Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines

Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines summarizes recent developments in Artificial Intelligence (AI)/Machine Learning (ML) and data driven optimization and calibration techniques for internal combustion engines. The book covers AI/ML and data driven methods to optimize fuel formulations and engine combustion systems, predict cycle to cycle variations, and optimize after-treatment systems and experimental engine calibration. It contains all the details of the latest optimization techniques along with their application to ICE, making it ideal for automotive engineers, mechanical engineers, OEMs and R&D centers involved in engine design. Provides AI/ML and data driven optimization techniques in combination with Computational Fluid Dynamics (CFD) to optimize engine combustion systems Features a comprehensive overview of how AI/ML techniques are used in conjunction with simulations and experiments Discusses data driven optimization techniques for fuel formulations and vehicle control calibration INDICE: 1. Active-learning for fuel optimization 2. High throughput screening for fuel formulation 3. Engine optimization using computational fluid dynamics-Genetic algorithms (CFD-GA) 4. Engine optimization using computational fluid dynamics-design of experiments (CFD-DoE) 5. Engine optimization using machine learning-genetic algorithms (ML-GA) 6. Machine learning driven sequential optimization using dynamic exploration and exploitation 7. Optimization of after-treatment systems using machine learning 8. Engine cycle-to-cycle variation control 9. Prediction of low pressure preignition using machine learning 10. AI aided optimization of experimental engine calibration 11. AI aided optimization of vehicle control calibration

• ISBN: 978-0-323-88457-0
• Editorial: Elsevier
• Encuadernacion: Rústica
• Páginas: 260
• Fecha Publicación: 28/01/2022
• Nº Volúmenes: 1
• Idioma: Inglés