2023 Fiscal Year Final Research Report
Prediction of scattering properties of gas molecule based on machine learning and search for functional nano-interfaces
| Project/Area Number |
18K03960
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Review Section |
Basic Section 19010:Fluid engineering-related
|
|---|
| Research Institution | Kochi National College of Technology |
|---|
Principal Investigator |
Takeuchi Hideki 高知工業高等専門学校, ソーシャルデザイン工学科, 教授 (30435474)
|
|---|
| Project Period (FY) |
2018-04-01 – 2024-03-31
|
| Keywords | 高Knudsen数流れ / Gas-Surface Interaction / 反射境界条件 / 分子速度分布関数 / 適応係数 / 機械学習 |
|---|
| Outline of Final Research Achievements |
Accurate understanding of the thermal flow characteristics of gases in high Knudsen number flows requires an appropriate treatment of the gas molecular reflection boundary condition at the object interface. A reflection model based on machine learning was constructed to predict the scattering characteristics of gas molecules, considering various factors, including the thermal flow conditions of the flow field and the state of the interface. It was confirmed that the model is effective in predicting macroscopic physical quantities such as the accommodation coefficient. Furthermore, the usefulness of the constructed model in considering functional nano-interfaces was also indicated.
|
| Free Research Field |
分子熱流体工学
|
| Academic Significance and Societal Importance of the Research Achievements |
高Knudsen数流れの系での気体の熱的・流体力学的特性の理解には，物体界面での気体分子散乱特性の解明が重要となる．流れ場の様々な因子を考慮した分子シミュレーション解析に基づく気体分子散乱データから気体分子散乱挙動の予測に有効な反射モデルを機械学習により構築する方法を実現した．構築モデルより求められる分子速度分布関数から，界面構造の違いによる流れ場への影響を予測することで，機能性ナノ界面の把握に有効となる．
|
