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Research Papers

Optimization of Turbomachinery Flow Surfaces Applying a CFD-Based Throughflow Method

[+] Author and Article Information
David Pasquale

Dipartimento di Ingegneria
Meccanica e Industriale,
Universita degli Studi di Brescia,
Via Branze 38,
Brescia 25123, Italy
e-mail: david.pasquale@ing.unibs.it

Giacomo Persico

Laboratorio di Fluidodinamica delle Macchine,
Dipartimento di Energia,
Politecnico di Milano,
via Lambruschini 4,
Milano 21056, Italy
e-mail: giacomo.persico@polimi.it

Stefano Rebay

Dipartimento di Ingegneria
Meccanica e Industriale,
Universita degli Studi di Brescia,
Via Branze 38,
Brescia 25123, Italy
e-mail: stefano.rebay@ing.unibs.it

Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received May 9, 2013; final manuscript received May 19, 2013; published online September 26, 2013. Editor: David Wisler.

J. Turbomach 136(3), 031013 (Sep 26, 2013) (11 pages) Paper No: TURBO-13-1072; doi: 10.1115/1.4024694 History: Received May 09, 2013; Revised May 19, 2013

This work proposes an automated strategy for the preliminary design of turbomachinery, based on the application of a throughflow code and of a highly flexible and efficient optimization strategy. The code solves for the circumferentially-averaged flow equations, including the effects of aerodynamic and friction forces and of blade thickness; the outcome of the code is the flow distribution on the meridional surface. The fluid-dynamic solver is coupled with the optimization tool in order to determine the “optimal” mean flow surface, as a result of a multiobjective optimization procedure, in which nonconcurrent goals are simultaneously considered. A global optimization strategy is applied, based on the combination of a Genetic Algorithm with a metamodel to tackle the computational cost of the process. The optimization method is applied to a low speed axial compressor, for which the optimization goals are the minimization of aerodynamic loss and discharge kinetic energy at the exit of the stage, as well as the uniformity of work exchange along the blade span. The method proves to match all the objectives, providing a clear improvement with respect to classical and well-established design methods. The optimization provided by the automated design is finally assessed by high-fidelity calculations performed with a fully three-dimensional CFD code on both the baseline and optimized machine configurations. Improvements are confirmed for all the goals specified in the optimization strategy, resulting in a more efficient machine.

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References

Figures

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Fig. 1

3D CFD–TzFlow comparison (a) total pressure field (P0); (b) entropy field (s); (c) spanwise profiles of relative flow angle (β) at stage inlet and rotor exit; (d) spanwise profiles of absolute flow angle (α) at rotor exit and stage exit

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Fig. 2

Layout of the optimization procedure

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Fig. 3

Example of 3D blade geometries constructed by the use of 2D multiple profiles

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Fig. 4

Axial compressor stage geometry—baseline configuration

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Fig. 5

Convergence history of the optimization. (a) Comparison between Kriging and FFNN metamodels. (b) Comparison between the computed and predicted (meta-) values by Kriging metamodel for objective function φ and constraint χm·.

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Fig. 6

Flow surface in bladed regions for the baseline and optimized configurations

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Fig. 7

Total enthalpy distribution in the baseline and optimized configurations

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Fig. 8

Entropy distribution in the baseline and optimized configurations

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Fig. 9

Spanwise distributions of relative (a) and absolute (b) flow angles, tangential velocity (c), and total enthalpy (d) for the baseline and optimized configurations

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Fig. 10

Blade profile comparison between optimized and baseline configurations

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Fig. 11

Spanwise profiles of β at stage inlet and rotor exit (a), α at rotor exit and stage exit (b), total enthalpy rise (c), and total pressure rise (d) for the baseline and optimized configurations as computed with the high-fidelity CFD model

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