Machine Learning and Its Application to Reacting Flows : : ML and Combustion.
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Superior document: | Lecture Notes in Energy Series ; v.44 |
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TeilnehmendeR: | |
Place / Publishing House: | Cham : : Springer International Publishing AG,, 2023. Ã2023. |
Year of Publication: | 2023 |
Edition: | 1st ed. |
Language: | English |
Series: | Lecture Notes in Energy Series
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Online Access: | |
Physical Description: | 1 online resource (353 pages) |
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Table of Contents:
- Intro
- Preface
- Contents
- Contributors
- Introduction
- 1 Combustion Technology Role
- 2 Governing Equations
- 3 Equations for LES
- 3.1 SGS Closures
- 3.2 LES Challenges and Role of MLA
- 4 Objectives
- References
- Machine Learning Techniques in Reactive Atomistic Simulations
- 1 Introduction and Overview
- 1.1 Molecular Dynamics, Reactive Force Fields and the Concept of Bond Order
- 1.2 Accuracy, Complexity, and Transferability
- 2 Machine Learning and Optimization Techniques
- 2.1 Continuous Optimization for Convex and Non-convex Optimization
- 2.2 Discrete Optimization
- 3 Machine Learning Models
- 3.1 Unsupervised Learning
- 3.2 Supervised Learning
- 3.3 Software Infrastructure for Machine Learning Applications
- 4 ML Applications in Reactive Atomistic Simulations
- 4.1 ML Techniques for Training Reactive Atomistic Models
- 4.2 Accelerating Reactive Simulations
- 5 Analyzing Results from Atomistic Simulations
- 5.1 Representation Techniques
- 5.2 Dimensionality Reduction and Clustering
- 5.3 Dynamical Models and Analysis
- 5.4 Reaction Rates and Chemical Properties
- 6 Concluding Remarks
- References
- A Novel In Situ Machine Learning Framework for Intelligent Data Capture and Event Detection
- 1 Introduction
- 1.1 Overview of Related Work
- 1.2 Contributions and Organization
- 2 Approach
- 3 Results
- 3.1 Data Capture for Optimal I/O: Mantaflow Experiments
- 3.2 Detecting Physical Phenomena: Marine Ice Sheet Instability (MISI)
- 3.3 Reduced Order Modeling: Sample Mesh Generation for Hyper-Reduction
- 3.4 HPC Experiments
- 4 Conclusion
- References
- Machine-Learning for Stress Tensor Modelling in Large Eddy Simulation
- 1 Introduction
- 2 Classic Stress Tensor Models
- 2.1 Smagorinsky
- 2.2 Scale Similarity
- 2.3 Gradient Model
- 2.4 Clark Model.
- 2.5 Wall-Adapting Local Eddy-Viscosity (WALE)
- 3 Deconvolution-Based Modelling
- 4 Machine-Learning Based Models
- 4.1 Type (a)
- 4.2 Type (b)
- 4.3 Type (c)
- 5 A Note: Sub-grid Versus Sub-filter
- 6 Challenges of Data-Based Models
- 6.1 Universality
- 6.2 Choice and Pre-processing of Data
- 6.3 Training, Validation, Testing
- 6.4 Network Structure
- 6.5 LES Mesh Size
- 6.6 Performance Metrics
- 7 Summary
- References
- Machine Learning for Combustion Chemistry
- 1 Introduction and Motivation
- 2 Learning Reaction Rates
- 2.1 Chemistry Regression via ANNs
- 3 Learning Reaction Mechanisms
- 3.1 Learning Observables in Complex Reaction Mechanisms
- 3.2 Chemical Reaction Neural Networks
- 3.3 PCA-Based Chemistry Reduction and Other PCA Applications
- 3.4 Hybrid Chemistry Models and Implementation of ML Tools
- 3.5 Extending Functional Groups for Kinetics Modeling
- 3.6 Fuel Properties' Prediction Using ML
- 3.7 Transfer Learning for Reaction Chemistry
- 4 Chemistry Integration and Acceleration
- 5 Conclusions
- References
- Deep Convolutional Neural Networks for Subgrid-Scale Flame Wrinkling Modeling
- 1 Introduction
- 2 Wrinkling Models
- 3 Convolutional Neural Networks
- 3.1 Artificial Neural Networks
- 3.2 Convolutional Layers
- 3.3 From Segmentation to Predicting Physical Fields with CNNs
- 4 Training CNNs to Model Flame Wrinkling
- 4.1 Data Preparation
- 4.2 Building and Analyzing the U-Net
- 4.3 A Priori Validation
- 5 Discussion
- 6 Conclusion
- References
- Machine Learning Strategy for Subgrid Modeling of Turbulent Combustion Using Linear Eddy Mixing Based Tabulation
- 1 Introduction
- 2 ML for Modeling of Turbulent Combustion
- 2.1 ANN Model for Chemistry
- 2.2 LES of Turbulent Combustion Using ANN
- 3 Mathematical Formulation with ANN
- 3.1 Governing Equations and Subgrid Models.
