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Record W3201876376 · doi:10.1088/1361-6501/ac2ca5

Simultaneous measurements of velocity, gas concentration, and temperature by way of thermal-anemometry-based probes

2021· article· en· W3201876376 on OpenAlexafffund
Alaïs Hewes, Laurent Mydlarski

Bibliographic record

VenueMeasurement Science and Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThermometerMechanicsTurbulenceIsothermal processTemperature measurementScalar (mathematics)ThermalMaterials scienceCoaxialCompensation (psychology)PhysicsThermodynamicsMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Many natural and engineering flows transport more than one scalar. Moreover, to study the scalar mixing therein, knowledge of the velocity field is also essential. For this reason, the present work describes the development of a three-wire thermal-anemometry-based probe to simultaneously measure velocity, helium concentration, and temperature in turbulent flows. It is first demonstrated, both theoretically and experimentally, that the temperature measured by a cold-wire thermometer is effectively insensitive to helium concentration. Then, building on recent work by Hewes and Mydlarski (2021 Meas. Sci. Technol. 32 105305), which pertains to the design of interference probes (i.e. thermal-anemometry-based probes used to measure velocity and gas concentration), a novel temperature compensation technique is proposed to extend their use to non-isothermal flows. The performance of the compensation technique is validated in turbulent coaxial jets by combining the cold-wire thermometer and interference probe to form a three-wire probe. Given that the three-wire probe can be employed to obtain simultaneous measurements of velocity and multiple scalars, it can therefore be used investigate phenomena such as multi-scalar mixing, including differential diffusion.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.438

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 communication0.0000.000
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 head0.010
GPT teacher head0.200
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 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

Citations8
Published2021
Admission routes2
Has abstractyes

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