Lade Inhalt...

Simulation of a Gas Turbine Combustor Test Rig using a Reactor Network Approach with Detailed Chemistry

Masterarbeit 2014 55 Seiten

Ingenieurwissenschaften - Energietechnik

Leseprobe

Contents

Abstract

Acknowledgment

Contents

List of Figures

List of Tables

Nomenclature

Abbreviations

1. Introduction
1.1 Background
1.2 Objectives of this work
1.3 LOGE AB
1.4 Siemens Industrial Turbomachinery AB

2. Theory
2.1 Gas turbine principles
2.2 Combustion
2.3 Governing equations of fluid flow and heat transfer
2.4 Flame basic definitions
2.5 Emissions
2.5.1 Carbon dioxide
2.5.2 Carbon Monoxide
2.5.3 Nitric Oxides
2.6 Reactor models
2.6.1 PSR - Perfectly Stirred Reactor
2.6.2 PFR - Plug-Flow Reactor
2.6.3 PaSR - Partially Stirred Reactor
2.7 Reactor network building

3. Software overview
3.1 Interface and features
3.2 Testing and improvement
3.3 Modules data and settings

4. Sandia Flame D
4.1 Reactor network set-up. Approach 1
4.2 Reactor network set-up. Approach 2
4.2.1 Parameter study

5. Siemens atmospheric combustion test rig
5.1 Mechanism validation
5.2 Reactor network set-up
5.3 Parameter study

6. Discussion and conclusions

7. Future works

References

List of Figures

Figure 1. Gas turbine structure

Figure 2. Theoretical PSR

Figure 3. Theoretical PFR

Figure 4. Conceptual diagram of PaSR reactor

Figure 5. Flame Zone Mapping Based on CFD Result with Swirl Angle of 45[0]

Figure 6. Schematic Layout of 6-Element CRN Model

Figure 7. LOGEsoft ReactorNetwork main interface

Figure 8. Available modules

Figure 9. Example of a reactor network

Figure 10. Reactor data

Figure 11. Calculation and output parameters

Figure 12. Reactor output

Figure 13. Flame D

Figure 14. Sandia Flame D validation

Figure 15a. Sandia Flame D Approach 1. Principle

Figure 15b. Sandia Flame D Approach 1. CRN

Figure 16. (a): temperature profile; (b): CH4 concentration, linear; (c): CH4 conc. Logarithmic; (d): OH conc.; (e): CO2 conc.; (f): H2O conc

Figure 17. (a): CH4 concentration, linear; (b): CH4 conc. Logarithmic; (c): OH conc.; (d): CO2 conc.; (e): H2O conc

Figure 18a. Sandia Flame D Approach 2. Principle

Figure 18b. Sandia Flame D Approach 2. CRN

Figure 19. (a): temperature profile; (b): CH4 concentration, linear; (c): CH4 conc. Logarithmic; (d): OH conc.; (e): CO2 conc., (f): H2O conc

Figure 20. (a): temperature profile; (b): CH4 concentration; (c): OH conc.; (d): CO2 conc.; (e): H2O conc

Figure 21. (a): temperature profile; (b): CH4 concentration; (c): OH conc.; (d): CO2 conc.; (e): H2O conc

Figure 22. (a): temperature profile; (b): CH4 concentration; (c): OH conc.; (d): CO2 conc.; (e): H2O conc

Figure 23. Schematic layout of the combustion test rig

Figure 24. 2D model of the test rig combustion chamber

Figure 25. (a): CH4,; (b): CO2; (c): CO; (d): H2O concentrations vs hydrogen content in the mixture

Figure 26. Velocity magnitude of the flow and schematic zones mapping

Figure 27. Combustion rig chemical reactor network

Figure 28. Thermal NOx formation trend along the central axis

Figure 29. (a): NOx vs hydrogen content; (b): Temperature vs hydrogen content

Figure 30. (a): NOx/Temp. vs inlet mass flow rate; (b): NOx/Temp. vs inlet temperature of the mixture

Figure 31. (a): NOx and (b): temperature sensitivity to different reaction schemes

List of Tables

Table1. Sandia Flame D flow conditions

Table 2. CPU times in case of two schemes in the first approach

Table 3. Time step size influence (Output results for reactor Nr 9)

Table 4. Mechanisms CPU times

Table 5. Time step size influence (Output results for outlet)

Table 6. CPU times

Nomenclature

illustration not visible in this excerpt

Abbreviations

illustration not visible in this excerpt

Abstract

Use of gas turbines as one of the most effective power generation technologies has ecological concerns caused by polluting combustion products. To reduce emissions different fuel compositions are being constantly investigated and gas turbines are developed by means of experiments or less expensive numerical simulations. Combustion processes can be modeled in computational fluid dynamics (CFD) with a good accuracy but it is time consuming and rather complicated in case of detailed chemistry. To overcome this issue a processing of CFD solution can be applied for a further building of equivalent chemical reactor networks (CRN) that allow to reduce calculation times and take minor species into account.

The aim of this work is to choose a proper technique of CRN set-up and apply it for engineering tasks with the software tool 'LOGEsoft ReactorNetwork'.

The first part of the thesis is devoted to investigation of existing CRN approaches, CFD processing instruments and testing and improvement of the 'LOGEsoft ReactorNetwork'. That software is successfully examined on the Sandia Flame D and a parameter study of the reactor network is carried out.

The second part involves mechanism validation for methane/hydrogen mixtures and development of an equivalent reactor network for the Siemens atmospheric combustion test rig that serves as an experimental facility for enhancement of the 3rd generation dry low emission burner. The obtained CRN is validated against experimental data of NOx measurements and it showed reasonable results with deviations. A parameter study and mechanism sensitivity of the model is also conducted and some ways for the future improvement are suggested.

Keywords: gas turbine, combustion, emissions, NOx, CFD, chemical reactor, reactor network, CRN, ERN, hydrogen-enriched.

Acknowledgment

This thesis is a final part of the Master program of Power Engineering at Brandenburg University of Technology Cottbus-Senftenberg. The work is carried out at LOGE GmbH (Cottbus, Germany) in cooperation with LOGE AB (Lund, Sweden) and Siemens Industrial Turbomachinery AB (SIT) (Finspång, Sweden).

I would like to thank my supervisors Dipl.-Ing. Lars Seidel and Prof. Dr.-Ing. F. Mauß for giving me such a pleasant experience and support. I also want to thank M.Sc. Andrea Matrisciano at LOGE GmbH and M.Sc. Cathleen Perlman and Thommie Nilsson at LOGE AB for a great help during the work.

