Integrated Engine and Turbomachinery Optimization Using Analytical Derivatives

Date of Award

8-15-2026

Degree Name

M.S. in Aerospace Engineering

Department

Department of Mechanical and Aerospace Engineering

Advisor/Chair

Markus Rumpfkeil

Abstract

This thesis presents a study on engine design optimization, with a focus on the integration of mean-line geometry design. The motivation for optimization is to shift design integration to earlier phases of the design process in order to reduce development time and cost. This work compares two engine design tools and their ability to perform engine-level optimizations. These tools are Numerical Propulsion System Simulation (NPSS) and pyCycle, both developed by the National Aeronautics and Space Administration (NASA). A new, analytically differentiated mean-line design tool for turbomachinery was developed and integrated into pyCycle. For comparison, the Object-Oriented Turbomachinery Analysis Code (OTAC) was utilized within NPSS. Both turbojet and turbofan engine models, including single and multi-stage turbine configurations, were evaluated in a series of optimization studies. These optimizations aimed to maximize thrust by varying geometric parameters such as mean radius, blade heights, aspect ratios, and Zweifel coefficients, while respecting constraints on reaction rates, mechanical stress, and Mach numbers. The results consistently demonstrate the significant advantages of using an optimization framework with analytic derivatives. While both NPSS and pyCycle yielded similar optimal design points, pyCycle achieved these results with substantially lower computational cost and was able to converge to stricter optimality tolerances. NPSS, relying on finite-differencing for sensitivity analysis, exhibited challenges with derivative accuracy, leading to longer run times and convergence difficulties, particularly as the complexity of the optimization problem increased. This research demonstrates the benefits of employing analytically derived sensitivities in multidisciplinary design optimization for advanced aerospace propulsion systems.

Keywords

Engineering

Rights Statement

Copyright 2026, author

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