Combination of group method of data handling neural network with multi-objective gray wolf optimizer to predict the viscosity of MWCNT-TiO2-oil SAE50 nanofluid
dc.authorscopusid | 59424156600 | |
dc.authorscopusid | 59375113300 | |
dc.authorscopusid | 57216923851 | |
dc.authorscopusid | 59424116200 | |
dc.authorscopusid | 58852252200 | |
dc.authorscopusid | 57212897458 | |
dc.authorscopusid | 23028598900 | |
dc.contributor.author | Zhou, Hongfei | |
dc.contributor.author | Ali, Ali B. M. | |
dc.contributor.author | Zekri, Hussein | |
dc.contributor.author | Abdulaali, Hanaa Kadhim | |
dc.contributor.author | Bains, Pardeep Singh | |
dc.contributor.author | Sharma, Rohit | |
dc.contributor.author | Hashemian, Mohammad | |
dc.date.accessioned | 2024-12-15T15:40:59Z | |
dc.date.available | 2024-12-15T15:40:59Z | |
dc.date.issued | 2024 | |
dc.department | Okan University | en_US |
dc.department-temp | [Zhou, Hongfei] Hebei Petr Univ Technol, Dept Comp & Informat Engn, Chengde 067000, Hebei, Peoples R China; [Ali, Ali B. M.] Univ Warith Al Anbiyaa, Coll Engn, Air Conditioning Engn Dept, Karbala, Iraq; [Zekri, Hussein] Univ Zakho, Coll Engn, Dept Mech Engn, Zakho, Kurdistan, Iraq; [Abdulaali, Hanaa Kadhim] Univ Technol Iraq, Dept Chem Engn, Baghdad, Iraq; [Bains, Pardeep Singh] JAIN Deemed To Be Univ, Fac Engn & Technol, Dept Mech Engn, Bengaluru 560069, Karnataka, India; [Bains, Pardeep Singh] Vivekananda Global Univ, Dept Mech Engn, Jaipur 303012, Rajasthan, India; [Sharma, Rohit] Shobhit Univ, Sch Engn & Technol, Gangoh 247341, Uttar Pradesh, India; [Sharma, Rohit] ARKA Jain Univ, Dept Mech Engn, Jamshedpur 831001, Jharkhand, India; [Abduvalieva, Dilsora] Shahrekord Univ, Dept Mech Engn, Shahrekord 8818634141, Iran; [Baghoolizadeh, Mohammadreza] Tashkent State Pedag Univ, Dept Math & Informat Technol, Bunyodkor Ave 27, Tashkent 100070, Uzbekistan; [Salahshour, Soheil] Istanbul Okan Univ, Fac Engn & Nat Sci, Istanbul, Turkiye; [Salahshour, Soheil] Bahcesehir Univ, Fac Engn & Nat Sci, Istanbul, Turkiye; [Salahshour, Soheil] Lebanese Amer Univ, Dept Comp Sci & Math, Beirut, Lebanon; [Hashemian, Mohammad] Islamic Azad Univ, Khomeinishahr Branch, Dept Mech Engn, Khomeinishahr, Iran | en_US |
dc.description.abstract | Background: Nanofluids are the most widely used materials in various engineering fields. They have different properties under different conditions, and predicting their properties requires several experiments. Artificial intelligence can predict the properties of nanofluids in the shortest time and cost. Methodology: This study aims to predict the viscosity and share rate of MWCNT-TiO2 (40-60)-oil SAE50 nano-lubricant (NL). Machine learning algorithms and neural networks can respond best to this important matter. For this purpose, the Group Method of Data Handling (GMDH) neural network is combined with the meta-heuristic algorithm Multi-Objective Gray Wolf Optimizer (MOGWO). This way, the experimental data is first given to the artificial neural network (ANN). Then, the meta-heuristic algorithm optimizes the hyperparameters of the ANN to bring the predicted results closer to the experimental data and minimize the error. The MOGWO algorithm's regulators are the number of iterations and the number of wolves investigated in this study to better select this algorithm. Then, these modes are measured using two criteria, correlation coefficient (R) and rote mean squared error (RMSE), to choose the best mode. Finally, by using the extracted equations by the GMDH neural network, the best models or the Pareto front can be obtained using the MOGWO meta-heuristic algorithm. Results: The error histogram diagram shows the excellent performance of the combination of the GMDH neural network and the MOGWO meta-heuristic algorithm. The values of R and RMSE for viscosity and shear rate are equal to 0.99217, 15.8749, and 0.99031, 68.7723, respectively. The optimization results showed that the best conditions to meet viscosity and cutting rate are when phi, T, and gamma equal 1.21*e-5, 46.71, and 50.11. | en_US |
dc.description.woscitationindex | Science Citation Index Expanded | |
dc.identifier.citation | 0 | |
dc.identifier.doi | 10.1016/j.csite.2024.105541 | |
dc.identifier.issn | 2214-157X | |
dc.identifier.scopus | 2-s2.0-85210126534 | |
dc.identifier.scopusquality | Q1 | |
dc.identifier.uri | https://doi.org/10.1016/j.csite.2024.105541 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14517/7526 | |
dc.identifier.volume | 64 | en_US |
dc.identifier.wos | WOS:001369556900001 | |
dc.identifier.wosquality | Q1 | |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Multi-objective gray wolf optimizer | en_US |
dc.subject | Viscosity | en_US |
dc.subject | Nano-lubricant | en_US |
dc.title | Combination of group method of data handling neural network with multi-objective gray wolf optimizer to predict the viscosity of MWCNT-TiO2-oil SAE50 nanofluid | en_US |
dc.type | Article | en_US |
dspace.entity.type | Publication |