MétaCan
Menu
Back to cohort

Cooling Performance Analysis of ThePrimary Cooling System ReactorTRIGA-2000Bandung

2018· article· en· W2791718322 on OpenAlexfundno aff
Ign. Djoko Irianto, Sukmanto Dibyo, Syaiful Bakhri, Geni Rina Sunaryo

Bibliographic record

VenueJournal of Physics Conference Series · 2018
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsnot available
FundersMcMaster University
KeywordsCoolantNuclear engineeringPressurizerWater coolingTRIGAVolumetric flow rateMass flowMass flow rateHeat transferMechanicsMaterials scienceThermodynamicsChemistryResearch reactorNuclear physicsEngineeringPhysics

Abstract

fetched live from OpenAlex

The conversion of reactor fuel type will affect the heat transfer process resulting from the reactor core to the cooling system. This conversion resulted in changes to the cooling system performance and parameters of operation and design of key components of the reactor coolant system, especially the primary cooling system. The calculation of the operating parameters of the primary cooling system of the reactor TRIGA 2000 Bandung is done using ChemCad Package 6.1.4. The calculation of the operating parameters of the cooling system is based on mass and energy balance in each coolant flow path and unit components. Output calculation is the temperature, pressure and flow rate of the coolant used in the cooling process. The results of a simulation of the performance of the primary cooling system indicate that if the primary cooling system operates with a single pump or coolant mass flow rate of 60 kg/s, it will obtain the reactor inlet and outlet temperature respectively 32.2 °C and 40.2 °C. But if it operates with two pumps with a capacity of 75% or coolant mass flow rate of 90 kg/s, the obtained reactor inlet, and outlet temperature respectively 32.9 °C and 38.2 °C. Both models are qualified as a primary coolant for the primary coolant temperature is still below the permitted limit is 49.0 °C.

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)0.000
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 score0.233
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.200
Teacher spread0.187 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Physics Conference SeriesSame topicNuclear reactor physics and engineeringFrench-language works237,207