[american institute of aeronautics and astronautics 43rd aiaa/asme/sae/asee joint propulsion...

American Institute of Aeronautics and Astronautics

1

Study of VSV Effects on Performance via Integrated

Aerodynamic Component Zooming Process

Arjun Bala1 and Vishal Sethi

2

Cranfield University, Cranfield, Bedfordshire, MK43 0AL

Enrico lo Gatto3 and Vassilios Pachidis

4


and

Pericles Pilidis5


The work presented in this paper adapts a modern simulation approach of “Integrated

Mini-Map based Component Zooming” in order to investigate the effects of variable stator

vanes on performance of both, the first stage of a Fan component at (3-D) component level

and on overall engine at (0-D) engine system level, during part speed operation. This paper

also highlights the structure and operation of the custom developed integrated workflow

controller framework. This single algorithmic framework facilitates optimal use of readily

available simulation technology at different fidelity levels.

Nomenclature α = Alpha or Stagger Angle VSV = Variable Stator Vanes

0-D = Non-Dimensional Wc = Corrected Mass Flow Rate

3-D = Three Dimensional

AVG = Averaged

BETA = Blade Angle

CFD = Computational Fluid Dynamics

DCA = Double Circular Arc

DP = Design Point

EFF = Isentropic Efficiency

GT = Gas Turbine

GUI = Graphical User Interface

IWC = Integrated Workflow Controller

LBR = Low Bypass Ratio

LP = Low Pressure

OD = Off Design

PCN = Non-Dimensional Speed

PR = Pressure Ratio

R&D = Research and Development

SSH = Secure Shell Network Protocol

VIGV = Variable Inlet Guide Vanes

1 SOE Researcher, Department of Power and Propulsion, Cranfield University.

2 SOE Researcher, Department of Power and Propulsion, Cranfield University.

3 Visiting Researcher, Department of Power and Propulsion, Cranfield University.

4 Research Fellow, Department of Power and Propulsion, Cranfield University.

5 Head of Department, Department of Power and Propulsion, Cranfield University.

43rd AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit 8 - 11 July 2007, Cincinnati, OH

AIAA 2007-5046

Copyright © 2007 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.


2

I. Introduction

or a variety of GT engine applications, it is

important to design a multi-stage high

performance axial compressor system with an

extensive range of operating flow rates and

efficiencies. It has been observed that the operating

range of these compressors is restricted due to internal

physical phenomenon and instabilities, which either

result in a complete breakdown of flow or create large

flow pulsations through the compressor stages. This

phenomenon is generally termed “Surge”.1 Depending

upon the engine application, efficiency, surge margin

improvement and weight reduction are compromised

according to certain design criteria. These design

criterions are generally set around an operating region

where the engine or in this context the compressor is

required to operate continuously for a majority of it

working life. Furthermore, for most compressors the

DP occurs typically at or near the maximum rotational

speed and PR the machine can deliver. Thus, each

stage blading, flow cross-sectional areas and other

geometric parameters are chosen to be appropriate for

this DP mass flow rate, pressure rise and rotational

speed. Most compressors operate at least for some part

of their life at pressure ratios or rotational speeds other

than at DP. For this reason, it is essential that an

adequate PR and EFF are achieved at these off-design

conditions e.g. during part speed operation.2

From experience, it is also observed that designing

and optimizing compressors for high-speed operating

conditions often lead to part speed performance

limitations. As shown in Fig.1, this typically affects the

front stage (or stages) of the multi-stage axial

compressor system. In other words, stages of

compressor are correctly matched only at one point

(i.e. DP) on the performance characteristics. During

part speed operations the compressor is operated at a

point (i.e. OD) further away from the original point

(i.e. DP) which leads to a greater mismatching between

stages. This prevents the compressor to operate

satisfactorily at part speeds and requires special

arrangements such as VSV, inter-stage bleeds, or their

combination to improve its part speed performance.3

As the stage geometries are fixed for design operating

conditions, these alternative corrective techniques

adopted prevents the front stage (or stages) from

“Stall” and subsequent compressor “Surge”. The

present work mainly focuses on the special

arrangement of using VSV to improve the part speed

performance.

Long before the advent of sophisticated and

powerful computers, various complex studies such as

the one discussed in this paper were based on large

scale and expensive experimental tests.4,5

This

approach was an integral part of the complete GT

engine R&D process, all the way from preliminary to

the detailed design phase. This design approach was

adopted primarily to check for various design impacts

on the component and overall engine performance. The

process involved several, hardware based design-build-

test cycles required to fully identify the

multidisciplinary design effects and reach the desired

engine system performance, which require increased

resources in-terms of time and costs.

Ever since the introduction of digital computers, a

large amount of effort has been put into the

development of several simulation tools and to apply

this technology to the field of gas turbine engineering.

The fidelity of these simulation tools depends mainly

on the part of the engine life cycle being considered.

