Optimisation sous contrainte de fiabilité d une coque imparfaite

National audienceSee http://hal.archives-ouvertes.fr/docs/00/59/27/95/ANNEX/r_S5P52J4K.pd

Approche fiabiliste pour l’optimisation locale d’un problème couplé fluide-structure

Le projet OSYCAF (Optimisation d’un Système Couplé fluide/structure représentant une Aile Flexible) a pour objectif de proposer une méthodologie d’optimisation multidisciplinaire dans un contexte aéronautique. Plus précisément, il s’agit d’optimiser une aile d’avion en tenant compte des interactions fluide-structure. Les modèles de mécanique des fluides et des structures sont des disciplines devant communiquer entre elles et avec l’optimiseur global. L’optimisation est réalisée sur deux niveaux : par rapport aux paramètres globaux, communs aux deux disciplines, et par rapport aux paramètres locaux, propres à chacune. Le travail présente l’optimisation de la structure par rapport aux paramètres locaux. Dans ce cadre, il est proposé d’introduire des incertitudes probabilistes permettant de tenir compte de contraintes de fiabilité

Local Reliability Based Sensitivity Analysis with the Moving Particles Method

Local reliability sensitivity methods aim at determining the partial derivatives of the failure probability or the reliability index with respect to model parameters. For efficient local reliability based sensitivity analysis, it is important to avoid repeated evaluations of the performance function. To this end, an extension of the moving particles method to local reliability based sensitivity analysis is presented that is completely based on the already evaluated samples for the reliability estimate and thus avoids repeated evaluations of the performance function. In order to further reduce the variance of the estimator and to increase the efficiency, a multilevel variant of the estimator is proposed. The method is discussed in detail and illustrated by means of examples

Reliability-based design optimization using kriging surrogates and subset simulation

The aim of the present paper is to develop a strategy for solving reliability-based design optimization (RBDO) problems that remains applicable when the performance models are expensive to evaluate. Starting with the premise that simulation-based approaches are not affordable for such problems, and that the most-probable-failure-point-based approaches do not permit to quantify the error on the estimation of the failure probability, an approach based on both metamodels and advanced simulation techniques is explored. The kriging metamodeling technique is chosen in order to surrogate the performance functions because it allows one to genuinely quantify the surrogate error. The surrogate error onto the limit-state surfaces is propagated to the failure probabilities estimates in order to provide an empirical error measure. This error is then sequentially reduced by means of a population-based adaptive refinement technique until the kriging surrogates are accurate enough for reliability analysis. This original refinement strategy makes it possible to add several observations in the design of experiments at the same time. Reliability and reliability sensitivity analyses are performed by means of the subset simulation technique for the sake of numerical efficiency. The adaptive surrogate-based strategy for reliability estimation is finally involved into a classical gradient-based optimization algorithm in order to solve the RBDO problem. The kriging surrogates are built in a so-called augmented reliability space thus making them reusable from one nested RBDO iteration to the other. The strategy is compared to other approaches available in the literature on three academic examples in the field of structural mechanics.Comment: 20 pages, 6 figures, 5 tables. Preprint submitted to Springer-Verla

Reliability analysis of bistable composite laminates

Bistable composite laminates are smart composites that have been employed for engineering structures due to their superlative offering of features like ability to change shape and low densities. Because of the embedded geometrical nonlinearity factor, a small variation of input parameters leads to significant changes in the response of the bistable composite laminates. In other words, Uncertainty Quantification (UQ) makes a change in the bistability characteristics. As a result, bistability behavior is extremely reliant on geometrical dimensions and elastic material properties as design parameters. Reliability analysis deals with the quantitative assessment of the occurrence probability due to UQ. In this regard, the reliability and sensitivity analysis of bistable composite plate are investigated through the Monte Carlo Simulation (MCS) and multiple types of uncertain parameters, geometry and material properties, are assumed as random variables. The results indicate bistable composite plates have a high probability to be bistability behavior with the assumed statistical properties. Moreover, the sensitivity reliability analysis illustrates that the thickness and coefficient of thermal expansion have more effect on the bistability behavior in comparison to other input parameters. The results are confirmed by comparing them with those determined by the Finite Element Method (FEM)

