Intelligent design of high-performance fluids for thermal management: integrating response surface methodology, weighted Tchebycheff method, and strength Pareto evolutionary algorithm II.

Optimizing nanofluid thermophysical properties (TPPs) is essential for advancing heat transfer applications; however, most studies focus on two-objective optimization, limiting their real-world applicability. This study presents a novel multi-objective optimization framework integrating response surface methodology (RSM) with enhanced hill climbing (EHC) algorithm and strength Pareto evolutionary algorithm II (SPEA-II) to optimize multiple TPPs. The weighted Tchebycheff method (WTM) is employed for decision-making, ensuring a balanced and application-specific selection of nanofluids. The RSM models demonstrated high predictive accuracy, with R values exceeding 0.99 for all key TPPs. The quartic model for density ratio (DR) and cubic model for viscosity ratio (VR) confirmed the framework's reliability with R values of 0.9982 and 0.9938, respectively. The fifth-order models for specific heat capacity ratio (SHCR) and thermal conductivity ratio (TCR) achieved R values of 0.9999 and 0.9971, respectively. The four-objective optimization using SPEA-II and WTM provided optimal nanofluid selection based on specific priorities. When all objectives are equally weighted, ZnO at 35.409 °C and 1.150% volume fraction (VF) offers a balanced performance. Prioritizing density reduction shifts the selection to ZnO at 25 °C and 0.860% VF, improving flowability. Emphasizing viscosity reduction selects CeO at 37.772 °C and 0.985% VF, while maximizing SHCR leads to CeO at 42.078 °C and 0.875% VF, enhancing heat storage. TCR optimization favors CeO at 37.313 °C and 1.399% VF, demonstrating that higher VF enhances conductivity. The results confirm ZnO's versatility, AlO's advantage in heat storage, and CeO's effectiveness in high-temperature applications, ensuring optimal selection for engineering applications.