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…mehr
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.
Jihad Badra is the Engine Combustion Team leader in the Transport Technologies Research and Development Division at Saudi Aramco. He joined Saudi Aramco in 2014 after working as a Postdoctoral Researcher in the Clean Combustion Research Center at King Abdullah University of Science and Technology (KAUST). Jihad's research interest is in developing and optimizing internal combustion engine technologies with decreased net environmental impact. Jihad's current focus is on fuel formulation for advanced engines and engine modelling using computational fluid dynamics. Jihad has more than 50 peer-reviewed journal papers. Jihad received his BASc in Mechanical Engineering from the University of Balamand, Lebanon and MASc and PhD degrees in Combustion Research in Mechanical Engineering at the University of Sydney, Australia.
Pinaki Pal is a research scientist in Argonne's Energy Systems division. His research interests broadly lie in the areas of computational fluid dynamics (CFD), turbulent combustion modeling, machine learning, computational science, and high-performance computing, for a wide range of applications, such as propulsion (automotive and aerospace) and material synthesis. Dr. Pal received his PhD from University of Michigan-Ann Arbor (2015) in Mechanical Engineering, with specialization in turbulent combustion modeling and CFD for low temperature combustion applications in both internal combustion engines and gas turbines. He also holds a Bachelor of Technology in Mechanical Engineering from the Indian Institute of Technology Kharagpur (India) (2011).
Yuanjiang Pei is a Technical Specialist at Aramco Americas: Aramco Research Center - Detroit working on the co-optimization of fuels and engines in pursuit of higher internal combustion engine efficiency. Pei has more than 10 years of experience working in the engine research and automotive industry. He joined Aramco in late 2015 after previously working more than 2 years on engine combustion modeling at Argonne National Laboratory and 5 years on engine management system calibration and project management at Delphi. Pei is actively involved in the organization of several international conferences, including Society of Automotive Engineers (SAE) World Congress, American Society of Mechanical Engineers (ASME) Internal Combustion Engine Fall (ICEF) Conference and Engine Combustion Network (ECN) workshops.
Inhaltsangabe
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
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
Es gelten unsere Allgemeinen Geschäftsbedingungen: www.buecher.de/agb
Impressum
www.buecher.de ist ein Shop der buecher.de GmbH & Co. KG Bürgermeister-Wegele-Str. 12, 86167 Augsburg Amtsgericht Augsburg HRA 13309
