Uncertainty Analysis of Store Separation Aerodynamic Data at the NRC 1.5 m Trisonic Wind Tunnel
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-2520.vid An uncertainty analysis of wind tunnel data for stores separation testing was performed. The Taylor Series Method was utilized for this analysis and applied to all three methods of stores separation wind tunnel testing used at the NRC 1.5 m (5 ft) Trisonic Wind Tunnel: captive carriage, grid survey and freestream. This method required a detailed analysis of the data reduction routines employed and was implemented through a series of Matlab programs on a per-run basis. Measurement uncertainties were calculated for all flow conditions and measured loads of the model. This method was validated against a Monte Carlo method approach as well as through repeat runs from various test entries. The uncertainty analysis was then applied to data acquired during a wind tunnel test to investigate the effects of mounting the Forward-Looking Infrared (FLIR) Pod on the centerline of the CF188 Hornet. Using a comparative method in conjunction with the measurement uncertainty, detailed in this paper, provided confidence that the variation in measured loads was not simply due to measurement uncertainty, but rather due to changes in the aircraft centerline configuration.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".