Flow over an espresso cup: Inferring 3D velocity and pressure fields from tomographic background oriented schlieren videos via physics-informed neural networks
TLDR
In this article, the authors proposed a new method based on physics-informed neural networks (PINNs) to infer the full continuous 3D velocity and pressure fields from snapshots of 3D temperature fields obtained by Tomo-BOS imaging.Abstract:
Tomographic background oriented schlieren (Tomo-BOS) imaging measures density or temperature fields in 3D using multiple camera BOS projections, and is particularly useful for instantaneous flow visualizations of complex fluid dynamics problems. We propose a new method based on physics-informed neural networks (PINNs) to infer the full continuous 3D velocity and pressure fields from snapshots of 3D temperature fields obtained by Tomo-BOS imaging. PINNs seamlessly integrate the underlying physics of the observed fluid flow and the visualization data, hence enabling the inference of latent quantities using limited experimental data. In this hidden fluid mechanics paradigm, we train the neural network by minimizing a loss function composed of a data mismatch term and residual terms associated with the coupled Navier-Stokes and heat transfer equations. We first quantify the accuracy of the proposed method based on a 2D synthetic data set for buoyancy-driven flow, and subsequently apply it to the Tomo-BOS data set, where we are able to infer the instantaneous velocity and pressure fields of the flow over an espresso cup based only on the temperature field provided by the Tomo-BOS imaging. Moreover, we conduct an independent PIV experiment to validate the PINN inference for the unsteady velocity field at a center plane. To explain the observed flow physics, we also perform systematic PINN simulations at different Reynolds and Richardson numbers and quantify the variations in velocity and pressure fields. The results in this paper indicate that the proposed deep learning technique can become a promising direction in experimental fluid mechanics.read more
Citations
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A general Neural Particle Method for hydrodynamics modeling
Jinshuai Bai,Ying Zhou,Yu-Jie Ma,Hyogu Jeong,Hai-yi Zhan,C. M. Rathnayaka,Emilie Sauret,YuanTong Gu +7 more
TL;DR: In this paper , the authors proposed a general Neural Particle Method (gNPM) for viscous hydrodynamics modeling, where a single pressure is output as the predicted pressure field rather than the multiple pressures in the original NPM.
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Physics-informed neural networks for phase-field method in two-phase flow
TL;DR: In this article , a physics-informed neural network for the phase-field method (PF-PINNs) in two-dimensional immiscible incompressible two-phase flow was developed.
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When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?
TL;DR: In this article , a prior generalization bound via the complexity of the target functions in the PDE problem, and a posterior generalization constraint via the posterior matrix norms of the networks after optimization were provided for general multi-layer PINNs and extended PINNs.
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Volumetric emission tomography for combustion processes
TL;DR: A comprehensive, critical, and pedagogical review of volumetric emission tomography for combustion processes can be found in this article , where four emission modalities are considered: chemiluminescence, laser-induced fluorescence, passive incandescence, and laser-induced fluorescence.
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Estimating density, velocity, and pressure fields in supersonic flows using physics-informed BOS
TL;DR: In this article , a physics-informed neural network (PINN) is used to produce flow fields that simultaneously satisfy the measurement data and governing equations, which is the first use of a PINN to reconstruct a supersonic flow from experimental data.
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