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Università di Pisa

Scuola di Dottorato in Ingegneria "Leonardo da Vinci"

Corso di Dottorato di Ricerca in

Ingegneria Chimica e dei Materiali

Tesi di Dottorato di Ricerca

Experimental and Numerical Investigation

of Advanced Systems forHydrogen-based Fuel Combustion

Autore:

Alessandro Parente

Relatori:

Prof. Ing. Leonardo Tognotti

Dott. Ing. Chiara Galletti

Anno 2008


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Università di Pisa

Scuola di Dottorato in Ingegneria "Leonardo da Vinci"

Corso di Dottorato di Ricerca in

Ingegneria Chimica e dei Materiali

Tesi di Dottorato di Ricerca

Sperimentazione e Modellazione

di Sistemi Avanzati per laCombustione di Idrogeno e sue Miscele

Autore:

Alessandro Parente

Relatori:

Prof. Ing. Leonardo Tognotti

Dott. Ing. Chiara Galletti

Anno 2008


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Ad Aurora

Il est très simple: on ne voit bien qu’avec le cœur.

L’essentiel est invisible pour les yeux.(Le Petit Prince - Antoine de Saint-Exupéry)


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Summary

The hydrogen economy has received considerable attention during the last years,in the academic, industrial and political fields, for its potential to deal withthe urgent issues related to the world energy scenario. Hydrogen can be pro-duced directly from all primary energy sources, allowing fuel diversification

and energy independence. In particular, hydrogen containing fuels can be ob-tained from the thermochemical conversion of coal and renewable sources suchas biomasses. The combination of the gasification technologies with carbon cap-ture and storage (CCS) is particularly attracting, to increase H2 purity whilereducing greenhouse gas emissions, i.e. CO2.

However, numerous challenges related to production, distribution and enduse still need to be faced for H2 to become an energy carrier. For example,hydrogen purity is a major issue in fuel cells, as impurities can adversely affectperformances and durability. Fuel composition does not represent a priori a

concern for combustion systems; however, H2 properties may negatively affectconventional combustion systems, leading to stability problems and large NOxemissions. Therefore, efforts must be spent to develop technologies able to dealwith the complexities of hydrogen containing mixtures. To this purpose, hydro-gen/natural gas fuels represent a more realistic alternative to pure hydrogen ina short term perspective, as they provide a ready alternative to pure fossil fuelsand retain the H2 potentials from the point of view of greenhouse gas reductionand efficiency increase.

The present Thesis reports numerical and experimental investigations of

advanced systems for hydrogen-based fuel combustion. In particular, atten-tion is devoted to a novel combustion technology, named flameless combustion,which allows to control NOx emissions while ensuring very high combustionand thermal efficiencies. Flameless combustion is based on the modificationof the traditional flame structure: the system is driven towards homogeneous(temperature and species) conditions, thus allowing to smooth the effect of the oxidation process on the temperature distribution. Such effect is certainlybeneficial for controlling NOx formation and shows potentials for limiting thereactivity of hydrogen-based fuels.

Three different case studies have been taken into account. Two of them aresemi-industrial systems, both installed at ENEL Ricerca facilities of Livorno,Italy: a self recuperative burner used in the steel industry for heating appli-

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cations and a micro-CHP unit for the distributed cogeneration of heat andpower. The third system is a lab-scale burner, developed at the Politecnicodi Milano. An integrated approach, based on both numerical modeling andexperiments, has been employed to assess the feasibility of flameless combus-tion with hydrogen-enriched fuels, being the investigated devices designed forburning natural gas.

Recognizing the complexity of the aforementioned systems, a hierarchicalapproach is proposed to address the different chemical and physical processeswhich are involved in the overall operations. In particular, from a modelingprospective, the proper representation of turbulence-chemistry interactions isfundamental to correctly capture the principal features of the combustion pro-cess. Therefore, a fundamental study on turbulence-chemistry interactions inturbulent reacting flows has been carried out, to determine the modeling ingre-dients required for an accurate description of the flameless combustion regime.A novel methodology based on Principal Component Analysis is presented forthe identification of the parameters controlling the evolution of a reacting sys-tem and for the development of optimal combustion models. To this purpose,high fidelity experimental and numerical data, available in the literature forreference systems, have been used.

The results obtained from such fundamental activity have supported thenumerical modeling of the burners for hydrogen-enriched fuel combustion. Themajor focus of the numerical simulation is represented by the choice of the com-bustion model and kinetic mechanism and the sensitivity of the final predictions

on such choices. It is known that flameless combustion relies on a competitionbetween chemistry and turbulent mixing, ensured by either exhaust recircu-lation or fuel dilution. Therefore, turbulence-chemistry interactions requirespecial treatment and simple combustion models are unsuited, as they cannotcapture the volumetric and diffuse features of the regime. Recent works onthe topic have suggested that only the use of detailed kinetic mechanisms canlead to reliable results; however further investigation is needed, especially whenhydrogen is added to the fuel.

The numerical simulations of the flameless systems have been carried out

with commercial numerical tools; however, state-of-the-art physical modelsfrom the literature have been coupled to the main code solvers, to enhancetheir modeling capabilities. In particular, heat transfer models have been im-plemented to simulate the system interactions with the surroundings and non-conventional NO formation routes, have been introduced to account for NOformation at low temperatures and with H2 in the fuel.

The quantitative validation of the computational approaches has repre-sented a fundamental moment for the present Thesis, to critically identifypotentials and limits in the mathematical models and to plan further improve-

ments and developments. Therefore, the availability of the experimental datahas been crucial for judging the actual predictive capabilities of the numericalsimulations, over a wide range of operating conditions. In particular, the as-

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sessment of the level of agreement between experimental data and numericalsimulations has been based on the Verification and Validation (V&V) method-ology, which allows to determine the uncertainty in the modeling results byestimating both the experimental and numerical errors.

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Contents

List of Figures v

List of Tables xv

Nomenclature xix

1 Advanced systems for hydrogen-based fuel combustion 1

1.1 NOx formation and control in combustion processes . . . . . . . 21.1.1 Homogeneous NOx formation mechanisms . . . . . . . . 3

1.1.1.1 Thermal NO mechanism . . . . . . . . . . . . . 51.1.1.2 N2O intermediate NO mechanism . . . . . . . 71.1.1.3 NNH intermediate NO mechanism . . . . . . . 71.1.1.4 Prompt NO mechanism . . . . . . . . . . . . . 81.1.1.5 Fuel NO mechanism . . . . . . . . . . . . . . . 10

1.1.2 NOx control in combustion . . . . . . . . . . . . . . . . 101.2 Flameless combustion . . . . . . . . . . . . . . . . . . . . . . . 11

1.2.1 Operating principles of flameless combustion . . . . . . 121.2.2 Review of the relevant literature in flameless combustion 15

1.2.2.1 Flameless combustion of hydrogen-enriched fuels 201.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2 Turbulent combustion modeling 23

2.1 Governing equations for turbulent reacting flows . . . . . . . . 252.1.1 Constitutive equations . . . . . . . . . . . . . . . . . . . 26

2.2 Turbulent combustion . . . . . . . . . . . . . . . . . . . . . . . 282.2.1 Reynolds and Favre averaging . . . . . . . . . . . . . . . 292.2.2 Closure of Favre Averaged Navier-Stokes Equations (FANS) 31

2.3 Combustion models for mean reaction rates . . . . . . . . . . . 322.3.1 Eddy Break-up model and Eddy Dissipation Model . . . 342.3.2 Eddy Dissipation Concept . . . . . . . . . . . . . . . . . 35

2.3.2.1 Energy cascade model . . . . . . . . . . . . . . 36

2.3.2.2 Fine structures . . . . . . . . . . . . . . . . . . 382.3.2.3 Reactor model . . . . . . . . . . . . . . . . . . 39

2.3.3 The primitive variable approach . . . . . . . . . . . . . . 41

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2.3.3.1 The mixture fraction variable . . . . . . . . . . 422.3.4 The Burke-Schumann solution . . . . . . . . . . . . . . . 442.3.5 Flamelet model . . . . . . . . . . . . . . . . . . . . . . . 45

2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

3 Methodology and Case Studies 49

3.1 Experimental equipment . . . . . . . . . . . . . . . . . . . . . . 503.2 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523.3 Verification and Validation . . . . . . . . . . . . . . . . . . . . . 53

3.3.1 Validation hierarchies . . . . . . . . . . . . . . . . . . . 553.3.2 Solution Verification . . . . . . . . . . . . . . . . . . . . 563.3.3 Assessing the level of agreement: validation metrics . . . 58

3.3.3.1 Definition of a confidence interval for the exper-imental data . . . . . . . . . . . . . . . . . . . 58

3.3.3.2 Validation metrics based on confidence intervals 593.3.3.3 Global metrics . . . . . . . . . . . . . . . . . . 61

3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

4 Principal Components Analysis for turbulence-chemistry inter-action modeling 63

4.1 Definition and derivation of Principal Components . . . . . . . 654.2 Sample PCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

4.2.1 Optimal properties of the PCA reduction . . . . . . . . 68

4.2.2 Data preprocessing: centering and scaling . . . . . . . . 694.2.2.1 Outlier detection and removal with PCA . . . 71

