A coupled parametric and nonparametric approach for modal analysis of a satellite

This study takes place in the context of dynamical prediction for satellite structures. Aims of such studies are to survey the dynamical response of satellite equipment and components, to check that requirements are correct and to give prediction of vibration levels, which are inputs for the experimental test validation according to the launcher specification. This prediction is done by modal analysis performed on a numerical model built by finite element method. Uncertainties on equipment and components properties lead to random frequency response function (FRF). This paper aims at understanding how modal approach can be adapted to probabilistic framework in order to calculate cumulative density function or, at least, some quantiles of a FRF. Starting point of deterministic modal analysis is the study of a single degree of freedom (DOF) system. C. Heinkelé analytically expresses the probability density function (PDF) of the FRF of an oscillator with a random natural pulsation following a uniform law. We have generalized this work to random natural pulsation following a law of finite variance. Then, the expression of a FRF between two DOF of the structure is a linear function of random oscillators FRF and DOF components of random eigenvectors. Assuming that random eigenvectors are close to their means, we have access to the characteristic function of the random FRF between two DOF as a multi-dimensional integral with respect to the joint PDF of the oscillators FRF. This paper mainly focuses on two major points which are calculation of oscillators joint PDF and inversion of characteristic functions. The first one is tackled by copulas theory. Dependence structure of random eigenvalues are identified and modeled by a copula. Then we apply results on copulas transformations to obtain joint PDF of oscillators FRF. Classical results concern monotonic transformations but we extended these ones to non-monotonic cases. Concerning inversion of characteristic function, several methods are studied to numerically compute the Gil-Pelaez formula. This approach allows to access some FRF quantiles by numerical integration which error can be controlled. Moreover, an interesting point is the flexibility in the identification of the random eigenvalues PDF. This is especially interesting in order to couple parametric identification with nonparametric one, when only few dispersion informations are given for equipments, housed in the satellite primary structure

Determination of Bootstrap confidence intervals on sensitivity indices obtained by polynomial chaos expansion

L’analyse de sensibilité a pour but d’évaluer l’influence de la variabilité d’un ou plusieurs paramètres d’entrée d’un modèle sur la variabilité d’une ou plusieurs réponses. Parmi toutes les méthodes d’approximations, le développement sur une base de chaos polynômial est une des plus efficace pour le calcul des indices de sensibilité, car ils sont obtenus analytiquement grâce aux coefficients de la décomposition (Sudret (2008)). Les indices sont donc approximés et il est difficile d’évaluer l’erreur due à cette approximation. Afin d’évaluer la confiance que l’on peut leur accorder nous proposons de construire des intervalles de confiance par ré-échantillonnage Bootstrap (Efron, Tibshirani (1993)) sur le plan d’expérience utilisé pour construire l’approximation par chaos polynômial. L’utilisation de ces intervalles de confiance permet de trouver un plan d’expérience optimal garantissant le calcul des indices de sensibilité avec une précision donnée

Etude numérique de l'influence de la structure de dépendance des valeurs propres en synthèse modale probabiliste

Ce travail a pour cadre la détermination de fonctions de réponse en fréquence (FRF) par synthèse modale. La modélisation probabiliste des paramètres d'entrée du modèle conduit à un problème aux valeurs propres aléatoires. Nous nous intéressons à la représentation de la structure de dépendance entre les valeurs propres et son influence sur la densité de probabilité de la FRF . Cette structure de dépendance est modélisée par une copule identifiée à partir de simulations de Monte-Carlo. En adaptant les travaux de C. Heinkelé au cas de l'amortissement critique, nous obtenons les expressions analytiques des densités de probabilité de la FRF d'un oscillateur harmonique. Nous utilisons ces résultats afin d'exprimer la densité jointe d'un vecteur de N oscillateurs connaissant la loi jointe des N premières valeurs propres du système

Synthèse modale probabiliste de systèmes à plusieurs degrés de liberté

Notre travail concerne les études dynamiques basse fréquence de satellites. Le but est d’étendre l’analyse modale en prenant en compte les incertitudes sur les paramètres d’entrée du modèle. Pour cela, l’approche probabiliste a été choisie. Les paramètres incertains du modèle sont donc définis par des variables aléatoires de lois connues. L’objectif de cette analyse est de déterminer la variabilité d’une fonction de réponse en fréquence (FRF) entre deux points de la structure. Nous supposons qu’il est possible d’identifier les lois de probabilité des valeurs propres du système et que l’amortissement modal est déterministe. Nous présentons une expression analytique des densités de probabilité de la FRF, ainsi qu’une méthodologie permettant de les calculer y compris dans le cas où les valeurs propres sont corrélées

