Effect of effusion hole arrangement on jet array impingement heat transfer

Choi, Won-Woo; Kim, Sung-Min

doi:10.1016/j.ijheatmasstransfer.2022.122900

Cited by 19 publications

(1 citation statement)

References 79 publications

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“…Under uniform wall heat flux, the tests were carried out for turbulent flow with Reynolds numbers varied from 5,000 to 23,000. Choi et al [24] studied the impact of using a jet array of effusion holes for gas turbine cooling. For the five hole arrangements, they developed a new correlation to estimate the average Nusselt number for impingement/effusion process.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks Application on Average and Stagnation Nusselt Number Prediction for Impingement Cooling of Flat Plate With Helically Coiled Air Jet

Fawaz,

Osama,

Maghrabie

2023

Journal of Thermal Science and Engineering Applications

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In order to estimate the average and stagnation Nusselt numbers for turbulent flow for impingement cooling of a flat plate with a helically coiled air jet, a new artificial neural network (ANN) model is presented in the present study. A new dataset of stagnation and average Nusselt numbers as a function of Reynolds number (Re) varied from 5000 to 30,000, nozzle plate spacing ratio changed from 2 to 8, and jet helical angles of 0 deg, 20 deg, 30 deg, 40 deg, and 60 deg was created based on an experimental investigation. The ANN structure is composed of three layers with hidden neurons of 14–10–8. The training process comprises feed-forward propagation of the selected input parameters, back-propagation with biases and weight adjustments, and loss function evaluation for the training and validation datasets. The activation function of the output layer is a linear function, and the rectified linear unit activation function is utilized in the hidden layers. The adaptive moment estimation algorithm is employed to minimize the loss function to accelerate the ANN training. To prevent an increase in training time caused by the marked discrepancy in the gradients of loss function considering the values of the weights, the “MinMax” normalization strategy was used. For the ANN model, the mean absolute percent error values were 2.35% for the average Nusselt number and 2.52% for the stagnation Nusselt number. According to the comparison of projected data with the outcomes of earlier experiments, the derived model’s performance was validated and the findings showed outstanding accuracy.

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Section: Introductionmentioning

confidence: 99%