4.2.3 Choosing a subset of Principal Components or Variables 744.2.3.1 Cumulative percentage of total variance . . . . 744.2.3.2 Variance of Principal Components . . . . . . . 754.2.3.3 Broken Stick Model . . . . . . . . . . . . . . . 754.2.3.4 Scree plot . . . . . . . . . . . . . . . . . . . . . 764.2.3.5 Choosing a subset of Variables . . . . . . . . . 76

4.2.4 Interpretation of principal components . . . . . . . . . . 79

4.3 Local Principal Components Analysis . . . . . . . . . . . . . . . 814.4 Data sets for model validation . . . . . . . . . . . . . . . . . . . 84

4.4.1 Experimental data . . . . . . . . . . . . . . . . . . . . . 844.4.2 Numerical data . . . . . . . . . . . . . . . . . . . . . . . 86

4.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 864.5.1 PCA for the identification of low-dimensional manifolds 86

4.5.1.1 GPCA of experimental data sets . . . . . . . . 874.5.1.2 LPCA of experimental and numerical data sets 106

4.6 Development of a PCA based combustion model . . . . . . . . 122

4.6.1 Transport equations for the PCs . . . . . . . . . . . . . 1244.6.2 PCA Modeling Approach . . . . . . . . . . . . . . . . . 1254.6.3 Parametrizing the State Variables . . . . . . . . . . . . . 126

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4.6.4 Parametrizing Source Terms . . . . . . . . . . . . . . . . 1294.6.5 Global versus Semi-Local PCA . . . . . . . . . . . . . . 131

4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

5 Experimental and numerical investigation of a self-recuperative flameless burner 135

5.1 Description of the burner . . . . . . . . . . . . . . . . . . . . . 1355.1.1 Experimental campaign n.1 . . . . . . . . . . . . . . . . 140

5.1.1.1 Extrapolation of NO emissions to steady state 1425.1.2 Experimental campaign n. 2 . . . . . . . . . . . . . . . . 146

5.2 Numerical modeling of the self-recuperative burner . . . . . . . 1465.2.1 Modeling approach for experimental campaign n.1 . . . 149

5.2.1.1 Flameless combustion of methane . . . . . . . 149

5.2.1.2 Flameless combustion of methane-hydrogen mix-tures . . . . . . . . . . . . . . . . . . . . . . . . 154

5.2.2 Modeling approach for experimental campaign n.2 . . . 1585.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

5.3.1 Experimental campaign n.1 . . . . . . . . . . . . . . . . 1615.3.1.1 Flameless combustion of methane . . . . . . . 1625.3.1.2 Flameless oxidation of methane-hydrogen mix-

tures . . . . . . . . . . . . . . . . . . . . . . . . 1695.3.2 Experimental campaign n.2 . . . . . . . . . . . . . . . . 183

5.3.2.1 CFD analysis . . . . . . . . . . . . . . . . . . . 1865.3.2.2 Kinetic post-processing . . . . . . . . . . . . . 194

5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

6 Experimental and numerical investigation of the flameless com-bustion of hydrogen-enriched fuels in a lab-scale burner 197

6.1 Description of the burner and experimental campaigns . . . . . 1976.2 Numerical modeling of the lab-scale burner . . . . . . . . . . . 200

6.2.1 Computational domain and grid . . . . . . . . . . . . . 2006.2.2 Boundary conditions . . . . . . . . . . . . . . . . . . . . 2036.2.3 Physical models . . . . . . . . . . . . . . . . . . . . . . . 203

6.2.3.1 Turbulence/chemistry interactions and kineticmechanisms . . . . . . . . . . . . . . . . . . . . 203

6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2046.3.1 Flow-field characterization . . . . . . . . . . . . . . . . . 2046.3.2 Effect of turbulence/chemistry interaction models . . . . 2056.3.3 Effect of dilution on the combustion regime . . . . . . . 209

6.3.4 Model validation: comparison of simulations and experi-ments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212

6.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216

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7 Experimental and numerical investigation of a micro-CHP flame-less unit 2197.1 Distributed combined heat and power generation . . . . . . . . 2207.2 Description of the system . . . . . . . . . . . . . . . . . . . . . 220

7.2.1 Stirling engine . . . . . . . . . . . . . . . . . . . . . . . 2247.3 Experimental campaign . . . . . . . . . . . . . . . . . . . . . . 2267.4 Numerical modeling of the micro-CHP flameless unit . . . . . . 229

7.4.1 Computational domain and grid . . . . . . . . . . . . . 2297.4.2 Boundary conditions . . . . . . . . . . . . . . . . . . . . 2317.4.3 Physical models . . . . . . . . . . . . . . . . . . . . . . . 234

7.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2367.5.1 Flow field and main operating features . . . . . . . . . . 2367.5.2 Effect of the kinetic mechanism on the flame structure . 2387.5.3 Effect of H2 addition to the fuel . . . . . . . . . . . . . . 2417.5.4 Effect of burner load . . . . . . . . . . . . . . . . . . . . 2427.5.5 Model validation . . . . . . . . . . . . . . . . . . . . . . 246

7.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249

Concluding remarks 251

Bibliography 253

List of Publications 265

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List of Figures

1.1 Combustion of hydrogen containing fuels: issues and solutions. 21.2 Flammable range for methane-air mixture as a function of the

recirculation degree, kv[33]. . . . . . . . . . . . . . . . . . . . . 13

1.3 Stability diagram for methane-air combustion, as a function of the recirculation degree and operating temperature [2]. . . . . . 14

1.4 Idealized flameless oxidation process [2]. . . . . . . . . . . . . . 141.5 High velocity burner for operation in flame and flameless regime. 151.6 Experimental apparatus used by Wünning and Wünning [2]. . . 161.7 FLOX® recuperative burner [2]. . . . . . . . . . . . . . . . . . 161.8 Time resolved temperature measurement for flame, unstable and

flameless regime [2]. . . . . . . . . . . . . . . . . . . . . . . . . 171.9 Temperature and OH radical concentration in flame and flame-

less regimes [35]. . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.1 Verification and Validation methodology [1]. . . . . . . . . . . . 242.2 Energy cascade model [65, 66, 71]. . . . . . . . . . . . . . . . . 372.3 Mass flow through a cylinder. . . . . . . . . . . . . . . . . . . . 392.4 PSR model for the fine structures. . . . . . . . . . . . . . . . . 402.5 Burke-Schumann solution as a function of the mixture fraction. 45

3.1 Lab-scale burner (a), FLOX® burner with air preheater (b) andSOLO Stirling 161 Cogeneration Unit (c). . . . . . . . . . . . . 51

3.2 Integrated CFD–experimental approach. . . . . . . . . . . . . . 523.3 Verification and Validation: objectives to quantify and tools. . . 533.4 Two phases of validation activities . . . . . . . . . . . . . . . . 543.5 V&V hierarchy for a flameless system. . . . . . . . . . . . . . . 56

4.1 Principal components scores with (a) and without (b) outliers. . 734.2 Eigenvalues size with (a) and without (b) outliers. . . . . . . . 734.3 Size reduction process with PCA. . . . . . . . . . . . . . . . . . 744.4 Schematic illustration of the VARIMAX rotation [110]. . . . . . 81

4.5 Schematic illustration of the VQPCA algorithm [92] . . . . . . . 834.6 Schematic illustration of the FPCA algorithm [92] for a CO/H2

flame [112].. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

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4.7 Scree-graph and histograms of the q largest eigenvalues for the jet flame data set, preprocessed with auto scaling. . . . . . . . . 88

4.8 Parity plots of temperature (a), H2O (b), H2 (c), CO (d), OH(e) and NO (f) mass fractions illustrating the GPCA (q = 2)

reduction of the jet flame data set. Scaling criterion adopted:auto scaling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 914.9 Parity plots of temperature (a), H2O (b), H2 (c), CO (d), OH

(e) and NO (f) mass fractions illustrating the GPCA (q = 3)reduction of the jet flame data set. Scaling criterion adopted:auto scaling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

4.10 Scree-graph and histograms of the q largest eigenvalues for FlameD (a), Flame F (b) and JHC (c). Scaling criterion adopted: autoscaling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

4.11 Parity plots of temperature (a), H2O (b), CO (c), H2 (d), OH(e) and NO (f) mass fractions illustrating the GPCA (q = 3)reduction of Flame F. Scaling criterion adopted: auto scaling. 98

4.12 Parity plots of temperature (a), H2O (b), CO (c), H2 (d), OH(e) and NO (f) mass fractions illustrating the GPCA (q = 4)reduction of Flame F. Scaling criterion adopted: auto scaling. 99

4.13 Parity plots of temperature (a), H2O (b), CO (c), H2 (d), OH (e)and NO (f) mass fractions illustrating the GPCA (q = 4) reduc-tion of JHC data set. Scaling criterion adopted: auto scaling.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

4.14 Parity plots of temperature (a), H2O (b), H2 (c), CO (d), OH (e)and NO (f) mass fractions illustrating the GPCA (q = 5) reduc-tion of JHC data set. Scaling criterion adopted: auto scaling.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

4.15 Scree-graph and histograms of the q largest eigenvalues for the jet flame data set, preprocessed with range (a), vast (b), level(c) and max (d) scaling. . . . . . . . . . . . . . . . . . . . . . . 107

4.16 Parity plots of temperature (a), H2O (b), CO (c), H2 (d), OH (e)and NO (f) mass fractions illustrating the VQPCA (q = 2, k =

