Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network

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Détails bibliographiques
Publié dans:	arXiv.org (Dec 23, 2024), p. n/a
Auteur principal:	Rostamijavanani, Abdolvahhab
Autres auteurs:	Li, Shanwu, Yang, Yongchao
Publié:	Cornell University Library, arXiv.org
Sujets:	Accuracy Parameter identification Parameterization Generative adversarial networks Fluid flow Parameter modification Two dimensional flow System effectiveness Nonlinear systems Dynamical systems Reynolds number Fluid dynamics Nonlinear dynamics
Accès en ligne:	Citation/Abstract Full text outside of ProQuest
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Description
Résumé:	This work presents a data-driven solution to accurately predict parameterized nonlinear fluid dynamical systems using a dynamics-generator conditional GAN (Dyn-cGAN) as a surrogate model. The Dyn-cGAN includes a dynamics block within a modified conditional GAN, enabling the simultaneous identification of temporal dynamics and their dependence on system parameters. The learned Dyn-cGAN model takes into account the system parameters to predict the flow fields of the system accurately. We evaluate the effectiveness and limitations of the developed Dyn-cGAN through numerical studies of various parameterized nonlinear fluid dynamical systems, including flow over a cylinder and a 2-D cavity problem, with different Reynolds numbers. Furthermore, we examine how Reynolds number affects the accuracy of the predictions for both case studies. Additionally, we investigate the impact of the number of time steps involved in the process of dynamics block training on the accuracy of predictions, and we find that an optimal value exists based on errors and mutual information relative to the ground truth.
ISSN:	2331-8422
Source:	Engineering Database