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Optimization of Turbine Engine Cycle Analysis with Analytic DerivativesA new engine cycle analysis tool, called Pycycle, was recently built using the OpenMDAO framework. This tool uses equilibrium chemistry based thermodynamics, and provides analytic derivatives. This allows for stable and efficient use of gradient-based optimization and sensitivity analysis methods on engine cycle models, without requiring the use of finite difference derivative approximation methods. To demonstrate this, a gradient-based design optimization was performed on a multi-point turbofan engine model. Results demonstrate very favorable performance compared to an optimization of an identical model using finite-difference approximated derivatives.
Document ID
20170002541
Acquisition Source
Glenn Research Center
Document Type
Presentation
Authors
Hearn, Tristan
(NASA Glenn Research Center Cleveland, OH United States)
Hendricks, Eric
(NASA Glenn Research Center Cleveland, OH United States)
Chin, Jeffrey
(NASA Glenn Research Center Cleveland, OH United States)
Gray, Justin
(NASA Glenn Research Center Cleveland, OH United States)
Moore, Kenneth T.
(NASA Glenn Research Center Cleveland, OH United States)
Date Acquired
March 27, 2017
Publication Date
June 16, 2016
Subject Category
Aircraft Propulsion And Power
Computer Programming And Software
Report/Patent Number
GRC-E-DAA-TN32882
Meeting Information
Meeting: AIAA Aviation 2016 Conference
Location: Washington, DC
Country: United States
Start Date: June 13, 2016
End Date: June 19, 2016
Sponsors: American Inst. of Aeronautics and Astronautics
Funding Number(s)
WBS: WBS 109492.02.03.01.10
CONTRACT_GRANT: NNC14BA04B
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
Keywords
Turbine engine
Analytic derivatives
OpenMDAO
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