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Lehrstuhl fr Strmungsmechanik

Universitt Erlangen-NrnbergCauerstr. 4

91058 Erlangen

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NUMERICAL SHAPE OPTIMIZATION OF TURBOMACHINES FOROPTIMUM HYDRAULIC PERFORMANCE AND LOW NOISE

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1 INTRODUCTION AND AIM OF THE WORK

Conventionally, axial and radial impeller design is made according to formulae based

on one dimensional, inviscid and incompressible flow considerations. Besides theo-retical knowledge, the experience also plays an important role in the design process.Experimentation and computational fluid dynamics (CFD) simulations are also exten-sively used for the evaluation of the different impeller geometries which serve as in-termediate designs on the way to the final product. Schematically, conventional de-sign approach may be represented by figure 1. The main drawback of conventionaldesign is that, human intuition becomes a significant part of the design job, especiallywhen the number of design parameters considerably increases and the theory is notdirectly applicable to the complex design problem in question.

Fig.1 Conventional design and optimization method

Numerical shape optimization in fluid mechanics has been of major interest in recentyears with the increasing computer resources and the competitive CFD codes thatappear on the market [1]. What we mean by numerical shape optimization, is thecomputer based optimization of any shape directly based on numerical fluid flowsimulations or experimental data. Current CFD technology offers the possibility ofaccurate performance evaluation of flow machines with the help of the simulations offluid flow and heat transfer, etc. For this reason, an efficient comparison of the twodifferent geometries is possible through CFD simulations.

Still, in the design of complex systems in which the number of shape parameters islarge and the physical phenomena interacting with the shape are complex, the de-

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signer is overstrained. The aim of the numerical shape optimization is to replace thedecision making process of human with the computer based decision mechanisms asmuch as possible. This is achieved by integration of CFD tools into the numerical op-timization process.

At LSTM-Erlangen, one CFD code was successfully integrated to performance opti-mization of radial impellers by using gradient based optimization technique as shownin Fig.2. The peculiarity of the integration is the automatic differentiation (AD)method used for finding the gradients of the cost functions with respect to thechanges on the shape. With the help of AD the speed of optimization process wasboosted when compared to finite differences. However, this integration needs inter-vention in the source code of the simulation program.

Fig.2 Numerical shape optimization process diagram

In addition to the gradient based optimization methods, one can use other optimiza-tion methods, such as genetic algorithms. Readily, there are optimization platformswhich combine the commercial simulation software with optimization algorithms. Oneexample is the ModeFrontier of Esteco. This platform has been successfully appliedin the design of heat exchangers and aircraft wing shapes (in cooperation withEADS) at LSTM-Erlangen. Hence, LSTM-Erlangen has enough experience on theintegration of CFD programs to an optimization algorithm both at source level and atapplication level.

Nowadays the acoustic noise generated by fluid flow treated to be an important issuein many technological applications. Therefore, besides numerical simulation of the

flow, the correct prediction of the acoustic noise generated by the flow is becomingan important component of design considerations.The aim of the proposed project is to develop a numerical shape optimization algo-rithm in order to optimize performance and minimize acoustic noise.

2 METHODOLOGY

Presently available acoustic noise prediction tools have been extensively used andanalyzed at LSTM-Erlangen. The acoustic computation methods, which use time re-solved flow simulation results obtained either by Large eddy simulation (LES) or Di-rect numerical simulation (DNS), are expensive to use in optimization process. Thereare also fast acoustic simulations tools, however they are not capable capturing theright tendencies of acoustic noise generation. The bottle neck of the numericalacoustic and performance optimization is our ability to model the acoustic noise gen-

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eration. In order to overcome this bottleneck and to perform optimization, a fastacoustic computation method which can deliver qualitatively correct tendencies ofgenerated acoustic noise when the shape is changed, should be developed.For the optimization two strategies can be chosen. First strategy is to use a commer-cially available optimization platform so as to integrate a commercial or a house CFD

code and the developed acoustic noise simulator. Second strategy would be to writea tailored optimization code which integrates a CFD code with known source codeand the developed acoustic simulation software. This code base on the reconstruc-tion of the acoustic source terms by using the turbulent kinetic energy and the coher-ence length. The results come out from e.g. RANS simulations. The simulation mustbe very efficient in a short computation time. It is not so important to give an exactsolution of the sound pressure level, but is must deliver the right tendency. So in theproject different computation will be tested.

So the first strategy has the advantage that various optimization methods can beused and various simulation tools can be combined. Moreover companies will get

continuous support service and updates from the commercial partner. The disadvan-tages would be the longer time required for routine optimization tasks. The secondstrategy would deliver optimized results in a shorter time period since the used turbu-lence and acoustic models would be internal components of the optimization programand the optimization algorithm can also be tailored according to the needs of the op-timization problem.

Based on the above considerations the proposed project involves the following majorsteps

1. Development of a fast acoustic prediction tool

2. Development of a optimization strategy for a combined performance andacoustic optimization.

3. Implementation of the optimization method and verification.

For testing and validation of the optimization tool it will used experimental data.These experiments will be done parallel to the numerically work at LSTM.

4. Contact person:

Dr. -Ing. Stefan BeckerLehrstuhl fr Strmungsmechanik

Universitt Erlangen-NrnbergCauerstrae 4D-91058 ErlangenTelefon +49 9131 8528272Fax +49 9131 8529503e-mail: sbecker@lstm.uni-erlangen.de

Dr. -Ing. szgr ErtuncLehrstuhl fr Strmungsmechanik

Universitt Erlangen-NrnbergCauerstrae 4D-91058 ErlangenTelefon +49 9131 8529472Fax +49 9131 8529503e-mail: ertunc@lstm.uni-erlangen.de