In addition, I would like to express my appreciation of all the encourage from Dr. Daniel Lörstad at SIT for provision of necessary data for the investigation.

Cottbus, July 2014

Oleg Bosyi

1. Introduction

1.1 Background

One of the most important power generation technologies is utilization of gas turbines - facilities that burn gaseous fuels and convert chemical energy into electricity and heat. However, in terms of environment safety different combustion products can be undesirable pollutants such as COx and NOx. Gas turbines mostly operate on natural gas as a fuel which composition can vary depending on the location it has been transported from. Also it is essential to use additives or diluents in its composition to improve combustion properties and reduce polluting emissions, for example, by blending natural gas with hydrogen. When burning there is a very complex process of interaction between the flow and flame, the chemical reactions depend on many factors. That leads to a need of adequate estimation and study of the combustion process.

An effective way to avoid expensive and time consuming full-scale experiments is numerical simulation. Nowadays it is possible to predict the most important flame properties such as heat release, velocity and main species concentration by means of computational fluid dynamics (CFD) with a good accuracy. However, in order to predict minor species such as CO, NOx, SOx and so forth it is necessary to have a more detailed view of fuel chemical composition and combustion reaction chemistry. CFD has computational limits such as a huge time consumption, so there is a need of an alternative approach. Use of a chemical reactor network (CRN) can be such a method. Its concept is based on extraction of an ‘‘equivalent’’ network of ideal chemical reactors as a simple flow model from CFD simulation that previously was performed using a simplified kinetics mechanism on a fine grid. The resulting CRN significantly reduces computational times to calculate minor species concentrations using detailed chemical reaction schemes.

1.2 Objectives of this work

1. Investigate different existing approaches of building up equivalent reactor networks based on CFD solution.
2. Test the software 'LOGEsoft ReactorNetwork' by LOGE AB and improve it.
3. Set up a CRN for the Siemens atmospheric combustion test rig, perform parameter studies and evaluate sensitivity to different reaction schemes.

1.3 LOGE AB

LOGE, Lund Combustion Engineering, is a software development company based in the cities of Lund, Sweden and Cottbus, Germany. The main competence of the company is the development of software tools used for simulating chemical processes, such as the combustion in technical devices (engines, furnaces), or chemical processes on surfaces (christal growth, catalysts). The company's main simulation tool is its own software suite LOGEsoft, which is a comprehensive solution for detailed chemical kinetics modeling of engineering applications.[15]

1.4 Siemens Industrial Turbomachinery AB

Siemens Industrial Turbomachinery AB (SIT) develops, manufactures, markets and services individual gas turbines all the way to complete power plants on a global market. The business comprise of approximately 2800 employees with an annual turnover of more than 1 billion EUR. The head office is located to Finspång.

SIT supply customers all over the world with various gas turbine solutions. The turbines are characterized by low environmental impact and high efficiency. Within the Siemens group, SIT is responsible for industrial steam turbines with power of 60-180 MW and for gas turbines with power levels of 15-50 MW.

SIT gas turbine offering consists mainly of five lines: SGT-500, SGT-600, SGT-700, SGT-750 and SGT-800.[1]

2. Theory

Combustion is a complex physical and chemical process of converting raw materials into combustion products during exothermic reactions, accompanied by a heat release. The chemical energy stored in the components of the original mixture can be released in form of heat and light radiation. The luminous zone is called flame front or flame.

2.1 Gas turbine principles

illustration not visible in this excerpt

Figure 1. Gas turbine structure.[2]

A gas turbine is an engine of continuous action in which scapular device energy of compressed and/or warm gas is transformed to mechanical work on its shaft. Burning of fuel can occur both out of the turbine, and inside of the turbine. Basic elements of its design are: a rotor (the working shovels fixed on disks) and a stator made in form of the leveling device.

Gas turbines are used as parts of gas-turbine engines, stationary gas turbine units and steam-gas turbine units. A gas turbine unit consists of two main parts: a power turbine and a generator. The stream of gas of high temperature moves the shovels of the power turbine. Heat used by a heat exchanger or a copper-utilizator provides increase in the general efficiency of the whole plant. Gas turbines can work both with liquid and with gaseous fuel: in a usual operating mode — natural gas, and in reserve (emergency) mode — it is automatically switched to a diesel fuel.

2.2 Combustion

Three factors necessary for combustion:

- fuel
- oxygen
- temperature

Flammable mixture: fuel combined with a sufficient amount of oxygen is ignited at a certain temperature. The main combustible components in the fuel are: carbon (C), hydrogen (H2) and mixtures formed during the combustion.

Complete combustion, also known as stoichiometric combustion, in theory it is an ideal combustion process as a result of which the fuel burns completely. An example of complete burning can be expressed as:

illustration not visible in this excerpt

Chemical reaction schemes, or mechanisms, describe in a stepwise manner the exact collisions and events that are required for the conversion of reactants into products. Mechanisms achieve that goal by breaking up the overall balanced chemical equation into a series of elementary steps. An elementary step is written to mean a single collision or molecular vibration that results in a chemical reaction.[3]

2.3 Governing equations of fluid flow and heat transfer

If a chemically reacting flow is considered, the system at each point in space and time is completely described by specification of pressure, density, temperature, velocity of the flow, and concentration of each species. These properties can be changing in time and space. The changes are the result of fluid flow (called convection), chemical reaction, molecular transport (e.g., heat conduction, diffusion, and viscosity), and radiation. A mathematical description of flames therefore has to account for each of these processes.

Some properties in reacting flows are characterized by the fact that they are conserved. Such properties are the energy, the mass, and the momentum. Summation over all the processes that change the conserved properties leads to the conservation equations, which describe the changes in reacting flow; accordingly, these equations are often called the equations of change. These equations of change (an extended set of the so-called Navier-Stokes Equations) are the general starting point for mathematical descriptions of chemically reacting flows. Because all systems are described by the conservation equations, the main difference from one system to another are the boundary conditions and physicochemical conditions.[4]

Mass conservation:

illustration not visible in this excerpt

Momentum conservation:

illustration not visible in this excerpt

where is the mixture mass density, u - the flow velocity vector, t - the time, x - the spatial coordinate, g - the gravitational acceleration field, P = pI -and is the stress tensor with p as thermodynamic pressure, I - the unit tensor and is the stress tensor which is calculated using Fick's Law of friction for a compressible mixture.

