Hydraulisk modellering är ett viktigt verktyg vid utvärdering av lämpliga åtgärder för torrfåror. Modelleringen påverkas dock alltid av osäkerheter och om dessa är stora kan en modells simuleringsresultat bli opålitligt. Det kan därför vara viktigt att presentera dess simuleringsresultat tillsammans med osäkerheter. Denna studie utreder olika typer av osäkerheter som kan påverka hydrauliska modellers simuleringsresultat. Dessutom utförs känslighetsanalyser där en andel av osäkerheten i simuleringsresultatet tillskrivs de olika inmatningsvariablerna som beaktas. De parametrar som ingår i analysen är upplösningen i använd terrängmodell, upplösning i den hydrauliska modellens beräkningsnät, inflöde till modellen och råheten genom Mannings tal. Studieobjektet som behandlades i denna studie var en torrfåra som ligger nedströms Sandforsdammen i Skellefteälven och programvaran TELEMAC-MASCARET nyttjades för samtliga hydrauliska simuleringar i denna studie.  För att analysera osäkerheter kopplade till upplösning i en terrängmodell och ett beräkningsnät användes ett kvalitativt tillvägagångsätt. Ett antal simuleringar utfördes där alla parametrar förutom de kopplade till upplösning fixerades. Simuleringsresultaten illustrerades sedan genom profil, sektioner, enskilda raster och raster som visade differensen mellan olika simuleringar. Resultaten för analysen visade att en låg upplösning i terrängmodeller och beräkningsnät kan medföra osäkerheter lokalt där det är högre vattenhastigheter och där det finns stor variation i geometrin. Några signifikanta effekter kunde dock inte skönjas på större skala.  Separat gjordes kvantitativa osäkerhets- och känslighetsanalyser för vattendjup och vattenhastighet i torrfåran. Inmatningsparametrarna inflöde till modellen och råhet genom Mannings tal ansågs medföra störst påverkan och övriga parametrar fixerades således. Genom script skapade i programmeringsspråket Python tillsammans med biblioteket OpenTURNS upprättades ett stort urval av möjliga kombinationer för storlek på inflöde och Mannings tal. Alla kombinationer som skapades antogs till fullo täcka upp för den totala osäkerheten i inmatningsparametrarna. Genom att använda urvalet för simulering kunde osäkerheten i simuleringsresultaten också beskrivas. Osäkerhetsanalyser utfördes både genom klassisk beräkning av statistiska moment och genom Polynomial Chaos Expansion. En känslighetsanalys följde sedan där Polynomial Chaos Expansion användes för att beräkna Sobols känslighetsindex för inflödet och Mannings tal i varje kontrollpunkt. Den kvantitativa osäkerhetsanalysen visade att det fanns relativt stora osäkerheter för både vattendjupet och vattenhastighet vid behandlat studieobjekt. Flödet bidrog med störst påverkan på osäkerheten medan Mannings tals påverkan var insignifikant i jämförelse, bortsett från ett område i modellen där dess påverkan ökade markant. / Hydraulic modelling is an important tool when measures for dry river stretches are assessed. The modelling is however always affected by uncertainties and if these are big the simulation results from the models could become unreliable. It may therefore be important to present its simulation results together with the uncertainties. This study addresses various types of uncertainties that may affect the simulation results from hydraulic models. In addition, sensitivity analysis is conducted where a proportion of the uncertainty in the simulation result is attributed to the various input variables that are included. The parameters included in the analysis are terrain model resolution, hydraulic model mesh resolution, inflow to the model and Manning’s roughness coefficient. The object studied in this paper was a dry river stretch located downstream of Sandforsdammen in the river of Skellefteälven, Sweden. The software TELEMAC-MASCARET was used to perform all hydraulic simulations for this thesis.  