Abstract
Aviation researchers are increasingly focusing on unconventional vehicle designs with tightly integrated propulsion systems to improve overall aircraft performance and reduce environmental impact. Properly analyzing these types of vehicle and propulsion systems requires multidisciplinary models that include many design variables and physics-based analysis tools. This need poses a challenge from a propulsion modeling standpoint because current state-of-the-art thermodynamic cycle analysis tools are not well suited to integration into vehicles level models or to the application of efficient gradient-based optimization techniques that help to counteract the increased computational costs. Therefore, the objective of this research effort was to investigate the development a new thermodynamic cycle analysis code, called pyCycle, to address this limitation and enable design optimization of these new vehicle concepts. This paper documents the development, verification, and application of this code to the design optimization of an advanced turbofan engine. The results of this study show that pyCycle models compute thermodynamic cycle data within 0.03% of an identical Numerical Propulsion System Simulation (NPSS) model. pyCycle also provides more accurate gradient information in three orders of magnitude less computational time by using analytic derivatives. The ability of pyCycle to accurately and efficiently provide this derivative information for gradient-based optimization was found to have a significant benefit on the overall optimization process with wall times at least seven times faster than using finite difference methods around existing tools. The results of this study demonstrate the value of using analytic derivatives for optimization of cycle models, and provide a strong justification for integrating derivatives into other important engineering analyses.
Highlights
Thermodynamic cycle analysis is a fundamental technique for the analysis and design of gas turbine engines
Based on the extensive body of work across multiple fields demonstrating the effectiveness of gradient-based optimization with analytic derivatives, this paper proposes that the approach is well suited to tackle the coupled multidisciplinary design problems that are emerging in propulsion system design trends
The calculations executed in the Cycle block do not explicitly result in a valid model as there are physical dependencies and design rules which must be satisfied. These physical dependencies and design rules are captured in the Balance block as a set of implicit state variables and associated nonlinear residual equations that are converged by the Solver. (In Numerical Propulsion System Simulation (NPSS), implicit state variables are called “independent variables” and residual equations are called “dependent equations”, but pyCycle adopts the terminology used in the multidisciplinary optimization (MDO) field.) Lastly, there is the Optimizer, which finds the design variable values that satisfy the constraints and minimizes the objective specified for the problem
Summary
Thermodynamic cycle analysis is a fundamental technique for the analysis and design of gas turbine engines. (In NPSS, implicit state variables are called “independent variables” and residual equations are called “dependent equations”, but pyCycle adopts the terminology used in the MDO field.) Lastly, there is the Optimizer, which finds the design variable values that satisfy the constraints and minimizes the objective specified for the problem Developing these four blocks and combining them into a complete cycle analysis and optimization tool involves the challenge of combining thermodynamic analysis, software engineering, numerical methods, and optimization techniques. OpenMDAO provides functionality for automatically computing derivatives across large, complex models for use with efficient gradient based optimization techniques In combination, these three characteristics facilitated the development of pyCycle to satisfy the primary research objective of creating a modular cycle analysis tool suitable for integration into a multidisciplinary aircraft design optimization process. This automatic total derivative calculation enabled the formulation of a modular cycle analysis code and guided the decisions about how to decompose the various cycle analysis calculations described
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
