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Record W4402528426 · doi:10.1115/1.4066523

Studies of the Thermalhydraulics Subchannel Code ASSERT-PV 3.2-SC for Supercritical Applications

2024· article· en· W4402528426 on OpenAlexaffabout
Armando Nava Domínguez, Xianmin Huang, Songyu Liu

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2024
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsAtomic Energy (Canada)Canadian Nuclear Laboratories
Fundersnot available
KeywordsSupercritical fluidNuclear engineeringCode (set theory)Materials scienceComputer scienceProcess engineeringThermodynamicsEnvironmental scienceProgramming languagePhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract Over the last decade, several international thermalhydraulics benchmarking efforts have been carried out to support the development of the Generation IV supercritical water-cooled reactor (SCWR) concept. These benchmarking efforts aimed to assess the readiness of computer codes to predict the thermalhydraulics behavior of supercritical fluids for nuclear fuel assembly applications. The results from the benchmarking also shed light on knowledge gaps. Throughout the years, several advancements in this area have been achieved, resulting in relevant conclusions and observations. Furthermore, experimental campaigns have been carried out worldwide to further our knowledge on the thermalhydraulics of supercritical fluids. The nuclear industry uses the subchannel approach to study the thermalhydraulics behavior of nuclear fuel assemblies in detail. In Canada, the subchannel code advanced solution of subchannel equations in reactor thermalhydraulics—pressure velocity (ASSERT-PV) is the qualified code for subchannel applications. ASSERT-PV was modified to handle supercritical conditions, resulting in an interim code version. This publication presents relevant subchannel analyses using the interim supercritical version of ASSERT-PV for fuel assemblies cooled with supercritical fluids.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.136

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 head0.026
GPT teacher head0.293
Teacher spread0.267 · 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
Published2024
Admission routes2
Has abstractyes

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