Application of machine learning to optimized design of layer structured particles
Direct absorption solar collectors are a solution to the problem of energy depletion: they work by dispersing plasmonic nanoparticles in a liquid and exposing them directly to sunlight. Improving the sunlight absorption performance of plasmonic nanoparticles is an important issue to increase their u...
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| Format: | Article |
| Language: | English |
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The Japan Society of Mechanical Engineers
2024-10-01
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| Series: | Journal of Thermal Science and Technology |
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| Online Access: | https://www.jstage.jst.go.jp/article/jtst/19/2/19_24-00236/_pdf/-char/en |
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| author | Hiroki GONOME Hirotake SATO Tatsuro HIRAI |
| author_facet | Hiroki GONOME Hirotake SATO Tatsuro HIRAI |
| author_sort | Hiroki GONOME |
| collection | DOAJ |
| description | Direct absorption solar collectors are a solution to the problem of energy depletion: they work by dispersing plasmonic nanoparticles in a liquid and exposing them directly to sunlight. Improving the sunlight absorption performance of plasmonic nanoparticles is an important issue to increase their utilization efficiency and reduce their production cost. To solve this problem, we have proposed metal-insulator-magnet plasmonic nanoparticles with a layered structure consisting of a spherical insulator sandwiched between thin films. These particles can be easily fabricated by sputtering a thin film on a spherical insulator, which significantly reduces the amount of material used and the process is inexpensive. However, the combination of factors that determine the radiative properties of the particles is enormous. Therefore, the goal of this study is to find the optimal particle design using machine learning. Three types of machine learning were used: neural networks, support vector machines, and light gradient boosting machines. Learning is done by performing an electromagnetic field analysis based on the finite element method and using the calculated radiative properties as the correct values. The accuracy of the machine learning was evaluated by predicting the absorption property from the particle parameters. The constructed machine learning code was then used to optimize the particle parameters. It was shown that machine learning is effective for optimization design of objects with a large number of parameters. |
| format | Article |
| id | doaj-art-9da5bce96deb491b9648d87c3e2d7b5a |
| institution | DOAJ |
| issn | 1880-5566 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | The Japan Society of Mechanical Engineers |
| record_format | Article |
| series | Journal of Thermal Science and Technology |
| spelling | doaj-art-9da5bce96deb491b9648d87c3e2d7b5a2025-08-20T02:57:25ZengThe Japan Society of Mechanical EngineersJournal of Thermal Science and Technology1880-55662024-10-0119224-0023624-0023610.1299/jtst.24-00236jtstApplication of machine learning to optimized design of layer structured particlesHiroki GONOME0Hirotake SATO1Tatsuro HIRAI2Graduate School of Science and Engineering, Yamagata UniversityGraduate School of Science and Engineering, Yamagata UniversityGraduate School of Science and Engineering, Yamagata UniversityDirect absorption solar collectors are a solution to the problem of energy depletion: they work by dispersing plasmonic nanoparticles in a liquid and exposing them directly to sunlight. Improving the sunlight absorption performance of plasmonic nanoparticles is an important issue to increase their utilization efficiency and reduce their production cost. To solve this problem, we have proposed metal-insulator-magnet plasmonic nanoparticles with a layered structure consisting of a spherical insulator sandwiched between thin films. These particles can be easily fabricated by sputtering a thin film on a spherical insulator, which significantly reduces the amount of material used and the process is inexpensive. However, the combination of factors that determine the radiative properties of the particles is enormous. Therefore, the goal of this study is to find the optimal particle design using machine learning. Three types of machine learning were used: neural networks, support vector machines, and light gradient boosting machines. Learning is done by performing an electromagnetic field analysis based on the finite element method and using the calculated radiative properties as the correct values. The accuracy of the machine learning was evaluated by predicting the absorption property from the particle parameters. The constructed machine learning code was then used to optimize the particle parameters. It was shown that machine learning is effective for optimization design of objects with a large number of parameters.https://www.jstage.jst.go.jp/article/jtst/19/2/19_24-00236/_pdf/-char/endirect absorption solar collector (dasc)machine learningneural networksupport vector machine (svm)light gradient boosting machine (light gbm) |
| spellingShingle | Hiroki GONOME Hirotake SATO Tatsuro HIRAI Application of machine learning to optimized design of layer structured particles Journal of Thermal Science and Technology direct absorption solar collector (dasc) machine learning neural network support vector machine (svm) light gradient boosting machine (light gbm) |
| title | Application of machine learning to optimized design of layer structured particles |
| title_full | Application of machine learning to optimized design of layer structured particles |
| title_fullStr | Application of machine learning to optimized design of layer structured particles |
| title_full_unstemmed | Application of machine learning to optimized design of layer structured particles |
| title_short | Application of machine learning to optimized design of layer structured particles |
| title_sort | application of machine learning to optimized design of layer structured particles |
| topic | direct absorption solar collector (dasc) machine learning neural network support vector machine (svm) light gradient boosting machine (light gbm) |
| url | https://www.jstage.jst.go.jp/article/jtst/19/2/19_24-00236/_pdf/-char/en |
| work_keys_str_mv | AT hirokigonome applicationofmachinelearningtooptimizeddesignoflayerstructuredparticles AT hirotakesato applicationofmachinelearningtooptimizeddesignoflayerstructuredparticles AT tatsurohirai applicationofmachinelearningtooptimizeddesignoflayerstructuredparticles |