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Record W2902034931 · doi:10.2172/1484143

Coupled Mass and Heat Transport Models for Nuclear Fuels using Thermodynamic Calculations

2018· report· en· W2902034931 on OpenAlexaff
Srdjan Simunovic, Jake Mcmurray, Theodore M. Besmann, Emily E. Moore, M.H.A. Piro

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsOntario Tech University
FundersU.S. Department of Energy
KeywordsFinite element methodThermodynamic integrationCoupling (piping)Nuclear fuelThermodynamicsPhysicsStatistical physicsEnergy (signal processing)Materials scienceNuclear physics

Abstract

fetched live from OpenAlex

We have developed a theoretical framework for incorporation of thermodynamic equilibrium calculations into engineering models of nuclear fuel problems based on the finite element method (FEM). The framework is based on linear irreversible thermodynamics and their incorporation into integral equations for balancing energy and mass. For coupled energy and mass transport, thermodynamic calculations are performed at the nodal points of the FEM mesh and are used at the integration points to calculate the driving forces and fluxes for irreversible thermodynamic problems. The consistent coupling of thermodynamic models with isotopic calculations and other nuclear fuel physics models has been described. The approach has been demonstrated on simple unit problems. Implementation in practical nuclear fuel problems will be developed follow-on research.

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)0.001
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: none
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.239
Teacher spread0.207 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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