Comparative study of numerical approaches to adaptive gas turbine cycle analysis
-
Michael Palman
Abstract
Significant increase in task complexity for modern gas-turbine propulsion systems drives the need for future advanced cycles’ development. Further performance improvement can be achieved by increasing the number of engine controls. However, there is a lack of cycle analysis tools, suitable for the increased complexity of such engines. Towards bridging this gap, this work focuses on the computation time optimization of various mathematical approaches that could be implemented in future cycle-solving algorithms. At first, engine model is described as a set of engine variables and error functions, and is solved as an optimization problem. Then, the framework is updated to use advanced root-finding paradigms. Starting with Newton-Raphson, the model is improved by applying Broyden’s and Miller’s schemes and implementing solution existence validation. Finally, algorithms are compared in representative condition using increasingly complex turbojet and adaptive cycle turbofan configurations. As evaluation cases become more time consuming, associated time benefits also improve.
Funding source: U.S. Office of Naval Research Global
Award Identifier / Grant number: N62909-17-1-217
Funding source: Minerva Research Center, Max Planck Society
Award Identifier / Grant number: AZ5746940764
Funding source: Peter Munk Research Institute
Award Identifier / Grant number: 110101
Funding source: Bernard M. Gordon Center for Systems Engineering
Award Identifier / Grant number: 1017930
-
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
-
Research funding: The present research effort was partially supported by the U.S. Office of Naval Research Global under award number N62909-17-1-217; Peter Munk Research Institute under award number 110101; and the Bernard M. Gordon Center for Systems Engineering under award number 1017930. Also, supported by Minerva Research Center (Max Planck Society Contract No. AZ5746940764).
-
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. WILLIAM, K. Pratt and Whitney receives $437M for continued adaptive engine development. Available from: https://www.sae.org/news/2018/09/pratt--whitney-receives-usd437m-for-continued-adaptive-engine-development.Search in Google Scholar
2. Rolls-Royce. Rolls-Royce North American Technologies, Inc. Selected by U.S. Air Force to complete ADVENT research demonstrator Program. Available from: http://www.defense-aerospace.com/articles-view/release/3/109107/rolls_royce-selected-for-advent-demonstrator.html.Search in Google Scholar
3. Kowalski, M. Adaptive jet engines. J KONES Powertrain Transport 2011;18.Search in Google Scholar
4. Patel, HR, Wilson, DR. Parametric cycle analysis of adaptive. AIAA Propul Energy 2018. 2018 Joint Propulsion Conference, July 9-11, 2018, Cincinnati, Ohio, US. https://doi.org/10.2514/6.2018-4521.Search in Google Scholar
5. Visser, MOWPJ, Kogenhop, O. A generic approach for gas turbine adaptive modeling. J Eng Gas Turbines Power 2006;128:13–19. https://doi.org/10.1115/1.1995770.Search in Google Scholar
6. Palman, M, Leizeronok, B, Cukurel, B. Mission analysis and operational optimization of adaptive cycle microturbofan engine in surveillance and firefighting scenarios. J Eng Gas Turbines Power 2019;141. https://doi.org/10.1115/1.4040734.Search in Google Scholar
7. Kadosh, K Cukurel, B. Micro-turbojet to turbofan conversion via continuously variable transmission: thermodynamic performance study. ASME J Eng Gas Turbines Power 2017;139. https://doi.org/10.1115/1.4034262.Search in Google Scholar
8. Lichtsinder, M, Levy, Y. Jet engine model for control and real-time simulations. J Eng Gas Turbines Power 2006;128:745–53. https://doi.org/10.1115/1.1915391.Search in Google Scholar
9. Powell, MJD. The theory of radial basis function approximation in 1990. In: Light, WA, editor. Advances in numerical analysis, volume 2, wavelets, subdivision algorithms, and radial basis functions. Oxford: Clarendon Press; 1992:105–210 pp.10.1093/oso/9780198534396.003.0003Search in Google Scholar
10. Regis, RG, Shoemaker, CA. A stochastic radial basis function method for the global optimization of expensive functions. Inf J Comput 2007;19:497–509. https://doi.org/10.1287/ijoc.1060.0182.Search in Google Scholar
