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8/8/2019 (eBook CFD) - 2D_3D CFD Design Optimization Using the Federated Intelligent Product Environment (FIPER) Technology
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GE Global Research
______________________________________________________________
2D/3D CFD Design OptimizationUsing the Federated Intelligent Product
Environment (FIPER) Technology
R. Sampath, R.M. Kolonay and C. Kuhne
2002GRC202, August 2002
Class 1
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Copyright © 2002 General Electric Company. All rights reserved.
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GE Global Research
Technical Report Abstract Page
Title 2D/3D CFD Design Optimization Using the Federated Intelligent Product Environment(FIPER) Technology
Author(s) R. Sampath Phone (518)387-6401R.M. Kolonay 8*833-6401C. Kuhne*
Component Advanced Mechanical Technologies, Niskayuna
ReportNumber 2002GRC202 Date August 2002
Number
of Pages 8 Class 1
Key Words MDA/MDO, FIPER technology, Turbine Aerodynamics, Response-SurfaceMethodology
This paper describes the application and implementation of an aircraft engine turbine bladeaerodynamic design optimization problem using a response-surface based methodology withinthe Federated Intelligent Product EnviRonment (FIPER) framework. The design problemconsidered is a shape optimization problem, in which, important blade shape parameters such asthe stagger angle, trailingedge thickness, leading-edge radius, over-turning and wedge angle arechosen such that an optimal design is achieved. In this work, the criterion for optimality is takenas the blade-row efficiency. The approach used in this work to solve this optimization problem is
to start with a baseline design, perturb the variables within a given range of validity, conduct adesign-ofexperiments (DOE) using an automated, distributed analysis DOE tool developed andgenerate a response-surface. The resulting response surface is used along with a gradient-basedoptimizer to calculate the optimal solution. The core of this paper describes in detail the FIPERarchitecture and addresses specific implementation issues in the context of solving the aboveshape optimization problem.
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AIAA-2002-5479
2D/3D CFD DESIGN OPTIMIZATION USING THE FEDERATED INTELLIGENT
PRODUCT ENVIRONMENT (FIPER) TECHNOLOGY
Rajiv Sampath, Raymond M. Kolonay+
GE Global Research Center
Schenectady, NY 12301
Craig M. Kuhne
GE Aircraft Engines
Cincinnati, OH 45215
ABSTRACT
This paper describes the application and
implementation of an aircraft engine turbine blade
aerodynamic design optimization problem using a
response-surface based methodology within the
Federated Intelligent Product EnviRonment (FIPER)
framework. The design problem considered is a shape
optimization problem, in which, important blade
shape parameters such as the stagger angle, trailing-
edge thickness, leading-edge radius, over-turning andwedge angle are chosen such that an optimal design is
achieved. In this work, the criterion for optimality is
taken as the blade-row efficiency. The approach used
in this work to solve this optimization problem is to
start with a baseline design, perturb the variables
within a given range of validity, conduct a design-of-
experiments (DOE) using an automated, distributed
analysis DOE tool developed and generate a
response-surface. The resulting response surface isused along with a gradient-based optimizer to
calculate the optimal solution. The core of this paper
describes in detail the FIPER architecture and
addresses specific implementation issues in the
context of solving the above shape optimization
problem.
INTRODUCTION
Gas turbine engine development is a highly coupledmultidisciplinary process involving several
disciplines such as aerodynamics, solid mechanics
and heat transfer. In a market with ever-increasing
demands in terms of reduced design cycle time,
reduced life cycle costs and performance
improvements turbine engineers can no longer
competitive market. The need of the day is a truly
digitized design process that can provide true
concurrency between design and manufacturing. In
order to address these challenges, GE has teamed up
with Goodrich, Parker Hannifin, Engineous Software,
Ohio University, Stanford University and OAI to
develop a concurrent engineering design system as a
part of a four-year advanced technology program
supported by the National Institute of Standards and
Technology (NIST). This system referred to as the
“Federated Intelligent Product EnviRonment”
(FIPER) strives to drastically reduce design cycle
time and time-to-market by intelligently automating
elements of the design process in a linked, associative
environment, thereby providing true concurrency in
the design process. Such an approach allows for
distributed design of robust and optimized products
by employing an open, network-centric, web-based
environment1
(Fig. 1).
