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

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Pubblicato in:arXiv.org (Dec 24, 2024), p. n/a
Autore principale: Sarker, Apurba
Pubblicazione:
Cornell University Library, arXiv.org
Soggetti:
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
Accesso online:Citation/Abstract
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Abstract: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
Fonte:Engineering Database