This technology has essentially sought its application

in the areas of GT design and optimization,

performance modeling and simulation, and

diagnostics.6 From Ref.7 and Ref.8, it is clear that a

greater use of predictive simulation tools saves some

costs directly associated with hardware development

and testing. It also enables engine design trade-offs and

component interactions to be studied in detail earlier

on, during the preliminary design phase, before a

commitment is made towards a final design. Experts

have estimated a reduction of 30-40% in development

time and costs when such modern simulation

techniques are implemented.8

Recently, GT performance simulation codes have

been evolving for a wide range of applications. These

applications range from the early stages of preliminary

design through to in-service support.6 Traditionally, in

the design cycle of an engine, the overall engine

performance is first estimated at the engine level with a

(0-D) cycle code – assuming generic component

characteristics (such as efficiencies, flow capacities,

etc). Along with performance, averaged aero-

thermodynamic conditions (temperature, pressure, etc)

are assessed in the engine with integral conservation

equations. In a (0-D) cycle code such as the one used

during the current study “PYTHIA”, individual

components are mathematically modeled using suitable

thermodynamic relations and component

characteristics via non-dimensional look-up tables

(maps). These maps are typically generated either

experimentally or are based on empirical calculations

and contain correction (or adjustment) factors for off-

design effects such as variable geometry, Reynolds

F


3

number effect, gamma corrections, tip clearance, etc.9

The conventional (0-D) cycle code offers excellent

performance predicting capabilities for the complete

engine system but fails to capture different complex

physical phenomena and analyze the performance of

the individual GT components in detail e.g. the effect

of geometric change on compressor performance.

Furthermore, they do not account for any axial, radial

or circumferential resolution within a given component

during simulation runs at both, DP and OD

conditions.10

As a solution, in the past various numerical codes

have been developed for various component specific

analyses. An excellent example in the context is a

numerical algorithm developed for predicting and

optimizing the stator vane settings in multistage axial

compressor system based on overall performance

requirements.1, 11

More recently, major advancements in the

computational speed and power, have enabled

sophisticated (3-D) computational simulation

techniques (or CFD) to be applied in the area of

complex GT design and performance simulation. Once

again, the use of CFD for studying complex physical

phenomena, design impact on component performance

and inter-component interaction at a very high

resolution can lead to a significant saving of resources

without compromising on the resolution of the

computed results. Another excellent example in this

context is the work discussed in Ref.12 and Ref.13

where CFD has been used to investigate the effect of

compressor entry distortion and to investigate the

turbine design and performance at large tip clearance

of un-shrouded rotor cascade, respectively.

From Ref.12 and Ref.13, it is clear that although

the CFD analysis of GT components yields very

detailed performance data at unique component

operating points, it fails to systematically account for

interactions between various engine components at

engine system level. It is also known that the overall

engine performance fundamentally depends on the

engine components working together in the most

efficient way over a range of operating conditions.10

It

can therefore be concluded that both simulation tools

have their own advantages and limitations. The best

possible solution would be to develop the (3-D) model

of the complete engine system and simulate it at

maximum resolution using CFD. Though the

predictions obtained would be very accurate, the

limiting factor for its implementation would be the

increased computational wall clock time and costs.8

For a given level of computational and financial

resources, the technique of, “Integrated Component

Zooming” seems more an effective and feasible

solution. This particular approach couples both

simulation environments at different fidelity levels

effectively, thus allowing for a “Variable Complexity

Analysis” to be achieved satisfactorily at component

and engine system level. Other research efforts with

similar focal points have been reported in the past.8, 14-

16

II. Simulation Strategy

The primary objective of the presented work is to

investigate the effects of VSV on the part speed

performance of the first stage of a fan component, and

subsequently on the overall engine performance. The

analysis involves; a (3-D) component level simulation

and a (0-D) engine system level simulation, utilizing

limited and readily available computing resources.

After a careful study of different methods 9, 15, 17-19

, this

study adapts a simulation strategy which easily allows

the GT component performance characteristics

generated from a detailed, high fidelity (3-D) CFD

analysis, to be integrated into a (0-D) engine system

analysis. This objective has been achieved though an

“Integrated Workflow Controller” (IWC) as explained

in Section IX.

This strategy has the potential to conduct the study

across multiple engineering domains (in this case,

Aerodynamic and Thermodynamic domain) and also

incorporate the modern distributed computing

architecture to reduce computational wall clock time.

This strategy clearly allows for a “Variable Complexity

Analysis” to be performed, satisfactorily. This strategy

is referred to as “Integrated Mini-Map based

Component Zooming”. 15, 17-19

This zooming technique, as illustrated in Fig.2,

uses component “Mini-Maps” generated from a high-

fidelity (3-D) computational analysis. Initially, a (0-D)

engine system model is simulated for both DP and a

number of OD conditions (e.g. different power

settings) using a standard empirically generated default

component characteristics in order to generate a

database of component specific entry and exit

boundary conditions. These boundary conditions are

subsequently used for the (3-D) CFD analysis. Each (3-

D) simulation produces a single averaged component

characteristic point for a given boundary condition and

power setting (rotational speed), once complete

convergence is achieved. Several CFD runs for a fixed

power setting (rotational speed) results in a range of

characteristic points and joining all points establishes a

single fan speed line.19

Repeating the simulation for


4

different power settings (rotational speed) and slightly

modified stage exit boundary conditions, generates the

complete high-fidelity component performance

characteristics “Mini-Maps”. These performance

“Mini-Maps”, fully define the characteristic of the

component at several operating conditions and power

settings.10

This procedure is repeated to generate high-

fidelity component “Mini-Maps”, for other two

different VSV geometric configurations. The process is

automated via the “Integrated Workflow Controller”

(IWC). The format of these “Mini-Maps” is compatible

with the (0-D) cycle code. These “Mini-Maps” replace

the default empirical maps thereby providing a more

accurate physics-based estimate of the performance of

the fan component.

