Robust turbine blade optimization in the face of real geometric variations

J Kamenik, I Voutchkov, DJJ Toal, AJ Keane… - Journal of Propulsion …, 2018 - arc.aiaa.org
Because of manufacturing variations, no real turbine blade exactly conforms to its nominal
geometry. Even minimal deviations are known to affect aerodynamic performance, blade …

Turbomachinery active subspace performance maps

P Seshadri, S Shahpar… - Journal of …, 2018 - asmedigitalcollection.asme.org
Turbomachinery active subspace performance maps are two-dimensional (2D) contour plots
that illustrate the variation of key flow performance metrics with different blade designs …

Novel compressor blade shaping through a free-form method

A John, S Shahpar, N Qin - Journal of …, 2017 - asmedigitalcollection.asme.org
This paper describes the use of the free-form-deformation (FFD) parameterization method to
create a novel blade shape for a highly loaded, transonic axial compressor. The novel …

Leakage uncertainties in compressors: The case of rotor 37

P Seshadri, GT Parks, S Shahpar - Journal of Propulsion and Power, 2015 - arc.aiaa.org
This paper revisits an old problem of validating computational fluid dynamics simulations
with experiments in turbomachinery. The case considered here is NASA rotor 37. Prior …

Affordable uncertainty quantification for industrial problems: application to aero-engine fans

T Ghisu, S Shahpar - Journal of Turbomachinery, 2018 - asmedigitalcollection.asme.org
Uncertainty quantification (UQ) is an increasingly important area of research. As
components and systems become more efficient and optimized, the impact of uncertain …

High-dimensional uncertainty quantification of high-pressure turbine vane based on multifidelity deep neural networks

Z Li, F Montomoli, N Casari… - Journal of …, 2023 - asmedigitalcollection.asme.org
In this work, a new multifidelity (MF) uncertainty quantification (UQ) framework is presented
and applied to the LS89 nozzle modified by fouling. Geometrical uncertainties significantly …

Bayesian assessments of aeroengine performance with transfer learning

P Seshadri, AB Duncan, G Thorne, G Parks… - Data-Centric …, 2022 - cambridge.org
Aeroengine performance is determined by temperature and pressure profiles along various
axial stations within an engine. Given limited sensor measurements, we require a …

A density-matching approach for optimization under uncertainty

P Seshadri, P Constantine, G Iaccarino… - Computer Methods in …, 2016 - Elsevier
Modern computers enable methods for design optimization that account for uncertainty in
the system—so-called optimization under uncertainty (OUU). We propose a metric for OUU …

Gradient-enhanced least-square polynomial chaos expansions for uncertainty quantification and robust optimization

T Ghisu, DI Lopez, P Seshadri, S Shahpar - AIAA AVIATION 2021 …, 2021 - arc.aiaa.org
View Video Presentation: https://doi. org/10.2514/6.2021-3073. vid Regression-based
Polynomial Chaos expansions offer several advantages over projection-based approaches …

Effects of rotor tip blade loading variation on compressor stage performance

A Tiralap, CS Tan, E Donahoo… - Journal of …, 2017 - asmedigitalcollection.asme.org
Changes in loss generation associated with altering rotor tip blade loading of an embedded
rotor–stator compressor stage are assessed with unsteady three-dimensional computations …