Concentration Fluctuations in Smoke Plumes Released Near the Ground

Jørgensen, Hans Ejsing; Mikkelsen, Torben; Pécseli, H. L.

doi:10.1007/s10546-010-9532-x

Cited by 8 publications

(7 citation statements)

References 42 publications

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“…We used the sonic temperature as an approximation of virtual temperature as discussed in, e.g. Kaimal and Finnigran (1994). As a consistency check, the flux Richardson number was calculated at z = 5.4 m using…”

Section: A3 Data Processing Of the Eddy Covariance Measurementsmentioning

confidence: 99%

Observation of turbulent dispersion of artificially released SO<sub>2</sub> puffs with UV cameras

et al. 2018

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Abstract. In atmospheric tracer experiments, a substance is released into the turbulent atmospheric flow to study the dispersion parameters of the atmosphere. That can be done by observing the substance's concentration distribution downwind of the source. Past experiments have suffered from the fact that observations were only made at a few discrete locations and/or at low time resolution. The Comtessa project (Camera Observation and Modelling of 4-D Tracer Dispersion in the Atmosphere) is the first attempt at using ultraviolet (UV) camera observations to sample the three-dimensional (3-D) concentration distribution in the atmospheric boundary layer at high spatial and temporal resolution. For this, during a three-week campaign in Norway in July 2017, sulfur dioxide (SO2), a nearly passive tracer, was artificially released in continuous plumes and nearly instantaneous puffs from a 9 m high tower. Column-integrated SO2 concentrations were observed with six UV SO2 cameras with sampling rates of several hertz and a spatial resolution of a few centimetres. The atmospheric flow was characterised by eddy covariance measurements of heat and momentum fluxes at the release mast and two additional towers. By measuring simultaneously with six UV cameras positioned in a half circle around the release point, we could collect a data set of spatially and temporally resolved tracer column densities from six different directions, allowing a tomographic reconstruction of the 3-D concentration field. However, due to unfavourable cloudy conditions on all measurement days and their restrictive effect on the SO2 camera technique, the presented data set is limited to case studies. In this paper, we present a feasibility study demonstrating that the turbulent dispersion parameters can be retrieved from images of artificially released puffs, although the presented data set does not allow for an in-depth analysis of the obtained parameters. The 3-D trajectories of the centre of mass of the puffs were reconstructed enabling both a direct determination of the centre of mass meandering and a scaling of the image pixel dimension to the position of the puff. The latter made it possible to retrieve the temporal evolution of the puff spread projected to the image plane. The puff spread is a direct measure of the relative dispersion process. Combining meandering and relative dispersion, the absolute dispersion could be retrieved. The turbulent dispersion in the vertical is then used to estimate the effective source size, source timescale and the Lagrangian integral time. In principle, the Richardson–Obukhov constant of relative dispersion in the inertial subrange could be also obtained, but the observation time was not sufficiently long in comparison to the source timescale to allow an observation of this dispersion range. While the feasibility of the methodology to measure turbulent dispersion could be demonstrated, a larger data set with a larger number of cloud-free puff releases and longer observation times of each puff will be recorded in future studies to give a solid estimate for the turbulent dispersion under a variety of stability conditions.

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Section: A3 Data Processing Of the Eddy Covariance Measurementsmentioning

confidence: 99%

Observation of turbulent dispersion of artificially released SO<sub>2</sub> puffs with UV cameras

et al. 2018

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“…A number of observations of low frequency turbulence in magnetized plasmas have demonstrated a systematic relationship between skewness S and kurtosis K of the probability density of some fluctuating quantity [4][5][6][7][8], the density for instance. Earlier, similar relations have been noted and studied for a variety of conditions for neutral atmospheric turbulence [9][10][11][12][13][14] where local concentrations of contaminants were analyzed. It is often found that the skewness-kurtosis relations associated with these data can be modeled by a parabolic expression like…”

Section: Introductionmentioning

confidence: 71%

“…Generally it can be shown that K S 1 2  + holds for any probability distribution [35], with the exception of PDF's with 0, s = which are singular in the present context. The simple analytical model (3) and its generalizations have been widely discussed in, for instance, studies of concentration fluctuations in turbulent environments [9][10][11][12][13][14].…”

