One of the major factors in RF circuit design is the ability to predict the performance of these circuits in the presence of uncertainty in the key design parameters. This is referred to as uncertainty quantification in the mathematical literature. Uncertainty about the key design parameters arises mainly from the difficulty of controlling the physical or geometrical features of the underlying design, especially at the nanometer level. With the constant trend to scale down the process feature size, uncertainty quantification becomes crucial in shortening the design time. This thesis presents a new approach to statistically characterize the variability of the Harmonic Balance analysis and its application to Intermodulation distortion analysis in the presence of uncertainty in the design parameters. The new approach is based on the concept of Polynomial Chaos (PC) and Stochastic Galerkin (SG) methods. However, unlike the traditional PC, the proposed approach adopts a new mathematical formulation that decouples the Polynomial Chaos problem into several problems whose sizes are equal to the size of the original Harmonic Balance problem. The proposed algorithm produces significant CPU savings with equivalent accuracy to traditional Monte Carlo and standard PC approaches.

Wind energy is a field with a large number of uncertainties. The random nature of the weather conditions, including wind speed, wind direction, and turbulence intensity, influences the energy output and the structural safety of a wind farm, making its performance fluctuate and difficult to predict. The uncertainties presented in the energy output and structure lifetime lead to increased investment risk. There are possibilities to reduce the risk associated with these uncertainties by optimizing the design of the farm or the wind turbine, with respect to the stochastic parameters. The goal of this project is to improve the wind farm optimization problem by providing accurate and computationally efficient annual energy production (AEP) estimates, which is a uncertainty quantification that is required in every optimization step. Uncertainty quantification has been recognized as a challenge in the wind energy industry, as the chaotic nature of the weather condition complicates the prediction of energy production. High-fidelity wind farm models usually employ advanced models like Large Eddy Simulation or Reynolds averaged Navier-Stokes equation for better accuracy. However, the prolonged computation time of these high-fidelity models make the traditional uncertainty quantification approach like the Monte-Carlo simulation or other integration techniques infeasible for larger wind farms.  To overcome this limitation, the report proposes the use of generalized polynomial chaos expansion (PCE) to characterize the AEP as a function of wind speed and wind direction. PCE is a technique that approximates a random variable using a series of orthogonal polynomials, the polynomials are chosen based on the target distribution. This report explains how a surrogate model of the AEP can be constructed using PCE, which can be used in optimization or model analysis. The objective of the thesis work is to minimize the number of model evaluations required for obtaining an accurate energy response surface. Different ideas of non-intrusive PCE are implemented and explored in this project. The report demonstrates that, the multi-element polynomial chaos fitted by point collocation, with a dependent polynomial basis, is not only able to make accurate and stable (with respect to the placement of the measurements) energy predictions, but also produces realistic energy response surface. / Vindkraft är en bransch med många osäkerheter, där väderförhållandena påverkar energiproduktionen och strukturens livslängd. Denna osäkerhet ökar investeringsrisken, men kan minskas genom optimering av vindkraftverkets design med hänsyn till de stokastiska parametrarna. Syftet med denna rapport är att förbättra optimeringsproblemet för vindkraftverk genom att ge noggranna och effektiva årliga energiproduktionsberäkningar (AEP), vilket krävs vid varje optimeringssteg. I rapporten används polynomial chaos expansion (PCE) för att approximera AEP och minska antalet nödvändiga modellutvärderingar. Resultaten visar att PCE är en effektiv metod för att göra energiprognoser.

Vehicle characteristics have a significant impact on handling, stability, and rollover propensity. This research is dedicated to furthering the research in and modeling of vehicle dynamics and parameter estimation. Parameter estimation is a challenging problem. Many different elements play into the stability of a parameter estimation algorithm. The primary trade-off is robustness for accuracy. Lyapunov estimation techniques, for instance, guarantee stability but do not guarantee parameter accuracy. The ability to observe the states of the system, whether by sensors or observers is a key problem. This research significantly improves the Generalized Polynomial Chaos Extended Kalman Filter (gPC-EKF) for state-space systems. Here it is also expanded to parameter regression, where it shows excellent capabilities for estimating parameters in linear regression problems. The modeling of ground vehicles has many challenges. Compounding the problems in the parameter estimation methods, the modeling of ground vehicles is very complex and contains many difficulties. Full multibody dynamics models may be able to accurately represent most of the dynamics of the suspension and vehicle body, but the computational time and required knowledge is too significant for real-time and realistic implementation. The literature is filled with different models to represent the dynamics of the ground vehicle, but these models were primarily designed for controller use or to simplify the understanding of the vehicle’s dynamics, and are not suitable for parameter estimation. A model is devised that can be utilized for the parameter estimation. The parameters in the model are updated through the aforementioned gPC-EKF method as applies to polynomial systems. The mass and the horizontal center of gravity (CG) position of the vehicle are estimated to high accuracy. The culmination of this work is the estimation of the normal forces at the tire contact patch. These forces are estimated through a mapping of the suspension kinematics in conjunction with the previously estimated vehicle parameters. A proof of concept study is shown, where the system is mapped and the forces are recreated and verified for several different scenarios and for changing vehicle mass. / Ph. D.

