Assessing Explosion Hazards in Gas Turbine Enclosures

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American Institute of Aeronautics and Astronautics

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Assessing Explosion Hazards in Gas Turbine Enclosures

Oliver R. Heynes1

MMI Engineering, Oakland, CA 94612, USA

J. Keith. Clutter 2

MMI Engineering, Houston, TX 77077, USA

The use of gas turbines (GTs) is widespread within the power and oil and gas industries,

for the electricity generation, pumping and compression, and power transmission. The fuel

supply to the GT is usually at high pressure and can be natural gas, liquefied petroleum gas,

diesel, syngas or one of several alternatives. However, in all cases, a complex fuel manifold

containing high pressure, flammable fuel is in close proximity to a number of ignition

sources including the hot surfaces of the GT, which may exceed the auto-ignition

temperature for methane. Thus the potential hazard is present for explosive atmospheres to

develop and subsequently ignite following an accidental release from the fuel manifold. The

focus of this paper is the use of Computational Fluid Dynamics (CFD) to quantify this

hazard, though detailed modeling of the ventilation, fuel gas releases from the fuel manifold,and subsequent explosion of the flammable environment. Significant emphasis is placed on

the explosion model iflow which uses adaptive meshing techniques to accurately calculate the

propagation of pressure waves through the enclosure, and can output peak overpressures

from several release scenarios. These results are useful to rapidly and cheaply assess the

structural integrity of current GT enclosures, define the necessary detection systems, and

highlight improvements to the ventilation systems that can reduce the potential explosion

risk.

Nomenclature

h = adaptive meshing parameter

K = adaptive meshing parameter

P = pressure

t = time

ε = adaption criteria

ξ = refinement parameter

I. Introduction

This paper concerns the modeling and assessment of accidental fuel gas releases and subsequent explosions

within gas turbine (GT) acoustic enclosures. The potential for such accidental releases is significant due to the

complexity of the pipe work and manifolds supplying fuel under high pressure to the combustion chambers of the

GT. The high number of ignition sources in the enclosure, including the hot surfaces of the GT which may be

approaching the auto-ignition temperature of the fuel gas, means that the released fuel is likely to ignite either

immediately, or after a flammable atmosphere has developed. In the latter case, the subsequent explosion couldcause overpressures in the enclosure high enough to cause failure of the internal equipment, escalation, or structural

failure of the enclosure itself.

This hazard is increasingly brought into focus due to the increasing popularity of using natural gas as a fuel

source (now approaching 25% of the total US power output1) within combined cycle power plants, which employ

large industrial GTs which must be kept within acoustic enclosures to control noise. While (to the author’s

knowledge) statistics concerning accidental fuel gas releases have not been collected within the US power industry

1 Engineer, MMI Engineering Inc., 475 14th Street, Suite 400, Oakland, CA 94612, USA2 Associate, MMI Engineering Inc., 11490 Westheimer Road, Suite 150, Houston, TX 77077, USA

49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition4 - 7 January 2011, Orlando, Florida

AIAA 2011-527

Copyright © 2011 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.




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as a whole, such studies have been completed within the United Kingdom2,3 where the electricity consumption is

about a tenth of that of the United States and a similar proportion from natural gas. These figures showed that 134

natural gas releases occurred over a 13 year period, with 30 of those releases going undetected by gas detectors

within the enclosure. Of those 30 undetected releases, 18 ignited causing fires within the enclosures. By contrast,

only one detected release ignited. These statistics highlight the importance of well designed gas detection to reduce

the risk of fires and explosions within GT enclosures.