Section: Simple Model Based On Coherent Structuresmentioning

confidence: 99%

Models for the probability densities of the turbulent plasma flux in magnetized plasmas

et al. 2015

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Observations of turbulent transport in magnetized plasmas indicate that plasma losses can be due to coherent structures or bursts of plasma rather than a classical random walk or diffusion process. A model for synthetic data based on coherent plasma flux events is proposed, where all basic properties can be obtained analytically in terms of a few control parameters. One basic parameter in the present case is the density of burst events in a long time-record, together with parameters in a model of the individual pulse shapes and the statistical distribution of these parameters. The model and its extensions give the probability density of the plasma flux. An interesting property of the model is a prediction of a near-parabolic relation between skewness and kurtosis of the statistical flux distribution for a wide range of parameters. The model is generalized by allowing for an additive random noise component. When this noise dominates the signal we can find a transition to standard results for Gaussian random noise. Applications of the model are illustrated by data from the toroidal Blaamann plasma.

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“…Models have been developed to evaluate the intensity of fluctuations and the statistics of exceeding specific concentration thresholds [13][14][15][16][17][18]. Remote-sensing with LIDAR systems has also been used to characterize fluctuations in full scale plumes [19,20]. In Fackrell and Robins [6] wind tunnel simulations were used to investigate the effect of source size on concentration fluctuations in single plumes.…”

Section: Introductionmentioning

confidence: 99%

Statistical properties of concentration fluctuations in two merging plumes

2013

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The statistics of the fluctuating concentration field within a plume is important in the analysis of atmospheric dispersion of toxic, inflammable and odorous gases. Previous work has tended to focus on concentration fluctuations in single plumes released in the surface layer or at ground level and there is a general lack of information about the mixing of two adjacent plumes and how the statistical properties of the concentration fluctuations are modified in these circumstances. In this work, data from wind tunnel experiments are used to analyse the variance, skewness, kurtosis, intermittency, probability density function and power spectrum of the concentration field during the mixing of two identical plumes and results are compared with those obtained for an equivalent single plume. The normalised variance, skewness and kurtosis on the centre-lines of the combined plume increase with distance downwind of the stack and, in the two-source configuration, takes lower values than those found in the single plumes. The results reflect the merging process at short range, which is least protracted for cases in which the sources are in-line or up to 30 • off-line. At angles of 45 • and more, the plumes are effectively side-by-side during the merging process and the interaction between the vortex pairs in each plume is strong. Vertical asymmetry is observed between the upper and the lower parts of the plumes, with the upper part having greater intermittency (i.e. the probability that no plume material is present) and a more pronounced tail to the concentration probability distribution. This asymmetry tends to diminish at greater distances from the source but occurs in both buoyant and neutral plumes and is believed to be associated with the 'bending-over' of the emission in the cross-flow and the vortex pair that this generates. The results allowed us to identify three phases in plume development. The first, very near the stack, is dominated by turbulence generated within the plume and characterised by concentration spectra with distinct peaks corresponding to scales comparable with those of the counter-rotating vortex pair. A second phase follows at somewhat greater distances 123Environ Fluid Mech downwind, in which there are significant contributions to the concentration fluctuations from both the turbulence internal to the plume and the external turbulence. The third phase is one in which the concentration fluctuations appear to be controlled by the external turbulence present in the ambient flow.

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Concentration Fluctuations in Smoke Plumes Released Near the Ground

Cited by 8 publications

References 42 publications

Observation of turbulent dispersion of artificially released SO<sub>2</sub> puffs with UV cameras

Observation of turbulent dispersion of artificially released SO<sub>2</sub> puffs with UV cameras

Models for the probability densities of the turbulent plasma flux in magnetized plasmas

Statistical properties of concentration fluctuations in two merging plumes

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Concentration Fluctuations in Smoke Plumes Released Near the Ground

Cited by 8 publications

References 42 publications

Observation of turbulent dispersion of artificially released SO&lt;sub&gt;2&lt;/sub&gt; puffs with UV cameras

Observation of turbulent dispersion of artificially released SO&lt;sub&gt;2&lt;/sub&gt; puffs with UV cameras

Models for the probability densities of the turbulent plasma flux in magnetized plasmas

Statistical properties of concentration fluctuations in two merging plumes

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Product

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Observation of turbulent dispersion of artificially released SO<sub>2</sub> puffs with UV cameras

Observation of turbulent dispersion of artificially released SO<sub>2</sub> puffs with UV cameras