A Hopfield neural network for flow field computation based on particle image velocimetry/particle tracking velocimetry image sequences

Author(s):  
M. Knaak ◽  
C. Rothlubbers ◽  
R. Orglmeister
2021 ◽  
Vol 928 ◽  
Author(s):  
Jiaqi Li ◽  
Aliza Abraham ◽  
Michele Guala ◽  
Jiarong Hong

We present a field study of snow settling dynamics based on simultaneous measurements of the atmospheric flow field and snow particle trajectories. Specifically, a super-large-scale particle image velocimetry (SLPIV) system using natural snow particles as tracers is deployed to quantify the velocity field and identify vortex structures in a 22 m  $\times$  39 m field of view centred 18 m above the ground. Simultaneously, we track individual snow particles in a 3 m  $\times$  5 m sample area within the SLPIV using particle tracking velocimetry. The results reveal the direct linkage among vortex structures in atmospheric turbulence, the spatial distribution of snow particle concentration and their settling dynamics. In particular, with snow turbulence interaction at near-critical Stokes number, the settling velocity enhancement of snow particles is multifold, and larger than what has been observed in previous field studies. Super-large-scale particle image velocimetry measurements show a higher concentration of snow particles preferentially located on the downward side of the vortices identified in the atmospheric flow field. Particle tracking velocimetry, performed on high resolution images around the reconstructed vortices, confirms the latter trend and provides statistical evidence of the acceleration of snow particles, as they move toward the downward side of vortices. Overall, the simultaneous multi-scale particle imaging presented here enables us to directly quantify the salient features of preferential sweeping, supporting it as an underlying mechanism of snow settling enhancement in the atmospheric surface layer.


2000 ◽  
Author(s):  
Shankar Devasenathipathy ◽  
Joshua I. Molho ◽  
James C. Mikkelsen ◽  
Juan G. Santiago ◽  
Kohsei Takehara

Abstract A micron-resolution particle image velocimetry (PIV) system has been developed to spatially and temporally resolve electroosmotic flow fields within microfluidic bioanalytical devices. A second diagnostic technique, particle tracking velocimetry (PTV) has been used to determine the distribution of electrophoretic mobilities of seed particles and thereby make the PIV measurements quantitative. This second particle tracking technique has been used to determine probability distribution functions of the seed particles. Results from simulations of electric fields yield local electric field strengths in the geometries of interest. The measured mean mobility of the seed particles (obtained from PTV measurements) is then multiplied by the local electric field vector to obtain the electrophoretic velocity. The variance on the particle mobility measurement influences the errors introduced in the electroosmotic flow measurements. After total particle velocities are measured within a microfluidic system of interest, the seed particle electrophoretic velocities are subtracted from the PIV total velocity data to obtain electroosmotic flow field velocities. Ensemble-averaged velocity field measurements for electroosmotic flow at the intersection of a cross-channel are presented.


2018 ◽  
Vol 140 (4) ◽  
Author(s):  
Kaushik Sampath ◽  
Thura T. Harfi ◽  
Richard T. George ◽  
Joseph Katz

Contrast ultrasound is a widely used clinical tool to obtain real-time qualitative blood flow assessments in the heart, liver, etc. Echocardiographic particle image velocimetry (echo-PIV) is a technique for obtaining quantitative velocity maps from contrast ultrasound images. However, unlike optical particle image velocimetry (PIV), routine echo images are prone to nonuniform spatiotemporal variations in tracer distribution, making analysis difficult for standard PIV algorithms. This study introduces optimized procedures that integrate image enhancement, PIV, and particle tracking velocimetry (PTV) to obtain reliable time-resolved two-dimensional (2D) velocity distributions. During initial PIV analysis, multiple results are obtained by varying processing parameters. Optimization involving outlier removal and smoothing is used to select the correct vector. These results are used in a multiparameter PTV procedure. To demonstrate their clinical value, the procedures are implemented to obtain velocity and vorticity distributions over multiple cardiac cycles using images acquired from four left ventricular thrombus (LVT) patients. Phase-averaged data elucidate flow structure evolution over the cycle and are used to calculate penetration depth and strength of left ventricular (LV) vortices, as well as apical velocity induced by them. The present data are consistent with previous time-averaged results for the minimum vortex penetration depth associated with LVT occurrence. However, due to decay and fragmentation of LV vortices, as they migrate away from the mitral annulus, in two cases with high penetration, there is still poor washing near the resolved clot throughout the cycle. Hence, direct examination of entire flow evolution may be useful for assessing risk of LVT relapse before prescribing anticoagulants.


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