Mass conservation of species:

illustration not visible in this excerpt

Energy conservation of species:

illustration not visible in this excerpt

In these equations, c p is the specific heat, T is the temperature and is the thermal conductivity of the mixture, i = i / is the mass fraction, h the enthalpy, the molar net change rate, M the molar mass, W the molecular weight, and Le i is the Lewis number of species i.

Lewis number is a dimensionless number which expresses the ratio of thermal diffusivity (/c p) to species mass diffusivity ( D m,i) as:

illustration not visible in this excerpt

where D m,i is the mixture-averaged diffusion coefficient. Effects of Lewis number become substantial when the thermal and mass diffusivity of the fuel differ and Le 1.[5]

2.4 Flame basic definitions

In combustion processes, fuel and oxidizer (typically air) are mixed and burned. It is useful to identify several combustion categories based upon whether the fuel and oxidizer is mixed first and burned later (premixed) or whether combustion and mixing occur simultaneously (nonpremixed). Each of these categories is further subdivided based on whether the fluid flow is laminar or turbulent.

Laminar Premixed Flames: In laminar premixed flames, fuel and oxidizer are pre-mixed before combustion and the flow is laminar.

A premixed flame is said to be stoichiometric, if fuel (e.g., a hydrocarbon) and oxidizer (e.g., oxygen O2) consume each other completely, forming only carbon dioxide (CO2) and water (H2O). If there is an excess of fuel, the system is called fuel-rich, and if there is an excess of oxygen, it is called fuel-lean.

Premixtures of fuel and air are characterized by the air equivalence ratio, , (sometimes air number) or the reciprocal value, the fuel equivalence ratio [4]

illustration not visible in this excerpt

Accordingly, premixed combustion processes can now be divided into three groups,

- rich combustion:[illustration not visible in this excerpt]>1
- stoichiometric combustion:[illustration not visible in this excerpt]=1
- lean combustion:[illustration not visible in this excerpt]<1

The burning of freely burning premixed laminar flat flames can be characterized by the laminar burning velocity v L (e.g., in m/s); other names in the literature are flame velocity or flame speed. It depends only on the mixture composition, the pressure and the initial temperature.

Sometimes premixed flame fronts burn and propagate into a turbulent fluid flow. If the turbulence intensity is not too high, curved laminar premixed flame fronts are formed. The turbulent flame can then be viewed as an ensemble of many premixed laminar flames.

The advantage of premixed combustion is that much greater control of the combustion is possible. By lean premixing ( <1), high temperatures are obtained and hence combustion with low production of soot and unburned hydrocarbons is accomplished. However, due to higher heat more NOx is formed.

Despite the advantages, premixed combustion is not widely used because of the potential for accidental collection of large volumes of premixed reactants, which could burn in an uncontrolled explosion.

Laminar Nonpremixed Flames: In laminar nonpremixed flames (laminar diffusion flames), fuel and oxidizer are mixed during the combustion process itself. The flow is laminar.

Turbulent Nonpremixed Flames: In this case nonpremixed flames burn in a turbulent flow field, and for low turbulence intensities the so-called flamelet concept can be used. Nonpremixed flames are mostly used in industrial furnaces and burners. Unless very sophisticated mixing techniques are used, nonpremixed flames show a yellow luminescence, caused by glowing soot particles formed by fuel-rich chemical reactions in the rich domains of the nonpremixed flames.[4]

Adiabatic Flame Temperature is the flame temperature under constant pressure, with no heat exchange and when the combustion is complete. This is the highest possible flame temperature.

2.5 Emissions

One of the driving factors in modern gas turbine design is reducing emissions, and the combustor is the primary contributor to a gas turbine's emissions. Generally speaking, there are five major types of emissions from gas turbine engines: smoke/soot, carbon dioxide (CO2), carbon monoxide (CO), unburned hydrocarbons (UHC), and nitrogen oxides (NOx , which is a sum of NO and NO2) .

2.5.1 Carbon dioxide

It is a harmful pollutant and takes place in the global warming process, it is a product of the combustion process and is primarily mitigated by reducing fuel usage. On average, 1 kg of jet fuel burned produces 3.2 kg of CO2. Carbon dioxide emissions will continue to drop as manufacturers make gas turbine engines more efficient.

2.5.2 Carbon Monoxide

Carbon monoxide is dangerous for human health and environment in terms of poisoning. Carbon monoxide is often formed in fuel-rich conditions, because of the lack of adequate oxygen to produce CO2. Although, in stoichiometric or fairly lean conditions, a high amount of CO is found due to the dissociation of CO2. In reality, CO emissions tend to be the highest at low-load conditions. The reason might be due to:

- Deficient burning rates due to a too small equivalence ratio, or too low residence time.
- Non-uniformity in equivalence ratio which creates spots with too lean or too rich mixtures.

Combustion efficiency and thus CO emissions are highly influenced by engine and combustor inlet temperatures, combustion pressure and primary-zone fuel to air ratio (or equivalence ratio).[5]

2.5.3 Nitric Oxides

This is a pollutant that participates in a chain reaction removing ozone from the stratosphere, that lead to increase of ultraviolet radiation on the earth's surface.

NOx is a common term to describe NO, NO2 and N2O. Gas turbine combustor mostly contains NO. There are four general mechanisms of NOx formation: Thermal NOx, Prompt NOx, N2O and Fuel NOx.

Thermal NO x (Zeldovich NO x ): it usually has a large concentration at temperatures more than 1750 K. For this type there are three reactions of formation:

illustration not visible in this excerpt

Formation of thermal NOx is largely controlled by flame temperature. As flame temperature rises, NOx production boosts up. Nevertheless, although temperatures are higher at the rich side, thermal NO peaks at the lean side. This is because of the competition between fuel and nitrogen for the available oxygen. On the lean side, there is an excess of oxygen which can be consumed by nitrogen. On the rich side, however, oxygen is mainly used by the fuel.[7]When the air is preheated thermal NOx is the first NOx formation mechanism.

Prompt NO x (Fenimore NO x ): in temperature lower than 1800 K, HCN is oxidized to NO in the flame front and prompt NOx is formed:

illustration not visible in this excerpt

Prompt NOx is important in fuel rich condition and is formed in relatively low temperature (about 1000 K).[8]

Fuel NO x : this type of NOx is associated with the presence of N2 in the fuel and in general is not important for gas turbines because of low nitrogen presence in natural gas and other common fuels.

