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Research Papers: Gas Turbines: Combustion, Fuels, and Emissions

Isothermal Combustor Prediffuser and Fuel Injector Feed Arm Design Optimization Using the prometheus Design System

[+] Author and Article Information
Xu Zhang, Andy J. Keane

Faculty of Engineering and the Environment,
University of Southampton,
Southampton SO16 7QF, UK

David J. J. Toal

Faculty of Engineering and the Environment,
University of Southampton,
Southampton SO16 7QF, UK
e-mail: djjt@soton.ac.uk

Frederic Witham, Jonathan Gregory

Rolls-Royce Plc.,
Bristol BS34 7QE, UK

Murthy Ravikanti, Emmanuel Aurifeille, Simon Stow, Mark Rogers, Marco Zedda

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

1Corresponding author.

Contributed by the Combustion and Fuels Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received August 18, 2015; final manuscript received September 8, 2015; published online November 17, 2015. Editor: David Wisler.

J. Eng. Gas Turbines Power 138(6), 061504 (Nov 17, 2015) (17 pages) Paper No: GTP-15-1411; doi: 10.1115/1.4031711 History: Received August 18, 2015; Revised September 08, 2015

The prometheus combustor design system aims to reduce the complexity of evaluating combustor designs by automatically defining preprocessing, simulation, and postprocessing tasks based on the automatic identification of combustor features within the computer-aided design (CAD) environment. This system enables best practice to be codified and topological changes to a combustor's design to be more easily considered within an automated design process. The following paper presents the prometheus combustor design system and its application to the multiobjective isothermal optimization of a combustor prediffuser and the multifidelity isothermal optimization of a fuel injector feed arm in combination with a surrogate modeling strategy accelerated via a high-performance graphical processing unit (GPU).

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References

Figures

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Fig. 1

A graphical representation of the geometry centric prometheus optimization workflow [11]

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Fig. 2

A graphical representation of the operations carried out by the prometheus CAD plugin [11]

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Fig. 3

An illustration of a prometheus generated fluid volume [11]

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Fig. 4

An example mesh generated using an ICEM script created automatically by prometheus [11]

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Fig. 5

Cross section through the baseline computational mesh illustrating refinement zones around the injector, dilution ports, and liner holes

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Fig. 6

A closeup view of the air swirler and combustor in the original (a) and modified geometry (b) [11]

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Fig. 7

A closeup view of the aerothermal network model resulting from the modified combustor design

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Fig. 8

An illustration of a prometheus generated aerothermal network model [11]

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Fig. 9

Cross section of the mesh through the outer (a) and inner (b) secondary rows of dilution ports resulting from the prometheus generated meshing script for the modified combustor

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Fig. 10

Contours of normalized velocity magnitude through the secondary row of dilution ports for the original design

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Fig. 11

Contours of normalized velocity magnitude through the secondary row of dilution ports for the modified design

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Fig. 12

Co-Kriging example using the Forrester function [22]

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Fig. 13

Comparison of concentrated log-likelihood evaluation costs for 5- (a), 10- (b), and 15-dimensional problems when using a i7-2860 CPU and Quadro 2000M and Tesla K20C GPUs

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Fig. 14

Comparison of Kriging predictor evaluation costs for a five-dimensional problem when using an i7-2860 CPU and Quadro 2000M and Tesla K20C GPUs and evaluating 1000 points in parallel

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Fig. 15

Comparison of Kriging error evaluation costs for a five-dimensional problem when using an i7-2860 CPU and Quadro 2000M and Tesla K20C GPUs and evaluating 1000 points in parallel

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Fig. 16

Double sector CAD model of the baseline combustor experimental rig including injectors and prediffuser

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Fig. 17

Single (a) and double (b) sector fluid volumes generated by prometheus including postprocessing planes

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Fig. 18

Graphical illustration of the prediffuser design parameters

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Fig. 19

Pareto front of inner and outer annuli pressure losses with those designs constrained by performance at MTO highlighted

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Fig. 20

Surrogate models of inner (a) and outer (b) annuli pressure losses with the constrained Pareto front highlighted

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Fig. 21

Illustration of the baseline injector feed arm geometry (a), maximum major to minor ellipse axis ratio (b) and with three independently twisted sections

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Fig. 22

Two variable feed arm search histories for single and multifidelity design optimizations as a function of total simulation time

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Fig. 23

Illustration of the injector stem geometry resulting from a two variable single- (a) and multifidelity (b) design optimization, note that there is no real discernible difference in the geometries

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Fig. 24

Six variable feed arm search histories for single and multifidelity design optimizations as a function of total simulation time

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Fig. 25

Illustration of the injector stem geometry resulting from a six variable single- (a) and multifidelity (b) design optimization

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