Characterization of the NASA Langley Arc Heated Scramjet Test Facility using NO PLIF
Bibliographic record
Abstract
The nitric oxide planar laser-induced fluorescence (NO PLIF) imaging was used to characterize the air flow of the NASA Langley Arc Heated Scramjet Test Facility (AHSTF) configured with a Mach 6 nozzle. The arc raises the enthalpy of the test gas in AHSTF, producing nitric oxide. Nitric oxide persists as the temperature drops through the nozzle into the test section. NO PLIF was used to qualitatively visualize the flowfield at different experimental conditions, measure the temperature of the gas flow exiting the facility nozzle, and visualize the wave structure downstream of the nozzle at different operating conditions. Uniformity and repeatability of the nozzle flow were assessed. Expansion and compression waves on the free-jet shear layer as the nozzle flow expands into the test section were visualized. The main purpose of these experiments was to assess the uniformity of the NO in the freestream gas for planned experiments, in which NO PLIF will be used for qualitative fuel-mole-fraction sensitive imaging. The shot-to-shot fluctuations in the PLIF signal, caused by variations in the overall laser intensity as well as NO concentration and temperature variations in the flow was 20-25% of the mean signal, as determined by taking the standard deviation of a set of images obtained at constant conditions and dividing by the mean. The fluctuations within individual images, caused by laser sheet spatial variations as well as NO concentration and temperature variations in the flow, were about 28% of the mean in images, determined by taking standard deviation within individual images, dividing by the mean in the same image and averaged over the set of images. Applying an averaged laser sheet intensity correction reduced the within-image intensity fluctuations to about 10% suggesting that the NO concentration is uniform to within 10%. There was no significant difference in flow uniformity between the low and high enthalpy settings. While not strictly quantitative, the temperature maps show qualitative agreement with the computations of the flow.
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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 | 0.000 |
| 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".