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Record W3084116143 · doi:10.32393/csme.2020.1133

The Icing Process of Water Droplet Postponed on the Zinc-Deposited Steel Surface in a Low-Temperature Environment

2020· article· en· W3084116143 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProgress in Canadian Mechanical Engineering. Volume 3 · 2020
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsIcingZincProcess (computing)Materials scienceZinc compoundsProcess engineeringMetallurgyEnvironmental scienceComputer scienceEngineeringMeteorologyOperating systemPhysics

Abstract

fetched live from OpenAlex

Ice formation and accretion lead to serve problems for power transmission lines, aircraft, wind turbines, and offshore structures. Superhydrophobic surfaces are featured as static contact angle (CA) above 150and low CA hysteresis (CAH, < 10) and present promising anti-icing capability. These surfaces can delay water freezing and reduce ice adhesion. However, the icing process of water droplets is complicated when this process happens on roughened hydrophobic/ superhydrophobic surfaces. Three (super)hydrophobic surfaces were fabricated by electrodepositing a zinc layer on stainless-steel substrates and coating with stearic acid. The primary testing substrates are 30 30 0.8 mm stainless-steel tiles . A Vaniman Problast micro-abrasive sandblaster (Problast-8008, Vaniman Manufacturing) is used for sandblasting as pre-treatment for the stainless-steel tiles. The material of sandblasting media is Al2O3 (White Fused Alumina, Vaniman Manufacturing), and the sizes are in 100 m and 250 m, respectively. Zinc Chloride (ZnCl2) (ACS grade, 97%, Caledon), Ammonium Chloride (NH4Cl) (ACS grade, 99.5%, ACP) and Stearic Acid (95%, Sigma-Aldrich) are used for the electrodeposition. The prepared samples were placed in a cold room, and the temperature was set at -10 1 C, 13 1 C and -15 1C. A syringe pump generates a water droplet of 23.6 1.4 l, and the distance between the needle of the syringe pump and the sample surface is 4.88 0.15 cm. A thermocouple was used to monitor the temperature of initial water droplets. A thermal bath controls the metal surface temperature at -14.73 2.01 C. A high-speed camera (Phantom V611, Vision research) recorded the whole dynamic and icing process of the water droplet. Wthe temperature of the sample approximately equals to the temperature of the environment (at -10 1 C and -15 1 C ), the icing process of the water droplet (the initial droplet temperature is 5.1 0.1C) on sample ED100SS-SA (sandblasted by 100 m Al2O3, then Zinc electrodeposited, followed with the stearic acids coating) can be postponed for more than an hour. After the temperature of the cold room was decreased while maintaining the same temperature of the samples, the icing delay time dropped significantly. Besides, the results show that the freezing rate is proportional to the final contact area between the droplet and the surface. The more hydrophobic surface leads to a smaller final contact area, which causes longer icing delay on the surface.

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.

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: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.692

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 science0.0010.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.178
Teacher spread0.173 · 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