- 3.2 ANN Based Modeling
- 4 Example Applications
- 4.1 Premixed Flame Turbulence
- 4.2 Non-premixed Temporally Evolving Jet Flame
- 4.3 SPRF Combustor
- 4.4 Cavity Strut Flame-Holder for Supersonic Combustion
- 5 Limitations of Past Studies
- 6 Summary and Outlook
- References
- On the Use of Machine Learning for Subgrid Scale Filtered Density Function Modelling in Large Eddy Simulations of Combustion Systems
- 1 Introduction
- 2 FDF Modelling
- 3 DNS Data Extraction and Manipulation
- 3.1 Low-Swirl Premixed Flame
- 3.2 MILD Combustion
- 3.3 Spray Combustion
- 4 Deep Neural Networks for Subgrid-Scale FDFs
- 4.1 Low-Swirl Premixed Flame
- 4.2 MILD Combustion
- 4.3 Spray Flame
- 5 Main Results
- 5.1 FDF Predictions and Generalisation
- 5.2 Reaction Rate Predictions
- 6 Conclusions and Prospects
- References
- Reduced-Order Modeling of Reacting Flows Using Data-Driven Approaches
- 1 Introduction
- 2 Governing Equations for Multicomponent Mixtures
- 3 Obtaining Data Matrices for Data-Driven Approaches
- 4 Reduced-Order Modeling
- 4.1 Data Preprocessing
- 4.2 Reducing the Number of Governing Equations
- 4.3 Low-Dimensional Manifold Topology
- 4.4 Nonlinear Regression
- 5 Applications of the Principal Component Transport in Combustion Simulations
- 5.1 A Priori Validations in a Zero-Dimensional Reactor
- 5.2 A Posteriori Validations on Sandia Flame D and F
- 6 Conclusions
- References
- AI Super-Resolution: Application to Turbulence and Combustion
- 1 Introduction
- 2 PIESRGAN
- 2.1 Architecture
- 2.2 Algorithm
- 2.3 Implementation Details
- 3 Application to Turbulence
- 3.1 Case Description
- 3.2 A Priori Results
- 3.3 A Posteriori Results
- 3.4 Discussion
- 4 Application to Reactive Sprays
- 4.1 Case Description
- 4.2 Results
- 4.3 Discussion
- 5 Application to Premixed Combustion.
- 5.1 Case Description
- 5.2 A Priori Results
- 5.3 A Posteriori Results
- 5.4 Discussion
- 6 Application to Non-premixed Combustion
- 6.1 Case Description
- 6.2 A Priori Results
- 6.3 A Posteriori Results
- 6.4 Discussion
- 7 Conclusions
- References
- Machine Learning for Thermoacoustics
- 1 Introduction
- 1.1 The Physical Mechanism Driving Thermoacoustic Instability
- 1.2 The Extreme Sensitivity of Thermoacoustic Systems
- 1.3 The Opportunity for Data-Driven Methods in Thermoacoustics
- 2 Physics-Based Bayesian Inference Applied to a Complete System
- 2.1 Laplace's Method
- 2.2 Accelerating Laplace's Method with Adjoint Methods
- 2.3 Applying Laplace's Method to a Complete Thermoacoustic System
- 3 Physics-Based Statistical Inference Applied to a Flame
- 3.1 Assimilating Experimental Data with an Ensemble Kalman Filter
- 3.2 Assimilating with a Bayesian Neural Network Ensemble
- 4 Identifying Precursors to Thermoacoustic Instability with BayNNEs
- 4.1 Laboratory Combustor
- 4.2 Intermediate Pressure Industrial Fuel Spray Nozzle
- 4.3 Full Scale Aeroplane Engine
- 5 Conclusion
- References
- Summary
- Index.