Scaling laws of tracer dispersion in a multi-compartment structure

Skvortsov, Alex; Suendermann, Brigitta; Gamble, Grant; Roberts, Michael D.; Ilaya, Omar; Pitaliadda, Dinesh

doi:10.1016/j.expthermflusci.2012.10.012

Cited by 2 publications

(4 citation statements)

References 28 publications

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“…As we can see from Figure 2, at each location, the signal generated by sensor tracer can be characterised by a number of parameters such as 'arrival time', 'saturation limit' and 'fluctuation level'(intensity of tracer fluctuations). The statistics of arrival times and saturation limits have been previously reported (Skvortsov et al (2013)). Our aim in this paper is to investigate tracer fluctuations in different locations inside a multicompartment structure.…”

Section: Experimental Datasetmentioning

confidence: 98%

“…Flow paths corresponding to two such configurations, denoted as OPEN and CLOSE, are shown in Figures 1(b) and 1(c), respectively. More details of the experimental setup are available in Skvortsov et al (2013).…”

Section: Experimental Datasetmentioning

confidence: 99%

“…This deficiency necessitates the development of simplified (but still scientifically consistent) models of tracer transport in confined environments that can easily be implemented and calibrated with a limited amount of experimental data. Revealing examples of such approaches are the compartment-based approach (Chen (2009)) and scaling models (Bakunin (2008), Skvortsov et al (2013), Skvortsov et al (2012)).…”

Section: Introductionmentioning

confidence: 99%

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Statistics of chemical tracer concentration in a multi-compartment structure measured with a sensor network

Karunasekera

Skvortsov

Gunatilaka

et al. 2013

Piantadosi, J., Anderssen, R.S. And Boland J. (Eds) MODSIM2013, 20th International Congress on Modelling and Simulation

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An understanding of the statistics of concentrations of chemical tracers dispersing through multicompartment structures such as buildings, ships, and aircraft is important in many applications (e.g., industrial, military, and ecological). Such an understanding can be useful for early detection of hazardous releases, source backtracking, and assessment of their impact on engineered structures (e.g., buildings, aircraft, ships). It may be also important for design of monitoring sensor systems. We present the statistics of chemical tracer concentrations obtained by analysing recent experimental data obtained in a study of tracer dispersion in a complex structure. The experiment involved the release of a tracer (dyed salt solution) from a point source placed inside a multi-compartment structure (Fig. 1) embedded in a water tank and measuring the salt concentration at different locations within the compartments using a network of conductivity sensors. We show that moments of measured concentration at a given sensor depend on the Euclidean distance between the source and the sensor, resembling the behaviour observed in advection-diffusion transport in porous media. (a) (b) (c) Figure 1. The experimental setup: (a) Image of the multi-compartment structure used for the experiment; (b) the flow path in the OPEN configuration; and, (c) the flow path in the CLOSE configuration.

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Section: Experimental Datasetmentioning

confidence: 98%

Section: Experimental Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Statistics of chemical tracer concentration in a multi-compartment structure measured with a sensor network

Karunasekera

Skvortsov

Gunatilaka

et al. 2013

Piantadosi, J., Anderssen, R.S. And Boland J. (Eds) MODSIM2013, 20th International Congress on Modelling and Simulation

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“…2, which is often used in modeling transport in porous media 13,43,44 and multi-compartment environments. 45 Unbiased diffusion in such systems is anisotropic and characterized by two effective diffusiveness, D (x) eff in the xdirection, which is parallel to the wide tube axes, and D (⊥) eff in the plane normal to the x-direction. These two diffusivities can be found by extending formalisms developed in Refs.…”

Section: Discussionmentioning

confidence: 99%

Aris-Taylor dispersion in tubes with dead ends

Dagdug

Berezhkovskii

Skvortsov

2014

The Journal of Chemical Physics

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This paper deals with transport of point Brownian particles in a cylindrical tube with dead ends in the presence of laminar flow of viscous fluid in the cylindrical part of the tube (Poiseuille flow). It is assumed that the dead ends are identical and are formed by spherical cavities connected to the cylindrical part of the tube by narrow necks. The focus is on the effective velocity and diffusivity of the particles as functions of the mean flow velocity and geometric parameter of the tube. Entering a dead end, the particle interrupts its propagation along the tube axis. Later it returns, and the axial motion continues. From the axial propagation point of view, the particle entry into a dead end and its successive return to the flow is equivalent to the particle reversible binding to the tube wall. The effect of reversible binding on the transport parameters has been previously studied assuming that the particle survival probability in the bound state decays as a single exponential. However, this is not the case when the particle enters a dead end, since escape from the dead end is a nonMarkovian process. Our analysis of the problem consists of two steps: First, we derive expressions for the effective transport parameters in the general case of non-Markovian binding. Second, we find the effective velocity and diffusivity by substituting into these expressions known results for the moments

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Scaling laws of tracer dispersion in a multi-compartment structure

Cited by 2 publications

References 28 publications

Statistics of chemical tracer concentration in a multi-compartment structure measured with a sensor network

Statistics of chemical tracer concentration in a multi-compartment structure measured with a sensor network

Aris-Taylor dispersion in tubes with dead ends

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