8) reduction of the jet flame data set. GSRE,n = 0.08. . . . . . 1094.17 Parity plots of temperature (a), H2O (b), CO (c), H2 (d), OH (e)

and NO (f) mass fractions illustrating the VQPCA (q = 3, k =8) reduction of Flame F data set. GSRE,n = 0.08. . . . . . . . . 110

4.18 Parity plots of temperature (a), H2O (b), CO (c), H2 (d), OH (e)and NO (f) mass fractions illustrating the VQPCA (q = 3, k =6) reduction of JHC data set. GSRE,n = 0.08. . . . . . . . . . . 111

4.19 Original (a) and conditioned (b) temperature field for DNS2 dataset at time step t = 1.5e

−03 s. . . . . . . . . . . . . . . . . . . 113

4.20 Contour plots of original and recovered temperature (a, a’) andOH mass fraction (b, b’) distribution for DNS1. VQPCA reduc-tion with q = 3 and k = 8. GSRE,n = 0.01. . . . . . . . . . . . 115

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4.21 Parity plots of original and recovered temperature (a, a’) and OHmass fraction (b, b’) distribution for DNS1. VQPCA reductionwith q = 3 and k = 8. GSRE,n = 0.04. . . . . . . . . . . . . . . 116

4.22 Contour plots of original (a, b) and recovered (a’, b’) temper-

ature distribution for DNS2, at two different time steps, i.e.t = 1.5e − 03 s (a, a’) and t = 2.0e − 03 s (b, b’). VQPCAreduction with q = 4 and k = 8. GSRE,n = 0.04. . . . . . . . . 117

4.23 Parity plots of temperature (a) and OH (b) mass fraction illus-trating the VQPCA (q = 4, k = 8) reduction of DNS2 data set.GSRE,n = 0.04. . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

4.24 Values of GSRE,n as a function of the number of clusters, k, andretained PCs, q , for the jet flame , Flame F and JHC data sets. 118

4.25 Values of GSRE,n as a function of the number of clusters, k, and

retained PCs, q , for the DNS1 and DNS2 data sets. . . . . . . . 1194.26 Temperature as a function of mixture fraction in the two clustersselected by VQPCA for the jet flame. q = 2 and GSRE,n = 0.21. 119

4.27 Temperature as a function of mixture fraction in the two clustersselected by VQPCA for Flame F. q = 3 and GSRE,n = 0.21. . . 120

4.28 Temperature as a function of mixture fraction in the two clustersselected by VQPCA for the JHC data set. q = 3 and GSRE,n =0.21. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

4.29 Parity plots of temperature (a), H2O (b), CO (c), H2 (d), OH (e)

and NO (f) mass fractions illustrating the VQPCA (q = 3, k =6) reduction of JHC data set. GSRE,n = 0.08. . . . . . . . . . . 123

4.30 CPU time associated with the FPCA and VQPCA reductions.asa function of the number of clusters, k, and retained PCs, q , forthe experimental (a) and numerical (b) data sets. . . . . . . . . 123

4.31 Parametrization of temperature at f st by χ (a) and z1 (b) forcase B. Solid lines are the doubly-conditional mean temperature.R2 is calculated from Eq. (4.54). . . . . . . . . . . . . . . . . . 127

4.32 Parametrization of OH mass fraction at f st by χ (a) and z1 (b)

for case B. Solid lines are the doubly-conditional mean temper-ature. R2 is calculated from Eq. (4.54). . . . . . . . . . . . . . 129

4.33 Parametrization of ω̇z1 at f st by z1 for case B. Solid line: doubly-conditional mean value of ω̇z1 . R

2 is calculated from Eq. (4.54). 130

5.1 Longitudinal section of the FLOX®burner. . . . . . . . . . . . 1365.2 3D view of the combustion chamber. . . . . . . . . . . . . . . . 1375.3 Outer surface of the air preheater. . . . . . . . . . . . . . . . . 1375.4 Inconel® shield (a) and outer insulation layer (b). . . . . . . . 138

5.5 Water heat exchanger during the first (a) and second (b) exper-imental campaigns. . . . . . . . . . . . . . . . . . . . . . . . . . 139

5.6 Outer surface of the air preheater. . . . . . . . . . . . . . . . . 139

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5.7 NO concentration as a function of time during a typical run of the first experimental campaign. . . . . . . . . . . . . . . . . . 140

5.8 O2 concentration in the flue gases with NG (a) and NG-H2 mix-tures (b), for the experimental campaign n. 1 on the FLOX®

burner. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1425.9 NO emissions for NG and NG-H2 mixtures for the experimentalcampaign n. 1 on FLOX® burner. . . . . . . . . . . . . . . . . 143

5.10 Raw data, experimental mean and standard deviation for theNO emissions vs. time. Experimental run n. 5, Table 5.1. . . . 144

5.11 Experimental mean plus 98% confidence intervals and compari-son with the mathematical models for the NO emissions vs. time.Experimental run n. 5, Table 5.11. . . . . . . . . . . . . . . . . 145

5.12 Estimated error plus 98% confidence interval showing the level

of agreement between experimental measurements and mathe-matical models. Experimental run n. 5, Table 5.12. . . . . . . . 1455.13 NO emissions as a function of the H2 load (a) and O2 concentra-

tion in the flue gases (b). Experimental campaign n. 2 on theFLOX® burner (Table 5.3). . . . . . . . . . . . . . . . . . . . . 148

5.14 Temperature measurements on the radiant tube (a), on the in-ternal (b) and external (c) surface of the Inconel® shield and onthe internal surface of the water heat exchanger (d). H2 ther-mal input = 30%. Experimental campaign n. 2 on the FLOX®

burner (Table 5.3). . . . . . . . . . . . . . . . . . . . . . . . . . 1485.15 3D (a) and axisymmetric (b) grids . . . . . . . . . . . . . . . . 1515.16 Heat exchange between the coaxial cylindrical shields represent-

ing the radiant tube, the internal and external insulation layersof the Inconel® shield and the water heat exchanger. . . . . . . 152

5.17 Grid Convergence Index (GCI) as a function of the coarseningfactor, r, for the set of 2D grids investigated in the numericalsimulation of the FLOX® burner. Assumed order of convergence

p = 1 [81]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156

5.18 Computational mesh for FLUENT simulations. . . . . . . . . . 1565.19 Estimated error, δ RE , for the profiles of axial velocity obtained

at x = 0.25 m (a), x = 0.35 m (b) and x = 0.45 m (c) with theselected grid. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

5.20 Conceptual scheme of the methodology adopted for the numerical sim-ulation of the experimental campaign n. 2 on the FLOX burner ®. . 161

5.21 Measured and computed temperature profiles along the radianttube for two different burner loads, i.e. Q̇in = 8.5 kW andQ̇in = 9.5 kW. . . . . . . . . . . . . . . . . . . . . . . . . . . . 162

5.22 Flow pattern inside the combustion chamber, determined by thehigh-momentum air jet issuing into the flame tube. Run 1, Table5.4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163

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5.37 Comparison of predicted and measured NO emissions in the fluegases. Runs 5, 20, 22 and 23 in Table 5.4): . . . . . . . . . . . . 183

5.38 Effect of molecular diffusion on the radial profiles of H2 massfraction (a, b), CO2 mass fraction (c, d) and temperature (e, f)

at different axial locations along the burner axis. Combustionmodel and kinetic mechanism: EDC with DRM- 19. Runs 5, 20,1m and 2m, Table 5.4. . . . . . . . . . . . . . . . . . . . . . . . 184

5.39 Laminar to turbulent H2 diffusion coefficient with increasing H2mass fraction in the fuel: 0% (a), 5.5% (b), 10% (c) and 20%(d). Runs 5, 20, 1m and 2m, Table 5.4. . . . . . . . . . . . . . . 185

5.40 Temperature field in the burner fed with increasing H2 in thefuel. H2 content corresponding to 10% (a), 20% (b), 30% (c),50% (d), 60% (e) and 70% (f) of the total burner thermal input,10 kW. Kinetic mechanism: KEE-58. Runs 4-9, Table 5.3. . . . 186

5.41 OH radical mass fraction distribution in the burner fed with in-creasing H2 in the fuel. H2 content corresponding to 10% (a),20% (b), 30% (c), 50% (d), 60% (e) and 70% (f) of the to-tal burner thermal input, 10 kW. Kinetic mechanism: KEE-58.Runs 4-9, Table 5.3. . . . . . . . . . . . . . . . . . . . . . . . . 187

5.42 CH2O radical mass fraction distribution in the burner fed withincreasing H2 in the fuel. H2 content corresponding to 10% (a),

20% (b), 30% (c), 50% (d), 60% (e) and 70% (f) of the to-tal burner thermal input, 10 kW. Kinetic mechanism: KEE-58.Runs 4-9, Table 5.3. . . . . . . . . . . . . . . . . . . . . . . . . 187

5.43 Sample mean and standard deviation for the average radianttube temperature (a) and NO concentration in the flue gases(b), when increasing the hydrogen thermal input from 10% to70%. Runs 4-9, Table 5.3. The sample mean and the standarddeviations are plotted on different scales. . . . . . . . . . . . . . 188

