(EBook PDF) Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines 1st Edition by Jihad Badra 032388458X 9780323884587 full chapters

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Product details:

ISBN-10 :  032388458X 

ISBN-13 :9780323884587

Author: Jihad Badra 

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.

 

Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines 1st Table of contents:

Chapter 1. Introduction
1. Industrial revolution
2. Artificial intelligence, machine learning, and deep learning
3. Machine learning algorithms
4. Artificial intelligence–based fuel-engine co-optimization
5. Summary
Section 1. Artificial Intelligence to optimize fuel formulation
Chapter 2. Optimization of fuel formulation using adaptive learning and artificial intelligence
1. Introduction and motivation
2. Mixed-mode combustion and fuel performance metrics
3. A neural network model to predict fuel research octane numbers
4. Optimization problem formulation and description of solution approaches
5. Numerical experiments and results
6. Discussion
7. Summary and concluding remarks
Chapter 3. Artificial intelligence–enabled fuel design
1. Transportation fuels
2. Application of artificial intelligence to fuel formulation
3. Conclusions and perspectives
Section 2. Artificial Intelligence and computational fluid dynamics to optimize internal combustion engines
Chapter 4. Engine optimization using computational fluid dynamics and genetic algorithms
1. Introduction
2. Modeling framework and acceleration strategies
3. Optimization methods
4. Summary and concluding remarks
Chapter 5. Computational fluid dynamics–guided engine combustion system design optimization using design of experiments
1. Introduction
2. Methodologies
3. A recent application
4. Recommendations for best practice
5. Conclusions and perspectives
Chapter 6. A machine learning-genetic algorithm approach for rapid optimization of internal combustion engines
1. Introduction
2. Engine optimization problem setup
3. Training and data examination
4. Machine learning-genetic algorithm approach
5. Automated machine learning-genetic algorithm
6. Summary
Chapter 7. Machine learning–driven sequential optimization using dynamic exploration and exploitation
1. Introduction
2. Active ML optimization (ActivO)
3. Case study 1: two-dimensional cosine mixture function
4. Case study 2: computational fluid dynamics (CFD)-based engine optimization
5. Conclusions
Section 3. Artificial Intelligence to predict abnormal engine phenomena
Chapter 8. Artificial-intelligence-based prediction and control of combustion instabilities in spark-ignition engines
1. Introduction
2. Case study: artificial-intelligence-enhanced modeling of dilute spark-ignition cycle-to-cycle variability
3. Case study: neural networks for combustion stability control
4. Case study: learning reference governor for model-free dilute limit identification and avoidance
5. Summary
Chapter 9. Using deep learning to diagnose preignition in turbocharged spark-ignited engines
1. Introduction
2. Preignition detection using machine learning algorithm
3. Activation functions
4. Experiments and data extraction
5. Machine learning methodology
6. Model 1: Input from principal component analysis
7. Model 2: Time series input
8. Model metrics
9. Results and discussion
10. Conclusions

 

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