assessing explosion hazards in gas turbine enclosures
TRANSCRIPT
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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
16
, 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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American Institute of Aeronautics and Astronautics
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