Intelligent design of high-performance fluids for thermal management: integrating response surface methodology, weighted Tchebycheff method, and strength Pareto evolutionary algorithm II
Abstract 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 res...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-07132-0 |
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| author | Mohamed Bechir Ben Hamida Ali Basem Neeraj Varshney Loghman Mostafa |
| author_facet | Mohamed Bechir Ben Hamida Ali Basem Neeraj Varshney Loghman Mostafa |
| author_sort | Mohamed Bechir Ben Hamida |
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| description | Abstract 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 R2 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 R2 values of 0.9982 and 0.9938, respectively. The fifth-order models for specific heat capacity ratio (SHCR) and thermal conductivity ratio (TCR) achieved R2 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 CeO2 at 37.772 °C and 0.985% VF, while maximizing SHCR leads to CeO2 at 42.078 °C and 0.875% VF, enhancing heat storage. TCR optimization favors CeO2 at 37.313 °C and 1.399% VF, demonstrating that higher VF enhances conductivity. The results confirm ZnO’s versatility, Al2O3’s advantage in heat storage, and CeO2’s effectiveness in high-temperature applications, ensuring optimal selection for engineering applications. |
| format | Article |
| id | doaj-art-d2021cce7af34d11a29f8dbbc24875e1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d2021cce7af34d11a29f8dbbc24875e12025-08-20T04:01:41ZengNature PortfolioScientific Reports2045-23222025-07-0115113310.1038/s41598-025-07132-0Intelligent design of high-performance fluids for thermal management: integrating response surface methodology, weighted Tchebycheff method, and strength Pareto evolutionary algorithm IIMohamed Bechir Ben Hamida0Ali Basem1Neeraj Varshney2Loghman Mostafa3Deanship of Scientific Research, Imam Mohammad Ibn Saud Islamic University (IMSIU)Faculty of Engineering, Warith Al-Anbiyaa UniversityDepartment of Computer Engineering and Applications, Institute of Engineering and Technology, GLA UniversityCollege of Technical Engineering, Islamic Azad University, Urmia BranchAbstract 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 R2 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 R2 values of 0.9982 and 0.9938, respectively. The fifth-order models for specific heat capacity ratio (SHCR) and thermal conductivity ratio (TCR) achieved R2 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 CeO2 at 37.772 °C and 0.985% VF, while maximizing SHCR leads to CeO2 at 42.078 °C and 0.875% VF, enhancing heat storage. TCR optimization favors CeO2 at 37.313 °C and 1.399% VF, demonstrating that higher VF enhances conductivity. The results confirm ZnO’s versatility, Al2O3’s advantage in heat storage, and CeO2’s effectiveness in high-temperature applications, ensuring optimal selection for engineering applications.https://doi.org/10.1038/s41598-025-07132-0Hybrid nanofluidResponse surface methodologyMulti-objective optimizationStrength Pareto evolutionary algorithm IIEnhanced hill climbing algorithmWeighted Tchebycheff method |
| spellingShingle | Mohamed Bechir Ben Hamida Ali Basem Neeraj Varshney Loghman Mostafa Intelligent design of high-performance fluids for thermal management: integrating response surface methodology, weighted Tchebycheff method, and strength Pareto evolutionary algorithm II Scientific Reports Hybrid nanofluid Response surface methodology Multi-objective optimization Strength Pareto evolutionary algorithm II Enhanced hill climbing algorithm Weighted Tchebycheff method |
| title | Intelligent design of high-performance fluids for thermal management: integrating response surface methodology, weighted Tchebycheff method, and strength Pareto evolutionary algorithm II |
| title_full | Intelligent design of high-performance fluids for thermal management: integrating response surface methodology, weighted Tchebycheff method, and strength Pareto evolutionary algorithm II |
| title_fullStr | Intelligent design of high-performance fluids for thermal management: integrating response surface methodology, weighted Tchebycheff method, and strength Pareto evolutionary algorithm II |
| title_full_unstemmed | Intelligent design of high-performance fluids for thermal management: integrating response surface methodology, weighted Tchebycheff method, and strength Pareto evolutionary algorithm II |
| title_short | Intelligent design of high-performance fluids for thermal management: integrating response surface methodology, weighted Tchebycheff method, and strength Pareto evolutionary algorithm II |
| title_sort | intelligent design of high performance fluids for thermal management integrating response surface methodology weighted tchebycheff method and strength pareto evolutionary algorithm ii |
| topic | Hybrid nanofluid Response surface methodology Multi-objective optimization Strength Pareto evolutionary algorithm II Enhanced hill climbing algorithm Weighted Tchebycheff method |
| url | https://doi.org/10.1038/s41598-025-07132-0 |
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