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Record W4394747396 · doi:10.1007/s00348-024-03803-2

Calorimetric wall-shear-stress microsensors for low-speed aerodynamics

2024· article· en· W4394747396 on OpenAlexfundno aff
Julien Weiss, Alain Giani

Bibliographic record

VenueExperiments in Fluids · 2024
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsnot available
FundersTechnische Universität BerlinUniversité de MontpellierÉcole de technologie supérieure
KeywordsLaminar flowMaterials scienceAirfoilTurbulenceAerodynamicsShear stressMechanicsBubbleComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract This article describes the design, calibration, and testing of new calorimetric microsensors for the measurement of wall shear stress in low-speed aerodynamic flows. The sensors are made of three beams of platinum-plated silicon nitride suspended over a small cavity. Their range of operation and their bandwidth are of the order of $$\pm 10$$ ± 10 Pa and 1 kHz, respectively. Results from experimental campaigns in a laminar separation bubble, a turbulent separation bubble, and on a NACA 0015 airfoil at low Reynolds number indicate a high sensitivity and an inherent capacity to measure instantaneous backflow. This demonstrates the capability of the new sensors to accurately determine the mean and fluctuating wall shear stress in laminar, transitional, and turbulent separating and reattaching flows.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.256
Teacher spread0.245 · 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

Citations10
Published2024
Admission routes1
Has abstractyes

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