Tesis sobre el tema "Polynomial chao"
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Xiaochen, Liu. "Statistical Analysis of Integrated Circuits Using Decoupled Polynomial Chaos". Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34836.
Texto completoYorke, Rory. "Chaos control using local polynomial approximation". Master's thesis, University of Cape Town, 2001. http://hdl.handle.net/11427/5075.
Texto completoChaotic systems may be defined as those whose behaviour is sensitively dependent on initial conditions. Such systems may be made periodic using small input perturbations, as proposed in [OGY90]; this is called Ott-Grebogi-Yorke (OGY) chaos control. The original method used a linear model for controller design; a later development of chaos control was [CCdF99], in which a polynomial model is used. This dissertation proposes using local Taylor polynomial models as a basis for chaos control.
Templeton, Brian Andrew. "A Polynomial Chaos Approach to Control Design". Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/28840.
Texto completoPh. D.
Whittle, Lisa. "Stochastic Optimal Trajectory Generation via Multivariate Polynomial Chaos". Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-65746.
Texto completoSzepietowska, Katarzyna. "POLYNOMIAL CHAOS EXPANSION IN BIO- AND STRUCTURAL MECHANICS". Thesis, Bourges, INSA Centre Val de Loire, 2018. http://www.theses.fr/2018ISAB0004/document.
Texto completoThis thesis presents a probabilistic approach to modelling the mechanics of materials and structures where the modelled performance is influenced by uncertainty in the input parameters. The work is interdisciplinary and the methods described are applied to medical and civil engineering problems. The motivation for this work was the necessity of mechanics-based approaches in the modelling and simulation of implants used in the repair of ventral hernias. Many uncertainties appear in the modelling of the implant-abdominal wall system. The probabilistic approach proposed in this thesis enables these uncertainties to be propagated to the output of the model and the investigation of their respective influences. The regression-based polynomial chaos expansion method is used here. However, the accuracy of such non-intrusive methods depends on the number and location of sampling points. Finding a universal method to achieve a good balance between accuracy and computational cost is still an open question so different approaches are investigated in this thesis in order to choose an efficient method. Global sensitivity analysis is used to investigate the respective influences of input uncertainties on the variation of the outputs of different models. The uncertainties are propagated to the implant-abdominal wall models in order to draw some conclusions important for further research. Using the expertise acquired from biomechanical models, modelling of historic timber joints and simulations of their mechanical behaviour is undertaken. Such an investigation is important owing to the need for efficient planning of repairs and renovation of buildings of historical value
Nydestedt, Robin. "Application of Polynomial Chaos Expansion for Climate Economy Assessment". Thesis, KTH, Optimeringslära och systemteori, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-223985.
Texto completoInom klimatekonomi används integrated assessment models (IAMs) för att förutspå hur klimatförändringar påverkar ekonomin. Dessa IAMs modellerar komplexa interaktioner mellan geofysiska och mänskliga system för att kunna kvantifiera till exempel kostnaden för den ökade koldioxidhalten på planeten, i.e. Social Cost of Carbon (SCC). Detta representerar den ekonomiska kostnaden som motsvaras av utsläppet av ett ton koldioxid. Faktumet att både de geofysiska och ekonomiska submodulerna är stokastiska gör att SCC-uppskattningar varierar mycket även inom väletablerade IAMs som PAGE och DICE. Variationen grundar sig i skillnader inom modellerna men också från att val av sannolikhetsfördelningar för de stokastiska variablerna skiljer sig. Eftersom IAMs ofta är formulerade som optimeringsproblem leder dessutom osäkerheterna till höga beräkningskostnader. I denna uppsats introduceras en ny IAM, FAIR/DICE, som är en diskret tids hybrid av DICE och FAIR. Den utgör en potentiell förbättring av DICE eftersom klimat- och kolmodulerna i FAIR även behandlar återkoppling från klimatmodulen till kolmodulen. FAIR/DICE är analyserad med hjälp av Polynomial Chaos Expansions (PCEs), ett alternativ till Monte Carlo-metoder. Med hjälp av PCEs kan de osäkerheter projiceras på stokastiska polynomrum vilket har fördelen att beräkningskostnader reduceras men nackdelen att lagringskraven ökar. Detta eftersom många av beräkningarna kan sparas från första simuleringen av systemet, dessutom kan statistik extraheras direkt från PCE koefficienterna utan behov av sampling. FAIR/DICE systemet projiceras med hjälp av PCEs där en osäkerhet är introducerad via equilibrium climate sensitivity (ECS), vilket i sig är ett värde på hur känsligt klimatet är för koldioxidförändringar. ECS modelleras med hjälp av en fyra-parameters Beta sannolikhetsfördelning. Avslutningsvis jämförs resultat i medelvärde och varians mellan PCE implementationen av FAIR/DICE och en Monte Carlo-baserad referens, därefter ges förslag på framtida utvecklingsområden.
