Prediction of Viscosity of Cobalt Ferrite/SAE50 Engine Oil based Nanofluids using well Trained Artificial Neutral Network (ANN) and Response Surface Methodology (RSM)
Heat transmission by ordinary fluids such as pure water, oil, and ethylene glycol is inefficient due to their low viscosity. To boost the efficiency of conventional fluids, very small percent of nanoparticles are added to the base fluids to prepare nanofluid. The impact of changing in viscosity can...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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V.N. Karazin Kharkiv National University Publishing
2023-09-01
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| Series: | East European Journal of Physics |
| Subjects: | |
| Online Access: | https://periodicals.karazin.ua/eejp/article/view/21732 |
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| author | Malik Muhammad Hafeezullah Abdul Rafay Ghulam Mustafa Muhammad Khalid Zubair Ahmed Kalhoro Abdul Wasim Shaikh Ahmed Ali Rajput |
| author_facet | Malik Muhammad Hafeezullah Abdul Rafay Ghulam Mustafa Muhammad Khalid Zubair Ahmed Kalhoro Abdul Wasim Shaikh Ahmed Ali Rajput |
| author_sort | Malik Muhammad Hafeezullah |
| collection | DOAJ |
| description | Heat transmission by ordinary fluids such as pure water, oil, and ethylene glycol is inefficient due to their low viscosity. To boost the efficiency of conventional fluids, very small percent of nanoparticles are added to the base fluids to prepare nanofluid. The impact of changing in viscosity can be used to investigate the rheological properties of nanofluids. In this paper, (CoFe2O4)/engine oil based nanofluids were prepared using two steps standard methodology. In first step, CoFe2O4 (CF) were synthesized using the sol-gel wet chemical process. The crystalline structure and morphology were confirmed using X-Ray diffraction analysis (XRD) and scanning electron microscopy (SEM), respectively. In second step, the standard procedure was adapted by taking several solid volume fractions of CF as Ø = 0, 0.25, 0.50, 0.75, and 1.0 %. Such percent of concentrations were dispersed in appropriate volume of engine oil using the ultrasonication for 5 h. After date, the viscosity of prepared five different nanofluids were determined at temperatures ranging from 40 to 80 °C. According to the findings, the viscosity of nanofluids (µnf) decreased as temperature increased while increased when the volume percentage of nanofluids Ø raised. Furthermore, total 25 experimental observations were considered to predict viscosity using an artificial neural network (ANN) and response surface methodology (RSM). The algorithm for building the ideal ANN architecture has been recommended in order to predict the fluid velocity of the CF/SAE-50 oil based nanofluid using MATLAB software. In order to determine the correctness of the predicted model, the mean square error (MSE) was calculated 0.0136. |
| first_indexed | 2024-03-12T01:39:30Z |
| format | Article |
| id | doaj.art-cda2529e4058458f921e57327b720dd5 |
| institution | Directory Open Access Journal |
| issn | 2312-4334 2312-4539 |
| language | English |
| last_indexed | 2024-03-12T01:39:30Z |
| publishDate | 2023-09-01 |
| publisher | V.N. Karazin Kharkiv National University Publishing |
| record_format | Article |
| series | East European Journal of Physics |
| spelling | doaj.art-cda2529e4058458f921e57327b720dd52023-09-10T16:51:58ZengV.N. Karazin Kharkiv National University PublishingEast European Journal of Physics2312-43342312-45392023-09-01347948910.26565/2312-4334-2023-3-5421732Prediction of Viscosity of Cobalt Ferrite/SAE50 Engine Oil based Nanofluids using well Trained Artificial Neutral Network (ANN) and Response Surface Methodology (RSM)Malik Muhammad Hafeezullah0Abdul Rafay1Ghulam Mustafa2Muhammad Khalid3Zubair Ahmed Kalhoro4Abdul Wasim Shaikh5Ahmed Ali Rajput6Institute of Computer Science and Mathematics, University of Sindh, Jamshoro, Pakistan; Department of