| Title | Machine Learning Computational Fluid Dynamics |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Usman, Ali, Rafiq, Muhammad, Saeed, Muhammad, Nauman, Ali, Almqvist, Andreas, Liwicki, Marcus |
| Conference Name | 2021 Swedish Artificial Intelligence Society Workshop (SAIS) |
| Keywords | composability, compositionality, computational fluid dynamics, Computational Intelligence, Computational modeling, flow past a cylinder, fluid flow, fluid-structure interaction, Fluids, machine learning, Neural networks, numerical analyses, Partial differential equations, Predictive models, pubcrawl |
| Abstract | Numerical simulation of fluid flow is a significant research concern during the design process of a machine component that experiences fluid-structure interaction (FSI). State-of-the-art in traditional computational fluid dynamics (CFD) has made CFD reach a relative perfection level during the last couple of decades. However, the accuracy of CFD is highly dependent on mesh size; therefore, the computational cost depends on resolving the minor feature. The computational complexity grows even further when there are multiple physics and scales involved making the approach time-consuming. In contrast, machine learning (ML) has shown a highly encouraging capacity to forecast solutions for partial differential equations. A trained neural network has offered to make accurate approximations instantaneously compared with conventional simulation procedures. This study presents transient fluid flow prediction past a fully immersed body as an integral part of the ML-CFD project. MLCFD is a hybrid approach that involves initialising the CFD simulation domain with a solution forecasted by an ML model to achieve fast convergence in traditional CDF. Initial results are highly encouraging, and the entire time-based series of fluid patterns past the immersed structure is forecasted using a deep learning algorithm. Prepared results show a strong agreement compared with fluid flow simulation performed utilising CFD. |
| DOI | 10.1109/SAIS53221.2021.9483997 |
| Citation Key | usman_machine_2021 |
Machine Learning Computational Fluid Dynamics