  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.
