From local to global importance measures of uncertainty propagation in composite structures

2015

Int J Mech Mater Des

Self Cite

A robust design optimization (RDO) approach for minimum weight and safe shell composite structures with minimal variability into design constraints under uncertainties is proposed. A new concept of feasibility robustness associated to the variability of design constraints is considered. So, the feasibility robustness is defined through the determinant of variance-covariance matrix of constraint functions introducing in this way the joint effects of the uncertainty propagations on structural response. A new framework considering aleatory uncertainty into RDO of composite structures is proposed. So, three classes of variables and parameters are identified: deterministic design variables, random design variables and random parameters. The bi-objective optimization search is performed using on a new approach based on two levels of dominance denoted by CoDominance-based Genetic Algorithm (CoDGA). The use of evolutionary concepts together sensitivity analysis based on adjoint variable method is a new proposal. The examples with different sources of uncertainty show that the Pareto front definition depends on random design variables and/or random parameters considered in RDO. Furthermore, the importance to control the uncertainties on the feasibility of constraints is demonstrated. CoDGA approach is a powerfully tool to help designers to make decision establishing the priorities between performance and robustness.

Section: Sensitivity Analysis Using Adjoint Variable Methodsmentioning

confidence: 99%

Section: Uncertainty Analysis For Robustness Definitionmentioning

confidence: 99%

Section: Uncertainty Analysis For Robustness Definitionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal design of composite shells based on minimum weight and maximum feasibility robustness

2015

Int J Mech Mater Des

Self Cite

“…A number of approaches to uncertainty and sensitivity analysis have been developed, including differential analysis, response surface methodology, Monte Carlo analysis, and variance decomposition procedures. Reviews of these methodologies are available in the bibliography [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

An approach for reliability-based robust design optimisation of angle-ply composites

2009

Composite Structures

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A B S T R A C TVariations of manufacturing process parameters and environmental aspects may affect the quality and performance of composite materials, which consequently affects their structural behaviour. Reliability-based design optimisation (RBDO) and robust design optimisation (RDO) searches for safe structural sys-tems with minimal variability of response when subjected to uncertainties in material design parameters. An approach that simultaneously considers reliability and robustness is proposed in this paper. Depending on a given reliability index imposed on composite structures, a trade-off is established between the performance targets and robustness. Robustness is expressed in terms of the coefficient of variation of the constrained structural response weighted by its nominal value. The Pareto normed front is built and the nearest point to the origin is estimated as the best solution of the bi-objective optimisation problem.

“…The uncertainty propagation and the importance measure of input parameters are analyzed using an Artificial Neural Network-based Monte Carlo simulation approach (ANN-MCS). The proposed methodology uses a Monte Carlo procedure together an Artificial Neural Network surrogate model based on supervised evolutionary learning [14].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty assessment approach for composite structures based on global sensitivity indices

2013

Composite Structures

The problem of uncertainty propagation in composite laminate structures is studied. An approach based on the optimal design of composite structures to achieve a target reliability level is proposed. Using the Uniform Design Method (UDM), a set of design points is generated over a design domain centred at mean values of random variables, aimed at studying the space variability. The most critical Tsai number, the structural reliability index and the sensitivities are obtained for each UDM design point, using the max-imum load obtained from optimal design search. Using the UDM design points as input/output patterns, an Artificial Neural Network (ANN) is developed based on supervised evolutionary learning. Finally, using the developed ANN a Monte Carlo simulation procedure is implemented and the variability of the struc-tural response based on global sensitivity analysis (GSA) is studied. The GSA is based on the first order Sobol indices and relative sensitivities. An appropriate GSA algorithm aiming to obtain Sobol indices is proposed. The most important sources of uncertainty are identified.