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In the time of increased awareness about the environment problems by the public opinion and also intensive international efforts to reduce emissions of greenhouse gases, as well increase of the generation of electrical energy to facilitate industrial growth, the conference offers broad contribution towards achieving the goals of diversification and sustainable development.
Focus of the student conference is to promote the discussion of views from scientists and students from Wroclaw University of Technology, Technical University of Ostrava and Brandenburg University of Technology Cottbus-Senftenberg.
The conference is a unique teaching session motivating the students to write science related papers, prepare presentations and discus future developments. It is a formal part of the curriculum offered as an elective module. Accompanied by a technical excursion.
In recent years parcel volumes reached record highs. The logistics industry is seeking new innovative concepts to keep pace. For densely populated areas delivery robots are a promising alternative to conventional trucking. These electric robots drive autonomously on sidewalks and deliver urgent goods, such as express parcels, medicine, or meals. The limited cargo space and battery capacity of these vehicles necessitates a depot visit after each customer served. The problem can be formulated as an electric vehicle routing problem with soft time windows and a single unit capacity. The goal is to serve all customers such that the quadratic sum of delays is minimized and each vehicle operates within its battery bounds. To solve this problem, we formulate an MIQP and present an expanded formulation based on a layered graph. For this layered graph we derive two solution approaches based on relaxations, which use less nodes and arcs. The first, Iterative Refinement, always solves the current relaxation to optimality and refines the graph if the solution is not feasible for the expanded formulation. This is repeated until a proven optimal solution is found. The second, Branch and Refine, integrates the graph refinement into a branch and bound framework avoiding restarts. Computational experiments performed on modified Solomon instances demonstrate the advantage of using our solution approaches and show that Branch and Refine outperforms Iterative Refinement in all studied parameter configurations.
In wire-arc additive manufacturing (WAAM), the desired workpiece is built layerwise by a moving heat source depositing droplets of molten wire on a substrate plate. To reduce material accumulations, the trajectory of the weld source should be continuous, but transit moves without welding, called deadheading, are possible. The enormous heat of the weld source causes large temperature gradients, leading to a strain distribution in the welded material which can lead even to cracks. In summary, it can be concluded that the temperature gradient reduce the quality of the workpiece. We consider the problem of finding a trajectory of the weld source with minimal temperature deviation from a given target temperature for one layer of a workpiece with welding segments broader than the width of the weld pool. The temperature distribution is modeled using the finite element method. We formulate this problem as a mixed-integer linear programming model and demonstrate its solvability by a standard mixed-integer solver.
Within the last decades, the number of social networks is growing fast. The competition of retaining the customers to grow their platform and increase their profitability is rising. That is why companies need to detect possible churners to retain these. The problem of predicting the users’ lifetime, churning users, and the reasons for churning can be tackled by using machine learning.
The goal of this bachelor thesis is to build machine learning models to predict user churn and the user lifetime within the social network Jodel, a location-based anonymous messaging application for Android and iOS.
To get the best possible prediction results, we have started with extensive literature research, whose approaches we have tested and added to a machine learning pipeline to build predictive models. With these models, we have investigated the performance after different observation time windows and have finally compared the strongest models to detect similarities and understand the insights to learn their behaviour.
The results of this thesis are machine learning models for a selected representative set of communities varying in size within the Kingdom of Saudi Arabia and a country model leveraging all data. These models are used for a regression task by predicting the lifetime of a user and a multi-label classification of a user into six different churn classes. Additionally, we have also given models for a binary classification, where the model will predict if the user will churn within a given time or not. These models have shown general strong predictive power, which is shrinking when limiting the observation time window. Especially the binary classification yielded high accuracy of over 99%.
The best models have been used for predicting user churn within other communities to detect communities with possible similar behaviour. These similarities then have been determined by features’ importance, where the most important features have got fed back into empirics. This has shown statistically significant differences between user groups with a different active time but as of today no clear trends were visible that had led us to define the communities’ behaviours.
Since the competition of social networks is still growing, the retaining of users will stay a core marketing strategy, which will need to be tackled by machine learning and artificial intelligence. The created models could be useful for predicting churning users within the platform Jodel to detect these customers that will churn within a given time.
Researches did not focus much on anonymous and location-based messaging. That is why the results of this thesis on the anonymous messaging application Jodel opens a variety of possible tasks for the future in this context.