Mechanical systems operate under parametric and external excitation uncertainties. The polynomial chaos approach has been shown to be more efficient than Monte Carlo approaches for quantifying the effects of such uncertainties on the system response. This work uses the polynomial chaos framework to develop new methodologies for the simulation, parameter estimation, and control of mechanical systems with uncertainty. This study has led to new computational approaches for parameter estimation in nonlinear mechanical systems. The first approach is a polynomial-chaos based Bayesian approach in which maximum likelihood estimates are obtained by minimizing a cost function derived from the Bayesian theorem. The second approach is based on the Extended Kalman Filter (EKF). The error covariances needed for the EKF approach are computed from polynomial chaos expansions, and the EKF is used to update the polynomial chaos representation of the uncertain states and the uncertain parameters. The advantages and drawbacks of each method have been investigated. This study has demonstrated the effectiveness of the polynomial chaos approach for control systems analysis. For control system design the study has focused on the LQR problem when dealing with parametric uncertainties. The LQR problem was written as an optimality problem using Lagrange multipliers in an extended form associated with the polynomial chaos framework. The solution to the Hâ problem as well as the H2 problem can be seen as extensions of the LQR problem. This method might therefore have the potential of being a first step towards the development of computationally efficient numerical methods for Hâ design with parametric uncertainties. I would like to gratefully acknowledge the support provided for this work under NASA Grant NNL05AA18A. / Ph. D.

Ce sujet de thèse s'intéresse à l'étude des effets des charges aérodynamiques sur le comportement dynamique non linéaire d'une éolienne à axe vertical de type Darrieus. Cette dernière présente, comparativement aux autres éoliennes, des profits très importants à exploiter, notamment dans les milieux urbains. Il s'agit d'une technologie fiable caractérisée surtout par son fonctionnement omnidirectionnel ainsi que son adaptation à tout type de vent. Généralement, ces éoliennes, ayant des phénomènes aérodynamiques complexes, sont affectées par des vibrations au niveau de leur système de transmission de puissance. En fait, ces vibrations commencent à se manifester à partir des pales du rotor jusqu'au génératrice. L'écoulement autour de ses pales présente également un fort caractère instationnaire. Cette caractéristique augmente d'avantage les vibrations aérodynamiques, qui sont automatiquement transmise au système d'engrenage d'éolienne. À ce niveau, nous avons développé un code de calcul numérique permettant de simuler la complexité des aspects aérodynamiques instationnaires tout en gardant un compromis entre la fiabilité des prédictions et la rapidité de calcul. Les simulations sont réalisées suivant une méthode de mécanique des fluides numérique (CFD) instationnaire bidimensionnel. Les résultats de simulation comparés avec ceux disponibles dans la littérature sont en bonne concordance, le rendement aérodynamique étant optimisé, qui présente un apport scientifique notable. Cette étude numérique a été l'objectif de l'analyse de l'impact des charges aérodynamiques vis-à-vis le comportement dynamique du système d'engrenage de l'éolienne en régime non-stationnaire. Dans ce contexte, une étude paramétrique a été développée afin d'établir le fonctionnement optimal de l'éolienne, caractérisé par un couple aérodynamique plus performant associé à des niveaux de vibrations dynamiques acceptables. En général, il est difficile d'identifier précisément la réponse dynamique des éoliennes à cause du caractère turbulent et stochastique des charges aérodynamiques. Par conséquent, il est indispensable de tenir en compte la variabilité des paramètres d'entrée pour assurer la robustesse du système étudié. Adoptons l'objectif de dimensionnement robuste. Une méthode d'évaluation basée sur des approches stochastiques, particulièrement la méthode du Chaos Polynomial, est utilisée pour simuler le comportement dynamique non-linéaire du système d'engrenage d'éolienne, en tenant compte des incertitudes. Ces dernières sont au niveau des charges aérodynamiques, inhérentes au calcul des niveaux vibratoires du système d'engrenage. Ce qui implique un apport scientifique important. Les résultats obtenus par l'approximation par Chaos Polynomial démontrent une forte dispersion des charges aérodynamiques aléatoires dans la réponse dynamique du système d'engrenage, contrairement aux études déterministes. Ce qui prouve l'insuffisance de telles études pour une analyse de robustesse. Les résultats mettent également en évidence la forte corrélation entre les phénomènes aérodynamiques complexes et les vibrations dynamiques. Le couplage établi constitue l'originalité