Optimizing ternary hybrid nanofluids using neural networks, gene expression programming, and multi-objective particle swarm optimization: a computational intelligence strategy
Abstract The performance of nanofluids is largely determined by their thermophysical properties. Optimizing these properties can significantly enhance nanofluid performance. This study introduces a hybrid strategy based on computational intelligence to determine the optimal conditions for ternary hy...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-85236-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832594874433536000 |
|---|---|
| author | Tao Hai Ali Basem As’ad Alizadeh Pradeep Kumar Singh Husam Rajab Chemseddine Maatki Nidhal Becheikh Lioua Kolsi Narinderjit Singh Sawaran Singh H. Maleki |
| author_facet | Tao Hai Ali Basem As’ad Alizadeh Pradeep Kumar Singh Husam Rajab Chemseddine Maatki Nidhal Becheikh Lioua Kolsi Narinderjit Singh Sawaran Singh H. Maleki |
| author_sort | Tao Hai |
| collection | DOAJ |
| description | Abstract The performance of nanofluids is largely determined by their thermophysical properties. Optimizing these properties can significantly enhance nanofluid performance. This study introduces a hybrid strategy based on computational intelligence to determine the optimal conditions for ternary hybrid nanofluids. The goal is to minimize dynamic viscosity and maximize thermal conductivity by varying the volume fraction, temperature, and nanomaterial mixing ratio. The proposed strategy integrates machine learning, multi-objective optimization, and multi-criteria decision-making. Three machine learning techniques—GMDH-type neural network, gene expression programming, and combinatorial algorithm—are applied to model dynamic viscosity and thermal conductivity as functions of the input variables. Then, the high-performing models provide the foundation for optimization using the well-established multi-objective particle swarm optimization algorithm. Finally, the decision-making technique TOPSIS is employed to identify the most desirable points from the Pareto front, based on various design scenarios. To validate the proposed strategy, a ternary hybrid nanofluid composed of graphene oxide (GO), iron oxide (Fe₃O₄), and titanium dioxide (TiO₂) was employed as a case study. The results demonstrated that the combinatorial approach excelled in accurately modeling (R = 0.99964–0.99993). The optimization process revealed that optimal VFs span a broad range across all mixing ratios, while optimal temperatures were consistently near the maximum value (65 °C). The decision-making outcomes indicated that the mixing ratio was consistent across all design scenarios, with the volume fraction serving as the key differentiating factor. |
| format | Article |
| id | doaj-art-ff92e50d27a64f4cb851265d0539abb1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ff92e50d27a64f4cb851265d0539abb12025-01-19T12:19:31ZengNature PortfolioScientific Reports2045-23222025-01-0115112610.1038/s41598-025-85236-3Optimizing ternary hybrid nanofluids using neural networks, gene expression programming, and multi-objective particle swarm optimization: a computational intelligence strategyTao Hai0Ali Basem1As’ad Alizadeh2Pradeep Kumar Singh3Husam Rajab4Chemseddine Maatki5Nidhal Becheikh6Lioua Kolsi7Narinderjit Singh Sawaran Singh8H. Maleki9State Key Laboratory of Public Big Data, Guizhou UniversityFaculty of Engineering, Warith Al-Anbiyaa UniversityDepartment of Civil Engineering, College of Engineering, Cihan University-ErbilDepartment of Mechanical Engineering, Institute of Engineering and Technology, GLA UniversityCollege of Engineering, Department of Mechanical Engineering, Najran UniversityDepartment of Mechanical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU)Mining Research Center, Northern Border UniversityDepartment of Mechanical Engineering, College of Engineering, University of Ha’ilFaculty of Data Science and Information Technology, INTI International UniversityRenewable Energy Research GroupAbstract The performance of nanofluids is largely determined by their thermophysical properties. Optimizing these properties can significantly enhance nanofluid performance. This study introduces a hybrid strategy based on computational intelligence to determine the optimal conditions for ternary hybrid nanofluids. The goal is to minimize dynamic viscosity and maximize thermal conductivity by varying the volume fraction, temperature, and nanomaterial mixing ratio. The proposed strategy integrates machine learning, multi-objective optimization, and multi-criteria decision-making. Three machine learning techniques—GMDH-type neural network, gene expression programming, and combinatorial algorithm—are applied to model dynamic viscosity and thermal conductivity