Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning
<p dir="ltr">This study focuses on predicting the dynamic viscosity of nanofluids, specifically Polyalpha-Olefin-hexagonal boron nitride (PAO-hBN) using machine learning models. The primary goal of this research is to assess and contrast the effectiveness of three distinct machine le...
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2023
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| _version_ | 1864513564350349312 |
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| author | Ahmad K. Sleiti (15955149) |
| author2 | Samer Gowid (15955152) Wahib A. Al-Ammari (15955155) Yazeed AbuShanab (15955156) |
| author2_role | author author author |
| author_facet | Ahmad K. Sleiti (15955149) Samer Gowid (15955152) Wahib A. Al-Ammari (15955155) Yazeed AbuShanab (15955156) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ahmad K. Sleiti (15955149) Samer Gowid (15955152) Wahib A. Al-Ammari (15955155) Yazeed AbuShanab (15955156) |
| dc.date.none.fl_str_mv | 2023-05-25T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.heliyon.2023.e16716 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Accurate_prediction_of_dynamic_viscosity_of_polyalpha-olefin_boron_nitride_nanofluids_using_machine_learning/23259701 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Chemical sciences Macromolecular and materials chemistry Engineering Mechanical engineering Information and computing sciences Machine learning Nano-fluids Dynamic viscosity Adaptive neuro-fuzzy inference Artificial neural network System Prediction Correlation |
| dc.title.none.fl_str_mv | Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">This study focuses on predicting the dynamic viscosity of nanofluids, specifically Polyalpha-Olefin-hexagonal boron nitride (PAO-hBN) using machine learning models. The primary goal of this research is to assess and contrast the effectiveness of three distinct machine learning models: Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The main objective is the identification of a model that demonstrates the highest level of accuracy in predicting a nanofluid’s viscosity namely, PAO-hBN nanofluids. The models were trained and validated using 540 experimental data points, where the mean square error (MSE) and the coefficient of determination R<sup>2</sup> were utilized for performance evaluation. The results demonstrated that all three models could predict the viscosity of PAO-hBN nanofluids accurately, but the ANFIS and ANN models outperformed the SVR model. The ANFIS and ANN models had similar performance, but the ANN model was preferred due to its faster training and computation time. The optimized ANN model had an R<sup>2</sup> of 0.99994, which indicates a high level of accuracy in predicting the viscosity of PAO-hBN nanofluids. The elimination of the shear rate parameter from the input layer improved the accuracy of the ANN model to an absolute relative error of less than 1.89% over the full temperature range (−19.7 °C–70 °C) compared to 11% in the traditional correlation-based model. These results suggest that the use of machine learning models can significantly improve the accuracy of predicting the viscosity of PAO-hBN nanofluids. Overall, this study demonstrated that the use of machine learning models, specifically ANN, can be effective in predicting PAO-hBN nanofluids’ dynamic viscosity. The findings provide a new perspective on how to predict the thermodynamic properties of nanofluids with high accuracy, which could have important applications in various industries.</p><h2>Other Information</h2><p dir="ltr">Published in: Heliyon<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://doi.org/10.1016/j.heliyon.2023.e16716" target="_blank">https://doi.org/10.1016/j.heliyon.2023.e16716</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_73d5df0b23b2e08cc0f5792cae942a1a |
| identifier_str_mv | 10.1016/j.heliyon.2023.e16716 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/23259701 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learningAhmad K. Sleiti (15955149)Samer Gowid (15955152)Wahib A. Al-Ammari (15955155)Yazeed AbuShanab (15955156)Chemical sciencesMacromolecular and materials chemistryEngineeringMechanical engineeringInformation and computing sciencesMachine learningNano-fluidsDynamic viscosityAdaptive neuro-fuzzy inferenceArtificial neural networkSystemPredictionCorrelation<p dir="ltr">This study focuses on predicting the dynamic viscosity of nanofluids, specifically Polyalpha-Olefin-hexagonal boron nitride (PAO-hBN) using machine learning models. The primary goal of this research is to assess and contrast the effectiveness of three distinct machine learning models: Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The main objective is the identification of a model that demonstrates the highest level of accuracy in predicting a nanofluid’s viscosity namely, PAO-hBN nanofluids. The models were trained and validated using 540 experimental data points, where the mean square error (MSE) and the coefficient of determination R<sup>2</sup> were utilized for performance evaluation. The results demonstrated that all three models could predict the viscosity of PAO-hBN nanofluids accurately, but the ANFIS and ANN models outperformed the SVR model. The ANFIS and ANN models had similar performance, but the ANN model was preferred due to its faster training and computation time. The optimized ANN model had an R<sup>2</sup> of 0.99994, which indicates a high level of accuracy in predicting the viscosity of PAO-hBN nanofluids. The elimination of the shear rate parameter from the input layer improved the accuracy of the ANN model to an absolute relative error of less than 1.89% over the full temperature range (−19.7 °C–70 °C) compared to 11% in the traditional correlation-based model. These results suggest that the use of machine learning models can significantly improve the accuracy of predicting the viscosity of PAO-hBN nanofluids. Overall, this study demonstrated that the use of machine learning models, specifically ANN, can be effective in predicting PAO-hBN nanofluids’ dynamic viscosity. The findings provide a new perspective on how to predict the thermodynamic properties of nanofluids with high accuracy, which could have important applications in various industries.</p><h2>Other Information</h2><p dir="ltr">Published in: Heliyon<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://doi.org/10.1016/j.heliyon.2023.e16716" target="_blank">https://doi.org/10.1016/j.heliyon.2023.e16716</a></p>2023-05-25T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.heliyon.2023.e16716https://figshare.com/articles/journal_contribution/Accurate_prediction_of_dynamic_viscosity_of_polyalpha-olefin_boron_nitride_nanofluids_using_machine_learning/23259701CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/232597012023-05-25T00:00:00Z |
| spellingShingle | Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning Ahmad K. Sleiti (15955149) Chemical sciences Macromolecular and materials chemistry Engineering Mechanical engineering Information and computing sciences Machine learning Nano-fluids Dynamic viscosity Adaptive neuro-fuzzy inference Artificial neural network System Prediction Correlation |
| status_str | publishedVersion |
| title | Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning |
| title_full | Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning |
| title_fullStr | Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning |
| title_full_unstemmed | Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning |
| title_short | Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning |
| title_sort | Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning |
| topic | Chemical sciences Macromolecular and materials chemistry Engineering Mechanical engineering Information and computing sciences Machine learning Nano-fluids Dynamic viscosity Adaptive neuro-fuzzy inference Artificial neural network System Prediction Correlation |