As discussed in Ref.9 and Ref.19, the “Integrated

Mini-Map based Component Zooming” approach has

several advantages

over the other approaches

including:

� This approach does not require a parallel or

iterative execution of the (0-D) Cycle Code and

high-fidelity (3-D) CFD simulation tool for the

generating the characteristic “Mini-Maps”.

� It is more stable and allows the user to take

intermediate corrective actions to prevent

oscillatory behavior during the simulation, which

may lead to non convergence or possibly a

complete simulation crash.

� This approach can be easily implemented, with

appropriate programmatic modifications, for all the

GT engine components.

� It is independent of the operating environment on a

computational machine and additionally it can be

developed using the modern distributed computing

architecture to significantly reduce computational

wall clock time and increase accuracy.

� This process can be easily controlled remotely.

III. “Zooming” Tools

The “Integrated Mini-Map based Component

Zooming” approach was facilitated utilizing the

following tools:

� “PYTHIA”9, 21, 22

- The in-house (0-D) GT engine

performance modeling and simulation code,

developed by Cranfield University (UK).

� The ANSYS commercial CFD simulation package

comprising:

� ANSYS BladeGen 27

� ANSYS CFX Turbo-Grid 28

� ANSYS CFX 10.0 29

� The Integrated Workflow Controller (IWC)33

IV. (0-D) Engine Model

A two shaft LBR mixed exhaust turbofan engine

system model was developed using “PYTHIA”, based

on the engine schematic illustrated in Fig.3. This

engine model is based on a typical military engine and

the overall engine DP performance is summarized in

Table.1.

Table 1

Engine Parameter Value

Engine Dry Thrust (kN) 60

Engine Thrust with Reheat (kN) 90

Overall LPC Pressure Ratio 4.21

Bypass Ratio 0.4

Dry SFC (g/kNs) 21-23

Reheated SFC (g/kNs) 47-49

Total Mass Flow Rate (kg/s) 75-77

Fan entry Diameter (mm) 740

Overall Engine Length (mm) 4000

Engine Weight (kg) 1040

Operating Altitude (m) 11,000

Flight Mach number 1.23

As shown in Fig.3, a separate compressor

component module, between engine stations 2 and 2A,

is used to simulate the performance of the first stage of

the fan. Engine stations, 2A – 3 correspond to the

coupled second and third stage LP compressor

component. This baseline engine model is simulated

for both DP and a number of OD conditions in order to

generate a database of entry and exit boundary

conditions for the first stage of the fan component.

V. (3-D) Fan Component Model

The three stage LP compressor was designed from

scratch and optimized based on the preliminary design

methodology discussed in Ref.2, Ref.3 and Ref.23-

Ref.26. During the design process, boundary

conditions from the baseline engine model simulation

were used to satisfy various physical relations e.g.

continuity equation, Euler’s Turbo-machinery

equation, etc.

Equipped with the knowledge that for a multistage

axial compressor it is typically the first stage which

suffers during part-speed operation 3, 26

and due to the

limited computational resources available, the CFD

analysis was only performed on the first stage (rotor

and variable stator) of the three stage LP compressor.

Some of the optimized design specifications of the first

stage are outlined in Table.2.


5

Table 2

Design Parameter Value

Pressure Ratio 1.701

Isentropic Efficiency 0.8689

Mass Flow Rate (kg/sec) 37.67

Rotor Tip Relative Mach number 1.40

AVG. Rotor Blade Aspect Ratio 1.87

AVG. Stator Blade Aspect Ratio 1.50

AVG. Rotor space/chord Ratio 0.76

AVG. Stator space/chord Ratio 0.77

Rotor Blade Number 19

Stator Blade Number 23

AVG. Rotor Chord Length (m) 0.116

AVG. Stator Chord Length (m) 0.109

AVG. Rotor Solidity 1.39

AVG. Stator Solidity 1.41

As shown in Fig.4, a complete (3-D) model for the

first stage was generated, using “ANSYS CFX-

BladeGen”, based on the geometry data obtained from

the preliminary design process.

