Investigation of Compressor Cascade Flow Using Physics- Informed Neural Networks with Adaptive Learning Strategy

TL;DR

This paper explores the use of Physics-Informed Neural Networks with an adaptive learning strategy to predict compressor cascade flow fields, demonstrating advantages over traditional CFD, especially with incomplete data and uncertainties.

Contribution

It introduces a novel adaptive learning strategy for PINNs and applies it to compressor cascade flow prediction, showing improved convergence and performance in inverse problems.

Findings

PINNs accurately predict compressor flow fields.

PINNs outperform traditional CFD in incomplete data scenarios.

PINNs are robust against data uncertainties.

Abstract

In this study, we utilize the emerging Physics Informed Neural Networks (PINNs) approach for the first time to predict the flow field of a compressor cascade. Different from conventional training methods, a new adaptive learning strategy that mitigates gradient imbalance through incorporating adaptive weights in conjunction with dynamically adjusting learning rate is used during the training process to improve the convergence of PINNs. The performance of PINNs is assessed here by solving both the forward and inverse problems. In the forward problem, by encapsulating the physical relations among relevant variables, PINNs demonstrate their effectiveness in accurately forecasting the compressor's flow field. PINNs also show obvious advantages over the traditional CFD approaches, particularly in scenarios lacking complete boundary conditions, as is often the case in inverse engineering problems. PINNs successfully reconstruct the flow field of the compressor cascade solely based on partial velocity vectors and near-wall pressure information. Furthermore, PINNs show robust performance in the environment of various levels of aleatory uncertainties stemming from labeled data. This research provides evidence that PINNs can offer turbomachinery designers an additional and promising option alongside the current dominant CFD methods.