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Abstract

This paper describes the procedure for developing a new airfoil family. This airfoil family applies to heavy-duty, industrial, and aero-derivative gas turbine compressors ranging from subsonic to transonic flow regimes. The airfoil family is generated by filling a database with optimized airfoil geometries. This database is structured in six dimensions, called design space parameters, including inlet Mach number, inlet flow angle, outlet flow angle, axial velocity density ratio, maximum thickness to chord ratio, and solidity. This six-dimensional space includes all compressor blades used in stationary gas turbine compressors. Each set of these design space parameters is related to an optimal geometry produced by the optimization system. The optimization system includes a parametrized airfoil generator, an accurate, fast blade-to-blade flow solver, and an evolutionary optimization algorithm. Airfoils of different stationary gas turbine compressor types are investigated to cover the required design space. Four hundred thirty airfoils, denoted as reference airfoils, are used to define design space borders. Comparing the newly optimized airfoils with reference airfoils revealed superior performance throughout the entire design space. They incorporate these optimized airfoils into a surrogate model, resulting in a fast, optimized airfoil generator (airfoil family). The transonic rotor of the existing multistage compressor has been redesigned according to the developed airfoil family. 3D computational fluid dynamics showed a 2% efficiency improvement for optimized blade row over the original design. Integrating this airfoil family and a streamline curvature code as part of a compressor design system is the main application of this advanced airfoil family.

Keywords

Axial compressor heavy-duty industrial airfoil family optimization operating range machine learning artificial intelligence

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References

Energy Information Administration . U.S. energy information administration. Washington, DC: Energy Information Administration, 2020. Available from: https://ourworldindata.org/electricity-mix

Ikeguchi

Matsuoka

Sakai

, et al. Design and development of a 14-stage axial compressor for industrial gas turbine. In: ASME turbo expo 2012: turbine technical conference and exposition, Copenhagen, Denmark, 11–15 June 2012: American Society of Mechanical Engineers (ASME).

Schnoes

Nicke

. Exploring a database of optimal airfoils for axial compressor design. Toulouse, France: ISABE, 2017.

Cumpsty

. Compressor aerodynamics. Harlow, UK: Longman Scientific and Technical, 1989.

Schnös

. Eine auslegungsmethodik für mehrstufige axialverdichter auf basis einer profildatenbank. Bochum, Germany: Ruhr-Universität Bochum, 2020.

Bakhtiari

Wartzek

Leichtfuss

, et al. Design and optimization of a new stator for the transonic compressor rig at TU darmstadt. Bonn, Germany: Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V, 2015.

Voß

Aulich

Raitor

. Metamodel assisted aeromechanical optimization of a transonic centrifugal compressor. Honolulu, USA: International Symposium on Transport Phenomena and Dynamics of Rotating Machinery (ISROMAC), 2014.

Abate

. Aerodynamic optimization of a transonic axial compressor rotor. Master’s thesis. Padua, Italy: University of Padua, 2012.

Johnsen

Bullock

. Aerodynamic design of axial-flow compressors: NASA SP-36. Washington, DC: NASA Special Publication, 1965, p. 36.

10.

Hobbs

Weingold

. Development of controlled diffusion airfoils for multistage compressor application. J Eng Gas Turbines Power 1984; 106(2): 271–278.

11.

Benini

Toffolo

. Development of high-performance airfoils for axial flow compressors using evolutionary computation. J Propuls Power 2002; 18(3): 544–554.

12.

Buche

Guidati

Stoll

. Automated design optimization of compressor blades for stationary, large-scale turbomachinery. In: ASME turbo expo 2003, collocated with the 2003 international joint power generation conference, Atlanta, GA, 16–19 June 2003.

13.

Lee

S-Y

Kim

K-Y

. Design optimization of axial flow compressor blades with three-dimensional Navier-Stokes solver. KSME Int J 2000; 14: 1005–1012.

14.

Jiang

Zheng

Zhang

, et al. Advanced axial compressor airfoils design and optimization. In: ASME turbo expo 2015: turbine technical conference and exposition, Montreal, Qc, Canada, 15–19 June 2015: American Society of Mechanical Engineers (ASME).

15.

Tao

Ding

, et al. Maximum thickness location selection of high subsonic axial compressor airfoils and its effect on aerodynamic performance. Front Energy Res 2021; 9: 791.

16.

Kipouros

Jaeggi

Dawes

, et al. Biobjective design optimization for axial compressors using tabu search. AIAA J 2008; 46(3): 701–711.

17.

Safari

Hajikolaei

Lemu

, et al. A high-dimensional model representation guided PSO methodology with application on compressor airfoil shape optimization. In: ASME turbo expo 2016: turbomachinery technical conference and exposition, Seoul, South Korea, 13–17 June 2016: American Society of Mechanical Engineers (ASME).

18.

Giesecke

Bullert

Friedrichs

, et al. Optimization of high subsonic, high reynolds number axial compressor airfoil sections for increased operating range. In: Proceedings of the global power and propulsion society forum, Montreal, Qc, Canada, 9 July 2018.

