Session: 37-01 Neural Network based surrogate models and optimization

Paper Number: 101698

101698 - High-Dimensional Uncertainty Quantification of High-Pressure Turbine Vane Based on Multi-Fidelity Deep Neural Networks 

In this work a new multi-fidelity (MF) uncertainty quantification (UQ) framework is presented and applied to LS89 nozzle modified by fouling. Geometrical uncertainties significantly influence aerodynamic performance of gas turbines. One representative example is given by the airfoil shape modified by fouling deposition, as in turbine nozzle vanes, which generates high-dimensional input uncertainties. However, the traditional UQ approaches suffer from the curse of dimensionality phenomenon in predicting the influence of high-dimensional uncertainties. Thus, a new approach based on multi-fidelity deep neural networks (MF-DNN) was proposed in this paper to solve the high-dimensional UQ problem. The basic idea of MF-DNN is to ensure the approximation capability of neural networks based on abundant low-fidelity (LF) data and few high-fidelity (HF) data. The prediction accuracy of MF-DNN was first evaluated using a 15-dimensional benchmark function. An affordable turbomachinery UQ platform was then built based on the MF-DNN model, the sampling-, the parameterization- and the statistical processing modules. The impact of fouling deposition on LS89 nozzle vane flow was investigated using the proposed UQ framework. In detail, the MF-DNN was fine-tuned based on bi-level numerical simulation results: the 2D Euler flow field as low-fidelity data and the 3D Reynolds-Averaged Navier-Stokes (RANS) flow field as high-fidelity data. The UQ results showed that the total pressure loss of LS89 vane was increased up to 17.1 % or reduced up to 4.3 %, while the mean value of loss was increased by 3.4 % compared to the baseline. The main reason for relative changes in turbine nozzle performance is that the geometric uncertainties induced by fouling deposition significantly altered the intensity of shock waves near the throat area and trailing edge. The developed UQ platform could provide a useful tool in the design and optimization of advanced turbomachinery considering high-dimensional input uncertainties.

Presenting Author: Zhihui Li Imperial College London

Presenting Author Biography: I am the Individual Research Fellow (funded by EU Marie Sklodowska-Curie Individual Fellowship) at Department of Aeronautics of Imperial College London. My research interests mainly focus on the uncertainty quantification and robust optimisation of turbomachinery based on the advanced machine learning technique.

Authors:

Zhihui Li Imperial College London
Francesco Montomoli Imperial College London
Nicola Casari University of Ferrara
Michele Pinelli University of Ferrara