de notre travail. / This thesis focuses on the study of the aerodynamic loads effects on the nonlinear dynamic behavior of Darrieus--type vertical axis wind turbine. The latter has received more attention due to its efficiency in urban regions compared to other wind turbines. In fact, the wind flow speed in urban regions continuously changes direction and is extremely turbulent. The inherent characteristics of its omni-directionality make it more suitable to harnessing this kind of flow. It is known that Darrieus wind turbine is characterized by an inherently unsteady aerodynamic behavior and a complex flow around rotor blades. The non-stationary behavior of the mentioned turbine increases vibration. These aerodynamic vibrations are transmitted to the gearing mechanism. We have, firstly, developed a numerical simulation, allowing to simulate the complexity of the unsteady aerodynamic phenomena keeping a compromise between the reliability of prediction and the rapidity of calculation. This numerical simulation has been carried out using a two-dimensional unsteady Computational Fluid Dynamics (CFD) method. Simulation results compared to those available in the literature are in good agreement. The Darrieus turbine efficiency is also optimized; thus introducing a significant scientific contribution. The latter is the objective of analyzing the aerodynamic load impact in the dynamic behavior of the Darrieus turbine in non-stationary regime. In this context, a parametric study has been developed in order to find optimal functioning of the studied turbine, which is characterized by the most performing aerodynamic torque associated with acceptable levels of dynamic vibration. In general, it is difficult to predict the dynamic response of the wind turbine with a good level of accuracy due to the aerodynamic loads turbulence and uncertain characteristics. It becomes necessary to take into account the uncertainty in the input parameters to ensure the robustness of the Darrieus turbine geared system. In a robustness study objective, the Polynomial Chaos method is adopted to predict the nonlinear dynamic behavior of the gearing system taking into account uncertainties which are associated to the performance coefficient of the input aerodynamic torque. This leads to an important scientific research contribution. The results have shown a large dispersion of the random parameter in the dynamic response of the gearing system compared to the deterministic study. That proves the insufficiency of that study for a robustness analyses. They have also proved that the Polynomial Chaos method is an efficient probabilistic tool for uncertainty propagation. Finally, the new proposed robust mechanical analysis indicates a good capacity to investigate the dynamic behavior of the Darrieus turbine thanks to its superior predictive capabilities in coupling complex aerodynamic phenomena with a mechanical gearing system vibration. Where the originality of such correlation in our work.

Ce travail de thèse porte sur l’analyse robuste et l’optimisation fiabiliste des problèmes vibro-acoustiques (ou en interaction fluide-structure) en tenant en compte des incertitudes des paramètres d’entrée. En phase de conception et de dimensionnement, il parait intéressant de modéliser les systèmes vibro-acoustiques ainsi que leurs variabilités qui peuvent être essentiellement liées à l’imperfection de la géométrie ainsi qu’aux caractéristiques des matériaux. Il est ainsi important, voire indispensable, de tenir compte de la dispersion des lois de ces paramètres incertains afin d’en assurer une conception robuste. Par conséquent, l’objectif est de déterminer les capacités et les limites, en termes de précision et de coûts de calcul, des méthodes basées sur les développements en chaos polynomiaux en comparaison avec la technique référentielle de Monte Carlo pour étudier le comportement mécanique des problèmes vibro-acoustique comportant des paramètres incertains. L’étude de la propagation de ces incertitudes permet leur intégration dans la phase de conception. Le but de l’optimisation fiabiliste Reliability-Based Design Optimization (RBDO) consiste à trouver un compromis entre un coût minimum et une fiabilité accrue. Par conséquent, plusieurs méthodes, telles que la méthode hybride (HM) et la méthode Optimum Safety Factor (OSF), ont été développées pour atteindre cet objectif. Pour remédier à la complexité des systèmes vibro-acoustiques comportant des paramètres incertains, nous avons développé des méthodologies spécifiques à cette problématique, via des méthodes de méta-modèlisation, qui nous ont permis de bâtir un modèle de substitution vibro-acoustique, qui satisfait en même temps l’efficacité et la précision du modèle. L’objectif de cette thèse, est de déterminer la meilleure méthodologie à suivre pour l’optimisation fiabiliste des systèmes vibro-acoustiques comportant des paramètres incertains. / This 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.

