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Automatisierte Fahrfunktionen haben sich fest in der Automobilindustrie etabliert. Insbesondere Einparkvorgänge lassen sich aufgrund der geringen Geschwindigkeiten und des vergleichsweise kleinen Betriebsraums sehr gut automatisieren. Um von den Insassen akzeptiert zu werden, muss eine Pkw-Parkfunktion jedoch souverän und mindestens so schnell agieren wie der Mensch. IPG Automotive, die Hochschule Kempten und MdynamiX zeigen, wie die eigenentwickelte Vehicle-in-the-Loop-Methode die durchgängige Bewertung automatisierter Fahrfunktionen unterstützen kann.
Obesity is one of the major challenges of modern societies. The food industry is under increasing pressure from government and the consumer side to reformulate foods to reduce the fat, sugar and salt content. However, if fat or sugar is reduced the overall flavor perception of the food is changed. In the case of fat reduction these changes are caused by a different aroma release during in mouth processing.
Additionally the texture is perceived as less creamy. It was recently found that aeration can increase aroma release and change texture perception in terms of creaminess.
The goal of this work is to study both effects in perspective of fat reduction in fermented dairy products.
Various mathematical models will be used to understand and describe the different effects involved. Since temperature is a critical parameter for aroma release and foam stability, heat transfer into a simplified mouth model for aroma release analysis was studied using the finite element method. In a second step the aroma release during foam collapse will be modelled and validated by aroma analysis data. In the last step the model linking the sensory perception of creaminess to the physical parameters of the foam (friction coefficient, viscosity, bubble size distribution) and the chemical attributes of the aroma substance (hydrophobicity, volatility) will be shown.
The findings from this work, both from the effect of aeration and the methodological approach may be transferred to other food categories as well.
Fat reduction is a major challenge for food manufacturers, because the desired calorie reduction goes along with a different aroma perception of low-fat products. To compensate for these changes, this work investigated the effect of foaming on aroma release from a fat-free dairy matrix. A mouth model and an analytical method based on HS-SPME-GC-IMS were designed and validated to quantify dynamic aroma release during thermally induced foam collapse. It was demonstrated that foaming significantly changes the release of aroma compounds. Depending on the hydrophobicity of the aroma substance, release was either increased or decreased after the inclusion of a gas phase.
Since aeration can increase aroma perception, it may be used to compensate the perceived aroma loss of low-fat dairy products. To be able to study the effect of aeration on aroma release systematically in the future, a temperature study was conducted on an in vitro model built to study aroma release at temperatures relevant for oral processing. The studied dairy foam contained 4% (w/w) milk protein and 0.75% (w/w) alginate and had a gas volume fraction of 71% after whipping. The temperature development during incubation of the foam at 40 °C was calculated using a numeric simulation. Thermal equilibrium was reached after 50 min. Sampling for dynamic aroma release was found to be ideal at 2, 7 and 15 min, because mean foam temperatures were 25, 30 and 35 °C. Experimental temperature analysis of the foam core was in good agreement with the simulation (R2 = 0.95).
When we are trying to decrease caloric intake by reducing fat content, aroma perception of the food is changed as well. Since previous studies indicated that foaming changes the aroma release, it was our goal to understand the physico-chemical background of this effect. Therefore, ten aroma compounds were added to a foamed acidified dairy matrix (4% milk protein, 1% gelatin, 60% gas volume). We simulated oral temperature conditions in a simplistic way through incubation at 40 °C and analyzed aroma release using headspace-solid phase microextraction-gas chromatography-ion mobility spectrometry. Significantly more highly hydrophobic aroma compounds were released from the foamed matrix than the unfoamed matrix, while compounds of intermediate hydrophobicity were released more from unfoamed matrix. The effect was independent from foam collapse and persisted for hours afterwards. Analytical results were complemented by orthonasal and retronasal sensory perception studies, which confirmed significant differences between aroma release behavior from foamed foods.
The production of food is highly complex due to the various chemo-physical and biological processes that must be controlled for transforming ingredients into final products. Further, production processes must be adapted to the variability of the ingredients, e.g., due to seasonal fluctuations of raw material quality. Digital twins are known from Industry 4.0 as a method to model, simulate, and optimize processes. In this vision paper, we describe the concept of a digital food twin. Due to the variability of the raw materials, such a digital twin has to take into account not only the processing steps but also the chemical, physical, or microbiological properties that change the food independently from the processing. We propose a hybrid modeling approach, which integrates the traditional approach of food process modeling and simulation of the bio-chemical and physical properties with a data-driven approach based on the application of machine learning. This work presents a conceptual framework for our digital twin concept based on explainable artificial intelligence and wearable technology. We discuss the potential in four case studies and derive open research challenges.
To understand aroma perception from complex food matrices‚ determination of dynamic aroma release during simulated oral processing is necessary. In this study optimization, validation and application of a novel method coupling headspace-solid phase microextraction (HS-SPME) with gas chromatography-ion mobility spectrometry (GC-IMS) is presented. Thirteen character impact compounds imparting different chemical properties are studied to understand capabilities and limitations of the method. It was shown for the first time that the temperature of the IMS sample inlet can be increased up to 200 °C without instrumental constraints. Linear calibration was possible for eleven of the thirteen compounds with one decade dynamic range. The limit of detection and quantitation were 2.1–63.0 ppb and 7.2–210.1 ppb, respectively. Diacetyl could be detected in negative polarity mode of IMS, however with lower precision compared to the compounds detected in positive mode. Limitations of the method were short HS-SPME extraction time, which in the case of caproic acid was not sufficient for reliable quantification. Additionally, δ-decalactone could not be detected due to maximum GC temperature of 200 °C. Application of the method to determine dynamic aroma release from a dairy matrix was successfully shown for nine compounds. Analysis of complex food matrix was performed with similar precision compared to analysis in aqueous solution, thus proving high robustness of the method towards matrix effects.
Eine Vielzahl von Produkten in den Supermarktregalen basiert auf Milch als Ausgangsprodukt. Dabei finden unterschiedliche Aufbereitungen der Rohmilch Anwendung. Der wohl wichtigste Prozess hierbei ist die Homogenisierung, welche vorrangig für die Stabilität des Produktes wichtig ist. An genau diesem Punkt kommt die Analyse der Partikelgröße ins Spiel.
Tensile properties of 2D braid reinforced composites are analyzed with regards to process-induced fiber architecture variations. First, a parametric study concerning the effect of radial braiding parameters on the braid characteristics is performed and a quantitative quality index based on visual attributes is introduced. Second, test laminates are produced with vacuum resin infusion to conduct tensile tests with biaxial and triaxial braids of varying qualities. To correlate fiber architecture variations with mechanical properties, each braided layer is scanned and analyzed by means of gray scale based image processing. This allows for determining specimen-specific fiber orientations and correlating them with mechanical properties. The results show strong correlations of measured fiber architectures and mechanical properties. Thus, the study suggests that detailed information about the (local) fiber architecture should allow for significantly improving the prediction of 2D braided composite properties.
This paper provides an approach for controlling the level of risk when operating highly automated transportation systems like cars, trains and similar. Such systems replace human perception and decision-making by employing highly sophisticated solutions based on electronics, IT, and AI. Such systems have demonstrated the potential for building highly automated vehicles, but, as of today, encounter challenges in correctly understanding the extremely complex open contexts, into which such vehicles could be deployed. Its key focus is on bounding the risks stemming from uncertainty in the perception of the environment.
Using a Machine Learning Regression Approach to Predict the Aroma Partitioning in Dairy Matrices
(2024)
Aroma partitioning in food is a challenging area of research due to the contribution of several physical and chemical factors that affect the binding and release of aroma in food matrices. The partition coefficient measured by the Kmg value refers to the partition coefficient that describes how aroma compounds distribute themselves between matrices and a gas phase, such as between different components of a food matrix and air. This study introduces a regression approach to predict the Kmg value of aroma compounds of a wide range of physicochemical properties in dairy matrices representing products of different compositions and/or processing. The approach consists of data cleaning, grouping based on the temperature of Kmg analysis, pre-processing (log transformation and normalization), and, finally, the development and evaluation of prediction models with regression methods. We compared regression analysis with linear regression (LR) to five machine-learning-based regression algorithms: Random Forest Regressor (RFR), Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGBoost, XGB), Support Vector Regression (SVR), and Artificial Neural Network Regression (NNR). Explainable AI (XAI) was used to calculate feature importance and therefore identify the features that mainly contribute to the prediction. The top three features that were identified are log P, specific gravity, and molecular weight. For the prediction of the Kmg in dairy matrices, R2 scores of up to 0.99 were reached. For 37.0 °C, which resembles the temperature of the mouth, RFR delivered the best results, and, at lower temperatures of 7.0 °C, typical for a household fridge, XGB performed best. The results from the models work as a proof of concept and show the applicability of a data-driven approach with machine learning to predict the Kmg value of aroma compounds in different dairy matrices.
Effiziente Kühlung und Schmierung für Fräsprozesse: Frästechnologie und Hochdruckkühlschmierstoff
(2024)
Insbesondere bei der 5-achsigen Fräsbearbeitung existiert noch kein System, das die optimale bzw. minimale Hochdruckkühlschmierstoffmenge bei jeweils gegebenen Bearbeitungszuständen beschreibt.
Die Entwicklung, Umsetzung und Potentiale eines seriennah einsetzbaren, externen und vom Werkzeugmaschinenhersteller unabhängigen Systems zur optimierten Bereitstellung von Hochdruckkühlschmierstoff werden gezeigt.
Bisher existiert noch kein System, das die optimale bzw. minimale Kühlschmierstoffmenge bei jeweils gegebenen Bearbeitungssituationen unter besonderer Berücksichtigung der zu verarbeitenden Werkstoffe und deren signifikantem Verschleißverhalten beschreibt.
Die Entwicklung eines seriennahen unabhängigen Systems zur optimierten Bereitstellung von Kühlschmierstoffen mit angepassten Drücken für die jeweilige Werkstoffapplikationen wird gezeigt.
Diese Veröffentlichung beleuchtet die Herausforderungen und Lösungsansätze zur Bewältigung des Fachkräftemangels in der deutschen produzierenden Industrie. Der Mangel an qualifizierten Arbeitskräften verursacht erhebliche Kosten und verringert das Produktionspotenzial. Hauptursachen sind demografische Veränderungen und veränderte Wertvorstellungen der Beschäftigten. Die Attraktivität des Arbeitsumfelds soll durch die Sichtbarmachung individueller Beiträge und die Stärkung des Gemeinschaftsgefühls erhöht werden. Maßnahmen aus dem Umfeld von Large-Language-Models werden vorgestellt. Abschließend wird die Vision einer modernen, vollvernetzten und arbeitnehmerfreundlichen Produktionsstätte skizziert.
In an industrial context, AI-based methods are becoming increasingly important in the optical systems used for identification, inspection and classification. The reasons for this are that AI-based image processing algorithms are easy to use on the operator side and often achieve superior results. E.g. in complex classification tasks. In the sand cast industry, the complexity in optical inspection of cast parts is connected with strong variations in the local surface topography and in the global object geometry change. Despite the great potential of AI-based methods, application is often hindered by the immense effort involved in acquiring a suitable training dataset. This refers not only to the acquisition of the required number of images but also to the tedious labelling. In this work, we investigate the capabilities and limits of synthetic training data on an AI-based optical scanner used to identify and track cast parts. The optical scanner is capable of detecting and classifying a codification specifically designed for the casting industry. By reading the code, the scanner can deduce the specific number of the cast part. For synthetic image generation, we use physically based rendering, which has advantage of full control over all rendering parameters. This allows for both a systematic investigation of the importance of the parameters and, an automatic labelling process of the training datasets. Our results show that, in particular, a detailed geometric modelling of the local surface topography and global object geometry of the pins have a positive influence on the recognition rate of the neural network. With that accuracy rates up to 56 % are achieved using synthetic training datasets, only.
Large Language Models (LLMs) have become widely adopted recently. Research explores their use both as autonomous agents and as tools for software engineering. LLM-integrated applications, on the other hand, are software systems that leverage an LLM to perform tasks that would otherwise be impossible or require significant coding effort. While LLM-integrated application engineering is emerging as new discipline, its terminology, concepts and methods need to be established. This study provides a taxonomy for LLM-integrated applications, offering a framework for analyzing and describing these systems. It also demonstrates various ways to utilize LLMs in applications, as well as options for implementing such integrations.
Following established methods, we analyze a sample of recent LLM-integrated applications to identify relevant dimensions. We evaluate the taxonomy by applying it to additional cases. This review shows that applications integrate LLMs in numerous ways for various purposes. Frequently, they comprise multiple LLM integrations, which we term ``LLM components''. To gain a clear understanding of an application's architecture, we examine each LLM component separately. We identify thirteen dimensions along which to characterize an LLM component, including the LLM skills leveraged, the format of the output, and more. LLM-integrated applications are described as combinations of their LLM components. We suggest a concise representation using feature vectors for visualization.
