Prognostic machine learning models for thermophysical characteristics of nanodiamond-based nanolubricants for heat pump systems

Abstract

Lubricants for compressor oil significantly enhance the energy efficiency and performance of heat pump (HP) systems. This study compares prognostic machine learning (ML) models designed to predict the thermal conductivity and viscosity of nanolubricants used in HP compressors. Nanodiamond (ND) nanoparticles were mixed in Polyolester (POE) oil at volume concentrations ranging from 0.05 to 0.5 vol.% and temperatures ranging from 10 to 100 °C. The data collected from the experimental research were used to build prognostic models using modern supervised ML techniques, including Gaussian process regression (GPR) and boosted regression tree (BRT). The GPR model demonstrated superior performance compared to the BRT model, achieving coefficient of correlation (R) values of 0.9996 and 0.9991 for thermal conductivity and viscosity, respectively. The reliability of the GPR and BRT models was further validated through comprehensive validation, sensitivity analysis, and extrapolation assessment using both empirical and unseen dataset references from the literature. When validated against an empirical correlation, the ML models exhibited a mean absolute error (MAE) of 0.17% for thermal conductivity and below 8% for viscosity. Additionally, when the GPR-based model was extended up to 120 °C, the parametric analysis confirmed the reliability and accuracy of thermal conductivity and viscosity within a relative error of 5%. Furthermore, in the extrapolation analysis, despite changes in oil grade and nanolubricant concentrations, the GPR-based model showed a maximum absolute error (AE) of 19% compared to non-trained experimental data. Overall, the developed ML models can aid in designing and optimizing ND/POE nanolubricants for HP applications, achieving desired performance parameters while remaining economically viable and reducing the need for time-consuming laboratory-based testing.