A Comparative Study of Experimental and Simulation Data for Micrometer Particle Acceleration in a Low-Pressure Supersonic Jet Impingement System
Bibliographic record
Abstract
Micrometer-sized particles impacting high-speed vehicles encounter a wide range of conditions, including gas rarefaction and high relative speeds, which significantly affect their drag. Recent attention has been given to understanding hypersonic vehicle behavior in particle laden environments, where accurate prediction of particle drag under a wide range pressure, temperature and gas velocities is paramount to predicting damage to surfaces. To this end, we design an experimental system to measure particle acceleration and declaration in a low pressure supersonic jet impingement with gas Mach reaching a value of 5. Pure nitrogen gas was used with gas pressure and gas temperature near standard conditions upstream of a de Laval nozzle and flow driven by a 266 Pa downstream pressure. Well controlled single sized solid spherical particles in the size range of 0.7 to 7 μm in diameter and density of 2.7 g cm⁻³ and 1.5 g cm⁻³ are generated by a vibrating orifice generator. The velocities of particles within the supersonic jet are measured using laser Doppler velocimetry at various downstream locations. Particle velocities are compared with numerical models that incorporate state-of-the-art drag laws suitable for the high Mach number and high Knudsen number regimes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".