To analyze the uncertainties related to the resolution for the terrain model and the mesh a qualitative approach was used. Several simulations were run where all parameters except those linked to the resolution were fixed. The simulation results were illustrated through individual rasters, profiles, sections and rasters that showed the differences between different simulations. The results of the analysis showed that a low resolution for terrain models and meshes can lead to uncertainties locally where there are higher water velocities and where there are big variations in the geometry. However, no significant effects could be discerned on a larger scale.  Separately, quantitative uncertainty and sensitivity analyzes were performed for the simulation results, water depth and water velocity for the dry river stretch. The input parameters that were assumed to have the biggest impact were the inflow to the model and Manning's roughness coefficient. Other model input parameters were fixed. Through scripts created in the programming language Python together with the library OpenTURNS, a large sample of possible combinations for the size of inflow and Manning's roughness coefficient was created. All combinations were assumed to fully cover the uncertainty of the input parameters. After using the sample for simulation, the uncertainty of the simulation results could also be described. Uncertainty analyses were conducted through both classical calculation of statistical moments and through Polynomial Chaos Expansion. A sensitivity analysis was then conducted through Polynomial Chaos Expansion where Sobol's sensitivity indices were calculated for the inflow and Manning's M at each control point. The analysis showed that there were relatively large uncertainties for both the water depth and the water velocity. The inflow had the greatest impact on the uncertainties while Manning's M was insignificant in comparison, apart from one area in the model where its impact increased.

In this thesis, abstract bounds for the normal approximation of Poisson functionals are computed by the Malliavin-Stein method and used to derive central limit theorems for problems from stochastic geometry. As a Poisson functional we denote a random variable depending on a Poisson point process. It is known from stochastic analysis that every square integrable Poisson functional has a representation as a (possibly infinite) sum of multiple Wiener-Ito integrals. This decomposition is called Wiener-Itô chaos expansion, and the integrands are denoted as kernels of the Wiener-Itô chaos expansion. An explicit formula for these kernels is known due to Last and Penrose. Via their Wiener-Itô chaos expansions the so-called Malliavin operators are defined. By combining Malliavin calculus and Stein's method, a well-known technique to derive limit theorems in probability theory, bounds for the normal approximation of Poisson functionals in the Wasserstein distance and vectors of Poisson functionals in a similar distance were obtained by Peccati, Sole, Taqqu, and Utzet and Peccati and Zheng, respectively. An analogous bound for the univariate normal approximation in Kolmogorov distance is derived. In order to evaluate these bounds, one has to compute the expectation of products of multiple Wiener-Itô integrals, which are complicated sums of deterministic integrals. Therefore, the bounds for the normal approximation of Poisson functionals reduce to sums of integrals depending on the kernels of the Wiener-Itô chaos expansion. The strategy to derive central limit theorems for Poisson functionals is to compute the kernels of their Wiener-Itô chaos expansions, to put the kernels in the bounds for the normal approximation, and to show that the bounds vanish asymptotically. By this approach, central limit theorems for some problems from stochastic geometry are derived. Univariate and multivariate central limit theorems for some functionals of the intersection process of Poisson k-flats and the number of vertices and the total edge length of a Gilbert graph are shown. These Poisson functionals are so-called Poisson U-statistics which have an easier structure since their Wiener-Itô chaos expansions are finite, i.e. their Wiener-Itô chaos expansions consist of finitely many multiple Wiener-Itô integrals. As examples for Poisson functionals with infinite Wiener-Itô chaos expansions, central limit theorems for the volume of the Poisson-Voronoi approximation of a convex set and the intrinsic volumes of Boolean models are proven.