The taxonomy is effective for describing LLM-integrated applications. It can contribute to theory building in the nascent field of LLM-integrated application engineering and aid in developing such systems. Researchers and practitioners explore numerous creative ways to leverage LLMs in applications. Though challenges persist, integrating LLMs may revolutionize the way software systems are built.
Clubfoot is a common congenital foot deformity that leads to constant pain and significant limitations if left untreated or not treated adequately. The most used method for treating clubfoot is the Ponseti method. It involves a correction phase where about five plaster casts are applied and changed weekly. This treatment lasting about 2 to 3 months, is the most chosen method due to its high success rate. However, treated babies often experience skin complications caused by stiff and tight casts. Previous research showed that viable solutions already exist including orthoses. In this research, a developed method known as VDI 2221 was applied and the printable orthosis using 3D printer was selected as an alternative to Ponseti method. Calculations and finite element method (FEM) analysis demonstrated that the orthosis made of PA6-CF provides sufficient stiffness and strength, assuming the weight force of the foot is 10 N. The selected design was developed based on requirements and functional analysis, effectively mitigating the disadvantages of the Ponseti method. The developed orthosis can be manufactured globally using the 3D printing process, with a manufacturing cost of around €150, excluding assembly costs. In summary, a new solution was proposed within the same treatment method, effectively eliminating skin complications, and enabling cost-effective manufacturability on a global scale.
Algorithms for causal discovery have recently undergone rapid advances and increasingly draw on flexible nonparametric methods to process complex data. With these advances comes a need for adequate empirical validation of the causal relationships learned by different algorithms. However, for most real and complex data sources true causal relations remain unknown. This issue is further compounded by privacy concerns surrounding the release of suitable high-quality data. To tackle these challenges, we introduce causalAssembly, a semisynthetic data generator designed to facilitate the benchmarking of causal discovery methods. The tool is built using a complex real-world dataset comprised of measurements collected along an assembly line in a manufacturing setting. For these measurements, we establish a partial set of ground truth causal relationships through a detailed study of the physics underlying the processes carried out in the assembly line. The partial ground truth is sufficiently informative to allow for estimation of a full causal graph by mere nonparametric regression. To overcome potential confounding and privacy concerns, we use distributional random forests to estimate and represent conditional distributions implied by the ground truth causal graph. These conditionals are combined into a joint distribution that strictly adheres to a causal model over the observed variables. Sampling from this distribution, causalAssembly generates data that are guaranteed to be Markovian with respect to the ground truth. Using our tool, we showcase how to benchmark several well-known causal discovery algorithms.
Energy storage is a crucial flexibility measure to temporally decouple power generation from power demand and is touted as the missing link in realizing a decarbonized energy system based on renewable energy. Energy storage capacity buildup at all levels of the global energy system is expected to accelerate the decarbonization process. To this end, a coherent mathematical framework to ascertain the carbon footprint of localized energy systems with energy storage is indispensable. This article presents an open-source energy system simulation program — Energy System Network (ESN). A variety of energy system configurations can be simulated with the Python program, which incorporates key energy system components such as generation, grid, storage, and loads. ESN features an integrated bottom-up approach that combines energy system modeling with streamlined life cycle assessment techniques to quantify the carbon footprint of all components in a localized energy system. The lifecycle phases of each component, including production, operation, and end-of-life treatment, can be considered. Carbon footprint values are obtained for two demonstrative case studies with lithium-ion battery applications: energy arbitrage and home energy systems. The metric Levelized Emissions of Energy Supply (LEES) has been used to evaluate the carbon footprint of each application. An unconventional energy arbitrage strategy designed to exploit the grid carbon intensity spreads instead of the energy price spreads manages to achieve a LEES value about 17% lower than the conventional variant. The influence of rooftop solar generation, battery energy storage system, and the energy management strategy on the LEES values for a home energy system is explored. A maximum LEES reduction of over 37% vis-á-vis the base scenario was observed with optimal energy management for the solar generation and the battery system. The open-source availability of ESN can contribute to transparency, comparability, and reproducibility in carbon footprint assessments of localized energy systems with energy storage.
At the current development stage, the lower airspace above urban areas is only used to a very limited extent. Recent developments in the drone industry are making this area more accessible. The leading use case for drone applications is currently seen in the medical sector. Individual evidence shows that the use of drones to transport medical personnel for first medical response brings significant improvements in terms of cost and response time. Advantages in urban applications are seen as promising by some research projects, but this is not scientifically proven yet. This study deals with the simulation of transportation times of medical proposals by ambulance and drone in the metropolitan region of Stavanger and the comparison of transport times. The proposed methodology develops transferable results from concrete use-cases. By using a drone 80% of the expected operations can benefit from a reduction in transport time of up to 10 minutes with a variation of +/- 4 minutes through adjusted flight speeds due to weather conditions and variations in ambulance travel times due to different traffic volumes. The data set cleaned for the local special cases shows potential for a reduction of up to 20 minutes for the remaining operations, while the extracted individual cases even showing improvements of up to 60 minutes.
Dispatch of a grid energy storage system for arbitrage is typically formulated into a rolling-horizon optimization problem that includes a battery aging model within the cost function. Quantifying degradation as a depreciation cost in the objective can increase overall profits by extending lifetime. However, depreciation is just a proxy metric for battery aging; it is used because simulating the entire system life is challenging due to computational complexity and the absence of decades of future data. In cases where the depreciation cost does not match the loss of possible future revenue, different optimal usage profiles result and this reduces overall profit significantly compared to the best case (e.g., by 30–50%). Representing battery degradation perfectly within the rollinghorizon optimization does not resolve this—in addition, the economic cost of degradation throughout life should be carefully considered. For energy arbitrage, optimal economic dispatch requires a trade-off between overuse, leading to high return rate but short lifetime, vs. underuse, leading to a long but not profitable life. We reveal the intuition behind selecting representative costs for the objective function, and propose a simple moving average filter method to estimate degradation cost. Results show that this better captures peak revenue, assuming reliable price forecasts are available.
The electrification of transportation modes such as cars, buses, and boats offers the potential of providing vehicle-to-X services during idle times. Pools of vehicles can provide balancing power, trade on the electricity market, or be used for load peak shaving. In this work, the usage patterns of electric cars, electric buses, and electric boats are investigated, and the provision of vehicle-to-X with these vehicles is simulated using an open-source simulation tool. A data analysis and a vehicle usage pattern assessment show that especially private electric cars behave predictably at night. It also reveals that the vehicle-to-X availability varies over the week for all vehicle types and is highest at night for cars and buses. During the day on weekdays, private cars are available for vehicle-to-X 30 to 70% of the time, the analyzed buses 5 to 50% of the time, and the availability of the boats depends on their primary use as ferries or private boats. If the three transportation modes provide vehicle-to-X during idle times, the equivalent full cycles that the lithium-ion batteries complete increase at different rates depending on the vehicle pool size, while the mean charging rates decrease. Furthermore, an exemplary aging analysis shows that the additional load of vehicle-to-X provision slightly increases the capacity loss of the car batteries compared to a paused unidirectional charging strategy.
Die Veröffentlichung beleuchtet die Herausforderungen und Lösungsansätze zur Bekämpfung des Fachkräftemangels innerhalb der produzierenden Industrie am Standort Deutschland. Der Mangel an qualifizierten Arbeitskräften führt zu erheblichen Kosten und reduziert das Produktionspotenzial. Hauptursachen sind der demographische Wandel und veränderte Wertvorstellungen der Beschäftigten. Es gillt, das Arbeitsumfeld Produktion attraktiver zu gestalten, indem individuelle Beiträge sichtbarer gemacht und das Gemeinschaftsgefühl gestärkt werden. Zudem soll die wahrgenommene Komplexität reduziert und die Autonomie des Fertigungspersonals erhöht werden. Beispiele wie ein digitales Ampelsystem und die Visualisierung individueller Beiträge verdeutlichen diese Ansätze. Die Zukunft der Produktion wird durch sieben Thesen skizziert, die die Bedeutung einer partizipativen Planung, die Veränderung der Anforderungen und die Notwendigkeit eines Kommunikationsraums betonen. Die Präsentation endet mit der Vision einer modernen, vollvernetzten und arbeitnehmerfreundlichen Produktionsstätte.
Driving simulators are used to test under reproducible conditions, however, they must be validated for each application. This guarantees that the gathered data on the simulator is representative of real vehicle data. This paper examines and compares objective data from 20 drivers that are recorded on a six degrees of freedom (DOF) high dynamic driving simulator and a passenger vehicle in the compact class on a proving ground. The purpose of this study is to investigate the comparability of the behavior of the subjects in their driving task on the driving simulator compared to the real driving test. The driving maneuvers include the 18 m slalom and an ISO double lane change (ISO 3888-2). The real car’s measurement setup is composed of an inertial measurement unit and access to the chassis CAN messages. The driving simulator is equipped with the same real electrical power steering as the test vehicle. Furthermore, a fully validated vehicle model is used in the simulation. Objective key performance indicators such as maximum steering wheel angle, steering wheel torque, lateral acceleration, yaw rate, and yaw gain deviate from around -18% to 10% in the slalom, with the majority of parameters not showing significant differences. Bigger differences are found for the double lane change. Overall, the results demonstrate a satisfactory degree of correlation between the driver behavior on the driving simulator and the real vehicle, even up to achieving absolute validity.
Comfort evaluation on a dynamic driving simulator with advanced tire, road and vehicle models
(2024)
The topics of automated driving and digitization are becoming increasingly im-portant and will shape the future of mobility. The potential of this technology is enormous. Concurrently, manufacturers want to continue to differentiate them-selves in driving characteristics typical of their brands. Rapid developments re-garding technological changes as well as legal regulations combined with short development times present new challenges for the entire automotive industry. In this context, virtualization and front-loading methods play a major role within the vehicle development. There has been a clear trend of pushing virtual devel-opment via simulation to reduce the number of necessary prototypes. Since however, both engineers and management still rely heavily on the crucial in-sights gained by real road tests, subjective closed-loop assessment must remain a part of this virtual process. Driving simulators have the potential to bridge these gaps, allowing engineers and test drivers to subjectively experience and assess new systems in an early virtual phase of development.
Kempten University of Applied Sciences is working with research and technol-ogy partners to research and further develop their dynamic driving simulator. With the goal to develop use-case specific methods for virtual vehicle develop-ment, the simulator’s novel motion platform is used specifically for research projects in areas requiring high dynamic performance such as vehicle dynamics and ride. This paper describes the methods and solutions developed in an R&D project investigating the simulator’s capabilities for ride comfort evaluation, such as primary & secondary ride. With the goal to enable experienced test drivers to perform a subjective ride evaluation in a very early development phase, the simulator’s real-time environment was extended with the highly so-phisticated tire model FTire. This paper provides an overview of the system’s performance regarding subjective ride assessment. It presents a brief insight into the detailed road modelling and describes the measures taken to ensure real-time capability of the individual model interfaces. Objective performance evaluation shows the benefit of this work for comfort evaluation in early phases of virtual development.
