Coupling STAR-CCM+ with Optimization Software...

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Coupling STAR-CCM+ with Optimization Software

IOSO by the example of axial 8-stages jet engine

compressor.

Folomeev V., (Sarov Engineering Center)

Iakunin A., (JSC Klimov)

STAR Global Conference 2014, March 17-19, Vienna

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Objectives


To create the procedure for coupling CFD code STAR-

CCM+ with optimization software IOSO

To improve the law of turning guide vanes for the first

tree stages of axial compressor.

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Main steps


Create axial 8-stages compressor CFD model

Develop the optimization task

IOSO supports only windows platform when clusters

usually operate on Linux, thus we need do develop the

procedure for net connection

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Typical design iterations


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CFD model


Steady model with

mixing plane interfaces.

Low-Reynolds k-omega

turbulence model

Turbulence

Suppression model

Polyhedral mesh about

15 mln cells with near

wall prism layers

Optimization process

involved 500 cores of

IBM cluster

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CFD modeling visualization


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First rotating row

Pressure side Suction side

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CFD modeling convergence history


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Results of CFD calculation


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Main features of the IOSO optimization technology


IOSO Technology is based on the response surface technology. That is why the strategy differs

significantly from the well-known approaches to optimization. The strategy has higher efficiency and

provides wider range of capabilities than standard algorithms. The main advantage one can get from using

the IOSO Technology is ability to solve very complex optimization tasks.

IOSO Technology algorithms:

Are independent of the optimization task types

Have good global properties and in the majority of the cases are able to find the global optimum

Have high convergence rate and allow to quickly and efficiently find the region where optimum is located

Are highly robust with the respect to the computational process

Allow for robust solving of stochastic optimization tasks, even if such problems have high level of noise

Provide the capabilities to solve real-life optimization tasks that involve complex modern high fidelity

mathematical models or engineering applications (for example, 3D CAD, CFD or FEA software).

Allow for solving robust design optimization tasks, including multidimensional single and multiobjective

optimization tasks.

Are very simple to use even for solving complex practical tasks of nonlinear optimization.

Have full-automatic optimization algorithms which do not need to be tuned up by a user.

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Optimization task


Independent

parameters

Leading vane angle

1-stage stator angle

2-stage stator angle

constrained

parameters

Mass flow

Pressure rise

Objective

function

efficiency

For each rotational rate we solve independent optimization task

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Setting the optimization task


IOSO uses file base coupling with STAR-CCM+

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Scripting STAR-CCM+ macro


STAR-CCM+ macro provides full java

functionality including net sockets and

supports ssh libraries. This

was used in creation client-server

application.

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Scheme of coupling


IOSO generates text file with input parameters

IOSO runs Server.java

Server.java reads

input file

Server.java runs ssh on

remote cluster and

starts STAR-CCM+

with macro

Server.java opens

server sockets and

sends input

variables

STAR-CCM+ with macro receives

input variables, performs CFD

calculations and sends back to

Server.java output variables

Server.java receives

output values and

writes output file

for IOSO

IOSO reads output file and analyses parameters

for a next iteration

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Results of optimization


The normal rotation rate regime was examined firstly

It was found that optimal configuration is nearly to

zero for all variable angles with variation less than 0.5

degree. This is consistent with analytical compressor

design methods.

The convergence of the optimization process was

reached within 30 iterations. It approximately corresponds

to 1 week of calculation on 500 cores of modern cluster

solution.

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Conclusions


The main task of this work was to develop procedure

for coupling CFD code STAR-CCM+ with optimization

software IOSO. This task was reached.

Axial 8-stages compressor served as an example for

the main task. The robust CFD model of the compressor

was created and tested. This model is appropriate for the

automatic calculations with varying geometry parameters.

The results of the optimization process are in

agreement with other analytical methods of prediction

compressor performance. It is presumed that the accuracy

may be improved with complication of CFD model, for

instance including harmonic balance model.

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Thank you for your attention


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