MétaCan
Menu
Back to cohort
Record W1976264099 · doi:10.1243/0954406001523957

Pressure wave attenuation in an air pipe flow

2000· article· en· W1976264099 on OpenAlexaff
Hao Wang, G. H. Priestman, S.B.M. Beck, R.F. Boucher

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · 2000
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFlow measurementAttenuationThermal mass flow meterAcousticsFlow (mathematics)Pipe flowUltrasonic flow meterMechanicsVolumetric flow ratePressure measurementFluidicsEngineeringMaterials scienceMass flow meterMechanical engineeringElectrical engineeringOpticsPhysicsTurbulence

Abstract

fetched live from OpenAlex

Pressure wave transmission attenuation in an air pipe flow is investigated both theoretically and experimentally. This investigation is to ensure the viability of remote flow measurement in an air pipe flow using a new fluidic pressure-pulse-transmitting flowmeter. The novel flowmeter produces self-induced oscillations, whose frequency is proportional to the flowrate. These pressure waves are transmitted via the flowing fluid and can be detected far downstream of the device. Experimental work has been conducted to ascertain how much the pressure waves are attenuated in air flow in a pipeline. This was done by using pipes of 0.05m diameter and both 4.7 and 28.5m long installed downstream of the flowmeter. A method of network simulation known as transmission line modelling (TLM), which has been programmed as the Sheffield University Network Analysis Software (SUNAS) code, is described and utilized to predict the wave decay through the air pipe flow. The theoretical and experimental results were found to give good agreement, demonstrating both the value of the modelling software and the viability of the remote flow measurement concept.

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)0.001
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 score0.086
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.201
Teacher spread0.190 · 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

Citations10
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering ScienceSame topicWater Systems and OptimizationFrench-language works237,207