Density-Matching for Turbomachinery Optimization Under Uncertainty

TL;DR

This paper introduces a monotonic density-matching optimization method tailored for turbomachinery, effectively reducing the impact of uncertainties like rear-seal leakage on engine performance.

Contribution

It develops a non-kernel density variant of density-matching specifically for monotonic turbomachinery problems, demonstrating its application to engine fan stage optimization under uncertainty.

Findings

Successfully de-sensitized rear-seal leakage uncertainty effects

Applicable to monotonic turbomachinery performance metrics

Improved robustness in engine design optimization

Abstract

A monotonic, non-kernel density variant of the density-matching technique for optimization under uncertainty is developed. The approach is suited for turbomachinery problems which, by and large, tend to exhibit monotonic variations in the circumferentially and radially mass-averaged quantities--such as pressure ratio, efficiency and capacity--with common aleatory turbomachinery uncertainties. The method is successfully applied to de-sensitize the effect of an uncertainty in rear-seal leakage flows on the fan stage of a modern jet engine.