En conception de produits ou de systèmes, les approches d'optimisation déterministe sont de nos jours largement utilisées. Toutefois, ces approches ne tiennent pas compte des incertitudes inhérentes aux modèles utilises, ce qui peut parfois aboutir à des solutions non fiables. Il convient alors de s'intéresser aux approches d'optimisation stochastiques. Les approches de conception robuste à base d'optimisation stochastique (Reliablity Based Robust Design Optimization, RBRDO) tiennent compte des incertitudes lors de l'optimisation au travers d'une boucle supplémentaire d'analyse des incertitudes(Uncertainty Anlysis, UA). Pour la plupart des applications pratiques, l'UA est réalisée par une simulation de type Monte Carlo (Monte Carlo Simulation, MCS) combinée avec l’analyse structurale. L'inconvénient majeur de ce type d'approche réside dans le coût de calcul qui se révèle être prohibitif. Par conséquent, nous nous sommes intéressés dans nos travaux aux développements de méthodologies efficaces pour la mise en place de RBRDO s'appuyant sur une analyse MCS. Nous présentons une méthode d'UA s'appuyant sur une analyse MCS dans laquelle la réponse aléatoire est approximée sur une base du chaos polynomial (Polynomial Chaos Expansion, PCE). Ainsi, l'efficacité de l'UA est grandement améliorée en évitant une trop grande répétition des analyses structurales. Malheureusement, cette approche n'est pas pertinente dans le cadre de problèmes en grande dimension, par exemple pour des applications en dynamique. Nous proposons ainsi d'approximer la réponse dynamique en ne tenant compte que de la résolution aux valeurs propres aléatoires. De cette façon, seuls les paramètres structuraux aléatoires apparaissent dans le PCE. Pour traiter le problème du mélange des modes dans notre approche, nous nous sommes appuyés sur le facteur MAC qui permet de le quantifier. Nous avons développé une méthode univariable permettant de verifier quelle variable générait un mélange de modes de manière à le réduire ou le supprimer. Par la suite, nous présentons une approche de RBRDO séquentielle pour améliorer l'efficacité et éviter les problèmes de non-convergence présents dans les approches de RBRDO. Dans notre approche, nous avons étendu la stratégie séquentielle classique, visant principalement à découpler l'analyse de fiabilité de la procédure d'optimisation, en séparant l'évaluation des moments de la boucle d'optimisation. Nous avons utilisé une approximation exponentielle locale autour du point de conception courant pour construire des objectifs déterministes équivalents ainsi que des contraintes stochastiques. De manière à obtenir les différents coefficients pour notre approximation, nous avons développé une analyse de sensibilité de la robustesse basée sur une distribution auxiliaire ainsi qu'une analyse de sensibilité des moments basée sur l'approche PCE. Nous montrons la pertinence ainsi que l'efficacité des approches proposées au travers de différents exemples numériques. Nous appliquons ensuite notre approche de RBRDO pour la conception d'un amortisseur dans le domaine du contrôle passif vibratoire d'une structure présentant des grandeurs aléatoires. Les résultats obtenus par notre approche permettent non seulement de réduire la variabilité de la réponse, mais aussi de mieux contrôler l'amplitude de la réponse au travers d'un seuil choisi par avance. / Deterministic design optimization is widely used to design products or systems. However, due to the inherent uncertainties involved in different model parameters or operation processes, deterministic design optimization without considering uncertainties may result in unreliable designs. In this case, it is necessary to develop and implement optimization under uncertainties. One way to deal with this problem is reliability-based robust design optimization (RBRDO), in which additional uncertainty analysis (UA, including both of reliability analysis and moment evaluations) is required. For most practical applications however, UA is realized by Monte Carlo Simulation (MCS) combined with structural analyses that renders RBRDO computationally prohibitive. Therefore, this work focuses on development of efficient and robust methodologies for RBRDO in the context of MCS. We presented a polynomial chaos expansion (PCE) based MCS method for UA, in which the random response is approximated with the PCE. The efficiency is mainly improved by avoiding repeated structural analyses. Unfortunately, this method is not well suited for high dimensional problems, such as dynamic problems. To tackle this issue, we applied the convolution form to compute the dynamic response, in which the PCE is used to approximate the modal properties (i.e. to solve random eigenvalue problem) so that the dimension of uncertainties is reduced since only structural random parameters are considered in the PCE model. Moreover, to avoid the modal intermixing problem when using MCS to solve the random eigenvalue problem, we adopted the MAC factor to quantify the intermixing, and developed a univariable method to check which variable results in such a problem and thereafter to remove or reduce this issue. We proposed a sequential RBRDO to improve efficiency and to overcome the nonconvergence problem encountered in the framework of nested MCS based RBRDO. In this sequential