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Record W4399907546 · doi:10.1080/00295639.2024.2356418

Assessment of CFD Predictions Using Experiments from a Heated Gas-Cooled Pebble Bed Facility

2024· article· en· W4399907546 on OpenAlex

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

VenueNuclear Science and Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsCanadian Nuclear Laboratories
FundersAtomic Energy of Canada Limited
KeywordsPebbleComputational fluid dynamicsNuclear engineeringEnvironmental scienceMechanicsMaterials sciencePhysicsGeologyEngineering

Abstract

fetched live from OpenAlex

This paper presents computational fluid dynamics (CFD) simulations of the coolant gas flow in a pebble bed reactor core to assess the suitability of CFD models to accurately predict the temperature distribution and possible occurrence of local hot spots that may affect pebble integrity. This study assessed CFD predictions against temperature distribution measurements from the SANA test facility at the Research Center Jülich in Germany. A realistic pebble bed structure of randomly packed 1584 pebbles was produced using the discrete element method to model the pebble packing in detail. A total of 96 experimental temperature pebble points were used for the assessments, covering a broad range of heating powers (10 kW ≤ Poperation ≤ 35 kW). A good agreement between the CFD predictions and the SANA measurements was obtained for two coolants, nitrogen and helium, along the height of the pebble bed. It is anticipated that a better understanding of the suitability of the existing CFD models gained through this study will aid in the identification of gaps and areas of improvement for CFD to support the design and safety evaluations for pebble bed small modular 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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score0.478

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 head0.014
GPT teacher head0.245
Teacher spread0.231 · 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