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The main purpose of this investigation was to explore the heat transfer and flow characteristics of aero-foil-shaped fins combined with extended jet holes, specifically focusing on their feasibility in cooling turbine blades. In this study, a comprehensive investigation was carried out by applying impinging jet array cooling (IJAC) on a semi-circular curved surface, which was roughened using aerofoil-shaped fins. Numerical computations were conducted under three different Reynolds numbers (Re) ranging from 5000 to 25,000, while nozzle-to-target surface spacings (S/d) ranged from 0.5 to 8.0. Furthermore, an assessment was made of the impact of different fin arrangements, single-row (L1), double-row (L2), and triple-row (L3), on convective heat transfer. Detailed examinations were performed on area-averaged and local Nusselt (Nu) numbers, flow properties, and the thermal performance criterion (TPC) on finned and smooth target surfaces. The study’s results revealed that the use of aerofoil-shaped fins and the reduction in S/d, along with surface roughening, led to significant increases in the local and area-averaged Nu numbers compared to the conventional IJAC scheme. The most notable heat transfer enhancement was observed at S/d = 0.5 utilizing extended jets and the surface design incorporating aerofoil-shaped fins. Under these specific conditions, the maximum heat transfer enhancement reached 52.81%. Moreover, the investigation also demonstrated that the highest TPC on the finned surface was achieved when S/d = 2.0 for L2 at Re = 25,000, resulting in a TPC value of 1.12. Furthermore, reducing S/d and mounting aerofoil-shaped fins on the surface yielded a more uniform heat transfer distribution on the relevant surface than IJAC with a smooth surface, ensuring a relatively more uniform heat transfer distribution to minimize the risk of localized overheating.
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.