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Fiber Bragg grating sensor for simultaneous measurement of flow rate and direction

2008· article· en· W2003731425 on OpenAlexafffund
Ping Lü, Qiying Chen

Bibliographic record

VenueMeasurement Science and Technology · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaMemorial University of NewfoundlandCanada Research Chairs
KeywordsFiber Bragg gratingMaterials scienceOpticsCantileverPHOSFOSUltrasonic gratingWavelengthGratingLong-period fiber gratingFiber optic sensorHolographic gratingFiberOptoelectronicsDispersion-shifted fiberDiffraction gratingComposite materialPhysics

Abstract

fetched live from OpenAlex

A new fiber-optic sensor system consisting of a fiber Bragg grating cantilever as a transducer is proposed and demonstrated to realize simultaneous measurement of fluid flow rate and direction. For the fiber Bragg grating mounted on either a stainless steel or a spring steel substrate, a change in the water flow rate gives rise to a monotonic shift in the Bragg resonance wavelength of the grating while the flow direction results in either a redshift or a blueshift in the Bragg wavelength due to a stretched or shrunk state of the grating. Shifts in the Bragg resonance wavelength of 0.077 and 0.826 nm at a water flow rate of 90 cm 3 s −1 were achieved with the fiber Bragg grating stainless steel and spring steel cantilever sensors, respectively. The experimental results are in good agreement with the theoretical analysis.

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)0.001
metaresearch head score (Gemma)0.000
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.025
GPT teacher head0.219
Teacher spread0.194 · 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

Citations57
Published2008
Admission routes2
Has abstractyes

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