Derivation of the Gaussian plume model in three dimensions

Gaussian plume model is a common model to study advection diffusion equation which is solved in three dimensions by using Laplace transformation considering constant eddy diffusivity and wind speed power law. Different schemes such as Irwin, Power Law, Briggs and Standard methods are used to obtain crosswind integrated concentration. Statistical measures are used in this paper to know which is the best scheme which agrees with the observed concentration data obtained from Copenhagen, Denmark. The results of model are compared with observed data.

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Gaussian plume model and advection-diffusion equation: An attempt to connect the two approaches

Atmospheric Environment (1967) ◽

10.1016/0004-6981(79)90041-6 ◽

1979 ◽

Vol 13 (8) ◽

pp. 1169-1174 ◽

Cited By ~ 8

Author(s):

R. Lupini ◽

T. Tirabassi

Keyword(s):

Diffusion Equation ◽

Gaussian Plume Model ◽

Plume Model ◽

Advection Diffusion Equation ◽

Advection Diffusion ◽

Gaussian Plume

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Estimation of Radioactivity Release Activity Using Non-Linear Kalman Filter-Based Estimation Techniques

Energies ◽

10.3390/en13153985 ◽

2020 ◽

Vol 13 (15) ◽

pp. 3985

Author(s):

Victor M. Becerra ◽

Vineet Vajpayee ◽

Nils Bausch ◽

T.V. Santhosh ◽

Gopika Vinod ◽

...

Keyword(s):

Kalman Filter ◽

Dose Rate ◽

Gaussian Plume Model ◽

Estimation Techniques ◽

Plume Model ◽

Dose Rates ◽

Statistical Measures ◽

Cubature Kalman Filter ◽

Non Linear ◽

Gaussian Plume

The estimation of radioactivity release following an accident in a nuclear power plant is crucial due to its short and long-term impacts on the surrounding population and the environment. In the case of any accidental release, the activity needs to be estimated quickly and reliably to effectively plan a rapid emergency response and design an appropriate evacuation strategy. The accurate prediction of incurred dose rate during normal or accident scenario is another important aspect. In this article, three different non-linear estimation techniques, extended Kalman filter, unscented Kalman filter, and cubature Kalman filter are proposed in order to estimate release activity and to improve the prediction of dose rates. Radionuclide release rate, average wind speed, and height of release are estimated using the dose rate monitors data collected in proximity of the release point. Further, the estimates are employed to improve the prediction of dose rates. The atmospheric dispersion phenomenon of radioactivity release is modelled using the Gaussian plume model. The Gaussian plume model is then employed for the calculation of dose rates. A variety of atmospheric and accident related scenarios for single source and multiple sources are studied in order to assess the efficacy of the proposed filters. Statistical measures have been used in order to validate the performance of the proposed approaches.

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Maximum crosswind integrated ground level concentration in two stability classes

MAUSAM ◽

10.54302/mausam.v64i4.748 ◽

2021 ◽

Vol 64 (4) ◽

pp. 655-662

Author(s):

M.ABDEL WAHAB ◽

KHALED SMESSA ◽

M. EMBABY ◽

SAWSAN EMELSAID

Keyword(s):

Wind Speed ◽

Laplace Transformation ◽

Ground Level ◽

Eddy Diffusivity ◽

Concentration Data ◽

Transformation Technique ◽

Advection Diffusion Equation ◽

Advection Diffusion ◽

Ground Level Concentration ◽

Vertical Height

bl 'kks/k i= esa fu"izHkkoh vkSj vfLFkj fLFkfr;ksa esa ØkWliou lekdfyr lkanz.k ysus ds fy, nks fn’kkvksa esa vfHkogu folj.k lehdj.k ¼ADE½ dks gy fd;k x;k gSA ykIykl :ikarj.k rduhd dk mi;ksx rFkk m/okZ/kj Å¡pkbZ ij vk/kkfjr iou xfr vkSj Hkaoj folj.k’khyrk dh leh{kk djrs gq, ;g gy fudkyk x;k gSA blds lkFk gh Hkw&Lrj vkSj vf/kdre lkanz.kksa dk Hkh vkdyu fd;k x;k gSA geus bl ekWMy esa iwokZuqekfur vkSj izsf{kr lkanz.k vk¡dM+ksa ds e/; rqyuk djus ds fy, dksiugsxu ¼MsuekdZ½ ls fy, x, vkuqHkfod vk¡dM+ksa dk mi;ksx fd;k gSA The advection diffusion equation (ADE) is solved in two directions to obtain the crosswind integrated concentration in neutral and unstable conditions. The solution is solved using Laplace transformation technique and considering the wind speed and eddy diffusivity depending on the vertical height. Also the ground level and maximum concentrations are estimated. We use in this model empirical data from Copenhagen (Denmark) to compare between predicted and observed concentration data.

