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research-article

A SINGLE FORMULATION FOR UNCERTAINTY PROPAGATION IN TURBOMACHINERY: SAMBA

[+] Author and Article Information
Richard Ahlfeld

Uncertainty Quantification Lab, Department of Aeronautics, Imperial College of London, UK
r.ahlfeld14@imperial.ac.uk

Francesco Montomoli

Uncertainty Quantification Lab, Department of Aeronautics, Imperial College of London, UK
f.montomoli@imperial.ac.uk

1Corresponding author.

ASME doi:10.1115/1.4037362 History: Received August 03, 2016; Revised July 25, 2017

Abstract

This work newly proposes an uncertainty quantification method named SAMBA PC (Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos) that offers a single solution to many current problems in turbomachinery applications. At the moment every specific case is characterized by a variety of different input types such as histograms (from experimental data), normal PDFs(design rules) or fat tailed PDFs (for rare events). Thus, the application of UQ requires the adaptation of ad hoc methods fore ach individual case. A second problem is that parametric PDFs have to be determined for all inputs. This is difficult if only few samples are available. In this work it is shown that the fitting of parametric PDFs to small data sets can lead to large bias and the direct use of the available data is more accurate. SAMBA is numerically efficient compared to other UQ implementations. The relationship between the number of random variables and number of simulation is linear (only 21 simulations for 10 input random variables are required). It is shown that SAMBA's algorithm can propagate a high number of input distributions through a set of nonlinear analytic test functions.Doing this the code needs a very small number of simulations and preserves a 5% error margin. SAMBA's flexibility to handle different forms of input distributions and a high number of in-put variables is shown on a low pressure turbine blade based on H2 profile. The relative importance of manufacturing errors indifferent location of the blade is analyzed.

Copyright (c) 2017 by ASME
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