WorldCat Identities

Gogu, Christian

Works: 17 works in 25 publications in 2 languages and 222 library holdings
Genres: Academic theses 
Roles: Editor, Author, Other, Opponent, Thesis advisor, Publishing director
Publication Timeline
Most widely held works by Christian Gogu
Mechanical engineering under uncertainties : from classical approaches to some recent developments( )

4 editions published in 2021 in English and held by 146 WorldCat member libraries worldwide

Ingénierie mécanique en contexte incertain : des approches classiques à quelques développements récents by Christian Gogu( Book )

3 editions published between 2020 and 2021 in French and held by 35 WorldCat member libraries worldwide

Considérer le contexte incertain en ingénierie mécanique dans le but d'améliorer les performances des futurs produits ou systèmes apparaît désormais comme un avantage compétitif, voire une nécessité pour garantir une exigence de sûreté de plus en plus élevée. Ingénierie mécanique en contexte incertain traite de la modélisation, de la quantification et de la propagation d'incertitudes. Il étudie également la prise en compte des incertitudes dans l'analyse de la fiabilité et dans l'optimisation sous incertitudes. Le spectre des méthodes présentées va des approches classiques aux développements plus récents et les méthodologies sont illustrées par des exemples concrets dans des domaines variés de la mécanique (génie civil, génie mécanique et mécanique des fluides).Cet ouvrage s'adresse aussi bien à un public de chercheurs que d'ingénieurs s'intéressant à la thématique du traitement des incertitudes en ingénierie mécanique
Mechanical engineering in uncertainties : from classical approaches to some recent developments by Christian Gogu( )

3 editions published in 2021 in English and held by 21 WorldCat member libraries worldwide

Faciliter l'identification bayesienne des propriétés élastiques par réduction de dimensionnalité et la méthode des surfaces de réponse by Christian Gogu( Book )

2 editions published in 2009 in English and held by 2 WorldCat member libraries worldwide

The Bayesian method is a powerful approach to identification since it allows to account for uncertainties that are present in the problem as well as to estimate the uncertainties in the identified properties. Due to computational cost, previous applications of the Bayesian approach to material properties identification required simplistic uncertainty models and/or made only partial use of the Bayesian capabilities. We propose the use of response surface methodology, dimensionality reduction methods including nondimensionalization, global sensitivity analysis and proper orthogonal decomposition to alleviate computational cost and allow full use of the Bayesian approach. We apply the proposed Bayesian approach to two identification problems of orthotropic elastic constants: first from natural frequencies of a vibrating composite plate, then from full field displacement measurements on a plate with a hole
Optimal design of computer experiments for surrogate models with dimensionless variables by Ion Hazyuk( )

1 edition published in 2017 in English and held by 2 WorldCat member libraries worldwide

Weighted Average Continuity Approach and Moment Correction: New Strategies for Non-consistent Mesh Projection in Structural Mechanics by Simone Coniglio( )

1 edition published in 2018 in English and held by 2 WorldCat member libraries worldwide

Generalized Geometry Projection: A Unified Approach for Geometric Feature Based Topology Optimization by Simone Coniglio( )

1 edition published in 2019 in English and held by 2 WorldCat member libraries worldwide

Linear regression-based multifidelity surrogate for disturbance amplification in multiphase explosion by M. Giselle Fernández-Godino( )

1 edition published in 2019 in English and held by 2 WorldCat member libraries worldwide

Stratégies d'optimisation par modélisation explicite des différents acteurs influençant le processus de conception by Garrett Waycaster( )

1 edition published in 2015 in English and held by 1 WorldCat member library worldwide

