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Contents

Acknowledgments i

Summary iii

Contents viii

List of Figures xv

List of Tables xviii

Nomenclature xix

1. Introduction 1

1.1. The importance of scale-bridging modeling . . . . 1

1.2. Case studies . . . . 4

1.2.1. Thermal conversion of coal . . . . 4

1.2.2. NO

x

formation in combustion systems . . . . 9

1.3. Objective of the Thesis . . . 15

2. Methodology 19 2.1. Validation and Uncertainty Quantification . . . 19

2.2. Design of Experiments . . . 23

2.3. Surrogate modeling . . . 24

2.4. Bound-to-Bound Data Collaboration . . . 26

2.5. Gaussian Process for Regression . . . 28

3. A reduced-order model of coal devolatilization 33 3.1. Modeling of coal devolatilization . . . 34

3.2. Benchmark assessment of devolatilization one-step models . . . 38

3.2.1. Experimental data . . . 39

3.2.2. Derivation of SFOR kinetic parameters . . . 42

3.2.3. Simulation settings . . . 44

3.2.4. Results and discussion . . . 46

3.3. A novel yield model of coal devolatilization . . . 52

3.3.1. Experimental data . . . 55

3.3.2. Numerical settings . . . 55

3.3.3. Preliminary CFD simulations . . . 55

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3.3.4. Model construction procedure . . . 58

3.3.5. Results and discussion . . . 62

3.3.6. Final validation . . . 68

3.4. Concluding remarks . . . 69

4. A reduced-order model of char combustion 73 4.1. Modeling of char conversion processes . . . 74

4.2. V/UQ workflow . . . 80

4.2.1. Experimental data . . . 81

4.2.2. Instrument modeling . . . 83

4.2.3. Char combustion modeling . . . 86

4.3. Results and discussion . . . 92

4.3.1. Sensitivity analysis . . . 97

4.3.2. Surrogate modeling challenges . . . 101

4.3.3. Consistency . . . 106

4.4. Concluding remarks . . . 118

5. Scale-bridging modeling for NO

x

formation 121 5.1. Modeling of NO

x

emissions in CFD simulations . . . 123

5.2. Eddy Dissipation Concept . . . 126

5.3. Partially Stirred Reactor . . . 127

5.4. Experimental data . . . 131

5.5. Numerical setup . . . 132

5.6. Results and discussion . . . 135

5.7. Concluding remarks . . . 149

6. Conclusions 153

A. Sandia Experimental Dataset 157

B. Bayesian Linear Regression 163

Bibliography 167

List of publications 185

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