Solar air heater with rotating circular ribs: Hybrid CFD-ANN approach for prediction of thermo-hydraulic performance

A hybrid computational fluid dynamics-artificial neural network approach is implemented to predict the thermo-hydraulic performance factor of a solar air heater with rotating circular ribs. Effects of rib height, rib pitch, and rib rotating speed are investigated for Reynolds numbers ranging from 3800 to 18000. A total of 432 different cases are simulated using CFD. Based on these data sets, an optimized ANN model is developed to predict thermo-hydraulic performance. The optimized ANN model has an architecture of 4-17-17-1 and it has a mean squared error of 3.89 x 10(-6) for the testing data sets. The maximum thermo-hydraulic performance achieved is 1.57 for rib height of 1 4 mm, rib pitch of 25 mm, rib rotating speed of 10000 rpm at Reynolds number = 3800. The generalization capacity of the optimized ANN model is tested using experimental results and it is found that ANN predictions are in agreement with CFD simulation results. A comparison of computational time is also performed and approximately 100% reduction in computational time is achieved using the optimized artificial neural network model. This research establishes that ANN can be an excellent design tool for assessing the performance of novel solar air heaters. (C) 2022 The Author(s). Published by Elsevier Ltd.

Automated summary

A hybrid CFD-ANN approach is used to predict the thermo-hydraulic performance of a solar air heater with rotating circular ribs. An optimized ANN model is developed based on CFD simulations, and it shows good agreement with experimental results. The optimized ANN model significantly reduces the computational time compared to CFD simulations.