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Record W1992603711 · doi:10.1115/fedsm2014-21650

Impact of Micro-Droplets on Superhydrophobic and Hydrophilic Surfaces

2014· article· en· W1992603711 on OpenAlexaff
Hany Gomaa, Moussa Tembely, Nabil Esmail, Ali Dolatabadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsConcordia University
Fundersnot available
KeywordsMaterials scienceRange (aeronautics)Deposition (geology)Weber numberDeformation (meteorology)AluminiumWork (physics)IcingSurface (topology)NanotechnologyComposite materialMechanicsMeteorologyMechanical engineeringGeologyGeometryPhysicsReynolds number

Abstract

fetched live from OpenAlex

Ice accretion is a major threat to all exposed structures such as wind turbines, overhead power cables, offshore structures and aircrafts. Such deposition starts by an impact of water droplets of different sizes on the surface of the exposed structure. This work aims to shed more light on the difference in the dynamics occurring upon the impact of microdroplets on substrates with various wettabilities, hydrophilic (aluminum) and Superhydrophobic (Aluminum + WX2100) surfaces. Experiments are conducted on a wide range of diameters, between cloud sized droplets with diameters ranging down to 20μm, and 10 times larger droplets with a diameter of 250 μm. A comparison in the impact (through deformation) results is made all through the wide range and explained using the two extremes. This is done experimentally by analyzing the maximum spread diameter on the hydrophillic surface and superhydrophobic surface and maximum height as a function of time on the hydrophillic surface. Both parameters are visualized experimentally, simulated numerically for the same impact velocities and then results are compared for verification.

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 categoriesInsufficient payload (model declined to judge)
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 score0.099
Threshold uncertainty score0.999

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)0.0020.000

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 head0.014
GPT teacher head0.260
Teacher spread0.245 · 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.

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

Citations1
Published2014
Admission routes1
Has abstractyes

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