Diploma thesis is focused on advanced reliability analysis of structures solved by non--linear finite element analysis. Specifically, semi--probabilistic methods for determination of design value of resistance, sensitivity analysis and surrogate model created by polynomial chaos expansion are described in the diploma thesis. Described methods are applied on prestressed reinforced concrete roof girder.

Being able to accurately approximate outputs of computationally expensive simulations for arbitrary input parameters, also called missing points estimation, is central in many different areas of research and development with applications ranging from uncertainty propagation to control system design to name a few. This project investigates the potential of kernel transformations and nonlinear autoencoders as methods of improving the accuracy of the proper orthogonal decomposition method combined with regression. The techniques are applied on aerodynamic pressure CFD data around airplane wings in both two- and three-dimensional settings. The novel methods show potential in select situations, but cannot at this stage be generally considered superior. Their performances are similar although the procedure of design and training of a nonlinear autoencoder is less straight forward and more time demanding than using kernel transformations. The results demonstrate the regression bottleneck of the proper orthogonal decomposition method, which partially is improved with the new methods. Future studies should focus on adapting the autoencoder training strategy to the architecture and data as well as improving the regression stage of all methods.

Indiana University-Purdue University Indianapolis (IUPUI) / With the recent advancement of computation and imaging technology, Image-based computational fluid dynamics (ICFD) has emerged as a great non-invasive capability to study biomedical flows. These modern technologies increase the potential of computation-aided diagnostics and therapeutics in a patient-specific environment. I studied three components of this image-based computational fluid dynamics process in this work. To ensure accurate medical assessment, realistic computational analysis is needed, for which patient-specific image segmentation of the diseased vessel is of paramount importance. In this work, image segmentation of several human arteries, veins, capillaries, and organs was conducted to use them for further hemodynamic simulations. To accomplish these, several open-source and commercial software packages were implemented. This study incorporates a new computational platform, called InVascular, to quantify the 4D velocity field in image-based pulsatile flows using the Volumetric Lattice Boltzmann Method (VLBM). We also conducted several parametric studies on an idealized case of a 3-D pipe with the dimensions of a human renal artery. We investigated the relationship between stenosis severity and Resistive index (RI). We also explored how pulsatile parameters like heart rate or pulsatile pressure gradient affect RI. As the process of ICFD analysis is based on imaging and other hemodynamic data, it is often time-consuming due to the extensive data processing time. For clinicians to make fast medical decisions regarding their patients, we need rapid and accurate ICFD results. To achieve that, we also developed surrogate models to show the potential of supervised machine learning methods in constructing efficient and precise surrogate models for Hagen-Poiseuille and Womersley flows.

Neutrino astronomy is expanding into the ultra-high energy (>1017eV) frontier with the use of in-ice detection of Askaryan radio emission from neutrino-induced particle showers. There are already pilot arrays for validating the technology and the next few years will see the planning and construction of IceCube-Gen2, an upgrade to the current neutrino telescope IceCube. This thesis aims to facilitate that planning by providing faster simulations using deep learning surrogate models. Faster simulations could enable proper optimisation of the antenna stations providing better sensitivity and reconstruction of neutrino properties. The surrogates are made for two parts of the end-to-end simulations: the signal generation and the signal propagation. These two steps are the most time-consuming parts of the simulations. The signal propagation is modelled with a standard fully connected neural network whereas for the signal generation a conditional Wasserstein generative adversarial network is used. There are multiple reasons for using these types of models. For both problems the neural networks provide the speed necessary as well as being differentiable -both important factors for optimisation. Generative adversarial networks are used in the signal generation because of the inherent stochasticity in the particle shower development that leads to the Askaryan radio signal. A more standard neural network is used for the signal propagation as it is a regression task. Promising results are obtained for both tasks. The signal propagation surrogate model can predict the parameters of interest at the desired accuracy, except for the travel time which needs further optimisation to reduce the uncertainty from 0.5 ns to 0.1 ns. The signal generation surrogate model predicts the Askaryan emission well for the limited parameter space of hadronic showers and within 5° of the Cherenkov cone. The two models provide a first step and a proof of concept. It is believed that the models can reach the required accuracies with more work.