As an application of the FIPER system, here, a novel
implementation of a turbomachinery blade design
system is discussed. The design problem being
considered is a shape-optimization problem in which
the task is to optimize the blade cross-section by
varying shape parameters such as the stagger angle,
trailing-edge thickness, leading-edge radius, over-
turning and wedge angle in order to maximize the
blade-row efficiency. In the main part of this paper,this design problem is discussed along with the
techniques used to solve this problem within the
FIPER framework.
BACKGROUND
FIPER draws extensively on several prior GE
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AIAA-2002-5479
projects like the top-down Product Control Structure
(PCS), Fig. 2, and the Linked Model Environment
(LME), Fig. 3, form the backbone of the geometry
aspects of FIPER. Since these projects have been
described in previous publications2,3
, only key
the design and manufacturing process, so that at all
times participants in the design and manufacturing
process have access to the same valid geometry
information. Taking advantage of the
UNIGRAPHICS®
WAVE top-down geometry
linking capability, a PCS for turbine engines was
developed, specifying interfaces between the different
engine subsystems and giving control of theseinterfaces to the groups responsible for system-level
design. Each subsystem group owns its own interfaces
between individual components.
Understanding that different disciplinary engineering
design and analysis tools require geometry at
different levels of detail, the concept of a "context
model" was introduced. The context model represents
a disciplinary context-specific, yet fully associative,
"view" of the master model geometry. Feature
suppression is extensively used in context models.
For example, a bolt hole, which is important for the
stress analyst, may not be required for a thermal or
CFD analysis and therefore would be suppressed in
the thermal/fluids context model. These context
models are then linked to the respective disciplinary
analysis tools, e.g. FEA, CFD, cost, producibility,
etc, in the LME , see Figure 3.
FIPER extends the concept of the geometric Master
Model with the introduction of the “Intelligent Master
Model” (IMM). Intelligence is added through
extensive use of Knowledge-Based Engineering
(KBE) systems in the design process. Initially, rules
for geometry generation were captured separately
Figure 1: The FIPER Project
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AIAA-2002-5479
Figure 3: Linked Model Environment
FIPER ARCHITECTURE
Traditionally MDA and MDO efforts have led to
systems that are monolithic, hard-wired systems, that
tend to provide only limited capabilities for
distributed computing and collaboration of variousdesigners located at spatially disjoint facilities
5.
Moreover, they rarely support a plug-and-play type
architecture where one analysis or simulation tool can
easily be substituted by another essentially fulfilling
the same function, with either a faster newer method,
or potentially at a higher level of fidelity. Today, the
web offers the vehicle for efficient data transfer
across the globe, but its capabilities are so far only
beginning to be utilized by the engineering and designcommunity. This is the case even though much of
engineering development in a project these days
occurs at spatially different locations. FIPER
addresses this deficiency by providing a network-
centric infrastructure supporting distributed
engineering services in a Peer-to-Peer paradigm.
FIPER federates processes, tools, methods,
documents, knowledge bases, and data into a
dynamic, distributed environment with its underlyingservices. Some services are generic (for example,
optimization algorithms or knowledge-based
systems), and thus, are not associated with a
particular IMM context but are globally available
within FIPER. Members of a federation agree on
basic notions of administration, identification, and
FIPER is composed of various service providers; any
of these can come and go and the system can respond
to changes in its environment in a reliable way
(network centricity). The services connected to
FIPER discover each other and cooperate in a
distributed environment (service centricity). Users
can request to use multiple services and check the
status of their submissions in different locations
through HTTP portal with thin web clients (web
centricity).
The three neutralities FIPER deploys are location
neutrality, protocol neutrality, and implementationneutrality, Figure 4. Services need not be co-located;
they are discovered and then join the federation,
which simplifies management of the entire software
environment (location neutrality). In addition, the
way clients communicate with a service provider is
not essential. A service proxy can use any protocol,
for example, Remote Method Invocation (RMI), IIOP
or even a plain socket communication. Clients are not
aware of what protocols are used and where theimplementations reside (protocol neutrality).