In response to the increased awareness of explosion hazards in GT enclosures, a Joint Industry Project was

formed under the United Kingdom’s Health and Safety Executive which aimed to provide an assessment of criterion

used as a basis of safety4,5,6. Following careful validation exercises, the study promoted the use of Computational

Fluid Dynamics (CFD) to calculate the volume of the flammable atmosphere that could develop following an

accidental release, and yet remain undetected (the gas detectors are usually set to trip at some percentage of the

lower flammable limit (LFL) of the fuel gas, rather than anything above zero concentration, in order to avoid

spurious detections and unnecessary shutdowns). The work presented in this paper demonstrates this technique for

hazard assessments in power plants within the United States, and goes further than previous studies by performing a

CFD analysis not only of the development of a flammable atmosphere, but also the modeling of explosions to

monitor peak overpressures. The explosion modeling is carried out using iflow, an adaptive mesh code, while the

ventilation assessment and gas releases are modeled using ANSYS-CFX7.

II. Ventilation Assessment in GT Enclosures

Fans placed within ducting either upstream or downstream of the enclosure provide ventilation at a flow rateusually calculated from the required heat rejection rate. However, due to the placement of equipment within the

enclosure and the design of the ventilation system itself, not all parts of the enclosure are ventilated equally well. In

some spaces (close to the ventilation inlets, for example) the air may change rapidly, while in others the air may

remain relatively stagnant. It is the size and extents of the stagnation regions that are crucial to the ventilation

assessment from the perspective of diluting a fuel gas release, as the worst case is that the release is directed towards

these stagnation zones and is therefore allowed to build up into a large flammable volume which could potentially

ignite before being detected.

Computational Fluid Dynamics (CFD) is particularly well-suited tool for perform such a ventilation assessment,

as not only can the fluid flow within the enclosure be calculated, but the “age” of the air can be determined either

through a “mean-age-of-air” analysis or through a residence time distribution. Physical measurements would be

extensive and costly to provide this data close to this utility, however spot velocity measurements can usefully be

taken at specific locations to validate the CFD analysis. The geometry of the enclosure must either be read into the

CFD from CAD files, or be created from scratch using technical drawings. The latter approach is frequently required

due to the lack of electronic data of the enclosure, however a fairly detailed model may still be created. The left

Figure 1. CAD geometry (right) of GT enclosure showing GT (blue), large equipment and pipework, ventilation

inlets (green) and outlets (red). Surface mesh on internal equipment shown on left.




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subfigure in Figure 1 shows an example of a GT enclosure model. The GT itself is highlighted in blue, while the

major equipment such as the central exhaust duct, compressors, turbine shield and pipe work are colored brown.

These components are visible as many of the surfaces of the enclosure have been made transparent – in the actual

CFD model they are solid. In the figure, ventilation is provided through two inlets highlighted in green (note that

fans, ducting and filters upstream of these inlets have not been included in the CFD model) while the outlets of the

ventilation are highlighted in red. In the figure, the mean flow due to the ventilation is roughly from left to right. As

with all CFD simulations, the modeling domain must be subdivided into small volumes on which the discretized

transport equations for momentum, mass, energy and species can be solved using iterative techniques. A

representation of the unstructured mesh used is shown on the right subfigure of Figure 1. The mesh is particularly

refined around primary areas of interest such as the GT, and uses prismatic inflation layers near all the solid surfaces

to refine the near-wall flows to a suitable level of accuracy. The total number of cells in this example is 4 million,

resulting in run-times for ventilation and release scenarios of a few hours on a single core analysis machine.

The boundary conditions of the CFD simulation are relatively simple for the velocity and pressure fields, since

the ventilation flow rate is generally known and may be applied at the ventilation inlet, while the ventilation outlet

boundaries may be specified as constant pressure outlets. The boundary conditions for the energy equation are more

complex, and require a surface temperature profile to be specified on the surface of the GT. As the GT surface can

approach 900°F, it is also necessary to model radiation in the simulation, and therefore to provide surface

emissivities as boundary conditions. Due to the uncertainty in these boundary conditions, it is often well advised 6 to

perform validation of the thermal field by obtaining several physical measurements of the air temperature (with

radiation shielding if possible) using thermocouples installed inside the enclosure. In all ventilation assessments

performed by MMI Engineering, such validation of the thermal field has played an important role. Once the boundary conditions are established, the ventilation flow may be solved. The numerical techniques of industrial

CFD packages such as ANSYS-CFX7, as well as best-practice guidelines for CFD6, are well-established and will not

be reproduced here.

