Optimization of Turbine Engine Cycle Analysis with Analytic Derivatives
Author(s) -
Tristan A. Hearn,
Eric S. Hendricks,
Jeffrey Chin,
Justin S. Gray
Publication year - 2016
Publication title -
12th aiaa/issmo multidisciplinary analysis and optimization conference
Language(s) - English
Resource type - Conference proceedings
DOI - 10.2514/6.2016-4297
Subject(s) - turbine , computer science , combined cycle , gas turbines , automotive engineering , environmental science , aerospace engineering , engineering , mechanical engineering
A new engine cycle analysis tool, called Pycycle, was built using the OpenMDAO framework. Pycycle provides analytic derivatives allowing for an efficient use of gradient-based optimization 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 turbofan engine model. Results demonstrate very favorable performance compared to an optimization of an identical model using finite-difference approximated derivatives.
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