Estimating density, velocity, and pressure fields in supersonic flows using physics-informed BOS

We report a new workflow for background-oriented schlieren (BOS), termed “physics-informed BOS,” to extract density, velocity, and pressure fields from a pair of reference and distorted images. Our method uses a physics-informed neural network (PINN) to produce flow fields that simultaneously satisf...

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Vydané v:	Experiments in fluids Ročník 64; číslo 1
Hlavní autori:	Molnar, Joseph P, Venkatakrishnan, Lakshmi, Schmidt, Bryan E, Sipkens, Timothy A, Grauer, Samuel J
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Nature 01.01.2023 Springer Berlin Heidelberg Springer Nature B.V
Predmet:	Algorithms Compressible flow Computer vision Density Engineering Engineering Fluid Dynamics Engineering Thermodynamics Estimates Fluid- and Aerodynamics Heat and Mass Transfer Image reconstruction Interpolation Inverse problems Inviscid flow Neural networks Physics Research Article Supersonic flow Workflow
ISSN:	0723-4864, 1432-1114, 1432-1114
On-line prístup:	Získať plný text
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