The commercial success or failure of engineered systems has always been significantly affected by their interactions with competing designs, end users, and regulatory bodies. Designs which deliver too little performance, have too high a cost, or are deemed unsafe or harmful will inevitably be overcome by competing designs which better meet the needs of customers and society as a whole. Recent efforts to address these issues have led to techniques such as design for customers or design for market systems. In this dissertation, we seek to utilize a game theory framework in order to directly incorporate the effect of these interactions into a design optimization problem which seeks to maximize designer profitability. This approach allows designers to consider the effects of uncertainty both from traditional design variabilities as well as uncertain future market conditions and the effect of customers and competitors acting as dynamic decision makers. Additionally, we develop techniques for modeling and understanding the nature of these complex interactions from observed data by utilizing causal models. Finally, we examine the complex effects of safety on design by examining the history of federal regulation on the transportation industry. These efforts lead to several key findings; first, by considering the effect of interactions designers may choose vastly different design concepts than would otherwise be considered. This is demonstrated through several case studies with applications to the design of commercial transport aircraft. Secondly, we develop a novel method for selecting causal models which allows designers to gauge the level of confidence in their understanding of stakeholder interactions, including uncertainty in the impact of potential design changes. Finally, we demonstrate through our review of regulations and other safety improvements that the demand for safety improvement is not simply related to ratio of dollars spent to lives saved; instead the level of personal responsibility and the nature and scale of potential safety concerns are found to have causal influence on the demand for increased safety in the form of new regulations
Quantification d'incertitudes aléatoires et épistémiques dans la prédiction d'instabilités aéroélastiques by Christian Thomas Nitschke( )

1 edition published in 2018 in English and held by 1 WorldCat member library worldwide

The critical flutter velocity is an essential factor in aeronautic design because it caracterises the flight envelope outside which the aircraft risks to be destroyed. The goal of this thesis is the study of the impact of uncertainties of aleatory and epistemic origin on the linear stability limit of idealised aeroelastic configurations. First, a direct propagation problem of aleatory uncertainties related to manufacturing parameters of a rectangular plate wing made of a laminated composite material was considered. The representation of the material through the polar method alleviates the constraint of the high number of dimensions of the initial stochastic problem, which allows the use of polynomial chaos. However, the correlation which is introduced by this parametrisation requires an adaption of the polynomial basis. Finally, a machine learning algorithm is employed for the treatment of discontinuities in the modal behaviour of the aeroelastic instabilities. The second part of the thesis is about the quantification of modelling uncertainties of epistemic nature which are introduced in the aerodynamic operator. This work, which is conducted based on a Bayesian formalism, allows not only to establish model probabilities, but also to calibrate the model coefficients in a stochastic context in order to obtain robust predictions for the critical velocity. Finally, a combined study of the two types of uncertainty allows to improve the calibration process
Analyse de sensibilité fiabiliste avec prise en compte d'incertitudes sur le modèle probabiliste - Application aux systèmes aérospatiaux by Vincent Chabridon( )

1 edition published in 2018 in English and held by 1 WorldCat member library worldwide

Les systèmes aérospatiaux sont des systèmes complexes dont la fiabilité doit être garantie dès la phase de conception au regard des coûts liés aux dégâts gravissimes qu'engendrerait la moindre défaillance. En outre, la prise en compte des incertitudes influant sur le comportement (incertitudes dites « aléatoires » car liées à la variabilité naturelle de certains phénomènes) et la modélisation de ces systèmes (incertitudes dites « épistémiques » car liées au manque de connaissance et aux choix de modélisation) permet d'estimer la fiabilité de tels systèmes et demeure un enjeu crucial en ingénierie. Ainsi, la quantification des incertitudes et sa méthodologie associée consiste, dans un premier temps, à modéliser puis propager ces incertitudes à travers le modèle numérique considéré comme une « boîte-noire ». Dès lors, le but est d'estimer une quantité d'intérêt fiabiliste telle qu'une probabilité de défaillance. Pour les systèmes hautement fiables, la probabilité de défaillance recherchée est très faible, et peut être très coûteuse à estimer. D'autre part, une analyse de sensibilité de la quantité d'intérêt vis-à-vis des incertitudes en entrée peut être réalisée afin de mieux identifier et hiérarchiser l'influence des différentes sources d'incertitudes. Ainsi, la modélisation probabiliste des variables d'entrée (incertitude épistémique) peut jouer un rôle prépondérant dans la valeur de la probabilité obtenue. Une analyse plus profonde de l'impact de ce type d'incertitude doit être menée afin de donner une plus grande confiance dans la fiabilité estimée. Cette thèse traite de la prise en compte de la méconnaissance du modèle probabiliste des entrées stochastiques du modèle. Dans un cadre probabiliste, un « double niveau » d'incertitudes (aléatoires/épistémiques) doit être modélisé puis propagé à travers l'ensemble des étapes de la méthodologie de quantification des incertitudes. Dans cette thèse, le traitement des incertitudes est effectué dans un cadre bayésien où la méconnaissance sur les paramètres de distribution des variables d'entrée est caractérisée par une densité a priori. Dans un premier temps, après propagation du double niveau d'incertitudes, la probabilité de défaillance prédictive est utilisée comme mesure de substitution à la probabilité de défaillance classique. Dans un deuxième temps, une analyse de sensibilité locale à base de score functions de cette probabilité de défaillance prédictive vis-à-vis des hyper-paramètres de loi de probabilité des variables d'entrée est proposée. Enfin, une analyse de sensibilité globale à base d'indices de Sobol appliqués à la variable binaire qu'est l'indicatrice de défaillance est réalisée. L'ensemble des méthodes proposées dans cette thèse est appliqué à un cas industriel de retombée d'un étage de lanceur
Détermination des incertitudes de mesures de charge en essais en vol by Marion Gonzalez( )

