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Record W4386114732 · doi:10.11159/htff23.173

Molecular Dynamics Simulation Of Adiabatic Two-Phase Flow In Nanochannels

2023· article· en· W4386114732 on OpenAlexvenueno aff
Yunmin Ran, Volfango Bertola

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicNanopore and Nanochannel Transport Studies
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilChina Scholarship Council
KeywordsAdiabatic processFlow (mathematics)Molecular dynamicsTwo-phase flowStatistical physicsPhase (matter)MechanicsDynamics (music)Computer sciencePhysicsThermodynamicsAcoustics

Abstract

fetched live from OpenAlex

The two-phase flow of argon fluid in a nanochannel is investigated by molecular dynamics simulations (MDS).The nanochannel consists of two parallel copper plates with the length of 2000 Å and the height of 120 Å, where the density of liquid argon is 1316.5 kg/m 3 at 100 K.An external driving force, ranging from 0.001 to 0.005 eV/ Å, was applied along the flow direction to fluid particles at the channel inlet during simulations.The flow patterns were obtained from the density distribution.Based on flow patterns, the void fraction was discussed as a function of the mean flow velocity.When the initial filling ratio is 67 %, the void fractions of different velocities are all close to 33%, which means these simulations are consistent with the homogeneous model.Different filling ratios were also analysed.As the filling ratio is increased, the bubble size decreases and the void fraction also decreases.Molecular dynamics simulation show that under these conditions the flow is adequately described by the homogeneous model.

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 score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.226
Teacher spread0.219 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Mechanical, Chemical, and Material EngineeringSame topicNanopore and Nanochannel Transport StudiesFrench-language works237,207