Data Mining und Knowledge Discovery in Databases (KDD) sind Forschungs- und Anwendungsgebiete, die sich mit der Extraktion von nutzbarem Wissen aus Daten befassen. Dazu werden unter anderem Methoden des maschinellen Lernens eingesetzt. Die Induktive Logikprogrammierung ist ein Teilgebiet des Maschinellen Lernens, dessen Gegenstand das Lernen aus multi-relational und prädikatenlogisch repräsentierten Daten ist, während andere Lernverfahren üblicherweise Daten voraussetzen, die in Form einer einzelnen Attribut-Wert-Tabelle vorliegen. Der Einsatz von ILP-Methoden für KDD und Data Mining wird auch als Relationales Data Mining bezeichnet. Ein Anwendungsgebiet von KDD und Data Mining ist die Teilgruppenanalyse. Dabei wird eine Population von Fällen, die in einer Datenbank repräsentiert sind, nach besonders interessanten Teilgruppen der Population durchsucht, indem mögliche Teilgruppen generiert und mithilfe geeigneter Interessantheitsfunktionen bewertet werden. Die vorliegende Arbeit hat sich zum Ziel gesetzt, Methoden zur sicheren Beschränkung des Suchraums bei der Suche nach interessanten Teilgruppen in multi-relationalen Daten zu erarbeiten und zu evaluieren. Dazu wird ein Verfahren zur Suche nach interessanten Teilgruppen in multi-relationalen Datenbanken entwickelt, das verschiedene Methoden zur Suchraumbeschränkung integriert. Die verschiedenen Methoden zur Suchraumbeschränkung werden in Experimenten evaluiert und das entwickelte Verfahren zur Bearbeitung eines echten Data Mining-Problems eingesetzt. Die Arbeit bietet im einzelnen: (1) eine Formalisierung der Teilgruppenanalyse im Rahmen der ILP, (2) Optimumschätzfunktionen zu ausgewählten Interessantheitsfunktionen, (3) eine Erweiterung des bekannten Apriori-Suchverfahrens zur Warenkorbanalyse, die es erlaubt, die von Apriori durchsuchte Hypothesensprache einzuschränken, (4) einen ILP-Sprachbias für die Teilgruppenanalyse, der die Anwendung der Teilmengenbedingung des Apriori-Suchverfahrens zur Beschränkung eines ILP-Suchraums erlaubt, (5) einen SQL-Sprachbias für die Teilgruppenanalyse in multi-relationalen Datenbanken, (6) einen Ansatz zur Integration der Suchraumbeschränkung anhand von Taxonomien in einen Apriori-artigen Suchalgorithmus, (7) eine Methode zur Behandlung diskretisierter numerischer Attribute, die die Suchraumbeschränkung anhand von Allgemeinerbeziehungen zwischen Intervallen vereinheitlicht mit der Suchraumbeschränkung anhand von Taxonomien, (8) Experimente zur Wirksamkeit der verschiedenen Möglichkeiten zur Suchraumbeschränkung, (9) die Anwendung der entwickelten Ansätze auf ein echtes Data Mining-Problem mit Bank-Daten und ausführliche Vergleiche mit verwandten Arbeiten. Die Experimente wurden mit einer prototypischen Implementation der in dieser Arbeit entwickelten Ansätze durchgeführt. Dabei haben sich Teilmengenbedingung und Optimumschätzfunktionen als wirkungsvolle und zuverlässige Methoden zur Beschränkung des Suchraums erwiesen, während der Beitrag der Taxonomien zur Suchraumbeschränkung zwischen verschiedenen Anwendungen stark schwankte und in einigen Fällen nur gering war. Ein wichtiges Ergebnis der Versuche ist, daß die Teilmengenbedingung, die bisher nur zur Suchraumbeschränkung in ein-relationalen Datenbanken eingesetzt werden konnte, für multi-relationale Datenbanken und ILP-Sprachen genauso wirkungsvoll sein kann wie für ein-relationale Datenbanken.
VBA bietet das Potenzial, effektive Digitalisierungslösungen mit geringem Aufwand zu realisieren. “VBA für Office-Automatisierung und Digitalisierung" zeigt mit vielen Codebeispielen die Automatisierung von Excel, Word, Outlook, PowerPoint, SAP ERP und SOLIDWORKS und das Zusammenwirken dieser Systeme. Auch Webservices und Rest APIs werden mit VBA angesprochen und erschließen interessante Möglichkeiten bis hin zu KI. Das Buch erläutert wichtige Konzepte und gibt viele Tipps, um VBA-Anwendungen mit einfachen Mitteln unternehmenstauglich und administrierbar zu gestalten.
„Hybride Montage“ als Antwort auf Modell Mix und Variantenvielfalt im produzierenden Mittelstand
(2024)
Die "Hybride Montage" als Kombination aus konventioneller Fließfertigung und innovativer Matrixproduktion offeriert vielfältige Potentiale, dem zunehmenden Maß an Variantenvielfalt in der produzierenden Industrie Rechnung zu tragen. Im Zeitalter des Industrial Metsverse und der damit verbundenen "Servitisierung" und des "Manufacturing as a Service" - d.h. der Güterproduktion als Dienstleistung - ermöglicht die Hybride Montage außerdem eine flexiblere Arbeitsplanung.
Die produzierende Industrie des DACH-Raumes erlebt im Jahr 2023 vielfältige Herausforderungen. Gerade scheint die Covid-Pandemie überwunden, welche die Vulnerabilität globaler Lieferketten schonungslos offenbart hat, treten neue Herausforderungen zutage. Veränderungen der gesetzlichen Anforderungen (wie EU Data Act), steigende Ansprüche an ökologische Nachhaltigkeit (z.B. Kreislaufwirtschaft) oder veränderte Kundenbedürfnisse (wie insbesondere Servitisierung) führen – bei einem konstant hohen Niveau an Variantenvielfalt – zu erheblichen technologischen Herausforderungen. Diese werden darüber vielerorts flankiert und im negativen Sinne überlagert durch einen erheblichen Mangel an Arbeits- und Fachkräften in der Produktion. Das Produktionssystem der Zukunft wird unserer Einschätzung nach daher nicht nur unternehmensübergreifenden Datenaustausch ermöglichen, Kreislaufwirtschaft befähigen und verstärkt kundenzentriert ausgerichtet sein. Es wird Arbeits- und Fachkräfte in der Produktion auf vielfältige Art und Weise „begeistern“. Auf diese Weise wird Fluktuation reduziert, generisches Wissen im Unternehmen gehalten und so die Grundlage für nachhaltigen Geschäftserfolg und technologischen Fortschritt sowie die langfristige Sicherung von attraktiven Arbeitsplätzen in der produzierenden Industrie geschaffen.
Nach unserer Überzeugung setzen sich in der produzierenden Industrie des DACH-Raumes diesbezüglich zwei Erkenntnisse durch:
1. Der „kritische Wettbewerb“ stammt selten aus Europa
2. Für die Bewerkstelligung dieser Herausforderungen sind authentische Impulse von außen – insbesondere von anderen, vergleichbaren Unternehmen – ein zentraler Erfolgsfaktor.
In dieser Gemengelage haben wir mit dem SUMMIT ALLGÄU eine Managementkonferenz „von der Industrie für die Industrie“ ins Leben gerufen. Im Zentrum des Veranstaltungskonzeptes stehen hierbei keine wissenschaftlichen Fachvorträge, sondern authentische Erfahrungsberichte hochkarätiger Referenten aus der industriellen Praxis. Beim ersten SUMMIT ALLGÄU Produktion am 23. und 24. Oktober 2023 in Marktoberdorf standen inhaltlich insbesondere die Themenkomplexe „Transformation & Nachhaltigkeit“, „Faktor Mensch in der Produktion“ sowie „Variantenvielfalt“ im Fokus. Auf überfachlicher Ebene wurden vor allem der persönliche Austausch und das Networking zwischen den zahlreichen Teilnehmern, Referenten und Ausstellern fokussiert.
Portable Emission Measurement Technology and RDE on Motorcycles as Instruments for Future Challenges
(2020)
The packaging of fresh meat has been studied for decades, leading to improved packaging types and conditions such as modified atmosphere packaging (MAP). While commonly used meat packaging uses fossil fuel-based materials, the use of biodegradable packaging materials for this application has not been studied widely. This study aimed at evaluating the sustainability of biodegradable packaging materials compared to established conventional packaging materials through analyses of the quality of freshly packaged pork. The quality was assessed by evaluating sensory aspects, meat color and microbiological attributes of the pork products. The results show no significant differences (p > 0.05) in ground pork and pork loin stored in biodegradable MAP (BioMAP) and conventional MAP for the evaluated sensory attributes, meat color or total bacterial count (TBC) over extended storage times. The data suggest that BioMAP could be a viable alternative to MAP using conventional, fossil fuel-based materials for the storage of fresh meats, while simultaneously fulfilling the customers’ wishes for a more environmentally friendly packaging alternative.
Die Rolle von Batteriespeichern im Kontext eines künftigen Energiesystems: Smart Energy Systems
(2023)
Welche Bedeutung haben Batteriespeicher für künftige Energiesysteme? Können wir mit Batteriespeichern eine sichere, stabile und bezahlbare Energieversorgung erreichen? Wie flexibel muss unser Stromsystem sein?
In seiner Präsentation im erläutert Prof. Dr. Holger Hesse anhand anschaulicher Beispiele und seiner jahrelangen Erfahrung, ob und wie Elektrofahrzeuge künftig auch durch "Vehicle-to-Grid" einen Beitrag leisten können, wann "Second-life" eine wirkliche Option sein kann und in welchen Bereichen es schon heute wirtschaftlich sein kann, einen Batteriespeicher zu betreiben.
Battery electric buses (BEBs) are gaining prominence in public transportation systems. In this paper, we investi-gate the impact of road grade, passenger load, and recuperation power limits on the energy consumption of BEBs using a physics-based model with tuned parameters. The model was employed to conduct a sensitivity analysis taking into account different altitude data sources, passenger load assumptions, and maximum recuperation power limits. The results highlight the importance of considering the route topology and its interaction with dynamic passenger loading for energy consumption predictions. Further-more, the results indicate that various altitude data sources are feasible to estimate the road grade for this purpose. Apart from that, the sensitivity for recuperation power limitations is shown and put into context. Within a broader framework, the findings suggest that physics-based energy consumption models with optimised parameters can serve as a powerful tool for enhanced operations and planning of BEBs.
AI in Battery Storage
(2023)
Energy storage is a crucial flexibility measure to temporally decouple power generation from power demand and is touted as the missing link in realizing a decarbonized energy system based on renewable energy. Energy storage capacity buildup at all levels of the global energy system is expected to accelerate the decarbonization process. To this end, a coherent mathematical framework to ascertain the carbon footprint of localized energy systems with energy storage is indispensable. This article presents an open-source energy system simulation program — Energy System Network (ESN). A variety of energy system configurations can be simulated with the Python program, which incorporates key energy system components such as generation, grid, storage, and loads. ESN features an integrated bottom-up approach that combines energy system modeling with streamlined life cycle assessment techniques to quantify the carbon footprint of all components in a localized energy system. The lifecycle phases of each component, including production, operation, and end-of-life treatment, can be considered. Carbon footprint values are obtained for two demonstrative case studies with lithium-ion battery applications: energy arbitrage and home energy systems. The metric Levelized Emissions of Energy Supply (LEES) has been used to evaluate the carbon footprint of each application. An unconventional energy arbitrage strategy designed to exploit the grid carbon intensity spreads instead of the energy price spreads manages to achieve a LEES value about 17% lower than the conventional variant. The influence of rooftop solar generation, battery energy storage system, and the energy management strategy on the LEES values for a home energy system is explored. A maximum LEES reduction of over 37% vis-á-vis the base scenario was observed with optimal energy management for the solar generation and the battery system. The open-source availability of ESN can contribute to transparency, comparability, and reproducibility in carbon footprint assessments of localized energy systems with energy storage.
Predictive battery life models are commonly utilized to extrapolate degradation trends observed during accelerated aging tests for simulation of degradation in real-world applications. Thus, fitting accelerated aging data as accurately as possible and with low uncertainty is crucial for making believable projections of battery lifetime, but it is challenging to identify algebraic expressions that accurately fit multivariate degradation trends. A review of models published in literature reveal some common expressions for fitting calendar aging data, which is only dependent on temperature and state-of-charge, but no consistency across many models for fitting cycle aging data, indicating the need for a statistically rigorous data driven approach for developing empirical models. This talk will describe a machine-learning assisted method for identification of predictive battery life models utilizing bilevel optimization and symbolic regression. Bilevel optimization with cross-validation is used to statistically determine cell- and stress-dependent model parameters, while symbolic regression identifies both linear and multiplicative candidate expressions to predict stress-dependent degradation rates by selecting low-order subsets of features from a generated feature library. Because model expressions are identified empirically, it is crucial to ensure resulting models behave according to physical expectations, so the stability of models for interpolation or extrapolation is interrogated qualitatively through simulation and quantitatively through cross-validation and uncertainty quantification via bootstrap resampling. This model identification approach substantially improves upon models identified purely using expert judgement in terms of both accuracy and uncertainty. Model simulation and validation is then conducted by deriving a state-equation form of the predictive model, enabling simulation of battery aging under dynamic stresses. This enables validation of the predictive battery model on lab-based tests with varying conditions or on drive-cycle or application-cycle testing protocols. Parameter uncertainty can be carried forward into model simulation, giving lifetime estimates and confidence windows for cell- or system-level lifetime. The financial impact of battery model uncertainty can be estimated by incorporating uncertainty into a technoeconomic model.
Das Wissen um den zukünftigen Wärmebedarf gewinnt, bei komplexer werdenden Fernwärmesystemen mit volatilen und heterogenen Erzeugungsanlagen, immer weiter an Bedeutung. Denn zur Vermeidung von ineffizienten und unwirtschaftlichen Betriebszuständen muss für eine passende Einsatzoptimierung eine möglichst präzise und zuverlässige Wärmelastprognose vorliegen. Maschinelle Lernverfahren, steigende Datengrundlagen und Verfügbarkeit von ausreichender Rechenleistung bergen hierbei erhebliches Verbesserungspotential. Das von der Hochschule für angewandte Wissenschaften Kempten durchgeführte Forschungsvorhaben „DeepDHC - Untersuchung und Weiterentwicklung modernster maschineller Lernverfahren für die hochgenaue Lastprognose in Fernwärmenetzen“ (FKZ: 03EN3017) befasste sich mit der Performance unterschiedlicher maschineller Lernverfahren zur Wärmelastprognose in Fernwärmenetzen. Unter Berücksichtigung unterschiedlicher Fernwärmenetztopologien, der Einbindung von Smart Meter Daten und der automatisierten Berücksichtigung von Veränderungen im Fernwärmenetz wurden hierbei besonders relevante Fragestellungen aufgegriffen und bearbeitet.