A coupled parametric and nonparametric approach for modal analysis of a satellite

International audienceThis study takes place in the context of dynamical prediction for satellite structures. Aims of such studies are to survey the dynamical response of satellite equipment and components, to check that requirements are correct and to give prediction of vibration levels, which are inputs for the experimental test validation according to the launcher specification. This prediction is done by modal analysis performed on a numerical model built by finite element method. Uncertainties on equipment and components properties lead to random frequency response function (FRF). This paper aims at understanding how modal approach can be adapted to probabilistic framework in order to calculate cumulative density function or, at least, some quantiles of a FRF. Starting point of deterministic modal analysis is the study of a single degree of freedom (DOF) system. C. Heinkelé analytically expresses the probability density function (PDF) of the FRF of an oscillator with a random natural pulsation following a uniform law. We have generalized this work to random natural pulsation following a law of finite variance. Then, the expression of a FRF between two DOF of the structure is a linear function of random oscillators FRF and DOF components of random eigenvectors. Assuming that random eigenvectors are close to their means, we have access to the characteristic function of the random FRF between two DOF as a multi-dimensional integral with respect to the joint PDF of the oscillators FRF. This paper mainly focuses on two major points which are calculation of oscillators joint PDF and inversion of characteristic functions. The first one is tackled by copulas theory. Dependence structure of random eigenvalues are identified and modeled by a copula. Then we apply results on copulas transformations to obtain joint PDF of oscillators FRF. Classical results concern monotonic transformations but we extended these ones to non-monotonic cases. Concerning inversion of characteristic function, several methods are studied to numerically compute the Gil-Pelaez formula. This approach allows to access some FRF quantiles by numerical integration which error can be controlled. Moreover, an interesting point is the flexibility in the identification of the random eigenvalues PDF. This is especially interesting in order to couple parametric identification with nonparametric one, when only few dispersion informations are given for equipments, housed in the satellite primary structure

Snapshot Mueller matrix polarimeter by wavelength polarization coding

International audienceWe present a new, to the best of our knowledge, experimental configuration of Mueller matrix polarimeter based on wavelength polarization coding. This is a compact and fast technique to study polarization phenomena. Our theoretical approach, the necessity to correct systematic errors and our experimental results are presented. The feasibility of the technique is tested on vacuum and on a linear polarizer

Systematic errors specific to a snapshot Mueller matrix polarimeter

International audienceSystematic errors specific to a snapshot Mueller matrix polarimeter are studied. Their origins and effects are highlighted, and solutions for correction and stabilization are proposed. The different effects induced by them are evidenced by experimental results acquired with a given setup and theoretical simulations carried out for more general cases. We distinguish the errors linked to some imperfection of elements in the experimental setup from those linked to the sample under study

Two-channel snapshot Mueller matrix polarimeter

International audienceWe describe a new setup for a snapshot Mueller matrix polarimeter (SMMP). It relies on the separation and orthogonal polarization of two light beams by a Wollaston prism located at the setup output. The simultaneous treatment of the two spectra allows an enhancement of accuracy for real-time measurements through reduction of the effects caused by random noise and systematic errors. Moreover, it gives insight into the nonuniform spectral response of the medium under study. Experimental results support the feasibility of the proposed technique

Superposition modale probabiliste : identification de la loi jointe des valeurs propres et paramètres effectifs par la théorie des copules

Afin de prendre en compte les incertitudes de conception dans le calcul prédictif des fonctions de réponse en fréquence (FRF), une adaptation de la méthode de superposition modale au cadre probabiliste est proposée. L’identification de la loi de probabilité du vecteur composé des valeurs propres et paramètres effectifs aléatoires est réalisée en 3 étapes : sélection des modes prépondérants, discrimination des lois jointes à identifier, identification de ces lois de probabilité de grandes dimensions par décomposition Vine(copules bidimensionnelles)

Construction of bootstrap confidence intervals on sensitivity indices computed by polynomial chaos expansion

Sensitivity analysis aims at quantifying influence of input parameters dispersion on the output dispersion of a numerical model. When the model evaluation is time consuming, the computation of Sobol' indices based on Monte Carlo method is not applicable and a surrogate model has to be used. Among all approximation methods, polynomial chaos expansion is one of the most efficient to calculate variance-based sensitivity indices. Indeed, their computation is analytically derived from the expansion coefficients but without error estimators of the meta-model approximation. In order to evaluate the reliability of these indices, we propose to build confidence intervals by bootstrap re-sampling on the experimental design used to estimate the polynomial chaos approximation. Since the evaluation of the sensitivity indices is obtained with confidence intervals, it is possible to find a design of experiments allowing the computation of sensitivity indices with a given accuracy