Soft computing approaches for prediction of specific heat capacity of hybrid nanofluids

Abstract

AbstractNanofluids are thermoelectric substance with a greater thermal transmission effectiveness than traditional thermal fluids. Nanofluid's specific heat capacity (SHC) is a crucial thermophysical parameter since it controls the fluid's heat transfer coefficient. Surprisingly few research investigations have been done on the prognostic modelling of the specific heat capacity of fusion nanofluids, despite the fact that numerous experiments have been conducted on the heat transfer capacitance of blended nanofluids. This study focuses on the use of algorithms based on machine learning procedures (MLP) to estimate the SHC of blended nanofluids. Numerous numbers of hybrid nanofluids are investigated in this investigation. Total of 984 samples of hybrid nanofluids for specific heat capacity has been collected from nine experimental research papers. Various MLP techniques were utilized in this analysis, including extreme boost gradient boost regression (XGB), support vector regression augmented with a genetic algorithm (support vector regression [SVR]‐genetic algorithm [GA]), grid search optimized based gradient boost regression algorithm (GBR) (GBR‐GSO), and voting ensemble procedure (VE). The obtained correlation coefficients of the SVR‐GA, XGB, VE and GBR‐GSO models for the testing dataset are 98.43%, 96.29%, 94.4% and 96.55% respectively. The SVR‐GA model showed a better predictive accuracy relative to other ML models. This SVR‐GA anticipated model could be used for quick and reliable prediction of the SHC of blended nanofluids which reduces the burden associated with experimental measurement possible future work includes applying a machine‐learning strategy to the problem of determining the diffusivity of hybrid nanofluids.