MétaCan
Menu
Back to cohort
Record W2972951685 · doi:10.1115/1.4044747

Sensitivity Analysis of Unsteady Corium Solidification in an Initially Emptied Horizontal Turbulent Pipe Flow

2019· article· en· W2972951685 on OpenAlexafffund
Shawn Somers‐Neal, Vien Nguyen, Edgar Matida, Vinh Tang, Tarik Kaya

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2019
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsCarleton University
FundersCanadian Nuclear Safety Commission
KeywordsCoriumMaterials scienceTurbulenceMechanicsHeat transferReynolds numberPenetration (warfare)Heat sinkThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract In a reactor core meltdown under postulated severe accidents, the molten material called corium could be ejected or relocated through existing vessel penetrations. There exists, however, a potential for plugging of melt flow due to its complete solidification providing the availability of an adequate heat sink. Simulations of the melt flow in a horizontal tube were carried out to conduct a sensitivity study on the effect of key parameters on the melt penetration distance and bulk temperature distribution of the corium. The Reynolds number was varied from 10,000 to 20,000, inlet temperature was varied from 2600 K to 3000 K, the corium thermal conductivity was varied from 10 W/m·K to 20 W/m·K, and the pipe diameter was varied from 0.0095 m to 0.019 m. In addition, a comparison was made with an analytical model based on a modified Epstein's model and a previous numerical model. The study provided insight into the lower bound, which was found to be 98 mm, and the upper bound was 258 mm when predicting the potential penetration length of corium in horizontal pipes.

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)0.001
metaresearch head score (Gemma)0.003
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.007
GPT teacher head0.220
Teacher spread0.212 · 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

Citations3
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Nuclear Engineering and Radiation ScienceSame topicMetallurgical Processes and ThermodynamicsFrench-language works237,207