Furthermore, the clients who use the FIPER services
do not need to know what languages are used and
how a service is implemented (implementation
neutrality). In all, FIPER provides accessibility
through a web centric architecture, self-manageability
using federated services, scalability via network
centricity, and adaptability with the power of plug-
and-play capability.
Proxy ProxyClientService
Provider
FIPER
Federation of Services
Proxy
discover
and join
register
and publish
protocol
(protocol
neutrality)
(location
neutrality)
(implementation
neutrality)
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AIAA-2002-5479
A set of components that provides the
infrastructure for federating services in a
distributed environment
A programming model that supports the
production of reliable distributed
environment
The functionality to register services and
resolve service requests
Java and the emerging JiniTM technology are at the
heart of this system. Services are found and resolved
through a "lookup" service (Figure 5). New servicesare added to the look-up service by a process called
"discover and join". When plugged into the
environment, the service first uses a discovery
protocol to locate an appropriate lookup service and
then joins, or registers, with the lookup service.
Services can communicate with any other generic
service in the entire federated product space. In the
case of FIPER, this is achieved by an IMM context,
user, or service posting a need which is resolved by alookup service. The lookup service connects the
requesting entity to an entity that has the functionality
to supply the service. Figure 5 illustrates this in a
given space with four services; CAD, KBE,
Optimization and Robust Design, and the Simulation
Engine. Each service provider must be Java wrapped
in order to join the federation, but it can have its own
framework of execution. A service could be based on
RMITM, CORBATM, Java Native Interface (JNITM),
Microsoft COMTM /DCOMTM, or even simple socket
connection.
FIPER. For the purpose of the NIST project FIPER
focuses on the services necessary for the design and
manufacture of a product. Specifically, the domains
of Design for Six Sigma/ Multidisciplinary
Optimization (DFSS/MDO), CAD/KBE, Engineering
Analysis & Sensitivities, Pre/Post processing, and
Data Repositories are addressed. Services are
provided by a service provider (which is typically a
computer program, for example, an engineering
analysis package, that has been wrapped to operate
within the FIPER environment). A service provider
can offer multiple services, and the same service can
be offered by multiple service providers. The FIPERtask submitted by the client is matched against the
services offered by the various service providers, and
the proper service is selected based on service
attributes.
Several engineering service providers are currently
implemented in the FIPER environment such as
UNIGRAPHICS®
for a variety of CAD and geometry
services, PATRAN
®
and ICEM
®
primarily for finiteelement and CFD meshing services, ANSYS®
for
finite element meshing, boundary condition, analysis,
and post processing services. Several in-house GE
proprietary codes are used as service providers for
addressing the deterministic as well as probabilistic
design of turbomachinery components.
Members of a federation agree on the basic notions of
administration, identification, and policy. The
resulting federation provides the simplicity of access,ease of administration and support for sharing
services provided by a large monolithic system, while
retaining the flexibility, and control provided by a
plug-and-play architecture.
Clients define and submit their jobs via web
browsers. A FIPER service manager then dispatches
each job into tasks. These tasks (or exertions) can be
executed sequentially, in parallel, or combination of
both in the FIPER environment, depending on their
input/output data dependency. If a parallel strategy is
chosen, tasks are dropped into spaces (by using
JavaSpacesTM, for example) for distributed
computation. Each service provider agent, if present,
picks up appropriate tasks and generates results and
t th b k t th O th th h d
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AIAA-2002-5479
also allows for real-time monitoring and editing of
jobs submitted through the system. This allows the
user the flexibility to modify a submitted job during
any stage of its execution. This is particularly
important in multidisciplinary optimization problems,
wherein, there is greater probability for user errors or
service failures. If such a situation arises, the FIPER
job monitor would be very effective in
reviewing/editing the failed task and resuming the
execution of the optimization problem.
TURBINE BLADE AERODYNAMIC DESIGN
The design of a new aircraft engine from scratch is anextremely rare occurrence in today’s competitive
environment. “New” designs are most often derived
from existing engines. Derivative engine design is
accomplished by first scaling from an existing design,
then optimizing this derivative geometry on the basis
of a list of design criteria. Modifications from this
scaled geometry are made as required to meet the
design detailed criteria, such as component life and
aerodynamic performance.
