Towards a new paradigm in intelligence-driven computational fluid dynamics simulations

• Published in: Engineering applications of computational fluid mechanics Vol. 18; no. 1
• Main Authors: Chen, Xinhai, Wang, Zhichao, Deng, Liang, Yan, Junjun, Gong, Chunye, Yang, Bo, Wang, Qinglin, Zhang, Qingyang, Yang, Lihua, Pang, Yufei, Liu, Jie
• Format: Journal Article
• Language: English
• Published: Taylor & Francis Group 31.12.2024
• Subjects: Computational fluid dynamics; deep learning; intelligent workflow; prototype platform; science paradigm
• ISSN: 1994-2060; 1997-003X
• DOI: 10.1080/19942060.2024.2407005

Computational Fluid Dynamics (CFD) plays a crucial role in investigating new physical phenomena and exploring the principles of fluid mechanics. However, CFD numerical methods often face the challenges of long research cycles, high costs, and extensive human-computer interactions due to the growing complexity of computational tasks. To meet the burgeoning requirements of contemporary physical sciences, in recent years, the coupling of traditional scientific computing techniques with promising deep learning techniques well-known from computer science have emerged as a new research paradigm. This paradigm aims to create automated, intelligent tools for obtaining valuable insights as well as being able to categorize, predict, and make evidence-based decisions in novel ways. These tools can be used to reduce the reliance on expert experience and laborious computations inherent in existing numerical theories and methods. In this paper, we delve into the essence of science paradigms, the evolution of computing intelligence, and provide a comprehensive overview of the key applications driving the development of a new intelligence paradigm in CFD simulations. In addition, we outline a prototype platform for CFD simulations within this new paradigm. Based on this platform, three intelligent workflows are proposed, anticipating to serve as a reference source for future research and foster the emergence of innovative applications in the field of CFD.Highlights Deep learning techniques emerged as a new method to create automated, intelligent tools for CFD simulations.A review of deep learning methods for mesh pre-processing.A review of deep learning methods for numerical solving.A review of deep learning methods for post-processing visualization.A prototype platform for CFD simulations within the new paradigm.Perspectives on challenges and future directions.