0
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).

Copyright © 2016 by ASME
Your Session has timed out. Please sign back in to continue.

References

Painchaud-Ouellet, S. , Tribes, C. , Trépanier, J. , and Pelletier, D. , 2006, “ Airfoil Shaped Optimization Using a Nonuniform Rational B-Spline Parameterization Under Thickness Constraint,” AIAA J., 44(10), pp. 2170–2178. [CrossRef]
Shahrokhi, A. , and Jahangirian, A. , 2007, “ Airfoil Shape Parameterization for Optimum Navier–Stokes Design With Genetic Algorithm,” Aerosp. Sci. Technol., 11(6), pp. 443–450. [CrossRef]
Brooks, C. , Forrester, A. , Keane, A. , and Shahpar, S. , 2011, “ Multi-Fidelity Design Optimisation of a Transonic Compressor Rotor,” 9th European Turbomachinery Conference, Istanbul, Mar. 21–25, pp. 1267–1276.
Epstein, B. , Jameson, A. , Peigin, S. , Roman, D. , Harrison, N. , and Vassberg, J. , 2009, “ Comparative Study of Three-Dimensional Wing Drag Minimization by Different Optimization Techniques,” J. Aircr., 46(2), pp. 526–541. [CrossRef]
Peigin, S. , and Epstein, B. , 2007, “ Efficient Approach for Multipoint Aerodynamic Wing Design of Business Jet Aircraft,” AIAA J., 45(11), pp. 2612–2621. [CrossRef]
Despierre, A. , Stuttaford, P. , and Rubini, P. , 1997, “ Preliminary Gas Turbine Combustor Design Using a Genetic Algorithm,” ASME Paper No. 97-GT-072.
Rogero, J. , and Rubini, P. , 2001, “ Optimisation of Combustor Wall Heat Transfer and Pollutant Emissions for Preliminary Design Using Evolutionary Techniques,” 15th International Symposium on Airbreathing Engines (ISOABE), Bangalore, India, Sept. 2–7, pp. 605–614.
Duchaine, F. , Morel, T. , and Gicquel, M. , 2009, “ Computational-Fluid-Dynamics-Based Kriging Optimization Tool for Aeronautical Combustion Chambers,” AIAA J., 47(3), pp. 631–645. [CrossRef]
Voutchkov, I. , Keane, A. , and Fox, R. , 2006, “ Robust Structural Design of a Simplified Jet Engine Model Using Multiobjective Optimization,” AIAA Paper No. 2006-7003.
Toal, D. , Keane, A. , Benito, D. , Dixon, J. , Yang, J. , Price, M. , Robinson, T. , Remouchamps, A. , and Kill, N. , 2014, “ Multi-Fidelity Multidisciplinary Whole Engine Thermo-Mechanical Design Optimization,” J. Propul. Power, 30(6), pp. 1654–1666. [CrossRef]
Zhang, X. , Toal, D. , Bressloff, N. , Keane, A. , Witham, F. , Gregory, J. , Stow, S. , Goddard, C. , Zedda, M. , and Rodgers, M. , 2014, “ Prometheus: A Geometry-Centric Optimisation System for Combustor Design,” ASME Paper No. GT2014-25886.
Stuttaford, P. , and Rubini, P. , 1997, “ Preliminary Gas Turbine Combustor Design Using a Network Approach,” ASME J. Eng. Gas Turbines Power, 119(3), pp. 546–552. [CrossRef]
Simpson, T. , Peplinski, J. , Kock, P. , and Allen, J. , 2001, “ Metamodels for Computer-Based Engineering Design: Survey and Recommendations,” Eng. Comput., 17(2), pp. 129–150. [CrossRef]
Queipo, N. , Haftka, R. , Shyy, W. , Goel, T. , Vaidyanathan, R. , and Tucker, P. , 2005, “ Surrogate-Based Analysis and Optimization,” Prog. Aerosp. Sci., 41(1), pp. 1–28. [CrossRef]
Forrester, A. , and Keane, A. , 2009, “ Recent Advances in Surrogate-Based Optimization,” Prog. Aerosp. Sci., 45(1–3), pp. 50–79. [CrossRef]
Sacks, J. , Welch, W. , Mitchell, T. , and Wynn, H. , 1989, “ Design and Analysis of Computer Experiments,” Stat. Sci., 4(4), pp. 409–435. [CrossRef]
Jones, D. , 2001, “ A Taxonomy of Global Optimization Methods Based on Response Surfaces,” J. Global Optim., 21(4), pp. 345–383. [CrossRef]
Jones, D. , Schonlau, M. , and Welch, W. , 1998, “ Efficient Global Optimization of Expensive Black-Box Functions,” J. Global Optim., 13(4), pp. 455–492. [CrossRef]
Toal, D. , Forrester, A. , Bressloff, N. , Keane, A. , and Holden, C. , 2009, “ An Adjoint for Likelihood Maximization,” Proc. R. Soc. A, 465(2111), pp. 3267–3287. [CrossRef]
Toal, D. , Bressloff, N. , Keane, A. , and Holden, C. , 2011, “ The Development of a Hybridized Particle Swarm for Kriging Hyperparameter Tuning,” Eng. Optim. 43(6), pp. 675–699. [CrossRef]
Toal, D. , 2015, “ Some Considerations Regarding the Use of Multi-Fidelity Kriging in the Construction of Surrogate Models,” Struct. Multidiscip. Optim., 51(6), pp. 1223–1245. [CrossRef]
