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This case study investigates the thermal performance of a novel axial finned-tube heat exchanger using machine learning, specifically the k-Nearest Neighbor (k-NN) algorithm for regression analysis. The study analyzes the effects of fin spacing, thickness, material, and convective heat transfer coefficient on efficiency and effectiveness, concluding that efficiency increases with fin thickness and decreases with fin spacing. The k-NN algorithm demonstrated high prediction accuracy, making it a valuable tool

Author

Debabrata panda

Language

EN