Aerodynamic Characterization of a Closed-Loop, Semi-Open Jet Wind Tunnel using Experimental and Computational Methods
Bibliographic record
Abstract
Aerodynamic characterization literature generally resolves streamwise pressure gradients, velocity distributions, non-uniformity, and turbulence intensity; yet, evaluation of turbulence in the frequency domain and comparison of experimental measurements with transient computational results are less common.Thus, an in-depth aerodynamic characterization of a closed-loop, semi-open jet wind tunnel was completed using experimental and computational methods.The work aims to improve the characterization process by evaluating turbulence scales and comparing experimental and transient computational results.Velocity profiles were experimentally measured and compared with computational results.The facilities' dormant heat exchanger was also evaluated using simplified models.Agreement was observed between experimental and computational velocity profiles and turbulence intensity distributions.A 400 mm long, 140 mm high, and 300 mm wide testing envelope was revealed with competitive non-uniformities and turbulence intensities less than 1%.Boundary layer growth occurred at an approximate rate of 3 mm every 200 mm streamwise for all velocities along with a near-zero pressure gradient.Agreement was also observed between heat exchanger models, indicating adequate cooling capabilities.Results conclude that the test section's flow field is comparable to existing facilities.iii I would like to express my gratitude for the supervision and guidance that I have received from my supervisor Dr. Joana Rocha.In times of frustration, you always encouraged me to persevere and had faith in my abilities.Thank you for your dedication.I thank all of the Mechanical
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".