Streamlines showing the flow inside the enclosure are shown on Figure 2. The direction of the streamlines from

the inlets is clear, and they become aligned with the vector normal to the outlet planes near to the ventilation outlet,

but within the bulk of the enclosure the flow patterns are complex and unclear. A useful technique to simplify the

picture is to add an additional scalar transport equation to the analysis with a source term set to the fluid density. The

resulting scalar is equivalent to the mean age of fluid at any one point. To clarify, the mean age of fluid in a cell (i.e.

the length of time that has passed since it was released through the ventilation inlet) is the average of all the various

ages of fluid molecules within the cell. The ages of the

individual fluid molecules are clearly not calculated

individually, but on the bulk scale of approximation in

the CFD analysis, the average age of those moleculesis the mean age. Contours of the mean fluid age are

shown on Figure 3. The blue areas show relatively

“young” fluid, which, as expected, is close to the

ventilation inlets. One does expect the fluid to age

naturally as it flows through the enclosure, however

large regions of red indicate “old” fluid or stagnation

regions, in other words, parts of the enclosure that are

not well-ventilated. It is these areas that are of

particular concern for fuel gas releases, as if the

release is directed towards them, it will not be well

diluted by the ventilation and is more likely to result in

the build-up of a large flammable volume that may

ignite and cause a subsequent explosion. The purposeof the ventilation analysis is therefore to highlight

these stagnation zones, thereby informing the

subsequent CFD modeling of fuel releases in the

enclosure of the most conservative, or “worst-case”,

release direction.

Figure 2. Streamlines colored by velocity magnitude

(red is 10 m/s, blue is 0 m/s).




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Figure 3. Mean age of air contours shown on central cut plane (left) and lateral cut plane (right). Blue

coloration indicates “fresh” air, while red coloration indicates “old” air or stagnation zones.

III. CFD Modeling of High Pressure Releases

Once the ventilation assessment has been performed, CFD simulations may be run of fuel releases inside the

enclosure. Since the mass balance for released gas and “clean” ventilation air is readily calculated, it is useful to setthe flow rate of the release to that which would result in a particular concentration at the ventilation outlet. For

example, a “20%LFL release” refers to a release with flow rate that results in a concentration of 20% of the lower-

flammable-limit (LFL) for the released gas. For methane, the LFL is usually taken to be 4.4%v. The overall strategy

is to perform several release simulations, directed into stagnation zones highlighted by the ventilation analysis, at

flow rates equivalent to plausible gas detector settings at the ventilation outlet, such as 10%LFL, 20%LFL and

30%LFL. For each of these simulations, the flammable volume of gas can be calculated as a percentage of the

enclosure volume, and once the volume of gas becomes higher than a pre-determined value, the detectors are set to a

sensible value lower than the flow rate which resulted in those high flammable volumes. This ensures that the

likelihood of flammable volumes developing that are larger than the pre-determined maximum volume are small.

However, the maximum allowable flammable volume can be difficult to determine if it is not set by regulators (as it

is in the United Kingdom as 0.1% of the Net Enclosure Volume4). To address this uncertainty, MMI Engineering

have combined the CFD analysis of releases in GT enclosures with a dedicated explosion modeling software called

iflow, which calculates the overpressures in the enclosure due to an explosion of the flammable atmosphere (detailsof this model are given in the subsequent sections of this paper).