Advancing towards `better' wind turbine designs engineers face two central challenges: first, current aerodynamic models (based on Blade Element Momentum theory) are inherently limited to comparatively simple designs of flat rotors with straight blades. However, such designs present only a subset of possible designs. Better concepts could be coning rotors, swept or kinked blades, or blade tip modifications. To be able to extend future turbine optimization to these new concepts a different kind of aerodynamic model is needed. Second, it is difficult to include long term loads (life time extreme and fatigue loads) directly into the wind turbine design optimization. This is because with current methods the assessment of long term loads is computationally very expensive -- often too expensive for optimization. This denies the optimizer the possibility to fully explore the effects of design changes on important life time loads, and one might settle with a sub-optimal design. In this dissertation we present work addressing these two challenges, looking at wing aerodynamics in general and focusing on wind turbine loads in particular. We adopt a Lagrangian vortex model to analyze bird wings. Equipped with distinct tip feathers, these wings present very complex lifting surfaces with winglets, stacked in sweep and dihedral. Very good agreement between experimental and numerical results is found, and thus we confirm that a vortex model is actually capable of analyzing complex new wing and rotor blade geometries. Next stochastic methods are derived to deal with the time and space coupled unsteady aerodynamic equations. In contrast to deterministic models, which repeatedly analyze the loads for different input samples to eventually estimate life time load statistics, the new stochastic models provide a continuous process to assess life time loads in a stochastic context -- starting from a stochastic wind field input through to a stochastic solution for the load output. Hence, these new models allow obtaining life time loads much faster than from the deterministic approach, which will eventually make life time loads accessible to a future stochastic wind turbine optimization algorithm. While common stochastic techniques are concerned with random parameters or boundary conditions (constant in time), a stochastic treatment of turbulent wind inflow requires a technique capable to handle a random field. The step from a random parameter to a random field is not trivial, and hence the new stochastic methods are introduced in three stages. First the bird wing model from above is simplified to a one element wing/ blade model, and the previously deterministic solution is substituted with a stochastic solution for a one-point wind speed time series (a random process). Second, the wind inflow is extended to an $n$-point correlated random wind field and the aerodynamic model is extended accordingly. To complete this step a new kind of wind model is introduced, requiring significantly fewer random variables than previous models. Finally, the stochastic method is applied to wind turbine aerodynamics (for now based on Blade Element Momentum theory) to analyze rotor thrust, torque, and power. Throughout all these steps the stochastic results are compared to result statistics obtained via Monte Carlo analysis from unsteady reference models solved in the conventional deterministic framework. Thus it is verified that the stochastic results actually reproduce the deterministic benchmark. Moreover, a considerable speed-up of the calculations is found (for example by a factor 20 for calculating blade thrust load probability distributions). Results from this research provide a means to much more quickly analyze life time loads and an aerodynamic model to be used a new wind turbine optimization framework, capable of analyzing new geometries, and actually optimizing wind turbine blades with life time loads in mind. However, to limit the scope of this work, we only present the aerodynamic models here and will not proceed to turbine optimization itself, which is left for future work. / Graduate / 0538 / 0548 / mfluck@uvic.ca

This diploma thesis deal with evaluation of the impact in the scale of uncertaintly stiffness on the tool deviation during grooving process. By the affect of the insufficient stiffness in each parts of the machine, there is presented a mechanical vibration during the cutting process which may cause a damage to the surface of the workpiece, to the tool or to the processing machine. The change of the stiffness is caused in the result of tool wear, impact of setted cutting conditions and many others. In the first part includes teoretical introduction to field of the uncertainty and choosing suitable methods for the solutions. Chosen methods are Monte Carlo and polynomial chaos expansion which are procced in the interface of MATLAB. Both of the methods are primery tested on the simple systems with the indefinited enters of the stiffness. These systems replace the parts of the stiffness characteristics of the each support parts. After that, the model is defined for the turning during the process of grooving with the 3 degrees of freedom. Then the analyses of the uncertainity and also sensibility analyses for uncertainity entering data of the stiffness are carried out again by both methods. At the end are both methods compared in the points of view by the time consuption and also by precission. Judging by gathered data it is clear that the change of the stiffness has significant impact on vibration in all degrees of freedome of the analysed model. As the example a maximum and a minimum calculated deviation of the workpiece stiffness was calculated via methode of Monte Carlo. The biggest impact on the finall vibration of the tool is found by stiffness of the ball screw. The solution was developed for the more stabile cutting process.

