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Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines
von: Jihad Badra, Pinaki Pal, Yuanjiang Pei, Sibendu Som
Elsevier Reference Monographs, 2022
ISBN: 9780323884587 , 262 Seiten
Format: ePUB, PDF, Online Lesen
Kopierschutz: DRM
Preis: 175,00 EUR
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Front Cover
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ARTIFICIAL INTELLIGENCE AND DATA DRIVEN OPTIMIZATION OF INTERNAL COMBUSTION ENGINES
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ARTIFICIAL INTELLIGENCE AND DATA DRIVEN OPTIMIZATION OF INTERNAL COMBUSTION ENGINES
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Copyright
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Contents
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Contributors
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Foreword
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Preface
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1 - Introduction
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1. Industrial revolution
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2. Artificial intelligence, machine learning, and deep learning
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3. Machine learning algorithms
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4. Artificial intelligence-based fuel-engine co-optimization
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4.1 Optimization of internal combustion engine
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4.1.1 Design of experiments
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4.1.2 Genetic algorithm
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4.1.3 Machine learning-based algorithms
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4.2 Optimization of fuel formulation
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4.3 Mitigation of rare combustion events
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5. Summary
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References
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1 - Artificial Intelligence to optimize fuel formulation
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2 - Optimization of fuel formulation using adaptive learning and artificial intelligence
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1. Introduction and motivation
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2. Mixed-mode combustion and fuel performance metrics
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3. A neural network model to predict fuel research octane numbers
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4. Optimization problem formulation and description of solution approaches
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4.1 Constrained optimization formulation
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4.2 Genetic algorithm
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4.3 Gaussian process–based surrogate model optimization algorithm
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5. Numerical experiments and results
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6. Discussion
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7. Summary and concluding remarks
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Acknowledgments
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References
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3 - Artificial intelligence–enabled fuel design
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1. Transportation fuels
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1.1 Fuel representation
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1.2 Fuel formulation workflow
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1.3 Artificial intelligence modeling approaches
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2. Application of artificial intelligence to fuel formulation
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2.1 High throughput screening: finding a needle in the haystack
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2.2 Fuel property prediction by machine learning models
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2.3 Reaction discovery
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2.4 Fuel-engine co-optimization
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3. Conclusions and perspectives
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Acknowledgments
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References
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2 - Artificial Intelligence and computational fluid dynamics to optimize internal combustion engines
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4 - Engine optimization using computational fluid dynamics and genetic algorithms
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1. Introduction
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2. Modeling framework and acceleration strategies
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2.1 Computational fluid dynamics acceleration techniques
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2.1.1 Adaptive mesh refinement
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2.1.2 Detailed chemistry acceleration strategies
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2.2 Engine geometry generation
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2.2.1 Method of splines
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2.2.2 Method of forces
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2.3 Virtual injection model
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3. Optimization methods
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3.1 Fundamentals of genetic algorithms
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3.2 Pioneering investigations
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3.3 Multiobjective framework
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3.4 Convergence acceleration
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4. Summary and concluding remarks
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References
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5 - Computational fluid dynamics–guided engine combustion system design optimization using design of experiments
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1. Introduction
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2. Methodologies
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2.1 Design space construction
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2.2 Response surface model formulation
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2.3 Model-based design optimization and verification
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3. A recent application
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3.1 Engine and fuel specifications
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3.2 Computational fluid dynamic model setup and validation
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3.3 Design variables
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3.4 Objective variables and evaluation method
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3.5 Data fitting and optimization
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4. Recommendations for best practice
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4.1 Adequate computational fluid dynamic model validation
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4.2 Efficient geometry and mesh manipulation
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4.3 Sample size
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4.4 Optimization across full engine operation range
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4.5 Computational efficiency
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5. Conclusions and perspectives
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Acknowledgments
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References
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6 - A machine learning-genetic algorithm approach for rapid optimization of internal combustion engines
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1. Introduction
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2. Engine optimization problem setup
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3. Training and data examination
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4. Machine learning-genetic algorithm approach
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4.1 Optimization methodology
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4.2 Repeatability of machine learning-genetic algorithm
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4.2.1 Extension of variable domain
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4.3 Postprocessing and robustness
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5. Automated machine learning-genetic algorithm
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5.1 Hyperparameter selection
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5.1.1 Manual selection
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5.1.2 Automated strategies for selecting hyperparameters
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5.2 Problem setup
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5.3 Results
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6. Summary
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Acknowledgments
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References
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7 - Machine learning–driven sequential optimization using dynamic exploration and exploitation
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1. Introduction
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2. Active ML optimization (ActivO)
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2.1 Basic algorithm
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2.2 Query strategies
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2.3 Convergence criteria
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2.4 Dynamic exploration and exploitation
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3. Case study 1: two-dimensional cosine mixture function
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4. Case study 2: computational fluid dynamics (CFD)-based engine optimization
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5. Conclusions
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Acknowledgments
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References
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3 - Artificial Intelligence to predict abnormal engine phenomena
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8 - Artificial-intelligence-based prediction and control of combustion instabilities in spark-ignition engines
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1. Introduction
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1.1 Artificial intelligence applications to engine controls
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1.2 Dilute combustion instability background
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2. Case study: artificial-intelligence-enhanced modeling of dilute spark-ignition cycle-to-cycle variability
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3. Case study: neural networks for combustion stability control
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3.1 Artificial neural networks
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3.2 Spiking neural networks
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4. Case study: learning reference governor for model-free dilute limit identification and avoidance
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4.1 Constrained combustion phasing control problem
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4.2 Learning reference governor for avoiding misfire events
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5. Summary
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References
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9 - Using deep learning to diagnose preignition in turbocharged spark-ignited engines
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1. Introduction
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1.1 Fault detection
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1.2 Optimization and control
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1.3 Predicting combustion parameters (phasing and cycle-to-cycle variation) and emissions
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2. Preignition detection using machine learning algorithm
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2.1 Feed forward multilayer neural networks
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2.2 Convolutional neural networks
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2.3 Recurrent neural networks
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3. Activation functions
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4. Experiments and data extraction
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5. Machine learning methodology
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6. Model 1: Input from principal component analysis
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7. Model 2: Time series input
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8. Model metrics
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9. Results and discussion
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9.1 Training and validation losses
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10. Conclusions
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References
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Further reading
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Index
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A
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B
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C
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D
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E
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F
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G
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H
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I
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K
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L
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M
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N
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O
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P
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Q
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R
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S
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T
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U
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V
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Z
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Back Cover
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