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Record W2307724024

Stefan's Problem: Validation of a One-Dimensional Solid-Liquid Phase Change Heat Transfer Process

2010· article· en· W2307724024 on OpenAlexaff
Wilson Ogoh, Dominic Groulx

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsLatent heatStefan problemMultiphysicsHeat transferPhase-change materialThermal conductionThermodynamicsThermal energy storageTransient (computer programming)Process (computing)Materials scienceMechanicsThermal energyEnergy (signal processing)ThermalPhase changeRange (aeronautics)Computer sciencePhysicsMathematicsFinite element method
DOInot available

Abstract

fetched live from OpenAlex

Abstract: One way of storing thermal energy is through the use of latent heat energy storage systems. One of the thermal/energy processes that needs to be properly modeled in designing such system is the transient latent heat storage encountered during phase change of the material used. This paper presents a numerical validation of such a process encountered in a simple geometry defined in Stefan's problem, where heat transfer by conduction and phase change are the only two processes present. It is shown that the physical processes encountered can be modeled numerically using COMSOL Multiphysics with a modification of the specific heat of the PCM accounting for the increase amount of energy, in the form of latent heat, needed to melt the PCM over its melting temperature range. This modification enables the simulation of the behavior of the melting front. The effects of the PCM melting temperature range, i.e., the presence of a mushy region, is also investigated and compared to the analytical solution obtained for Stefan's problem.

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)0.002
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 score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0020.001
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 head0.066
GPT teacher head0.341
Teacher spread0.275 · 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

Citations27
Published2010
Admission routes1
Has abstractyes

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