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The analog linear interpolation appr...

Zhang, Wenchao.

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The analog linear interpolation approach for Monte Carlo simulation of prompt gamma-ray neutron activation analysis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	The analog linear interpolation approach for Monte Carlo simulation of prompt gamma-ray neutron activation analysis./
Author:	Zhang, Wenchao.
Description:	117 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-02, Section: B, page: 0712.
Contained By:	Dissertation Abstracts International64-02B.
Subject:	Chemistry, Nuclear. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3081756

The analog linear interpolation approach for Monte Carlo simulation of prompt gamma-ray neutron activation analysis.
Zhang, Wenchao.

The analog linear interpolation approach for Monte Carlo simulation of prompt gamma-ray neutron activation analysis. - 117 p.

Source: Dissertation Abstracts International, Volume: 64-02, Section: B, page: 0712.

Thesis (Ph.D.)--North Carolina State University, 2003.

The Monte Carlo code (CEARPGA 1) was developed to generate the elemental library spectra required for implementing the Monte Carlo Library Least-Squares algorithm for prompt gamma-ray neutron activation analysis (PGNAA). The existing big weight problem in which a few histories yield very large weights with very large variance has been investigated thoroughly. It has been found that the expected value splitting technique, a powerful variance reduction technique used in the code is the primary cause of this problem.Subjects--Topical Terms:

1916260
Chemistry, Nuclear.

The analog linear interpolation approach for Monte Carlo simulation of prompt gamma-ray neutron activation analysis.
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100 1 $a Zhang, Wenchao. $3 1949453
245 1 4 $a The analog linear interpolation approach for Monte Carlo simulation of prompt gamma-ray neutron activation analysis.
300 $a 117 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-02, Section: B, page: 0712.
500 $a Chair: Robin P. Gardner.
502 $a Thesis (Ph.D.)--North Carolina State University, 2003.
520 $a The Monte Carlo code (CEARPGA 1) was developed to generate the elemental library spectra required for implementing the Monte Carlo Library Least-Squares algorithm for prompt gamma-ray neutron activation analysis (PGNAA). The existing big weight problem in which a few histories yield very large weights with very large variance has been investigated thoroughly. It has been found that the expected value splitting technique, a powerful variance reduction technique used in the code is the primary cause of this problem.
520 $a Two Monte Carlo simulation approaches have been investigated to eliminate the big weight problem while still maintaining high efficiency. They are (1) score importance map with batch tracking and (2) analog linear interpolation. Both approaches demonstrated to be feasible for solving the big weight problem.
520 $a The analog linear interpolation approach was finally selected and implemented in the new CEARPGA Monte Carlo code (CEARPGA II). A comparison of the simulated results by CEARPGA I, CEARPGA II and MCNP with the experimentally measured data shows that the big weight problem has been successfully eliminated, the accuracy of the simulation has improved greatly, and the simulated results agree very well with the measured data.
520 $a In addition, some other important improvements to this code to enhance its accuracy and efficiency have also been introduced, including: (1) adding the tracking of annihilation gamma rays outside of the detector, (2) using the improved detector response functions, (3) generating individual natural background libraries, (4) adding the neutron activation backgrounds, and (5) adopting a general geometry package etc.
590 $a School code: 0155.
650 4 $a Chemistry, Nuclear. $3 1916260
650 4 $a Physics, Radiation. $3 1019212
650 4 $a Engineering, Nuclear. $3 1043651
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710 2 0 $a North Carolina State University. $3 1018772
773 0 $t Dissertation Abstracts International $g 64-02B.
790 1 0 $a Gardner, Robin P., $e advisor
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791 $a Ph.D.
792 $a 2003
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