Development of a PN Surrogate Model Based on Mixture Quality in a GDI Engine
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- 版元よりダウンロードリンクを連絡
- 形態
- 価格
- 一般価格(税込)：￥6,600 会員価格(税込)：￥5,280
- 文献・情報種別
- SAE Paper
No.2021-24-0013
- 掲載ページ
- 1-12(Total 12 p)
- 発行年月
- 2021年 9月
- 出版社
- SAE International
- 言語
- 英語
- イベント
- International Conference on Engines and Vehicles 2021
書誌事項
| 著者（英） | 1) Davide Domenico Sciortino, 2) Mark Cary, 3) Sunny Verma, 4) Federico Biagiotti, 5) Edward Hopkins, 6) Changzhao Jiang, 7) Dennis Witt, 8) Fabrizio Bonatesta |
|---|---|
| 勤務先（英） | 1) Oxford Brookes University, 2) Oxford Brookes University, 3) Oxford Brookes University, 4) Oxford Brookes University, 5) Oxford Brookes University, 6) Loughborough Univ, 7) Ford Motor Company, 8) Oxford Brookes University |
| 抄録（英） | A novel surrogate model is presented, which predicts the engine-out Particle Number (PN) emissions of a light-duty, spray-guided, turbo-charged, GDI engine. The model is developed through extensive CFD analysis, carried out using the Siemens Simcenter STAR-CD, and considers a range of part-load operating conditions and single-variable sweeps where control parameters such as start of injection and injection pressure are varied in isolation. The work is attached to the Ford-led APC6 DYNAMO project, which aims to improve efficiency and reduce harmful emissions from the next generation of gasoline engines. The CFD work focused on the air exchange, fuel spray and mixture preparation stages of the engine cycle. A combined Rosin-Rammler and Reitz-Diwakar model, calibrated over a wide range of injection pressure, is used to model fuel atomization and secondary droplets break-up. A validated approach, based on the Bai-Onera model of droplet-wall interaction, is used to capture the details of liquid film formation. A multi-component surrogate fuel blend model reproduces the relevant characteristics of the E5 95RON gasoline used in parallel experiments. A fixed, but region-specific, wall temperature scheme is used for the in-cylinder simulations, based on available experimental data. An Elastic Net (EN) regression technique was used to construct a novel PN surrogate model, through the identification of relevant relationships between experimental engine-out PN emission levels and modelled air-fuel mixture quality indicators. To maximize model usefulness and applicability, these indicators are then correlated through sub-models to engine control parameters and easily-accessible measurements. The sub-models are obtained via Radial Basis Function (RFB) or a combination of RBF and EN regression. Within limits, engine sooting tendencies can be reliably predicted without reliance on combustion characteristics, which are complex to measure in real time. 翻訳 |