1 edition published in 2015 in French and held by 1 WorldCat member library worldwide

The loads on the wings of aircraft cannot be directly measured in flight. These loads are most of the time estimatedfrom the strains of the wing, which are measured by strain gages bridges. The relation between the strains and theloads is typically modeled by a linear regression model. The estimation of flight loads is so performed by a methodin 2 steps, known as the Skopinski method :- the ground calibration : tests are performed in order to identify the model parameters linking the strains, measuredon ground, to the loads, known from the loads which are applied on the structure.- the flight tests : the loads are estimated from the strains, measured in flight, and from the model parameters,identified on ground.In this method, the existing uncertainties at each step are not taken into account. These uncertainties correspond tothe measurement noises and the modeling errors. Furthermore, the model is applied in a domain which is differentfrom the domain where its parameters are identified. Indeed, the model is calibrated on ground in pressure, thermaland loading conditions which are different from those existing in flight. The aim of this PhD is to develop a methodtaken into account these different sources of uncertainties to better identify the model on one hand and to quantifythe uncertainty which is caused by its use
Optimisation de structures aéronautiques : une nouvelle méthode à fidélité adaptative by Moindzé Soilahoudine( Book )

1 edition published in 2016 in French and held by 1 WorldCat member library worldwide

The surrogate based optimization method with adaptive enrichment (Efficient Global Optimization type approach) may, in spite of its strengths, be prohibitive in terms of computational cost when applied to large scale problems with several local minima. They require the resolution of a full numerical model for each simulation, which can lead to intractable studies or to simulation times incompatible with the times allotted for the design of a product. This PhD thesis falls within the scope of optimizing expensive simulator codes by using substitution models of the simulator. These substitutions models can be of two types: a metamodel or a reduced order model. We have proposed here a new methodology for global optimization of mechanical systems by coupling adaptive surrogate based optimization methods with the reduced order modeling methods. The surrogate based optimization methods aim to reduce the number of objective function evaluations while the reduced order model methods aim to reduce the dimensionality of a model and thus its computational cost. The objective of the methodology proposed in this thesis is thus to reduce the number of the objective function evaluations while at the same time significantly reducing the computational expense to the resolutions of the full mechanical model. The basic idea of the proposed approach resides in the adaptive construction the metamodel of the objective function. This construction fuses full and reduced order models and thus adapts the model fidelity to the accuracy requirements of the optimization at the current iteration. The efficiency of our proposed algorithms was illustrated on two types of applications: i. a problem of identification of orthotropic elastic constants from full field displacement measurements based on a tensile test on a plate with a hole ii. a problem of stiffness maximization of laminated plates. The results have shown that our methodology provides a significant speed-up in terms of computational cost, compared to the traditional EGO algorithm
Analyse des tolérances des systèmes complexes - Modélisation des imperfections de fabrication pour une analyse réaliste et robuste du comportement des systèmes by Edoh Goka( )

1 edition published in 2019 in French and held by 1 WorldCat member library worldwide

