Abstract

Modern high-pressure turbine (HPT) blade design stands out due to high complexity comprising three-dimensional blade features, multipassage cooling system (MPCS), and film cooling to allow for progressive thermodynamic process parameters. During the last decade, probabilistic design approaches have become increasingly important in turbomachinery to incorporate uncertainties such as geometric variations caused by manufacturing scatter. In Part B of this two-part article, real geometry effects are considered within a probabilistic finite element (FE) analysis that aims at sensitivity evaluation. The knowledge about the geometric variability is derived based on a blade population of more than 400 individuals by means of parametric models that are introduced in Part A. The HPT blade population is statistically assessed, which allows for reliable sensitivity analysis and robustness evaluation taking the variability of the airfoil, profiled endwalls (PEWs) at hub and shroud, wedge surfaces (WSFs), and the MPCS into account. The probabilistic method—Monte Carlo simulation (MCS) using an extended Latin hypercube sampling (eLHS) technique—is presented subsequently. Afterward, the FE model that involves thermal, linear-elastic stress, and creep analysis is described briefly. Based on this, the fully automated process chain involving computer-aided design (CAD) model creation, FE mesh morphing, FE analysis, and postprocessing is executed. Here, the mesh morphing process is presented involving a discussion of the mesh quality. The process robustness is assessed and quantified referring to the impact on input parameter correlation. Finally, the result quantities of the probabilistic FE simulation are evaluated in terms of sensitivities. For this purpose, regions of interest are determined, wherein the statistical analysis is conducted to achieve the sensitivity ranking. A significant influence of the considered geometric uncertainties onto mechanical output quantities is observed, which motivates to incorporate these in modern design strategies or robust optimization.

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