Decoupled UMDO formulation for interdisciplinary coupling satisfaction under uncertainty

Loïc Brévault 1 Mathieu Balesdent 1 Nicolas Bérend 2 Rodolphe Le Riche 3
1 ONERA - The French Aerospace Lab [Palaiseau]
2 ONERA - The French Aerospace Lab [Châtillon]
3 DEMO
LIMOS - Laboratoire d'Informatique, de Modélisation et d'optimisation des Systèmes, DEMO-ENSMSE - Département Décision en Entreprise : Modélisation, Optimisation
Abstract : At early design phases, taking into account uncertainty for the optimization of a multidisciplinary system is essential to establish the optimal system characteristics and performances. Uncertainty Multidisciplinary Design Optimization (UMDO) formulations have to eciently organize the di erent disciplinary analyses, the uncertainty propagation, the optimization, but also the handling of interdisciplinary couplings under uncertainty. A decoupled UMDO formulation (Individual Discipline Feasible - Polynomial Chaos Expansion) ensuring the coupling satisfaction for all the instantiations of the uncertain variables is presented in this paper. Ensuring coupling satisfaction in instantiations is essential to ensure the equivalence between the coupled and decoupled UMDO problem formulations. The proposed approach relies on the iterative construction of surrogate models based on Polynomial Chaos Expansion in order to represent at the convergence of the optimization problem, the coupling functional relations as a coupled approach under uncertainty does. The performances of the proposed formulation is assessed on an analytic test case and on the design of a new Vega launch vehicle upper stage.
Keywords : MULTIDISCIPLINARY DESIGN OPTIMIZATION UNCERTAINTY INTERDISCIPLINARY COUPLINGS POLYNOMIAL
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Conference papers
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DEMO-ENSMSE | CNRS | LIMOS | ONERA | FAYOL-ENSMSE | EMSE | INSTITUT-TELECOM | PRES_CLERMONT | TDS-MACS | SIGMA-CLERMONT

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Loïc Brévault, Mathieu Balesdent, Nicolas Bérend, Rodolphe Le Riche. Decoupled UMDO formulation for interdisciplinary coupling satisfaction under uncertainty. 15th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Jun 2014, Atlanta, United States. 32p., ⟨10.2514/6.2014-3014⟩. ⟨emse-01012119⟩

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