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Record W4225151944 · doi:10.3390/met12050761

Effect of Radiation Defects on Thermo–Mechanical Properties of UO2 Investigated by Molecular Dynamics Method

2022· article· en· W4225151944 on OpenAlexaff
Ziqiang Wang, Miaosen Yu, Chen yang, Xuehao Long, Ning Gao, Zhongwen Yao, Limin Dong, Xuelin Wang

Bibliographic record

VenueMetals · 2022
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsQueen's University
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceMolecular dynamicsCrystallographic defectIrradiationFrenkel defectRadiationWork (physics)Radiation damageElastic modulusThermalComposite materialThermal expansionReduction (mathematics)ModulusThermodynamicsCondensed matter physicsComputational chemistryOpticsChemistryNuclear physicsPhysics

Abstract

fetched live from OpenAlex

Nuclear fuel performance is deteriorated due to radiation defects. Therefore, to investigate the effect of irradiation-induced defects on nuclear fuel properties is essential. In this work, the influence of radiation defects on the thermo-mechanical properties of UO2 within 600–1500 K has been studied using the molecular dynamics method. Two types of point defects have been investigated in the present work: Frenkel pairs and antisites with concentrations of 0 to 5%. The results indicate that these point defects reduce the thermal expansion coefficient (α) at all studied temperatures. The elastic modulus at finite temperatures decreases linearly with the increase in concentration of Frenkel defects and antisites. The extent of reduction (R) in elastic modulus due to two different defects follows the trend Rf > Ra for all studied defect concentrations. All these results indicate that Frenkel pairs and antisite defects could degrade the performance of UO2 and should be seriously considered for estimation of radiation damage in nuclear fuels used in nuclear reactors.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.012
GPT teacher head0.241
Teacher spread0.229 · 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
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

Citations5
Published2022
Admission routes1
Has abstractyes

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