| Project/Area Number |
18K03974
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 19020:Thermal engineering-related
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| Research Institution | Niigata University |
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Principal Investigator |
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2019: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2018: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | 熱ふく射 / 機械学習 / 電磁波 / メタマテリアル |
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| Outline of Final Research Achievements |
In this study, we have tried to apply machine learning to the calculation of thermal radiation properties. The thermal radiation properties of the multilayer film were analyzed numerically using the rigorous coupled wave analysis method, which expands Maxwell's equations in the frequency domain. By combining this electromagnetic wave analysis method with machine learning, we aimed to establish a design method for multilayer structures that maximizes the thermal radiation performance (in this case, narrowing the thermal radiation spectrum). As a result, we found an aperiodic metamaterial structure with high thermal radiation performance. Based on this prediction, we have successfully demonstrated an ultra-narrowband thermal emitter and clarified its physical mechanism.
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| Academic Significance and Societal Importance of the Research Achievements |
熱ふく射スペクトル制御の応用例として、波長選択性太陽光吸収材料を用いた太陽熱発電、熱光起電力発電、大気の窓領域に高い放射率を持つスカイラジエータ、有機溶剤の吸光スペクトルに合わせた波長選択性赤外線ヒーター等が挙げられる。このように可視光から赤外光まで多種多様な社会・産業界からのニーズがある中で、何兆通りにも存在する組み合わせから高性能なメタマテリアルを導き出すことはできるのだろうか？その課題解決のため、機械学習を用いた熱ふく射物性計算方法を確立した。さらに、機械学習から導かれた非周期的なメタマテリアル構造体ではどのような新しい熱ふく射特性が発現するのかといった学術的知見を得ることができた。
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