Perez, Rafael A. "Uncertainty Analysis of Computational Fluid Dynamics Via Polynomial Chaos". Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/28984.
Texto completoPh. D.
Ishak, Hassoun. "Étude stochastique de l'impact des défauts de porosités et de plissements dans les matériaux composites". Thesis, Nantes, 2017. http://www.theses.fr/2017NANT4090/document.
Texto completoComposite materials are increasingly used in various fields such as aerospace or renewable marine energies due to their excellent specific properties. However, the manufacturing processes of the composite structures are complex, which can lead to the appearance of defects, particularly wrinkles and porosities, which affect the mechanical properties of the structure. Based on conservative criteria, a system of non-destructive testing of composite parts thus makes it possible to judge their conformity. In case of non-conformity, those components are rejected, with non-negligible economic consequences. The objective of this study is to quantify the impact of the defects and associated uncertainties on the behavior of composite parts. In this work, we adopt a parametric vision of the uncertainties consisting in representing the probabilistic content through a finite set of random variables. We focus on the propagation of uncertainties based on spectral stochastic methods. The study involving porosity is done at the micro-scale and then at the macro-scale. The random input parameters are related to the geometry of the porosities and their rates. The study of the wrinkle defect, done at the mesoscopic scale, is based on a parametric representation of the geometry of the wrinkle. The random input parameters then represent the shape and size of this defect. It is therefore possible to analyze the impact of these two manufacturing defects at a structural scale through classical mechanical quantities and check the failure of the structure with failure criteria
Koehring, Andrew. "The application of polynomial response surface and polynomial chaos expansion metamodels within an augmented reality conceptual design environment". [Ames, Iowa : Iowa State University], 2008.
Buscar texto completoFisher, James Robert. "Stability analysis and control of stochastic dynamic systems using polynomial chaos". [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-2853.
Texto completoAyres, Daniel. "Uncertainty quantification in nuclear criticality modelling using methods of polynomial chaos". Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/26993.
Texto completoBlatman, Géraud. "Adaptive sparse polynomial chaos expansions for uncertainty propagation and sensitivity analysis". Clermont-Ferrand 2, 2009. https://tel.archives-ouvertes.fr/tel-00440197.
Texto completoPrice, Darryl Brian. "Estimation of Uncertain Vehicle Center of Gravity using Polynomial Chaos Expansions". Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/33625.
Texto completoMaster of Science
Shimp, Samuel Kline III. "Vehicle Sprung Mass Parameter Estimation Using an Adaptive Polynomial-Chaos Method". Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/32056.
Texto completoMaster of Science
SOLANO, ALEJANDRA CAMACHO. "UNCERTAINTY QUANTIFICATION IN OIL RESERVOIR SIMULATION VIA GENETIC PROGRAMMING AND CHAOS POLYNOMIAL". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2015. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=26216@1.