Tolerance analysis aims toward the verification of the impact of individual tolerances on the assembly and functional requirements of a mechanical system. The manufactured products have several types of contacts and their geometry is imperfect, which may lead to non-functioning and non-assembly. Traditional methods for tolerance analysis do not consider the form defects. This thesis aims to propose a new procedure for tolerance analysis which considers the form defects and the different types of contact in its geometrical behavior modeling. A method is firstly proposed to model the form defects to make realistic analysis. Thereafter, form defects are integrated in the geometrical behavior modeling of a mechanical system and by considering also the different types of contacts. Indeed, these different contacts behave differently once the imperfections are considered. The Monte Carlo simulation coupled with an optimization technique is chosen as the method to perform the tolerance analysis. Nonetheless, this method is subject to excessive numerical efforts. To overcome this problem, probabilistic models using the Kernel Density Estimation method are proposed
Facilitating Bayesian identification of elastic constants through dimensionality reduction and response surface methodology by Christian Gogu( )

1 edition published in 2009 in English and held by 1 WorldCat member library worldwide

We then introduce the Bayesian identification approach, first on a simple three bar truss problem, then on a plate vibration problem. On these problems we find three general situations in which the Bayesian approach has a significant advantage over least squares identification: large differences in the response sensitivities to the material properties, large differences in the response uncertainties and their correlation (Chapter 3). We then move to the identification problem of orthotropic elastic constants from the natural frequencies of free plates. To maintain reasonable computational cost, response surface approximations of the natural frequencies are constructed aided by nondimensionalization. We show that the fidelity of the approximations is essential for accurate identification (Chapter 4). We then apply the Bayesian approach to identify the posterior probability distributions of the orthotropic elastic constants (Chapter 5) from the vibration test. Some of the properties could only be identified with high uncertainty, which partly illustrates the difficulties of accurately identifying the ply elastic constants from frequency measurements on a multi-ply multiorientation structure. The final two chapters look at identifying the four orthotropic elastic constants from full field displacement measurements taken on a tensile test on a plate with a hole. To make the Bayeian approach tractable the proper orthogonal decomposition method is used to reduce the dimensionality of the fields (Chapter 6). Finally we present the results of the Bayesian identification for the open hole tension test, first on a simulated experiment, then on a Moiré interferometry experiment that we carried out (Chapter 7). As for the vibration based identification we find that the different properties are identified with different uncertainties, which we are able to quantify
Determination of Paris' law constants and crack length evolution via Extended and Unscented Kalman filter: An application to aircraft fuselage panels( )

1 edition published in 2016 in English and held by 1 WorldCat member library worldwide

Abstract: Prediction of fatigue crack length in aircraft fuselage panels is one of the key issues for aircraft structural safety since it helps prevent catastrophic failures. Accurate estimation of crack length propagation is also meaningful for helping develop aircraft maintenance strategies. Paris' law is often used to capture the dynamics of fatigue crack propagation in metallic material. However, uncertainties are often present in the crack growth model, measured crack size and pressure differential in each flight and need to be accounted for accurate prediction. The aim of this paper is to estimate the two unknown Paris' law constants m and C as well as the crack length evolution by taking into account these uncertainties. Due to the nonlinear nature of the Paris' law, we propose here an on-line estimation algorithm based on two widespread nonlinear filtering techniques, Extended Kalman filter (EKF) and Unscented Kalman filter (UKF). The numerical experiments indicate that both EKF and UKF estimated the crack length well and accurately identified the unknown parameters. Although UKF is theoretical superior to EKF, in this Paris' law application EKF is comparable in accuracy to UKF and requires less computational expense. Highlights: EKF involves significantly lower computational cost than UKF. Both EKF and UKF algorithms show good identification accuracy. On-line estimation algorithms based on EKF and UKF are proposed. Estimation of Paris' law constants is formalized as a nonlinear filtering problem
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Mechanical engineering under uncertainties : from classical approaches to some recent developments Mechanical engineering in uncertainties : from classical approaches to some recent developments
Mechanical engineering in uncertainties : from classical approaches to some recent developments
English (19)

French (6)