Quantification of Supersonic Impulse Flow Conditions via High-Bandwidth Wavelength Modulation Absorption Spectroscopy
Bibliographic record
Abstract
A tunable diode laser-based absorption diagnostic has been developed to characterize transient phenomena within the Stanford Expansion Tube Flow Facility. A measurement rate of 25 kHz was achieved to identify and investigate transient flow structures within the facility test time. Key parameters provided by the diagnostic include the temperature, mole fraction, and velocity time histories of the supersonic test gas, as well as the structure and timing of the test gas contact surfaces. The presence of water within the test gas affords well-established rovibrational transitions near that are probed using calibration-free, scanned wavelength-modulation absorption spectroscopy with second harmonic detection. This technique offers the sensitivity and noise resistance necessary for in situ temperature, velocity, and mole fraction measurements within the harsh conditions of the expansion tube. A rigorous scanned wavelength-modulation absorption spectroscopy with second harmonic detection theory has been employed to account for nonideal laser performance associated with high-bandwidth tunable diode laser absorption spectroscopy measurements. The measurements carried out with this sensor provide a quantitative characterization of the test gas conditions produced in the expansion tube and thus lay an essential foundation for supersonic combustion experimentation within the facility.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".