Variance-based reliability sensitivity with dependent inputs using failure samples

Reliability sensitivity analysis is concerned with measuring the influence of a system's uncertain input parameters on its probability of failure. Statistically dependent inputs present a challenge in both computing and interpreting these sensitivity indices; such dependencies require discerning between variable interactions produced by the probabilistic model describing the system inputs and the computational model describing the system itself. To accomplish such a separation of effects in the context of reliability sensitivity analysis we extend on an idea originally proposed by Mara and Tarantola (2012) for model outputs unrelated to rare events. We compute the independent (influence via computational model) and full (influence via both computational and probabilistic model) contributions of all inputs to the variance of the indicator function of the rare event. We compute this full set of variance-based sensitivity indices of the rare event indicator using a single set of failure samples. This is possible by considering $d$ different hierarchically structured isoprobabilistic transformations of this set of failure samples from the original $d$-dimensional space of dependent inputs to standard-normal space. The approach facilitates computing the full set of variance-based reliability sensitivity indices with a single set of failure samples obtained as the byproduct of a single run of a sample-based rare event estimation method. That is, no additional evaluations of the computational model are required. We demonstrate the approach on a test function and two engineering problems

Application of subset simulation in reliability estimation of underground pipelines

This paper presents a computational framework for implementing an advanced Monte Carlo simulation method, called Subset Simulation (SS) for time-dependent reliability prediction of underground flexible pipelines. The SS can provide better resolution for low failure probability level of rare failure events which are commonly encountered in pipeline engineering applications. Random samples of statistical variables are generated efficiently and used for computing probabilistic reliability model. It gains its efficiency by expressing a small probability event as a product of a sequence of intermediate events with larger conditional probabilities. The efficiency of SS has been demonstrated by numerical studies and attention in this work is devoted to scrutinise the robustness of the SS application in pipe reliability assessment and compared with direct Monte Carlo simulation (MCS) method. Reliability of a buried flexible steel pipe with time-dependent failure modes, namely, corrosion induced deflection, buckling, wall thrust and bending stress has been assessed in this study. The analysis indicates that corrosion induced excessive deflection is the most critical failure event whereas buckling is the least susceptible during the whole service life of the pipe. The study also shows that SS is robust method to estimate the reliability of buried pipelines and it is more efficient than MCS, especially in small failure probability prediction

Dynamic reliability sensitivity analysis for inter-story isolation structure under stochastic excitations

Considering the uncertainties of seismic excitation and stochastic parameters of the Inter-story Isolation Structure, the implicit structural response function is transformed into an explicit one by the Stochastic Response Surface Method (SRSM), combined with a probabilistic collocation method based on the linear independence principle. By using the reliability analysis method based on the first-passage failure, the reliability of isolation structure is analyzed and the sensitivity of the reliability of each sub-structure is further analyzed. The results show that conducting sensitivity research based on reliability analysis can provide an important theoretical basis for an optimized and robust design of a random vibration system. The changes of the stiffness and damping ratio of the Isolation Layer have a great influence on the reliability of the system and have different effects on the reliability of different parts of the system. The analysis results of reliability sensitivity could provide system reliability indexes to rank stochastic parameters by their importance, thus improving the efficiency of the structure reliability analysis and optimization design

Augmented line sampling for approximation of failure probability function in reliability-based analysis

Abstract(#br)An efficient approach, called augmented line sampling, is proposed to locally evaluate the failure probability function (FPF) in structural reliability-based design by using only one reliability analysis run of line sampling. The novelty of this approach is that it re-uses the information of a single line sampling analysis to construct the FPF estimation, repeated evaluations of the failure probabilities can be avoided. It is shown that, when design parameters are the distribution parameters of basic random variables, the desired information about FPF can be extracted through a single implementation of line sampling. Line sampling is a highly efficient and widely used reliability analysis method. The proposed method extends the traditional line sampling for the failure probability estimation to the evaluation of the FPF which is a challenge task. The required computational effort is neither relatively sensitive to the number of uncertain parameters, nor grows with the number of design parameters. Numerical examples are given to show the advantages of the approach