5.44 95% confidence intervals for the average temperature of the ra-diant tube and comparison with the numerical results (a). 95%confidence interval for the true error and estimated errors for theaverage temperature of the radiant tube (b). Runs 4-9, Table 5.3. 189

5.45 95% confidence intervals for the average NO emission from theburner and comparison with the numerical results (a). 95% con-fidence intervals for the true error and estimated errors for theaverage NO emissions from the burner (b). Runs 4-9, Table 5.3 190

5.46 Radial profiles of temperature, H, O and OH radical mass frac-

tions at different axial locations along the burner, as predictedby the KEE-58 and DRM-19 kinetic mechanisms, for a hydrogenthermal input of 20%. Run 4, Table 5.3 . . . . . . . . . . . . . 192

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5.47 Radial profiles of temperature, H, O and OH radical mass frac-tions at different axial locations along the burner, as predictedby the KEE-58 and DRM-19 kinetic mechanisms, for a hydrogenthermal input of 70%. Run 9, Table 5.3 . . . . . . . . . . . . . 193

5.48 Relative importance of NO formation routes as a function of hydrogen thermal input for the complete NO mechanism . . . . . 193

6.1 Laboratory scale experimental equipment. Sketch and main di-mensions [31]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198

6.2 Sketch of the exhaust gas outlet sections. . . . . . . . . . . . . . 199

6.3 3D computational domain and grid. . . . . . . . . . . . . . . . . 202

6.4 2D computational domain and grid. . . . . . . . . . . . . . . . . 202

6.5 Velocity streamlines. Run 6, Table 6.1. . . . . . . . . . . . . . . 205

6.6 Temperature distribution in the burner fed with CH4 (Run 1,Table 6.1), predicted by ED/FR with global chemistry (a), EDCwith global chemistry (b), EDC with KEE-58 (c), DRM-19 (d)and GRI-3.0 (e). . . . . . . . . . . . . . . . . . . . . . . . . . . 206

6.7 Radial profiles of temperature at different axial locations alongthe axis, i.e. x = 0.06 m (a) and x = 0.10 m (b), predictedby different combustion models and kinetic mechanisms. Run 1,Table 6.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207

6.8 Temperature distribution in the burner fed with a mixture con-taining 60% by vol. of H2 (Run 6, Table 6.1), predicted byED/FR with global chemistry (a), EDC with global chemistry(b), EDC with KEE-58 (c), DRM-19 (d) and GRI-3.0 (e). . . . 208

6.9 Radial profiles of temperature at different axial locations alongthe axis, i.e. x = 0.06 m (a) and x = 0.10 m (b), predictedby different combustion models and kinetic mechanisms. Run 6,Table 6.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209

6.10 Temperature distribution in the combustion chamber fed with

CH4/H2 (60% H2 by vol.), predicted with EDC/GRI-3.0 for dif-ferent dilution ratios, kv: 7.3 (a), 10.6 (b), 11.2 (c), 13.2 (d) and19.1 (e). Runs 2-6, Table 6.1. . . . . . . . . . . . . . . . . . . . 210

6.11 Radial profiles of temperature (a, b) and OH radical mass frac-tion (a’, b’), at different axial locations along the axis, i.e. x =0.06 m (a, a’) and x = 0.10 m (b, b’), predicted with EDC/GRI-3.0 for different dilution ratios, kv: 7.3 (FLAME), 10.6 (TR1),11.2 (TR2), 13.2 (TR3) and 19.1 (MILD). Runs 2-6, Table 6.1. 211

6.12 OH radical distribution in the combustion chamber fed with

CH4/H2 (60% H2 by vol.), predicted with EDC/GRI-3.0 for dif-ferent dilution ratios, kv: 10.6 (a), 11.2 (b), 13.2 (c) and 19.1(d). Runs 3-6, Table 6.1. . . . . . . . . . . . . . . . . . . . . . . 212

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6.13 CH2O mass fraction distribution in the combustion chamber fedwith CH4/H2 (60% H2 by vol.), predicted with EDC/GRI-3.0for different dilution ratios, kv: 10.6 (a), 11.2 (b), 13.2 (c) and19.1 (d). Runs 3-6, Table 6.1. . . . . . . . . . . . . . . . . . . . 213

6.14 Comparison of measured and predicted temperatures inside thecombustion chamber, T middle, x = 0.18 m and r = 0.014 m, Runs2-6, Table 6.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . 213

6.15 NO emissions in the flue gases obtained with CH4/H2 fuel (60%H2 by vol.) for different dilution ratios, kv. Temperature andspecies fields predicted by different combustion models and ki-netic mechanisms. Runs 2-6, Table 6.1. . . . . . . . . . . . . . . 216

6.16 Contribution of different formation routes to total NO emissionswith CH4/H2 fuel (60% H2 by vol.), for different dilution ratios,kv

. Temperature and species fields predicted by EDC with GRI-3.0. Runs 2-6, Table 6.1. . . . . . . . . . . . . . . . . . . . . . . 217

7.1 SOLO Stirling 161 Cogeneration Unit. . . . . . . . . . . . . . . 2217.2 Flame tube (a) and finned heat exchanger (b). . . . . . . . . . 2227.3 Air (a) and fuel (b) feeding system. . . . . . . . . . . . . . . . . 2237.4 Schematic diagram of flow streams and heat exchanges. . . . . 2247.5 Thermocouples on the heat exchanger (a) and on the top of the

expansion cylinder (b). . . . . . . . . . . . . . . . . . . . . . . . 2257.6 Functional diagram of Stirling engine. . . . . . . . . . . . . . . 225

7.7 Sketch of the combustion chamber and details about the feedingsystem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230

7.8 Computational domain and grid with details of the injection noz-zles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231

7.9 Grid Convergence Index (GCI) as a function of the coarseningfactor, r, for the set of 3D grids investigated in the numericalsimulation of the FLOX® burner. Assumed order of convergence

p = 1 [81]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2327.10 Estimated error, δ RE , for the profiles of axial velocity obtained

at x = 0.25 m (a), x = 0.35 m (b) and x = 0.45 m (c) with theselected grid. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233

7.11 Control region for the evaluation of the heat transferred to theoperating fluid of the Stirling cycle. . . . . . . . . . . . . . . . . 234

7.12 Angular Section (180 degrees) of the fluid volume correspondingto the flue gases/helium heat exchanger. Run 12, Table 7.2. . . 235

7.13 Flow pattern inside the combustion chamber, determined by thehigh-momentum air jet issuing into the flame tube. Run 8, Table7.2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237

7.14 Temperature distribution in the combustion chamber fed withCH4, predicted by the 4-step (a) and KEE-58 (b) kinetic mech-anisms. P He = 90 bar. Runs 5, Table 7.2. . . . . . . . . . . . . 239

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List of Figures

7.15 Temperature distribution in the combustion chamber fed withCH4/H2 (H2 9% by wt.), predicted by the 4-step (a) and KEE-58 (b) kinetic mechanisms. P He = 90 bar. Runs 8, Table 7.2. . 240

7.16 Radial temperature profiles at different axial locations, i.e. x =

0.075 m (a), x = 0.150 m (b), x = 0.250 m (c) and x = 0.350 m(d), predicted by the 4-step and KEE-58 mechanisms for a hydro-gen content in the fuel equal to 7 and 9% (by wt.), respectively..P He = 90 bar. Runs 7 and 8, Table 7.2. . . . . . . . . . . . . . 241

7.17 Temperature distribution in the combustion chamber fed withCH4/H2 mixtures containing 0 (a), 7 (b) and 9 (c) % (by wt.) of H2, predicted by the KEE-58 kinetic mechanism. P He = 90 bar.Runs 5, 7 and 8, Table 7.2. . . . . . . . . . . . . . . . . . . . . 243

7.18 OH radical mass fraction distribution in the combustion chamberfed with CH4/H2 mixtures containing 0 (a), 7 (b) and 9 (c) %(by wt.) of H2, predicted by the KEE-58 kinetic mechanism.P He = 90 bar. Runs 5, 7 and 8, Table 7.2. . . . . . . . . . . . . 244

7.19 Radial temperature profiles at different axial locations, i.e. x =0.075 m (a), x = 0.150 m (b), x = 0.250 m (c) and x = 0.350 m(d), predicted by the KEE-58 kinetic mechanism for a hydrogencontent in the fuel equal to 0, 7 and 9% (by wt.), respectively..P He = 90, 120 bar. Runs 5, 7-9, 11 and 12, Table 7.2. . . . . . 245

7.20 Temperature distribution in the combustion chamber fed withCH4, predicted by the KEE-58 kinetic mechanisms. P He =120 bar. Runs 9, Table 7.2. . . . . . . . . . . . . . . . . . . . . 246

7.21 Comparison between measured and predicted CO (a) and NO(b) emissions. Experimental data with 95% confidence intervals.Runs 5, 7-9, 11 and 12, Table 7.2. . . . . . . . . . . . . . . . . . 247

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List of Tables

1.1 Reaction parameters for the extended Zeldovich mechanism andfor the NO2 intermediate NO mechanism. Units: mol, cm, s, K. 6

4.1 Covariance matrix for the jet flame data set. Scaling criterionadopted: auto scaling. . . . . . . . . . . . . . . . . . . . . . . . 89

4.2 Total, tq, and individual variance, tq,j , accounted for the jet flamedata set, as a function of the number of retained PCs, q , and thepreprocessing criterion. . . . . . . . . . . . . . . . . . . . . . . . 90