We study the action of the nonlinear mapping G[z] between real or complex Banach spaces in the vicinity of a given curve with respect to possible linearization, emerging patterns of level sets, as well as existing solutions of G[z]=0. The results represent local generalizations of the standard implicit or inverse function theorem and of Newton's Lemma, considering the order of approximation needed to obtain solutions of G[z]=0.
The main technical tool is given by Jordan chains with increasing rank, used to obtain an Ansatz, appropriate for transformation of the nonlinear system to its linear part. The family of linear mappings is restricted to the case of an isolated singularity.
Geometrically, the Jordan chains define a generalized cone around the given curve, composed of approximate solutions of order 2k with k denoting the maximal rank of Jordan chains needed to ensure k-surjectivity of the linear family. Along these lines, the zero set of G[z] in the cone is calculated immediately, agreeing up to the order of k−1 with the given approximation. Hence, the results may also be interpreted as a version of Tougeron's implicit function theorem or Hensel's Lemma in Banach spaces, essentially restricted to the arc case of a single variable.
Finally, by considering a left shift of the Jordan chains, the Ansatz can be modified in a systematic way to obtain a sequence of refined versions of linearization theorems and Newton Lemmas in Banach spaces.
Intensive research in the field over the past decades highlighted the complexity of aroma partition. Still, no general model for predicting aroma matrix interactions could be described. The vision outlined here is to discover the blueprint for the prediction of aroma partitioning behavior in complex foods by using machine learning techniques. Therefore, known physical relationships governing aroma release are combined with machine learning to predict the 𝐾𝑚𝑔 value of aroma compounds in foods of different compositions. The approach will be optimized on a data set of a specific food product. Afterward, the model should be transferred using explainable artificial intelligence (XAI) to a different food category to validate its applicability. Furthermore, we can transfer our approach to other relevant questions in the food field such as aroma quantification, extraction processes, or food spoilage.
The effect of ionomer to carbon (I/C) weight ratio and relative humidity (RH) on cathode catalyst degradation was investigated by comprehensive in situ characterization. Membrane electrode assemblies (MEA) with I/C ratios of 0.5, 0.8 and 1.2 were subjected to an accelerated stress test performed at 40, 70 and 100% RH. The results show an increasing loss in electrochemical active surface area (ECSA) for both higher I/C ratios and RH during voltage cycling. To differentiate between ionomer and water connected ECSA, carbon monoxide stripping measurements were performed at varying RH. Before degradation, all MEAs show comparable total ECSA values, while higher I/C ratios lead to a larger fraction of ionomer connected ECSA. After degradation, ECSA measurements of the lowest I/C ratio showed a relatively higher loss of Pt in contact with ionomer than Pt in contact with water, while an opposite trend was observed for higher I/C ratios. H2 /N2 impedance measurements showed drastically increasing protonic catalyst layer resistances for decreasing RH especially at low I/C ratios, which might hinder Pt 2+ ion diffusion towards the membrane, hence decreasing the ECSA loss. Limiting current measurements show increasing molecular O 2 diffusion resistances at end of test for samples with higher I/C ratios and higher ECSA loss.
As the adoption of battery electric buses (BEBs) in public transportation systems grows, the need for precise energy consumption forecasting becomes increasingly important. Accurate predictions are essential for optimizing routes, charging schedules, and ensuring adequate operational range. This paper introduces an innovative forecasting methodology that combines a propulsion and auxiliary energy model with a novel concept, the environment generator. This approach addresses the primary challenge in electric bus energy forecasting: estimating future environmental conditions, such as weather, passenger load, and traffic patterns, which significantly impact energy demand. The environment generator plays a crucial role by providing the energy models with realistic input data. This study validates various models with different levels of model complexity against real-world operational data from a case study of over one year with 16 electric buses in Göttingen, Germany. Our analysis thoroughly examines influencing factors on energy consumption, like altitude, temperature, passenger load, and driving patterns. In order to comprehensively understand energy demands under varying operational conditions, the methodology integrates data-driven models and physical simulations into a modular and highly accurate energy predictor. The results demonstrate the effectiveness of our approach in providing more accurate energy consumption forecasts, which is essential for efficient electric bus fleet management. This research contributes to the growing body of knowledge in electric vehicle energy prediction and offers practical insights for transit authorities and operators in optimizing electric bus operations.
Im vorliegenden Beitrag wird eine Methode zur subjektiven und objektiven Charakterisierung von aktiven Fahrstreifenwechselfunktionen sowie eine Korrelationsanalyse zur Ermittlung optimaler Funktionseigenschaften vorgestellt. Zur Quantifizierung maßgeblicher subjektiver Eigenschaften wurden Bewertungskategorien und -kriterien aus den Bereichen Fahrerkooperation, Funktionsperformance, Entlastungsgrad und Sicherheitsgefühl erarbeitet, deren Beurteilung im Rahmen einer umfassenden Fahrstudie erfolgte. Die beurteilten Fahrzeuge wurden hinsichtlich ihrer unterschiedlichen Funktionsausprägungen anschließend in einem neuartigen fahrmanöverbasierten Prüfverfahren vermessen. Das Verfahren umfasst hierbei drei Typen von Fahrstreifenwechselszenarien in welchen unter anderem die Eigen- und Relativbewegung von Ego- und Target-Fahrzeug sowie die Funktionsrückmeldung am Lenkrad und im Kombi-Instrument des Egofahrzeugs messtechnisch erfasst wurden. Die Auswertung des hiermit aufgezeichneten objektiven Funktionsverhaltens geschieht durch eine automatisierte KPI-basierte Softwareumgebung. Ausgehend von der korrelativen Gegenüberstellung aller Subjektivkriterien mit den ermittelten KPI-Kennwerten können wichtige Trends und Zusammenhänge geprüft, erkannt und nutzbringend in die Festlegung optimaler Wertbereiche eingearbeitet werden. Die vorgestellte Methodik ermöglicht somit eine zielgerichtete Auslegung und Abstimmung der Eigenschaften einer aktiven Fahrstreifenwechselfunktion.
Social Selling im Maschinen- und Anlagebau : Ein Fallbeispiel aus dem Bereich Antriebstechnik
(2023)
Social Selling: Ist das ein Hype oder ein neuer Vertriebsansatz? Diese Frage haben sich viele Geschäftsführer und Vertriebsmanager zu Beginn der Corona-Pandemie gestellt. Zu dem Zeitpunkt galt Social Selling, also die beziehungsorientierte Nutzung von sozialen Medien zur Erreichung von Vertriebszielen, als eine Lösung, um Kunden- kontakte trotz Reiserestriktionen zu ermöglichen. Viele Unternehmen haben ihre Vertriebsmitarbeiter im Umgang mit LinkedIn, der führendenden SocialMedia-Plattform im B2B-Geschäft, geschult. Nun stellt sich die Frage, ob diese Bemühungen erfolg reich waren und welche Erfolgsfaktoren für die Implementierung existieren. In dem Fallbeispiel aus dem Bereich Antriebstechnik werden die Vorgehensweise einer Implementierung und die Ergebnisse einer Befragung unter den Mitarbeitern vorgestellt. Damit lassen sich erste Rückschlüsse auf Erfolgsfaktoren für Social Selling in B2B-Unternehmen ziehen.
Decarbonisation of heat generation has become a priority for district heating network operators. In order to avoid the use of fossil-fired boilers, operators need to know peaks in heat demand in advance. Accurate thermal load forecasting is playing an increasingly important role in this respect. This paper presents the final results of the research project “deepDHC” (deep learning for district heating and cooling) funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK). The three-year project focused on systematically benchmarking thermal load forecasts for district heating networks, based on state-of-the-art machine learning methods. The analysis covers a variety of machine learning techniques, such as neural networks – including latest deep learning methods – (e.g. LSTM, TFT, ESN, RC), decision trees (random forests, adaptive boosting, XGB) and statistical methods (SARIMAX). In addition, the impact of combining methods by so-called “stacking” was investigated. Training and validation of the machine learning algorithms was based on historical operating data from the district heating network for the city of Ulm in Germany, in combination with historical weather data, and weather forecasts. Thermal load forecasts – typically for three days ahead – are presented and compared against one another. An automatic tuning routine was developed as part of the project, which enables regular re-training of the machine learning algorithms based on the latest operating data from the heating network. Furthermore, a web interface for real-time forecasting was developed and implemented at the power station.
The battery pack lifetime is severely affected by the State-of-Charge (SOC) and operating temperature. This paper proposes a real-time SoC-Temperature balance power-sharing algorithm for the battery racks to optimize the battery life. The control scheme takes into account the SoC disparity and temperature deviation simultaneously to calculate the active power set-points for battery energy storage system (BESS) units. The proposed algorithm can serve as an alternative to the state of the health (SoH) based power sharing algorithms, which require complex SoH estimation procedures and extensive data for battery age prediction. This method can be particularly useful for second-life batteries, which often show significant heterogeneity in age, internal resistance, and capacity, necessitating a generic yet robust control strategy for their optimal utilization and to minimize degradation. Simulation results demonstrate (i) the suitability of the proposed control scheme for real-time implementation, (ii) the controller efficacy to limit high battery temperatures, which can help to slow down the ageing process. Overall, this research enables the easy integration of second-life batteries for grid ancillary services, obviating the need for complex SoH estimation procedures by considering battery temperature and SoC.
The electrification of the transportation sector leads to an increased deployment of lithium-ion batteries in vehicles. Today, traction batteries are installed, for example, in electric cars, electric buses, and electric boats. These use-cases place different demands on the battery. In this work, simulated data from 60 electric cars and field data from 82 electric buses and six electric boats from Germany are used to quantify a set of stress factors relevant to battery operation and life expectancy depending on the mode of transportation. For this purpose, the open-source tool SimSES designed initially to simulate battery operation in stationary applications is extended toward analyzing mobile applications. It now allows users to simulate electric vehicles while driving and charging. The analyses of the three means of transportation show that electric buses, for example, consume between 1 and 1.5 kWh/km and that consumption is lowest at ambient temperatures around 20 °C. Electric buses are confronted with 0.4–1 equivalent full cycle per day, whereas the analyzed set of car batteries experience less than 0.18 and electric boats between 0.026 and 0.3 equivalent full cycles per day. Other parameters analyzed include mean state-of-charges, mean charging rates, and mean trip cycle depths. Beyond these evaluations, the battery parameters of the transportation means are compared with those of three stationary applications. We reveal that stationary storage systems in home storage and balancing power applications generate similar numbers of equivalent full cycles as electric buses, which indicates that similar batteries could be used in these applications. Furthermore, we simulate the influence of different charging strategies and show their severe impact on battery degradation stress factors in e-transportation. To facilitate widespread and diverse usage, all profile and analysis data relevant to this work is provided as open data as part of this work.
Battery systems are extensively used in smart energy systems in many different applications, such as Frequency Containment Reserve or Self-Consumption Increase. The behavior of a battery in a particular operation scenario is usually summarized using different key performance indicators (KPIs). Some of these indicators such as efficiency indicate how much of the total electric power supplied to the battery is actually used. Other indicators, such as the number of charging-discharging cycles or the number of charging-discharging swaps, are of relevance for deriving the aging and degradation of a battery system. Obtaining these indicators is very time-demanding: either a set of lab experiments is run, or the battery system is simulated using a battery simulation model. This work instead proposes a machine learning (ML) estimation of battery performance indicators derived from time series input data. For this purpose, a random forest regressor has been trained using the real data of electricity grid frequency evolution, household power demand, and photovoltaic power generation. The results obtained in the research show that the required KPIs can be estimated rapidly with an average relative error of less than 10%. The article demonstrates that the machine learning approach is a suitable alternative to obtain a very fast rough approximation of the expected behavior of a battery system and can be scaled and adapted well for estimation queries of entire fleets of battery systems
Oxygen scavengers are used to reduce the oxygen permeation of packaging (active barrier) and to absorb oxygen from its direct environment, e.g., a headspace of packaged food. Few oxygen scavenger coatings have been developed. Therefore, in this study, a novel oxygen scavenger coating has been developed. It is based on inorganic–organic polymers (ORMOCER®). The oxygen absorption reaction is activated by UV light. The scavenger was synthesized, coated on aluminum foil, subsequently dried and afterwards laminated with a polyethylene sealing layer. UV light activates the oxygen scavenging reaction. The oxygen absorption capacity, measured at 23 °C and 0% r.h., was 242 ± 8 mg oxygen/g scavenger coating. When the oxygen scavenger coating layer was laminated by using a two-component polyurethane laminating adhesive, the absorption capacity was hardly reduced, with a measured absorption capacity of 223 ± 18 mg oxygen/g scavenger coating. In an experimental packaging sample with the oxygen scavenger coating with a thickness (dry) of 3 µm and 18 µm, near-zero mbar oxygen partial pressure was reached by the non-laminated oxygen scavenger coatings within two days, and within about 20 days when laminated with a polyurethane laminating adhesive and a PE-layer on the oxygen scavenger layer. The oxygen partial pressure was kept near zero mbar for 500 days, whereas in the experimental packaging without oxygen scavenger, the oxygen partial pressure increased to 110 mbar during this time. The developed oxygen scavenger based on inorganic–organic polymers can be applied as wet chemical coating on various surfaces with standard application procedures. Application scenarios are oxygen-sensitive goods such as food, pharmaceutical products and cosmetics.
