Research Papers

An Integrated System for the Aerodynamic Design of Compression Systems—Part I: Development

[+] Author and Article Information
Tiziano Ghisu1

Engineering Design Centre, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, Cambridgeshire CB2 1PZ, UKtg269@cam.ac.uk

Geoffrey T. Parks, Jerome P. Jarrett, P. John Clarkson

Engineering Design Centre, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, Cambridgeshire CB2 1PZ, UK

Multidisciplinary design optimization (MDO) is an active area of research, concerned with “how to analyze efficiently and design optimally a system governed by multiple coupled disciplines or made of coupled components” (9).

While only the first 20 principal components are varying, the remaining components might also assume nonzero (although small) values if less than 100% of the total variance is captured by the selected PCs. Only if the values for the fixed components of a are zero for all of the Pareto-optimal designs can the system in Eq. 12 be simplified by reducing Φ and a accordingly. In any event, the computational cost associated with this matrix multiplication is negligible compared with the cost of running the evaluation tools.


Corresponding author.

J. Turbomach 133(1), 011011 (Sep 21, 2010) (10 pages) doi:10.1115/1.4000534 History: Received January 10, 2009; Revised July 21, 2009; Published September 21, 2010; Online September 21, 2010

The design of gas turbine engines is a complex problem. This complexity has led to the adoption of a modular design approach, in which a conceptual design phase fixes the values for some global parameters and dimensions in order to facilitate the subdivision of the overall task into a number of simpler subproblems. This approach, while making a complex problem more tractable, necessarily has to rely on designer experience and simple evaluations to specify these process-intrinsic constraints at a point in the design process where very little knowledge about the final design exists. Later phases of the design process, using higher-fidelity tools but acting on a limited region of the design space, can only refine an already established design. While substantial improvements in performance have been possible with the current approach, further gains are becoming increasingly hard to achieve. A gas turbine is a complex multidisciplinary system: a more integrated design approach can facilitate a better exploitation of the trade-offs between different modules and disciplines, postponing the setting of these critical interface parameters (such as flow areas, radii, etc.) to a point where more information exists, reducing their impact on the final design. In the resulting large, possibly multimodal, highly constrained design space, and with a large number of objectives to be considered simultaneously, finding an optimal solution by simple trial-and-error can prove extremely difficult. A more intelligent search approach, in which a numerical optimizer takes the place of the human designer in seeking optimal designs, can enable the design space to be explored significantly more effectively, while also yielding a substantial reduction in development times thanks to the automation of the design process. This paper describes the development of a system for the integrated design and optimization of gas turbine engines, linking a metaheuristic optimizer to a geometry modeler and to evaluation tools with different levels of fidelity. In recognition of the substantial increase in design space size required by the integrated approach, an improved parameterization based on the concept of principal components’ analysis was implemented, allowing a rotation of the design space along its most significant directions and a reduction in its dimensionality, proving essential for a faster and more effective exploration of the design space.

Copyright © 2011 by American Society of Mechanical Engineers
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Figure 1

A more integrated engine design (13)

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Figure 2

A schematic meridional view of a core compression system (IPC, duct, and HPC)

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Figure 3

Optimization history and optimizer control thresholds

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Figure 4

Early evolution of the Pareto front

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Figure 5

Later evolution of the Pareto front

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Figure 6

A different basis can reduce the effective dimensionality of the problem

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Figure 7

Difference between original and optimal basis for expressing the nondominated solutions

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Figure 8

Comparison between optimization in the original design space and in the reduced principal components’ design space (after 1000 optimization steps)

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Figure 9

Optimization in the full principal components’ design space




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