Texto completoCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Os modelos de simulação de reservatórios estão sujeitos à incerteza presente em uma grande variedade de seus parâmetros de entrada. Esta incerteza é o resultado da heterogeneidade das formações geológicas, erros nas medições dos dados e da modelagem petrofísica, estrutural e do transporte dos fluidos no meio poroso. Uma quantificação precisa da incerteza requer, na maioria dos casos, uma quantidade elevada de simulações, o que é usualmente inviável se considerarmos o tempo consumido para simular modelos de grande escala. Por outro lado, uma avaliação adequada da incerteza aumenta a qualidade e robustez das decisões tomadas para o gerenciamento dos campos de petróleo. Com esta motivação, foi investigado o método das Expansões por Caos Polinomial (PCE, por suas siglas em inglês). PCE é uma técnica de convergência rápida utilizada para analisar como se propaga, na saída de um modelo, a incerteza presente nos parâmetros de entrada. Mediante PCE, pode-se representar a resposta aleatória de um modelo de simulação de reservatórios de petróleo como um polinômio, construído a partir de uma base de funções que dependem da distribuição de probabilidade das variáveis incertas de entrada. Por outro lado, quando a relação entre os parâmetros de entrada e a saída do modelo têm um componente não polinomial, o algoritmo de Programação Genética (PG) pode ser utilizado para representar esta dependência utilizando funções ou operadores mais complexos. PG é um algoritmo de regressão simbólica capaz de encontrar uma expressão aleatória explícita, que aproxime a saída de um modelo de simulação de reservatórios de petróleo, conhecendo-se a priori a distribuição de probabilidade dos parâmetros de entrada. Neste trabalho foram aplicadas as duas técnicas, antes mencionadas, num modelo de simulação de reservatórios baseado no campo PUNQ-S3, considerando até vinte e três parâmetros incertos durante um período de produção de 13 anos. Foi feita uma análise de incerteza, calculando-se a distribuição de probabilidade completa da saída do simulador. Os resultados foram comparados com o método de Monte Carlo, indicando um alto desempenho em termos de custo computacional e acurácia. Ambas as técnicas conseguem níveis de ajuste superiores a 80 porcento com uma quantidade de simulações consideravelmente baixa.
Reservoir simulation models are subject to uncertainty in a wide variety of its inputs. This uncertainty is a result of the heterogeneity of the geological formations, data measurement errors, and petrophysical, structural, and fluid transport in porous media modelling. An accurate uncertainty quantification requires, in most cases, a large number of simulations, which is unviable considering the time it takes to simulate large scale models. On the other hand, a proper uncertainty assessment, increases the robustness of the decision making process for the oil field management. To this end, the method of Polynomial Chaos Expansions (PCE) was studied. PCE is a fast paced convergence technique, used to analyze the uncertainty propagation of the input parameters all the way to the output of the model. Through PCE is possible to represent the response of an oil reservoir simulation model as a polynomial, built from a function basis, that depend on the probability distribution of the uncertain input variables. Furthermore, when the relationship between the input and output parameters of the model has a non-polynomial component, the algorithm of Genetic Programming (GP) can be used to represent this dependency by more elaborate functions or operators. GP is a symbolic regression algorithm, capable of finding an explicit expression that approximates the output of a reservoir simulation model, with prior knowledge of the probability distribution of the input parameters. In this work, the two previously mentioned techniques were applied in a reservoir simulation model, based on the oil field PUNQ-S3, considering up to twenty three uncertain parameters during a simulation period of 13 years. An uncertainty analysis of the output of the simulator was conducted, calculating the entire probability distribution. The results were compared to the Monte Carlo simulation method, presenting a satisfactory performance in terms of accuracy and computational cost. Both techniques show adjustment levels higher than 80 percent, with a considerable small amount simulations.
Lee, Hyunwook. "A Polynomial Chaos Approach for Stochastic Modeling of Dynamic Wheel-Rail Friction". Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/77195.
Texto completoPh. D.
Beddek, Karim. "Propagation d’incertitudes dans les modèles éléments finis en électromagnétisme : application au contrôle non destructif par courants de Foucault". Thesis, Lille 1, 2012. http://www.theses.fr/2012LIL10175.
Texto completoThe uncertainty quantification technique aims to quantify the effect of uncertainties of input parameters of numerical models, e.g. material, geometry, source terms, on the quantity of interest. In this thesis, we focus on probabilistic approaches in order to spread uncertainties of magnetic and electric behavior laws over large scale electromagnetic finite element models. The main objective of this work is to compare two spectral stochastic methods (Non Intrusive Spectral Projection (NISP) and Spectral Stochastic Finite Element Method (SSFEM)), which are based on chaos polynomial representation of the random quantities. The comparison between the NISP and the SSFEM is carried out by confronting the computational costs and the precision when scalar and vector complex quantities of interest are computed. The numerical applications show that the SSFEM method become as competitive as the NISP method in terms of computational cost when solving probabilistic problems with large number of random parameters. Thus, the SSFEM method is chosen as the best adapted to solve electromagnetic problems when the behavior laws are random. In fact, the NISP method is inappropriate to compute vector complex quantities when equipped with adaptive sparse grid procedures. Finally, the NISP and SSFEM methods are used to study the clogging of the Tube Support Plate (TSP) of steam generators of nuclear power plants. The effect of uncertainties of the permeability and the conductivity of the TSP and the magnetite (clogging product) on the control signal and the SAX ratio is investigated
Blatman, Géraud. "Chaos polynomial creux et adaptatif pour la propagation d'incertitudes et l'analyse de sensibilité". Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2009. http://tel.archives-ouvertes.fr/tel-00440197.