Nitrous NO x (from N 2 O): N2O is important in high pressure and high temperature conditions. Emission of N2O is not significant, but it can serve as an intermediate to NOx emissions.[9]

2.6 Reactor models

2.6.1 PSR - Perfectly Stirred Reactor

illustration not visible in this excerpt

Figure 2. Theoretical PSR.[10]

The perfectly stirred reactor is an ideal chemical reactor in which perfect mixing is achieved inside the control volume. This means that the composition within the reactor is everywhere the same[11]. The gas from the inlet is instantly mixed with the existing reactor content and the composition in the outlet will be the same as in the reactor, so called back mixing[12]. The mixture in a perfectly stirred reactor is blended with the combustion products and heated so quickly that in passing through the chamber with a sampling probe it is difficult to locate any regions with composition or temperature different than in other regions. Due to intense recirculation in the chamber, no defined directions of the flow can be distinguished either[13].

The basic equations in the PSR model are the following.

Mass conservation:

illustration not visible in this excerpt

with indicating the mass flow rate and indexes in and out representing the inlet and outlet flows from the control volume, respectively.

Considering [accumulation] = [generation] + [in] − [out], the above equation becomes:

illustration not visible in this excerpt

where V is the volume of the reactor.

This creates N s equations with N s + 1 unknowns. The additional equation is obtained from the energy balance.

Energy conservation of species:

illustration not visible in this excerpt[14]

2.6.2 PFR - Plug-Flow Reactor

illustration not visible in this excerpt

Figure 3. Theoretical PFR.

In the ideal plug-flow reactor or tube reactor no recirculation occurs and the flow is homogeneous with respect to velocity and species concentration in a radial direction from the axis of symmetry. The physical interpretation is that the flow is trapped in between two membranes/pistons that move with the same velocity as the flow along the tube, thus inhibiting any recirculation/mixing in the axial direction of the tube.[16]

There is a clear analogy to an ideal perfectly mixed mixing tube of a gas turbine burner. For the plug-flow reactor some assumptions can be made for ignition delay applications:

- Steady state, one-dimensional flow
- Only gas-phase reactions
- Ideal gas behavior
- Ideal frictionless flow

Applying the above simplifications the mass conservation equation can be written as:

illustration not visible in this excerpt

A x is the cross-sectional flow area. The conservation of species can be formulated as '

illustration not visible in this excerpt

The energy equation is formulated as

illustration not visible in this excerpt

[illustration not visible in this excerpt]is the mean heat capacity per unit mass of gas, Q e the heat flux from the surroundings to the outer wall of the tube whose area per length unit is a e.[17]

2.6.3 PaSR - Partially Stirred Reactor

In many practical combustion devices, e.g., gas turbines, the characteristic time scales for mixing are of the same order of magnitude as the time scales for chemical kinetics. When modeling such practical combustion devices it is important to account for both effects. However in order to include detailed reaction chemistry, simplifying assumptions regarding the fluid flow description are necessary to avoid excessive computational and storage expenses. The partially stirred reactor (PaSR) is one such model based on the probability density function (PDF) transport equation of the physical quantities, assuming statistical spatial homogeneity. The model accounts for mixing and is computationally efficient for large coupled chemical reaction mechanisms involving many chemical species.

The PaSR model can be derived from the one point joint scalar PDF. The PDF equation is solved numerically using a Monte Carlo particle method with time splitting techniques. This method involves approximating the PDF by an ensemble of stochastic particles, and has been successfully exploited for solving high dimensional PDF equations. [18]

illustration not visible in this excerpt

Figure 4. Conceptual diagram of PaSR reactor (the reaction zone is painted).[19]

In the PaSR approach, a computational cell is split into two different zones: in one zone all reactions occur, while in the other one there are no reactions (Fig. 4). Therefore, the composition changes due to mass exchange with the reacting zone. In addition. the reaction zone is treated as a PSR, in which all reactants are assumed to be perfectly mixed with each other. This allows to neglect any fluctuations when calculating the chemical source terms. Three average concentrations are presented in the reactor, the mean mixture concentration of the feed c [0], the mixture concentration in the reaction zone c, the mixture concentration at the exit of the reactor c [1].

The whole combustion process is regarded as two processes. In the first process initial concentration in the reaction zone changes from c [0] to c, in the second process the reacted mixture (with concentration c) is mixed with the un-reacted mixture (with concentration c [0] by turbulence), the results is the averaged concentration c [1]. The reaction rate of this computational cell is determined by the fraction of the reactor in this cell. It seems quite clear that it should be proportional to the ratio of the chemical reaction time c to the total conversion time in the reactor, i.e. the sum of the micro-mixing time mix and reaction time c:

illustration not visible in this excerpt

The micro-mixing time mix characterizes the exchange process between reactant mixture and unburnt mixture. The overall reaction rate and the homogeneous reaction rate of this computational cell have the following relationship:[20]

illustration not visible in this excerpt

2.7 Reactor network building

In this section different techniques of CFD data extraction with further equivalent chemical reactor networks development are described. In principle, a CRN is developed by analyzing a flow field obtained with CFD. Then regions with certain characteristics are identified and modeled by chemical reactors, as a rule by PSRs and PFRs.

Method 1: On the most basic level, for very simple 2D geometries with no recirculating flows, the streamlines are divided into several plug flow reactors. The division is refined until no considerable change is observed in the results; or in other words a sensitivity analysis is implemented via the number of reactors.[21]

However, this approach is not suitable for complex geometries where a combination of recirculation zones play a big role and should be taken into account.

Method 2: Falcitelli et. al. [22] have divided the full combustor into many small perfectly stirred reactors. In this method, the flow field is broken down into many small regions based on temperature and composition parameters. The grouping is done regardless of the geometrical properties. The recycling flow (the flow that enters one reactor from another) is directly taken from the CFD calculations between the adjacent cells. As every few cells are grouped into one reactor, the network's resolution would not be far from the CFD's resolution therefore making it easier to obtain accurate results.[21]

In case of relatively simple flows this method makes sense, nevertheless, if the flow field is complicated and consists of thousands of cells it seems to be impossible to build up a network manually with several hundred reactors after a preliminary extraction of mass exchange and other parameters for each cell. This approach definitely needs to be automated by using a certain code coupled with computational resources.

