Insights into the Estimation of the Enhanced Thermal Conductivity of Phase Change Material-Containing Oxide Nanoparticles using Gaussian Process Regression Method
Thermal conductivity (TC) of a phase change material (PCM) may be enhanced by distributing nanostructured materials (NSMs) termed nano-PCM. It is critical to accurately estimate the TC of nano-PCM to assess heat transfer during phase transition processes, namely, solidification and melting. Here, we...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Journal of Chemical Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/7119336 |
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| author | Tzu-Chia Chen Hasan Sh. Majdi Aras Masood Ismael Jamshid Pouresmi Danial Ahangari Saja Mohammed Noori |
| author_facet | Tzu-Chia Chen Hasan Sh. Majdi Aras Masood Ismael Jamshid Pouresmi Danial Ahangari Saja Mohammed Noori |
| author_sort | Tzu-Chia Chen |
| collection | DOAJ |
| description | Thermal conductivity (TC) of a phase change material (PCM) may be enhanced by distributing nanostructured materials (NSMs) termed nano-PCM. It is critical to accurately estimate the TC of nano-PCM to assess heat transfer during phase transition processes, namely, solidification and melting. Here, we propose Gaussian process regression (GPR) strategies involving four various kernel functions (KFs) (including exponential (E), squared exponential (SE), rational quadratic (RQ), and matern (M)) to predict TC of n-octadecane as a PCM. The accessible computational techniques indicate the accuracy of our proposed GPR model compared to the previously proposed methods. In this research, the foremost forecasting strategy has been considered as a GPR method. This model consists of the matern KF whose R2 values of training and testing phases are 1 and 1, respectively. In the following, a sensitivity analysis (SA) is used to explore the effectiveness of variables in terms of outputs and shows that the temperature (T) of nanofluid (NF) is the most efficient input parameter. The work describes the physical properties of NFs and the parameters that should be determined to optimize their efficiency. |
| format | Article |
| id | doaj-art-89d9c05cc3ef4fb1ac1ccc71d4202f20 |
| institution | OA Journals |
| issn | 1687-8078 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Chemical Engineering |
| spelling | doaj-art-89d9c05cc3ef4fb1ac1ccc71d4202f202025-08-20T02:09:19ZengWileyInternational Journal of Chemical Engineering1687-80782022-01-01202210.1155/2022/7119336Insights into the Estimation of the Enhanced Thermal Conductivity of Phase Change Material-Containing Oxide Nanoparticles using Gaussian Process Regression MethodTzu-Chia Chen0Hasan Sh. Majdi1Aras Masood Ismael2Jamshid Pouresmi3Danial Ahangari4Saja Mohammed Noori5Department of Industrial Engineering and ManagementAl- Mustaqbal University CollegeInformation Technology DepartmentDepartment of Instrumentation and Industrial AutomationDepartment of GeologyDepartment of Computer NetworkThermal conductivity (TC) of a phase change material (PCM) may be enhanced by distributing nanostructured materials (NSMs) termed nano-PCM. It is critical to accurately estimate the TC of nano-PCM to assess heat transfer during phase transition processes, namely, solidification and melting. Here, we propose Gaussian process regression (GPR) strategies involving four various kernel functions (KFs) (including exponential (E), squared exponential (SE), rational quadratic (RQ), and matern (M)) to predict TC of n-octadecane as a PCM. The accessible computational techniques indicate the accuracy of our proposed GPR model compared to the previously proposed methods. In this research, the foremost forecasting strategy has been considered as a GPR method. This model consists of the matern KF whose R2 values of training and testing phases are 1 and 1, respectively. In the following, a sensitivity analysis (SA) is used to explore the effectiveness of variables in terms of outputs and shows that the temperature (T) of nanofluid (NF) is the most efficient input parameter. The work describes the physical properties of NFs and the parameters that should be determined to optimize their efficiency.http://dx.doi.org/10.1155/2022/7119336 |
| spellingShingle | Tzu-Chia Chen Hasan Sh. Majdi Aras Masood Ismael Jamshid Pouresmi Danial Ahangari Saja Mohammed Noori Insights into the Estimation of the Enhanced Thermal Conductivity of Phase Change Material-Containing Oxide Nanoparticles using Gaussian Process Regression Method International Journal of Chemical Engineering |
| title | Insights into the Estimation of the Enhanced Thermal Conductivity of Phase Change Material-Containing Oxide Nanoparticles using Gaussian Process Regression Method |
| title_full | Insights into the Estimation of the Enhanced Thermal Conductivity of Phase Change Material-Containing Oxide Nanoparticles using Gaussian Process Regression Method |
| title_fullStr | Insights into the Estimation of the Enhanced Thermal Conductivity of Phase Change Material-Containing Oxide Nanoparticles using Gaussian Process Regression Method |
| title_full_unstemmed | Insights into the Estimation of the Enhanced Thermal Conductivity of Phase Change Material-Containing Oxide Nanoparticles using Gaussian Process Regression Method |
| title_short | Insights into the Estimation of the Enhanced Thermal Conductivity of Phase Change Material-Containing Oxide Nanoparticles using Gaussian Process Regression Method |
| title_sort | insights into the estimation of the enhanced thermal conductivity of phase change material containing oxide nanoparticles using gaussian process regression method |
| url | http://dx.doi.org/10.1155/2022/7119336 |
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