This paper proposes a novel probabilistic approach for multidisciplinary design optimization (MDO) under uncertainty, especially for systems with feedback coupled analyses with multiple coupling variables. The proposed approach consists of four components: multidisciplinary analysis, Bayesian network, copula-based sampling, and design optimization. The Bayesian network represents the joint distribution of multiple variables through marginal distributions and conditional probabilities, and updates the distributions based on new data. In this methodology, the Bayesian network is pursued in two directions: (1) probabilistic surrogate modeling to estimate the output uncertainty given values of the design variables, and (2) probabilistic multidisciplinary analysis (MDA) to infer the distributions of the coupling and output variables that satisfy interdisciplinary compatibility conditions. A copula-based sampling technique is employed for efficient sampling from the joint and conditional distributions. The proposed MDO methodology is implemented within a framework of reliability-based design optimization. The proposed Bayesian network surrogate model and copula sampling are used for efficient reliability assessment within the optimization framework. A mathematical example and an aeroelastic aircraft wing design are used to demonstrate the proposed probabilistic MDO methodology


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multidisciplinary Optimization under Uncertainty Using Bayesian Network


    Additional title:

    Sae Int. J. Mater. Manf
    Sae International Journal of Materials and Manufacturing


    Contributors:

    Conference:

    SAE 2016 World Congress and Exhibition ; 2016



    Publication date :

    2016-04-05


    Size :

    11 pages




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    Bayesian Network Approach to Multidisciplinary, Multi-Objective Design Optimization under Uncertainty

    Nannapaneni, Saideep / Liang, Chen / Mahadevan, Sankaran | AIAA | 2017


    BAYESIAN NETWORK APPROACH TO MULTIDISCIPLINARY, MULTI-OBJECTIVE DESIGN OPTIMIZATION UNDER UNCERTAINTY (AIAA 2017-3825)

    Nannapaneni, Saideep / Liang, Chen / Mahadevan, Sankaran | British Library Conference Proceedings | 2017


    Multidisciplinary Optimization under Uncertainty for Preliminary Aircraft Sizing

    Bes, Christian / Gogu, Christian / Jaeger, Laure et al. | SAE Technical Papers | 2011


    Uncertainty-Based Multidisciplinary Design Optimization

    Kowal, M. / Mahadevan, S. / AIAA et al. | British Library Conference Proceedings | 1998


    Uncertainty-Based Multidisciplinary Design Optimization (UMDO)

    Brevault, Loïc / Balesdent, Mathieu | Springer Verlag | 2020