Method 3: Thanh Hao et. al. [23] have suggested to analyze the flow field information in CFD in order to determine combustion zones in the combustor. A gas turbine combustor can be divided into zones based on the flow velocity, temperature and species concentration. Figure 5 displays the flame shape and flow of gas from one zone to another.

illustration not visible in this excerpt

Figure 5. Flame Zone Mapping Based on CFD Result with Swirl Angle of 45[0] . [23]

Afterwards, the CRN has been built up and schematically shown in the Figure 6.

illustration not visible in this excerpt

Figure 6. Schematic Layout of 6-Element CRN Model . [23]

The method allows to simplify complicated schemes and manually extract necessary parameters. The authors have conducted validation of this approach on a variety of flame modifications against experiments and a good agreement has been obtained.

3. Software overview

3.1 Interface and features

The software tool 'LOGEsoft ReactorNetwork' developed by LOGE AB aims for creation of reactor networks. For all the further work in this thesis the version v1.00.006 was used. The Figure 7 shows its interface.

illustration not visible in this excerpt

Figure 7. LOGEsoft ReactorNetwork main interface.

In the Figure 8 the modules available to be connected into a network are listed. The Stochastic PSR has not been implemented yet at the time of this work.

illustration not visible in this excerpt

Figure 8. Available modules.

Some modules in a bigger scale can be seen in the Figure 9. A fuel-air mixture inlet is connected with PSRs. The mass exchange between the reactors is expressed in kg/s and given by the user. Also, for each module, except mixers and splitters, an initial gas composition with its temperature and pressure need to be specified. Figure 10 demonstrates what inputs a reactor has. A PSR requires specification of either volume or residence time. For a PFR the geometry is represented as length, diameter and surface area.

illustration not visible in this excerpt

Figure 9. Example of a reactor network.

illustration not visible in this excerpt

Figure 10. Reactor data.

The user may choose what tolerance the calculation should have as well as the time step size. The time step size does not serve for chemistry calculation but for the flow. Output frequency defines time steps to be written in output files. These options are shown in the Figure 11.

illustration not visible in this excerpt

Figure 11. Calculation and output parameters.

illustration not visible in this excerpt

Figure 12. Reactor output.

As shown in the Figure 12 the output for each reactor can be seen as a function of time.

3.2 Testing and improvement

First of all, the tool was needed to be validated before its implementation in real cases. The main purpose of the testing was to prove that it is working stably and all balances are kept. An examination of code and execution of that code in various environments and conditions has been conducted. After a debugging process some new features have been included to enhance performance and extend possibilities of the product.

3.3 Modules data and settings

All the chemical reactor networks in this thesis are built from homogeneous perfectly-stirred reactors using transient calculation. In terms of calculation simplification it was decided to specify volumes since there was no option to take residence times directly from the CFD.

Mass flow rates of a flow have been extracted from the CFD for each cell (and further summarizing of the corresponding cells) with the formula:

illustration not visible in this excerpt

where V is a reactor volume, A cell flow - cell surface area through which the flow goes.

Finally, the following parameters have been specified for the reactor networks:

- flow boundaries: inlet mixture composition, temperature and pressure (keep constant).
- mass flow rates between modules
- PSR/PFR: volume/geometry, initial mixture composition (air was used), temperature and pressure (change with time and mostly affect only on the convergence speed).

In all schemes in this work a spark of 0.01 s long in the first PSR was applied for ignition and a value of 1e-6 for a steady state tolerance was used. The time step size of 1e-3 seconds is treated as default and optimal for most cases but also investigated later on.

4. Sandia Flame D

In order to investigate the process of building up equivalent reactor networks and find their sensitivity to different parameters the Sandia Flame D [24] was used as a reference. Briefly, it is a methane/air jet flame. Figure 13 shows photographs of it.

illustration not visible in this excerpt

Figure 13. Flame D (left) with Nd:YAG laser beam and close-up of the pilot flame (right).

illustration not visible in this excerpt

Table1. Sandia Flame D flow conditions.

The experimental set-up has a main inlet, a pilot flow and a co-flow. They are described in the Table 1.

The flame with a corresponding set of boundary conditions had been simulated by Nilsson[25]and described in his thesis being written in parallel to this work. All flow calculations in the Nilsson's work use the time dependent form of the RANS equations (also known as unsteady RANS or U-RANS). Flow equations are solved using the finite volume-based CFD software STAR-CD.[25]

illustration not visible in this excerpt

Figure 14. Sandia Flame D validation.[25]

Mechanism with 163 species is used and described by Shenk et. al. [26]. The validation sample can be seen in the Figure 14. There is a good agreement between the CFD solution and experiments, therefore the resulting flow field can serve as a trusted reference for the further CRN development.

In order to model the flame with equivalent reactor network two different approaches have been verified and are described in the following sections.

4.1 Reactor network set-up. Approach 1

In the first and simplest approach the reactor network is based on a sequence of volumes into which the flame has been cut. These volumes are used as volumes of transient PSRs in the CRN. The reaction scheme used was the same as which had been used in STAR-CD[26]. The schematic principle and equivalent reactor network can be seen in the Figures 15a and 15b respectively.

illustration not visible in this excerpt

Figure 15a. Sandia Flame D Approach 1. Principle.

illustration not visible in this excerpt

Figure 15b. Sandia Flame D Approach 1. CRN.

Afterwards, the results from the CRN have been compared to those from the CFD solution, where the values are taken as median of all cells in a certain corresponding part of the flame. The comparison is shown in the Figures 16 (a-f).

illustration not visible in this excerpt

Figure 16. (a): temperature profile; (b): CH4 concentration, linear; (c): CH4 conc. logarithmic; (d): OH conc.; (e): CO2 conc.; (f): H2O conc.

As one can see the combustion prediction in the CRN differs from CFD. From the residence time formula it can be explained that combustion in the CRN directly depends on volumes chosen for the reactors and will be different according to different choice of the flame parts' size. In order to force the CRN combustion to go the way it goes in CFD, it was decided to use isothermal PSRs with a temperature profile from CFD, which is seen

in the Figure 16a. In such reactors under a constant temperature mass fractions may vary. The results are presented in the Figure 17 (a-e).

illustration not visible in this excerpt

Figure 17. (a): CH4 concentration, linear; (b): CH4 conc. logarithmic; (c): OH conc.; (d): CO2 conc.; (e): H2O conc.

illustration not visible in this excerpt

Table 2. CPU times in case of two schemes in the first approach.

It is obvious, that in case of isothermal PSRs the convergence has been achieved four times faster and the tendency of species concentrations also has become better but still the scheme does not predict this flame well enough.

The probable reason was that the reactors had been created from non-homogeneous parts of the flame, so that non-similar zones had been mixed.

Thus, it was decided to try another approach to take homogeneity into account.

