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Record W4200427504 · doi:10.2514/1.j060785

Design and Fabrication of Submillimeter Supersonic Wind Tunnels for Pressure Measurements

2021· article· en· W4200427504 on OpenAlexaboutno aff
Chih‐Yung Huang, Zih-Chen Lin, Chao‐Yu Chen

Bibliographic record

VenueAIAA Journal · 2021
Typearticle
Languageen
FieldMathematics
TopicGas Dynamics and Kinetic Theory
Canadian institutionsnot available
FundersMinistry of Science and Technology, Taiwan
KeywordsSupersonic wind tunnelHypersonic wind tunnelPlenum spaceNozzleWind tunnelSupersonic speedMach numberInletMechanicsAerodynamicsShock waveMaterials scienceStagnation pressureShock (circulatory)Discharge coefficientCylinderStatic pressureAerospace engineeringPhysicsMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

A de Laval supersonic nozzle with a test section was designed as a microsupersonic wind tunnel and subjected to numerical and experimental analysis. The height and depth of the throat of the nozzle were 500 and 150 μm, respectively. A plenum chamber was added to the inlet of the supersonic nozzle and a reservoir was connected to the outlet of the test section to ensure a steady pressure condition during experiments. The microsupersonic wind tunnel was fabricated through microelectromechanical lithography, and a pressure-sensitive paint (PSP) was applied to acquire the pressure contours inside the microsupersonic wind tunnel at various pressure conditions. A divergent angle of 4 deg was added to the test section to resolve the viscous layer growing from the side walls and to ensure a steady flow during the measurements. A flow speed of Mach 1.6 was achieved with inlet and outlet pressures of 100 and 20 kPa, respectively. A circular cylinder model with a diameter of 50 μm was positioned at the exit of the test section to examine the flowfield. Because of the viscous effect, the shock wave cannot be identified but local high- and low-pressure regions could still be observed through PSP measurements.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.828
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.092
GPT teacher head0.303
Teacher spread0.211 · 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 designTheoretical or conceptual
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
Published2021
Admission routes1
Has abstractyes

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