Dispersion modeling of thermal power plant emissions on stochastic space
Abstract This study aims to couple a deterministic atmospheric dispersion solver based on Gaussian model with a nonintrusive stochastic model to quantify the propagation of multiple uncertainties. The nonintrusive model is based on probabilistic collocation framework. The advantage of nonintrusive n...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gorle, J. M. R. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Systematik: |
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| Anmerkung: |
© Springer-Verlag Wien 2015 |
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| Übergeordnetes Werk: |
Enthalten in: Theoretical and applied climatology - Springer Vienna, 1986, 124(2015), 3-4 vom: 06. Mai, Seite 1119-1131 |
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| Übergeordnetes Werk: |
volume:124 ; year:2015 ; number:3-4 ; day:06 ; month:05 ; pages:1119-1131 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00704-015-1483-1 |
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OLC2048456510 |
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| 520 | |a Abstract This study aims to couple a deterministic atmospheric dispersion solver based on Gaussian model with a nonintrusive stochastic model to quantify the propagation of multiple uncertainties. The nonintrusive model is based on probabilistic collocation framework. The advantage of nonintrusive nature is to retain the existing deterministic plume dispersion model without missing the accuracy in extracting the statistics of stochastic solution. The developed model is applied to analyze the $ SO_{2} $ emission released from coal firing unit in the second stage of the National Thermal Power Corporation (NTPC) in Dadri, India using “urban” conditions. The entire application is split into two cases, depending on the source of uncertainty. In case 1, the uncertainties in stack gas exit conditions are used to construct the stochastic space while in case 2, meteorological conditions are considered as the sources of uncertainty. Both cases develop 2D uncertain random space in which the uncertainty propagation is quantified in terms of plume rise and pollutant concentration distribution under slightly unstable atmospheric stability conditions. Starting with deterministic Gaussian plume model demonstration and its application, development of stochastic collocation model, convergence study, error analysis, and uncertainty quantification are presented in this paper. | ||
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Dispersion modeling of thermal power plant emissions on stochastic space |
| abstract |
Abstract This study aims to couple a deterministic atmospheric dispersion solver based on Gaussian model with a nonintrusive stochastic model to quantify the propagation of multiple uncertainties. The nonintrusive model is based on probabilistic collocation framework. The advantage of nonintrusive nature is to retain the existing deterministic plume dispersion model without missing the accuracy in extracting the statistics of stochastic solution. The developed model is applied to analyze the $ SO_{2} $ emission released from coal firing unit in the second stage of the National Thermal Power Corporation (NTPC) in Dadri, India using “urban” conditions. The entire application is split into two cases, depending on the source of uncertainty. In case 1, the uncertainties in stack gas exit conditions are used to construct the stochastic space while in case 2, meteorological conditions are considered as the sources of uncertainty. Both cases develop 2D uncertain random space in which the uncertainty propagation is quantified in terms of plume rise and pollutant concentration distribution under slightly unstable atmospheric stability conditions. Starting with deterministic Gaussian plume model demonstration and its application, development of stochastic collocation model, convergence study, error analysis, and uncertainty quantification are presented in this paper. © Springer-Verlag Wien 2015 |
| abstractGer |
Abstract This study aims to couple a deterministic atmospheric dispersion solver based on Gaussian model with a nonintrusive stochastic model to quantify the propagation of multiple uncertainties. The nonintrusive model is based on probabilistic collocation framework. The advantage of nonintrusive nature is to retain the existing deterministic plume dispersion model without missing the accuracy in extracting the statistics of stochastic solution. The developed model is applied to analyze the $ SO_{2} $ emission released from coal firing unit in the second stage of the National Thermal Power Corporation (NTPC) in Dadri, India using “urban” conditions. The entire application is split into two cases, depending on the source of uncertainty. In case 1, the uncertainties in stack gas exit conditions are used to construct the stochastic space while in case 2, meteorological conditions are considered as the sources of uncertainty. Both cases develop 2D uncertain random space in which the uncertainty propagation is quantified in terms of plume rise and pollutant concentration distribution under slightly unstable atmospheric stability conditions. Starting with deterministic Gaussian plume model demonstration and its application, development of stochastic collocation model, convergence study, error analysis, and uncertainty quantification are presented in this paper. © Springer-Verlag Wien 2015 |
| abstract_unstemmed |
Abstract This study aims to couple a deterministic atmospheric dispersion solver based on Gaussian model with a nonintrusive stochastic model to quantify the propagation of multiple uncertainties. The nonintrusive model is based on probabilistic collocation framework. The advantage of nonintrusive nature is to retain the existing deterministic plume dispersion model without missing the accuracy in extracting the statistics of stochastic solution. The developed model is applied to analyze the $ SO_{2} $ emission released from coal firing unit in the second stage of the National Thermal Power Corporation (NTPC) in Dadri, India using “urban” conditions. The entire application is split into two cases, depending on the source of uncertainty. In case 1, the uncertainties in stack gas exit conditions are used to construct the stochastic space while in case 2, meteorological conditions are considered as the sources of uncertainty. Both cases develop 2D uncertain random space in which the uncertainty propagation is quantified in terms of plume rise and pollutant concentration distribution under slightly unstable atmospheric stability conditions. Starting with deterministic Gaussian plume model demonstration and its application, development of stochastic collocation model, convergence study, error analysis, and uncertainty quantification are presented in this paper. © Springer-Verlag Wien 2015 |
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| title_short |
Dispersion modeling of thermal power plant emissions on stochastic space |
| url |
https://doi.org/10.1007/s00704-015-1483-1 |
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| author2 |
Sambana, N. R. |
| author2Str |
Sambana, N. R. |
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| doi_str |
10.1007/s00704-015-1483-1 |
| up_date |
2024-07-03T18:48:36.906Z |
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