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Record W2555373703 · doi:10.1115/ht2016-7366

Modeling the Performance of Bi-Textured Micropillar Array as a Wicked Evaporator

2016· preprint· en· W2555373703 on OpenAlexaff
Hassan Azarkish, A. Behzadmehr, Luc G. Fréchette

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMaterials sciencePillarEvaporationEvaporatorCapillary actionBoilingMass flow rateComposite materialVolumetric flow rateNanotechnologyMechanicsMechanical engineeringThermodynamics

Abstract

fetched live from OpenAlex

In the present work, the performance of bi-textured micro pillar arrays has been modeled as a wicked evaporator to provide steam flow via the thin film evaporation mechanism. Bi-textured micro pillar evaporator consists of an array with rough hydrophilic pillar bases and smooth hydrophobic tips. Water wicks between the rough hydrophilic sections of the micro pillar array to cover the surface, and vaporizes from the thin films that are formed in the vicinity of the pillar walls. The stability of the phase change mechanism is increased due to the change in direction of the capillary forces at the rough-smooth interface of micro pillars. The experimental results show that the pure evaporation mechanism occurs for a surface temperature above saturation on the bi-textured micro pillar array. The numerical analysis shows that there are optimal micro pillar dimensions for each surface temperature. The evaporation mass flow rate at the optimum dimensions is higher than the pool boiling mass flow rate on a bare surface at the same surface temperature. However, the wicked evaporator performance decreases for larger evaporator sizes.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.699

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 head0.015
GPT teacher head0.226
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 teacher head, 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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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