Kapak Resmi

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

Kaydedildi:
Yayımlandı:arXiv.org (Dec 23, 2024), p. n/a
Yazar: Rostamijavanani, Abdolvahhab
Diğer Yazarlar: Li, Shanwu, Yang, Yongchao
Baskı/Yayın Bilgisi:
Cornell University Library, arXiv.org
Konular:
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
Online Erişim:Citation/Abstract
Full text outside of ProQuest
Etiketler: Etiketle
Etiket eklenmemiş, İlk siz ekleyin!
Özet: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
Kaynak:Engineering Database