A new artificial neural network approach for fluid flow simulations

In this research we describe our attempt to get instantaneous numerical simulation for fluid flow by using Artificial Neural networks (ANN). Such simulation should provide a reliable perception about the fluid behaviour with respect to both momentum and energy equations. In addition to the preceding recorded data, the proposed method consider the geometrical boundaries profile as a major contributions for ANN training phase. Our study is driven by the need of rapid response especially in medical cases, surgeon diagnosis, engineering emergency situations, and when novel circumstances occurs. Furthermore, the existing computational fluid dynamics tools require long time to response and the present of professional expert to set the parameters for the different cases. In fact, ANN can deal with the lack of proper physical models or the present of uncertainty about some conditions that usually affect the outcomes form the other approaches. We manage to get acceptable result for 1D-flow equations with respect to both energy and momentum equations. Our ANN approach is able to handle fluid flow prediction with known boundaries velocity. This approach can be the first step for neural network computational program that can tackle variance type of problems.