Thermal performance of microchannel heat sink integrated with porous medium, slip coefficient and phase change material and machine learning approach

Farahani, Somayeh Davoodabadi; Mamoei, Amirhossien Jazari; Alizadeh, As'ad

doi:10.1016/j.est.2023.109357

Cited by 2 publications

(1 citation statement)

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“…They also proposed multilayer perceptron neural networks and principal component analysis for dataset visualization and decreased computational power. In another study, Farahani et al [35] investigated the thermal performance of a microchannel heat sink in terms of the porous medium, phase change material, and shape of the microchannel using the finite volume method and group method of a data handling algorithm (GMDH). The findings demonstrated that wavy microchannels could enhance thermal performance by roughly 10.6% and 5% compared with smooth and converging microchannels.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Analysis of Thermal Performance Indicator of Heat Exchangers with Delta Wing Vortex Generators

Aksöz,

Günay,

Aziz

et al. 2024

Energies

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In this work, the design features of delta wing vortex generators (DWVGs) on the thermo-hydraulic performance of heat exchangers are investigated using machine learning. Reynolds numbers, attack angle, length, wing-to-width ratio, and relative pitch ratio of DWVGs were used as descriptor variables, with Nusselt numbers, friction factors, and performance evaluation criterion (PEC) serving as target variables. Decision tree classification revealed the pathways leading to high or low values of the performance variables. Among many of those pathways, it was found that high Reynolds numbers (between 8160 and 9800) and high attack angles (greater than or equal to 47.5°) lead to high Nusselt numbers. On the other hand, an attack angle between 41° and 60°, a Reynolds number less than 8510, and a wing-to-width ratio greater than or equal to 0.4 causes a high friction factor. Finally, the PEC is likely to enhance when the Reynolds number is higher than or equal to 10,300 and the attack angle is between 47.5° and 60°. In addition to the decision tree analysis, SHapley Additive exPlanations (SHAP) analysis (a part of explainable machine learning) was also applied to reveal the importance of design features and their positive and negative effects on the target variables. For example, for a Nusselt number as the target variable, the Reynolds number was found to be the most influential variable, followed by the attack angle and the relative pitch ratio, all of which had a positive impact on the target. It was then concluded that machine learning methods could help provide strong insights into the configuration design features of heat exchangers in DWVGs to improve their efficiency and save energy.

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Section: Introductionmentioning

confidence: 99%