Machine Learning and Its Application to Reacting Flows : : ML and Combustion.

• Superior document: Lecture Notes in Energy Series ; v.44
• Author: Swaminathan, Nedunchezhian.
• TeilnehmendeR: Parente, Alessandro.
• 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
• Physical Description: 1 online resource (353 pages)

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.