Experimental investigation and ANN-based prediction of average Nusselt number for a flat surface subjected to double inclined air jets

This study explores the thermal behavior of double-inclined circular air jets impinging on a flat plate. Using a jet diameter (D) of 9.5 mm, experimental analyses are carried out to examine the impact of jet inclination angles (ranging from 0° to 60°), spacing between jets (2D-8D), Reynolds numbers (10,000-40,000), and the distance from the nozzle to plate (2D-8D) on the heat transfer over the impingement plate. An Artificial Neural Network (ANN) is constructed using the experimental dataset to estimate the average Nusselt number based on these parameters. Thermal infrared imaging is employed to capture surface temperature distributions during the experiments. The results indicate that the most effective jet inclination angles, generally between 10° and 20°, enhance heat transfer by optimizing both direct impingement and interaction between jets. Optimal jet spacing, found to be in the range of 3D to 4D, contributes to improved thermal performance with reduced interference and a more uniform temperature field. The ADAM optimizer is used to train the ANN design, which has 16 hidden layers with 24 neurons each. The model employs linear activation function in the output layer, ReLU activation functions in the hidden layers, and a feedforward structure combined with backpropagation to iteratively refine weights and biases. The resulting model demonstrates high prediction accuracy, with R² and r values approximately 1 and low error values: MSE = 6.265, MAPE = 0.796%, MSLE = 9.82e-05, and log-cosh loss = 1.419.