The catalyst coated membrane represents the heart of a polymer electrolyte fuel cell (PEMFC) and is produced by directly applying a catalyst suspension on the membrane or a decal transfer foil. Understanding the rheology of catalyst inks is crucial to optimize the printed microstructure of the catalyst layer and hence the performance of the fuel cell. The suspension consists of platinum on carbon powder, ionomer dispersion (perfluorosulfonic acid ionomer) and solvents. Its rheology is directly affected by various material properties of all ink components. Therefore, measuring the flow curves represents a suitable tool for quality control at the beginning of the production line.
An artifact appearing during the cathodic transient of cyclic voltammograms (CVs) of low-loaded platinum on carbon (Pt/C) electrodes in proton exchange membrane fuel cells (PEMFCs) was examined. The artifact appears as an oxidation peak overlapping the reduction peak associated to the reduction of platinum oxide (PtOx). By varying the nitrogen (N2) purge in the working electrode (WE), gas pressures in working and counter electrode, upper potential limits and scan rates of the CVs, the artifact magnitude and potential window could be manipulated. From the results, the artifact is assigned to crossover hydrogen (H2X) accumulating in the WE, once the electrode is passivated towards hydrogen oxidation reaction (HOR) due to PtOx coverage. During the cathodic CV transient, PtOx is reduced and HOR spontaneously occurs with the accumulated H2X, resulting in the overlap of the PtOx reduction with the oxidation peak. This feature is expected to occur predominantly in CV analysis of low-loaded electrodes made of catalyst material, whose oxide is inactive towards HOR. Further, it is only measurable while the N2 purge of the WE is switched off during the CV measurement. For higher loaded electrodes, the artifact is not observed as the electrocatalysts are not fully inactivated towards HOR due to incomplete oxide coverage, and/or the currents associated with the oxide reduction are much larger than the spontaneous HOR of accumulated H2X. However, owing to the forecasted reduction in noble metal loadings of catalyst in PEMFCs, this artifact is expected to be observed more often in the future.
Nano-porous materials can be imaged spatially by focused ion beam scanning electron microscopy (FIB-SEM). This method generates a stack of SEM images that has to be segmented (or reconstructed) to serve as basis for structural characterization. To this end, we apply two state-of-the-art algorithms. We study the influence of the original image’s voxel size on estimates of morphological characteristics and effective permeabilities. Special attention is paid to analyzing anisotropies due to the FIB-SEM typical anisotropic sampling. Quantitative comparison of morphological descriptors and flow properties of reconstructed data is enabled by the use of synthetic FIB-SEM sets for which a ground truth is available. Moreover, in that case, reconstruction parameters can be chosen optimally, too.
We present a model of the cathode catalyst layer morphology before and after loading a porous catalyst support with Pt and ionomer. Support nanopores and catalyst particles within pores and on the support surface are described by size distributions, allowing for qualitative processes during the addition of a material phase to be dependent on the observed pore and particle size. A particular focus is put on the interplay of pore impregnation and blockage due to ionomer loading and the consequences for the Pt/ionomer interface, ionomer film thickness and protonic binding of particles within pores. We used the model to emulate six catalyst/support combinations from literature with different porosity, surface area and pore size distributions of the support as well as varying particle size distributions and ionomer/carbon ratios. Besides providing qualitatively and quantitatively accurate predictions, the model is able to explain why the protonically active catalyst surface area has been reported to not increase monotonically with ionomer addition for some supports, but rather decrease again when the optimum ionomer content is exceeded. The proposed model constitutes a fast translation from manufacturing parameters to catalyst layer morphology which can be incorporated into existing performance and degradation models in a straightforward way.
In this work, flatbed screen printing is evaluated regarding its capability to produce catalyst layers of PEM fuel cells. In the field of printed electronics, screen printing is regarded as robust and high-throughput coating technology. The possibility of in-plane structuring could be an additional degree of freedom, enabling more complex designs of catalyst layers in the future. In this study, process parameters are varied to investigate their effect on resulting layer thickness, homogeneity, and Pt-loading. With the usage of different screens, the Pt-loading can be adjusted. Additionally, two different pastes with and without water content are investigated. The catalyst paste without water showed a better process stability during printing and performed best under dry conditions (RH = 40%) and worst under wet conditions (RH = 100%) during electrochemical in-situ testing. Overall, the reproducibility of the CCM production process was verified. The viscosity of the catalyst paste with 19.55 wt% water in solvent was higher compared to the paste without water. Furthermore, a carbon paste (Pt-free) is developed in a similar viscosity range as the catalyst pastes. The main challenge of screen printing process development lies in the paste optimization to prevent evaporation effects over time, ensuring sufficient wetting of the paste on the substrate and sufficient fuel cell performance.
Lithium-ion cells are subject to degradation due to a multitude of cell-internal aging effects, which can significantly influence the economics of battery energy storage systems (BESS). Since the rate of degradation depends on external stress factors such as the state-of-charge, charge/discharge-rate, and depth of cycle, it can be directly influenced through the operation strategy. In this contribution, we propose a model predictive control (MPC) framework for designing aging aware operation strategies. By simulating the entire BESS lifetime on a digital twin, different aging aware optimization models can be benchmarked and the optimal value for aging cost can be determined. In a case study, the application of generating profit through arbitrage trading on the EPEX SPOT intraday electricity market is investigated. For that, a linearized model for the calendar and cyclic capacity loss of a lithium iron phosphate cell is presented. The results show that using the MPC framework to determine the optimal aging cost can significantly increase the lifetime profitability of a BESS, compared to the prevalent approach of selecting aging cost based on the cost of the battery system. Furthermore, the lifetime profit from energy arbitrage can be increased by an additional 24.9% when using the linearized calendar degradation model and by 29.3% when using both the linearized calendar and cyclic degradation model, compared to an energy throughput based aging cost model. By examining price data from 2019 to 2022, the case study demonstrates that the recent increases in prices and price fluctuations on wholesale electricity markets have led to a substantial increase of the achievable lifetime profit.
We give conditions for local diagonalization of analytic operator families acting between real or complex Banach spaces.The transformations are constructed from an operator Töplitz matrix obtained from Jordan chains of increasing length. The basic assumption is given by stabilization of the Jordan chains at length k in the sense that no root elements with finite rank above k are allowed to exist. Jordan chains with infinite rank may appear. These assumptions ensure finite pole order equal to k of the generalized inverse. The Smith form arises immediately.
Smooth continuation of kernels and ranges towards appropriate limit spaces is considered using associated families of analytic projection functions.
No Fredholm properties or other finiteness assumptions, besides the pole order, are assumed. Real and complex Banach spaces are treated without difference by elementary analysis of the system of undetermined coefficients.
Formal power series solutions of the system of undetermined coefficients are constructed, which are turning into convergent solutions, as soon as analyticity of the operator family and continuity of the projections is assumed. Along these lines, results concerning linear Artin approximation follow immediately, which are well known in finite dimensions. The main technical tool is given by a defining equation of Nakayama Lemma type.
KI-Anwendung in der Energietechnik: Einsatz maschineller Lernverfahren für die Wärmelastprognose
(2021)
Maschinelles Lernen gilt als eines der vielversprechendsten Teilgebiete der künstlichen Intelligenz (KI). Sein Einsatz hat in den vergangenen Jahren zu enormen Fortschritten sowohl in der Bild- und Texterkennung als auch bei Zeitreihenprognosen geführt.
Der Vortrag demonstriert dies am Beispiel von Wärmelastprognosen für die Fernwärmebranche. Dabei werden maschinelle Lernverfahren genutzt, um den Wärmebedarf in Fernwärme-netzen über mehrere Tage im Voraus hochgenau vorherzusagen. Auf diese Weise können Energieversorger den Einsatz ihrer Wärmeerzeugungsanlagen optimal planen. Beispielsweise können bei vorhersehbaren Lastspitzen Wärmespeicher frühzeitig mit erneuerbar erzeugter Wärme gefüllt und so der Betrieb fossiler Spitzenlastkraftwerke vermieden werden. Als Folge kann der Betreiber sowohl CO2-Emissionen als auch Emissions-, Brennstoff- sowie An- und Abfahrkosten einsparen.
Der Vortrag basiert auf Ergebnissen aus mehreren Forschungsprojekten, die an der Hochschule Kempten seit 2016 gemeinsam mit Fernwärmeversorgern durchgeführt wurden. Dabei wurden maschinelle Lernverfahren sehr unterschiedlicher Komplexität systematisch untersucht und bewertet – von „einfachen“ bis hin zu anspruchsvollen Verfahren aus dem Bereich des sogenannten „Deep Learning“. Die Wärmelastprognosen werden unter Verwendung historischer Betriebs- und Wetterdaten sowie von Wetterprognosen vollautomatisiert erstellt und dem Betreiber über eine Web-Schnittstelle zur Verfügung gestellt, die auch im Vortrag gezeigt wird.
Die vorgestellte Methode bietet erhebliche Einsparpotenziale für den Anlagenbetreiber. Sie ist zudem auch auf andere Branchen mit ähnlichen Zielgrößen bzw. Fragestellungen übertragbar.
Kompaktes Lehrbuch für Studium und Praxis.
Dieses Lehrbuch für Studium und Praxis ist speziell abgestimmt auf das Ingenieurwesen, das Wirtschaftsingenieurwesen und die Betriebswirtschaft. Es wird ein breites Wissen über die wesentlichen Elemente des Qualitätsmanagements dargestellt: vom Qualitätsmanagement in der Beschaffung, der Statistik, den Messsystemen, der ISO 9001, den Qualitätsmethoden, bis hin zum Projekt- und Risikomanagement und der Servicequalität. Zudem wird ein Schwerpunkt auf das internationale Qualitätsmanagement gelegt. Zentral sind kommunikative und interkulturelle Kompetenzen – sei es beim Umgang mit Fehlern, bei der Lösungssuche oder beim Aufbau eines Qualitätsmanagementsystems.
The energy absorption capacity of braided composite frames under bending loads was studied by conducting quasi-static four-point-bending tests. As specimen geometry C-shaped frame segments where chosen which show the typical failure behaviour of frames with open cross section, such as local buckling and crippling. The braiding manufacturing process offers the possibility to influence the fracture mechanics by a local hybridization of the braider yarns. Different hybridization concepts were investigated to identify design principles for braided frame structures with enhanced energy absorption capacity. The test results show that the post-failure energy absorption of braided frame segments can be significantly increased by a local modification of the braid architecture.
A successful application of braiding technology for the manufacturing of an industrial product is shown on the example of a carbon fiber-reinforced plastic (CFRP) manipulator. Due to its lightweight design, the CFRP system offers highly ergonomic advantages for the worker compared to the previously available steel version. In the course of the braided manipulator design, braid-specific manufacturing options were used to improve bending and torsion stiffness by combining design aspects and fiber architecture variations. It is also shown how braid-related manufacturing constraints can be overcome by additional subsystems, eg, to realize strongly curved part geometries. Finally, a process of the mechanical testing procedure for the whole system is presented which follows the classical building-block approach used for the validation of aeronautical structures.
Breaking down the cost structure of state-of-the-art CFRP part shows that a major share of the costs is caused by labor and equipment as well as process energy consumption.
Therefore, the main goal of the EU funded FP7 project LOWFLIP (Low Cost Flexible Integrated Composite Process) has been the reduction of these costs by introducing new technologies into CFRP production processes.
The LOWFLIP concept focuses on three main aspects:
• Development of a new out-of-autoclave (OOA) prepreg system with snap cure capabilities.
• Development of a direct 3D placement technology for plies and tapes.
• Development of energy efficient and fast heating toolings.
