Abstract
In recent years, progress in computational fluid dynamics (CFD) have made CFD-based design possible in various industrial fields that use fluids. On the other hand, however, the need for significant computation time and resources when performing fluid simulations for complex geometries has become an issue. To resolve this problem, approaches based on machine learning has been developing rapidly. In this study, we examine machine learning prediction of unsteady flow fields in complex channels. Specifically, we set an unsteady pulsating flow having one step in a channel, which causes rapid expansion or rapid contraction, as learning object, and set an unsteady pulsating flow having two steps in a channel as prediction object. In this paper, we report a result of applying convolutional neural network (CNN) + long short-term memory (LSTM) and signed distance function (SDF) to those.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The Proceedings of The Computational Mechanics Conference
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