4.2 Reactor network set-up. Approach 2

In terms of specification of reactor network parameters the second approach resembles the first one. The only difference is in the principle of zones derivation which were defined by the flame temperatures. That principle is demonstrated in the Figure 18a.

illustration not visible in this excerpt

Figure 18a. Sandia Flame D Approach 2. Principle.

In the Figure 18b an equivalent reactor network is shown.

illustration not visible in this excerpt

Figure 18b. Sandia Flame D Approach 2. CRN.

Then the results were compared to CFD and shown in the Figure 19 (a-f).

illustration not visible in this excerpt

Figure 19. (a): temperature profile; (b): CH4 concentration, linear; (c): CH4 conc. Logarithmic; (d): OH conc.; (e): CO2 conc., (f): H2O conc.

The temperature profile obtained in the CRN is very close to the one from CFD, it means the combustion went properly and caused an obvious relatively good agreement for the species concentrations as well. The scheme has been also tested with use of isothermal reactors but the results mostly did not change.

4.2.1 Parameter study

It was decided to carry out a parameter study for the reactor network made with the second approach to see its stability and sensitivity to various factors. The influence of inlet temperature of main inlet and co-flow, time step size and mechanism sensitivity have been investigated.

Time step size, t:

illustration not visible in this excerpt

Table 3. Time step size influence (Output results for reactor Nr 9).

With time step size of 0.1 and 0.02 the initial spark time has been increased as 1 in order to ensure ignition of the mixture. Despite residence times of some reactors are smaller and some are bigger than t, as seen in the Table 3 for the current scheme it does not have an influence on results and the change of time step size only between 1e-3 and 5e-4 seconds gives different values. So, for the scheme with these certain conditions the time step size of 5e-4 seconds leads to the goal results while consuming less computational time.

Inlet temperature:

Mixture temperature variety has been done in three cases and results for the main inlet and the co-flow are shown in the Figures 20 (a-e) and 21 (a-e) respectively. For the main inlet the next cases are used:[illustration not visible in this excerpt]

illustration not visible in this excerpt

Figure 20. (a): temperature profile; (b): CH4 concentration; (c): OH conc.; (d): CO2 conc.; (e): H2O conc.

No obvious difference for the combustion behavior can be seen. For the co-flow temperature the following cases are used: [illustration not visible in this excerpt]

illustration not visible in this excerpt

Figure 21. (a): temperature profile; (b): CH4 concentration; (c): OH conc.; (d): CO2 conc.; (e): H2O conc.

Increase of co-flow temperature causes a higher final flame temperature that logically reduces amount of methane and enlarges OH; concentration of the other species did not change that much.

Mechanism sensitivity:

For this research the following mechanisms have been chosen for comparison with the previous results: LOGEfuel C4 v1.0 with 235 species, Reduced Methane Mech. with 28 species and Optimized Methane Mech. with 20 species, all developed by LOGE AB. Their performance can be seen in the Figure 22 (a-e). Also, in the Table 4 computational times of the mechanisms are summarized.

illustration not visible in this excerpt

Figure 22. (a): temperature profile; (b): CH4 concentration; (c): OH conc.; (d): CO2 conc.; (e): H2O conc.

illustration not visible in this excerpt

Table 4. Mechanisms CPU times.

As we can see from the plots the Optimized mechanism is the most beneficial one in terms of computational time and rather good prediction.

In conclusion, it can be stated that the second approach is sufficiently enough to simulate the flame in terms of time costs and accuracy. However, in order to decrease the deviation the zones should also be splitted into more reactors to take into account a variety of mass flows within the flame, this process is just a matter of time. The current scheme has demonstrated that the software tool performs properly giving reasonable results, so it can be applied to other engineering problems.

5. Siemens atmospheric combustion test rig

Siemens Industrial Turbomachinery manufactures the SGT-800 that is the third generation dry low emission (DLE) gas turbine. For experimental studies of the burner an atmospheric combustion test rig situated in Finspång, Sweden, has been built. A detailed description of the rig can be found in[27]. A schematic setup is presented in the Figure 23.

illustration not visible in this excerpt

Figure 23. Schematic layout of the combustion test rig.[28]

CFD computations on the burner have been performed by Nilsson[25]in STAR-CD using two different 2D meshes provided by Siemens. These meshes do not include the mixing chamber and the swirl cone upstream of the burner; instead the velocity and turbulence profiles used at the inlet were taken from previous 3D calculations preformed by Bruneflod[29], which included those features. The chemical composition and temperature at the inlet are computed based on reported mass flow rates of fuel and air into the mixing chamber. [25] The 2D model imported in STAR-CD is shown in the Figure 24.

illustration not visible in this excerpt

Figure 24. 2D model of the test rig combustion chamber.

5.1 Mechanism validation

Gas turbine simulation is a complicated engineering task and has to be treated accordingly. First, in order to start modeling of combustion processes in the rig and trust results, the most important component should be examined - a reaction scheme. Four mechanisms were chosen to be tested:

- GRI3.0 with 53 species[30]
- Ranzi et. al. mechanism with 114 species, described in[31]
- Optimized mechanism.
- Reduced mechanism.

As the corresponding experimental data the work of Le Cong et. al. [32] on investigation of methane/hydrogen blends oxidation at atmospheric pressure in a PSR was chosen. All the four mechanisms have been tested and validated as shown in the Figures 25(a-d).

illustration not visible in this excerpt

Figure 25. (a): CH4,; (b): CO2; (c): CO; (d): H2O concentrations vs hydrogen content in the mixture.

As it seen from the plots above the mechanisms Reduced, Optimized and one by Ranzi et. al. show a rather good agreement with experiments. The Optimized mechanism is the most beneficial in terms of computational times and accurate prediction, whereas the GRI3.0 has the worst trends. However, since there was no experimental data for NOx measurements in that PSR case, all the four mechanisms are still interesting for combustion and emission simulation under gas turbine conditions.

5.2 Reactor network set-up

A reactor network for the rig has been developed according to the third method described in the Section 2.7. Only a cold-flow solution from [25] was available. The processing of the CFD results was based on determination of recirculation zones which are shown in the Figure 26.

illustration not visible in this excerpt

Figure 26. Velocity magnitude of the flow and schematic zones mapping.

All data required for reactors has been extracted in a similar way it had been done for the Sandia flame described in the Section 4.1. In the Figure 27 an equivalent CRN is given. The reactors represent the flame zones as following: PSRs 1 - dome recirculation, 2 - main flame, 3 - immediate post flame, 4 - main recirculation. Reactors 5 and 6 are PFRs and they model post flame zone and dilution zone respectively.

illustration not visible in this excerpt

Figure 27. Combustion rig chemical reactor network.

