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.