We consider the mission and flight planning problem for an inhomogeneous fleet of unmanned aerial vehicles (UAVs). Therein, the mission planning problem of assigning targets to a fleet of UAVs and the flight planning problem of finding optimal flight trajectories between a given set of waypoints are combined into one model and solved simultaneously. Thus, trajectories of an inhomogeneous fleet of UAVs have to be specified such that the sum of waypoint-related scores is maximized, considering technical and environmental constraints. Several aspects of an existing basic model are expanded to achieve a more detailed solution. A two-level time grid approach is presented to smooth the computed trajectories. The three-dimensional mission area can contain convex-shaped restricted airspaces and convex subareas where wind affects the flight trajectories. Furthermore, the flight dynamics are related to the mass change, due to fuel consumption, and the operating range of every UAV is altitude-dependent. A class of benchmark instances for collision avoidance is adapted and expanded to fit our model and we prove an upper bound on its objective value. Finally, the presented features and results are tested and discussed on several test instances using GUROBI as a state-of-the-art numerical solver.
For more than a decade, the interconnected structures of the Buddhist earthen heritage in Wanla village/Ladakh, illustrated most prominently by the historic Avalokiteshvara Temple, have been researched, conserved, and restored by the NGOs Achi Association and Achi Association India. In the fifteen articles of this publication, the interdisciplinary team of architects, art conservators, conservation scientists, art historians, and archaeologists present the results of their work with the village community for the preservation of this exceptional cultural heritage. They provide insights into a training programme for young people interested in their heritage, and explain the art historical details, construction techniques, building materials, building archaeology, and state of conservation of these structures, as well as the iconography of art works. This is all enriched by the conservation reports and documentation of several buildings.
All these efforts combined three major approaches: a fabric-based and a value-based analysis and assessment, along with a people-centred conservation.
In this thesis, a detailed chemical kinetic mechanism is developed to predict the oxidation of ammonia. The main aim is to cover the most important features of ammonia combustion - laminar flame speed, auto-ignition timing, emission formation, speciation in different reactors, and subsequently to study fuel/NOₓ interaction. Each elementary reaction in the mechanism is carefully reviewed based on several published literature, both experimental and theoretical rate parameters are selected accordingly. A wide range of published experimental data in multi-setup experiments are selected - in freely propagating and burner stabilized premixed flames and in shock tubes and jet-stirred and flow, reactor to assess the performance of the developed mechanism. The reaction mechanism also considers the formation of nitrogen oxides and the reduction of nitrogen oxides depending on the conditions of the surrounding gas phase. The experimental data from the literature are interpreted with the help of the kinetic model developed in this thesis.
A local algebraic simulation model was developed, to determine the characteristic length scales for dispersed phases. This model includes the Ishii- Zuber drag model, the lift, the wall lubrication force and the turbulent dispersion force as well. It is based on the Algebraic Interface Area Density (AIAD) model from the Helmholtz Zentrum Dresden Rossendorf (HZDR), which provides the morphology detection and the free surface drag model. The developed model is in agreement with the current state of knowledge based on an examination of the theory and of state of science models for interface momentum transfer.
This new simulation model was tested on three different experiments. Two experiments can be found in the literature, the Fabre 1987 and the Hewitt 1987 experiment. And the third simulation is based on a steam drum experiment. This steam drum experiment is designed with ERK Eckrohrkessel GmbH internals and was developed to examine the droplet mass flow out of the turbulent separation stage.
The implementation of all models and tests was performed using Ansys CFX. The first analysis was carried out to reproduce a wavy stratified flow to examine the effects of different simulation model set-ups according to the velocity and kinetic energy profiles, as well as the pressure drop gradient and the water level measured by Fabre 1987. The second analysis was a proof on concept for reproducing the vertical flow pattern by an experiment from Hewitt 1987. The third simulation analysed the water distribution in the steam drum and feeding pipes system as well as the droplet carryover into the gas phase in the turbulent separation region of the drum.
These simulations have shown, that the accuracy of the particle distribution model in interaction with the drag and non-drag forces is able to reproduce horizontal and vertical flow patterns. Higher deviations are recognised for the liquid volume fraction close above the interface. Generally, simulations can now be performed to optimise industrial steam drum designs.