The experiments had been carried out using natural gas as a fuel whereas in this work it was replaced by methane, since the composition of corresponding natural gas was not known. For the first simulation the mechanism Optimized M. with included thermal NOx (Zeldovich reactions) chemistry. That NOx part had been taken from the GRI3.0 mechanism. Referring to the section 2.5.3 such a decision was made based on neglection of a relatively small input of prompt NOx into the whole picture at high temperatures, whereas thermal NOx plays the most important role. Moreover, prompt NOx strongly depends on CH-radicals and should better be ignored for simplification. The resulting mechanism had 23 species. Outlet temperature and NOx concentration have been compared to experimental data and plotted below. In the Figure 28 NOx concentration from the CRN along the central horizontal axis of the rig starting from inlet is given. Due to confidential reasons all the values are scaled.

illustration not visible in this excerpt

Figure 28. Thermal NOx formation trend along the central axis.

The trend shows that the amount of NOx quickly increases in the flame zone, shortly drops afterwards and slightly forms through the rest part of the combustion chamber.

According to the experimental data provided by Siemens the CRN has been tested with corresponding conditions. The following Figures 29a and 29b compare NOx concentrations and temperatures obtained in the reactor network and compared to experimental values for five cases differed by hydrogen content in the fuel mixture.

illustration not visible in this excerpt

Figure 29. (a): NOx vs hydrogen content; (b): Temperature vs hydrogen content.

Based on the theory previously written and the last two plots above it can be stated that the NOx formation process mainly depends on temperature. The combustion test rig CRN captures a correct trend of such a dependency but not absolute values. Temperatures reached in the reactor network with no hydrogen in the mixture are about 30 K lower than the experimental, therefore NOx values do not meet logically. Probable reasons of those deviations will be discussed in the discussion part later on. Anyway, from the plots we see that with higher temperatures the NOx agreement is better, which can be explained by its formation mechanisms described in the Section 2.5.3.

5.3 Parameter study

The aim of this parameter study was to estimate prediction under different conditions and also see the reactor network sensitivity to other reaction schemes.

Time step size:

illustration not visible in this excerpt

Table 5. Time step size influence (Output results for outlet).

Based on results given in the Table 5 for the current reactor network at time step size of between 0.005 and 0.001 the values stop changing and further reduction of time step does not improve the accuracy, thus for this scheme the optimal time step size is 5e-3 seconds, since it takes less CPU time and leads to the goal results.

In the Figures 30a and 30b the dependency of NOx concentration and temperature of the flow on the mixture inlet velocity and temperature is shown.

illustration not visible in this excerpt

Figure 30. (a): NOx/Temp. vs inlet mass flow rate; (b): NOx/Temp. vs inlet temperature of the mixture.

Apparently, the faster mixture comes to the burner, the lower values of temperature and NOx amount the flow will have after combustion. Also, a higher temperature of incoming mixture leads to higher flame temperatures and, as it was mentioned before, leads to more intensive NOx production.

Mechanism sensitivity:

In the Figures 31a and 31b we can see sensitivity of this scheme to four chosen mechanisms, using methane with no hydrogen content as a fuel. The reaction schemes are: Optimized mechanism+Thermal NOx, Reduced+Thermal NOx (modified analogically) with 31 species, Ranzi et. al. and GRI3.0. The Table 6 shows the mechanisms computational time consumed for the simulation.

illustration not visible in this excerpt

Table 6. CPU times.

illustration not visible in this excerpt

Figure 31. (a): NOx and (b): temperature sensitivity to different reaction schemes.

In comparison to bigger mechanisms, as shown before, the Optimized+Thermal NOx mechaninism gives higher temperatures and lower NOx emissions and is still closer to the measured data. Its performance is also better in terms of computational time. That all proves its ability to be used as reaction scheme for the further CRN development of the combustion test rig.

Also, it is worth to notice that the detailed reaction schemes do not influe so much on the final temperature (<10 K difference), as on the NOx formation, so its prediction is directly dependent on the NOx chemistry of a mechanism.

6. Discussion and conclusions

In this thesis different techniques of a chemical reactor network set-up were investigated. An in-house software tool 'LOGEsoft Reactor Network' was tested, improved and applied to two cases in order to see its performance for emission prediction in parallel to low computational costs.

The first case was modeling of the Sandia Flame D based on its validated CFD solution. The CRN development has been tried with two approaches: splitting the flame by 'disks' in series and determination of homogeneous zones by temperature. The results were far not accurate in the first approach since non-homogeneous parts of the flame had been represented by PSRs where a perfect mixing occurs and combustion went other way. In order to force the combustion process resemble CFD, isothermal PSRs with the CFD temperature field were used. The results became better and trends got more reasonable but still the solution was incorrect. In the second approach a resulting CRN temperature profile nearly repeated the one from CFD. It meant the combustion behavior went right way, so the outcome for species concentrations was also in a good agreement with CFD. However, in order to decrease the deviation the zones should have been splitted into a bigger number of reactors in order to take into account different mass flow rates which vary along radius and height. The scheme has shown that the software tool performs properly, the results are reasonable and it can be applied to other engineering problems.

The second case has pictured such a problem. As it was mentioned before gas turbines are being constantly evolved in terms of emissions reduction and simulation tools are in charge to avoid real-scale experiments. An experimental atmospheric combustion test rig by SIT, which serves for investigation of DLE burner used in the SGT-800 turbine, was modeled in CFD with a subsequent data extraction. The equivalent reactor network has been built based on recirculation zones of the 'cold' flow. The outcome captured the main trends but not absolute values. Firstly, it might be caused by different heat release when burning methane whereas for the experiments natural gas was used. Secondly, for the hydrogen cases during the experiments by adjusting mass flow the temperature has been aimed to be the same, while in the CRN the flow does not change and thereafter deviations of temperature are bigger. Thirdly, a subjective factor that is expressed in manual data extraction with some essential deviations should be noticed. Also, of course, the reactor network had been built upon a 'cold-flow' solution which may differ from a 'hot' one. In this work the 'cold-flow' was a single available picture of how the flame would look like and the corresponding ERN could not capture the flame field for the hydrogen cases properly.

