Uncertainty Quantification and Optimization of Aeronautical Components

This project was part of a wider investigation performed on a set of 200 High Pressure Turbine (HPT) blades, dismounted after several hours of flight and characterized by in-service and manufacturing variations. The main objective of this project was to determine the impact of these variations on the aerodynamic performance of the rotor and to devise a strategy to design more robust geometries, i.e, less sensitive to the given uncertainties. The initial set of data consisted of the digitized versions of the blades (GOM scans). The geometrical deviations characterizing the blades from their hub until the 70% of the span were parametrized via a set of aerodynamic Engineering Parameters belonging to PADRAM design space (EP) and quantified via an inverse mapping procedure (P2S-PADRAM-SOFT); the visual inspection of the digital twins highlighted a considerable volume loss along the blade rim (tip region) due to localized erosion: the quantification of this metal degradation was performed via in-house Python scripts and expressed as indices of volume removal rate (PADRAM Squealer Tip option). The adjoint solver and the Polynomial Chaos Expansion (PCE) were used for the Uncertainty Propagation (UP) of the first set of uncertain variables, while for the erosion parameters only PCE was selected. The sensitivity of the nominal blade with respect to the typical erosion level was found to be higher than that of the geometrical deviations occurring beneath the tip. UP was followed by a series of gradient-based robust optimizations (RO) approaches. The rotor efficiency was selected as the figure of merit to be maximized by optimizing the EP values; in the first approach, the performance of a simplified geometry (without winglet and gutter) was slightly improved driving SLSQP Python optimizer via adjoint gradients. Following this, the nominal configuration, complete with winglet and gutter, was optimized providing SLSQP with the first order derivatives calculated via PCE. The same strategy for the calculation of the derivatives was used in the third approach, only this time the erosion level, characterizing the worst damaged rim, was included. A local, gradient-based, optimization was then performed in a larger design space: the final optimized configuration is then recovered a good percentage of rotor efficiency otherwise lost when the erosion occurs, thanks to an offloading of the tip region, while also improving the nominal rotor performance.