Radiation Source Localization Using Surrogate Models Constructed from 3-D Monte Carlo Transport Physics Simulations.
Saved in:
| Title: | Radiation Source Localization Using Surrogate Models Constructed from 3-D Monte Carlo Transport Physics Simulations. |
|---|---|
| Authors: | Miles, Paul R.1 (AUTHOR), Cook, Jared A.1 (AUTHOR), Angers, Zoey V.2 (AUTHOR), Swenson, Christopher J.2 (AUTHOR), Kiedrowski, Brian C.2 (AUTHOR), Mattingly, John3 (AUTHOR), Smith, Ralph C.1 (AUTHOR) rsmith@ncsu.edu |
| Source: | Nuclear Technology. Jan2021, Vol. 207 Issue 1, p37-53. 17p. |
| Abstract: | Recent research has focused on the development of surrogate models for radiation source localization in a simulated urban domain. We employ the Monte Carlo N-Particle (MCNP) code to provide high-fidelity simulations of radiation transport within an urban domain. The model is constructed to employ a source location ( x , y , z) as input and return the estimated count rate for a set of specified detector locations. Because MCNP simulations are computationally expensive, we develop efficient and accurate surrogate models of the detector responses. We construct surrogate models using Gaussian processes and neural networks that we train and verify using the MCNP simulations. The trained surrogate models provide an efficient framework for Bayesian inference and experimental design. We employ Delayed Rejection Adaptive Metropolis (DRAM), a Markov Chain Monte Carlo algorithm, to infer the location and intensity of an unknown source. The DRAM results yield a posterior probability distribution for the source's location conditioned on the observed detector count rates. The posterior distribution exhibits regions of high and low probability within the simulated environment identifying potential source locations. In this manner, we can quantify the source location to within at least one of these regions of high probability in the considered cases. Employing these methods, we are able to reduce the space of potential source locations by at least 60%. [ABSTRACT FROM AUTHOR] |
| Copyright of Nuclear Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 148112348 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Radiation Source Localization Using Surrogate Models Constructed from 3-D Monte Carlo Transport Physics Simulations. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Miles%2C+Paul+R%2E%22">Miles, Paul R.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cook%2C+Jared+A%2E%22">Cook, Jared A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Angers%2C+Zoey+V%2E%22">Angers, Zoey V.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Swenson%2C+Christopher+J%2E%22">Swenson, Christopher J.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kiedrowski%2C+Brian+C%2E%22">Kiedrowski, Brian C.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mattingly%2C+John%22">Mattingly, John</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Smith%2C+Ralph+C%2E%22">Smith, Ralph C.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> rsmith@ncsu.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Nuclear+Technology%22">Nuclear Technology</searchLink>. Jan2021, Vol. 207 Issue 1, p37-53. 17p. – Name: Abstract Label: Abstract Group: Ab Data: Recent research has focused on the development of surrogate models for radiation source localization in a simulated urban domain. We employ the Monte Carlo N-Particle (MCNP) code to provide high-fidelity simulations of radiation transport within an urban domain. The model is constructed to employ a source location ( x , y , z) as input and return the estimated count rate for a set of specified detector locations. Because MCNP simulations are computationally expensive, we develop efficient and accurate surrogate models of the detector responses. We construct surrogate models using Gaussian processes and neural networks that we train and verify using the MCNP simulations. The trained surrogate models provide an efficient framework for Bayesian inference and experimental design. We employ Delayed Rejection Adaptive Metropolis (DRAM), a Markov Chain Monte Carlo algorithm, to infer the location and intensity of an unknown source. The DRAM results yield a posterior probability distribution for the source's location conditioned on the observed detector count rates. The posterior distribution exhibits regions of high and low probability within the simulated environment identifying potential source locations. In this manner, we can quantify the source location to within at least one of these regions of high probability in the considered cases. Employing these methods, we are able to reduce the space of potential source locations by at least 60%. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Nuclear Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=148112348 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00295450.2020.1738796 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 37 Titles: – TitleFull: Radiation Source Localization Using Surrogate Models Constructed from 3-D Monte Carlo Transport Physics Simulations. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Miles, Paul R. – PersonEntity: Name: NameFull: Cook, Jared A. – PersonEntity: Name: NameFull: Angers, Zoey V. – PersonEntity: Name: NameFull: Swenson, Christopher J. – PersonEntity: Name: NameFull: Kiedrowski, Brian C. – PersonEntity: Name: NameFull: Mattingly, John – PersonEntity: Name: NameFull: Smith, Ralph C. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 00295450 Numbering: – Type: volume Value: 207 – Type: issue Value: 1 Titles: – TitleFull: Nuclear Technology Type: main |
| ResultId | 1 |
