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Record W4414863935 · doi:10.1080/14484846.2025.2568344

Error analysis on the fluid measurement of the Laval nozzle’s throat equivalent diameter

2025· article· en· W4414863935 on OpenAlexaboutno aff
Xiang Zhang, Hongyu Wei, Yang Wang, Guanglin Wang

Bibliographic record

VenueAustralian Journal of Mechanical Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor Technologies Research
Canadian institutionsnot available
Fundersnot available
KeywordsError analysisObservational errorStatistical analysisThroatRotation (mathematics)

Abstract

fetched live from OpenAlex

The mechanical dimensions of the throat are key parameters of the Laval nozzle, significantly influencing the performance of the servo system. An improved fluid-based measurement model for determining the throat diameter is presented, which employs a mandrel plug gauge (MPG) to enhance the measurement system’s resolution. Measurement errors resulting from oil temperature, MPG eccentricity, MPG tilt, non-circular cross-section, and throat length were analysed via flow field simulations. The results showed that oil temperature and MPG tilt had a considerable effect on measurement accuracy, while MPG eccentricity, throat shape, and throat length had negligible influence. Targeted improvements were subsequently made to the measurement system: a closed-loop temperature control was implemented to regulate oil temperature, and a non-fixed MPG was designed to reduce tilt-induced errors. Experimental results demonstrate that the enhanced fluid measurement system significantly reduces the non-repeatability error of the measurements.

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)0.003
metaresearch head score (Gemma)0.014
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 score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.048
GPT teacher head0.284
Teacher spread0.236 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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