As an application of the FIPER system, a turbine
blade aerodynamic design optimization problem is
presented. Blade shape parameters (see Fig 6.) such
as the stagger angle, trailing-edge thickness, unguided
turning, over-turning and wedge angle are chosen
such that the blade-row efficiency is maximized.
Blade row efficiency defines the overall aerodynamic
performance of the turbine blade-row and is an
indicator of the performance of the engine as a whole.
blade is considered (and this implies that 3D losses
are accounted for in the aerodynamic analysis). The
process map used in the 2D/3D CFD based design of
the blade is illustrated in Fig. 7. As described, the
first step in the design process is to start with a scaled
airfoil design. This design data is copied to the
optimization workspace to be modified by the design
process. This is followed by an update process in
which important blade shape parameters are modified
to enforce blade shape change. This is followed by
the execution of a blade generation/stacking program
in order to generate the appropriate blade shape.
Once the blade geometry is defined, the next step isthe generation of a 2D/3D grid. The 2D/3D grid is
used in a corresponding CFD analysis to evaluate the
performance of the blade design. This entire loop is
executed for each DOE run. The outputs from this
DOE are used to generate a response surface to
optimize the design. In this work, due to the non-
linear nature of the transfer function, a 2nd
order
central-composite design6
augmented with a one-
factor-at-a-time (OFAT) DOE design close to theoptimal point is employed. The upper bounds, lower
bounds, variable limits are chosen in order to ensure
an approximate transfer function that was within the
required accuracy bounds.
Figure 8 shows the internals of the 2D/3D CFD
design tool developed within the FIPER system. As
described in this figure, five major service providers
were developed to automate the 2D/3D CFD based
optimization process map shown in Fig. 7. A database
manager service to manage data transfer between the
aero database containing the scaled airfoil data and
the FIPER system. A morphing service to generate
the morphed or mapped geometry for each DOE run.
This service updates the airfoil shape parameters
(such as stagger, wedge angle, etc), regenerates the
airfoil design sections and then stacks the blade
sections to generate the 3D blade. A gridding or
meshing service provider is developed to generate the
2D/3D blade-to-blade grid. GE in-house application
is the service provider for the 3D gridding. Another
GE proprietary code is the service provider for the
2D gridding. A blade-to-blade CFD analysis service
provider is developed to execute the 2D/3D blade-to-
bl d CFD l i H i t ll d l d
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AIAA-2002-5479
Copy baseline design to
optimization workspace
Update airfoil shape
parameters
Morph the airfoil
sections
Stack the airfoil sections
to generate 3D airfoil
Generate 2D/3D grid
Execute 2D/3D CFD
Analysis
Calculate optimization
objective function
Evaluate design
constraints
Generate
DOE Table
Generate Response
SurfaceOptimizer
Figure 7: 2D/3D CFD Optimization Process Map
Figure 8: Integration of 2D/3D CFD Design
Process Using FIPER Architecture
All services are published at a remote HP
workstation. Emerging JavaSpacesTM technology is
used to develop a distributed DOE execution
environment. In particular, a FIPER space is created
in which the various DOE runs are dropped as tasks.
Depending on the availability of the various service
providers (multiple instances of these service
providers were published across the network), the
tasks are picked up by the various providers and are
executed and the results attached to the context model
and sent back to the user for review. After execution,
each service sends back a detailed report which can
be perused by the experienced engineer to make
appropriate design decisions. In order to speed-up theconvergence of the CFD analysis the flow results
from the baseline case were used as the initial starting
flow solution for the various DOE runs. Fig. 9 shows
the initial and optimal blade profiles calculated using
this system. This case corresponds to the 1st
rotor in a
2-stage high-pressure turbine. (Note: The turbine
blade example considered herein is strictly a
caricature of a real turbine blade problem; no
information contained in this paper is representativeof turbine blades produced by GE. Also, due to
restrictions dealing with sharing proprietary
information, only normalized quantities will be
presented in this paper). All the angle blade shape
parameters (stagger, wedge, overturning) were varied
from the baseline values by +/- 5 degrees while the
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AIAA-2002-5479
the nominal design as the starting flow solution (this
technique was employed to accelerate the
convergence of the CFD solution), the 3D-CFD
design took close to 45 minutes per DOE iteration
while the 2D-CFD case took about 10-15 minutes per
DOE iteration. All the DOE runs were executed in
parallel on 5 HP servers distributed across the
network. As an extension of this work, currently CFD
solution of multiple blade rows is being considered.
