Artificial Neural Network Models to Predict Heat Transfer Coefficients and Pressure Drops in Cold Plates with Surface Roughness

Abstract

– In the present study, artificial neural network (ANN) models are developed to predict heat transfer coefficient (ℎ) and pressure drop (∆𝑃𝑃) in cold plates (CPs) with surface roughness operating in turbulent flow. Roughness sizes range from zero (smooth surface) to 0.5 mm, and Reynolds numbers vary from 3,170 to 10,560. The RNG 𝑘𝑘 − 𝜀𝜀 model is used to simulate turbulent flow. Input data for the ANN models are prepared by simulating three-dimensional steady state turbulent flow and heat transfer inside the CPs. Separate multilayer neural networks are selected to predict ℎ and ∆𝑃𝑃 . Both ANN architectures include two hidden layers with 1,024 neurons in each layer. The accuracy of the training process and the neural network is assessed by the mean absolute error. Both ANN models show excellent predictions as the predicted ℎ and ∆𝑃𝑃 are within ±1.2% and ±2.6% of the simulated values, respectively. Since roughness is an inevitable consequence of additive manufacturing, the present study suggests that accurate ANN-based models can be used as promising design tools for optimizing additively manufactured CPs. While roughness improves heat transfer, it leads to a higher pressure drop. As a result, accurate ANN models can be used to design additively manufactured cooling systems with an optimized range of roughness to improve heat transfer while operating within the allowed pressure drop and pumping power.