Texto completoLi, Lin. "Treatment of Uncertainties in Vehicle and Terramechanics Systems Using a Polynomial Chaos Approach". Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/29030.
Texto completoPh. D.
Cooper, Rachel Gray. "Augmented Neural Network Surrogate Models for Polynomial Chaos Expansions and Reduced Order Modeling". Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103423.
Texto completoMaster of Science
The world is an elaborate system of relationships between diverse processes. To accurately represent these relationships, increasingly complex models are defined to better match what is physically seen. These complex models can lead to issues when trying to use them to predict a realistic outcome, either requiring immensely powerful computers to run the simulations or long amounts of time to present a solution. To fix this, surrogates or approximations to these complex models are used. These surrogate models aim to reduce the resources needed to calculate a solution while remaining as accurate to the more complex model as possible. One way to make these surrogate models is through neural networks. Neural networks try to simulate a brain, making connections between some input and output given to the network. In the case of surrogate modeling, the input is some current state of the true process, and the output is what is seen later from the same system. But much like the human brain, the reasoning behind why choices are made when connecting the input and outputs is often largely unknown. Within this paper, we seek to add meaning to neural network surrogate models in two different ways. In the first, we change what each piece in a neural network represents to build large polynomials (e.g., $x^5 + 4x^2 + 2$) to approximate the larger complex system. We show that the building of these polynomials via neural networks performs much better than traditional ways to construct them. For the second, we guide the choices made by the neural network by enforcing restrictions in what connections it can make. We do this by using additional information from the larger system to ensure the connections made focus on the most important information first before trying to match the less important patterns. This guiding process leads to more information being captured when the surrogate model is compressed into only a few dimensions compared to traditional methods. Additionally, it allows for a faster learning time compared to similar surrogate models without the information.
Prempraneerach, Pradya 1975. "Uncertainty analysis in a shipboard integrated power system using multi-element polynomial chaos". Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/40364.
Texto completoErrata, dated Oct. 30, 2007, inserted between pages 3 and 4 of text.
Includes bibliographical references (p. 301-307).
The integrated power system has become increasingly important in electric ships due to the integrated capability of high-power equipment, for example, electromagnetic rail guns, advance radar system, etc. Several parameters of the shipboard power system are uncertain, caused by a measurement difficulty, a temperature dependency, and random fluctuation of its environment. To date, there has been little if any studies which account for these stochastic effects in the large and complex shipboard power system from either an analytical or a numerical perspective. Furthermore, all insensitive parameters must be identified so that the stochastic analysis with the reduced dimensional parameters can accelerate the process. Therefore, this thesis is focused on two main issues - stochastic and sensitivity analysis - on the shipboard power system. The stochastic analysis of the large and complex nonlinear systems with the non-Gaussian random variables or processes, in their initial states or parameters, are prohibited analytically and very time consuming using the brute force Monte Carlo method. As a result, numerical stochastic solutions of these systems can be efficiently solved by the generalized Polynomial Chaos (gPC) and Probabilistic Collocation Method (PCM).
(cont.) In the case of the long-time integration and discontinuity in the stochastic solutions, the multi-element technique of PCM, which refines the solution in random space, can significantly improve the solutions' accuracy. Furthermore, the hybrid gPC+PCM is developed to extend the gPC ability to handle a system with nonlinear non-polynomial functions. Then, we systematically establish the convergence rate and compare the convergence performance among all numerical stochastic algorithms on various systems with both continuous and discontinuous solutions as a function of random dimension and the algorithms' accuracy governing parameters. To identify the most significant parameter in the large-scale complex systems, we propose new sensitivity analysis techniques - Monte Carlo Sampling, Collocation, Variance, and Inverse Variance methods - for static functions and show that they agree well with Morris method, which is one of the existing sensitivity analysis techniques for a function with large input dimensions. In addition, we extend the capability of the Sampling, Collocation, Variance, and the Morris methods to study both the parameters' sensitivity and the interaction of the ordinary differential equations.