This thesis aims at Multi-Objective Optimization under Uncertainty in structural design. We investigate Polynomial Chaos Expansion (PCE) surrogates which require extensive training sets. We then face two issues: high computational costs of an individual Finite Element simulation and its limited precision. From numerical point of view and in order to limit the computational expense of the PCE construction we particularly focus on sparse PCE schemes. We also develop a custom Latin Hypercube Sampling scheme taking into account the finite precision of the simulation. From the modeling point of view, we propose a multifidelity approach involving a hierarchy of models ranging from full scale simulations through reduced order physics up to response surfaces. Finally, we investigate multiobjective optimization of structures under uncertainty. We extend the PCE model of design objectives by taking into account the design variables. We illustrate our work with examples in sheet metal forming and optimal design of truss structures. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

The aim of this thesis is to simulate stochastic models that are driven by a fractional Brownian motion process and to apply these methods to financial applications related to yield rate and asset price modelling. Several rough volatility processes are used to model the asset price and yield dynamics. Firstly fractional processes of Cox-Ingersoll-Ross, CEV and Vasicek types are introduced as models for volatility and yield data. In this framework it holds that the Hurst parameter that determines the covariance structure of the fBM process can be directly estimated from observed data series using a least squares log-periodogram approach. The remaining parameters in the model are estimated using a combination of Maximum Likelihood estimates and expectation estimations. In the modelling and pricing of assets one model that is studied is the fractional Heston model, that is used to model an asset price process using both observed asset and volatility data. Similarly two other similar rough volatility models are also studied, which are constructed so as to have log-Normal returns. These processes which in the thesis are called the exponential models 1 and 2 have rough volatility that are characterized by the CEV and Vasicek processes. Additionally the first order Wiener Chaos Expansion is implemented and explored in two ways. Firstly the Chaos Expansion is applied to a parametric fractional stochastic model which is used to generate a Wick product process, which is found to resemble the underlying process. It is also used to generate an approximate expansion of real yield rate data using a bootstrap sampling approach. / Den här uppsatsen syftar till att simulera stokastiska modeller som drivs av fraktionell Brownsk rörelse och att använda dessa modeller i finansiella tillämpningar relaterade till räntor och finansiella tillgångar. Flera volatilitetsprocesser som är rough används för att modellera ränte- och aktiedynamiken. Först introduceras de fraktionella varianterna av Cox-Ingersoll-Ross, CEV och Vasicek processer, vilka används för att modellera volatilitet och ränteprocesser. Med detta tillvägagångssätt gäller det att Hurstparametern, vilken bestämmer covariansstrukturen för den fraktionella Brownska rörelsen, kan uppskattas direkt från observerad data med en minsta kvadrat log-periodogram-metod. Samtliga andra parametrar i modellen uppskattas med en kombination av Maximum Likelihood och uppskattning av väntevärden. I modelleringen och prissättningen av finansiella tillgångar är en model som studeras den fraktionella Hestonmodellen, som används för att modellera en tillgång baserat på både volatilitets- och aktiedata. Ytterligare två liknande modeller studeras, vilka också har volatilitet som är rough och är konstruerade så att deras avkastning är log-Normal. Dessa processer, vilka i uppsatsen är benämnda som de exponentiella modellerna 1 och 2 har volatilitet som karaktäriseras av CEV- och Vasicekprocesser. Ytterligare är Wiener's Kaosexpansion av första ordningen också implementerad och undersöks från två håll. Först används den på en parameterbestämd fraktionell stokastisk modell, vilken används för att generera en Wickproduktprocess. Expansionen används även med hjälp av en bootstrap-metod för att generera en process från observerad data.

