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

Bibliographic Details
Main Author: Swaminathan, Nedunchezhian.
Other Authors: Parente, Alessandro.
Format: eBook
Language:English
Published: Cham : Springer International Publishing AG, 2023.
Edition:1st ed.
Series:Lecture Notes in Energy Series
Subjects:
Electronic books.
Online Access:Click to View
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.

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