Prediction of heat transfer performance of CuO/water nanofluids flow in spirally corrugated helically coiled heat exchanger using fuzzy logic technique

• Fuzzy logic expert system has proposed to predict the heat transfer performance.
• Better heat transfer coefficient is observed for nanofluids compared to water.
• Lower friction factor is found for CuO/water nanofluids compared to water.
• The relative error of predicted values is noticed to be acceptable limit.
• The goodness of fit of predicted values is also achieved to be acceptable limit.

This paper presents the fuzzy logic expert system (FLES) for heat transfer performance investigation in helically coiled heat exchanger with spirally corrugated wall operated with water and CuO/water nanofluids. Compared with traditional logic model, fuzzy logic is more efficient in connecting the multiple units to a single output and is invaluable supplements to classical hard computing techniques. Hence, the main objective of this analysis is to investigate the relationship between heat exchanger working parameters and performance characteristics, and to determine how fuzzy logic expert system plays a significant role in prediction of heat transfer performance. Analytical values are taken in helically coiled heat exchanger with spirally corrugated wall operated with water and CuO/water nanofluids for investigation of heat transfer performance. The heat transfer coefficients of CuO/water nanofluids significantly increased about 5.90–14.24% with the increase of volume concentrations compared to water and while the values of the friction factor decreased with the increase in volume flow rate and volume concentration by using nanofluid instead of water. A fuzzy logic expert system model has developed for the prediction of heat transfer coefficient and friction factor. Verification of the developed fuzzy logic model was carried out through various numerical error criteria. For all parameters, the relative error of predicted values are found to be less than and/or slightly above the acceptable limit (5%). The goodnesses of fit of the prediction values from the fuzzy logic expert system model are found to be close to 1.0 as expected, and hence demonstrated the good performance of the developed system.