Treffer: A Review of Research on the Thermo‐Force Characteristics of Scramjet Engine Wall Based on Neural Networks.

With the development of high‐performance computing and advanced experimental methods, data‐driven machine learning, especially neural network technology, has shown great potential in fluid mechanics research and has become a fourth paradigm research tool. In particular, remarkable achievements have been made in turbulence modeling, near‐wall flow prediction, and combustion dynamic evolution. Researchers use neural network model to assist turbulence control, improve Reynolds average turbulence model, and harnesses the deep learning method to solve the problem of complex flow phenomenon prediction driven by large‐scale data, which effectively improves the accuracy and efficiency of internal flow and wall effect simulation of supersonic combustion ramjet (scramjet) engine. These studies not only promote the development of fluid mechanics but also provide strong support for the design optimization of scramjet engines. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Energy Research is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)