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Record W2972478362 · doi:10.1115/1.4044751

Enhanced Accident Tolerance of Thoria-Based Nuclear Fuels

2019· article· en· W2972478362 on OpenAlexaff
Barbara Szpunar, Jerzy A. Szpunar

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2019
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsNuclear fuelMaterials scienceThermal conductivityNuclear engineeringNuclear fission productMelting pointPelletsLight-water reactorNuclear transmutationSpent nuclear fuelOxygenFission productsThermodynamicsNuclear physicsChemistryComposite materialNeutron

Abstract

fetched live from OpenAlex

Abstract Many factors need to be investigated before alternative nuclear fuel can be adapted for service in the harsh environment of a nuclear reactor. Urania, used conventionally as a nuclear fuel, has a low thermal conductivity, which degrades with increasing stoichiometric deviation. Thoria-based fuel has been considered as an alternative fuel, since it does not oxidize and has a high melting point and higher thermal conductivity. Simulations have shown that the fuel melting observed in urania fuel rods during an accident with steam ingress should not be observed (or will be delayed) in thoria as its thermal conductivity remains high enough to dissipate excessive heat in the center of the fuel pellets. The thermal gradient also remains low and therefore thermal stress is reduced, which should improve the longevity of the fuel. Thoria also has some other desirable properties as our calculations predict a significantly higher temperature of oxygen lattice premelting than urania. Furthermore, we found that the diffusion of fission gas, e.g., helium, is strongly affected by oxygen diffusion and therefore is slower in thoria for the temperatures where the oxygen lattice premelts in urania, but not in thoria.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.348

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 communication0.0000.001
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 head0.006
GPT teacher head0.211
Teacher spread0.205 · 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 designBench or experimental
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

Citations2
Published2019
Admission routes1
Has abstractyes

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