Design of experiments coupled with Bayesian optimisation for nanolubricant formulation

Abstract

Nanoparticle-enhanced lubricants (nanolubricants) achieve tribological properties that can signficantly outperform the unmodified base oil. However, this performance enhancement is known to be extremely senstive to the concentration of nanoparticles used. Here, we introduce a new approach to optimizing nanolubricants combining design of experiments (DOE) with machine learning, addressing the challenge of determining optimal nanoparticle concentration in base oils and demonstrating significant performance improvements. Combining Box-Behnken design with Bayesian optimization, we identify an optimal concentration of 0.04 wt% copper oxide (CuO) nanoparticles in a standard engine oil (SAE 20W50), resulting in a 42% reduction in coefficient of friction (COF) relative to the base oil. Surface analysis (AFM, SEM) confirmed improvement of anti-wear and anti-friction characteristics. Signficant enhacements of thermophysical properties were observed (demonstrated by increased flash and pour points, by 10% and 7% respectively), signifying greater thermal stability. The dynamic viscosity remained consistent, with a beneficial reduction observed under conditions of high temperature and shear, which aligns with the demands of engine lubricant standards for PC-12B oils, with a reduction from 3.49 to 3.18 cP (8%). This research introduces a novel hybrid optimization technique that provides a versatile framework for optimizing nanofluids, addressing the critical challenge of determining optimal nanoparticle concentration.