ﻻ يوجد ملخص باللغة العربية
Considerable effort has been expended over the last 2 centuries into explaining the behavior of fluid flow after the onset of turbulence. While perturbations in the velocity field have been shown to explain turbulent transitions, a physical explanation of why flows become turbulent, based on the forces felt by the fluid particles, has remained elusive. In this work a new theory is proposed that attempts to explain the transition of fluid flow from laminar to turbulent as explained by the fluid material undergoing failure. In a vaguely similar sense to how fractures can occur in solids once the balance of momentum exceeds the capacity of the material, so too in a fluid, after sufficient kinetic energy has been achieved by a fluid packet, the viscous forces are unable to maintain the laminar behavior and the fluid packets receive a boost as the stored energy in the viscous bonds are transferred to the kinetic energy of the fluid. This new model is described in terms of fluid packets and the forces on a mass element and commonly-known turbulent flows are used as examples to test the theory. Predicted flow profiles from the theory match the experimental observations of averaged flow profiles and a new equation to predict the onset of turbulence for any flow is presented. This process of the fluid undergoing failure can be seen as a natural continuation of the prevailing wisdom of turbulence when viewed from a different frame of reference.
The transitional and well-developed regimes of turbulent shear flows exhibit a variety of remarkable scaling laws that are only now beginning to be systematically studied and understood. In the first part of this article, we summarize recent progress
The acoustic radiation force produced by ultrasonic waves is the workhorse of particle manipulation in acoustofluidics. Nonspherical particles are also subjected to a mean torque known as the acoustic radiation torque. Together they constitute the me
There exists continuous demand of improved turbulence models for the closure of Reynolds Averaged Navier-Stokes (RANS) simulations. Machine Learning (ML) offers effective tools for establishing advanced empirical Reynolds stress closures on the basis
Ice crystals settling through a turbulent cloud are rotated by turbulent velocity gradients. In the same way, turbulence affects the orientation of aggregates of organic matter settling in the ocean. In fact most solid particles encountered in Nature
The macroscopic dynamics of a droplet impacting a solid is crucially determined by the intricate air dynamics occurring at the vanishingly small length scale between droplet and substrate prior to direct contact. Here we investigate the inverse probl