The main content of this paper is detailed information on a novel direct 3D prepreg layup process for automated production of large-scale fiber reinforced parts of small and medium lot sizes. The advanced ply placement process, which is able to drape and compact unidirectional prepreg tapes with currently up to 300 mm ply width directly into a double curved tooling, is being introduced. Two large-scale demonstrator parts from the transport and aerospace sector will be presented. Experiences gained during prototype manufacturing will be reflected and benchmarks of the equipment are presented.
The effect of the cover factor on the in-plane mechanical properties of biaxial and triaxial braided carbon fiber composites is investigated. Low braid coverage occurs if the mandrel circumference exceeds a specific length, so that gaps occur in between the different yarn systems.
Braids are manufactured on different mandrel sizes with a braid angle of ±45°. The cover factor is calculated analytically and measured by means of gray scale analysis of scanned samples. A new analytical model is presented which allows for calculating the cover factor of triaxial braids in good approximation with real results, taking into account the off-centered alignment of the axial yarns. Tensile and compression test results are presented for the thermoset resin systems EPIKOTE MGS® RIM 235 and HexFlow® RTM6. The results show that there is a significant influence of decreased cover factors on the mechanical properties, especially in case of triaxial braids tested in axial direction. Micro computed tomography images based on synchrotron radiation reveal that low coverage leads to an increased fiber undulation of the axial yarns, which mainly determine the mechanical properties of triaxial braids. An influence of the different resin systems is observed for matrix-dominated properties of biaxial braids, where the composite failure strain is in the range of neat resin failure strain.
Scope of the presented work is a detailed comparison of a macroscopic draping model with real fibre architecture on a complex non-crimp-fabric preform using a new robot-based optical measurement system. By means of a preliminary analytical process design approach, a preforming test centre is set up to manufacture dry non-crimp-fabric preforms. A variable blank holder setup is used to investigate the effect of different process parameters on the fibre architecture. The real fibre architecture of those preforms is captured by the optical measurement system, which generates a three-dimensional model containing information about the fibre orientation along the entire surface of the preform. The measured and calculated fiber orientations are then compared with the simulation results in a three-dimensional overlay file. The results show that the analytical approach is able to predict local hot spots with high shear angles on the preform. Macroscopic simulations show a higher sensitivity towards changes in blank holder pressure than reality and limit the approach to precisely predict fibre architecture parameters on complex geometries.
Carbon fiber reinforced plastics (CFRP) allow to redesign well established metallic structures like manipulators in order to decrease their weight and to increase their level of performance. This task can only be successful, when the CFRP-design process does not copy the metallic structure, but solves the underlying functions in a new way. The present study shows, how an I-beam steel manipulator was transferred to a braided design with hollow cross sections and locally adapted material properties. Step by step we show, how to define the manipulator’s functions and mechanics and how they can be considered with a braided CFRP-structure. This includes analytical pre-design as well as detailed finite element method (FEM) design. A mechanical testing program was performed in order to measure input-values for the numerical calculations on coupon-level and validate the simulation on system level. Static and dynamic tests of the CFRP-manipulator showed that it fulfils all requirements to be certified for sale. Therefore, the braiding process for commercial production was investigated for two machine setups and the state of serial production was reached. The braided CFRP-manipulator is 70% lighter than the steel reference and has increased handling properties because of locally adapted material properties.
Ziel der vorliegenden Arbeit ist die Entwicklung eines messtechnischen Verfahrens, um prozessbedingte Variationen der Faserarchitektur textiler Preforms im Entwicklungsprozess von Bauteilen aus Faser-Kunststoff-Verbunden berücksichtigen zu können. Anhand mechanischer Prüfreihen von geflecht- und gelegeverstärkten Verbundwerkstoffen können klare Wirkzusammenhänge zwischen visuellen Textilmerkmalen und den mechanischen Eigenschaften identifiziert werden. Schwerpunkt der Untersuchungsreihen sind Auswirkungen von Lücken, Ondulationen und Streuungen des Faserwinkels auf die Zug- und Druckeigenschaften kohlenstofffaserverstärkter Kunststoffe. Die Erkenntnisse fließen in die Entwicklung eines optischen Messsystems zur 3D-Preformanalyse ein, welches mittels grauwertbasierter Bildverarbeitung die vollflächige Texturanalyse komplexer, dreidimensionaler Oberflächen ermöglicht. In der Arbeit werden zuletzt Anwendungspotenziale der detaillierten Faserarchitekturanalyse zur Optimierung von Fertigungsprozessen und zur Bauteildimensionierung vorgestellt.
This work describes why additive manufacturing is a key technology for efficient design iterations and rapid production ramp-up with large-scale manufacturing technologies. Laser cutting, injection moulding and folding were used to increase the production capacity of face shields for health care workers during the COVID-19 pandemic. We applied systematic learnings from the iterative processes used for additive manufacturing to these large-scale manufacturing technologies and the respective face shield designs. In cooperation with medical experts, structural and functional design requirements of face shields were identified and are described in detail in this work. The regulatory design requirements according to EN 166 are introduced, which were considered to receive a CE certification for three of the presented designs. The employed manufacturing techniques are specified and the respective implications on the design solutions are discussed. The paper concludes with a summary of the production initiative at the research campus ARENA2036 with a total output of over 13 000 face shields from April to June 2020, which were distributed internationally.
This volume of the series ARENA2036 compiles the outcomes of the first Stuttgart Conference on Automotive Production (SCAP2020).
It contains peer-reviewed contributions from a theoretical as well as practical vantage point and is topically structured according to the following four sections: It discusses (I) Novel Approaches for Efficient Production and Assembly Planning, (II) Smart Production Systems and Data Services, (III) Advances in Manufacturing Processes and Materials, and (IV) New Concepts for Autonomous, Collaborative Intralogistics.
Given the restrictive circumstances of 2020, the conference was held as a fully digital event divided into two parts. It opened with a pre-week, allowing everyone to peruse the scientific contributions at their own pace, followed by a two-day live event that enabled experts from the sciences and the industry to engage in various discussions. The conference has proven itself as an insightful forum that allowed for an expertly exchange regarding the pivotal Advances in Automotive Production and Technology.
In case of electric vehicles (EV) powered by lithium ion traction batteries (LIB), remanufacturing processes nbecome increasingly important due to their rising market share and valuable raw materials. LIB can account for up to 40% of the total EV cost. Often, only a small portion of the cells are significantly degraded when the usable battery capacity falls below 80%, which is currently considered the standard end-of-life criterion. However, in order to enable efficient remanufacturing, novel battery design principles are required. This paper discusses the requirements, opportunities and challenges of future remanufacturing processes of LIBs down to the cell level using a battery system of a commercial vehicle as an example. It gives an overview of the current state-of-the-art manufacturing processes of battery systems and shows the developed overall remanufacturing process including condition assessment, disassembly and reassembly. Subsequently, requirements on future designs are discussed. The state-of-the-art of EV batteries is evaluated based on these requirements to determine where incompatible connections such as welded contacts or adhesive joints conflict with remanufacturing design principles.
Social Selling: Ist das ein Hype oder ein neuer Vertriebsansatz? Diese Frage haben sich viele Geschäftsführer und Vertriebsmanager zu Beginn der Pandemie gestellt, als die beziehungsorientierte Nutzung von sozialen Medien zur Erreichung von Vertriebszielen als Lösung für Kundenkontakte bei Reiserestriktionen beworben wurde. Viele Unternehmen haben ihre Vertriebsmitarbeiter im Umgang mit LinkedIn, der führenden Plattform im B2B-Geschäft geschult. Nun stellt sich die Frage, ob diese Bemühungen erfolgreich waren und welche Erfolgsfaktoren existieren. In dem Fallbeispiel aus dem Bereich Antriebstechnik werden die Vorgehensweise und die Ergebnisse einer Befragung unter den Mitarbeitern vorgestellt. Damit lassen sich erste Rückschlüsse auf Erfolgsfaktoren ziehen.
In unserer Zeit, geprägt von Digitalisierung, Sozialen Netzwerken, und eingeschränkten Kontaktmöglichkeiten, bedingt durch COVID, benötigen Unternehmen einen Weg, auch ohne Messen und Vertreterbesuche, Kontakte zu potenziellen Kunden aufzubauen und zu pflegen. Social Selling ist diesbezüglich in aller Munde. Möchten auch Sie erfahren, wie Sie Social Selling im B2B-Bereich erfolgreich in Marketing und Vertrieb einsetzen können? Dr. Philipp Schmid hat mit jahrelanger Erfahrung in diesem Ratgeber ein Werkzeug geschaffen, das Ihnen Schritt für Schritt hilft, Ihren Social-Selling-Prozess auf Linkedin einzurichten und anzuwenden. Weitere Experten ergänzen sein Werk mit ihren wertvollen Erfahrungen in Form von Interviews: Thomas Vierhaus, Michael Stenberg, Klaus Hengstmann, Simon Repp, Peter Schreiber, Mike (Michael) Spiewok, Maik Pfingsten, Markus Härlin und Klaus Eck
Keynote auf dem AGFW-Expertenforum "Fernwärme Digital" (18. - 19.04.2023, Frankfurt am Main):
Der Vortrag gibt einen Überblick über die aktuell zum Thema Digitalisierung in der Fernwärmeforschung laufenden Aktivitäten. Zudem werden die Forschungsprojekte deepDHC, KWKflex und HeatSHIFT der Hochschule Kempten zu diesem Thema vorgestellt.
Feature-conserving gradual anonymization of load profiles and the impact on battery storage systems
(2023)
Electric load profiles are highly relevant for battery storage research and industry as they determine system design and operation strategies. However, data obtained from electrical load measurements often cannot be shared or published due to privacy concerns. This paper presents a methodology to gradually anonymize load profiles while conforming to various degrees of anonymity. It segregates the original load profile into base and peak sequences and extracts features from each of the sequences. With the help of the features, a synthetic, anonymized load profile is created. Different levels of anonymization can be selected, which transform the original profile to the desired extent. A random permutation of the peak sequences or base sequences is used to achieve this transformation. Exemplary profiles from a household and an electric vehicle charging station are used to demonstrate the functionality of the anonymization. The indicators of the anonymized load profiles are compared with the original ones in both time and frequency domains, and the effects of load profile anonymization on the operation of battery storage systems in two scenarios are analyzed. While the anonymized load profiles retain the time-invariant indicators from the original profile, the permutation causes a loss of regularity in the load profiles. As a result, relevant indicators of battery storage systems subjected to these anonymized profiles deviate to a greater extent in time-dependent applications such as self-consumption increase. This is reflected in the overestimation of equivalent full cycles by up to 6% and underestimation of self-sufficiency by up to 9 percentage points. In time-independent applications such as peak shaving, however, the indicators can be well reproduced with deviations of up to 3% despite the lost regularity. In order to make the anonymization methodology usable for everyone, we present the open-source tool LoadPAT, in which users can anonymize their load profiles and choose their desired level of anonymization. This work is intended to further encourage the dissemination of open-source data.
Dieser Beitrag behandelt ein neu entwickeltes analytisches Berechnungsmodell zum mechanischen Verhalten von rechteckförmigen Mehrschraubenverbindungen (MV). Es werden die Modellvoraussetzungen, -einschränkungen, Theorie und Ergebnisgüte vorgestellt. Dazu werden die Ergebnisse des analytischen Berechnungsmodells den Ergebnissen der experimentellen Untersuchungen sowie den Finite-Elemente-Analysen (FEA) gegen übergestellt.
