Optimization and Modeling of Thermal Conductivity of Cu–H ₂ O Nanofluid Using Response Surface Methodology and Artificial Neural Network: A Comparative Study Between Statistical and Machine Learning Approach

Nanofluids have recently emerged as a significant working fluid in thermal management and fluid dynamics. Precise assessment of their thermophysical properties, particularly thermal conductivity, is essential for evaluating heat transfer efficiency. This paper introduces an innovative statistical approach for optimizing and accurately forecasting the thermal conductivity of Cu–H 2 O nanofluid via response surface methodology (RSM). An artificial neural network (ANN) was utilized to estimate the thermal conductivity of Cu–H 2 O nanofluid. To develop the proposed thermal conductivity model, 20 experimental data points were employed, using the volume % of the nanoparticle, temperature, and diameter of the copper nanoparticle as input variables in the models. The anticipated effectiveness of the established RSM and ANN techniques is validated by experimental data and existing empirical correlations. Various statistical metrics, including margin of deviation (MOD), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination ( R 2 ), have been utilized for the quantitative evaluation of the accuracy of the developed RSM and ANN models. The consequences affirm that the created ANN models serve as a viable instrument for analyzing the behavior of industrial applications utilizing nanofluids as the operating fluid. The consequences indicated that the developed model effectively predicts the thermal conductivity of Cu–H 2 O nanofluid throughout a broad spectrum of temperature, volume percentage, and nanoparticle diameter. The constructed ANN model demonstrated optimal fit and acceptable concordance with the experimental data. The suggested correlation demonstrates superior accuracy relative to all prior correlations for predicting thermal conductivity under varying conditions.