Artificial intelligence-based viscosity prediction of polyalphaolefin-boron nitride nanofluids

Predicting viscosity’s nanofluids can benefit all domains, including energy, thermofluids, power systems, energy storage, materials, cooling, heating, and lubrication. The objective of this study to predict the dynamic viscosity of polyalphaolefin-hexagonal boron nitride (PAO/hBN) nanofluids using four main parameters: shear rate, shear stress, nanomaterials mass fraction, and temperature. Moreover, three hybrid ensemble learning models (Bayesian ridge-random forest, Bayesian ridge-MLP regressor and Bayesian ridge-AdaBoost regressor) were developed for the current task. The forward sequential feature selector (FSFS) created four input combinations (models). Model 4 showed the best prediction accuracy, followed by models 2, 3 and 1. The computational findings showed that ensemble learner 1 was slightly outperformed by ensemble learner 3. Meanwhile, among the predictive models, ensemble learner 2 consistently placed third. Besides, the research results demonstrated that creating predictive models based on all input parameters can produce a precise prediction matrix. Overall, the study recommended exciting conclusions on predicting a nanolubricant’s viscosity for use in heat transfer applicants.