After a careful study of the different types of blade

profiles2, 3

, the use of DCA blades was considered

acceptable because:

� The first stage is part of a three stage “Transonic”

compressor and from earlier experimental tests, it

has been established that DCA blade profiles

provide realistic and acceptable performance during

transonic operating conditions.30-32

� DCA blade profiles involve a considerably

simplified design process.31

The (3-D) first stage model was subsequently

meshed using the commercial tool, “ANSYS CFX-

TurboGrid” as illustrated in Fig.5. The (3-D)

component grid has suitable “Inlet” and “Outlet”

domains. This provision considerably reduces any

second order effects at the actual entry and exit plane

which may arise from the imposed boundary

conditions at the domain extremities. A fully structured

grid type was chosen comprising the following sub-

types:

� “H, J, L” grid type applied to each mesh layer

� “O” Grid type applied around the blade profile with

suitable expansion ratio to account for boundary

layer as illustrated in Fig.6

� “H” grid type applied to both “Inlet” and “Outlet”

domains.

To establish the optimum grid-density that would

offer a best compromise between computational time

and accuracy a “Grid Independency” test was also

carried out. The results of this test are tabulated in

Table.3.

Table 3

Fan Stage

Parameter

Grid Density

250,000

Grid Density

400,000

Pressure Ratio 1.701 1.69987

Mass Flow (kg/s) 37.67 37.5884

Isentropic Efficiency 0.8689 0.86470

Following this test a grid density of 250,000 nodes

was chosen which lead to a reasonable computational

wall clock time of ~20 hours as opposed to ~40 hours

for the higher grid density of 400,000 nodes. The

relative discrepancies of all the three principle stage

performance parameters are close to 0.2%, which is

considered acceptable for this case study.

The method outlined above was used to model and

mesh three different stage configurations with a fixed

rotor stagger setting (or blade angle as in Fig.7) and the

following VSV stagger settings; 0° (DP), 10° and 15°.

The corresponding values of BETA for the hub, mean

and tip, for the rotor and each VSV setting are

highlighted in Table.4 and illustrated in Fig.8 – Fig.10.

Table 4

BETA (°) Stagger

Hub Mean Tip

Rotor - (DP) -20.6 -52.8 -67.7

Stator - (DP) 38.8 29.4 25.3

Stator - (10°) 49.1 39.1 34.6

Stator - (15°) 54.2 43.9 39.3

VI. (3-D) CFD Simulation and Mini-Map

Generation

The (3-D) model of each configuration was

assembled, simulated and post-processed using

“ANSYS CFX 10.0”. (3-D) CFD simulation is

performed based on Navier Stokes equations. To

obtain solutions for real flows a numerical approach

has been adopted whereby the equations are replaced

by algebraic approximations which may be solved

using a numerical method. The modern solver includes

“Discretisation of the Governing Equations”. This

approach involves discretising the spatial domain into

finite control volumes using a mesh. The governing

equations are integrated over each control volume,


6

such that the relevant quantity (mass, momentum,

energy etc.) is conserved in a discrete sense for each

control volume. In order to achieve more realistic and

accurate physic based solutions “k-e” turbulence model

available within the commercial tool was introduced

along with wall-functions to closely capture flow

phenomena near walls. The “k-e” turbulence model has

a proven track record in terms of stability, numerical

robustance and established regime of predictive

capability.29

The database of entry and exit boundary conditions

for the first stage of the fan component, obtained from

“PYTHIA”, along with the pre-defined solver settings

were used to run a series of CFD simulations

sequentially, for each stage configuration. All

simulations were carried out by imposing the following

boundary conditions:

� Stage entry total pressure.

� Stage entry total temperature.

� LP shaft rotational speed.

� Stage exit average static pressure.

Varying the stage exit average static pressure yields

the different fan operating points for a given non-

dimensional speed. To establish the surge limit, the

static pressure was gradually incremented from the DP

value until a point where the CFD solution did not

converge as a consequence of severe unstable and

turbulent flow conditions. Similarly, the choke limit

was established by gradually reducing the static

pressure.

The simulations were run using a distributed cluster

grid computing framework. Solutions were obtained

for residual values (RMS) of 10-7

for DP and from 10-5

to 10-6

for all OD operating conditions. Assuming that

the flow at the fan entry is uniform, only a single flow

passage was simulated as illustrated in Fig.11. The

solution for the whole cascade was then automatically

calculated by the CFD tool based on the solution

obtained for the single passage.

This series of CFD simulations generates a

database of performance parameters (PR, Wc and EFF)

which are automatically converted into individual

high-fidelity “Mini-Maps”, for each VSV stagger

setting, by the IWC. The IWC framework

automatically integrates these “Mini-Maps” into a

single variable geometry composite “Mini-Map” which

is consistent with the “PYTHIA” map file input format

as illustrated in Fig.13 and Fig.14.

VII. Fan Stage Behavior and Characteristics

(Including VSV in Operation)

The main characteristics of the variable geometry

composite “Mini-Map” [Fig.13 and Fig.14] are as

follows:

� The overall composite “Mini-Map” comprises three

distinct characteristic “Mini-Maps” corresponding

to all three VSV stagger settings. “Mini-Map 1”

corresponds to the DP stage configuration. “Mini-

Map 2” and “Mini-Map 3” correspond to the stage

configurations with VSV stagger settings of 10°

and 15°, respectively.

