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Record W2965177326 · doi:10.11159/htff19.179

Investigation of the Solid-Liquid Phase Change in the Presence of Gas Phase: Numerical Modeling and Validation

2019· article· en· W2965177326 on OpenAlexvenueno aff
Ravi Govindram Kewalramani, Sebastian Pose, Ingo Riehl, Tobias Fieback

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsnot available
FundersTechnische Universität Bergakademie Freiberg
KeywordsPhase (matter)Materials sciencePhase changeComputer scienceThermodynamicsChemistryPhysics

Abstract

fetched live from OpenAlex

In the present work a fundamental numerical model is implemented in the open source framework, finite volume method (FVM) based computational fluid dynamics (CFD) program OpenFOAM, to tackle the solid-liquid phase change phenomena in the presence of gas phase. The volume of fluid (VoF) method is employed to distinguish between the phase change material (PCM) and the gas phase, and the enthalpy-porosity method is adopted to capture the moving boundary phase change phenomena in the PCM. The discussed model is validated against the well documented cases in the literature i.e., 1D Stefan problem, melting of gallium and free surface thermocapillary flow with phase change. The simulation results are in very good agreement with those found in the literature. Finally experiments are performed with partially filled paraffin wax (Rubitherm RT31) in the presence of air in an enclosure. The left and right walls of the enclosure are made of aluminium plates and maintained at different temperatures while the remaining walls are made of Plexiglas. The temporal evolution of melting front during melting obtained from the discussed numerical model is compared with experiments.

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 score0.170
Threshold uncertainty score0.222

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.022
GPT teacher head0.253
Teacher spread0.231 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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Same venueProceedings of the World Congress on Mechanical, Chemical, and Material EngineeringSame topicnanoparticles nucleation surface interactionsFrench-language works237,207