Scaling laws of peripheral mixing of passive scalar in a wall-shear layer

Abstract

The scaling laws governing the concentration moments of a passive scalar released from a ground-level localized source in a neutrally stratified wall-shear layer are investigated using a theoretical framework recently formulated by Lebedev and Turitsyn [Phys. Rev. E 69, 036301 (2004)]. For the current application, this theoretical framework is generalized from the smooth random velocity field applicable in the viscous sublayer to the nonsmooth random velocity field that applies to the bulk of the wall-shear layer. Theoretical relationships for the passive scalar concentration moments are compared to a water-channel simulation of turbulent diffusion from a ground-level source in a wall-shear layer. The diffusion measurements in the wall-shear layer are shown to be consistent with the theoretical description and also imply the robustness of the identified scaling laws for the scalar concentration moments.

Statistical characterisation of bio-aerosol background in an urban environment

“…Amongst the different functional PDF forms of tracer concentration evaluated in studies of turbulent tracer dispersion, the Gamma form has recently emerged as one that adequately captures the physics of turbulent transport and provides a good match with a variety of experimental data (Yee and Chan (1997); Skvortsov and Yee (2011)). This model (see the Gamma form of (3)) can be analytically derived using a phenomenological analogy between the mixing and convolution processes by explicit modeling of the deformation of tracer blobs by random stretching and folding (Duplat and Villermaux (2008); Villermaux and Duplat (2003); Venaille and Sommeria (2008)).…”

Bayesian likelihood-free localisation of a biochemical source using multiple dispersion models

“…Use of these more physically realistic terms in the advection-diffusion equation imply fundamentally different solutions that are not of a Gaussian nature. More accurate analysis implies an "effective" plume convection velocity [18] that should be a function of downstream distance x s , but this increases significantly the dimension of the parameter space. Also, under different meteorological conditions the functional form given by (8) can vary in the manner that expression for σ ys in (12) cannot fully capture.…”

Modelling and Performance Analysis of a Network of Chemical Sensors with Dynamic Collaboration

“…The proposed framework allows to implement a reasonably realistic model of the con- The geometrical complexity of the turbulent flow can be incorporated in the theoretical framework (2) by assuming a temporal and spatial variability of the mean concentration filed C 0 ≡ C 0 (r, t). This way we can simulate various morphologies of the flow (jet, wake, boundary layer, compartment flow, etc) as well as various scenarios of hazardous release (plume, puff ), for details see [8] , [17]. For the sake of simplicity in the current paper we consider only case C 0 = const.…”

“…This way we can simulate various morphologies of the flow (jet, wake, boundary layer, compartment flow, etc.) as well as various scenarios of hazardous release (plume, puff), for details see [13,15]. For the sake of simplicity in the current paper, we consider only case C 0 = const.…”