Forrester, A. , Sóbester, A. , and Keane, A. , 2007, “ Multi-Fidelity Optimization Via Surrogate Modelling,” Proc. R. Soc. A, 463(2088), pp. 3251–3269. [CrossRef]
Kuya, Y. , Takeda, K. , Zhang, X. , and Forrester, A. , 2011, “ Multifidelity Surrogate Modeling of Experimental and Computational Aerodynamic Data Sets,” AIAA J., 49(2), pp. 289–298. [CrossRef]
Toal, D. , and Keane, A. , 2011, “ Efficient Multi-Point Aerodynamic Design Optimization Via Co-Kriging,” J. Aircr., 48(5), pp. 1685–1695. [CrossRef]
Yamazaki, W. , and Mavriplis, D. , 2013, “ Derivative-Enhanced Variable Fidelity Surrogate Modeling for Aerodynamic Functions,” AIAA J., 51(1), pp. 126–137. [CrossRef]
Ghoreyshi, M. , Badcock, K. , and Woodgate, M. , 2009, “ Accelerating the Numerical Generation of Aerodynamic Models for Flight Simulation,” J. Aircr., 46(3), pp. 972–980. [CrossRef]
Han, Z. , and Görtz, S. , 2012, “ Hierarchical Kriging Model for Variable-Fidelity Surrogate Modeling,” AIAA J., 50(9), pp. 1885–1896. [CrossRef]
Han, Z. , Görtz, S. , and Zimmermann, R. , 2013, “ Improving Variable-Fidelity Surrogate Modeling Via Gradient-Enhanced Kriging and a Generalised Hybrid Bridge Function,” Aerosp. Sci. Technol., 25(1), pp. 177–189. [CrossRef]
Han, Z. , Zimmermann, R. , and Görtz, S. , 2012, “ Alternative Cokriging Model for Variable-Fidelity Surrogate Modeling,” AIAA J., 50(5), pp. 1205–1210. [CrossRef]
Kennedy, M. , and O'Hagan, A. , 2000, “ Predicting the Output From a Complex Computer Code When Fast Approximations are Available,” Biometrika, 87(1), pp. 1–13. [CrossRef]
Keane, A. , 2006, “ Statistical Improvement Criteria for Use in Mulitobjective Design Optimization,” AIAA J., 44(4), pp. 879–891. [CrossRef]
Toal, D. , and Keane, A. , 2012, “ Non-Stationary Kriging for Design Optimization,” Eng. Optim., 44(6), pp. 741–765. [CrossRef]
Cha, C. , Ireland, P. , Denman, P. , and Savarianandam, V. , 2012, “ Turbulence Level are High at the Combustor–Turbine Interface,” ASME Paper No. GT2012-69130.
Deb, K. , 2001, Multi-Objective Optimization Using Evolutionary Algorithms, Wiley, Hoboken, NJ.

Figures

Grahic Jump Location
Fig. 1

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

Grahic Jump Location
Fig. 2

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

Grahic Jump Location
Fig. 3

An illustration of a prometheus generated fluid volume [11]

Grahic Jump Location
Fig. 4

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

Grahic Jump Location
Fig. 5

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

Grahic Jump Location
Fig. 6

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

Grahic Jump Location
Fig. 7

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

Grahic Jump Location
Fig. 8

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

Grahic Jump Location
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

Grahic Jump Location
Fig. 10

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

Grahic Jump Location
Fig. 11

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

Grahic Jump Location
Fig. 12

Co-Kriging example using the Forrester function [22]

Grahic Jump Location
Fig. 16

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

Grahic Jump Location
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

Grahic Jump Location
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

Grahic Jump Location
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

Grahic Jump Location
Fig. 17

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

Grahic Jump Location
Fig. 22

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

Grahic Jump Location
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

Grahic Jump Location
Fig. 18

Graphical illustration of the prediffuser design parameters

Grahic Jump Location
Fig. 19

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

Grahic Jump Location
Fig. 20

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

Grahic Jump Location
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

Grahic Jump Location
Fig. 24

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

Grahic Jump Location
Fig. 25

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

Tables

Errata

Discussions

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