Impinging jets play a crucial role in designing various engineering applications, including the complex mechanism of cooling gas turbine blades. In this study, we analyze the effect of the nozzle aspect ratio (AR) ranging from 1 to 4, with a hydraulic diameter of 8mm; the non-dimensional nozzle-to-plate distance ranging from 1 to 6, and Reynolds number ranging from 10535.05 to 28595.13 on the Nusselt number at the stagnation point, numerically. The numerical results of RNG k-ε, realizable k-ε, and SST k-ω are compared with the experimental results and found that SST k-ω predicts heat transfer and flow behavior better than other models. Additionally, we predict heat transfer in the impingement of a single air jet by implementing a supervised learning algorithm - artificial neural networks (ANNs). The numerical analysis was validated using experimental results, which revealed that Reynolds number primarily influences the Nusselt number at the stagnation point. Further observations showed that nozzles with a large aspect ratio perform better when the plate is situated within the potential core region. In contrast, smaller aspect ratio nozzles perform better outside the same region. A correlation is developed for the pressure at the stagnation point as a function of AR,  and Re, with an  value of 97.69%. Also, another correlation is developed for the Nu at the stagnation point as a function of AR,  and Re, with an  value of 97.03. An ANN model was also developed to predict the Nusselt number, which performed better than the regression model, with an average error of 1.28%. The best combination of network type, transfer function, and number of neurons was a single-layer neural network model with a layer recurrent network, log-sigmoid transfer function, and 11 neurons.

jet impingement, SST k-ω, turbulent intensity, Nusselt number, ANN.

Received: July 20, 2023; Revised: September 22, 2023; Accepted: October 31, 2023; Published: November 21, 2023

How to cite this article: B. Venkata Sai Raghu Vamsi, P. Raveendiran and Malladi R. Ch. Sastry, Heat transfer prediction in impinging jets using artificial neural networks, JP Journal of Heat and Mass Transfer 36 (2023), 103-142. http://dx.doi.org/10.17654/0973576323055

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

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