In this paper, we consider the physical mechanism for the clustering of inertial particles in the inertial range of isotropic turbulence. We analyze the exact, but unclosed, equation governing the radial distribution function (RDF) and compare the mechanisms it describes for clustering in the dissipation and inertial ranges. We demonstrate that in the limit Str≪1, where Str is the Stokes number based on the eddy turnover time scale at separation r, the clustering in the inertial range can be understood to be due to the preferential sampling of the coarse-grained fluid velocity gradient tensor at that scale. When Str≳O(1) this mechanism gives way to a nonlocal clustering mechanism. These findings reveal that the clustering mechanisms in the inertial range are analogous to the mechanisms that we identified for the dissipation regime [see New J. Phys. 16, 055013 (2014)]. Further, we discuss the similarities and differences between the clustering mechanisms we identify in the inertial range and the "sweep-stick" mechanism developed by Coleman and Vassilicos [Phys. Fluids 21, 113301 (2009)]. We show that the idea that initial particles are swept along with acceleration stagnation points is only approximately true because there always exists a finite difference between the velocity of the acceleration stagnation points and the local fluid velocity. This relative velocity is sufficient to allow particles to traverse the average distance between the stagnation points within the correlation time scale of the acceleration field. We also show that the stick part of the mechanism is only valid for Str≪1 in the inertial range. We emphasize that our clustering mechanism provides the more fundamental explanation since it, unlike the sweep-stick mechanism, is able to explain clustering in arbitrary spatially correlated velocity fields. We then consider the closed, model equation for the RDF given in Zaichik and Alipchenkov [Phys. Fluids 19, 113308 (2007)] and use this, together with the results from our analysis, to predict the analytic form of the RDF in the inertial range for Str1, which, unlike that in the dissipation range, is not scale invariant. The results are in good agreement with direct numerical simulations, provided the separations are well within the inertial range.
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http://dx.doi.org/10.1103/PhysRevE.92.023029 | DOI Listing |
Acta Bioeng Biomech
June 2024
1Department of Rehabilitation Medicine, Southern Medical University Nanfang Hospital, Guangzhou, China.
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Laboratoire de Simulation et Modélisation du Mouvement, École de Kinésiologie et des Sciences de l'Activité Physique, Université de Montréal, Montréal, QC, Canada.
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Sport and Physical Activity Research Centre, Sheffield Hallam University, Olympic Legacy Park, 2 Old Hall Rd, Sheffield S9 3TY, UK.
Our aim was to validate a sacral-mounted inertial measurement unit (IMU) for reconstructing running kinematics and comparing movement patterns within and between runners. IMU data were processed using Kalman and complementary filters separately. RMSE and Bland-Altman analysis assessed the validity of each filtering method against a motion capture system.
View Article and Find Full Text PDFAnimals (Basel)
January 2025
Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Yalelaan 112-114, NL-3584 CM Utrecht, The Netherlands.
Prior to international competitions, dressage horses are evaluated for fitness to compete while trotting in hand on a firm surface. This study compares the kinematics of experienced dressage horses trotting under fitness-to-compete conditions vs. performing collected and extended trot when ridden on a sand-fiber arena surface.
View Article and Find Full Text PDFSci Data
January 2025
Division of Biomechanics and Research Development, Department of Biomechanics, and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, NE, 68182, USA.
The continued effort to study gait kinematics and the increased interest in identifying individuals based on their gait patterns could be strengthened by the inclusion of data from older groups. To address this need and complement our previous database on healthy young adults, we present an addition to the Nonlinear Analysis Core (NONAN) GaitPrint database. We offer full-body inertial measurement data during self-paced overground walking on a 200 m indoor track of 41 older adults (56 + years old; 20 men and 21 women; age: 64.
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