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Record W4390696223 · doi:10.1063/5.0180908

Point source modelling approach for sessile droplet evaporation

2024· article· en· W4390696223 on OpenAlexaff
S.J. Malcolm, Ahmed Azzam, Alidad Amirfazli

Bibliographic record

VenuePhysics of Fluids · 2024
Typearticle
Languageen
FieldEngineering
TopicNanomaterials and Printing Technologies
Canadian institutionsYork University
FundersEuropean Space Agency
KeywordsEvaporationMechanicsIsothermal processFlux (metallurgy)RADIUSDiffusionMaterials scienceContact angleRange (aeronautics)ThermodynamicsPhysicsComposite materialComputer science

Abstract

fetched live from OpenAlex

Evaporation of sessile droplets from unheated solid surfaces is a ubiquitous process in many practical applications. A reduced order, analytical point source model (PSM) for the axisymmetric diffusion-dominated evaporation of an isolated sessile droplet surrounded by non-saturated, quiescent air was developed. The droplet is modeled as a dynamic point mass source in the limit of an isothermal system. The model also incorporates the spatial variation in the evaporative flux across the droplet free surface. The model is capable of considering the mode of evaporation, i.e., constant contact angle or contract radius. The PSM was simulated using the finite difference method in MATLAB R2020a. The model determines the vapor concentration distribution in the surrounding environment, the instantaneous evaporative flux averaged across the droplet surface and the overall evaporation rate. Calculating the evaporation rate assuming a spatially uniform evaporative flux under-predicts the evaporation rate by up to an order of magnitude. The model results agreed with experimental data in literature and sufficiently captures the evaporation process phenomena. The versatility and accurate predictive power of the PSM allows it to be a robust and computationally inexpensive modeling tool for studying sessile droplet evaporation in a wide range of technical applications.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.765
Threshold uncertainty score0.305

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 head0.024
GPT teacher head0.230
Teacher spread0.205 · 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 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

Citations4
Published2024
Admission routes1
Has abstractyes

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