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Physics-informed neural networks (PINN) are machine-learning methods that have been proved to be very successful and effective for solving governing equations of fluid flow. In this work we develop a robust and efficient model within this framework and apply it to a series of two-dimensional three-component (2D3C) stereo particle-image velocimetry datasets, to reconstruct the mean velocity field and correct measurements errors in the data. Within this framework, the PINNsbased model solves the Reynolds-averaged-Navier-Stokes (RANS) equations for zeropressure-gradient turbulent boundary layer (ZPGTBL) without a prior assumption and only taking the data at the PIV domain boundaries. The TBL data has different flow conditions upstream of the measurement location due to the effect of an applied flow control via uniform blowing. The developed PINN model is very robust, adaptable and independent of the upstream flow conditions due to different rates of wall-normal blowing while predicting the mean velocity quantities simultaneously. Hence, this approach enables improving the mean-flow quantities by reducing errors in the PIV data. For comparison, a similar analysis has been applied to numerical data obtained from a spatially-developing ZPGTBL and an adverse-pressure-gradient (APG) TBL over a NACA4412 airfoil geometry. The PINNs-predicted results have less than 1% error in the streamwise velocity and are in excellent agreement with the reference data. This shows that PINNs has potential applicability to shear-driven turbulent flows with different flow histories, which includes experiments and numerical simulations for predicting high-fidelity data.
The next generation of civil turbofan engines targets the by-pass ratios of up to 20:1, requiring an innovative fan design with a low fan pressure ratio, low specific thrust and a radically increased fan diameter. The aerodynamic stability of such a large slow rotating fan is very sensitive against the back-pressure variations in the by-pass duct, especially during the take-off operations. The back-pressure regulation can be achieved significantly through a Variable Area Fan Nozzle (VAFN). This work deals with the design development of VAFN concepts for ultra-high by-pass ratio engines which was researched in EU funded program ENOVAL and received funding under grant agreement number 604999.
A system engineering approach was implemented for the VAFN development by following the requirements in conceptual, preliminary and detailed design phases. The design domains in the rear nacelle and under the core fairing were selected for the concept generation. Several qualitative and quantitative trade studies were conducted to down-select the best-fit solution during each design phase. These included the kinematic simulations of various types of VAFN modulations; analytical calculations to understand the thermodynamics of the selected VAFN kinematics; aerodynamic performance predictions using CFD simulations on a large number of preliminary designs; 3D CFD simulations for detailed performance assessments including the design optimization of individual features and distortions due to failed modulations; and FEM calculations for the topology generation and optimization of structural components. The overall weighted effect was determined for each output parameter and the results were presented in percentile changes relative to that with a fixed nozzle reference geometry.
Two VAFN concepts were selected for the final detailed design phase, Flaps in rear nacelle domain and Variable Inner Fairing Structure (VIFS). Both concepts showed better outputs in terms of specific fuel consumption, noise emission and fan’s safety margin during the take-off, with an over-area exhaust position than those with a fixed nozzle operation. During the climb phase, with an under-area VAFN position, both concepts resulted in drawbacks due to higher aerodynamic losses relative to the fixed nozzle. During MCR, both the VAFN concepts with stowed positions caused losses mainly due to leakages and higher structural weights relative to the fixed nozzle configuration. For each VAFN concept, a detailed system definition was developed and the function trees for each operation were explained. A discrete modulation type with two positions was described and recommended for both concepts. This included an over-area deployed position for the take-off phase and a stowed position for the rest of the flight, based on the beneficial performance of the VAFN concepts over the fixed clean nozzle configuration.