(cont.) In each approach, both strength and limitations of the sensitivity ranking accuracy and the convergence performance are emphasized. The convergence rate of the Collocation and Variance methods are more than an order of magnitude faster than that of Morris and Sampling methods for low and medium parameters' dimensions. At last, we successfully apply both stochastic and sensitivity analysis techniques to the integrated shipboard power system, with both open-and close-loop control of the propulsion system, to study a propagation of uncertainties and rank parameters in the order of their importance, respectively.
by Pradya Prempraneerach.
Ph.D.in Ocean Engineering
Xu, Yijun. "Uncertainty Quantification, State and Parameter Estimation in Power Systems Using Polynomial Chaos Based Methods". Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/97876.
Texto completoPHD
Nabavi, Seyed Ghavamoddin. "Statistical Analysis of Steady State Response in RF Circuits via Decoupled Generalized Polynomial Chaos". Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35293.
Texto completoKersaudy, Pierric. "Modélisation statistique de l'exposition humaine aux ondes radiofréquences". Thesis, Paris Est, 2015. http://www.theses.fr/2015PESC1120/document.
Texto completoThe purpose of this thesis is to deal with the problem of the management and the characterization of the variability of the human exposure to radio frequency waves through the use of the numerical dosimetry. As a matter of fact, if the recent advances in the high performance computing domain led to reduce significantly the simulation duration for the evaluation of the human exposure, this computation of the specific absorption rate remains a time-consuming process. With the variability of the usage, this constraint does not allow the analysis of the influence of random input parameters on the exposure to be achieved with classical approaches such as Monte Carlo simulations. In this work, two approaches are proposed to address this problem. The first one is based on the use and the hybridization of construction methods of surrogate models in order to study the global influence of the input parameters. The second one aims at assessing efficiently the 95th-percentiles of the output distributions in a parcimonous way. It is based on the development of an adaptive and oriented methodology of design of experiments combined with the construction of surrogate models. In this manuscript, the proposed methods are compared and tested on analytical examples and then applicated to full-scale problems from the numerical dosimetry
Holdorf, Lopez Rafael. "Optimisation en présence d’incertitudes". Thesis, Rouen, INSA, 2010. http://www.theses.fr/2010ISAM0009.
Texto completoThe optimization is a very important tool in several domains. However, among its applications, it is hard to find examples of systems to be optimized that do not possess a certain uncertainty level on its parameters. The main goal of this thesis is the treatment of different aspects of the optimization under uncertainty. We present a brief review of the literature on this topic, which shows the lack of methods able to characterize the probabilistic properties of the optimum point of functions that depend on random parameters. Thus, the first main contribution of this thesis is the development of two methods to eliminate this lack: the first is based on Monte Carlo Simulation (MCS) (considered as the reference result) and the second is based on the polynomial chaos expansion (PCE). The validation of the PCE based method was pursued by comparing its results to those provided by the MCS method. The numerical analysis shows that the PCE method is able to approximate the probability density function of the optimal point in all the problems solved. It was also showed that it is able to approximate even high order statistical moments such as the kurtosis and the asymmetry. The second main contribution of this thesis is on the treatment of probabilistic constraints using the reliability based design optimization (RBDO). Here, a new RBDO method based on safety factors was developed. The numerical examples showed that the main advantage of such method is its computational cost, which is very close to the one of the standard deterministic optimization. This fact makes it possible to couple the new method with global optimization algorithms
Owen, Nathan Edward. "A comparison of polynomial chaos and Gaussian process emulation for uncertainty quantification in computer experiments". Thesis, University of Exeter, 2017. http://hdl.handle.net/10871/29296.
Texto completoBlanchard, Emmanuel. "Polynomial Chaos Approaches to Parameter Estimation and Control Design for Mechanical Systems with Uncertain Parameters". Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/26727.
Texto completoPh. D.
Alkhateeb, Osama. "DATA-DRIVEN UNCERTAINTY QUANTIFICATION IN APPLICATIONS OF ELECTROMAGNETICS AND WIRELESS COMMUNICATION VIA ARBITRARY POLYNOMIAL CHAOS". University of Akron / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=akron1509988525627307.
Texto completoCooper, Michele Desiree. "Control Design and Model Validation for Applications in Nonlinear Vessel Dynamics". Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/52905.
Texto completoPh. D.
Yadav, Vaibhav. "Novel Computational Methods for Solving High-Dimensional Random Eigenvalue Problems". Diss., University of Iowa, 2013. https://ir.uiowa.edu/etd/4927.