4.3 Covariance matrix for Flame D data set. Scaling criterion adopted:auto scaling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

4.4 Covariance matrix for Flame F data set. Scaling criterion adopted:auto scaling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

4.5 Covariance matrix for JHC data set. Scaling criterion adopted:auto scaling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

4.6 Total, tq, and individual variance, tq,j , accounted for Flame D,F and JHC data sets by the GPCA reduction, as a function of the number of retained PCs, q . . . . . . . . . . . . . . . . . . . 102

4.7 Retained (a) and rotated (b) eigenvectors for the jet flame dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

4.8 Retained (a) and rotated (b) eigenvectors for Flame D data set. 103

4.9 Retained (a) and rotated (b) eigenvectors for Flame F data set. 1034.10 Retained (a) and rotated (b) eigenvectors for JHC data set. . . 1044.11 Principal variables for the jet flame data set, as provided by the

different methods described in Section 4.2.3.5. . . . . . . . . . 1054.12 Principal variables for Flame D, F and the JHC data set. PV

method: MC2 (Section 4.2.3.5). . . . . . . . . . . . . . . . . . 1054.13 Values of GSRE,n associated with the GPCA, VQPCA and FPCA

reconstructions of the jet flame, flame F and JHC data set, as afunction of the number of clusters, k, and retained PCs, q . . . . 107

4.14 Total, tq, and individual variance, tq,j , accounted for by the re-tained PCs for the DNS1 and DNS2 data sets as a function of the number of components, q . . . . . . . . . . . . . . . . . . . . 112

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7.5 Macro indicators of the main operating features of the SOLOStirling CHP unit. Data extracted from the numerical simula-tions carried out with the KEE-58 kinetic mechanism. . . . . . 238

7.6 Relative and global validation metrics for NO and CO measure-

ments at different burner loads, obtained with the 4-step andKEE-58 kinetic mechanisms. Runs 5, 7-9, 11 and 12, Table 7.2. 248

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Nomenclature

a Oxygen reaction order in the one-step promptmechanism

ak Eigenvector corresponding to the kth largest

eigenvalue of S (or Σ)A Pre-exponential factor, reaction dependentA Constant of the Eddy Dissipation ModelA Matrix containing the eigenvectors of S A∗ Exchange surface of the fine structures, m2

bke Number of atoms of element e in a molecule of species k

B Constant of the Eddy Dissipation ModelB Rotation matrix

BC Boundary ConditionsC Confidence level adopted for the definition of the CIsC D1, C D2 Constants in the energy cascade modelC EBU Constant of the Eddy Break-up ModelC µ Constant in the k − modelc p Specific heat at constant pressure, J kg-1 K-1

CAE Computer Aided EngineeringCF D Computational Fluid DynamicsCHP Combined Heat and PowerCI Confidence intervalCM C Conditional Moment Closured Scaling parameter for variable x, variable dependentdet Determinant of a matrixD Diffusion coefficient, m2 s-1

D Matrix of scaling parameters, variable dependentDM Mahalanobis distanceDa Damkohler numberDN S Direct Numerical SimulationDO Discrete Ordinate Radiation ModelE True errorE Expectation operatorE a Activation energy, J kmol-1

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Nomenclature

E Estimated errorEB U Eddy Break-up ModelEDM Eddy Dissipation ModelED/FR Eddy Dissipation Finite Rate Model

f Volume force, m s-2

f Mixture fractionF Model solution of the SRQF ij View factor between shields i and jF s Safety factor for the estimation of the solution

uncertainty, U sverF 0 Exact value of the SRQFANS Favre Averaged Navier StokesF P C A Mixture Fraction Principal Components AnalysisF R Finite Rate ModelGCI Grid Convergence IndexGPCA Global Principal Components AnalysisGRE Global reconstruction errorGSRE Global scaled reconstruction errorh Specific enthalpy, m2 s-2

h Grid spacinght Total specific enthalpy, m2 s-2

h∗ Height of the cylindrically shaped fine structure, mHITAC High Temperature Air CombustionI Identity matrixIC Initial ConditionsIFCM Infinitely Fast Chemistry ModelJHC Jet issuing in a Hot Co-flowk Kinetic energy, m2 s-2

K eq Equilibrium constantkf Forward kinetic constant, reaction dependentkr Backward kinetic constant, reaction dependentkR Recirculation degree

kv Dilution factorKinPP Kinetic post-processinglk kth largest eigenvalue of the sample covariance

matrix S l Mean value of the eigenvalues of the sample

covariance matrix S L Length, mL Diagonal matrix containing the eigenvalues of S in

descending order

Le Lewis numberLEM Linear Eddy ModelLES Large Eddy Simulation

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Nomenclature

LPCA Local Principal Components Analysisṁ Mass flow rate, kg s-1

ṁ∗ Specific mass flow rate across the fine structures, s-1

M Symbol for chemical species˙

M Mass flow across the fine structures, kg s-1

MILD Moderate and Intense Low Oxygen DilutionM M S Method of Manufactured Solutionsn Number of experimental replicates for the definition

of CIsne Total number of chemical elementsN Total number of speciesN G Natural gasODT One Dimensional Turbulence

p Pressure, bar p Convergence order p Number of random variables of X P AH Polycyclic Aromatic HydrocarbonsP C Principal ComponentP CA Principal Components AnalysisP CC Principal Component ClassifierP DE Partial Differential EquationP DF Probability Density FunctionP F Principal featuresP r Prandtl numberP rt Turbulent Prandtl numberP V Principal variablesq Energy flux, J s-1s-2

q Energy dissipation in the energy cascade model, m2

s-3

Q Flow field propertyQ j Rate of progress of reaction j, kg m-3 s-1

Q̇ Heat source, W m-3

Q

Property fluctuationQ

Favre property fluctuationQ Property mean valueQ Favre property mean valuer Radial coordinate, mrh Grid refinement ratioR2 Correlation coefficientRA Aerodynamic recirculation ratioRANS Reynolds Averaged Navier Stokes

Re Reynolds numberRHS Right hand side

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Nomenclature

RSM Reynolds Stress ModelRT E Radiative Transfer Equations Sample standard deviationS Sample covariance matrix

S 22,1 Partial covariance matrix for X (2) given X (1)Sc Schmidt numberSct Turbulent Schmidt numberSLFM Steady Laminar Flamelet ModelSRQ System Response QuantityS V E R Solution Verificationt Time, st α2,,ν 1 − α2 quantile of a t-distribution with ν degrees of

freedomtq

Total variance explained by a q -dimensional subset of PCs

tq,j Variance accounted for each variable by aq -dimensional subset of PCs

tr Trace of a matrixT Temperature, KT Standardized random variableT N F Turbulent Non-premixed Flamesu Velocity, m s-1

U sver Solution verification uncertaintyU DF User Defined FunctionU HC Unburned HydrocarbonV Diffusion velocity, m s-1

V Volume, m3

V Transposed eigenvector matrix AV MAX Quantity maximized by the VARIMAX criterionV &V Verification and ValidationV AST Variable Stability ScalingV QP C A Vector Quantization Principal Components Analysis

w Production of turbulent kinetic energy, m2 s-2W Molecular weight, kg kmol-1

WSGG Weighted Sum of Gray Gases Modelx Axial coordinate, mx Observed mean for random variable xx Random variablex Scaled random variableX Mole fractionX Sample matrix

X (1), X (2) Subsets of X X Vector of sample means

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Nomenclature

X Scaled sample matrixye Sample mean based on n experimentsym Value of the SRQ given by the computational modelY Species mass fraction

zk kth PC of S (orΣ

)Z Matrix of PC scoresẐ Approximation of Z based on a subset of m variables

of X

Greek symbols

α Constant in the Richardson extrapolationβ Coupling function for the definition of the mixturefraction

β Temperature exponentγ ∗ Fine structure mass fractionδ Eigenvalues of S 22,1δ ij Kronecker symbolδ RE Numerical error estimate Turbulent energy dissipation rate, m2 s-3

Error from the PCA reconstruction of X air Air excessλ Thermal conductivity, W m-1 K-1

λ Lagrange multiplierλk kth largest eigenvalue of S (or Σ)µ Dynamic viscosity, kg m-1 s-1

µ True meanµt Turbulent viscosity, kg m-1 s-1

µ Mean of the infinite population X ν Degrees of freedom of the t-distributionρ Density, kg m-3

ρ Canonical correlations between X (1) and X (2)σ Boltzmann constant, J K-1

σ2 j Residual variance in predicting x j from Z Σ Covariance matrix of the infinite population X τ Characteristic time, sτ ∗ Residence time within the fine structures, sτ ij Viscous force tensor, kg m-1 s-2

υ Kinematic viscosity, m2 s-1

υ Net mass stoichiometric coefficientυ

Molar stoichiometric coefficient for forward reaction

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Nomenclature

υ

Molar stoichiometric coefficient for backward reactionφ Lagrange multiplierφ Optimal rotation angle determined with the

VARIMAX criterion

χ Scalar dissipation rateχ1 Probability of coexistence of reactants for thedefinition of χfs

χ2 Degree of heating for the definition of χfsχ3 Lack of reactants for the definition of χfsχfs Reacting fraction of the fine structures, χfs = χ1χ2χ3ω Strain rate, s-1

ω̇k Reaction rate for species k, kg m-3 s-1

Subscripts

1 Fuel stream2 Oxidizer streamA Airc Chemicale Chemical elementem Emitter (referred to the radiant tube surface)E Exhaust gasesF Fueli Direction indexi Observation indexins Insulation (referred to the insulation shields)in Incoming

j Direction index j Reaction index j Variable indexk Species indexm Number of retained variablesO OxidizerP Productsq Dimensionality of the truncated set of PCsst Stoichiometric conditionst Turbulentu Unburnt status

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Nomenclature

Superscripts

Transpose operator

,

, . . . ,n . . . ,∗ Levels in the energy cascade model∗

Fine structure properties1, 2, . . . , n Experiment replications for the definition of CIsk Cluster index

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Chapter 1

Advanced systems for

hydrogen-based fuel combustion

Hydrogen has recently received special attention in the scientific community dueto its promising role as future energy carrier. Hydrogen can be relatively easilystored and its utilization does not cause any local emission of CO2. Moreover,hydrogen combustion is relevant in many applications, including fuel cells after-burners, power production with CO2 sequestration and thermal conversion of CO2 neutral fuels such as biomasses and wastes.

