Particle image velocimetry analysis with simultaneous uncertainty quantification using Bayesian neural networks

Journal Article · Mon Jun 14 00:00:00 EDT 2021 · Measurement Science and Technology

DOI:https://doi.org/10.1088/1361-6501/abf78f· OSTI ID:1922758

Morrell, Mia Chase ^[1]; Hickmann, Kyle Scott ^[1]; Wilson, Brandon Merrill ^[1]

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1. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Particle image velocimetry (PIV) is an effective tool in experimental fluid mechanics for extracting flow fields from images. Recently, convolutional neural networks (CNNs) have been used to perform PIV analysis with accuracy on par with classical methods. Here we extend the use of CNNs to analyze PIV data while providing simultaneous uncertainty quantification on the inferred flow field. The method we apply in this paper is a Bayesian convolutional neural network (BCNN) which learns distributions of the CNN weights through variational Bayes. In order to demonstrate the utility of BCNNs for the PIV task, we compare the performance of three distinct BCNN models with simple architectures. The first network estimates flow velocity from image interrogation regions only. Our second model learns to infer velocity from both the image interrogation regions and interrogation region cross-correlation maps. Finally, our best performing network infers velocities from interrogation region cross-correlation maps only. We find that BCNNs using interrogation region cross-correlation maps as inputs perform better than those using interrogation windows only as inputs and discuss reasons why this may be the case. Additionally, we test the best performing BCNN on a full synthetic test image pair and a real image pair from the 1st International PIV Challenge. We show that ~98% of true particle displacements from the full synthetic image pair can be captured within the BCNN's 95% confidence intervals, and that the BCNN's performance on the real image pair is quantitatively similar to that of algorithms tested in the 1st International PIV Challenge. Finally, we show that BCNNs can be generalized to be used with multi-pass PIV algorithms with a moderate loss in accuracy, which may be overcome by future work on finetuning and training schemes. So to our knowledge, this is the first use of Bayesian neural networks to perform PIV.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1922758
• Alternate ID(s): OSTI ID: 23135831
• Report Number(s): LA-UR-20-29836
• Journal Information: Measurement Science and Technology, Journal Name: Measurement Science and Technology Journal Issue: 10 Vol. 32; ISSN 0957-0233
• Publisher: IOP Publishing
• Country of Publication: United States
• Language: English

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