Many techniques are available to model the release of high pressure gas from fuel lines, but perhaps the most

efficient whilst still retaining good accuracy is the sonic disk approach, whereby the under-expanded portion of the

jet as it expands, which is characterized by a series of diagonal shock waves, are not calculated. Instead, the jet is

modeled downstream of the sonic disk (located where the jet expands to atmospheric pressure), with the properties

the sonic disk calculated using isentropic expansion relations. This approach is efficient as the computational

resources required to calculate the under-expanded regime accurately are prohibitive. In addition, the assumption

that the expansion of the jet from its stagnation properties in the fuel line to atmospheric conditions is isentropic is

reasonable. Detailed CFD studies have confirmed that the sonic disk approach is an accurate method.

Figure 4 shows results from a CFD model of a release inside the enclosure. In both subfigures, the red isosurface

indicates the extent of concentrations of fuel gas above the LFL concentration. This is slightly larger for the figure

on the right, since the flow rate there is set to 30%LEL, while for the figure on the left the flow rate is 20%LEL. The

LFL clouds have somewhat unusual shapes, as they impact beams inside the enclosure – this is fairly typical sincethe GT enclosures can have several obstructions such as beams, pipe work and other process equipment. However,

the release is generally directed from the fuel manifold towards the stagnation zones highlighted by the ventilation

assessment. Full concentration data from these analyses may then be used for subsequent explosion modeling, as

shown in the following section.




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Figure 4. Red isosurface showing the extents of the flammable gas cloud resulting from a high pressure

release from the GT fuel manifold. 20%LEL release shown on left, 30%LEL release shown on right.

IV. Explosion Modeling Approach

After determining the details of the released gas in the gas turbine and its enclosure, the potential of an explosion

needs to be assessed. To perform this analysis both the geometry and explosion process must be represented.

General CFD codes such as ANSYS-CFX can be used to perform the explosion analysis. However, it has been

found that the representation of the explosion process can be very sensitive to the chemical kinetics scheme used in

the analysis as well as the resolution of the turbulence intensity in the flow field. This is typically not an issue when

performing more research oriented analysis. However, when simulations are in support of safety analysis for design

and operations, an approach that doesn’t underestimate the potential of an explosion must be taken.

The code used for the explosion analysis is call iflow and was developed specifically for explosively driven

flows. It uses algorithms validated to accurately represent the explosion problem8. The reaction process is

represented using a flame-speed based combustion model which is tailored for simulations used in safety studies9.

Details of the combustion model are provided in these earlier works. Here details of other aspects of the model are

presented. Here the focus is on an explosion in a single gas turbine enclosure. However the code used for the

analysis is also used for explosion analysis in complete facilities such as offshore rigs and petrochemical facilities.

The use of the code for these larger, much more complex domains has driven some of the selections of techniques

discussed here.

A. Geometry Definition and MeshingThe first requirement is the definition of the geometry and an example of the geometries that need to be

represented is shown in Figure 1. There exist a variety of meshing frameworks for use in computational modeling.

The main classes are (1) body-fitted curvilinear meshing, (2) unstructured tetrahedral meshing, and (3) Cartesian

meshing. All have pros and cons when considering their use in urban flow scenarios. The body-fitted approach

requires that the mesh be constructed around each body. In the case of the scenarios of interest here there are simply

too many bodies with too many orientation possibilities for this to be a viable and efficient method. This is even

truer when analysis of a complete facility is being performed.

The unstructured tetrahedral approach is a viable option for the vehicle scenario and has been successfully used

for problems such as airflow and pollutant in urban settings10. The construction of the mesh for the scenario of an

explosion in a processing facility can be much more involved since the mesh construction begins with the surfacesof the geometry and for the problems of interest here, there are many and they cover a large range of sizes.

The Cartesian meshing approach offers a very efficient option when building a scenario that contains several

bodies such as in the scenarios of interest here. The baseline mesh is refined only where there exist surfaces of the

geometry. The method employed here uses the basic principles found in previous work approaches11,12,13.