Les réseaux de distribution d’eau consistent en de grandes infrastructures réparties dans l’espace qui assurent la distribution d’eau potable en quantité et en qualité suffisantes. Les modèles mathématiques de ces systèmes sont caractérisés par un grand nombre de variables d’état et de paramètres dont la plupart sont incertains. Les temps de calcul peuvent s’avérer conséquents pour les réseaux de taille importante et la propagation d’incertitude par des méthodes de Monte Carlo. Par conséquent, les deux principaux objectifs de cette thèse sont l’étude des techniques de modélisation à ordre réduit par projection ainsi que la propagation spectrale des incertitudes des paramètres. La thèse donne tout d’abord un aperçu des méthodes mathématiques utilisées. Ensuite, les équations permanentes des réseaux hydrauliques sont présentées et une nouvelle méthode de calcul des sensibilités est dérivée sur la base de la méthode adjointe. Les objectifs spécifiques du développement de modèles d’ordre réduit sont l’application de méthodes basées sur la projection, le développement de stratégies d’échantillonnage adaptatives plus efficaces et l’utilisation de méthodes d’hyper-réduction pour l’évaluation rapide des termes résiduels non linéaires. Pour la propagation des incertitudes, des méthodes spectrales sont introduites dans le modèle hydraulique et un modèle hydraulique intrusif est formulé. Dans le but d’une analyse plus efficace des incertitudes des paramètres, la propagation spectrale est ensuite évaluée sur la base du modèle réduit. Les résultats montrent que les modèles d’ordre réduit basés sur des projections offrent un avantage considérable par rapport à l’effort de calcul. Bien que l’utilisation de l’échantillonnage adaptatif permette une utilisation plus efficace des états système pré-calculés, l’utilisation de méthodes d’hyper-réduction n’a pas permis d’améliorer la charge de calcul. La propagation des incertitudes des paramètres sur la base des méthodes spectrales est comparable aux simulations de Monte Carlo en termes de précision, tout en réduisant considérablement l’effort de calcul. / Water distribution systems are large, spatially distributed infrastructures that ensure the distribution of potable water of sufficient quantity and quality. Mathematical models of these systems are characterized by a large number of state variables and parameter. Two major challenges are given by the time constraints for the solution and the uncertain character of the model parameters. The main objectives of this thesis are thus the investigation of projection based reduced order modelling techniques for the time efficient solution of the hydraulic system as well as the spectral propagation of parameter uncertainties for the improved quantification of uncertainties. The thesis gives an overview of the mathematical methods that are being used. This is followed by the definition and discussion of the hydraulic network model, for which a new method for the derivation of the sensitivities is presented based on the adjoint method. The specific objectives for the development of reduced order models are the application of projection based methods, the development of more efficient adaptive sampling strategies and the use of hyper-reduction methods for the fast evaluation of non-linear residual terms. For the propagation of uncertainties spectral methods are introduced to the hydraulic model and an intrusive hydraulic model is formulated. With the objective of a more efficient analysis of the parameter uncertainties, the spectral propagation is then evaluated on the basis of the reduced model. The results show that projection based reduced order models give a considerable benefit with respect to the computational effort. While the use of adaptive sampling resulted in a more efficient use of pre-calculated system states, the use of hyper-reduction methods could not improve the computational burden and has to be explored further. The propagation of the parameter uncertainties on the basis of the spectral methods is shown to be comparable to Monte Carlo simulations in accuracy, while significantly reducing the computational effort.

