A Study on Optimization of Combustion Chamber Geometry in Heavy Duty Diesel Engines by Using Computer Thermo-Fluid Dynamics and Machine Learning
数値熱流体計算と機械学習を組み合わせた大型ディーゼルエンジンの燃焼室形状の最適化手法の研究
- Delivery
- Available on this site
- Format
- Price
- Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880
- Publication code
- 20266323
- Paper/Info type
- Proceedings (Autumn)
No.159-26
- Pages
- 1-2(Total 2 p)
- Date of publication
- Oct 2026
- Publisher
- JSAE
- Language
- Japanese
- Event
- 2026 JSAE Annual Congress (Autumn)
Detailed Information
| Author(J) | 1) 松前 諒大, 2) 松田 知紘, 3) 草鹿 仁 |
|---|---|
| Author(E) | 1) Ryo Matsumae, 2) Tomohiro Matsuda, 3) Jin Kusaka |
| Affiliation(J) | 1) 早稲田大学, 2) 早稲田大学, 3) 早稲田大学 |
| Affiliation(E) | 1) Waseda University, 2) Waseda University, 3) Waseda University |
| Abstract(J) | ディーゼルエンジン燃焼室のリップ径・リップ深さ・リエントラント角を対象に，熱効率，Soot，NOx排出量を指標とした最適化を実施した．燃焼室形状と性能指標の関係を推定するためANNモデルを構築し，最適化にはPSOを適用した．感度解析により各形状因子の影響を明確化し，熱効率向上と排出ガス低減を両立する形状を探索した． Translation |
| Abstract(E) | Optimization of diesel engine combustion chamber geometry, including lip diameter, lip depth, and re-entrant angle, was conducted using thermal efficiency, soot, and NOx emissions as objective indices. An ANN-based surrogate model was developed to estimate the relationship between combustion chamber geometry and performance metrics, and PSO was applied for optimization. Sensitivity analysis clarified the influence of each geometric parameter, and chamber geometries achieving both improved thermal efficiency and reduced emissions were explored. |