Auf Grundlage des bei Petersen (2013) und Petersen und Leidich (2013) vorgestellten Konzepts der Digitalisierung der Betriebskraftverteilung, wurde ein analytisches Berechnungsmodell entwickelt. Das Berechnungsmodell besteht aus zwei Teilmodellen, dem Platten- und dem Rahmensegmentmodell. Dem Plattenmodell liegt die Kirchhoff'sche Plattentheorie mit den Navier'schen Randbedingungen zugrunde, wobei die Ecken durch eine empirische Eck-Korrekturfunktion an die Realität angepasst werden. Das Rahmensegmentmodell wird mit einem modifizierten Berechnungsmodell nach Seidel (2001) umgesetzt. Um die Stützwirkung durch die Nachbarsegmente des Rahmens zu berücksichtigen, wird speziell für die eigenen Versuchskörper eine Korrekturfunktion bzw ein Korrekturfaktor bei den höchstbelasteten Schrauben entwickelt. Mit dieser Korrekturfunktion zeigt der Vergleich mit einer Messung und der entsprechenden FEA eine sehr gute Übereinstimmung. Damit zeigt sich eine befriedigende Validierung des analytischen Berechnungsmodells, wobei eine Abhängigkeit der Korrekturfunktion vom Gradienten der angreifenden Linienlast am jeweiligen Rahmensegment, d.h. der Schrägstellung derselben festgestellt wird. Weitere Untersuchungen zur Erstellung einer allgemeingültigen Korrekturfunktion sind notwendig. Eine zusätzliche Anwendung des analytischen Berechnungsmodells erfolgt in Petersen und Leidich (2013) an einem Versuchskörper von Grote (1984). Beim Vergleich der Berechnungs- mit den Messergebnissen von Grote ergeben sich große Abweichungen. Die Analyse dieser Messergebnisse zeigen bei kleinen Flanschdicken niedrigere Schraubenzusatzkräfte als bei den Großen. Dies wiederspricht den eigenen Untersuchungen sowie den Untersuchungen von Koch und Izykowski (1979). Die analytischen Ergebnisse zum Versuchskörper von Grote zeigen quantitativ nur eine als ausreichend zu bezeichnende Ergebnisgute Aufgrund der widersprüchlichen Messergebnisse von Grote können die Grunde dafür jedoch nicht abschließend geklärt werden, Petersen (2013). Die höchstbelastete Schraube kann mit dem analytischen Modell quantitativ und qualitativ sicher bestimmt werden. Mit den Parametervariationen bei den Messungen von Petersen und Leidich (2013) und den Berechnungen von Petersen und Walther (2013) konnte klar nachgewiesen werden, dass sich der Ort der höchstbelasteten Schraube bei einer Vorspannkraftänderung nicht ändert. Eine Reduzierung der Schraubenklemmlänge beeinflusst die Nachgiebigkeit der Schrauben wesentlich stärker als die Flanschnachgiebigkeit, d.h., die Schraube wird im Verhältnis steifer, als es der Flansch wird. Verlängerungshülsen für die Schrauben sind eine hilfreiche Maßnahme, wenn die Flanschdicke zu klein ist. Bei MV reduzieren hohe Vorspannkräfte, große Klemmlängen, bzw. Dehnschrauben und ein gegenüber dem Flanschblatt wesentlich steiferer Flanschanschlusskörper, die Schraubenzusatzkraft stark, wenn dabei die Richtwerte der VDI 2230 zum Schraubenabstand und zu der Trennfuge beachtet werden. Die quasistatischen Versuche haben ergeben, dass Klaffen bis ungefähr zum Beginn des Schraubendurchgangslochs zugelassen werden kann, wenn die konstruktiven Empfehlungen der VDI 2230 umgesetzt werden, was weitere Untersuchungen jedoch endgültig bestätigen müssen.
Untersuchungen zum Übertragungsverhalten von Mehrschraubenverbindungen: Finite-Elemente-Analysen
(2013)
Der Beitrag beschreibt FEA (Finite-Elemente-Analysen) zum mechanischen Verhalten rechteckförmiger Mehrfachschraubverbindungen und vergleicht die Ergebnisse dieser FEM-Simulationen mit den Ergebnissen experimenteller Untersuchungen, die in einem weiteren Beitrag mit dem Titel "Untersuchungen zum Übertragungsverhalten von Mehrschraubenverbindungen - Experimente" veröffentlicht wurden. Zunächst werden die FE-Geometrien der drei Versuchskörper am Beispiel des 'genuteten Flansches' vorgestellt, um anschließend den Simulationsablauf und die Ergebnisauswertung zu erläutern. Die FEA erfolgten mit der Software Abaqus ®, wobei der Prä-/Postprozessor Abaqus/CAE und der nichtlineare, implizite FE-Solver Abaqus/Standard verwendet wurden. Ziele der Untersuchungen waren die Überprüfung des analytischen Berechnungsmodells, die quantitative Analyse des Verformungsverhaltens der Versuchskörper sowie die Durchführung von Parameterstudien, die nicht durch die experimentellen Versuche abgedeckt waren. Im Rahmen der Verifizierung der Berechnungsergebnisse werden zentrische Druck- und Zugversuche am Nutflansch sowie exzentrische Lasteinleitungen am Nutflansch ausgewertet und mit den Simulationsergebnissen verglichen. Abschließend werden die Ergebnisse verschiedener Betriebslastparameterstudien am Nutflansch vorgestellt. Die Autoren kommen zum Schluss, dass der Vergleich der Messergebnisse mit den FE-Berechnungen sehr befriedigende Ergebnisse zeigt, sodass davon ausgegangen werden kann, dass das vorgestellte FE-Modell die realen Verhältnisse quantitativ gut beschreibt.
Die MV (Mehrschraubenverbindung) ist unter den lösbaren reibschlüssigen Verbindungen die am häufigsten verwendete Verbindungsart. MV werden z. B. als Verbindungsart bei Anschlageinrichtungen an Gebäuden, die Teil eines Absturzschutzsystems für eine Person ist, gewählt. Der Beitrag fokussiert auf experimentelle Untersuchungen zum ÜV (Übertragungsverhalten) von rechteckförmigen MV. Es werden neue Erkenntnisse hinsichtlich des Einflusses der Bauteilsteifigkeit auf das ÜV der MV, der Lage der höchstbelasteten Schraube im MS-Verband sowie des Entfalls einzelner Schrauben im Schraubenfeld, ohne die Betriebssicherheit zu gefährden, vorgestellt. Auf Basis des Konzeptes der BKEM (Betriebskrafteinleitungsmatrix) wurden drei Versuchskörper entwickelt, mit denen praxisnahe experimentelle Untersuchungen zum Übertragungsverhalten von rechteckförmigen MV (Mehrschraubenverbindungen) durchgeführt wurden. Die Lastfälle Zug an den Segmentpositionen 1, 3, 4, 5, 7 sowie 9 und Druck ausschließlich am Segment 5 der durchgeführten Experimente sind alle statisch. In diesen Versuchen wird festgestellt, dass bei den angewendeten Betriebsbelastungen die Eckschrauben der Versuchskörper nahezu keine Mehrbelastung erfahren. Wenn alle Schrauben eine ausreichende Vorspannung besitzen, könnte man bei den Zuglastfällen die Eckschrauben einsparen. Wenn zusätzlich Drehmomente oder Querkräfte in der Trennfuge auftreten, müssen zukünftige Untersuchungen zeigen, ob auch bei diesen Lastzuständen die Eckschrauben eingespart werden können. Ein Entfall der Eckschrauben 1, 8, 15 und 22 hat keine Auswirkungen auf das Übertragungsverhalten der MV bei den Flanschen und der Platte, wie beispielhaft mit Versuchen am Nutflansch gezeigt wird. Außerdem kann nachgewiesen werden, dass der Nutflansch im Vergleich zum Vollflansch bei den Zuglastfällen bis zu einer Betriebskraft von ca. 70 kN nahezu identische Schraubenzusatzkräfte bei den höchstbelasteten Schrauben zeigt. Die Plattenversuche ergeben ähnliche Ergebnisse; lediglich die Schraubenzusatzkräfte des Lastfalls Eckenzug weichen erheblich von den Schraubenzusatzkräften der beiden anderen Versuchskörper ab. Bei Betrachtung einer Schwingbeanspruchung wird festgestellt, dass sich ein maximal zulässiger Schraubenzusatzkraftbereich bis 3 kN für diese Beanspruchung ergibt und sich die Schraubenzusatzkräfte von Voll- und Nutflansch bis FB = 90 kN in diesem Bereich weitgehend identisch verhalten. D.h. die durch die Nuten bedingten Steifigkeitsunterschiede der beiden Flansche sind bis zu dieser Betriebskraft, mit Ausnahme des Zuglastfalls bei Position 5, nahezu bedeutungslos. Die Platte lässt nur bis zu einer Betriebskraft von circa 80 kN bei den mittleren Schrauben einen sehr ähnlichen Verlauf der Schraubenzusatzkräfte im Vergleich zu den beiden anderen Versuchskörpern erkennen. Beim Lastfall Eckenzug weicht die Platte dagegen quantitativ erheblich gegenüber den beiden anderen Flanschen ab. Vermutlich ist neben der niedrigeren Biegesteifigkeit der Platte die abweichende Betriebskrafteinleitung bei der Platte gegenüber der bei den beiden Flanschen dafür verantwortlich.
The Efficiency and Profitability of the Modular Multilevel Battery for Frequency Containment Reserve
(2021)
The modular multilevel battery (M2B) is a novel approach to integrate battery storage into the electricity grid. This paper obtains the efficiency and financial benefits of a working prototype system, compared to conventional systems for frequency containment reserve (FCR). The efficiency is determined with a low-level simulation that models the conduction losses on the circuit, the MOSFETs’ switching and conduction losses, and the system consumption. The simulation model shows, that the efficiency is superior to conventional inverters over the entire operating range. The operation for FCR shows a cost-reduction of 50% for transactions on the intraday market, resulting in a 72.9% higher net-profit after 10 years.
Battery energy storage systems (BESS) are used for a variety of applications, with their economic benefit often being the decisive factor for deployment. A multitude of physico-chemical aging mechanisms lead to capacity fade over a BESS life cycle. The models that are used to describe this capacity fade are prone to inherent model errors. Through a holistic techno-economic modelling approach, we investigate the impact of battery degradation modelling uncertainty on the economic benefit of representative BESS applications. Here, it is shown how improved parameter fit quality can reduce the resulting economic uncertainty. Furthermore, we highlight that the consideration of degradation modelling uncertainty is especially crucial when: (i) the cash flow highly depends on the available battery capacity, (ii) a fixed, e.g. warranty mandated, state of health limit acts as the threshold for battery end-of-life, (iii) long evaluation periods and low discount rates are the focus of economic evaluation.
Reducing time, costs and prototypes in vehicle development is a central objective. Previously, simulation and experimental testing are separate blocks in the development process chain that exchange information with each other. Theoretical preliminary considerations are realised by simulations, which are then tested by prototypes in real road trials. The use of driving simulators enables a synergetic solution to combine simulation and experimental testing. A process chain is presented in order to make it possible to experience the MBS model with damaged axle components on the driving simulator and thus also to evaluate them subjectively. Various deformation states of the components of the four-link rear axle are subjectively evaluated by several test drivers on the driving simulator in various driving manoeuvres. In the process, the components are ranked in terms of their damage criticality and the customer acceptance threshold is determined based on the component deformation. Furthermore, correlations between objective overall vehicle quantities and subjective driver evaluations are identified via linear regression models and artificial neural networks.
In der vorliegenden Arbeit sollen grundlegende Betrachtungen zum Übertragungsverhalten von ausschließlich rechteckförmigen Mehrschraubenverbindungen (MV) angestellt werden. Grund für diese Fokussierung ist die große Wissenslücke, die bis heute in diesem Bereich vorherrscht. Rechteckförmige MV sind liniensymmetrische Flanschverbindungen. Sie teilen sich auf in ein rechteckförmiges Flanschblatt, das als Kontaktstelle zum Gegenkörper fungiert und in einen nicht zwingend reckteckförmigen Anschlusskörper. Diese Art von MV kommt z.B. bei Lagerbefestigungen, Gehäuseverschraubungen und Zylinderkopfverschraubungen vor. [...]
Folgende wissenschaftliche Fragestellungen ergeben sich allgemein bezüglich MV:
- Wo liegt die höchstbelastete Schraube im Schraubenfeld (SF)?
- Welche Parameter sind ausschlaggebend und wie stark beeinflussen diese die Lage der höchstbelasteten Schraube?
- Welche konstruktiven Maßnahmen reduzieren die Schraubenzusatzkräfte in der MV?
- Wie weit kann Klaffen zugelassen werden, ohne die Verbindung bleibend zu schädigen?
Diese grundlegenden Fragen zu MV sollen im Rahmen dieser Arbeit für rechteckförmige MV konkret beantwortet werden.
The data represents tensile tests of 2D braided biaxial and triaxial composite laminates. Braids were produced on a Herzog radial braiding machine RF 1-176-100 with carbon fiber yarns (Toho Tenax F13 HTS40 12K 800 tex). Test laminates are infused with a VARI process with the 2-component epoxy resin system Hexion RIM235/RIMH237. The infusion is performed at vacuum pressure leves of 20-30 mbar and the resin is cured at room temperature for 24 hours at a pressure level of 250 mbar. A subsequent post-cure step maximizes matrix properties at 70°C for 10 hours.
The braid setups have been chosen to achieve four different braids with a fiber angle of 45° on braid mandrels with a diameter of 120 mm, two biaxial ones and two triaxial ones. By using different sizes of braiding ring diameters and yarn forces, homogeneous and inhomogeneous fiber architectures are generated.
Braiding setups 1 (biaxial) and 2 (triaxial) with 130 mm braiding ring diameter and 350 g yarn force result in braids with homogeneous fiber architecture. 3 (biaxial, 400 mm braiding ring, 600 g yarn force) and 4 (triaxial, 250 mm braiding ring, 600 g yarn force) in braids with inhomogeneous scattering of visual characteristics. Single layers are braided on mandrels with a length of 1500 mm and laminates with 6 layers of biaxial and 4 layers of triaxial braids are subsequently infused.
