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The Spatial Neutron Algorithmic Model
The Spatial Neutron Algorithmic Model
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260311091518.5
- ISBN
- 9798270229634
- DDC
- 628.4
- 서명/저자
- The Spatial Neutron Algorithmic Model / Cade Michael Bourque
- 발행사항
- [Sl] : The University of Texas at Austin, 2025
- 형태사항
- 1 electronic resource (132 pages)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-06, Section: B.
- 주기사항
- Advisors: Henzl, Vladimir; Charlton, William S. Committee members: Landsberger, Sheldon; Clarno, Kevin.
- 학위논문주기
- - Ph.D. : The University of Texas at Austin, 2025.
- 초록/해제
- 요약The Spatial Neutron Algorithmic Model (SNAM) represents a novel numerical technique which leverages random forest regression models to improve both the precision of passive, neutron-based nondestructive assay (NDA) estimates relative to what can be achieved with the standard Point Model. This advancement is facilitated by the emergence of full list-mode, multi-channel data acquisition technologies in neutron detectors. Traditional assay techniques use the Point Model to translate observed multiplicity count rates into SNM mass estimates. However, uncertainties regarding the spatial distribution of SNM within the detector and the material composition of the waste matrix undermine two of the key assumptions made under the Point Model, resulting in assay uncertainties up to 30%. Using full list-mode, multi-channel outputs, SNAM can examine the ratio of count rates between any and all channels/channel combinations to estimate the effective efficiency of the detection. Additionally, if represented visually, these ratios can be used by a trained individual to estimate the spatial distribution of source material within the detector. In being able to determine the efficiency of the measurement (rather than simply assuming an often inaccurate, static value), SNAM decouples the Point Model from the limitations of its assumptions, leading to improved assay precision. Additionally, SNAM expands the capability of neutron detectors beyond just NDA measurements - the decoding of spatial information from measurements grants waste inspectors access to new data streams which can be used to better understand their system and inspect their items in novel manners.
- 언어주기
- English
- 일반주제명
- Materials science
- 일반주제명
- Theoretical physics
- 일반주제명
- Computational physics
- 키워드
- Point Model
- 키워드
- Waste matrix
- 기타저자
- The University of Texas at Austin Mechanical Engineering
- 기본자료저록
- Dissertations Abstracts International. 87-06B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520260311091518.5
■006m o d
■007cr|nu||||||||
■020 ▼a9798270229634
■040 ▼aMiAaPQD▼beng▼cMiAaPQD▼erda
■082 ▼a628.4
■1001 ▼aBourque, Cade Michael▼eauthor.
■24510▼aThe Spatial Neutron Algorithmic Model ▼cCade Michael Bourque
■260 ▼a[Sl]▼bThe University of Texas at Austin▼c2025
■264 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a1 electronic resource (132 pages)
■336 ▼atext▼btxt▼2rdacontent
■337 ▼acomputer▼bc▼2rdamedia
■338 ▼aonline resource▼bcr▼2rdacarrier
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-06, Section: B.
■500 ▼aAdvisors: Henzl, Vladimir; Charlton, William S. Committee members: Landsberger, Sheldon; Clarno, Kevin.
■5021 ▼bPh.D.▼cThe University of Texas at Austin▼d2025.
■520 ▼aThe Spatial Neutron Algorithmic Model (SNAM) represents a novel numerical technique which leverages random forest regression models to improve both the precision of passive, neutron-based nondestructive assay (NDA) estimates relative to what can be achieved with the standard Point Model. This advancement is facilitated by the emergence of full list-mode, multi-channel data acquisition technologies in neutron detectors. Traditional assay techniques use the Point Model to translate observed multiplicity count rates into SNM mass estimates. However, uncertainties regarding the spatial distribution of SNM within the detector and the material composition of the waste matrix undermine two of the key assumptions made under the Point Model, resulting in assay uncertainties up to 30%. Using full list-mode, multi-channel outputs, SNAM can examine the ratio of count rates between any and all channels/channel combinations to estimate the effective efficiency of the detection. Additionally, if represented visually, these ratios can be used by a trained individual to estimate the spatial distribution of source material within the detector. In being able to determine the efficiency of the measurement (rather than simply assuming an often inaccurate, static value), SNAM decouples the Point Model from the limitations of its assumptions, leading to improved assay precision. Additionally, SNAM expands the capability of neutron detectors beyond just NDA measurements - the decoding of spatial information from measurements grants waste inspectors access to new data streams which can be used to better understand their system and inspect their items in novel manners.
■546 ▼aEnglish
■590 ▼aSchool code: 0227
■650 4▼aMaterials science
■650 4▼aTheoretical physics
■650 4▼aComputational physics
■653 ▼aSpatial Neutron Algorithmic Model
■653 ▼aPoint Model
■653 ▼aNeutron detectors
■653 ▼aWaste matrix
■7102 ▼aThe University of Texas at Austin▼bMechanical Engineering.▼edegree granting institution.
■7201 ▼aHenzl, Vladimir▼edegree supervisor.
■7201 ▼aCharlton, William S.▼edegree supervisor.
■7730 ▼tDissertations Abstracts International▼g87-06B.
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17361155▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