11. Wang, Y, Shoemaker, CA. A general stochastic algorithm framework for minimizing expensive black box objective functions based on surrogate models and sensitivity analysis. 2014. arXiv:1410.6271v1. Available from: https://arxiv.org/pdf/1410.6271.Search in Google Scholar
12. Gutmann, H-M. A radial basis function method for global optimization. J Global Optim 2001;19:201–27. https://doi.org/10.1023/a:1011255519438.10.1023/A:1011255519438Search in Google Scholar
13. Kennedy, J, Eberhart, R. Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks. 1995:1942–5.10.1109/ICNN.1995.488968Search in Google Scholar
14. Mezura-Montes, E, Coello, CA. Constraint-handling in nature-inspired numerical optimization: past, present and future. Swarm and Evolutionary Computation 2011;1:173–94. https://doi.org/10.1016/j.swevo.2011.10.001.Search in Google Scholar
15. Pedersen, ME. Good parameters for particle swarm optimization. Luxembourg City, Luxembourg: Hvass Laboratories; 2010.Search in Google Scholar
16. Ryaben’kii, VS, v Tsynkov, S. A theoretical introduction to numerical analysis. Boca Raton, Florida, US: CRC Press; 2006:243 p. ISBN 9781584886075.Search in Google Scholar
17. Broyden, CG. A class of methods for solving nonlinear simultaneous equations math. Math Comput 1965;19:577–93. https://doi.org/10.1090/s0025-5718-1965-0198670-6.Search in Google Scholar
18. Muller, DE. A method for solving algebraic equations using an automatic computer. MTAC; 1956. p. 208–15. https://doi.org/10.1090/s0025-5718-1956-0083822-0.Search in Google Scholar
19. Walsh, PP, Fletcher, P. Gas turbine performance. Hoboken, New Jersey, US: Blackwell Publishing; 2004.10.1002/9780470774533Search in Google Scholar
20. Gouy, I. The computer language benchmarks game. Available from: https://benchmarksgame-team.pages.debian.net/benchmarksgame.Search in Google Scholar
21. Aruoba, SB, Fernansez-Villaverde, J. A comparison of programming languages in macroeconomics. J Econ Dyn Control 2015;58:265–73.10.1016/j.jedc.2015.05.009Search in Google Scholar
© 2021 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Design and aerodynamic performance analysis of a variable geometry axisymmetric inlet for TBCC
- Effect of multi-hole arrangement on the effusion cooling with backward injection
- Research on a component characteristic adaptive correction method for variable cycle engines
- Optimization of a circumferential groove in a centrifugal compressor
- Comparative study of numerical approaches to adaptive gas turbine cycle analysis
- Numerical simulation of shock wave/tip leakage vortex interaction for a transonic axial fan rotor
- Experimental research on suppressing unbalanced vibration of rotor by integral squeeze film damper
- The influence of the geometry of V-gutter bluff body on transient vortex shedding
- Design and validation of a two-dimensional variable geometry inlet
- Computational assessment of performance parameters of an aero gas turbine combustor for full flight envelope operation
- Investigation of effect of atomization performance on lean blowout limit for gas turbine combustors by comparison of utilizing aviation kerosene and methane as fuel
- Design optimization of a supersonic through-flow fan rotor based on the blade profiles
Articles in the same Issue
- Frontmatter
- Design and aerodynamic performance analysis of a variable geometry axisymmetric inlet for TBCC
- Effect of multi-hole arrangement on the effusion cooling with backward injection
- Research on a component characteristic adaptive correction method for variable cycle engines
- Optimization of a circumferential groove in a centrifugal compressor
- Comparative study of numerical approaches to adaptive gas turbine cycle analysis
- Numerical simulation of shock wave/tip leakage vortex interaction for a transonic axial fan rotor
- Experimental research on suppressing unbalanced vibration of rotor by integral squeeze film damper
- The influence of the geometry of V-gutter bluff body on transient vortex shedding
- Design and validation of a two-dimensional variable geometry inlet
- Computational assessment of performance parameters of an aero gas turbine combustor for full flight envelope operation
- Investigation of effect of atomization performance on lean blowout limit for gas turbine combustors by comparison of utilizing aviation kerosene and methane as fuel
- Design optimization of a supersonic through-flow fan rotor based on the blade profiles