However, the use of hydrogen as a fuel is not straightforward due to some

specific properties which strongly deviate from those characterizing traditionalfuels such as methane and which make the existing combustion equipment un-suited (Figure 1.1). Hydrogen has a high molecular diffusivity, a wide flamma-bility range, a high laminar flame speed and a high energy density per unitmass. Therefore, hydrogen combustion could lead to very large NOx emissions,when operating with conventional diffusion burners. On the other hand, NOxemissions could be controlled by means of lean and partially premixed combus-tion. However, this may lead to stability problems and flashback phenomena.

A major technological challenge related to hydrogen utilization is, then,

to work with high hydrogen contents in the fuel ensuring, at the same time,acceptable levels of NOx emissions. This requires the re-conceptualization of the burner design with two main objectives: i) to upgrade conventional burnersand combustion chambers for H2-enriched fuels and ii) to design and developnew devices for pure H2 combustion.

Computational Fluid Dynamics (CFD) represents an established tool forthe design and development of combustion technologies. Efforts need to bespent to make such tool as reliable as possible, through a conscious choice of validated models and sub-models, e.g. turbulence and combustion models, ki-

netic mechanisms [1]. The present Chapter will firstly focus on a brief review of the main mechanisms responsible for the formation of NOx in flames. Partic-ular emphases will be devoted to homogeneous hydrocarbon-hydrogen flames;

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Chapter 1. Advanced systems for hydrogen-based fuel combustion

Figure 1.1: Combustion of hydrogen containing fuels: issues and solutions.

however, some references regarding solid fuel combustion, i.e. coal combustion,will be provided. Then, comprehensive modeling approaches for NOx forma-tion will be discussed. Finally, advanced technologies adopted to mitigate NOxformation during the combustion process and to control emissions in the postcombustion phase will be presented. In particular, attention will be devotedto a novel combustion regime, known as flameless combustion [2] (or MILD1

[3] or HITAC2 [4]. Such combustion regime appears particularly promising forhydrogen-based fuels, also because it overcomes the paradigm of the conflictbetween energy saving, usually achieved with an increase in energy efficiencyvia air preheating, and NO reduction.

1.1 NOx formation and control in combustion pro-

cesses

Modeling NOx formation in turbulent reacting systems requires the descriptionof several, inherently coupled, physical processes, including the general fluiddynamic, the local mixing process, heat transfer and chemical kinetics. Theliterature on nitrogen pollutant is extraordinary rich of kinetic mechanisms forthe description of the net rate of NOx formation in different types of flames.However, it is still unfeasible to directly couple very large kinetic mechanismswith the turbulent mixing process into a CFD code. Moreover, it is recognizedthat nitrogen pollutant chemistry barely affects the flame structure, which isdominated by the fast fuel-oxidizer reactions. Therefore, NOx formation models

are often decoupled from the generalized combustion models and they are gen-1Moderate and Intense Low Oxygen Dilution2High Temperature Air Combustion

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1.1. NOx formation and control in combustion processes

erally executed after the flame structure has developed. This approach is verycomputationally efficient and less complicated than solving the transport equa-tions for the pollutants and the reacting species jointly. It should be also notedthat the pollutant formation sub-models typically converge in a small fraction

(typically around 10%) of the CPU time required to solve the reacting speciesequation, thus allowing to investigate several NOx formation mechanisms onpre-calculated flame structures.

The arguments provided above regarding the poor effect of NOx chemistryon the overall temperature and flow fields have prompted the developmentof post-processing tools of CFD results which incorporate very large kineticmechanism for the prediction of pollutant emissions, i.e. NOx, CO, UHC3 andPAH4 [5, 6, 7, 8, 9]. According to such approach, the flow and temperaturefield are taken from the CFD simulation and a detailed kinetic calculation iscarried out on a reactor network built from the CFD grid using criteria basedon the similarities of local temperature and species concentration.

1.1.1 Homogeneous NOx formation mechanisms

Traditionally, the description of nitrogen oxides formation in gas-phase takesinto account the following three main routes:

• Thermal NO (Zeldovich mechanism)

• Prompt NO

• Fuel NO

However, it has been widely recognized in the literature that homogeneous NOformation may occur with alternative routes:

• N 2 O intermediate NO mechanism

• NNH route

These formation mechanisms can become relevant and contribute equally or

even more than the main routes mentioned above, when operating in non con-ventional conditions. In particular, technologies promoting low-temperaturecombustion and employing hydrogen enriched fuels may be characterized byvery large contributions of the N2O and NNH routes. For example, combustionequipment implementing flameless combustion typically operate at low tem-peratures, thus resulting in NOx emissions which cannot be explained only bythe thermal and prompt kinetic mechanisms, because of the significant roleplayed by the N2O and NNH intermediates. The latter may become dominant,when hydrogen is added to the fuel, to stabilize lean hydrocarbon flames and

to reduce greenhouse gas emissions.3Unburned Hydrocarbon4Polycyclic Aromatic Hydrocarbons

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NO formation via NNH formation was first proposed by Bozzelli and Dean[10], who deduced a large kinetic constant for the reaction employing the di-rect oxidation of NNH to NO. Such result was confirmed by Hayhurst andHutchinson [11], who performed measurements of NOx in a laminar, premixed,

flat CH4/H2. Results showed NOx concentration too high to be explained en-tirely by the Zeldovich mechanism. The inclusion of the NNH route proposedby Bozzelli and Dean [10] gave reasonable good agreement with experimentalobservations.

Konnov et al. [12] proposed an updated detailed H/N/O mechanism for theanalysis of NO formation during hydrogen combustion in well stirred reactors.Numerical results were compared to experiments carried out in the temperaturerange 1500-2200 K. The authors observed that the NNH route represents thedominant source of NO at 1500 K, not only in rich conditions [13], but alsoin lean mixtures and at stoichiometric conditions. They observed that, atstoichiometric and rich conditions, thermal NO formation became predominantto the NNH route later than in lean conditions, when increasing the operatingtemperature. This is easily explained considering that NO formation via NNHis proportional to H radicals concentration, which is higher in stoichiometricand rich mixtures. Finally, a possible new route for NO formation in richmixture and at low temperature via N2H3 radicals was identified.

Rørtveit et al. [14] investigated the effect of diluent addition on NOx forma-tion in hydrogen laminar counter-flow flames. They observed that the additionof CO2 and He to the fuel stream resulted in significantly lower temperaturepeaks and NOx with respect to the diluent N2. The authors also found sig-nificant contributions of the N2O and NNH mechanisms on the overall NOxproduction at low temperatures (∼1800 K). When raising the temperature upto 2100 K, these mechanisms showed a very mild temperature dependency, dif-ferently from the thermal mechanism, which increased 16 times in the 300 Ktemperature range investigated, becoming the dominant contribution.

Guo et al. [15] studied the effect of hydrogen addition to ultra-lean counter-flow CH4/air premixed flames on the flammability limits and NOx emissions.Results showed that the addition of hydrogen led to a substantial enlargement

of the flammable range and to an increase of the NO emissions, when operatingat constant equivalence ratios. With regards to the source of NO, the authorsfound that the N2O and NNH routes played a major role on NO formation,while the thermal and prompt mechanisms resulted in minor contributions,due to the lean conditions and the low temperatures.

Löffler et al. [13] recently developed a new simplified reaction scheme tomodel NOx formation in natural gas combustion. The model was tested ina wide range of conditions and compared to more detailed kinetic schemes[16] under premixed plug-flow reactor conditions. Results confirmed that NO

formation is dominated by the thermal mechanism at temperatures higher than1800 K. At lower temperatures and with increasing oxygen availability, theN2O mechanism gave significant contribution, due to the increased O radical

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concentration. On the contrary, NNH dominated below 1600 K and in fuel richconditions, when the concentration of H radicals is higher.