Texto completoAguirre, Luis Antonio. "Application of global polynomial models in the identification, analysis and control of nonlinear dynamics and chaos". Thesis, University of Sheffield, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324232.
Texto completoSchick, Michael [Verfasser] y V. [Akademischer Betreuer] Heuveline. "Uncertainty Quantification for Stochastic Dynamical Systems : Spectral Methods using Generalized Polynomial Chaos / Michael Schick. Betreuer: V. Heuveline". Karlsruhe : KIT-Bibliothek, 2012. http://d-nb.info/101936193X/34.
Texto completoSong, Chen [Verfasser] y Vincent [Akademischer Betreuer] Heuveline. "Uncertainty Quantification for a Blood Pump Device with Generalized Polynomial Chaos Expansion / Chen Song ; Betreuer: Vincent Heuveline". Heidelberg : Universitätsbibliothek Heidelberg, 2018. http://d-nb.info/1177252406/34.
Texto completoLangewisch, Dustin R. "Application of the polynomial chaos expansion to multiphase CFD : a study of rising bubbles and slug flow". Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/92097.
Texto completoCataloged from PDF version of thesis.
Includes bibliographical references (pages 157-167).
Part I of this thesis considers subcooled nucleate boiling on the microscale, focusing on the analysis of heat transfer near the Three-Phase (solid, liquid, and vapor) contact Line (TPL) region. A detailed derivation of one representative TPL model is presented. From this work, it was ultimately concluded that heat transfer in the vicinity of the TPL is rather unimportant in the overall quantification of nucleate boiling heat transfer; despite the extremely high heat fluxes that are attainable, it is limited to a very small region so the net heat transfer from this region is comparatively small. It was further concluded that many of the so-called microlayer heat transfer models appearing in the literature are actually models for TPL heat transfer; these models do not model the experimentally observed microlayer. This portion of the project was terminated early, however, in order to focus on the application of advanced computational uncertainty quantification methods to computational multiphase fluid dynamics (Part II). Part II discusses advanced uncertainty quantification (UQ) methods for long-running numerical models, namely computational multiphase fluid dynamics (CMFD) simulations. We consider the problem of how to efficiently propagate uncertainties in the model inputs (e.g., fluid properties, such as density, viscosity, etc.) through a computationally demanding model. The challenge is chiefly a matter of economics-the long run-time of these simulations limits the number of samples that one can reasonably obtain (i.e., the number of times the simulation can be run). Chapter 2 introduces the generalized Polynomial Chaos (gPC) expansion, which has shown promise for reducing the computational cost of performing UQ for a large class of problems, including heat transfer and single phase, incompressible flow simulations; example applications are demonstrated in Chapter 2. One of main objectives of this research was to ascertain whether this promise extends to realm of CMFD applications, and this is the topic of Chapters 3 and 4; Chapter 3 covers the numerical simulation of a single bubble rising in a quiescent liquid bath. The pertinent quantities from these simulations are the terminal velocity of the bubble and terminal bubble shape. the simulations were performed using the open source gerris flow solver. A handful of test cases were performed to validate the simulation results against available experimental data and numerical results from other authors; the results from gerris were found to compare favorably. Following the validation, we considered two uncertainty quantifications problems. In the first problem, the viscosity of the surrounding liquid is modeled as a uniform random variable and we quantify the resultant uncertainty in the bubbles terminal velocity. The second example is similar, except the bubble's size (diameter) is modeled as a log-normal random variable. In this case, the Hermite expansion is seen to converge almost immediately; a first-order Hermite expansion computed using 3 model evaluations is found to capture the terminal velocity distribution almost exactly. Both examples demonstrate that NISP can be successfully used to efficiently propagate uncertainties through CMFD models. Finally, we describe a simple technique to implement a moving reference frame in gerris. Chapter 4 presents an extensive study of the numerical simulation of capillary slug flow. We review existing correlations for the thickness of the liquid film surrounding a Taylor bubble and the pressure drop across the bubble. Bretherton's lubrication analysis, which yields analytical predictions for these quantities when inertial effects are negligible and Ca[beta] --> o, is considered in detail. In addition, a review is provided of film thickness correlations that are applicable for high Cab or when inertial effects are non-negligible. An extensive computational study was undertaken with gerris to simulate capillary slug flow under a variety of flow conditions; in total, more than two hundred simulations were carried out. The simulations were found to compare favorably with simulations performed previously by other authors using finite elements. The data from our simulations have been used to develop a new correlation for the film thickness and bubble velocity that is generally applicable. While similar in structure to existing film thickness correlations, the present correlation does not require the bubble velocity to be known a priori. We conclude with an application of the gPC expansion to quantify the uncertainty in the pressure drop in a channel in slug flow when the bubble size is described by a probability distribution. It is found that, although the gPC expansion fails to adequately quantify the uncertainty in field quantities (pressure and velocity) near the liquid-vapor interface, it is nevertheless capable of representing the uncertainty in other quantities (e.g., channel pressure drop) that do not depend sensitively on the precise location of the interface.
