Predictive modelling and rheological investigation of titanium oxide–enhanced silica-based shear-thickening fluids

A silica-based (SiO 2 ) shear-thickening fluid (STF) reinforced with titanium dioxide (TiO 2 ) nanoparticles was systematically examined across a broad range of temperature, shear rate, and applied stress. The TiO 2 /SiO 2 system showed a distinct, stress-dependent, nonlinear shear-thickening behavior, with an optimal nanoparticle concentration that maximized viscosity increase. The synergistic interaction between SiO 2 and TiO 2 nanoparticles promoted the formation of dense hydroclaster structures, significantly enhancing resistance to flow at high shear rates and improving rheological stability across different thermal conditions. To accurately model and predict highly nonlinear viscosity behavior, advanced machine learning (ML) models, including Support Vector Regression (SVR), Random Forest (RF), XGBoost, and CatBoost, were developed and fine-tuned using Bayesian Optimization (BO). CatBoost outperformed others, achieving a coefficient of determination (R 2 ) of 0.9991 and 0.9906 for training and testing datasets, respectively, along with a test root mean squared error (RMSE) of 7.2553 and a mean absolute error (MAE) of 4.0291. Additional validation confirmed the model’s generalization, with an R 2 of 0.9842. Moreover, Shapley additive explanation (SHAP) analysis was used to improve interpretability, revealing shear rate and shear stress as the most influential factors in viscosity changes. Meanwhile, temperature and TiO 2 concentration contributed positively within an optimal range. The close match between experimental results and ML predictions demonstrates the robustness of the proposed data-driven approach. This research highlights the potential of ML-based methods for the intelligent design and optimization of high-performance STFs for advanced impact mitigation, energy absorption, and vibration control applications.