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Lagrangian stochastic modeling for a...

Das, Sandipan Kumar.

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Lagrangian stochastic modeling for atmospheric turbulent dispersion of airport pollutants.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Lagrangian stochastic modeling for atmospheric turbulent dispersion of airport pollutants./
Author:	Das, Sandipan Kumar.
Description:	111 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4442.
Contained By:	Dissertation Abstracts International66-08B.
Subject:	Engineering, Mechanical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3186337
ISBN:	9780542285837

Lagrangian stochastic modeling for atmospheric turbulent dispersion of airport pollutants.
Das, Sandipan Kumar.

Lagrangian stochastic modeling for atmospheric turbulent dispersion of airport pollutants. - 111 p.

Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4442.

Thesis (Ph.D.)--Stanford University, 2005.

The objective of the study is to develop a suitable Lagrangian stochastic model (LSM) for turbulent dispersion in atmospheric flows and to apply it to a practical pollutant dispersion problem in an airport environment.

ISBN: 9780542285837Subjects--Topical Terms:

783786
Engineering, Mechanical.

Lagrangian stochastic modeling for atmospheric turbulent dispersion of airport pollutants.
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008 130610s2005 eng d
020 $a 9780542285837
035 $a (UnM)AAI3186337
035 $a AAI3186337
040 $a UnM $c UnM
100 1 $a Das, Sandipan Kumar. $3 1916879
245 1 0 $a Lagrangian stochastic modeling for atmospheric turbulent dispersion of airport pollutants.
300 $a 111 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4442.
500 $a Adviser: Paul A. Durbin.
502 $a Thesis (Ph.D.)--Stanford University, 2005.
520 $a The objective of the study is to develop a suitable Lagrangian stochastic model (LSM) for turbulent dispersion in atmospheric flows and to apply it to a practical pollutant dispersion problem in an airport environment.
520 $a A new methodology of constructing LSM's has been developed where the second moment equations derived from the set of stochastic differential equations (SDE's) of the LSM are the same as those of a general form of tensorially linear turbulence second-moment closure (SMC) model. The premise is that by following this route, the physics (e.g. buoyancy, anisotropy etc.) capturing capabilities encapsulated in the SMC models will be carried over to the corresponding LSM's. Additional terms are added to the LSM to satisfy the well-mixed constraint. The SMC model on which the LSM is based is calibrated for better match with homogeneous, stratified flow experiments. The stochastic model is at first applied to homogenous shear flow with neutral stratification where the prediction is found to agree very well with experimental data. Later stratification is introduced and the LSM is found to capture the relevant physics adequately.
520 $a A methodology has been developed for efficient application of the LSM to a practical problem involving atmospheric turbulent dispersion of pollutants released from air-traffic in an airport environment. The pollutant sources along aircraft flight paths are characterized as spheres. For more efficient computation with the same number of fluid particles, reverse trajectory formulation of the current LSM is introduced. The input Eulerian statistics are provided in terms of one-dimensional profiles constructed from given meteorological data. A simplified form of the LSM is devised which is expected to take care of essential physics of the current problem. Partitioning of the flow domain is carried out for faster search algorithms to determine source hits by the released fluid particles. Concentration calculations for a realistic airport problem are performed by the proposed LSM and some well-known past LSM's for different flow conditions with varying degrees of stability and a comparative analysis is carried out.
590 $a School code: 0212.
650 4 $a Engineering, Mechanical. $3 783786
650 4 $a Physics, Atmospheric Science. $3 1019431
690 $a 0548
690 $a 0608
710 2 0 $a Stanford University. $3 754827
773 0 $t Dissertation Abstracts International $g 66-08B.
790 1 0 $a Durbin, Paul A., $e advisor
790 $a 0212
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3186337