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Record W3119161808 · doi:10.2514/6.2021-0833

Design and Fabrication of Micro-Supersonic Wind Tunnels for Microscale Shock Wave Analysis

2021· article· en· W3119161808 on OpenAlexaboutno aff
Chih‐Yung Huang, Zih-Chen Lin, Chen-Yu Hsiao

Bibliographic record

VenueAIAA Scitech 2021 Forum · 2021
Typearticle
Languageen
FieldMathematics
TopicGas Dynamics and Kinetic Theory
Canadian institutionsnot available
Fundersnot available
KeywordsSupersonic speedSupersonic wind tunnelShock waveWind tunnelNozzleMicroscale chemistryMechanicsShock (circulatory)Materials scienceMicrometerMach numberHypersonic wind tunnelAerodynamicsPlenum spacePressure sensorMechanical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2021-0833.vid A de Laval supersonic nozzle with test section was designed as the micro-supersonic wind tunnel and it was analyzed numerically and experimentally in this study. The height of nozzle throat was 500 micrometer and the depth was 150 micrometer. A plenum chamber was added to the inlet of supersonic nozzle and a reservoir was connected to the outlet of test section to ensure the steady pressure condition during experiment. The micro-supersonic wind tunnel was fabricated with MEMS lithography technique and Pressure-Sensitive Paint technique was applied to acquire the pressure contours inside the micro-supersonic wind tunnel at various pressure conditions. Good agreement has been established between simulation and experiment. A 4 degree divergent angle was added to the test section to resolve the viscous layer growing from the side walls and ensure the steady flow speed during the measurements. With the inlet pressure of 100 kPa and outlet pressure of 20 kPa, a flow speed of Mach 1.6 can be achieved. A circular cylinder model with diameter of 50 micrometer was positioned at the exit of test section and the microscale shock wave pattern was observed. Due to the viscous effect, the shock wave was smeared but local high and low pressure region can still be identified by Pressure-Sensitive Paint 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)0.000
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: none
Teacher disagreement score0.456
Threshold uncertainty score0.619

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 head0.027
GPT teacher head0.272
Teacher spread0.246 · 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
Published2021
Admission routes1
Has abstractyes

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