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Record W2299770058 · doi:10.1115/1.4033088

Design of a Tritium-In-Air Monitor Using Field-Programmable Gate Arrays

2016· article· en· W2299770058 on OpenAlexaffabout
Phillip McNelles, Lixuan Lu

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2016
Typearticle
Languageen
FieldEngineering
TopicRadiation Effects in Electronics
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsField-programmable gate arrayMicroprocessorComputer scienceDetectorGate arrayEmbedded systemNuclear engineeringEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Field-programmable gate arrays (FPGAs) have recently garnered significant interest for certain applications within the nuclear field including instrumentation and control (I&C) systems, pulse measurement systems, particle detectors, and health physics. In CANada Deuterium Uranium (CANDU) nuclear power plants, the use of heavy water (D2O) as the moderator leads to increased production of tritium, which poses a health risk and must be monitored by tritium-in-air monitors (TAMs). Traditional TAMs are mostly designed using microprocessors. More recent studies show that FPGAs could be a potential alternative to implement the electronic logic used in radiation detectors, such as the TAM, more effectively. In this paper, an FPGA-based TAM is designed and constructed in a laboratory setting using an FPGA-based cRIO system. New functionalities, such as the detection of carbon-14 and the addition of noble-gas compensation, are incorporated into a new FPGA-based TAM along with the standard functions included in the original microprocessor-based TAM. The effectiveness of the new design is demonstrated through simulations as well as laboratory testing on the prototype system. Potential issues caused by radiation interactions with the FPGA are beyond the scope of this work.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.225
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes2
Has abstractyes

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