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Using the Cartesian approach, the domain of interest is defined as any rectangular shape. Also defined is the size

of the coarsest grid desired which is referred to as the Level 0 cells. The cells need not be cubic but larger aspect

ratio cells can introduce numerical error depending on the particular flow solver used. Also defined is the number of

adaptation levels desired, referred to here as n levels. The two parameters, Level 0 cell size and n levels will

influence both solution accuracy and efficiency.

The Cartesian mesh used here will be adapted

as needed to define either geometries or solutions.

The particulars of the adaptation will be presented

in following sections. Here the fundamentals of

the meshing structure are discussed. The domain

of interest is decomposed into a set of Level 0

cells. If increased resolution is needed these cells

are systematically divided into 8 children as

depicted in Figure 5. The requirement for

refinement is dependent on a local feature of

interest in the geometry or solution. In the current

implementation, the discontinuity between

neighboring cells is never greater than a 2:1 ratio.

This restriction does require that some additional

cells be refined than just those meeting specified criteria. However, it simplifies the flow solver and difference

equation solution algorithms and more than adequately accounts for any additional cost.To facilitate the management of the computational mesh and the solution process, there are some basic grid

accounting parameters that need to be associated with each cell. These include the following:

• level = level of the cell

• idiv = 0 if cell is not divided, = 1 if cell is divided

• Parent = pointer to a parent cell

• C2[i][j][k] = pointer to children cells: i=0,1; j=0,1; k=0,1

• xN = pointer to a neighboring cell: x=L(eft), R(ight), B(ottom), T(op), U(nder), O(ver)

The first three parameters are self evident. The third parameter defines the children cells if any cell at any level

is divide. The pointers are stored in

a three-dimensional array making it

easy to define algorithms such as

flow solvers. Figure 6 shows the

definition of the cells andorientation of the numbering. The

symbol # denotes that from that

vantage point the index could be 0

or 1.

The adaptation of the mesh is

first required to define the

geometry of interest. Here it is the gas turbine and enclosure shown in Figure 1 (left subfigure). The actual

construction of the geometry is typically done in a CAD package and can be exported as a collection of triangular

plates. Most of the common CAD programs allow such output as shown in on the right subfigure of Figure 1. The

adaptation to the geometry uses the plate information long with the defined grid domain, Level 0 cell size and the

parameter n levels to construct a representation of the geometry within the Cartesian mesh. Here every item in the

geometry is explicitly represented. However for other cases such as an explosion in a processing unit, large objects

can be explicitly represented and other items such as collections of pipes can be represented using an equivalent porosity approach14.

The adaptation is based on the method developed by Akenine-Moller for graphics programming 15 and the

algorithm is derived from the separating axis theorem (SAT). The model cycles through the set of plates, refining

only the cells which it intersects. The algorithm begins with the level 0 cells and whether the particular level 0 cell

has been divided or not, it does not search lower if the plate does not intersect that cell. If the intersection check

returns a true then the algorithm steps to the finer levels. If the cell has not been divided earlier, it is divided at that

time. This recursive process continues until the volume the plate occupies has been divided to the finest level

permitted. At that point, these finest of cells is defined to contain a geometric surface.

Figure 5. The cell division used when refinement is

required. A cell at level n is divided into 8 children at level

n+1.

Figure 6. Definition of the children cells.




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Figure 7 shows an example of the algorithm results for the gas turbine and enclosure in Figure 1. In this example

there are 5 levels of refinement. Cut planes have been used to show how the structures are represented in the model.

Those cells at the finest spacing that are colored red have been defined to contain a plate. The presence of these solid

surfaces is represented in the computation by applying the correct boundary conditions. Here it is adequate to treat

them as rigid surfaces.

Figure 7. How the geometry is defined within the Cartesian grid

system.