as functions of the input variables. Then, the high-performing models provide the foundation for optimization using the well-established multi-objective particle swarm optimization algorithm. Finally, the decision-making technique TOPSIS is employed to identify the most desirable points from the Pareto front, based on various design scenarios. To validate the proposed strategy, a ternary hybrid nanofluid composed of graphene oxide (GO), iron oxide (Fe₃O₄), and titanium dioxide (TiO₂) was employed as a case study. The results demonstrated that the combinatorial approach excelled in accurately modeling (R = 0.99964–0.99993). The optimization process revealed that optimal VFs span a broad range across all mixing ratios, while optimal temperatures were consistently near the maximum value (65 °C). The decision-making outcomes indicated that the mixing ratio was consistent across all design scenarios, with the volume fraction serving as the key differentiating factor.https://doi.org/10.1038/s41598-025-85236-3Ternary hybrid nanofluidMachine learningMulti-objective optimizationMulti-criteria decision-makingComputational intelligenceThermophysical properties |
| spellingShingle | Tao Hai Ali Basem As’ad Alizadeh Pradeep Kumar Singh Husam Rajab Chemseddine Maatki Nidhal Becheikh Lioua Kolsi Narinderjit Singh Sawaran Singh H. Maleki Optimizing ternary hybrid nanofluids using neural networks, gene expression programming, and multi-objective particle swarm optimization: a computational intelligence strategy Scientific Reports Ternary hybrid nanofluid Machine learning Multi-objective optimization Multi-criteria decision-making Computational intelligence Thermophysical properties |
| title | Optimizing ternary hybrid nanofluids using neural networks, gene expression programming, and multi-objective particle swarm optimization: a computational intelligence strategy |
| title_full | Optimizing ternary hybrid nanofluids using neural networks, gene expression programming, and multi-objective particle swarm optimization: a computational intelligence strategy |
| title_fullStr | Optimizing ternary hybrid nanofluids using neural networks, gene expression programming, and multi-objective particle swarm optimization: a computational intelligence strategy |
| title_full_unstemmed | Optimizing ternary hybrid nanofluids using neural networks, gene expression programming, and multi-objective particle swarm optimization: a computational intelligence strategy |
| title_short | Optimizing ternary hybrid nanofluids using neural networks, gene expression programming, and multi-objective particle swarm optimization: a computational intelligence strategy |
| title_sort | optimizing ternary hybrid nanofluids using neural networks gene expression programming and multi objective particle swarm optimization a computational intelligence strategy |
| topic | Ternary hybrid nanofluid Machine learning Multi-objective optimization Multi-criteria decision-making Computational intelligence Thermophysical properties |
| url | https://doi.org/10.1038/s41598-025-85236-3 |
| work_keys_str_mv | AT taohai optimizingternaryhybridnanofluidsusingneuralnetworksgeneexpressionprogrammingandmultiobjectiveparticleswarmoptimizationacomputationalintelligencestrategy AT alibasem optimizingternaryhybridnanofluidsusingneuralnetworksgeneexpressionprogrammingandmultiobjectiveparticleswarmoptimizationacomputationalintelligencestrategy AT asadalizadeh optimizingternaryhybridnanofluidsusingneuralnetworksgeneexpressionprogrammingandmultiobjectiveparticleswarmoptimizationacomputationalintelligencestrategy AT pradeepkumarsingh optimizingternaryhybridnanofluidsusingneuralnetworksgeneexpressionprogrammingandmultiobjectiveparticleswarmoptimizationacomputationalintelligencestrategy AT husamrajab optimizingternaryhybridnanofluidsusingneuralnetworksgeneexpressionprogrammingandmultiobjectiveparticleswarmoptimizationacomputationalintelligencestrategy AT chemseddinemaatki optimizingternaryhybridnanofluidsusingneuralnetworksgeneexpressionprogrammingandmultiobjectiveparticleswarmoptimizationacomputationalintelligencestrategy AT nidhalbecheikh optimizingternaryhybridnanofluidsusingneuralnetworksgeneexpressionprogrammingandmultiobjectiveparticleswarmoptimizationacomputationalintelligencestrategy AT liouakolsi optimizingternaryhybridnanofluidsusingneuralnetworksgeneexpressionprogrammingandmultiobjectiveparticleswarmoptimizationacomputationalintelligencestrategy AT narinderjitsinghsawaransingh optimizingternaryhybridnanofluidsusingneuralnetworksgeneexpressionprogrammingandmultiobjectiveparticleswarmoptimizationacomputationalintelligencestrategy AT hmaleki optimizingternaryhybridnanofluidsusingneuralnetworksgeneexpressionprogrammingandmultiobjectiveparticleswarmoptimizationacomputationalintelligencestrategy |