Mathematics, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta, PakistanDepartment of Physics, University of Karachi, Karachi, PakistanDepartment of Physics, NED University of Engineering and Technology, Karachi, PakistanDepartment of Physics, University of Karachi, Karachi, PakistanDepartment of Mathematics, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta, PakistanInstitute of Computer Science and Mathematics, University of Sindh, Jamshoro, PakistanDepartment of Physics, University of Karachi, Karachi, PakistanHeat transmission by ordinary fluids such as pure water, oil, and ethylene glycol is inefficient due to their low viscosity. To boost the efficiency of conventional fluids, very small percent of nanoparticles are added to the base fluids to prepare nanofluid. The impact of changing in viscosity can be used to investigate the rheological properties of nanofluids. In this paper, (CoFe2O4)/engine oil based nanofluids were prepared using two steps standard methodology. In first step, CoFe2O4 (CF) were synthesized using the sol-gel wet chemical process. The crystalline structure and morphology were confirmed using X-Ray diffraction analysis (XRD) and scanning electron microscopy (SEM), respectively. In second step, the standard procedure was adapted by taking several solid volume fractions of CF as Ø = 0, 0.25, 0.50, 0.75, and 1.0 %. Such percent of concentrations were dispersed in appropriate volume of engine oil using the ultrasonication for 5 h. After date, the viscosity of prepared five different nanofluids were determined at temperatures ranging from 40 to 80 °C. According to the findings, the viscosity of nanofluids (µnf) decreased as temperature increased while increased when the volume percentage of nanofluids Ø raised. Furthermore, total 25 experimental observations were considered to predict viscosity using an artificial neural network (ANN) and response surface methodology (RSM). The algorithm for building the ideal ANN architecture has been recommended in order to predict the fluid velocity of the CF/SAE-50 oil based nanofluid using MATLAB software. In order to determine the correctness of the predicted model, the mean square error (MSE) was calculated 0.0136.https://periodicals.karazin.ua/eejp/article/view/21732cobalt ferritenanofluidsviscositysolid volume fractionannrsm |
| spellingShingle | Malik Muhammad Hafeezullah Abdul Rafay Ghulam Mustafa Muhammad Khalid Zubair Ahmed Kalhoro Abdul Wasim Shaikh Ahmed Ali Rajput Prediction of Viscosity of Cobalt Ferrite/SAE50 Engine Oil based Nanofluids using well Trained Artificial Neutral Network (ANN) and Response Surface Methodology (RSM) East European Journal of Physics cobalt ferrite nanofluids viscosity solid volume fraction ann rsm |
| title | Prediction of Viscosity of Cobalt Ferrite/SAE50 Engine Oil based Nanofluids using well Trained Artificial Neutral Network (ANN) and Response Surface Methodology (RSM) |
| title_full | Prediction of Viscosity of Cobalt Ferrite/SAE50 Engine Oil based Nanofluids using well Trained Artificial Neutral Network (ANN) and Response Surface Methodology (RSM) |
| title_fullStr | Prediction of Viscosity of Cobalt Ferrite/SAE50 Engine Oil based Nanofluids using well Trained Artificial Neutral Network (ANN) and Response Surface Methodology (RSM) |
| title_full_unstemmed | Prediction of Viscosity of Cobalt Ferrite/SAE50 Engine Oil based Nanofluids using well Trained Artificial Neutral Network (ANN) and Response Surface Methodology (RSM) |
| title_short | Prediction of Viscosity of Cobalt Ferrite/SAE50 Engine Oil based Nanofluids using well Trained Artificial Neutral Network (ANN) and Response Surface Methodology (RSM) |
| title_sort | prediction of viscosity of cobalt ferrite sae50 engine oil based nanofluids using well trained artificial neutral network ann and response surface methodology rsm |
| topic | cobalt ferrite nanofluids viscosity solid volume fraction ann rsm |
| url | https://periodicals.karazin.ua/eejp/article/view/21732 |
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