Robust Design of Compressor Fan Blades Against Erosion

[+] Author and Article Information
Apurva Kumar, Andy J. Keane

Computational Engineering and Design Group, School of Engineering Sciences, University of Southampton, Highfield, Southampton SO17 1BJ, UK

Prasanth B. Nair1

Computational Engineering and Design Group, School of Engineering Sciences, University of Southampton, Highfield, Southampton SO17 1BJ, UKp.b.nair@soton.ac.uk

Shahrokh Shahpar

Aerothermal Methods, Rolls-Royce Plc. Derby, DE24 8BJ, UK


Corresponding author.

J. Mech. Des 128(4), 864-873 (Jan 18, 2006) (10 pages) doi:10.1115/1.2202886 History: Received September 16, 2005; Revised January 18, 2006

This paper is concerned with robust aerodynamic design of compressor blades against erosion. The proposed approach combines a multiobjective genetic algorithm with geometry modeling methods, high-fidelity computational fluid dynamics, and surrogate models to arrive at robust designs on a limited computational budget. The multiobjective formulation used here allows explicit trade-off between the mean and variance of the performance to be carried out. Detailed numerical studies are presented for robust geometric design of a typical compressor fan blade section to illustrate the proposed methodology. The performance of a selected robust optimal solution on the Pareto front is compared to a deterministic optimal solution to demonstrate that significant improvements in the mean shift and variance can be achieved.

Copyright © 2006 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.



Grahic Jump Location
Figure 9

Main effect plot for location

Grahic Jump Location
Figure 13

Flowchart for deterministic surrogate-assisted design optimization

Grahic Jump Location
Figure 8

Probability distribution of the pressure loss using MCS

Grahic Jump Location
Figure 10

Main effect plot for width

Grahic Jump Location
Figure 11

Main effect plot for height

Grahic Jump Location
Figure 12

Flowchart for robust design methodology

Grahic Jump Location
Figure 6

Predicted posterior mean versus observed values (R2=0.954)

Grahic Jump Location
Figure 7

SCVRi values using leave-one-out validation

Grahic Jump Location
Figure 14

Baseline and optimal blade shapes

Grahic Jump Location
Figure 1

Blades with variations in noise factors

Grahic Jump Location
Figure 2

Blades with variations in control factors

Grahic Jump Location
Figure 3

A typical C-O-H mesh used for CFD analysis

Grahic Jump Location
Figure 4

CFD static pressure plot

Grahic Jump Location
Figure 5

The scatter plot of pressure loss using the training data set

Grahic Jump Location
Figure 15

Histogram of pressure loss in presence of erosion

Grahic Jump Location
Figure 16

Plot of the initial data set with the initial Pareto front and all the search points. The plot also shows the last three Pareto fronts after which the search was terminated. Note that the 11th, 12th, and 13th Pareto fronts are the same and overlap, hence they are not distinguishable in the plot.

Grahic Jump Location
Figure 17

Shape of robust and baseline geometry

Grahic Jump Location
Figure 18

Histogram of pressure loss of the robust geometry

Grahic Jump Location
Figure 19

Histograms of robust and deterministic optimal geometries



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In