Efficient Aircraft Design Optimization Using Multi-Fidelity Models and Multi-fidelity Physics Informed Neural Networks

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Détails bibliographiques
Publié dans:	arXiv.org (Dec 24, 2024), p. n/a
Auteur principal:	Sarker, Apurba
Publié:	Cornell University Library, arXiv.org
Sujets:	Design analysis Accuracy Finite element method Finite volume method Mathematical models Reduced order models Neural networks Machine learning Design optimization Aircraft design Generative adversarial networks
Accès en ligne:	Citation/Abstract Full text outside of ProQuest
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Description
Résumé:	Aircraft design optimization traditionally relies on computationally expensive simulation techniques such as Finite Element Method (FEM) and Finite Volume Method (FVM), which, while accurate, can significantly slow down the design iteration process. The challenge lies in reducing the computational complexity while maintaining high accuracy for quick evaluations of multiple design alternatives. This research explores advanced methods, including surrogate models, reduced-order models (ROM), and multi-fidelity machine learning techniques, to achieve more efficient aircraft design evaluations. Specifically, the study investigates the application of Multi-fidelity Physics-Informed Neural Networks (MPINN) and autoencoders for manifold alignment, alongside the potential of Generative Adversarial Networks (GANs) for refining design geometries. Through a proof-of-concept task, the research demonstrates the ability to predict high-fidelity results from low-fidelity simulations, offering a path toward faster and more cost effective aircraft design iterations.
ISSN:	2331-8422
Source:	Engineering Database