Inter-model comparison of CFD and neural network analysis of natural convection heat transfer in a partitioned enclosure

CFD analysis of heat and mass flow due to natural convection in partitioned enclosures has recently been the focus of many CFD researchers. In some cases, it was reported in the literature that different CFD solutions (due to different numerical stability characteristics) were obtained for different mesh quality, time step, and discretization order. The objective of this paper is to investigate the feasibility of using neural networks (NNs) as a means lending support to the authenticity of steady-state CFD solutions for such ill-posed problems through inter-model comparisons. Attention is focused on using NNs trained on a database generated by numerically-stable CFD analysis to predict flow variables for the aforementioned ill-posed cases, thereby giving confidence in steady-state CFD results for these cases. Three types of NNs were evaluated and parametric studies were performed to optimize network designs for best predictions of the flow variables.