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Ultrasonic Anemometer Using Microphones

2025· article· en· W4413557080 on OpenAlexaff
G.R. Li, Mehrdad Moallem, Patrick Palmer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFlow Measurement and Analysis
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsUltrasonic sensorAnemometerAcousticsComputer sciencePhysicsWind speedMeteorology

Abstract

fetched live from OpenAlex

This study presents a microphone-based system for wind speed measurement, eliminating the need for traditional mechanical components. The primary objective is to demonstrate the feasibility of using an ultrasonic transducer and acoustic sensors, specifically digital or analog microphones, to detect the change of phase differences due to wind speed. In contrast to conventional anemometers which rely on moving parts, such as cups or vanes, the proposed system uses the change of the phase shift or pulse delay in the microphones as a measure of time-offlight (ToF). The research involves the design, calibration, and testing of a prototype that integrates sound wave analysis, wind velocity estimation, and computational methods. Key factors such as environmental noise, microphone response characteristics, electronic interference, and calibration were considered to optimize measurement accuracy and reliability. The results suggest that the microphone-based anemometer offers an inexpensive, viable solution with advantages including reduced mechanical wear, lower maintenance requirements, and lower manufacturing costs. The findings prove the concept of ultrasonic and microphone wind speed measurement, opening the door for further refinement of the technology. This work contributes to the growing need for acoustic wind speed measurement solutions for both environmental monitoring and industrial applications.

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)0.000
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 score0.265
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.211
Teacher spread0.200 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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