Optimizing Heat Transfer in Nanofluid Heat Pipes Using Fuzzy Logic: A Soft Computing Approach for Human-Machine Interface Systems

Abstract

Heat pipes, renowned for their efficient heat transfer capabilities, can be further enhanced by leveraging the power of nanofluids. However, optimizing their performance can be challenging due to the complex interplay between various factors. This work explores applying fuzzy logic, a powerful artificial intelligence (AI) technique, for optimizing heat transfer in cylindrical heat pipes filled with ZrO2-CeO2/Water-Ethylene Glycol (WEG) nanofluids. Heat pipes offer exceptional heat transfer capabilities due to capillary action and phase change. However, their performance can be further enhanced using nanofluids. This work explores the application of fuzzy logic for optimizing heat transfer in a cylindrical heat pipe filled with ZrO2-CeO2/Water-Ethylene Glycol (WEG) nanofluids. The fuzzy logic system takes power input and nanofluid concentration as inputs and predicts thermal Resistance and heat transfer rate as outputs. Membership functions and fuzzy rules are established to capture the relationships between these parameters. The effectiveness of the fuzzy logic system is evaluated by comparing its predicted optimal operating conditions with experimental results obtained in a heat pipe apparatus operating within a 30-60 W power range. This approach offers a novel and intelligent optimization technique for improving heat pipe efficiency with nanofluids.