0

Site Breadcrumb

Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines

Title: Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines
Author: Jihad Badra, Pinaki Pal, Sibendu Som, Yuanjiang Pei
ISBN: 0323884571 / 9780323884570
Format: Soft Cover
Pages: 260
Publisher: Elsevier
Year: 2022
Availability: 20-30 days

Tab Article

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.

Tab Article

Chapter 1 : Introduction
Chapter 2 : Optimization of fuel formulation using adaptive learning and artificial intelligence
Chapter 3 : Artificial intelligence–enabled fuel design
Chapter 4 : Engine optimization using computational fluid dynamics and genetic algorithms
Chapter 5 : Computational fluid dynamics–guided engine combustion system design optimization using design of experiments
Chapter 6 : A machine learning-genetic algorithm approach for rapid optimization of internal combustion engines
Chapter 7 : Machine learning–driven sequential optimization using dynamic exploration and exploitation
Chapter 8 : Artificial-intelligence-based prediction and control of combustion instabilities in spark-ignition engines
Chapter 9 : Using deep learning to diagnose preignition in turbocharged spark-ignited engines

 

 

Browse by subjects