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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 OpenAlex
Julien Weiss, Alain Giani

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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 $$\pm 10$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>±</mml:mo> <mml:mn>10</mml:mn> </mml:mrow> </mml:math> 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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.736
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
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