Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning
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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-06-01
|
| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023039233 |
| _version_ | 1797815493896175616 |
|---|---|
| author | Yazeed AbuShanab Wahib A. Al-Ammari Samer Gowid Ahmad K. Sleiti |
| author_facet | Yazeed AbuShanab Wahib A. Al-Ammari Samer Gowid Ahmad K. Sleiti |
| author_sort | Yazeed AbuShanab |
| collection | DOAJ |
| description | 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 R2 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 R2 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. |
| first_indexed | 2024-03-13T08:23:38Z |
| format | Article |
| id | doaj.art-4120b5ea76e342b385fb23b6afb713da |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-13T08:23:38Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-4120b5ea76e342b385fb23b6afb713da2023-05-31T04:47:25ZengElsevierHeliyon2405-84402023-06-0196e16716Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learningYazeed AbuShanab0Wahib A. Al-Ammari1Samer Gowid2Ahmad K. Sleiti3Corresponding author.; Department of Mechanical & Industrial Engineering, College of Engineering, Qatar University, QatarDepartment of Mechanical & Industrial Engineering, College of Engineering, Qatar University, QatarDepartment of Mechanical & Industrial Engineering, College of Engineering, Qatar University, QatarDepartment of Mechanical & Industrial Engineering, College of Engineering, Qatar University, QatarThis 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 R2 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 R2 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.http://www.sciencedirect.com/science/article/pii/S2405844023039233Nano-fluidsDynamic viscosityAdaptive neuro-fuzzy inferenceArtificial neural networkSystemPrediction |
| spellingShingle | Yazeed AbuShanab Wahib A. Al-Ammari Samer Gowid Ahmad K. Sleiti Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning Heliyon Nano-fluids Dynamic viscosity Adaptive neuro-fuzzy inference Artificial neural network System Prediction |
| 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 | Nano-fluids Dynamic viscosity Adaptive neuro-fuzzy inference Artificial neural network System Prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023039233 |
| work_keys_str_mv | AT yazeedabushanab accuratepredictionofdynamicviscosityofpolyalphaolefinboronnitridenanofluidsusingmachinelearning AT wahibaalammari accuratepredictionofdynamicviscosityofpolyalphaolefinboronnitridenanofluidsusingmachinelearning AT samergowid accuratepredictionofdynamicviscosityofpolyalphaolefinboronnitridenanofluidsusingmachinelearning AT ahmadksleiti accuratepredictionofdynamicviscosityofpolyalphaolefinboronnitridenanofluidsusingmachinelearning |