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Research Papers

Multidisciplinary Optimization of a Radial Compressor for Microgas Turbine Applications

[+] Author and Article Information
T. Verstraete

 von Karman Institute for Fluid Dynamics, Waterloose Steenweg 72, 1640 Sint-Genesius-Rode, Belgiumverstraete@vki.ac.be

Z. Alsalihi

 von Karman Institute for Fluid Dynamics, Waterloose Steenweg 72, 1640 Sint-Genesius-Rode, Belgiumalsalihi@vki.ac.be

R. A. Van den Braembussche

 von Karman Institute for Fluid Dynamics, Waterloose Steenweg 72, 1640 Sint-Genesius-Rode, Belgiumvdb@vki.ac.be

J. Turbomach. 132(3), 031004 (Mar 24, 2010) (7 pages) doi:10.1115/1.3144162 History: Received August 01, 2008; Revised February 26, 2009; Published March 24, 2010; Online March 24, 2010

A multidisciplinary optimization system and its application to the design of a small radial compressor impeller are presented. The method uses a genetic algorithm and artificial neural network to find a compromise between the conflicting demands of high efficiency and low centrifugal stresses in the blades. Concurrent analyses of the aero performance and stress predictions replace the traditional time consuming sequential design approach. The aerodynamic performance, predicted by a 3D Navier–Stokes solver, is maximized while limiting the mechanical stresses to a maximum value. The stresses are calculated by means of a finite element analysis, and controlled by modifying the blade camber, lean, and thickness at the hub. The results show that it is possible to obtain a significant reduction of the centrifugal stresses in the blades without penalizing the performance.

Copyright © 2010 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Layout of a microgas turbine; schematic view on the electrical generator, compressor and turbine

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Figure 2

Flow chart of the optimization algorithm

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Figure 3

Meridional contour defined by Bézier control points

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Figure 4

Definition of the blade camber line by β angle

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Figure 5

Thickness distribution along the camber line of the blade (not to scale)

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Figure 6

Negative loading and loading unbalance in a compressor with splitter vanes

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Figure 7

Convergence history of the optimization

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Figure 8

Aero penalty versus stress penalty for baseline, database, and optimization geometries

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Figure 9

Zoom on the low penalty region of Fig. 8

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Figure 10

Isentropic Mach number plot at 90% of the span (near the tip) of geometry 25

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Figure 11

von Mises stresses due to centrifugal loading in the baseline

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Figure 12

von Mises stresses due to centrifugal loading in iteration 25

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Figure 13

Blade lean versus stress and efficiency for database and optimization geometries

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Figure 14

Stress and efficiency versus blade hub leading edge thickness for database and optimization geometries

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Figure 15

Blade leading edge height versus stress and efficiency

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Figure 16

Blade trailing edge height versus stress and efficiency for database and optimization geometries

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