This thesis covers the process of automating the design of coolant nozzles used for cylindrical grinding. Coolant nozzles are used to supply coolant, an oil and water mixture used to cool the metal workpiece and lubricate the grinding wheel. In the automotive industry, grinding is used to reduce the surface roughness of the workpiece. However, a large amount of heat is generated, risking the heat treatment of the steel to be compromised, for this, coolant is supplied to minimize the heat caused by friction. A nozzle is used, aiming a jet to the zone that generates heat. Commonly used nozzles are adjustable, leading to variation in cooling performance if misaligned. The design of fixed nozzles is developed in this thesis to reduce variation and automatise the design for multiple applications. The automatically designed nozzles are fused deposition modeled and tested. The design automation tool is tested repeatedly and improved successively in the span of the thesis. This lead to a great extent of implementation of design automation. Which lead to a facilitation in reaching of the work zone and avoid obstacles. Also, the tool managed to create nozzle tubes for a multitude of machines. The tool is able to generate, aim, orient, and individually dimension multi-nozzle tubes. Design of Experiment methodology is implemented to find nozzle designs with improved velocity and flow rate and minimize the air mixture with the coolant. Several nozzle designs are tested and fitted into a surrogate model that is, in turn, optimized. The results of the tests led to a greater understanding of how the nozzle geometry restricts the flow rate when attempts of reaching higher velocities of the coolant jet are made. The surrogate models created, also made it possible to find the range of designs which best suits different applications, whereby a Pareto front was able to be populated with a range of different designs alternating in flow rate, velocity and coherency ratio.

[pt] A análise de fluxo de água sob condição transiente não saturada requer o conhecimento das propriedades hidráulicas do solo. Essas relações constitutivas, denominadas curva característica e função de condutividade hidráulica, são descritas através de modelos empíricos que geralmente possuem vários parâmetros que devem ser calibrados com relação a dados coletados. Muitos dos parâmetros nos modelos constitutivos não podem ser medidos diretamente em campo ou laboratório, mas somente podem ser inferidos de forma significativa a partir de dados coletados e da modelagem inversa. Para obter os parâmetros do solo com a análise inversa, um algoritmo de otimização de busca local ou global pode ser aplicado. As otimizações globais são mais capazes de encontrar parâmetros ótimos, no entanto, a solução direta, por meio da modelagem numérica é computacionalmente custosa. Portanto, soluções analíticas (modelo substituto) podem superar essa falha acelerando o processo de otimização. Nesta dissertação, apresentamos a Regressão Polinomial Evolucionária (EPR) como uma ferramenta para desenvolver modelos substitutos do fluxo não saturado. Um rico conjunto de dados de parâmetros hidráulicos do solo é usado para calibrar o nosso modelo, e dados do mundo real são utilizados para validar nossa metodologia. Nossos resultados demonstram que o modelo da EPR prevê com precisão os dados de carga de pressão. As simulações do modelo se mostram concordantes com as simulações do programa Hydrus. / [en] Water flow analyses under transient soil hydraulic conditions require knowledge of the soil hydraulic properties. These constitutive relationships, named soil-water characteristic curve (SWCC) and hydraulic conductivity function (HCF) are described through empirical models which generally have several parameters that must be calibrated against collected data. Many of the parameters in SWCC and HCF models cannot be directly measured in field or laboratory but can only be meaningfully inferred from collected data and inverse modeling. In order to obtain the soil parameters with the inverse process, a local or global optimization algorithm may be applied. Global optimizations are more capable of fiding optimum parameters, however the direct solution through numerical modeling are time consuming. Therefore, analytical solutions (surrogate models) may overcome this shortcomming by accelerating the optimization process. In this work we introduce Evolutionary Polynomial Regression (EPR) as a tool to develop surrogate models of the physically-based unsaturated flow. A rich dataset of soil hydraulic parameters is used to calibrate our surrogate model, and real-world data are then utilized to validate our methodology. Our results demonstrate that the EPR model predicts accurately the observed pressure head data. The model simulations are shown to be in good agreement with the Hydrus software package.