Afin d'estimer les forces statiques équivalentes du vent, qui produisent les réponses quasi-statiques et dynamiques extrêmes dans les structures soumises au champ de pression instationnaire induit par les effets du vent, une nouvelle méthode probabiliste est proposée. Cette méthode permet de calculer les forces statiques équivalentes du vent pour les structures avec des écoulements aérodynamiques complexes telles que les toitures de stade, pour lesquelles le champ de pression n'est pas gaussien et pour lesquelles la réponse dynamique de la structure ne peut être simplement décrite en utilisant uniquement les premiers modes élastiques (mais nécessitent une bonne représentation des réponses quasi-statiques). Généralement, les mesures en soufflerie du champ de pression instationnaire appliqué à une structure dont la géométrie est complexe ne suffisent pas pour construire une estimation statistiquement convergée des valeurs extrêmes des réponses dynamiques de la structure. Une telle convergence est nécessaire pour l'estimation des forces statiques équivalentes afin de reproduire les réponses dynamiques extrêmes induites par les effets du vent en tenant compte de la non-gaussianité du champ de pression aléatoire instationnaire. Dans ce travail, (1) un générateur de réalisation du champ de pression instationnaire non gaussien est construit en utilisant les réalisations qui sont mesurées dans la soufflerie à couche limite turbulente; ce générateur basé sur une représentation en chaos polynomiaux permet de construire un grand nombre de réalisations indépendantes afin d'obtenir la convergence des statistiques des valeurs extrêmes des réponses dynamiques, (2) un modèle d'ordre réduit avec des termes d'accélération quasi-statique est construit et permet d'accélérer la convergence des réponses dynamiques de la structure en n'utilisant qu'un petit nombre de modes élastiques, (3) une nouvelle méthode probabiliste est proposée pour estimer les forces statiques équivalentes induites par les effets du vent sur des structures complexes décrites par des modèles éléments finis, en préservant le caractère non gaussien et sans introduire le concept d'enveloppes des réponses. L'approche proposée est validée expérimentalement avec une application relativement simple et elle est ensuite appliquée à une structure de toiture de stade pour laquelle des mesures expérimentales de pressions instationnaires ont été effectuées dans la soufflerie à couche limite turbulente / In order to estimate the equivalent static wind loads, which produce the extreme quasi-static and dynamical responses of structures submitted to random unsteady pressure field induced by the wind effects, a new probabilistic method is proposed. This method allows for computing the equivalent static wind loads for structures with complex aerodynamic flows such as stadium roofs, for which the pressure field is non-Gaussian, and for which the dynamical response of the structure cannot simply be described by using only the first elastic modes (but require a good representation of the quasi-static responses). Usually, the wind tunnel measurements of the unsteady pressure field applied to a structure with complex geometry are not sufficient for constructing a statistically converged estimation of the extreme values of the dynamical responses. Such a convergence is necessary for the estimation of the equivalent static loads in order to reproduce the extreme dynamical responses induced by the wind effects taking into account the non-Gaussianity of the random unsteady pressure field. In this work, (1) a generator of realizations of the non-Gaussian unsteady pressure field is constructed by using the realizations that are measured in the boundary layer wind tunnel; this generator based on a polynomial chaos representation allows for generating a large number of independent realizations in order to obtain the convergence of the extreme value statistics of the dynamical responses, (2) a reduced-order model with quasi-static acceleration terms is constructed, which allows for accelerating the convergence of the structural dynamical responses by using only a small number of elastic modes of the structure, (3) a novel probabilistic method is proposed for estimating the equivalent static wind loads induced by the wind effects on complex structures that are described by finite element models, preserving the non-Gaussian property and without introducing the concept of responses envelopes. The proposed approach is experimentally validated with a relatively simple application and is then applied to a stadium roof structure for which experimental measurements of unsteady pressures have been performed in boundary layer wind tunnel