This will allow the designers to consider the entire
stage performance as the objective function rather
than limit themselves to blade-row efficiency
estimates.
Normalized Stagger = 1.0
Normalized leading edge radius = 1.0Normalized trailing edge radius = 1.0
Normalized over turning = 1.0
Normalized wedge angle = 1.0
Normalized blade row efficiency = 1.0
Normalized Stagger = 1.1208
Normalized leading edge radius = 1.009
Normalized trailing edge radius = 0.971
Normalized over turning = -0.4548
Normalized wedge angle = 0.8115
Normalized blade row efficiency = 1.0172
Figure 9: Initial and Optimal 3D Blade Designs
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AIAA-2002-5479
Normalized stagger = 1.0
Normalized leading edge radius = 1.0
Normalized trailing edge radius = 1.0
Normalized over turning = 1.0
Normalized wedge angle = 1.0
Normalized blade row efficiency = 1.0
Normalized stagger = 1.140
Normalized leading edge radius = 1.049
Normalized trailing edge radius = 1.1003
Normalized over turning = -0.0369
Normalized wedge angle = 1.0665
Normalized blade row efficiency = 1.021
Figure 10: Initial and Optimal 2D Blade Designs
CLOSING REMARKS
A robust network-centric MDA/MDO environment
has been developed and applied to an engineering
design problem. The concepts of an engineering
analysis code as a service provider and of a “FIPERcontext” as a generic means of supplying problem-
specific information to the generic service provider
have been implemented. The FIPER framework has
been applied to a number of sophisticated engineering
and design problems, one of which, the aerodynamic
analysis and CFD based design of a turbine blade,
forms the basis of this work. The framework takes
advantage of a number of key technologies for
distributed network-centric computing, primarilyJINITM, RMITM, and JavaSpacesTM.
The user has the ability to access the system via a
web browser from anywhere in the world to submit
jobs, check the status of jobs, or to review results.
The system also provides the user the capability to
JavaSpacesTM technology. Email notification after the
execution of jobs provides URL links to the
significant output which the user can study through a
web browser, from anywhere in the world.
Currently, the FIPER team is expanding the capability
to do system-level optimization studies wherein
disciplines such as aerodynamics, heat transfer and
mechanical analysis are all coupled. The results from
these efforts will be presented in subsequent
presentations.
Acknowledgments
This research is jointly funded through the National
Institute for Standards and Technology-Advanced
Technology Program (NIST-ATPTM
) and the General
Electric Company. The authors would like to
acknowledge this support, as well as the valuable
input from the whole FIPER team. In particular, the
authors would like to acknowledge the help and
valuable input from Shashi Talya, Anurag Gupta,
Sanjay Goel, Rohinton Irani and Michael Sobolewskiin developing the CFD-based optimization tool within
the FIPER system.
References
[1] Federated Intelligent Product Environment,
Technical Proposal, OAI, General Electric,
Goodrich, Parker Hannifin, Engineous Software,
Ohio University, Stanford University, April,
1999.
[2] Röhl P.J.; Kolonay R. M. et al.: A Federated
Intelligent Product Environment. Proceedings,
8th
AIAA/USAF/NASA/ISSMO Symposium on
Multidisciplinary Analysis and Optimization,
Long Beach, CA, September 2000.
[3] Rohl, P. J.; Kolonay R. M. et al.: Intelligent
Compressor Design in a Network-Centric
Environment. Proceedings, ASME Computers in
Engineering Conference, Pittsburgh, PA,
September 2001.
[4] Intent User Manual, Heide Corporation,
Medfield, MA, 2000.
[5] Rohl, P. J., A Multilevel Decomposition
Procedure for the Preliminary Wing Design of a
High-Speed Civil Transport Aircraft, Doctoral
Th i G i I tit t f T h l Atl t
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R. Sampath 2D/3D CFD Design Optimization Using the Federated Intelligent 2002GRC202
R.M. Kolonay Product Environment (FIPER) Technology August 2002
C. Kuhne