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An Application of the Gaussian Plume Model to Localization of an Indoor Gas Source with a Mobile Robot

Sensors ◽

10.3390/s18124375 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4375 ◽

Cited By ~ 8

Author(s):

Jorge Sánchez-Sosa ◽

Juan Castillo-Mixcóatl ◽

Georgina Beltrán-Pérez ◽

Severino Muñoz-Aguirre

Keyword(s):

Gas Sensors ◽

Robotic Systems ◽

Gaussian Plume Model ◽

Concentration Data ◽

Plume Model ◽

Gas Source ◽

Velocity Information ◽

Gaussian Plume ◽

Metal Oxide Gas Sensors ◽

Gas Leaks

The source localization of gas leaks is important to avoid any potential danger to the surroundings or the probable waste of resources. Currently there are several localization methods using robotic systems that try to find the origin of a gas plume. Many of these methods require wind velocity information involving the use of commercial anemometric systems which are extremely expensive compared to metal oxide gas sensors. This article proposes the validation of the Gaussian plume model inside an empty room and its application to localize the source of a gas plume without employing anemometric sensors, exclusively using concentration data. The model was selected due to its simplicity and since it easily admits variants closer to reality, explaining the behavior of pollutants transported by the wind. An artificial gas source was generated by a conventional fan and liquid ethanol as contaminant. We found that the physical fan, far from making the model impossible to implement, enriched the information and added realism. The use of a robotic system capable of autonomously mapping the room concentration distribution is described. The results showed that the Gaussian plume model is applicable to localize our experimental gas source. An estimated position of the source with a deviation of 14 cm (6.1%) was obtained.

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Crosswind integrated concentration for various dispersion parameter systems

MAUSAM ◽

10.54302/mausam.v64i4.747 ◽

2021 ◽

Vol 64 (4) ◽

pp. 645-654

Author(s):

KHALED SMESSA ◽

SOAD METMAN

Keyword(s):

Wind Profile ◽

Dispersion Parameter ◽

Diffusion Experiment ◽

Gaussian Plume Model ◽

Dispersion Parameters ◽

Plume Model ◽

Statistical Measures ◽

Gaussian Plume ◽

Release Height ◽

Logarithmic Wind Profile

LFkkuh; Lrj izdh.kZu ds fy, xkSlh;u fiPNd ekWMy ¼Gaussian Plume Model½ dk O;kid :i ls iz;ksx fd;k tkrk gSA vuqizLFk iou dh dqy lkanzrk Kkr djus ds fy, xkSlh;u lw= ¼QkWewyk½ dks laxfBr fd;k gSA vuqizLFk iou dh dqy lkanzrk dh x.kuk djus ds fy, izdh.kZu izkpyksa dh fHkUu&fHkUu iz.kkfy;ksa dk mi;ksx fd;k x;k gSA lrg Lrj esa Å¡pkbZ ds vuqlkj iou xfr dh fHkUurk dk o.kZu djus ds fy, ykxfjFehd foaM izksQkby dk mi;ksx fd;k x;k gSA blesa NksM+h tkus okyh izHkkoh Å¡pkbZ dks /;ku esa j[kk x;k gSA fHkUu fHkUu izdh.kZu izkpy iz.kkfy;ksa ds fy, iwokZuqekfur lkanzrkvksa vkSj dksisugsxu ds folj.k iz;ksx ls izkIr fd, x, izsf{kr vk¡dM+ksa dh rqyuk djus ds fy, lkaf[;dh; ifjekiksa dk mi;ksx fd;k x;k gSA The Gaussian plume model is the most widely used model for local scale dispersion. The Gaussian formula has been integrated to obtain the crosswind-integrated concentration. Different systems of dispersion parameters are used to calculate the crosswind integrated concentration. A logarithmic wind profile is used to describe the variation of wind speed with height in the surface layer. The effective release height was taken into consideration. Statistical measures are utilized in the comparison between the predicted concentrations for different dispersion parameter systems and the observed concentrations data obtained from Copenhagen diffusion experiment.

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