19.

Köller

Mönig

Küsters

, et al. Development of advanced compressor airfoils for heavy-duty gas turbines: part I — design and optimization. In: ASME 1999 international gas turbine and aeroengine congress and exhibition, Indianapolis, IN, 7–10 June 1999, p. 10.

20.

Koller

. Entwicklung einer fortschrittlichen profilsystematik fur stationare gasturbinenverdichter. Cologne, Germany: Deutsches Zentrum fur Luft-und Raumfahrt, 1999.

21.

Sieverding

Ribi

Casey

, et al. Design of industrial axial compressor blade sections for optimal range and performance. J Turbomach 2004; 126(2): 323–331.

22.

Okui

Verstraete

Van den Braembussche

, et al. Three-dimensional design and optimization of a transonic rotor in axial flow compressors. J Turbomach 2013; 135(3): 031009.

23.

Drela

Youngren

. MISES user manual. Cambridge, MA: MIT, 2008.

24.

Fuchs

Schreiber

Steinert

, et al. Ein verlustminimiertes verdichtergitter für einen transsonischen rotor-entwurf und analyse. Hannover, Germany: VDI-Berichte, 1998.

25.

Youngren

. Analysis and design of transonic cascades with splitter vanes. Cambridge, MA: Massachusetts Institute of Technology, 1991.

26.

Küsters

Schreiber

Köller

, et al. Development of advanced compressor airfoils for heavy-duty gas turbines: part II — experimental and theoretical analysis. In: ASME 1999 international gas turbine and aeroengine congress and exhibition, Indianapolis, IN, 7–10 June 1999, p. 10.

27.

Pieper

Schulte

JHG

, Verlustarme verdichterauslegung (experiment I und II): vorhaben nr. 466 und 538, experimentelle untersuchung einer invers ausgelegten beschaufelung eines einstufigen axialverdichters mit vorleitrad, instationäre messungen an einem invers ausgelegten einstufigen axialverdichter mit vorleitrad; abschlußbericht; beginn der arbeiten: 01.01.1990; ende der arbeiten, 1994. Forschungsberichte verbrennungskraftmaschinen. Frankfurt, Germany: FVV, 1995.

28.

Abu-Ghannam

Shaw

. Natural transition of boundary layers—the effects of turbulence, pressure gradient, and flow history. J Mech Eng Sci 2006; 22(5): 213–228.

29.

Drela

. MISES implementation of modified Abu-Ghannam/Shaw transition criterion. Mises user’s guide. Cambridge, MA: MIT, 1995.

30.

Mayle

. The role of laminar-turbulent transition in gas turbine engines. The 1991 ICTI scholar lecture. Berlin, Germany: Springer, 1991.

31.

Hong

T-P

Wang

H-S

. A dynamic mutation genetic algorithm. in 1996 IEEE international conference on systems, man and cybernetics: information intelligence and systems (cat. No. 96CH35929), Beijing, China, 14–17 October 1996: IEEE.

32.

Eiben

ÁE

Hinterding

Michalewicz

. Parameter control in evolutionary algorithms. IEEE Trans Evol Comput 1999; 3(2): 124–141.

33.

Ginder

Calvert

. The design of an advanced civil fan rotor. J Turbomachinery 1987; 109(3): 340–345. DOI: 10.1115/1.3262111.

34.

Bonaiuti

Pitigala

Zangeneh

, et al. Redesign of a transonic compressor rotor by means of a three-dimensional inverse design method: a parametric study. In: ASME turbo expo 2007: power for land, sea, and air, Montreal, Canada, 14–17 May 2007.

35.

Wadia

Copenhaver

. An investigation of the effect of cascade area ratios on transonic compressor performance. J Turbomachinery 1996; 118(4): 760–770. DOI: 10.1115/1.2840932.

36.

Seyler

Smith

Jr . Single stage experimental evaluation of high Mach number compressor rotor blading: part I-design of rotor blading. Washington, DC: NASA, 1967.

37.

Tsukuda

Akita

Arimura

, et al. The operating experience of the next generation M501G/M701G gas turbine. In: ASME turbo expo 2001: power for land, sea, and air, New Orleans, LA, June 4–7, 2001: American Society of Mechanical Engineers (ASME).

38.

Crouse

Janetzke

Schwirian

. A computer program for composing compressor blading from simulated circular-arc elements on conical surfaces. Washington, DC: NASA, 1969.

39.

DuToit

Steyn

AGW

Stumpf

. Graphical exploratory data analysis. Berlin, Germany: Springer Science and Business Media, 2012.

40.

Brownlee

. Deep learning with python: develop deep learning models on Theano and TensorFlow using Keras. Melbourne, Australia: Machine Learning Mastery, 2016.

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Utilizing artificial intelligence to develop an advanced compressor airfoil family for industrial,aero-derivative,and heavy-duty gas turbines

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