by Dustin R. Langewisch.
Ph. D.
Bazargan, Hamid. "An efficient polynomial chaos-based proxy model for history matching and uncertainty quantification of complex geological structures". Thesis, Heriot-Watt University, 2014. http://hdl.handle.net/10399/2757.
Texto completoDessombz, Olivier. "Analyse dynamique de structures comportant des paramètres incertains". Ecully, Ecole centrale de Lyon, 2000. http://www.theses.fr/2000ECDL0036.
Texto completoWe are interested in the modelling of structures with uncertain parameters. We focus on the characteristics of static and dynamic responses of such mechanical systems. We distinguish in this study two cases : first, the case of random parameters with a known probability law and second the case of variables of which only the bounds are known. In a first part, we investigate the case of structures with uncertain parameters modelled as random variables. We are particularly interested in the dynamic responses, as well the frequency response functions as the eigenmodes. An inovative method is carried out, which consists in a projection on orthogonal polynomial (polynomial chaos) that leads to the main stochastic characteristics of the responses. In a second part, we use the interval arithmetic to solve static and dynamic problems. We first propose an adapted formulation of the mathematical problems with respect to the finite element modeling of mechanical systems. We then introduce a new formulation of an iterative algorithm that leads to enveloppes of responses for interval linear systems
Kreuter, Daniel Christopher. "Bestimmung effektiver Materialkennwerte mit Hilfe modaler Ansätze bei unsicheren Eingangsgrößen". Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-191159.
Texto completoDe, La Torre Gerardo. "Autonomous suspended load operations via trajectory optimization and variational integrators". Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53932.
Texto completoMühlpfordt, Tillmann [Verfasser] y V. [Akademischer Betreuer] Hagenmeyer. "Uncertainty Quantification via Polynomial Chaos Expansion – Methods and Applications for Optimization of Power Systems / Tillmann Mühlpfordt ; Betreuer: V. Hagenmeyer". Karlsruhe : KIT-Bibliothek, 2020. http://d-nb.info/1203211872/34.
Texto completoGuerine, Ahmed. "Contribution à l'étude du comportement dynamique d'un système d'engrenage en présence d'incertitudes". Thesis, Rouen, INSA, 2016. http://www.theses.fr/2016ISAM0013/document.
Texto completoIn the present work, the dynamic behavior of a gear system with uncertain parameters is studied. One of the principal hypotheses in the use of methods for taking into account uncertainties is that the model is deterministic, that is to say that parameters used in the model have a defined and fixed value. Furthermore, the knowledge of variation response of a gear system involving damping coefficients, mesh stiffness, friction coefficient, assembly defect, manufacturing defect or the input blades in the case of wind turbine is essential. In the first part, we investigate the dynamic response of a gear system with uncertain parameters modeled as random variables. A Monte Carlo simulation, a perturbation method and a polynomial chaos method are carried out. In the second part, two approaches are used to analyze the dynamic behavior of a wind turbine gear system : the probabilistic approach and the interval analysis method. The objective is to compare the two approaches to define their advantages and disadvantages in terms of precision and computation time
Dammak, Khalil. "Prise en compte des incertitudes des problèmes en vibro-acoustiques (ou interaction fluide-structure)". Thesis, Normandie, 2018. http://www.theses.fr/2018NORMIR19/document.
