Application of Machine Learning to Gasoline Direct Injection Systems: Towards a Data-Driven Development
| Autor: | Massimiliano Botticelli, Erik Schünemann, Karl Georg Stapf, Paul Jochmann, Robin Hellmann |
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| Rok vydání: | 2020 |
| Předmět: |
Spray characteristics
Computer science 02 engineering and technology Computational fluid dynamics Combustion Machine learning computer.software_genre law.invention Data-driven 03 medical and health sciences chemistry.chemical_compound 0302 clinical medicine law Valve seat 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Gasoline direct injection business.industry Injector chemistry Petroleum 020201 artificial intelligence & image processing Artificial intelligence business computer 030217 neurology & neurosurgery |
| Zdroj: | ICMLA |
| DOI: | 10.1109/icmla51294.2020.00131 |
| Popis: | The physical phenomena occurring before, during and after the combustion in Gasoline Direct Injection engines are complex and include multiple interactions between liquids and gases. In the past years, several simulation tools and measurement techniques have been developed in order to understand and optimize the components involved in the engine combustion processes. However, due to strong non-linear and multidimensional implied problems, a huge effort is required in the analysis of the generated data and in the study of the most influencing factors. In the current paper the design space of multi-hole high-pressure injectors is explored and exploited using Machine Learning techniques. Data generated with 3D Computational Fluid Dynamics (CFD) simulations are explored through feature importance and partial dependencies in order to identify how the variations of injector valve seat geometry influence the spray characteristics. Afterwards, the trained models are exploited in order to discover new designs able to achieve predefined objectives and performance, avoiding large amount of expensive and time consuming simulations. |
| Databáze: | OpenAIRE |
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