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Record W2806420014 · doi:10.11159/ffhmt18.129

Impact Dynamics of a Droplet on a Heated Surface

2018· article· en· W2806420014 on OpenAlexvenueno aff
Prathamesh G. Bange, Nagesh D. Patil, Rajneesh Bhardwaj

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2018
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsnot available
FundersIndian Institute of Technology Bombay
KeywordsDynamics (music)Surface (topology)MechanicsMaterials sciencePhysicsMathematicsGeometryAcoustics

Abstract

fetched live from OpenAlex

The effect of impact velocity and substrate temperature on the impact dynamics of a water droplet on a non-heated and heated glass substrate is studied.A high-speed camera is utilized to record the time-varying droplet shapes during the impact.The initial spreading of the droplet is driven by large kinetic energy with large deformation of the free surface.The droplet recoils due to conversion of the surface energy to the kinetic energy and the internal flow reverses to radially inward.The liquid-gas surface oscillates along with the flow reversal from axially upward to axially downward, due to competition between the surface and kinetic energy.The amplitude of oscillation damps due to viscous dissipation and droplet assumes a spherical cap shape after it comes to rest.A lager impact velocity corresponds to higher kinetic energy and oscillations take longer time to dissipate.As the temperature of the substrate increases, the droplet spreads lesser due to larger resistance to the wetting at the contact line.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.235
Teacher spread0.220 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the ... International Conference on Fluid Flow, Heat and Mass TransferSame topicFluid Dynamics and Heat TransferFrench-language works237,207