Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines

Focuses on the recent developments in Artificial Intelligence/Machine Learning and data driven optimization and calibration techniques for internal combustion engines

Jihad Badra (Edited by), Pinaki Pal (Edited by), Yuanjiang Pei (Edited by), Sibendu Som (Edited by)

9780323884570, Elsevier Science

Paperback / softback, published 28 January 2022

260 pages, 100 illustrations (50 in full color)
22.9 x 15.2 x 1.7 cm, 0.43 kg

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.

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

Subject Areas: Automotive technology & trades [TRC]