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Design and optimization of dry powder jet cleaning nozzles for airport navigation lights

2023· article· en· W4320008061 on OpenAlexaboutno aff
Qiuhua Zhu, Xiaohong Ge, Xiaobin Gu, Hui Li, Jinhuo Wang, Zhiwen Zhang, Fuheng Lv

Bibliographic record

VenueJournal of Physics Conference Series · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleJet (fluid)Discharge coefficientAbrasiveFluentMaterials scienceCompressed airMechanicsMechanical engineeringFlow (mathematics)Computational fluid dynamicsEngineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract The research of this work is based on the nozzle design and optimization of dry powder (NaHCO3) jet cleaning system for airport navigation aid lamps. In this cleaning system, compressed air drives dry powder (NaHCO3) abrasive, and a gas-solid mixed high-speed jet is sprayed onto the surface of the luminaire through the nozzle to scour and clean the dirt attached to the surface of the luminaire. Based on the computational fluid dynamics and flow field theory, three types of nozzle structures, namely tapered nozzle, Laval nozzle, and flat nozzle, were studied; the DPM (Deformable part model) model in Fluent was used to analyze the internal and external flow fields of the nozzle mixed with the gas-solid two-phase flow to obtain the velocity field, pressure field and nozzle wear rate of the mixed gas, and the flat nozzle was selected and the nozzle parameters were optimized. The optimized level combination of flat type nozzle structure is obtained with a flat width of 4mm, flat height of 24.5mm, and flat length of 55mm.

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 score0.828
Threshold uncertainty score0.293

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 head0.023
GPT teacher head0.230
Teacher spread0.208 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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