Les conceptions récentes de turbomachines ont tendance à évoluer vers des liaisons entre étages de plus en plus souples et des niveaux d'amortissement faibles, donnant lieu à des configurations où les modes sont susceptibles de présenter des niveaux de couplages inter-étages forts. En général, les ensembles disques aubes multi-étagés n'ont aucune propriété de symétrie cyclique d'ensemble et l'analyse doit porter sur un modèle de la structure complète donnant lieu à des calculs très coûteux. Pour palier ce problème, une méthode récente appelée symétrie cyclique multi-étages peut être utilisée pour réduire le coût des calculs des rotors composés de plusieurs étages, même lorsque les étages ont un nombre différent de secteurs. Cette approche profite de la symétrie cyclique inhérente à chaque étage et utilise une hypothèse spécifique qui aboutit à des sous-problèmes découplés pour chaque ordre de Fourier spatial. La méthodologie proposée vise à étudier l'effet des incertitudes sur le comportement dynamique des rotors en utilisant l'approche de symétrie cyclique multi-étages et l'expansion en Chaos Polynomial. Les incertitudes peuvent découler de l'usure des aubes, des changements de température ou des tolérances de fabrication. En première approche, seules les incertitudes provenant de l'usure uniforme de l'ensemble des aubes sont étudiées. Celles-ci peuvent être modélisées en considérant une variation globale des propriétés du matériau de l'ensemble des aubes d'un étage particulier. L'approche de symétrie cyclique multi-étages peut alors être utilisée car l'hypothèse de secteurs identiques est respectée. La positivité des matrices aléatoires concernées est assurée par l'utilisation d'une loi gamma très adaptée à la physique du problème impliquant le choix des polynômes de Laguerre comme base pour le chaos polynomial. Dans un premier temps des exemples numériques représentatifs de différents types de turbomachines sont introduits dans le but d'évaluer la robustesse de la méthode de symétrie cyclique multi-étages. Ensuite, les résultats de l'analyse modale aléatoire et de la réponse aléatoire obtenus par le chaos polynomial sont validés par comparaison avec des simulations de Monte-Carlo. En plus des résultats classiquement rencontrés pour les fréquences et réponses forcées, les incertitudes considérées mettent en évidence des variations sur les déformées modales qui évoluent entre différentes familles de modes dans les zones de forte densité modale. Ces variations entraînent des modifications sensibles sur la dynamique globale de la structure analysée et doivent être considérées dans le cadre des conceptions robustes. / Recent designs in turbomachinery tend to have more flexible inter-stage rims and to be more lightly damped, resulting in configurations where modes might not be confined to only one stage. In general, multi-stage rotors have no particular axial symmetry property and the computationally costly analysis of the whole structure becomes mandatory. However, a multi-stage cyclic symmetry approach can be used for reducing the cost of modeling rotors composed of several stages even when the stages have different numbers of sectors. This approach takes advantage of the inherent cyclic symmetry of each stage and uses a specific assumption that results in decoupled subproblems for each spatial Fourier harmonic. The methodology proposed in this work allows including uncertainties in the analysis of multi-stage rotors using the multi-stage cyclic symmetry approach and the Polynomial Chaos Expansion. Uncertainties in rotors may arise from in-use wear of blades, temperature changes or manufacturing tolerances. As a first approach, only uncertainties arising from uniform in-use wear of the set of blades are included. These may be modeled by considering a global variation of the material properties of the set of blades of a particular stage. The multi-stage cyclic symmetry approach can then be used since the underlying assumption of identical sectors is respected. The positiveness of the random matrices involved is reached by using gamma-distributed random variables which imply the use of Laguerre's polynomials as basis for the polynomial chaos. Numerical examples representative of various types of turbomachinery are introduced in order to assess the robustness of the method of multi-stage cyclic symmetry. Uncertainties results for the free and forced response analyses obtained by the polynomial chaos are validated by comparison with Monte Carlo simulations. The considered uncertainties induce variations on the mode shapes that evolve between different families of modes in areas of high modal density. These variations result in significant changes in the global dynamics of the structure and must be considered in the context of robust designs.