� A set of operating points which define the design

non-dimensional speed line (PCN = 1.0) on “Map-

1” (baseline map) is highlighted in Table.5. The

values in bold correspond to the DP.

� Furthermore, as indicated in Table.5, the DP is not

the operating point with the maximum efficiency.

This is not an un-common design practice.

Choosing a DP at maximum efficiency results in a

reduced surge margin. Military engines (such as the

one used for this case study) typically experience

extreme transient operating conditions and are

generally compromised to operate at relatively

lower efficiencies to facilitate larger surge

margins.34

Table 5

Exit Static

Pressure (Pa)

Wc

(kg/sec) PR EFF

50000 69.99 1.45 0.769

55000 69.92 1.49 0.796

65000 69.63 1.60 0.841

70000 69.31 1.66 0.858

73500 68.94 1.70 0.869

76000 68.54 1.73 0.875

78000 68.06 1.76 0.878

79000 67.73 1.77 0.879

80000 67.32 1.79 0.879

82000 65.76 1.81 0.869

83000 64.12 1.88 0.854

� As expected the gradients of all the constant speed

lines decrease as the non-dimensional speed

reduces.

� A typical isentropic efficiency characteristic is

obtained [Fig.14], exhibiting a point of maximum

compressor isentropic efficiency, for each constant

speed line.

� As shown in Fig.13, at lower non-dimensional

speeds, the surge line characteristics exhibit a

“kink”. This kink is a consequence of the absence


7

of upstream VIGV. At low speeds and in the

absence of VIGV, it is typical for the front stage to

be heavily stalled because the rear stages are

operating close to choked conditions, as shown in

Fig.1. For a given axial velocity and blade speed

the incidence angle can be restored to an optimum

value by introducing VIGV. This would

substantially reduce the stage stall and smooth out

the “kinked” surge lines. 3, 26

This is a second order

effect of VIGV but it cannot be achieved by the use

of downstream VSV alone as in the case study.

However, studying the effects of upstream VIGV

was beyond the scope of this case study.

The main operation of VSV and VIGV [Fig.12] can

be easily explained in-terms of their effect on the stage

characteristics. It is important to understand that a

compressor map is unique for a given compressor entry

flow angle and geometry. Both VSV and VIGV are

generally rotated about their centroid in order to get

them re-staggered. At lower speeds they move the non-

dimensional speed lines approximately horizontally on

the stage characteristics; they are scheduled closed

(high rotative swirl angle) thus reducing the passage

area and further reducing the mass flow through the

stage at a given speed, and more importantly move the

surge line to the left. During OD operations the

compressor working line often alleviates towards surge

as an engine is throttled back and VSV/VIGV provide

a mechanism to mitigate this by raising the part speed

surge line. It is also important to note that to a first

order, the working line in terms of PR versus Wc is un-

affected by their setting.35

The composite “Mini-Map” [Fig.13] demonstrates

the expected trends with VSV operation and conforms

to the theory discussed above. There is clearly a

horizontal shift of non-dimensional speed lines and

also an increased part speed surge margin. Furthermore

it can be observed that the magnitudes of these shifts

are proportional to the stagger angle i.e. the greater the

stagger setting, more the shift and therefore the better

the part speed surge margin.

As shown in Fig.13, “Mini-Map 2” and “Mini-Map

3”capture the effects of VSV for part speed operation

only. The variable geometry composite “Mini-Map”

does not include non-dimensional speed lines above

the design speed. At high speeds the VSV are fully

open, the flow being axial (or providing small negative

incidence), as it is important that the compressor passes

as much mass flow as possible to maximize output

thrust.35

VIII. Overall Engine Performance

The final step in the integrated (3-D) aerodynamic

component zooming process is to replace the original

default map, of the separate compressor module with

the new high-fidelity composite “Mini-Map”. The new

composite “Mini-Map” provides a more accurate,

physics-based estimate of the component’s

performance, hence engine performance. The original

(0-D) engine system model is simulated for both DP

and a number of OD conditions (LP shaft rotational

speed as handle) in order to establish a working line.

The (0-D) simulation was initially run using “Mini-

Map 1” (the baseline map). However, in order to

extend the part speed operation of the engine, an

appropriate VSV schedule was devised so that when

the working line approached surge, “Mini-Map 2” (10°

VSV close) was selected automatically. Similarly, as

the working line approached surge again, (as the LP

shaft rotation speed was reduced even further) “Mini-

Map 3” (15° VSV close) was automatically selected.

In both cases the surge margin was improved as

illustrated in Fig.15. Additionally, as expected

changing the VSV stagger settings do not affect the

working line progressing across the three “Mini-

Maps”.

Subsequent to the completion of all (0-D) cycle

simulations, it has been observed that different feasible

VSV schedules only have a second order impact on the

relationships between leading engine non-dimensional

parameters i.e. generally, they remain virtually

unchanged, which is consistent with the theory

discussed in Ref.35. This is also illustrated in Fig.16,

Fig.17 and Fig.18.

