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CALCULATION OF AIRFLOW SPEED FROM A TURBINE WITH AN INTEGRATED LAVAL NOZZLE AT THE OUTLET FOR METAL COATING BY COLD SPRAYING METHOD

2023· article· en· W4387821218 on OpenAlexaboutno aff
Dmitry E. Pisarev, Sergey I. Mitrokhin

Bibliographic record

VenueArchitecture Construction Transport · 2023
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleAirflowMechanical engineeringTurbineImpellerCoatingWork (physics)Materials scienceMechanicsEngineeringMarine engineeringPhysicsComposite material

Abstract

fetched live from OpenAlex

The goal of this work was to test the practicability of using a device that provides airflow into the Laval nozzle channel to enable the application of metal coating by cold spraying. A turbine with an integrated Laval nozzle at the outlet was considered as a device to provide airflow into the Laval nozzle channel. The authors describe the equipment used in the calculations and the experimental study, the process of gasdynamic calculation of the output velocity of the airflow injected by the turbine impeller and the results of this calculation with precise indices. As a result, functional dependences of the indicators that provide the possibility of using the turbine as a source of airflow have been obtained, and a conclusion about the inexpediency of using a turbine with an integrated Laval nozzle at the outlet to provide the possibility of metal coating by cold spraying has been formed.

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 score0.001
Threshold uncertainty score0.004

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.252
Teacher spread0.237 · 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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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