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Record W2417438533 · doi:10.1177/0309524x16650766

A compact in-blade five hole pressure probe for local inflow study on a horizontal axis wind turbine

2016· article· en· W2417438533 on OpenAlexaff
Andrea Moscardi, David A. Johnson

Bibliographic record

VenueWind Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsInflowPressure sensorData acquisitionTurbineAerodynamicsPressure measurementEngineeringInstrumentation (computer programming)Marine engineeringWind tunnelTurbine bladeAcousticsWind speedSimulationMechanical engineeringAerospace engineeringComputer scienceMeteorologyPhysics

Abstract

fetched live from OpenAlex

Understanding local inflow conditions on a wind turbine blade on an operating wind turbine can further understanding of aerodynamic variations and help predict loads on the turbine. Turbine blades are generally designed with a two-dimensional steady approach, however the real wind conditions are highly three-dimensional (3D) and unsteady. Detailed measurements are not common for validating aerodynamic models. The aim of this theoretical and experimental study is to build, calibrate, install and test a compact in-blade five hole pressure probe system to be used to retrieve these measurements. Wind tunnel calibration of the five hole pressure probe has been successfully completed using an automated traversing system over a ±45° range, with 5° increments. Error analysis showed that the multi-zone pressure coefficient data reduction approach is the most suitable for this application. This approach not only extends the measurable local inflow angles up to ±70°, but also it allows any reference pressure for differential pressure readings. A new data acquisition system (DAQ) internal to a rotating small wind turbine blade section was developed. Space limitations resulted in a custom built DAQ of very contained dimensions. This included, among others, five pressure transducers on a printed circuit board, a 16 bit analog to digital converter, an Arduino microcontroller, and a Bluetooth transceiver to transmit the data wirelessly to the main computer. The new blade section was designed and 3D-printed in such a way that the DAQ instrumentation could be easily accessed and, at the same time, had an acceptable structural solidity. A series of tests were conducted on a 3.4 m diameter wind turbine in a large scale wind tunnel in order to assess the correct functioning of the probe system. As expected, the inflow measurement obtained while the turbine was operating under yawed conditions showed a periodically oscillating inflow vector. The period of this variation was the same as the period of the rotor rotation.

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)0.001
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.220
Teacher spread0.210 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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