IX. The IWC Framework

An essential aspect of the zooming process is the

development and application of the “Integrated

Workflow Controller” (IWC). As the source codes for

both “PYTHIA” and the “ANSYS-CFX” simulation

tools were not accessible it was necessary to develop a

single external workflow framework. The IWC

encapsulates both (0-D/3-D) simulation tools and is

able to perform the high-fidelity (3-D) aerodynamic

component zooming simulation in a synchronized and

controlled manner, automatically. The IWC was

custom developed for the case study discussed in

previous sections.

The IWC comprises a collection of individual

custom developed algorithms which, along with a

“SSH” daemon, are invoked and executed in a

synchronized manner as an integral part of the overall

(3-D) aerodynamic component zooming process. All


8

the algorithms have been based on “Shell” 36

and

“Perl” 37

LINUX scripting languages. The IWC acts as

a control system which also permits a network of

heterogeneous LINUX computers to be used as a

single and large computing system generally referred

to as a “Virtual Machine”.19

This collection of different

computers, provide dedicated and aggregate power for

solving large and computationally demanding

numerical problems.

In addition to the “Base Terminal” within the IWC,

the following the “Virtual Machine” elements are

linked together adapting to the distributed computing

architecture: 33

� Application server

� Cluster-grid machines

� Shared Drive Space

The IWC system is composed of a “Daemon”

which resides on all the machines making up the

“Virtual Machine”. The fundamental type of daemon

used in the current framework is known as “SSH

Daemon”.38

The use of “Daemon” was necessary

because, all the (3-D) CFD tools and IWC program

modules were located on the “Application Server”,

which had to be invoked from the “Base Terminal”,

remotely. Additionally, all the input-output files

continuously being generated and used during the (3-

D) zooming process were required to be stored and

handled from a remote “Shared Drive Space”. All the

complex computational simulations were carried out

across the “Cluster-Grid Machines”, once again

remotely.

The IWC was employed for handling a number of

different tasks during the (3-D) zooming process. As

highlighted in Fig.19, some of the key tasks handled by

the IWC, include:

� Data extraction and simultaneous data translation

between the (0-D) cycle code and the (3-D)

computational tool (MAKE-FILE and PRE-INPUT

FILE GENERATOR)

� Invoke and execute all the (3-D) CFD simulation

tools in a controlled and a synchronized manner

including:

� (3-D) grid model generation (GRID

CONTROLLER)

� Creation of numerous (3-D) CFD case

file definitions (PRE-CONTROLLER)

� Solve a large number of numerical

cases in a controlled manner over the

cluster-grid machines (SOLVER

CONTROLLER)

� Finally, post processing these large

amounts of averaged computational

results to extract the necessary sets of

component performance data (POST

CONTROLLER)

� Reverse, data extraction and simultaneous data

translation between the (3-D) computational tool

and (0-D) cycle code (MINI-MAP GENERATOR)

In addition to the above key tasks the IWC is also

responsible for automatically establishing remote

connections between the “Base Terminal”, and the

“Virtual Machine”. Furthermore, it is responsible for

data management in terms of different file formats,

their storage and systematic handling, to and from the

predefined directories located on the “Shared drive

Space”. Using this first generation of the IWC

framework, manual intervention was only necessary

for running the (0-D) cycle code (as illustrated in

Fig.19).

For the (3-D) zooming system to work properly and

successfully the user is required to actively monitor

and evaluate the validity of the simulation. The large

amount of data typically present in the (3-D) flow

solution, along with the large number of variables

affecting simulations, place a heavy demand on the

settings in the relevant IWC algorithms pre-

programmed by the user. This is particularly true in

this scenario where the method of selecting and

applying boundary conditions, is extremely important.

Incorrect boundary conditions and solver settings will

lead to numerical instabilities, poor convergence or

even complete simulation crash.19

For this reason, the

IWC makes use of an interactive GUI readily available

within “ANSYS-CFX-Solver”. This provides a powerful

tool which allows the user to actively monitor and

make necessary corrective changes to the numerical

problem during the actual (3-D) computational

simulation, [Fig.20]. Further detailed information on

the structure and working of the IWC tool can be

obtained from Ref.33.

X. Conclusion

This paper adopts a modern “Integrated Mini-Map

based Component Zooming” strategy. This particular

strategy allows the high-fidelity component

performance data to be utilized for a low fidelity

engine system analysis via component characteristic

“Mini-Maps”.

The (3-D) zooming process is controlled by a

custom developed IWC which encapsulates both, a (0-

D) cycle code “PYTHIA” and the (3-D) “ANSYS-

CFX” CFD simulation tool. An analysis (in the form of

a case study) has been carried out successfully using

the modern simulation strategy in order to investigate

the effect of VSV on both, first stage fan component


9

performance at (3-D) component level and on overall

engine performance at (0-D) engine system level

during part speed operation. The analysis performed,

gives a better understanding of the overall engine

handling mechanism and implementation of control

laws to achieve better engine performance. The

analysis involved the generation of individual sets of

fan “Mini-Maps” corresponding to the three different

VSV stagger settings. The IWC framework

automatically integrates these “Mini-Maps” into a

single variable geometry composite “Mini-Map” which

is consistent with the “PYTHIA” map file input format.