B. Solution AdaptionIn addition to refining the grid to represent the geometry, refinement is needed in areas of flow property

gradients to ensure accuracy while maintaining efficiency. The two primary approaches to setting the criteria for

adaptation are (1) using a measure of convergence of a solution or (2) using the local gradient information. Here the

second method, as demonstrated in earlier studies

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, will be used to determine an adaptation criteria parameterlabeled ε. This parameter will be set based on a series of tests evaluating whether the gradient in the flow parameters

meets a set condition. The parameter assigned to the cell and used in the adaptation decision will be the largest of

the set corresponding to the gradient between the cell and its six neighbors,

( )OU T B R Lε ε ε ε ε ε ε ,,,,,max=

where the parameter ε N corresponds to the largest value as determined by each flow parameter.

The local pressure gradient will be evaluated using

( ) ( )( )⎩⎨⎧ >−

=

otherwise

PPPPabsif S

N N

0

,min1 ξ ε

where P is the pressure in the cell being evaluated and P N is the pressure in the neighboring cells. S is a set value

that is used to determine if a cells needs to be refined or can be coarsened. For instance, if S is set to 0.1 then the

cell will be refined until the maximum relative increase in pressure between the cell under evaluation and any

neighbor is not greater than 10%. The refinement can be set as a function of other parameters but since the interesthere is on blast loadings pressure will be used. Figure 8 shows a simple example of a steady-state solution using this

adaptation method.




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Figure 8. Solution of a Mach 3 ramp flow problem using the adpatation method with 5 levles of refinement

and a gradient criterion of 10%.

For the steady-state problem it is rather straight forward to refine the mesh in the vicinity of gradients. However,

the main objective of this current work is the solution of problems involving unsteady wave motion impacting the

vehicle. For the cases involving moving fronts, if refinement is restricted only to the immediate area where the front

sets then refinement will have to occur frequently to ensure adequate adaptation is always present. If not critical

results such as the maximum blast pressure produced during the explosion will be under predicted. The requirement

may be that adaptation be performed essentially at every time step. This can introduce excessive computational time

and counter the goal of the adaptation in the first place.

Here an alternative approach is suggested. This method will map out the current location where key fronts are

located and project their motion. This additional area will be refined along with the current location of the front.

Such an approach will require that the grid be refined only at discrete times during the time integration. By

balancing the mapping and the interval between adaptation then both increased accuracy and efficiency can be

achieved.

To develop an algorithm to map out areas in the vicinity of the fronts for refinement, the adaptation criteriaalready discussed is used. However it is treated as a domain property similar to temperature and a convection-

diffusion equation is solved to perpetuate the property through the domain. At the intervals when grid refinement

occurs the convection-diffusion equation is integrated in pseudo time, off-line from the time integration of the

governing equations of the flow problem.

This is similar to the method used in earlier work 16. However, in this earlier work a reaction-diffusion equation

was employed. This was tested in the current study and it was found that the mapping was directly dependent on the

time over which the reaction-diffusion equation was integrated. A steady-state solution did not exist. That is why in

the current study this equation was replaced with the convection-diffusion equation which will converge to a steady-

state solution. This makes the mapping independent of the time over which the integration is performed. Therefore

the perpetuation of the adaptation property will depend solely on the nature of the flow field and the constants used

in the equations.

The approach to map out regions around fronts in unsteady problems that need adaptation begins with the same

process using in the steady-state situation. Those regions where the gradient warrants that the adaptation parameter,

ε, be set to a value of 1 are defined. These cells are maintained at a ε = 1 state. This parameter is then treated as a

property and perpetuated through the domain using the governing equation

( )∞

−−∇=

∂

∂ε ε ε

ε

hK t

2

patterned after the heat conduction-diffusion equation. Here, ε∞ is set to 0. To present the use of this approach to

map out the regions near fronts in need of refinement, a simple discontinuity in pressure and within a straight duct

was used. Then the parameters K and h were adjusted to determine the characteristics of the mapping procedure.

What was of interest was the size of the region over which ε perpetuated. Recall those cells where ε is greater than




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some specified value will be refined. All others will be coarsened. The governing partial differential equation is

integrated using a finite volume approach with explicit time integration.

