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Record W2115110607 · doi:10.2172/809884

Review of air flow measurement techniques

2002· report· en· W2115110607 on OpenAlexaff
Jennifer McWilliams

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsAirflowDuct (anatomy)TRACEREnvironmental scienceLeakFlow measurementIndoor air qualityParticle image velocimetryAcousticsMarine engineeringMechanicsEngineeringMechanical engineeringEnvironmental engineeringTurbulencePhysics

Abstract

fetched live from OpenAlex

Airflow measurement techniques are necessary to determine the most basic of indoor air quality questions: ''Is there enough fresh air to provide a healthy environment for the occupants of the building?'' This paper outlines airflow measurement techniques, but it does not make recommendations for techniques that should be used. The airflows that will be discussed are those within a room or zone, those between rooms or zones, such as through doorways (open or closed) or passive vents, those between the building and outdoors, and those through mechanical air distribution systems. Techniques that are highlighted include particle streak velocimetry, hot wire anemometry, fan pressurization (measuring flow at a given pressure), tracer gas, acoustic methods for leak size determination, the Delta Q test to determine duct leakage flows, and flow hood measurements. Because tracer gas techniques are widely used to measure airflow, this topic is broken down into sections as follows: decay, pulse injection, constant injection, constant concentration, passive sampling, and single and multiple gas measurements for multiple zones.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0080.008
Science and technology studies0.0010.001
Scholarly communication0.0030.005
Open science0.0040.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.008

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 head0.058
GPT teacher head0.273
Teacher spread0.216 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations58
Published2002
Admission routes1
Has abstractyes

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