Session: 24-01 Compressor aerodynamic damping

Paper Number: 81935

81935 - Uncertainty Quantification of Computational Flutter Estimates of a Compressor Cascade 

Aeroelastic instabilities such as flutter can greatly limit the
operating range and safety of modern aircraft engines. Current
computational methods have a central role in the evaluation of
turbomachinery blades stability, but can be affected by errors
if the investigated flow conditions break model assumptions or
are particularly sensitive to small changes in flow variables.
In this paper, a machine learning based method is proposed to
quantify the uncertainty of computational aerodynamic damping
predictions. The test case employed for this study is a two
dimensional compressor cascade, which resembles most of the
relevant aeroelastic features of modern fan and compressor
blades. A random forest based model is trained and tested to
construct a mapping between input features and aerodynamic
damping, both obtained from linearised CFD computations. The
input features concern simple, physically relevant quantities that
are available early on in design stage. The results show that the
machine learnt model can produce predictions, by interpolating
within the range of input features, with a coefficient of deter-
mination R2 ≈ 0.94. Moreover, the predictions are enhanced
with a measure of uncertainty in terms of confidence intervals.
The results show that the confidence intervals can accurately
portray the sensitivity of aerodynamic damping with respect to
the flow variables. Finally, to underline the relevance of such
an approach during design, the model is applied to obtain a
conservative flutter boundary on a compressor map, providing a
safer operating margin.

Presenting Author: Marco Rauseo Imperial College London

Presenting Author Biography: Marco earned his BSc in Mechanical Engineering at Politecnico di Milano, Milan, Italy. He then enrolled in the “THRUST” programme to earn a double degree between KTH in Stockholm and Duke University in the USA.<br/>He is currently a PhD student in the Mechanical Engineering Dept. at Imperial College London, working on modelling of compressor flutter with machine learning techniques.

Authors:

Marco Rauseo Imperial College London
Fanzhou Zhao Imperial College London
Mehdi Vahdati Imperial College London
Quentin Rendu Imperial College London