De nos jours, la plupart des analyses CEM d’équipements et systèmes électroniques sont basées sur des approches quasi-déterministes dans lesquelles les paramètres internes et externes des modèles sont supposés parfaitement connus et où les incertitudes les affectant sont prises en compte sur les réponses par le biais de marges de sécurité importantes. Or, l’inconvénient de telles approches est qu’elles sont non seulement trop conservatives, mais en outre totalement inadaptées à certaines situations, notamment lorsque l’objectif de l’étude impose de prendre en compte le caractère aléatoire de ces paramètres via des modélisations stochastiques appropriées de type variables, processus ou champs aléatoires. Cette approche probabiliste a fait l’objet ces dernières années d’un certain nombre de recherches en CEM, tant au plan national qu’au plan international. Le travail présenté dans cette thèse est une contribution à ces recherches et a un double objectif : (1) développer et mettre en œuvre une méthodologie probabiliste et ses outils numériques d’accompagnement pour l’évaluation de la fiabilité et l’analyse sensibilité des équipements et systèmes électroniques en se limitant à des modélisations stochastiques par variables aléatoires ; (2) étendre cette étude au cas des modélisations stochastiques par processus et champs aléatoires dans le cadre d’une analyse prospective basée sur la résolution de l’équation aux dérivées partielles des télégraphistes à coefficients aléatoires.L’approche probabiliste mentionnée au point (1) consiste à évaluer la probabilité de défaillance d’un équipement ou d’un système électronique vis-à-vis d’un critère de défaillance donné et à déterminer l’importance relative de chacun des paramètres aléatoires en présence. Les différentes méthodes retenues à cette fin sont des adaptations à la CEM de méthodes développées dans le domaine de la mécanique aléatoire pour les études de propagation d’incertitudes. Pour le calcul des probabilités de défaillance, deux grandes catégories de méthodes sont proposées : celles basées sur une approximation de la fonction d’état-limite relative au critère de défaillance et les méthodes de Monte-Carlo basées sur la simulation numérique des variables aléatoires du modèle et l’estimation statistique des probabilités cibles. Pour l’analyse de sensibilité, une approche locale et une approche globale sont retenues. Ces différentes méthodes sont d’abord testées sur des applications académiques afin de mettre en lumière leur intérêt dans le domaine de la CEM. Elles sont ensuite appliquées à des problèmes de lignes de transmission et d’antennes plus représentatifs de la réalité.Dans l’analyse prospective, des méthodes de résolution avancées sont proposées, basées sur des techniques spectrales requérant les développements en chaos polynomiaux et de Karhunen-Loève des processus et champs aléatoires présents dans les modèles. Ces méthodes ont fait l’objet de tests numériques encourageant, mais qui ne sont pas présentés dans le rapport de thèse, faute de temps pour leur analyse complète. / Nowadays, most EMC analyzes of electronic or electrical devices are based on deterministic approaches for which the internal and external models’ parameters are supposed to be known and the uncertainties on models’ parameters are taken into account on the outputs by defining very large security margins. But, the disadvantage of such approaches is their conservative character and their limitation when dealing with the parameters’ uncertainties using appropriate stochastic modeling (via random variables, processes or fields) is required in agreement with the goal of the study. In the recent years, this probabilistic approach has been the subject of several researches in the EMC community. The work presented here is a contribution to these researches and has a dual purpose : (1) develop a probabilistic methodology and implement the associated numerical tools for the reliability and sensitivity analyzes of the electronic devices and systems, assuming stochastic modeling via random variables; (2) extend this study to stochastic modeling using random processes and random fields through a prospective analysis based on the resolution of the telegrapher equations (partial derivative equations) with random coefficients. The first mentioned probabilistic approach consists in computing the failure probability of an electronic device or system according to a given criteria and in determining the relative importance of each considered random parameter. The methods chosen for this purpose are adaptations to the EMC framework of methods developed in the structural mechanics community for uncertainty propagation studies. The failure probabilities computation is performed using two type of methods: the ones based on an approximation of the limit state function associated to the failure criteria, and the Monte Carlo methods based on the simulation of the model’s random variables and the statistical estimation of the target failure probabilities. In the case of the sensitivity analysis, a local approach and a global approach are retained. All these methods are firstly applied to academic EMC problems in order to illustrate their interest in the EMC field. Next, they are applied to transmission lines problems and antennas problems closer to reality. In the prospective analysis, more advanced resolution methods are proposed. They are based on spectral approaches requiring the polynomial chaos expansions and the Karhunen-Loève expansions of random processes and random fields considered in the models. Although the first numerical tests of these methods have been hopeful, they are not presented here because of lack of time for a complete analysis.