RBRDO, we extended the conventional sequential strategy, which mainly aims to decouple the reliability analysis from the optimization procedure, to make the moment evaluations independent from the optimization procedure. Locally "first-torder" exponential approximation around the current design was utilized to construct the equivalently deterministic objective functions and probabilistic constraints. In order to efficiently calculate the coefficients, we developed the auxiliary distribution based reliability sensitivity analysis and the PCE based moment sensitivity analysis. We investigated and demonstrated the effectiveness of the proposed methods for UA as well as RBRDO by several numerical examples. At last, RBRDO was applied to design the tuned mass damper (TMD) in the context of passive vibration control, for both deterministic and uncertain structures. The associated optimal designs obtained by RBRDO cannot only reduce the variability of the response, but also control the amplitude by the prescribed threshold.

Process models are always associated with uncertainty, due to either inaccurate model structure or inaccurate identification. If left unaccounted for, these uncertainties can significantly affect the model-based decision-making. This thesis addresses the problem of model-based optimization in the presence of uncertainties, especially due to model structure error. The optimal solution from standard optimization techniques is often associated with a certain degree of uncertainty and if the model-plant mismatch is very significant, this solution may have a significant bias with respect to the actual process optimum. Accordingly, in this thesis, we developed new strategies to reduce (1) the variability in the optimal solution and (2) the bias between the predicted and the true process optima. Robust optimization is a well-established methodology where the variability in optimization objective is considered explicitly in the cost function, leading to a solution that is robust to model uncertainties. However, the reported robust formulations have few limitations especially in the context of nonlinear models. The standard technique to quantify the effect of model uncertainties is based on the linearization of underlying model that may not be valid if the noise in measurements is quite high. To address this limitation, uncertainty descriptions based on the Bayes’ Theorem are implemented in this work. Since for nonlinear models the resulting Bayesian uncertainty may have a non-standard form with no analytical solution, the propagation of this uncertainty onto the optimum may become computationally challenging using conventional Monte Carlo techniques. To this end, an approach based on Polynomial Chaos expansions is developed. It is shown in a simulated case study that this approach resulted in drastic reductions in the computational time when compared to a standard Monte Carlo sampling technique. The key advantage of PC expansions is that they provide analytical expressions for statistical moments even if the uncertainty in variables is non-standard. These expansions were also used to speed up the calculation of likelihood function within the Bayesian framework. Here, a methodology based on Multi-Resolution analysis is proposed to formulate the PC based approximated model with higher accuracy over the parameter space that is most likely based on the given measurements. For the second objective, i.e. reducing the bias between the predicted and true process optima, an iterative optimization algorithm is developed which progressively corrects the model for structural error as the algorithm proceeds towards the true process optimum. The standard technique is to calibrate the model at some initial operating conditions and, then, use this model to search for an optimal solution. Since the identification and optimization objectives are solved independently, when there is a mismatch between the process and the model, the parameter estimates cannot satisfy these two objectives simultaneously. To this end, in the proposed methodology, corrections are added to the model in such a way that the updated parameter estimates reduce the conflict between the identification and optimization objectives. Unlike the standard estimation technique that minimizes only the prediction error at a given set of operating conditions, the proposed algorithm also includes the differences between the predicted and measured gradients of the optimization objective and/or constraints in the estimation. In the initial version of the algorithm, the proposed correction is based on the linearization of model outputs. Then, in the second part, the correction is extended by using a quadratic approximation of the model, which, for the given case study, resulted in much faster convergence as compared to the earlier version. Finally, the methodologies mentioned above were combined to formulate a robust iterative optimization strategy that converges to the true process optimum with minimum variability in the search path. One of the major findings of this thesis is that the robust optimal solutions based on the Bayesian parametric uncertainty are much less conservative than their counterparts based on normally distributed parameters.

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