Nevertheless, the resulting values are reasonable in comparison to experimental data that proves a suitability of such a scheme to be developed onward. A way of improvement might be a more detailed zone mapping of the flame and accurate and full extraction of mass flows and other interactions between the zones. It is rather complicated and time consuming when this work is carried out by hand, that is why automation of the process by use of computational sources is absolutely rational.

7. Future works

The current reactor network method needs to be used for further development, in particular by means of two major features: using CFD 'hot-flow' solutions (for hydrogen cases also desirable) and implementation of automatic instruments for data extraction.

As an alternative approach partially-stirred (stochastic) reactors should be tested to take turbulent effects on the flow into account for a better simulation of flames under gas turbine conditions.

Despite the most frequently used above mechanism showed a good performance, its NOx chemistry has to be also further improved.

In addition, more experimental investigations are highly desirable as well as application of the reactor network to other similar gas turbine combustor modifications.

References

1. http://www.setatwork.eu/database/actors/A397.htm
2. ge-flexibility.com/static/global-multimedia/flexibility/photos/how-gt-work-lg.jpg
3. www.sparknotes.com/chemistry/kinetics/mechanisms/section1.html
4. Warnatz, J. et. al., Combustion: Physical and Chemical Fundamentals, Modeling and Simulation, Experiments, Pollutant Formation. 4th ed. Springer Berlin Heidelberg New York, 2006
5. Sepideh, S.M., Network Modeling Application to Laminar Flame Speed and NOx Prediction in Industrial Gas Turbines. Master's thesis, Linköping University, 2013
6. http://en.wikipedia.org/wiki/Combustor
7. Lefebvre, A.H. and Ballal, D.R. Gas Turbine Combustion: Alternative Fuels and Emissions. Third Edition. CRC Press, Taylor & Francis Group, 2010.
8. Al-Fawas, A.D., Dearden, L.M., Hedley, J.T., Missaghi, M., Pourkashanian, M., Williams, A. and Yap, L.T., NOx Formation in Geometrically Scaled Gas - Fired industrial Burners. 25th Symposium (International) Combustion, The Combustion Institute, Pittsburgh, 1994.
9. Hamedi, N., Numerical Study of NOx and Flame Shape of a DLE Burner. Master's thesis, Linköping University, 2012
10. http://www.maidhof.com/img/psr.gif
11. Turns, S.R., An introduction to Combustion, 1993
12. Andersson, S., Förbränningsteknik, 1996
13. Chomiak, J., Combustion: A study in theory, fact and application, 1990
14. Tillmark, C., Kinetic study of combustion behavior in a gas turbine - influence from varying natural gas composition. Lund Institute of Technology, Lund University, 2006.
15. http://www.loge.se/Company/company.html
16. Schmidt, L.D., “The Engineering of Chemical Reactions”. New York: Oxford University Press, 1998.
17. Daniel, J., Combustion Characteristics of MCV/LCV Fuel - A Numerical Chemical Kinetic Study at Gas Turbine Conditions. Master's thesis, Lund Institute of Technology, Lund University, 2007.
18. Bhave, A. and Kraft, M., Partially Stirred Reactor Model: Analytical Solutions and Numerical Convergence Study of PDF/Monte Carlo Method. Cambridge Centre for Computational Chemical Engineering, University of Cambridge, 2002.
19. Hua, W., Yong-chang, L., Ming-rui, W., Yu-sheng, Z., “Multidimensional modeling of Dimethyl Ether (DME) spray combustion in DI diesel engine”. Journal of Zhejiang University SCIENCE, 2005.
20. Hallaji, M. and Mazaheri, K., Numerical simulation of turbulent non-premixed combustion in diluted hot coflow using PaSR combustion model. Combustion Institute, MCS 7, 2011.
21. Ghazi-Hesami, S., Cost Effective Emissions and Minor Species Predictions via Coupling of Computational Fluid Dynamics and Chemical Reactor Network Analysis. Master's thesis, Concordia University Montreal, 2009.
22. Falcitelli, M., Tognotti, L., Pasini, S., An Algorithm for Extracting Chemical Reactor Network Models from CFD Simulation of Industrial Combustion Systems. Combustion Science and Technology, 2002.
23. Thanh Hao, N. and Jungkyu, P., CRN Application to Predict the NOx Emissions for Industrial Combustion Chamber. Asian Journal of Applied Science and Engineering, Volume 2, No 2, 2013.
24. http://www.sandia.gov/TNF/DataArch/FlameD.html
25. Nilsson, T., Development of a model for gas turbine combustion. Master's thesis, Combustion physics, Lund University (to be published later).
26. Schenk, M., Moshammer, K., Oßwald, P., Kohse-Höinghaus, K., Leon, L., Seidel, L., Mauss, F., and Zeuch, T., Detailed mass spectrometric and modeling study of isomeric butene flames. Combustion and Flame, 160(3), 2013.
27. Lindholm, A., Lörstad, D., Magnusson, P., Andersson, P. and Larsson, T., Combustion stability and emissions in a lean premixed industrial gas turbine burner due to changes in the fuel profile. ASME paper GT2009-59409, 2009.
28. Lörstad, D., Lindholm, A., Larfeldt, J., Lantz, A., Collin, R., Aldén, M., Investigation of hydrogen enriched natural gas flames in a SGT-700/800 burner using OH PLIF and chemiluminescence imaging. Proceedings of ASME Turbo Expo, 2014.
29. Bruneflod, S., Flow simulations of an axisymmetric two-dimensional 3:rd generation DLE burner, Master's thesis. Department of Applied Physics and Mechanical Engineering Division of Fluid Mechanics, Lule å University of Technology, 2010.
30. http://www.me.berkeley.edu/gri-mech/version30/text30.html
31. http://creckmodeling.chem.polimi.it/index.php/current-version-november-2013/c1c3-me chanism
32. Le Cong, T., Dagaut, P., Oxidation of H2/CO2 mixtures and effect of hydrogen initial concentration on the combustion of CH4 and CH4/CO2 mixtures: Experiments and modeling. Proceedings of the Combustion Institute 32, 2009.

Details

Seiten
55
Jahr
2014
ISBN (Buch)
9783656712374
Dateigröße
5.2 MB
Sprache
Englisch
Katalognummer
v278443
Institution / Hochschule
Brandenburgische Technische Universität Cottbus
Note
1.0
Schlagworte
combustion emissions NOx CFD reactor network CRN ERN hydrogen enriched chemical reactor gas turbine

Autor

Teilen

Zurück

Titel: Simulation of a Gas Turbine Combustor Test Rig using a Reactor Network Approach with Detailed Chemistry