Additive Manufacturing of metals has become relevant for industrial applications. The near net-shape production of components produced by Laser Powder Bed Fusion (PBF-LB/M) enables new possibilities in component design combined with a reduction of the amount of needed material. Omitting the extra material, that was part of conventionally produced components due to machining constraints, results in components which in consequence lack the inherent additional safety margins provided by the higher material consumption of conventionally produced components. Therefore, to use PBF-LB/M metals in safety critical applications an in-depth understanding of porosity and internal stresses in parts made by PBF-LB/M is needed. Only non-destructive testing methods—such as computed tomography and residual stress analysis using neutrons—enable the assessment of porosity and stresses in the whole part. In this thesis I tackled creep and tensile static deformed specimens to fill research gaps in this field in terms of analysing PBF-LB/M stainless steel AISI 316L: from structural properties to in-situ behaviour. The initial void population of AISI 316L is studied using X-ray and synchrotron micro computed tomography. Specimens produced with different process parameters were analysed to quantify the influence of process parameters on the initial void population. The possibility to close voids using the laser illumination of subsequent layers is discussed by a quantitative study of the ability of the laser to melt different multiples of the applied layer thickness. The formation of internal stress is inherent to components produced by the PBFLB/ M process. These stresses remain in the components after production as residual stresses. In this thesis a study is presented which aims to unravel the mechanisms that define the spatial distribution of the residual stresses, and their magnitude. In the end, the population of internal voids during mechanical testing is studied by X-ray micro computed tomography. The evolution of damage accumulation in creep specimens is studied at different stages of the creep test. Results are compared to a creep tested conventionally made specimen and to a PBF-LB/M specimen from a tensile test. An interconnection between the PBF-LB/M microstructure and the pattern of damage is revealed.
Nanopore sequencing, a third-generation sequencing technique that applies nanometre sized pores to transduce the physical and chemical properties of specific nucleobases into measurable electrical signals, shows attractive advantages over conventional next-generation sequencing techniques. However, primarily due to high sequencing error rates this technique has rarely been used so far in clinical laboratory diagnostics. In this cumulative dissertation Nanopore sequencing was established and validated in clinical diagnostics using the example of the molecular diagnosis of Familial Mediterranean fever (FMF) and SARS coronavirus-2 (SARS-CoV-2) infections. First, a novel data analysis pipeline for accurate single nucleotide polymorphism (SNP) genotyping using Nanopore sequencing data was developed and validated with the corresponding sequencing protocol against conventional Sanger sequencing using 47 samples of patients with clinical suspicion of FMF. This method comparison showed a perfect agreement between both methods rendering current Nanopore sequencing in principle suitable for SNP genotyping in human genetics.
The bioinformatic analysis of sequencing data is one of the most challenging parts in Nanopore sequencing experiments and complicates the application in a clinical diagnostic setting. Therefore, six different bioinformatic tools for sequence alignment were evaluated regarding their applicability to Nanopore sequencing data. This evaluation revealed a good suitability of all except one of these tools although differences in quality and performance exist.
Since Nanopore sequencing showed a robust performance in SNP genotyping, a SARS-CoV-2 whole genome sequencing (WGS) protocol was established to enable onside viral WGS in a clinical laboratory. This was especially important for viral molecular biological surveillance during the pandemic as shown by analysing viral genetic data over the course of one year. Applying this approach in a clinical research project to investigate host-virus interaction by aggregating for the first time viral genetic data, serological data and clinical data, showed diverse humoral immune responses to SARS-CoV-2, that appear to be influenced by age, obesity and disease severity. Further, even small viral genetic changes may influence the clinical presentation of the associated disease COVID-19. Additionally, a novel reverse transcriptase (RT)- loop mediated isothermal amplification (LAMP) assay for the detection of SARS-CoV-2 was developed and validated for diagnostic use by method comparison with conventional RT-polymerase chain reaction (PCR). In summary, by presenting advancements of sequencing and bioinformatic workflows with the focus on an application in clinical diagnostics, the results of this thesis may pave the way for a broader application of Nanopore sequencing in laboratory medicine in the near future.
Between 2000 and 2015, the services sector grew by 23% in OECD countries, with around two-thirds of the working population employed in this sector. In the European Union, 72.1% of the labor force worked in the services sector in 2018. With a growing global population, the demand for services is also increasing, making increasing service productivity an important research goal.
Public and private service companies are subject to different incentive structures that influence their approach to productivity. Public service enterprises, especially in the social, health, and education sectors, play a critical role in society. Improving public service productivity is therefore essential.