Skottene and Rian [17] compared different detailed mechanisms for the pre-diction of NOx formation in laminar hydrogen flames and turbulent hydrogen

jet flames. They considered the scheme by Glarborg et al. [16], the San Diegomechanism [18], and two other mechanisms obtained by replacing the H2/O2chemistry of the first two schemes with the kinetic model proposed by Li et al.[19]. Results showed the important role of the NNH pathway in NO forma-tion for all investigated flames. The performances of the different models werefound to be strongly dependent on their ability to properly capture the NNHroute. Therefore, the main drawback of the San Diego mechanism was relatedto the omission of an explicit pathway converting NNH to NO. On the otherhand, the mechanism by Glarborg et al. [16] was found to perform satisfactoryfor the turbulent jet flame, while its modified version with Li et al. [19] H2/O2chemistry gave the best prediction for the diffusion flames, although resultingin a slight overprediction of the NO levels.

1.1.1.1 Thermal NO mechanism

Thermal NO is formed from the oxidation of atmospheric nitrogen at relativelyhigh temperatures in fuel-lean conditions. The process is described by theso-called Zeldovich two-step mechanism:

N 2 + Okf,1

−−−−kr,1

N O + N (1.1)

N + O2kf,2−−−−kr,2

N O + O. (1.2)

An additional elementary reaction is added to the thermal mechanism whenoperating in fuel-rich conditions to obtain the so called extended Zeldovichmechanism:

N + OH kf,3

−−−−kr,3

N O + H. (1.3)

The extended Zeldovich mechanism includes the effects of O, H and OHradicals. Typically, their concentration is obtained from equilibrium consid-erations; however, this can result in a significant underestimation of the NOformation rates. Better agreement can be usually achieved if the radicals con-centration is estimated by means of detailed kinetic mechanisms for the gasphase oxidation [20]. The rate parameters for reactions (1.1)-(1.3) are listed inTable 1.1.

Reaction (1.1) is usually considered the rate limiting step of the thermal

formation process due to its high activation energy, which determines the hightemperature sensitivity of the mechanism. Assuming the N radical to be inquasi steady state, the overall rate of NO formation is given by:

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Table 1.1: Reaction parameters for the extended Zeldovich mechanism and for theNO2 intermediate NO mechanism. Units: mol, cm, s, K.

Reaction expressiona Forward Backward

A β E a/R A β E a/R

N 2 + Okr,1−−−−kf,1

N O + N 1.71 · 1014 0 38367 3.30 · 1012 0.3 0

N + O2kr,2−−−−kf,2

N O + O 6.40 · 109 1 3160 4.92 · 1012 0 20399

N 2 + Okr,3−−−−kf,3

N O + N 3.80 · 1013 0 0 1.10 · 1012 0 23911

N 2 + O + M kr,4−−−−kf,4

N 2O + M 4.13 · 1013 0 7890 4.00 · 1014 0 28200

N 2O + Okr,5−−−−kf,5

N O + N O 6.60 · 1013 0 13390 1.64 · 1012 0 32057

N 2O + Okr,4−−−−kf,4

N 2 + O2 1.00 · 1014 0 14080 5.26 · 1013 0 54745a k = AT β exp(−E/RT ).

d [N O]

dt = 2 [O]

kf,1 [N 2] − kr,1kr,2[NO]2

kf,2[O2]

1 + kr,1[NO]

kf,2[O2]+kf,3[OH ]

. (1.4)Assuming that the initial concentrations of NO and OH are small, only theforward rates of the Zeldovich mechanism are significant and Equation (1.4)becomes:

d [N O]

dt = 2kf,1 [O] [N 2] . (1.5)

Equations (1.4) and (1.5) are coupled to the fuel oxidation process throughcompetition of the O and OH radicals. Their local concentration can be esti-mated if a comprehensive kinetic model is adopted during the CFD calculation.Otherwise, estimation procedures based on equilibrium and partial-equilibriumapproaches are taken into account. Following Westenberg [21], oxygen atomscan be assumed to be in equilibrium with O2 in fuel lean zones, where CO isoxidized to CO. Therefore:

[O] = K eq [O2]1

2 . (1.6)

An improvement to the equilibrium approach is obtained by accounting forthird-body reactions in the O2 dissociation-recombination process [22]:

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O2 + M −− O + O + M which generally leads to higher O radical concentrations. In case the OH radicalis not negligible with respect to the O radical, i.e. fuel-rich conditions, the local

concentration of OH can be deduced from a comprehensive kinetic model orfrom a partial equilibrium assumption [23].

At mean temperatures below 1600-1800 K, thermal NO formation via theZeldovich scheme is significantly reduced and it does not represent a majorsource of NO. Other routes have been proposed to extend the thermal mecha-nism described above, in order to describe the observed NO emissions from realsystems. In particular, the so called N2O and NNH routes will be presentedbriefly.

1.1.1.2 N2O intermediate NO mechanism

Malte and Pratt [24] first proposed the formation of NO via the intermediatespecies N2O. The mechanism can contribute as much as 90% of the total NOformed when operating at temperatures below 1800 K, elevated pressures andfuel-lean conditions. Therefore, this mechanism can be particularly relevantfor gas turbine conditions. The N2O intermediate route is described by thereactions:

N 2 + O + M

kf,4

−−

−−kr,4 N 2O + M (1.7)

N 2O + Okf,5−−−−kr,5

N O + N O (1.8)

N 2O + Okf,6−−−−kr,6

N 2 + O2. (1.9)

The rate constants for Reactions (1.7)-(1.9) are listed in Table 1.1.

1.1.1.3 NNH intermediate NO mechanism

Bozzelli and Dean [10] proposed an alternative route for NO formation via NNHradicals. The mechanism can be particularly relevant at low temperatures, infuel rich conditions and for hydrogen enriched flames. The essential reactionsof the NNH route are:

N 2 + H kr,7−−−−kf,7

N N H (1.10)

N N H + Okr,8

−−−−kf,8

N O + N H. (1.11)

The NNH route is also linked to the N2O route via the reaction:

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N N H + Okr,9−−−−kf,9

N 2O + H. (1.12)

Assuming that the NH radicals formed in reaction (1.11) are completely

converted to NO and that reaction (1.10) is equilibrated, it is possible to derivea rate expression for NO formation via NNH radicals as follows:

d [N O]NNH dt

= 2kf,8K p,7 [N 2] [O] X H (1.13)

where K p,7 is the equilibrium constant of reaction (1.10) expressed in partialpressures and X H is the mole fraction of H radicals. Hayhurst and Hutchinson[11] estimated the product kf,8K p,7 in the range between 1800-2500 K, usingexperimental measurements of temperature and OH radical along the axis of

laminar, premixed, flat CH4/air and H2/air flames. The NNH contributionwas estimated as the NO exceeding the Zeldovich mechanism, leading to thefollowing value for the product kf,8K p,7:

kf,78K p,7 = 2.3 · 10−15exp (−2760/T ) cm3/molecules · s−1. (1.14)

Konnov et al. [25] extended the temperature range for kf,8K p,7 from 1800-2500 K [11] to 1400-2500 K, by comparing experiments on the lean combustionof hydrogen and air in stirred reactors with kinetic modeling, carried out with

different mechanisms. The resulting value for kf,8K p,7 is:

kf,8K p,7 = 2.5 · 10−15exp (−3600/T ) cm3/molecules · s−1. (1.15)

1.1.1.4 Prompt NO mechanism

Prompt NO is formed by the reaction of atmospheric nitrogen with hydrocar-bon radicals in fuel rich regions of the flame with subsequent oxidation of the

intermediate species to NO. The mechanism was first reported by Fenimore [26]and it can be described by the following sequence of reactions:

N 2 + CH x −− HCN + N + . . . (1.16)

N 2 + C 2 −− 2CN (1.17)

N + OH −− N O + H. (1.18)

The mechanism requires a hydrocarbon to initiate the reaction with nitro-gen; therefore, it is particularly relevant in rich conditions or in rich regions of the flame, at temperatures below 1800 K. The detailed prompt mechanism is

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usually neglected in most NO models for CFD calculations due to the increasedcomplexity of nitrogen chemistry and its intimate coupling with the fuel oxida-tion chemistry. Instead, simplified approaches are usually taken into account.De Soete [27] suggested the following one-step kinetic mechanism:

d [N O] promptdt

= k prompt [O2]a [N 2] [F ] (1.19)

where

k prompt = A promptexp

−T A,prompt

T

. (1.20)

In Eq. (1.20) a is the oxygen reaction order and F denotes the fuel. The kineticparameters are fuel dependent. De Soete [27] suggested the following values foracetylene fuel:

T A,prompt = 30215 K A prompt = 1.2 · 106 s−1. (1.21)As for the oxygen reaction order, it is uniquely related to oxygen mole fractionin the flame [27]:

a =

1.0 X O2 ≤

4.1

·10−3

−3.95 − 0.9 ln X O2 4.1 · 10−3 ≤ X O2 ≤ 1.11 · 10−2−0.35 − 0.1 ln X O2 1.11 · 10−2 ≤ X O2 ≤ 0.030 X O2 ≥ 0.03

. (1.22)

Eq. (1.21) was proposed for acetylene. Therefore, it may cause errors if used for other hydrocarbons, especially in fuel-rich conditions. The commercialCFD package FLUENT® employs a modified version of the model by De Soete[27], fitted using experimental data available from Backmier et al. [28]:

d [N O] promptdt

= f k

prompt [O2]a [N 2] [F ] (1.23)

with f being a correction factor, equal to:

f = 4.75 + 0.0819n − 23.2φ + 32φ2 − 12.2φ3. (1.24)In Eq. (1.24), n is the number of carbon atoms per molecule for the hydrocarbonfuel and φ is the equivalence ratio. The correction factor is valid for equivalence

ratios between 0.6 and 1.6. The values proposed for k

prompt are:

T

A,prompt = 36510 K A prompt = 6.4 · 106 s−1. (1.25)

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1.1.1.5 Fuel NO mechanism

Nitrogen-containing organic compounds can contribute to the total NO formedduring the combustion process through the fuel mechanism. Fuel NO is formedfrom nitrogen bound in the fuel, via intermediate species such as HCN and NH3,

which are oxidized to NO while being competitively reduced to N2 accordingto the overall reactions:

HCN/NH 3 + O2 −− N O + . . . (1.26)

N O + HCN/NH 3 −− N 2 + . . . (1.27)Fuel NO is a relevant source of NO for residual fuel oil and coal, which

typically contain 0.3-2% nitrogen by weight. On the other hand, natural gas

and hydrogen combustion is not interested by such formation mechanism.

