Estimating pressure fields from planar velocity data around immersed bodies; a finite element approach

Pirnia, Alireza; McClure, Jeffrey; Peterson, Sean D.; Helenbrook, Brian T.; Erath, Byron D.

doi:10.1007/s00348-020-2886-z

Cited by 8 publications

(4 citation statements)

References 59 publications

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“…The first computes the pressure field from the Poisson equation, i.e. as shown below for an inviscid flow ( Fujisawa et al, 2005 ; de Kat and and van Oudheusden, 2012 ; Shams et al, 2015 ; Neeteson and Rival, 2015 ; Pirnia et al, 2020 ): where p is the pressure, u is the velocity vector, ρ is the fluid density and D u /D t is the material derivative. However, Charonko et al (2010) and Pan et al (2016) have shown that the Poisson-based solvers are sensitive to the grid resolution, flow type, velocity measurement errors, the shape of the immersed body and the type of boundary conditions that are applied.…”

Section: Introductionmentioning

confidence: 99%

“…To then obtain the surface pressure, one would typically have to extrapolate from the nearest neighbor node in the surrounding pressure field. Pirnia et al (2020) demonstrated that such an approach can provide a very accurate prediction of the surface pressure around stationary objects. However, the error increases greatly when the object is free to deform.…”

Section: Introductionmentioning

confidence: 99%

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Reconstructing the pressure field around swimming fish using a physics-informed neural network

Calicchia

Mittal

Seo

et al. 2023

Journal of Experimental Biology

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Fish detect predators, flow conditions, environments, and each other through pressure signals. Lateral line ablation is often performed to understand the role of pressure sensing. In this study, the authors propose a non-invasive method for reconstructing the instantaneous pressure field sensed by a fish's lateral line system from 2D particle image velocimetry (PIV) measurements. The method uses a physics-informed neural network (PINN) to predict an optimized solution for the pressure field near and on the fish's body that satisfy both the Navier-Stokes equations and the constraints put forward by the PIV measurements. The method was validated using a direct numerical simulation of a swimming mackerel, Scomber scombrus, and was applied to experimental data of a turning zebrafish, Danio rerio. The results demonstrate that this method is relatively insensitive to the spatio-temporal resolution of the PIV measurements and accurately reconstructs the pressure on the fish's body.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reconstructing the pressure field around swimming fish using a physics-informed neural network

Calicchia

Mittal

Seo

et al. 2023

Journal of Experimental Biology

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“…Traditionally, there have been two main categories of this approach. The first computes the pressure field from the Poisson equation, i.e., as shown below for an inviscid flow (Fujisawa et al, 2005; de Kat et al, 2012; Shams et al, 2015; Neeteson et al, 2015; Pirnia et al, 2020). where p is the pressure, u is the velocity vector, ρ is the fluid density, and D / Dt is the material derivative.…”

Section: Introductionmentioning

confidence: 99%

Reconstructing the pressure field around a swimming fish using a physics-informed neural network

Calicchia

Mittal

Seo

et al. 2023

Preprint

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Hydrodynamic pressure is a physical quantity that is utilized by fish and many other aquatic animals to generate thrust and sense the surrounding environment. To advance our understanding of how fish react to unsteady flows, it is necessary to intercept the pressure signals sensed by their lateral line system. In this study, the authors propose a new, non-invasive method for reconstructing the instantaneous pressure field around a swimming fish from 2D particle image velocimetry (PIV) measurements. The method uses a physics-informed neural network (PINN) to predict an optimized solution for the velocity and pressure fields that satisfy in an L2 sense both the Navier Stokes equations and the constraints put forward by the measurements. The method was validated using a direct numerical simulation of a swimming mackerel, Scomber scombrus, and was applied to empirically obtained data of a turning zebrafish, Danio rerio. The results demonstrate that when compared to traditional methods that rely on directly integrating the pressure gradient field, the PINN is less sensitive to the spatio-temporal resolution of the velocity field measurements and provides a more accurate pressure reconstruction, particularly on the surface of the body.

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“…For example, one could include turbulence modelling via Reynolds Averaged Navier Stokes (RANS) formulations to compute averaged pressure fields (e.g., Gurka et al (1999); van Oudheusden et al (2007)) or leverage Taylor's frozen turbulence hypothesis to compute instantaneous pressures (de Kat and Oudheusden, 2011;der Kindere et al, 2019;Laskari et al, 2016). Within the class of Eulerian methods, more sophisticated methods include solvers based on pressurevelocity algorithms from CFD (Felis-Carrasco et al, 2021;Gunaydinoglu and Kurtulus, 2019) or immersed boundary techniques (Pirnia et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A Meshless Method to Compute Pressure Fields from Image Velocimetry

Sperotto,

Pieraccini,

Mendez

2021

Preprint

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We propose a meshless method to compute pressure fields from image velocimetry data, regardless of whether this is available on a regular grid as in cross-correlation based velocimetry or on scattered points as in tracking velocimetry. The proposed approach is based on Radial Basis Functions (RBFs) regression and relies on the solution of two constrained least square problems. The first one is the regression of the measurements to create an analytic representation of the velocity field. This regression can be constrained to impose boundary conditions (e.g. no-slip velocity on a wall or inlet conditions) or differential constraints (e.g. the solenoidal condition for an incompressible flow). The second one is the meshless integration of the pressure Poisson equation, achieved by seeking a solution in the form of a RBF expansion and using constraints to impose boundary conditions.We first illustrate the derivation of the two least square problems and the numerical techniques implemented for their solution. Then, we showcase the method with three numerical test cases of growing complexity. These are a 2D Gaussian Vortex, a 2D flow past a cylinder from CFD and a 3D Stokes flow past a sphere. For each case, we consider randomly sampled vector fields simulating particle tracking measurements and analyze the sensitivity to noise and seeding density.

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Estimating pressure fields from planar velocity data around immersed bodies; a finite element approach

Cited by 8 publications

References 59 publications

Reconstructing the pressure field around swimming fish using a physics-informed neural network

Reconstructing the pressure field around swimming fish using a physics-informed neural network

Reconstructing the pressure field around a swimming fish using a physics-informed neural network

A Meshless Method to Compute Pressure Fields from Image Velocimetry

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