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

Automated Design Optimization of a Small-Scale High-Swirl Cavity-Stabilized Combustor

[+] Author and Article Information
Alejandro M. Briones

University of Dayton Research Institute,
Dayton, OH 45469
e-mail: alejandro.briones@udri.udayton.edu

David L. Burrus

Innovative Scientific Solutions, Inc.,
Dayton, OH 45459
e-mail: dburrus1151@gmail.com

Joshua P. Sykes

Innovative Scientific Solutions, Inc.,
Dayton, OH 45459
e-mail: joshua.sykes.3.ctr@us.af.mil

Brent A. Rankin

Air Force Research Laboratory,
WPAFB, OH 45433
e-mail: brent.rankin.1@us.af.mil

Andrew W. Caswell

Air Force Research Laboratory,
WPAFB, OH 45433
e-mail: andrew.caswell.4@us.af.mil

Contributed by the Advanced Energy Systems of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received June 22, 2018; final manuscript received July 2, 2018; published online November 29, 2018. Editor: Jerzy T. Sawicki. This work is in part a work of the U.S. Government. ASME disclaims all interest in the U.S. Government's contributions.

J. Eng. Gas Turbines Power 140(12), 121509 (Nov 29, 2018) (10 pages) Paper No: GTP-18-1319; doi: 10.1115/1.4040821 History: Received June 22, 2018; Revised July 02, 2018

A numerical optimization study is performed on a small-scale high-swirl cavity-stabilized combustor. A parametric geometry is created in cad software that is coupled with meshing software. The latter automatically transfers meshes and boundary conditions to the solver, which is coupled with a postprocessing tool. Steady, incompressible three-dimensional simulations are performed using a multiphase Realizable k-ε Reynolds-averaged Navier-Stokes (RANS) approach with a nonadiabatic flamelet progress variable (FPV) model. There are nine geometrical input parameters. There are five output parameters, viz., pattern factor (PF), RMS of the profile factor deviation, averaged exit temperature, averaged exit swirl angle, and total pressure loss. An iterative design of experiments (DOE) with a recursive Latin hypercube sampling (LHS) is performed to filter the most important input parameters. The five major input parameters are found with Spearman's order-rank correlation and R2 coefficient of determination. The five input parameters are used for the adaptive multiple objective (AMO) optimization. This provided a candidate design point with the lowest weighted objective function, which was verified through computational fluid dynamic (CFD) simulation. The combined filtering and optimization procedures improve the baseline design point in terms of pattern and profile factor. The former halved from that of the baseline design point, whereas the latter turned from an outer peak to a center peak profile, closely mimicking an ideal profile. The exit swirl angle favorably increased 25%. The averaged exit temperature and the total pressure losses remained nearly unchanged from the baseline design point.

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Diwekar, U. M. , 2003, Introduction to Applied Optimization, Kluwer Academic Publishers, New York.
Davis, N. T. , and Samuelsen, G. S. , 1996, “ Optimization of Gas Turbine Combustor Performance Throughout the Duty Cycle,” Symp. (Int.) Combust., 26(2) pp. 2819–2825.
Epstein, B. , Peigin, S. , and Tsach, S. , 2006, “ A New Efficient Technology of Aerodynamic Design Based on CFD Driven Optimization,” Aerosp. Sci. Technol., 10(2), pp. 100–110. [CrossRef]
Lighthill, M. J. , “ A New Method of Two-Dimensional Aerodynamic Design,” Aeronautical Research Council, London, Rand Report No. M 2112.
Jameson, A. , Shankaran, S. , Matinelli, L. , and Haimes, B. , “ Aerodynamics Shape Optmization of Complete Aircraft Configurations Using Unstructured Grids,” AIAA Paper No. AIAA-2004-0533.
Thevenin, D. , and Janiga, G. , 2008, Optimization and Computational Fluid Dynamics, Springer, Berlin.
Wankhede, M. J. , Bressloff, N. W. , and Keane, A. J. , “ Combustor Design Optimization Using Co-Kriging of Steady and Unsteady Turbulent Combustion,” ASME J. Eng. Gas Turbines Power, 133(12), p. 121504. [CrossRef]
Fuligno, L. , Micheli, D. , and Poloni, C. , 2009, “ An Integrated Approach for Optimal Design of Macro Gas Turbine Combustors,” J. Therm. Sci., 18 (2), pp. 173–184. [CrossRef]
Mazaheri, K. , and Shakeri, A. , 2006, “ Numerical Optimization of Laboratory Combustor Geometry for NO Suppression,” Appl. Therm. Eng., 102, pp. 1328–1336. [CrossRef]
Torkzadeh, M. M. , Bolourchifard, F. , and Amani, E. , 2016, “ An Investigation of Air-Swirl Design Criteria for Gas Turbine Combustors Through a Multi-Objective CFD Optimization,” Fuel, 186, pp. 734–749. [CrossRef]
ANSYS, 2018, “ Workbench User's Guide, v18.0,” ANSYS Inc., Canonsburg, PA.
ANSYS, 2018, “ DesignXplorer User's Guide, v18.0,” ANSYS Inc., Canonsburg, PA.
ANSYS, 2018, “ DesignModeler User's Guide, v18.0,” ANSYS Inc., Canonsburg, PA.
ANSYS, 2018, “ Meshing User's Guide, v18.0,” ANSYS Inc., Canonsburg, PA.
ANSYS, 2018, “ Fluent User's Guide, v18.0,” ANSYS Inc., Canonsburg, PA.
ANSYS, 2018, “ Fluent Theory Guide, v18.0,” ANSYS Inc., Canonsburg, PA.
ANSYS, 2018, “ CFD-Post User's Guide, v18.0,” ANSYS Inc., Canonsburg, PA.
Montgomery, D. C. , and Runger, G. C. , 1998, Applied Statistics and Probability for Engineers, 2nd ed., Wiley, Hoboken, NJ.
Deb, K. , Pratap, A. , Agarwal, S. , and Meyrivan, T. , 2002, “ A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II,” IEEE Trans. Evol. Comput., 6(2), pp. 182–197. [CrossRef]
Lefebvre, A. , and Ballal, D. , 2010, Gas Turbine Combustion: Alternative Fuels and Emissions, 3rd ed., CRC Press, Boca Raton, FL.


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Fig. 1

Schematic of the small-scale high-swirl cavity-stabilized combustor baseline geometry. (a) Forward looking aft, (b) side, (c) aft looking forward, and (d) isometric views. The nine input parameter indices are indicated (in magenta) and correspond to those listed in Table 1.

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Fig. 2

Flowchart of the filtering operations algorithm used to identify the most important input parameters

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Fig. 3

Flowchart of the AMO algorithm used to optimize the input parameters

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Fig. 4

Spearman's order-rank correlation coefficient (rij) matrix

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Fig. 5

Clipped Spearman's order-rank correlation coefficients (rij). Values are clipped to zero when −0.313<rij<0.313, which corresponds to α = 0.02 (i.e., confidence level of 98%).

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Fig. 6

R2 coefficient of determination matrix

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Fig. 7

Clipped R2 coefficients of determination. Values are clipped to zero if Rij2<0.15.

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Fig. 8

(left) Baseline design and (right) optimized design in terms of (top) center plane and (bottom) exit plane temperature distributions

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Fig. 9

Profile factors as a function of combustor exit dimensionless height

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Fig. 10

(top) Plot of p-value or α versus relevance level and (bottom) plot of gain versus relevance level



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