Figure 9 shows the effect of the parameters K and h on the mapping of the parameter ε in a 1-D configuration

with a discontinuity in pressure.

There is some tradeoff between the

time required to map the parameter

and the size of the region defined

for refinement with more time

required for the parameter settings

that maps a larger region. Because

the interest is in unsteady problems,

the region refined needs to be large

enough to accommodate the

movement of the fronts over the

interval between when adaptation

occurs. The setting of K and h will

be evaluated using a representative

vehicle explosion problem. Note

the region over which the property

ε spreads is dependent on the ratio between K and h and not necessarily the numerical values.

The fluid dynamics that govern the propagation of blast is solved using a proven flow solver. The flow solver

method used here has been used and benchmarked in earlier studies17,18.

V. Example Explosion Modeling Results

To demonstrate the current approach for simulating explosions in gas turbines and enclosures the two release

cases were used. The gas concentration was imported into the mesh used by iflow and the explosion was simulated.

The ignition location was taken to be a location near the turbine inside the enclosure. Figure 10 shows contours of

the concentration of fuel and one of the combustion products shortly after ignition. Though not a substantial amount

of fuel has been burned the increase in pressure produced during the combustion results in overpressure that

migrates through the gas turbine and the enclosure as evident in Figure 11. The complex geometry drives the

migration of the waves and the enhancement of the blast loading on the equipment and enclosure. Evident in Figure

11 is how the mesh is adapted to the solution with refinement not only where the wave fronts are located but where

they will be migrating to.

Figure 12 shows the overpressure field some time later. Again it is evident that the blast loading is dominated by

the interaction of the waves with the geometry. The solution refinement is again evident. It is the pressure field that

dominates the criteria of the refinement.

Virtual pressure probes were included at two locations, one on the inner surface of the enclosure and one on the

surface of the turbine. Figure 13 shows a comparison of the pressure time histories recorded by the probes. The

loadings are similar in magnitude and shape. This can be contributed to the fact that the same ignition location was

assumed for each case. The gas field and concentrations are different but in the vicinity of the ignition they are

similar enough to produce similar pressure output immediately after ignition. The loadings are heavily influenced by

the geometry of the gas turbine and the enclosure and the interaction of the pressure waves with the geometry. This

suggests when performing such simulations for safety assessments it may not be required to simulate the explosion

for every release scenario. It may be sufficient to analyze a few representative cases.

Figure 9. Steady state mapping of ε for h=1 and 50 when K=100.




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Figure 10. The fuel and combustion product concentrations along cut planes through the enclosure

approximately 10 ms after ignition.

Figure 11. Contour of overpressure within the enclosure approximately 10 ms after ignition. The left image isa vertical cut plane and the right a plan view.

Figure 12. Contour of overpressure within the enclosure approximately 100 ms after ignition. The left image

is a vertical cut plane and the right a plan view.




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VI. Conclusions

A full computational methodology has been presented for assessing the explosion hazard due to accidental

releases within gas turbine (GT) enclosures. The methodology comprises three broad phases: a ventilation

assessment, modeling of worst-case fuel gas releases, explosion modeling of the flammable atmosphere. The first

two phases can be solved using commercially available CFD software, while the third has been solved using iflow,

an adaptive meshing code used to calculate the propagation of pressure waves following an explosion. This

computational methodology can readily be supported with validation exercises at relatively little cost. The benefits

of the analysis include the assessment of the current structural integrity of the enclosure, the required settings of gas

detectors in the ventilation outlets to prevent large explosions, and improvements to the ventilation system to betterdilute released gas. In all cases, the risk of failure of the enclosure, GT, and related escalation issues can be reduced

significantly.

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Figure 13. Comparison of explosion pressure loadings for the two release scenarios at a point on the

enclosure inner surface (probe 1) and on the equipment (probe 2).




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Assessing Explosion Hazards in Gas Turbine Enclosures