The high-fidelity component characteristic is used by

the (0-D) cycle code instead of the original (default)

empirically generated component characteristics during

the final step of the zooming process in order to

investigate what has been defined for the case study. It

was noted that VSV could be ganged in together and

scheduled against compressor shaft rotational speed,

very effectively thereby extending the overall engine

part speed operating range.

The custom developed IWC workflow management

tool can be used with suitable programmatic

modifications, in the future for various other high-

fidelity analyses. It can also be used to automatically

generate numerous turbomachinery characteristics in

batch mode. These high-fidelity component

characteristics can be used instead of the (default)

empirically generated characteristic maps within a (0-

D) Cycle code for more accurate GT performance

predictions.

This investigation justifies the extra time that is

usually required to develop and simulate (3-D) CFD

engine component, especially in cases where more

accurate, high fidelity engine performance simulations

are required and the strategy adopted indirectly extends

the validity of the (0-D) cycle code.

Due to the limited computational resources

available, the CFD analysis was only performed on the

first stage (rotor and variable stator) of the three stage

LP compressor. However, with adequate computing

power the analysis can be extended to the complete

three stage LP compressor. Furthermore, the

application of the IWC framework can also be

extended to other GT components, simultaneously,

with suitable programmatic modifications.

Further detailed information on any part of the work

presented in this paper can be obtained from Ref.33.

XI. References

1Sun, J., and Elder, R.L., “Numerical Optimization of

Stator Vane Setting in Multi-Stage Axial Flow

Compressors”, IMechE Proceedings, Vol.212 Part A,

Cranfield, England, 1998. 2Cumpsty, N.A., Compressor Aerodynamics, 1st ed.,

Krieger Publishing Company, Florida, 2004. 3McKenzie, A.B., Axial Flow Fans and Compressors, 1st

ed., Ashgate Publishing Limited, England, 2003. 4Moore, R.D., and Reid, L., “Aerodynamic Performance

of Axial-Flow Fan Stage operated at Nine Inlet Guide Vane

Angles”, NASA TP-1510, 1979. 5 Bobula, G.A., Soeder, R.H. and Burkardt, L.A., “Effect

of Variable guide Vanes on the Performance of a High-

Bypass turbofan Engine”, Journal of Aircraft, Vol.20,

Number 4, Ohio, United States of America, 1982. 6Alexiou, A., and Mathioudakis, K., “Development of

Gas Turbine Performance Models using a Generic

Simulation Tool”, ASME, GT-2005-68678, 2005. 7Lytle, J.K., Follen, G., Naiman, C., Veres, J.P., Owen,

K., and Lopez, I., “2001 Numerical Propulsion System

Simulation Review”, NASA TM-2002-211197, 2002. 8Follen, G., and auBuchon, M., “Numerical Zooming

between a NPSS Engine System Simulation and a One-

Dimensional High Compressor Analysis Code”, NASA TM-

2000-209913, 2000. 9Nantua, N., Pachidis, V., and Pilidis, P., “De-Coupled

Approach to Component Zooming Applied to a HBR

Turbofan Engine Intake”, Cranfield University Technical

Report, 2004. 10Melloni, L., Kotsiopoulos, P., Jackson, A., Pachidis, V.,

and Pilidis, P., “Military Engine Response to Compressor

Inlet Stratified Distortion by an Integrated CFD Analysis”,

ASME, GT-2006-90805, 2006. 11de Sousa Junior, F., da Silva, R.J., and Barbosa, J.R.,

“Single Objective Optimization of a Multistage Compressor

Using a Gradient Based Method Applied to a Specially

Developed Design Computer Code”, WCSMO, 2005. 12Charalambous, N., Ghisu, T., Iurisci, G., Pachidis, V.,

and Pilidis, P., “Axial Compressor Response to Inlet Flow

Distortion by CFD Analysis”, ASME, GT-2004-53846, 2004. 13Helmers, L., Steen, J., Ljungkrona, I., Brodin, S. and

Johnsson, R., “Turbine Design and Performance at large Tip

Clearance of Un-Shrouded Rotor Cascades”, AIAA, AIAA-

2003-4766, 2003. 14Reed, J.A. and Afjeh, A.A., “A Comparative Study of

High and Low Fidelity Fan Models for Turbofan Engine

System Simulation”, IASTED Proceedings, Banff, Canada,

1997. 15Turner, M.G., Reed, J.A., Ryder, R., and Veres, J.P.,

“Multi-fidelity Simulation of a Turbofan Engine with Results

Zoomed into Mini-Maps for a Zero-D Cycle Simulation”,

ASME, GT-2004-53956, 2004. 16Sampath, R., Irani, R. and Balasubramaniam, M.,

“High Fidelity System Simulation of Aerospace vehicles

using NPSS”, AIAA, AIAA-2004-371, 2004. 17Pachidis, V., Pilidis, P., Talhouarn, Kalfas, A., and

Templalexis, L., “A Fully Integrated Approach to

Component Zooming Using Computational Fluid

Dynamics”, ASME, GT-2005-68458, 2005.


