Enhanced prediction of heat transfer in jet impingement cooling using an artificial intelligence: A case study

OpenAlex İngilizce

This paper is about the gas turbine cooling model using the extended jet array. The aim of the present study is to explain the effect of the impinging jet configuration of elongated jet holes on the heat transfer distribution with a deep Convolutional Neural Network (CNN). CNNs preserve spatial relationships through convolutional operations—unlike traditional Artificial Neural Networks (ANNs), which require flattening of input data—allowing for higher accuracy with smaller datasets and faster training due to fewer trainable parameters. The goal is to understand how accurate and fast deep learning models with limited data can deliver predictions for complex systems such as jet impingement cooling. The proposed CNN model was compared with the numerical and experimental data. Jet impingement cooling was examined with four different Reynolds numbers (16250, 21700, 27100, 36250) and six dimensionless gaps between the jet and the target surface (G/D = 1, 2, 3, 4, 5, and 6). Mean and local Nusselt number (Nu) distributions were investigated in particular. G/D = 2 was selected as the test data for the model among the configurations. While the CFD model failed to identify this peak performance point, the CNN model accurately captured this maximum behavior despite being trained only on other configurations. Moreover, the CNN model demonstrated more suitable prediction accuracy in the first two jet impingement regions than the CFD data, especially in capturing peak Nusselt numbers and localized hot spots. It has been determined that the CNN model provided more accurate predictions of the local Nusselt numbers within the measurement region compared to the Computational Fluid Dynamics (CFD) study for the average Nu, the proposed CNN model showed a closer agreement to experimental data than numerical simulation by 4.15 % at the Re =32500 and G/D=2.