P7UC2022 - Recent progress in engineering design with MDO/AI

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Authors:

Joseph Morlier, Professor of Aerospace Engineering, ISAE-SUPAERO/ICA

Journal / Event:

pSeven User Conference 2022

Abstract:

This presentation attempts to demonstrate the contribution of reproducible research in MDO/AI with several recent open source attempts of Prof Morlier’s group. During the last few decades, surrogate modeling has gained in popularity, especially in engineering fields, where they are often used in design analysis and optimization to replace expensive numerical simulations. Coupled to Multidisciplinary Design Optimization (MDO) this process lead to an engineering design acceleration through AI. The first part of the talk will present some novelties in AI for Engineers such as the Surrogate Modeling Toolbox (SMT developed conjointly between ONERA, ISAE-SUPAERO/ICA, Nasa, and University of Michigan). SMT is a Python package that contains a collection of surrogate modeling methods (Mainly Kriging also known as Gaussian Processes in Machine Learning community), sampling techniques and benchmarks. SMT is different from existing surrogate modeling libraries because of its emphasis on derivatives that can be used directly in MDO processes. It also includes surrogate models or options that are not available elsewhere: KPLS (Kriging with Partial Least Square) for automatic inputs dimension reduction, mixture of experts (with automatic clustering) for simulation codes that include singularities (such as buckling in structural mechanics) and mixed variable design space (continuous, discrete, and categorical). The 2022 version currently available propose new applications such as multi-fidelity approach, Bayesian unconstrained optimization (EGO), fully inter-operable and automated. The second part will present some MDO applications in Aerospace sciences will be highlighted, including launcher and UAV, HALE optimal design especially in an environmental perspective.

Keywords: P7UC2022, Optimization, Aerospace