Texto completoThis PhD thesis deals with the robust analysis and reliability optimization of vibro-acoustic problems (or fluid-structure interaction) taking into account the uncertainties of the input parameters. In the design and dimensioning phase, it seems interesting to model the vibro-acoustic systems and their variability, which can be mainly related to the imperfection of the geometry as well as the characteristics of the materials. It is therefore important, if not essential, to take into account the dispersion of the laws of these uncertain parameters in order to ensure a robust design. Therefore, the purpose is to determine the capabilities and limitations, in terms of precision and computational costs, of methods based on polynomial chaos developments in comparison with the Monte Carlo referential technique for studying the mechanical behavior of vibro-acoustic problems with uncertain parameters. The study of the propagation of these uncertainties allows their integration into the design phase. The goal of the reliability-Based Design Optimization (RBDO) is to find a compromise between minimum cost and a target reliability. As a result, several methods, such as the hybrid method (HM) and the Optimum Safety Factor (OSF) method, have been developed to achieve this goal. To overcome the complexity of vibro-acoustic systems with uncertain parameters, we have developed methodologies specific to this problem, via meta-modeling methods, which allowed us to build a vibro-acoustic surrogate model, which at the same time satisfies the efficiency and accuracy of the model. The objective of this thesis is to determine the best methodology to follow for the reliability optimization of vibro-acoustic systems with uncertain parameters
Piprek, Patrick [Verfasser], Florian [Akademischer Betreuer] Holzapfel, Sébastien [Gutachter] Gros y Florian [Gutachter] Holzapfel. "Robust Trajectory Optimization Applying Chance Constraints and Generalized Polynomial Chaos / Patrick Piprek ; Gutachter: Sébastien Gros, Florian Holzapfel ; Betreuer: Florian Holzapfel". München : Universitätsbibliothek der TU München, 2020. http://d-nb.info/1211086992/34.
Texto completoKamath, Atul Krishna. "Surrogate-assisted optimisation-based verification & validation". Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/15637.
Texto completoYu, Hang. "Reliability-based design optimization of structures : methodologies and applications to vibration control". Phd thesis, Ecole Centrale de Lyon, 2011. http://tel.archives-ouvertes.fr/tel-00769937.
Texto completoNechak, Lyes. "Approches robustes du comportement dynamique des systèmes non linéaires : Application aux systèmes frottants". Phd thesis, Université de Haute Alsace - Mulhouse, 2011. http://tel.archives-ouvertes.fr/tel-00708215.
Texto completoPettersson, Per, Alireza Doostan y Jan Nordström. "On Stability and Monotonicity Requirements of Finite Difference Approximations of Stochastic Conservation Laws with Random Viscosity". Linköpings universitet, Beräkningsmatematik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-90995.
Texto completoJanya-anurak, Chettapong [Verfasser]. "Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos / Chettapong Janya-anurak". Karlsruhe : KIT Scientific Publishing, 2017. http://www.ksp.kit.edu.
Texto completoJanya-Anurak, Chettapong [Verfasser]. "Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos / Chettapong Janya-anurak". Karlsruhe : KIT Scientific Publishing, 2017. http://www.ksp.kit.edu.
Texto completoScott, Karen Mary Louise. "Practical Analysis Tools for Structures Subjected to Flow-Induced and Non-Stationary Random Loads". Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/38686.
Texto completoPh. D.
Notin, Alban. "Evaluation à moindre coût de la fiabilité des structures sollicitées en fatigue". Compiègne, 2010. http://www.theses.fr/2010COMP1877.
Texto completoThis thesis take place in the context of the estimation of the reliability of structures under fatigue loading. In the case of industrial applications, each model evaluation may be time and storage consuming. This way, only a few number of evaluations can be performed. This efficient estimation of the reliability of structures under fatigue loading implies to word on the reliability algorithm as well as the speeding up of mechanical computations. In this double issue lies the settlement of this thesis. Concerning the reliability part, the RPCM (Resampling Polynomial Chaos Method) method has been developed. The goal is to build the polynomial chaos basis in an adaptative way such that the troncature error is taken into account. This erros is estimad through confidence intervals on the reliability index. Numerical results show a very good behaviour of the proposed method in the case of smooth limit-state functions. However, metamodels are not the only way to speed up computations. Another strategy consists in accelerate the mechanical computations by approximating the closest calculi controlling the error. This is the idea of the SLDL T (Stochastic LDL T decomposition) approach which is based on a slight modification of the Cholesky decomposition assuming that the fluctuations of the lower matrix L are negligible in the domain of variation of the random inputs. The randonmess is put on the digonal matrix D, which is optimized such a way to minimize the error on the stiffness matrix. In the case of a linear elastic mechanical behaviour with the Young’s modulus modeled by a random field, results show a gain factor round to 180