Tensile tests are performed on a Schenck-Trebel RM250 universal testing machine according to AITM 1-0007. For each specimen, the individual fiber orientation in each layer is measured prior to infusion by image analysis performed on scans of the braided layers. The results show a strong dependence of the individual fiber orientation of single test specimens on their mechanical properties in case of the presented tensile tests
Modern electric power steering (EPS) systems allow a flexible adaptation of the same steering hardware to different vehicle types by calibration of the corresponding control unit (ECU). As the steering behavior is one of the major factors influencing the end customers` buying decision and driving experience, the steering ECU calibration has to fit very well to the customer requirements. A growing number of functions in the steering ECU offer increasing possibilities to influence comfort, safety and steering feel and must be validated in different driving maneuvers (figure 1). However, several trade-offs of these characteristics have to be solved such as the driver’s steering effort vs. the steering feedback while cornering.
Model-based Development Methods – What can Chassis and Powertrain Development Learn from Each Other?
(2015)
The biggest challenge for today’s vehicle development is the increasing number of vehicle variants for different markets, which have to be developed in ever shorter cycles. In addition, customers and governments have high demands when it comes to comfort, driving pleasure, consumption, security and CO2 emissions. The powertrain and the chassis domain have developed their own methods to solve these difficulties. Although there are some important differences, the question arises how one domain can profit from the methods of the other domain. For example, the strict emission legislation in the powertrain sector lead to advanced model-based calibration and testing methods, which could be useful also for other domains.
Zeitreihenprognosen können eine leistungsfähige Methode zur Vorhersage des Wärmebedarfs eines Fernwärmenetzes darstellen. Durch die Analyse historischer Daten über den Energieverbrauch und andere relevante Faktoren wie Wetterdaten kann ein Neuronales Netz Muster und Trends erkennen, die ihm helfen, genaue Vorhersagen über den künftigen Bedarf zu treffen. Auf diese Weise kann der Netzbetreiber die Ressourcennutzung der Anlage optimieren, Kosten einsparen und Emissionen reduzieren und sicherstellen, dass die Wärme effizient und kostengünstig an die Kunden geliefert wird. Darüber hinaus können genaue Prognosen dem Betreiber bei der Planung von Wartungs- und Reparaturarbeiten helfen und sicherstellen, dass das Netz auch in Zeiten hoher Nachfrage zuverlässig und funktionsfähig bleibt. Insgesamt kann die Zeitreihenprognose dazu beitragen, dass Fernwärmenetze effizienter arbeiten und Geld sparen, während sie den Kunden einen guten Servicebieten.
Eine mögliche Deep Learning Methode, die für die Zeitreihenprognose verwendet werden kann, ist das LSTM. Ein LSTM-Netzwerk (Long Short-Term Memory) ist ein Rekurrentes Neuronales Netzwerk (RNN), das sich besonders für die Verarbeitung und Vorhersage sequenzieller Daten eignet. Es verwendet eine komplexe Architektur von Zellen, die sich Werte über lange Zeiträume hinwegmerken können, so dass es Abhängigkeiten und Muster in sequentiellen Daten erfassen kann. In diesem kurzen Leitfaden wird anhand eines Beispiels gezeigt, wie thermische Last- und Wetterdatenverwendet werden können, um ein LSTM-Modell zu trainieren. Dieses Modell wird dann gespeichert und von der Festplatte geladen, um Lastvorhersagen für einen anderen Zeitraum zu machen. Darüber hinaus wird gezeigt, wie die Fehler der Vorhersagen berechnet werden können, wenn der tatsächliche Lastbedarf bekannt ist, wodurch ein kurzer Überblick über alle grundlegenden Komponentengegeben wird, die für die Erstellung eines Modells zur Vorhersage des Fernwärmebedarfs erforderlich sind.
Dieser Leitfaden wurde von der Hochschule Kempten im Rahmen des Forschungsprojekts "deepDHC" erstellt.
Vorstellung einer Vorgehensweise zum Einsatz des Maschinellen Lernens (ML) zur Klassifizierung einer Schraubenverbindung (SV) entsprechend der VDI 2230 Blatt 1 und 2 mittels einer Schraubenfeld-Ansicht der zu untersuchenden SV. Neben einem Ablaufplan zur Erstellung des ML-Programmes (MLP) wird ausführlich auf die effiziente Erstellung einer Bilddatenbank mit SV zum Trainieren, Verifizieren und Testen des Neuronalen Netzes eingegangen.
Der "musikalische Fingerabdruck von Glocken" bezeichnet ein Verfahren, mit dem auf Basis von Klanganalysen Ermüdungsrisse in Glocken in einem solch frühen Stadium ermittelt werden können, bevor diese zu hörbaren Klangveränderungen führen. Grundlage dieses Verfahrens ist die Eigenschaft rotationssymmetrischer Körper, wonach sich deren Eigenformen doppelt über den Umfang verdreht ausbilden. Während bei idealer Rotationssymmetrie die jeweils paarweise vorhandenen Eigenformen identische Eigenfrequenzen besitzen, spalten sich diese bei Abweichungen von der Rotationssymmetrie in zwei beachbare Eigenfrequenzen auf. Solche Abweichungen werden durch Fehlstellen hervorgerufen, die bedingt durch die Herstellung, die Oberflächengestaltung oder aufgrund von Ermüdungsrissen im Klangkörper von Glocken vorhanden sind. Abhängig von der Lage, Ausprägung und Art vorhandener Fehlstellen können charakteristische Veränderungen im Schwingverhalten von Glocken festgestellt werde, die eine Identifikation der verursachenden Fehlstelle möglich macht. Darüber hinaus können Fehlstellen durch die Lageveränderung der gespaltenen Eigenformen lokalisiert werden.
Der Klang einer läutenden Glocke wird neben ihrem eigenen Klangvermögen durch eine Vielzahl von Parametern beeinflusst. Äußere Parameter wie Läutewinkel, Glockenstube und Schalläden beeinflussen den Glockenklang außerhalb des Turmes. Die Abstimmung des dynamischen Systems Glocke mit Joch und Klöppelmaterial, -gewicht und -form sind bestimmend für die Lautstärke und die Anregung zum Schwingen. In einigen Projekten wurde darüber hinaus der Klöppel im Bereich des Vorschwungs gedrungen gestaltet, um damit eine wesentliche Verbesserung des Klanges zu erzielen. Der Klang wird als Wohltönend empfunden, wenn die hohen Teiltöne nicht vorherrschend schrill klingen und mehr die niederen Frequenzen im Klangbild entscheidend sind. Eine unterschiedliche Anregung verschiedener Teiltöne wird durch den unterschiedlichen Verlauf des Anschlages und hierbei insbesondere die Anschlagdauer erreicht.
Eine Veränderung des Klöppels bewirkt automatisch auch immer eine Veränderung der Läuteparameter, so dass eine gezielte Auslegung des Klangs durch Auslegung des Klöppels bisher nur erfahrungsbasiert und nicht parametergesteuert vorgenommen werden konnte. In diesem Beitrag wird über Computersimulationen bei erzielter Variation der einzelnen wesentlichen Parameter zur Klangbeeinflussung berichtet.
Die Ergebnisse erlauben die Abschätzung der Auswirkungen einer Variation von Anfluggeschwindigkeiten, Klöppelmasse, Ballendurchmesser, Schaftdicke und Vorschwungform, so dass nun gezielt ein Optimum der Läutebedingungen im Hinblick auf Lebensdauer und Klang eingestellt werden kann. Insbesondere konnte nachgewiesen werden, dass ein Klöppel durch eine gedrungenem Vorschwung als vorteilhaft hinsichtlich hoher Lebensdauer und grundtönigem Klang ausgelegt werden kann.
Das Kulturgut Glocke wird beim Läuten hohen Belastungen ausgesetzt. Zur Vermeidung von Schäden insbesondere von Ermüdungsrissen, wurden die Einflussgrößen für einen schonenden Umgang mit läutenden Glocken anhand umfangreicher Messungen und Analysen durch das ECC-ProBell ermittelt und daraus die Anschlagsintensität als Bewertungsmaßstab für den Grad der Schonung entwickelt. Die Beanspruchung von Glocken wird jedoch nicht nur durch dynamische Abstimmung und das Massenverhältnis zwischen Glocke und Klöppel bestimmt, vielmehr beeinflussen zahlreiche zusätzliche Randbedingungen das Beanspruchungsgeschen der läutenden Glocke. Aufgrund der Erfahrung aus zahlreichen Glockenmessungen in Kirchtürmen und auf Basis systematischer Untersuchungen und Tests im Labor werden der Einfluss des Jochs, der Läutemaschine sowie der Befestigung der Locke am Joch und der Installation des Klöppels in der Glocke erörtert, und Vorschläge zur nachhaltigen Schonung von Glocken erarbeitet und an konrekreten Beispielen vorgestellt.
Das Beanspruchungsgeschehen an Glocken wurden in den vergangenen zwei Jahrzehnten in national und europäisch geförderten Forschungsvorhaben umfassend untersucht. So konnten die Ursachen und Zusammenhänge für die Entstehung und Ausbreitung von Ermüdungsschäden und Schlagverschleiß an Glocken umfassend geklärt und einer ingenieursmäßigen Betrachtung zugänglich gemacht werden. Die abgeleiteten Auslegungsverfahren erlauben eine rechnerische Bestimmung des Risikos für Ermüdungsschäden an Glocken und für einen verstärkten Verschleiß an den Klöppelanschagstellen sowie die gezielte Beeinflussung der Klanglichen Eigenschaften der läutenden Glocke. Die Läutebedingungen lassen sich damit für die unterschiedlichen Anforderungen gezielt optimieren - sei es aus denkmalpflegerischer Sicht zur Minimierung des Schadensrisikos, sei es aus musikalischer Sicht die Verbesserung der Klangentfaltung oder Lautstärke.
The energy transition in the mobility sector is well underway. The electrification of road transport is resulting in a shift of the energy demand from the oil and gas sector to the electricity grid. Increasingly aggressive targets for low charging times for Electric Vehicles (EVs) are slated to raise the demand for High-Power Charging (HPC). This is likely to lead to bottlenecks and overloading in vulnerable sections of the electricity grid. Battery Assistance (BA) is a promising grid integration measure for High-Power Charging (HPC) to mitigate these problems. As decarbonization is the primary objective of the energy transition, the determination and comparison of the Global Warming Potential (GWP) footprints for HPC stations with BA is crucial. A comprehensive mathematical framework for the modelling and quantification of GWP footprints for HPC has been developed. The Levelized Emissions of Energy Supply (LEES) methodology has been extended and generalized to handle energy from the grid. A new state variable for the Battery Energy Storage System (BESS) — the State of Carbon Intensity (SOCI) has been introduced to calculate the operation phase GWP footprint of the BESS. The energy consumption GWP footprint for the load is also described by a new quantity — the Load Energy Consumption (LEC) emissions. The effect of incorporation of local Photovoltaic Solar (PV) generation in the energy flows is also investigated. An optimized Energy Management System (EMS) strategy with rolling horizon optimization to minimize emissions has been implemented to regulate energy flows in scenarios with BA and local PV generation. The Levelized Emissions of Energy Supply (LEES) values are obtained for all simulated scenarios and compared against a baseline rule-based EMS strategy. In combination with on-site PV generation, BA could achieve a reduction of 24% in the LEES vis-á-vis the baseline strategy. For reference, two scenarios with Grid Reinforcement (GR) for the grid section with and without local PV generation have also been simulated. With Grid Reinforcement (GR), a reduction of over 2% can be achieved with respect to the baseline EMS strategy for BA. Grid Reinforcement (GR) in conjunction with local PV generation can bring about a further reduction of about 6% with respect to the baseline EMS strategy for BA.
Design Science Research is a research paradigm suitable for application-oriented disciplines that develop (construct) artifacts as solutions to practical problems. Design Science Research is known to be a mainstream research paradigm in engineering and other disciplines. In recent years, Design Science Research (DSR) has become an established research approach in the field of Information Systems (IS). Nevertheless, there is an ongoing debate about the methodology and guidelines for Design Science Research in Information Systems (IS-DSR). This paper proposes to gather and leverage insights from other design disciplines, such as engineering, to provide clarity and inspiration for IS-DSR and to work towards a common understanding of design science research across disciplines. This paper provides results of an initial empirical analysis of research literature from engineering disciplines. The results provide suggestions for validating DSR results and contribute to the understanding of research guidelines for DSR. In addition, a novel, fine-grained, and operational framework for analyzing DSR papers and projects is presented. The third contribution is a proposal to develop a common basic schema for design science research, analogous to the standard IMRaD schema for empirical research. Based on the analysis of samples of papers, this paper proposes IDEaD as the standard scheme for Design Science Research, i.e., Introduction, Description, Evaluation, and Discussion.