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Record W4391693126 · doi:10.3390/radiation4010003

Characterizing the Early Acidic Response in Advanced Small Modular Reactors Cooled with High-Temperature, High-Pressure Water

2024· article· en· W4391693126 on OpenAlex
Abida Sultana, Jintana Meesungnoen, Jean‐Paul Jay‐Gerin

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueRadiation · 2024
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Nuclear Safety Commission
KeywordsModular designHigh pressureNuclear engineeringChemistryMaterials scienceProcess engineeringChemical engineeringThermodynamicsComputer scienceEngineeringPhysicsOperating system

Abstract

fetched live from OpenAlex

Utilizing Monte Carlo multi-track chemistry simulations along with a cylindrical instantaneous pulse (Dirac) irradiation model, we assessed the initial acidic response in both subcritical and supercritical water under high radiation dose rates. This investigation spans a temperature range of 300 to 500 °C at a nominal pressure of 25 MPa, aligning with the operational conditions anticipated in proposed supercritical water (SCW)-cooled small modular reactors (SCW-SMRs). A pivotal finding from our study is the observation of a significant ‘acid spike’ effect, which shows a notable intensification in response to increasing radiation dose rates. Our results bring to light the potential risks posed by this acidity, which could potentially foster a corrosive environment and thereby increase the risk of accelerated material degradation in reactor components.

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.

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)0.000
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 score0.396
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.003
GPT teacher head0.164
Teacher spread0.160 · 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