Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines

Jihad Badra, Pinaki Pal, Yuanjiang Pei, Sibendu Som 

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