Two research questions are addressed: What factors have the greatest impact on the performance of service workers? How can performance improvement be expressed by managing the influencing factors of frontline employees? By focusing on frontline employees, this dissertation recognizes their essential role in increasing service productivity. The goal of the thesis is to gain insight into the factors that influence their productivity in public service.
The dissertation reviews service productivity models in detail. Differences and connections between the various models and schools of models are discussed. Grönroos and Ojasalo's model of service productivity is selected as the theoretical framework for the empirical study to examine the factors influencing frontline employees. The systematic selection of influencing factors as well as the evaluation of previous research on these factors, reveals research gaps, which are translated into hypotheses. The majority of the data for the empirical part comes from public service companies. The designed questionnaire serves as a starting point for the development of a scale for empirical validation for one part, a component of the Grönroos-Ojasalo model. The research results offer a basis for action for practitioners who want to achieve productivity improvements.
Solving differential equations is still a topic of major interest, due to their appearance in many fields of science and engineering and a classic approach with neural networks builds upon trial solutions, the so-called neural forms. The latter are incorporated in a cost function that is subject to minimisation, to train the involved neural networks. Neural forms represent general and flexible tools for solving ordinary differential equations, partial differential equations as well as systems of each. However, the computational approach is in general highly dependent on a variety of computational parameters and the choice of the optimisation methods. Studying the solution of a simple but fundamental stiff ordinary differential equations with small feedforward neural networks and first order optimisation shows, that it is possible to identify preferable choices for parameters and methods. The neural network weight initialisation appears to be a sensitive topic, while having a major impact on the solution accuracy. Especially the use of non-random (deterministic) weights partially shows poor performance, but removes a stochastic component. Further research reveals, that a new polynomial representation of the neural forms can significantly increase the reliability of a deterministic initialisation (all weights have initially the same values assigned). In order to maintain smaller neural network architectures and solve the differential equation, even on fairly large domains, a new technique called domain segmentation (for initial value problems) is introduced. The solution domain splits into equidistant subdomains and the above-mentioned collocation polynomial neural forms are solved separately in each domain fragment. At the boundary of any subdomain, a new initial value is provided by the neural forms solution and directly incorporated in the adjacent one. In classic adaptive numerical methods for solving differential equations, the mesh as well as the domain may be refined or decomposed, respectively, in order to improve numerical accuracy. The subdomain distribution can also be connected with an adaptive refinement. That is, the neural network training status is combined with an adaptive subdomain size reduction in the new adaptive neural domain refinement algorithm. That is, each subdomain is reduced in size until the optimisation is resolved up to a predefined training accuracy. In addition, while the neural networks are by default small, the number of neurons may also be adjusted in an adaptive way. Conditions are introduced to automatically confirm the solution reliability and optimise computational parameters whenever it is necessary.
Multi- and manycore processors promise to combine high overall peak performance with moderate power consumption to meet the constantly growing demand for computational power under the energy constraints of today’s CMOS technology. Future systems with manycore processors are expected to contain a huge amount of cores, which exceeds the number of processes that will run simultaneously. Consequently, processor time sharing approaches, that introduce significant overhead from regular context switches in common OS, will no longer be necessary.
This work investigates mechanisms for scalable and energy-efficient spatial partitioning of multi- and manycore processor systems. In addition, it explores the implications of exclusive processor core allocation to user processes due to the absence of temporal multiplexing and offers approaches to ease the adaptation to the new programming model.
The proposed mechanisms achieved fast thread allocation which motivates applications for dynamic thread allocation and benefits performance as well as energy efficiency. The efficiency control and resource revocation mechanisms detect and prevent wasteful and inefficient resource occupation from poorly optimized or malicious processes. In this way, the global efficiency of the system is optimized. The dynamic processing resource allocation and revocation handling has been integrated into a task parallel runtime system, to disburden the application programmer from manual implementation and to increase productivity.
