MétaCan
Menu
Back to cohort
Record W7023444270

One-dimensional single phase natural circulation model of FLiNaK in Molten Salt Loop

2019· dissertation· en· W7023444270 on OpenAlexfundaboutno aff

Bibliographic record

Venuee-scholar@UOIT (University of Ontario Institute of Technology) · 2019
Typedissertation
Languageen
FieldMathematics
TopicRandom Matrices and Applications
Canadian institutionsnot available
FundersUniversity of Ontario Institute of Technology
KeywordsNucleofectionTSG101Fusible alloyTubulopathyDiafiltrationProteogenomicsHyporeflexia
DOInot available

Abstract

fetched live from OpenAlex

This work studied a coolant for a molten salt type reactor in terms of a natural circulation behavior. A natural circulation model using MATLAB was developed. This model is for the Molten Salt Loop currently under development at University of Ontario Institute of Technology to investigate aging related degradation effects and thermal storage applications of FLiNaK. The model???s purpose is to help design the flow loop and future experiments. Generation IV reactors were compared to provide an overview of new reactor concepts with an in-depth review of the Molten Salt Reactor MSR type. The molten salt history, design, and application were discussed. A review of thermophysical properties of different salt coolants is performed to understand the variation of cooling capability. The model???s results were compared to different fluid types to verify the trends. This gave a baseline upon which to refer when experimental results are ready and thus guide future research into molten salt studies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.265
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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
Published2019
Admission routes2
Has abstractyes

Explore more

Same venuee-scholar@UOIT (University of Ontario Institute of Technology)Same topicRandom Matrices and ApplicationsFrench-language works237,207