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Record W2809137116 · doi:10.22215/etd/2016-11341

An Experimental and Theoretical Investigation of Evaporating Meniscus Dynamics and Instabilities

2016· dissertation· en· W2809137116 on OpenAlexaff
John Polansky

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicFluid Dynamics and Thin Films
Canadian institutionsCarleton University
Fundersnot available
KeywordsSuperheatingInstabilityMeniscusMechanicsMaterials scienceEvaporationSurface tensionThermodynamicsOpticsPhysics

Abstract

fetched live from OpenAlex

High power density systems utilising phase change heat transfer devices such as heat pipes can be susceptible to evaporation driven meniscus dynamics and instability.To better understand this, an study of evaporating meniscus dynamics and instability was needed.A mathematical model describing evaporating meniscus dynamics was developed in which meniscus height was correlated with superheat.Subsequent validation experiments confirmed the model was consistent with the general trends including the superheat to meniscus height relation.The study of evaporating meniscus instability was investigated using a one-sided model and a linear stability analysis.The analysis considered the effects of long range molecular forces, surface tension, vapour recoil, evaporation, thermocapillarity and viscous forces.The potential for instability was studied for three film geometries, for which the potential for instability was found to be spatially dependent for the curved cases, with perturbation growth rates increasing with superheat.An experimental study of channel based evaporating meniscus instability was performed for eight channel widths and three fluids: n-pentane, iso-octane and acetone.The meniscus height to superheat correlation was used to infer the superheat at which the meniscus destabilised.The experiments revealed two kinds of instability.The first was localised to a narrow range of superheats and unique to the alkanes, the second common to all three fluids and sustained for higher superheats.The second kind of instability was found to require larger superheats for decreasing channel widths.iiiFirst and foremost, I would like to thank my thesis supervisor Professor Tarik Kaya for his unwaivering support and guidance throughout the course of my graduate studies.It is with his judicious application of wisdom that I have been able to grow as both a researcher and individual, for which I am truly grateful.Our many discussions and debates have been invaluable and it is my hope that our efforts will prove fruitful for many years to come.I would like to extend many thanks to professors Bruce Burlton and Edgar Matida for their support, guidance and varied perspectives that helped me to see the problems in a new light.Furthermore, the time and assistance from the technical staff is greatly appreciated as their efforts helped me to bring my experimental ambitions into reality. I would like to thank my father Steve

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.004
GPT teacher head0.215
Teacher spread0.211 · 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 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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