For the third time we were able to hold our PhD workshop "Research is Calling" on smart medical devices and systems, this time in Berlin again. This unique workshop brings together young scientists from computer science and medicine to provide a forum to discuss the very latest approaches to smart medical devices and systems. This year, for the first time, it is possible to publish corresponding workshop proceedings via the publication service of the Brandenburg Technical University (BTU). In our view, this format can create true interdisciplinarity because everyone presents their ideas and interim results to colleagues from a wide variety of disciplines and, conversely, hears and sees the latest from many other specialties. This forum is the vehicle for initiating new collaborations and planning joint research projects. The 'Cottbus project' emerged from personal partnerships between 'technicians' and 'physicians' and was planned from the beginning as a platform and an offer: as a platform for free and fully open scientific exchange and discourse, as an offer to all who are interested, also and gladly beyond Brandenburg. Begun as a "PhD Colloquium", Cottbus' "Research is Calling" is an ideas laboratory for young scientists. Cottbus is to establish a university medical faculty in the next few years as part of the structural change program following the coal phase-out. From our point of view, this lab is an important building block to establish cross-sectional issues right at the interface between medicine and technology, a culture of open discourse and scientific dialogue. The workshop covers the following topics in particular: Smart sensors for medical applications, Low-power wearable sensors, artificial intelligence for medical applications, medical robots, algorithms for medical applications, applications and case studies. Despite the pandemic situation the workshop could be held in presence. In five exciting presentations new ideas and cooperation possibilities were shown. Two keynotes (including Alina Nechyporenko, Marcus Frohme as special guests) and a dinner rounded off the program. At this point we would like to thank all participants, the reviewers, and the organizers, Stefan Scharoba and Kathleen Galke. We look forward to establishing this event as permanent and reaching an ever-growing audience.
Temperature variability may have direct and indirect impacts on the environments of the Accra and Kumasi Metropolises in Ghana. This study analysed temperature and trends in temperature in both cities using in-situ measurements from one meteorological station in both cities from 1986 to 2015. The temperature indices were computed using the RClimdex package from the Expert Team on Climate Change Detection Monitoring Indices (ETCCDMI). The temperature time series was pre-whitened before the Mann–Kendall trend and Sen’s slope estimator analysis were applied. Initial analysis revealed minimal variation in temperature in both cities. The results from the analysed temperature indices revealed an increase in warm days and a general rise in the minimum temperature compared to maximum temperatures. Mann Kendall and Sen’s slope revealed significant trends in the annual and seasonal (dry and wet seasons) in minimum temperature in both cities. These might lead to an increased rate of heat-stressed diseases and an overall rise in urban warming in both cities. The analysis of temperature, indices and trends provided comprehensive insights into the temperature of Accra and Kumasi. The results highlight the essence of evaluating temperature indices and trends in light of Climate Change concerns. It is recommended that urban green and blue spaces should be incorporated into land use plans as these policy directions can aid regulate the temperature in both cities.
Neuroadaptive technology (NAT) utilizes real-time measures of neurophysiological activity within a closed control loop to create intelligent software adaptation. Measures of electrocortical and neurovascular brain activity are quantified to provide a dynamic representation of the psychological state of the user, with respect to cognitions, emotions and motivation. As such, NAT can access unique aspects of human information processing, and human intelligence, which can subsequently be used to enable more versatile and more human-like forms of machine intelligence. Current trends in different scientific fields indicate an increased interest in integrating context-sensitive information from the human brain into Artificial Intelligence. NAT'22, the Neuroadaptive Technology Conference 2022, was intended to bring scientists interested in Physiological Computing, Applied Neurosciences and Passive Brain-Computer Interfaces together with experts from the fields of Artificial Intelligence, Machine Learning and Intelligent Systems. The main goals of the conference were an exchange of research questions and findings from these fields and the identification of common goals and joint ventures in the domain of Neuroadaptive Technology, including: real-time signal processing, unsupervised vs. supervised ML, designing neuroadaptive interaction, explainable AI (XAI), neuroadaptive applications, hybrid AI systems (DL + symbolic AI) for applied neurosciences, ethics of neurotechnology in real world (responsibility for action, security), cloud-based solutions for data management and more.
NAT'22 was held in Lübbenau, near Berlin, and organised by the Society for Neuroadaptive Technology. These Proceedings contain the abstracts of six keynote lectures and a total of 39 submissions in the categories of Brain-Computer Interface & Applications, Ethics & Perspectives, Artificial Intelligence & Machine Learning, and a poster session.