Self-Supervised Learning for Effective Denoising of Flow Fields

TL;DR

This paper introduces a self-supervised deep learning autoencoder approach for denoising noisy flow fields, demonstrating effectiveness across laminar and turbulent flows without requiring clean training data.

Contribution

It presents a novel self-supervised deep autoencoder model that effectively denoises flow fields, including turbulent flows, without needing clean data for training.

Findings

Effectively denoised turbulent and laminar flow data

Outperformed conventional noise filters in accuracy

Generalized well across different noise types and intensities

Abstract

In this study, we proposed an efficient approach based on a deep learning (DL) denoising autoencoder (DAE) model for denoising noisy flow fields. The DAE operates on a self-learning principle and does not require clean data as training labels. Furthermore, investigations into the denoising mechanism of the DAE revealed that its bottleneck structure with a compact latent space enhances denoising efficacy. Meanwhile, we also developed a deep multiscale DAE for denoising turbulent flow fields. Furthermore, we used conventional noise filters to denoise the flow fields and performed a comparative analysis with the results from the DL method. The effectiveness of the proposed DL models was evaluated using direct numerical simulation data of laminar flow around a square cylinder and turbulent channel flow data at various Reynolds numbers. For every case, synthetic noise was augmented in the data. A separate experiment used particle-image velocimetry data of laminar flow around a square cylinder containing real noise to test DAE denoising performance. Instantaneous contours and flow statistical results were used to verify the alignment between the denoised data and ground truth. The findings confirmed that the proposed method could effectively denoise noisy flow data, including turbulent flow scenarios. Furthermore, the proposed method exhibited excellent generalization, efficiently denoising noise with various types and intensities.