The procedure of joining flexible or nonrigid parts using applied loads is called compliant assembly, and it is widely used in automotive, aerospace, electronics, and appliance manufacturing. Uncontrolled assembly processes may produce geometric errors that can exceed design tolerances and induce an increment of elastic energy in the structure due to the accumulation of internal stresses. This condition might create unexpected deformations and residual stress distributions across the structure that compromise product functionality. This thesis presents a method based on nonlinear Finite Element Analysis (FEA), metamodelling, and optimization techniques to provide accurate and on-time shimming strategies to support the definition of optimum assembly strategies. An example of the method on a typical aerospace wing box structure is demonstrated in the present study. The delivered outputs intend to support the production line by anticipating the response of the structure under a specific assembly condition and presenting alternative assembly strategies that can be applied to address eventual predicted issues on product requirements. / Graduate

Tato diplomová práce se zabývá elektromagnetickou analýzou a modelováním asynchronního stroje s plným rotorem. Tato práce tedy zahrnuje literární rešerši na téma vysokootáčkových elektrických strojů s porovnáním s klasickými elektrickými stroji s převodovkou a popisem jejich výhod či nevýhod, rozdělení vysokootáčkových elektrických strojů s plnými rotory a srovnání jejich výhod či nevýhod, kde se tato práce nejvíce soustřeďuje na vysokootáčkové asynchronní stroje s plnými rotory a jejich použití v průmyslu. Dále se tato práce zabývá metodami výpočtu elektrických asynchronních strojů s plnými rotory. Proto jsou zde uvedeny a popsány metody výpočtu stroje mezi které patří analytické metody i metoda konečných prvků. Vzhledem k povaze elektrických strojů s plnými rotory je hlavně kladen důraz v této práci na výpočet stroje pomocí metody konečných prvků ve 2D prostoru s využitím korekčních činitelů konců plných rotorů, které jsou zde velmi detailně popsány a rozděleny. Na základě dostupné literatury je vypočítaný elektrický stroj s plným rotorem pomocí MKP analýzy. Elektromagnetický výpočet stroje je automatizován pomocí skriptu vytvořeného v Pythonu. Dalším hlavním cílem této práce je popis tzv. náhradních modelů, uvedení jejich výhod či nevýhod, použití v jiných průmyslových odvětvích a hlavně použití náhradních modelů na elektrický stroj s plným rotorem. S využitím náhradních modelů je dále optimalizovaný vybraný asynchronní stroj s plným rotorem a to pomocí programů SymSpace a Optimizer. Pro samotnou optimalizaci byly uvažovány 3 návrhy stroje, které byly na závěr mezi sebou porovnány a to hlavně z hlediska jejich elektromagnetického výkonu.

<p>  </p> <p>With the recent advancement of computation and imaging technology, Image-based computational fluid dynamics (ICFD) has emerged as a great non-invasive capability to study biomedical flows. These modern technologies increase the potential of computation-aided diagnostics and therapeutics in a patient-specific environment. I studied three components of this image-based computational fluid dynamics process in this work.</p> <p>To ensure accurate medical assessment, realistic computational analysis is needed, for which patient-specific image segmentation of the diseased vessel is of paramount importance. In this work, image segmentation of several human arteries, veins, capillaries, and organs was conducted to use them for further hemodynamic simulations. To accomplish these, several open-source and commercial software packages were implemented. </p> <p>This study incorporates a new computational platform, called <em>InVascular</em>, to quantify the 4D velocity field in image-based pulsatile flows using the Volumetric Lattice Boltzmann Method (VLBM). We also conducted several parametric studies on an idealized case of a 3-D pipe with the dimensions of a human renal artery. We investigated the relationship between stenosis severity and Resistive index (RI). We also explored how pulsatile parameters like heart rate or pulsatile pressure gradient affect RI.</p> <p>As the process of ICFD analysis is based on imaging and other hemodynamic data, it is often time-consuming due to the extensive data processing time. For clinicians to make fast medical decisions regarding their patients, we need rapid and accurate ICFD results. To achieve that, we also developed surrogate models to show the potential of supervised machine learning methods in constructing efficient and precise surrogate models for Hagen-Poiseuille and Womersley flows.</p>