Research on energy saving technologies surged in the last decades. One especially relevant technology regards thermal energy storage via phase change materials, or PCM. These materials function as regenerative thermal batteries that can absorb and release thermal energy via the latent heat associated with a phase change, while temperature is kept constant. The advantage of this technology is that due to the latent heat effect the energy density is very high, which reduces the required size of the medium and makes it easier to be coupled with heat loss sources, both in industrial and household applications. The challenge lies, however, in identifying correct PCMs for specific operation temperatures. The goal of the thesis is, then, to develop a novel thermodynamic database that describes the thermodynamic properties of salt mixtures with potential as phase change materials, both for high (up to 800 ℃) and low temperature (up to 100 ℃) applications; and, then, to perform a screening to identify potential PCM compositions in the database.
The database is created with FactSage, a Calphad software, and the systems covered are the CaCl₂-Ca(NO₃)₂-KCl-KNO₃-NaCl-NaNO₃, for high temperature PCMs, and the hydrated Mn(NO₃)₂-H₂O, Zn(NO₃)₂-H₂O, MgSO₄-H₂O and ZnSO₄-H₂O for low temperature PCMs. The liquid solution in all systems is modelled with the non-ideal associates model and, therefore, no aqueous solution model is required. The experimental data used for the assessments come from the literature and from new measurements performed by the partners of the PCM-Screening project (FKZ 03ET1441).
A new program called DataOptimizer has been developed to assist with the optimisation of thermodynamic databases. Relying on the ChemApp software and the NOMAD optimizer, DataOptimizer overcomes many shortcomings of similar database optimisation programs. A graphical user interface featuring a real-time plotting output is also implemented, which allows for a much easier and user-friendly experience. Details about the implementation and features of the program are given.
Finally, the identification of PCM candidates is performed using both phase diagrams calculated with FactSage and a new numerical screening algorithm, which relies on ChemApp. The screening algorithm proves to be capable of identifying eutectics in multicomponent systems automatically without the need for phase diagrams. As a result, twenty-two PCM candidates are identified for high temperature applications within the anhydrous system and, fourteen candidates, for low temperature applications within the hydrated systems.
The rapid detection of infectious diseases is still an unsolved problem since their identification must be carried out either by cultivation or DNA analysis in a laboratory. The development of point-of-care (PoC) is a current development trend that requires further technological impulses to produce reliable and cost-effective systems. By miniaturizing and integrating microfluidic and electronic components, the advantages of electronic methods can be transferred to the field of PoC testing. The combination of complementary metal-oxide-semiconductor (CMOS) technology with microfluidic platforms allowed the development of fully functional sample-to-result LoC setups, which served the portability of the device even out of the laboratory or hospitals. CMOS-based LoC device can control and manage the data from sensors, microfluidics, and actuators. Dielectrophoresis (DEP) is a non-destructive and non-invasive method promising to be used in PoC medical applications. Utilizing MEMS technology and fabrication of microelectrodes allow DEP to be applied in biomedical applications such as cell manipulation and separation with high speed, sensitivity and without any labeling. Cell detection and separation occupy an important place in diagnostics of viral and infectious diseases such as Influenza and COVID-19. Therefore, rapid, sensitive, and automated LoC devices are needed to detect such diseases. Starting from this point of view, manipulating the cells as a way to detect them using DEP was decided as the main objective of the thesis.
This work aimed at developing a miniaturized CMOS integrated silicon microfluidic device, in line with a standard CMOS procedure, for characterization and manipulation of live and dead yeast cells using the DEP technique. Understanding the relationship between the microelectrode’s geometry and the magnitude of DEP force, the microfluidic devices can be designed to produce the most effective DEP implication on biological samples. In this work, interdigitated electrode arrays (IDEs) were used to manipulate the cells. This microelectrode was primarily used to detect microorganisms in a solution, based on the measurement of the variation of the dielectric constant by the concentration of the microorganisms. Therefore, finite element simulations were performed to optimize this microelectrode and adapt it to our application. Thus, the IDEs were optimized as a function of finger width and spacing between adjacent fingers. One of the most serious matters related to DEP-based microfluidic devices is that the DEP spectra of the targeted cell should precisely be known. Therefore, the DEP spectrum analysis of various cell suspensions with different medium conductivities was studied comprehensively by finite element simulation and experimentally. This study presented an optimized trapping platform for both detection and separation applications in terms of electrode dimension and electrical parameters.