10

18Reed, J.A., and Afjeh, A.A., “Distributed and Parallel

Programming in Support of Zooming in Numerical

propulsion System Simulation”, ISPA, Ohio, United States of

America, 1994. 19Reed, J.A., and Afjeh, A.A., “An Interactive Graphical

System for Engine Component Zooming in a Numerical

Propulsion System Simulation”, AIAA, AIAA-1995-0118,

1994. 20GTPE Group, “The TURBOMATCH Scheme”, School

of Engineering, Cranfield University, 1999. 21GTPE Group, “The PYTHIA (OOP) Scheme”, School

of Engineering, Cranfield University, 2005. 22Pachidis, V., “Gas Turbine Performance Simulation”,

School of Engineering, Cranfield University, 2005. 23Ramsden, K.W., “Axial Compressor Design Manual”,

School of Engineering, Cranfield University, 2005. 24Ramsden, K.W., “Axial Compressor Design and

Performance Course Notes”, School of Engineering,

Cranfield University, 2005. 25Haslam, A., “Mechanical Design of Turbomachinery

Course Notes”, School of Engineering, Cranfield University,

2004. 26Saravanamuttoo, H.I.H., Gas Turbine Theory, 5th ed.,

Pearson Education Ltd., England, 2001. 27ANSYS CFX-BladeGen, User Manual, Version 10.0,

2005. 28ANSYS CFX-TurboGrid, User Manual, Version 10.0,

2005. 29ANSYS CFX-Pre/Solver/Post, User Manual, Version

10.0, 2005. 30Gelder, T.F., Schmidt, J.F., Suder, L., and Hathaway,

M.D., “Design and Performance of Controlled Diffusion

Stator Compared with Original Double –Circular-Arc

Stator”, NASA TM-100141, TR-87-C-25, 1987. 31Steinke, R.J., “Design of 9.271 Pressure Ratio Five

Stage Core Compressor and Overall Performance for Three

Stages”, NASA TP-2697, 1986. 32McGuire, A.G., “Determination of Boundary Layer

Transition and Separation on Double Circular Arc

Compressor Blades in a Large Subsonic Cascade”, MSc.

Thesis, Naval Postgraduate School Monterey, CA, 1983. 33Bala, A, “Poly-Dimensional Gas Turbine System

Modeling and Simulation ”, PhD. Third Year Review Report,

Department of Power and Propulsion, School of Engineering,

Cranfield University, England, 2007. 34Pilidis, P., and Palmer, P., “Gas Turbine Theory and

Performance Course Notes”, School of Engineering,

Cranfield University, 2004. 35Walsh, P.P., and Fletcher, P., Gas Turbine

Performance, 2nd ed., Blackwell Science Ltd., England, 2004. 36Arthur, L.J., and Burns, T., UNIX-Shell Programming,

3rd ed., John Wiley and Sons Inc., Canada, 1994. 37Wall, L., Christiansen, T., and Orwant, J.,

Programming Perl, 3rd ed., O’ Reilly Media Inc., United

States of America, 2000. 38Rubini, V., “VNC and SSH Tunneling with PUTTY”,

School of Engineering, Cranfield University, 2005.


11

Figure 1. Multistage Axial Compressor

Behaviour (Ref.2)

Figure 2. Integrated Mini-Map based Component

Zooming Technique

Figure 3. LBR Turbofan Engine Artistic

Schematic

Figure 4. Optimized First Stage Fan Rotor and

Stator (3-D) Model

Figure 5. Optimized First Stage Fan Rotor and

Stator (3-D) Grid Model


12

Figure 6. “O” Grid type for Realistic Boundary

Layer Effect around Blade Profile

Figure 7. Optimized Rotor 0° Close Blade

Angle Contour Plot

Figure 8. Optimized Variable

Stator 0° Close Blade Angle

Contour Plot



Contour Plot



Contour Plot

Figure 11. Optimized First Stage Fan (3-D) Grid

Model Assembly

Figure 12. VIGV and VSV arrangement

Schematic (Ref.3)


13

Figure 13. Variable Geometry 1

st Stage Fan Composite Map (PR Vs Wc)


st Stage Fan Composite Map (EFF Vs PR)


st Stage Fan (Compressor) Working Line Plot (PR Vs Wc)


14

Figure 16. LBR Turbofan Engine Performance Plot (Specific Fuel Consumption Vs TET)

Figure 17. LBR Turbofan Engine Performance Plot (Fuel Flow Rate Vs TET)

Figure 18. LBR Turbofan Engine Performance Plot (Net Thrust Vs TET)


15

Figure 19. Integrated (3-D) Aerodynamic Component Zooming Process using IWC

Figure 20. ANSYS “CFX-Solver” Interactive Graphical User Interface (Ref.29)

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