Machine learning framework to predict flows over arbitrarily arranged solid arrays

Flow past an array of solid obstacles have been studied in a wide range of fluid engineering applications, such as heat exchanger, particulate filter, and fuel cells. Existing studies have focused on flows over homogeneous arrangements with different geometrical setups and attained the explicit correlation between the inlet and outlet. However, it is impractical to obtain the explicit correlation in flows over heterogeneous arrangements due to the case-specific nature for countless geometries. In the present study, we devised a machine learning framework to tackle the case-specific nature in flows over arbitrarily arranged solid arrays, including heterogeneous arrangements. The training datasets can be generated systematically and automatically with recursively performed CFD simulations, without the need to set up all possible geometric configurations of a solid array. The prediction performance with high robustness was verified by comparison of the predicted results to the target data retrieved from the numerical simulations in various geometries and flow regimes. Furthermore, we also showed that the proposed model can cover untrained flow regimes.