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Record W2939343726 · doi:10.1109/pgsret.2018.8685948

KANUPP Reactor Core Model and its Validation

2018· article· en· W2939343726 on OpenAlexaboutno aff
M. Sajjad, M. Rizwan Ali, M. Naveed Ashraf, Rustam Khan, Tasneem Fatima

Bibliographic record

Venue2018 International Conference on Power Generation Systems and Renewable Energy Technologies (PGSRET) · 2018
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsnot available
Fundersnot available
KeywordsNuclear engineeringMonte Carlo methodNuclear decommissioningCoolantCore (optical fiber)Core modelNuclear reactor coreEnvironmental scienceComputer scienceSimulationNuclear physicsEngineeringMechanical engineeringStatisticsPhysicsMathematics

Abstract

fetched live from OpenAlex

KArachi NUclear Power Plant (KANUPP), CANadian Deuterium Uranium (CANDU) type, is the first power reactor of Pakistan. It has been in operation to produce electricity since 1972. After the successful completion of its life span, it has been planned to be decommissioned in 2020. In this research work, a Monte Carlo model of KANUPP fresh core is developed using open computer simulation code OpenMC and validated against the reference results. For validation, the variation of excess reactivity with moderator level (critical moderator height) along with the important feedback effects like moderator temperature coefficient and coolant temperature coefficient are calculated and compared with the reference data. Reasonable agreement is obtained between the simulated and reference values for the above mentioned parameters. This detailed and validated simulation model may be used for the study of material changes of the in-core and out-core components for decommissioning of the reactor.

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)0.001
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 score0.021
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.044
GPT teacher head0.239
Teacher spread0.195 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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