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ICOPE-15-1158 Supersonic air and wet steam jet using simplified de Laval nozzle

2015· article· en· W2721511385 on OpenAlexaboutno aff
Takumi Komori, Masahiro Miura, Sachiyo Horiki, Masahiro Osakabe

Bibliographic record

VenueThe Proceedings of the International Conference on Power Engineering (ICOPE) · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicErosion and Abrasive Machining
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleJet (fluid)Supersonic speedPetroleum engineeringEnvironmental scienceMaterials scienceMechanicsWaste managementMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Usually, Trichloroethane has been used for the de-oiling and cleaning of machine parts. But its production and import have been prohibited since 1995 because of its possibility to destroy the ozone layer. Generally for biological and environmental safety, the de-oiling should be done with the physical method instead of the chemical method using detergent or solvent. As one of the physical method, a cleaning by a supersonic wet steam jet has been proposed. The steam jet with water droplets impinges on the oily surface of machine parts and removes the oil or smudge. In the present study, the low-cost and taper-shaped nozzles were fabricated with an electric discharge machining. The jet behaviors from the taper-shaped nozzles were carefully observed by using air and wet steam. The non-equilibrium model of wet steam was proposed and compared with the experimental results. The spatial distribution of low density regions along the jet axis was considered to contribute the cleaning and de-oiling. However, the condensate generated with the depressurization of steam depressed the injected steam mass flow rate. Furthermore, the steam cleaning was conducted for the plastic coin and the good cleaning effect could be confirmed in spite of the short cleaning duration.

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 score0.520
Threshold uncertainty score0.449

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 integrity0.0000.000
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 head0.033
GPT teacher head0.251
Teacher spread0.218 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

Explore more

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