1.1.2 NOx control in combustion

Nitrogen oxides control is a main goal for Researchers and Engineers. NOxrepresent a significant threat for the environment and combustion systems area major source of such pollutants. Many efforts have been spent during thelast decades to improve the understanding of NOx formation mechanisms and,then, to develop effective NOx control technologies. In general, when refer-ring to NOx control, it is possible to distinguish between post-combustion NOxclean up and modification of the combustion process. Typical post-flame tech-niques include selective catalytic reduction (SCR) and selective non-catalyticreduction (SNCR). In SCR ammonia is injected near the combustor exit, al-lowing NOx reductions by as much as 90%. SCR is very effective in a broadrange of applications but it can be relatively expensive, due to the high costof the catalyst. With SNCR, ammonia is injected in a higher temperature re-gion (1173-1373 K), to ensure the effective action of NH3. SNCR is a viableoption only in a narrow temperature range, which precludes its use in manyapplications.

The suppression of NOx formation during combustion can be accomplishedwith different approaches, often used in combination with each other. The mostcommon are burner design, fuel staging, flame cooling, ultra-lean combustionand flue-gas recirculation. All these approaches share the same objective of suppressing the temperature peaks and reducing the residence time and theoxygen concentration in the high-temperature regions. Low-NOx burners forcoal combustion accomplish this with swirling and an appropriate combinationof secondary and tertiary air, to generate a fuel rich region where NOx canbe reduced to N2. Staging is often used to create a fuel rich region near the

burner through the staged introduction of fuel, fuel staging , and oxidizer, air staging . Staging can result in significant NOx suppression, especially for hydro-gen combustion, due to the absence of prompt mechanism for such flames [29].

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1.2. Flameless combustion

However, the implementation costs of staging are usually very high, resultingin lower overall performances. Flame cooling adopts steam injection in thecombustion chamber to reduce the temperature levels, thus allowing to reduceNOx formation but also determining significant CO emissions in the exhausts.

Ultra-lean combustion can be effectively exploited to reduce NOx emissions dueto the reduced temperature peaks. However, careful attention must be paid tothe flame stability, to avoid extinction and ensure safety of operation. As men-tioned before, the addition of hydrogen to these flames may be of interest toenlarge the flammability limits [15, 14]. Mastorakos et al. [30] observed thatthe techniques involving flue-gas recirculation are the most effective in accom-plishing NOx reduction. For sake of clarity, it is worth distinguishing betweenexternal and internal recirculation. The first option is accomplished by recy-cling part of the exhaust gases to the inlet section, to dilute the fresh reactants.On the other hand, internal recirculation is achieved through a special aero-dynamic design of the burner, based on the entrainment of the exhaust gasesdriven by the high momentum of the fresh reactants.

Flameless combustion is based on the internal recirculation of exhaust gases.The investigation of advanced combustion systems operating in flameless com-bustion has represented the main objective for the present Thesis, as they showgreat potentials, not only for the efficient and environmental friendly oxidationof conventional fuels, but also for the exploitation of more complex ones, suchas hydrocarbon-hydrogen mixtures and low-calorific value fuels enriched withH2. The theory and applications of flameless combustion are discussed in thefollowing Sections.

1.2 Flameless combustion

Flameless combustion couples high combustion efficiencies with very low pol-lutant emissions [2, 4, 3]. Such a combustion regime needs the reactants tobe preheated above the self-ignition temperature and enough inert combustionproducts to be entrained in the reaction region. The former requirement en-

sures high thermal efficiency, whereas the latter allows diluting the flame andreducing the final temperature well below the adiabatic flame temperature.As a result, a flame front is no longer identifiable, thus the name flameless ,and the combustion process is no longer restricted to the flame front regionbut extended to a larger portion of the combustion chamber. The system ap-proaches perfectly stirred reactor (PSR) conditions and it is characterized bya more uniform temperature field than traditional combustion systems. Byavoiding temperature peaks, NOx formation is largely reduced and the effecton the materials is beneficial. Soot formation is also suppressed, due to the

lean conditions and the large CO2 concentration in the exhausts [3]. Fromthe operation point of view, it has been observed that ignition and extinctionphenomena do not occur in flameless combustion because of the small temper-

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ature difference between burnt and unburnt gases. Flame stabilization occursnaturally as the reactants temperature exceeds the self-ignition temperature.Therefore, a large degree of freedom in the choice of the fluid dynamical con-figuration of the combustion chamber is allowed. Several studies have been

devoted to understanding the operating principles of flameless combustion [31]as well as its mechanisms and critical parameters [32]. An extensive review onflameless combustion features considering physical, chemical, and thermody-namic aspects has been provided by Cavaliere and de Joannon [3]. Gupta [4]also provides a detailed review on HITAC, describing benefits (energy savings,CO2 and NOx reduction, equipment size reduction) and main features of thetechnology.

However, flameless combustion appears to be still worthy of further inves-tigations and attention. In particular, from the modeling perspective, it is stillnot clear how to treat turbulence-chemistry interactions in such regime andto which extent the kinetic mechanisms adopted need to be detailed . More-over, the uncertainly of these topics is increased by the wide range of solutionsadopted to achieve flameless operations, through the competition of turbulencemixing and chemical kinetics.

In the following Section a brief description of the operating principles of flameless combustion is presented, followed by an exhaustive review of the rele-vant literature in the field. In particular, emphasis will be put on the literatureregarding the flameless combustion of hydrogen-based fuels.

1.2.1 Operating principles of flameless combustion

In contrast to stabilized flame combustion, flameless oxidation is mixture andtemperature controlled, and it is achieved with specific flow and temperatureconditions. A prerequisite for a stable flame in traditional combustion systemsis represented by the balance between flow velocity and flame velocity. Creatingflow conditions for flame stabilization is an essential burner design criterion,for both premixed and diffusion flames. Usually, swirl or bluff body are usedto create stagnation points or areas of low velocity for stabilization. Internal

exhaust gas recirculation is sometimes used to reduce NOx formation. FollowingWünning and Wünning [2], it is possible to quantify the amount of exhaustsrecirculated in the reaction zone as:

kR = ṁE

ṁA + ṁF (1.28)

where ṁE is the net mass flow rate of recirculated flue gas, whereas ṁF andṁA are the fuel and air mass flow rates, respectively. It has been shown [33]that it is not possible to achieve flammable mixtures of hydrocarbon and air

for values of kR ≥ 0.5 (Figure 1.2). Above this value, extinction occurs, due tothe lower oxygen concentration and higher inerts in the mixture.

However, it was found [34] that a stable form of combustion is also possible

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1.2. Flameless combustion

Figure 1.2: Flammable range for methane-air mixture as a function of therecirculation degree, kv[33].

for much higher recirculation rates. Under ideal conditions, such combustionprocess takes place without any visible or audible flame. Figure 1.3 showsa stability diagram for the combustion of methane in air, as a function of the recirculation degree, kR, and the operating temperature. Three differentcombustion modes or regimes are identified: stable flames, unstable flamesand flameless oxidation. Burner stabilized flames (A) are possible over theentire range of operating temperatures, but only for recirculation degrees upto 30%. For higher recirculation values of kR, the flame becomes unstable

(B), lifts off and finally blow out, for temperatures below self ignition. If thefurnace temperature is sufficiently high, the fuel can react in a steady, stableform of flameless oxidation (C), without limitations of the recirculation degree.As it can be deduced from Figure 1.3, it is not possible to operate a burnerin flameless mode in a cold combustion chamber. Therefore, the combustionchamber must be heated up in conventional mode and switched to flamelessoxidation only when the operating temperature exceeds the fuel self-ignitiontemperature.

An idealized operating scheme for flameless oxidation is shown in Figure

1.4. Combustion air is mixed with the exhaust gases (region I); then, fuel isadded in region II and combustion takes place. The maximum temperature risedue to the oxidation process can be effectively reduced to few hundred Kelvin, if enough product gases are recirculated in the reaction zone. In region III, energyis withdrawn from the combustion products to control the temperature level inthe combustion chamber. However, the temperature has to be kept at a levelhigh enough to guarantee reaction in region II. To increase energy efficiency,the gases leaving the system could be used for air-preheating, although this isnot compelling to ensure the flameless oxidation process.

The technical realization of the idealized process in Figure 1.4 can be ac-complished by means of a high velocit

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