Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines
Paperback 260 pages English Jan 21, 2022
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Elsevier
Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines
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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.
- Author:
- Jihad Badra, Pinaki Pal, Yuanjiang Pei, Sibendu Som
- Publisher:
- Elsevier
- Publication Date:
- Jan 21, 2022
- Number of pages:
- 260 pages
- Language:
- English
- Binding:
- Paperback
- ISBN-10:
- 0323884571
- ISBN-13:
- 9780323884570