Modification and Tests of Particle Probe Tips to Mitigate Effects of Ice Shattering
Bibliographic record
Abstract
Abstract Ice particle shattering may significantly contaminate measurements taken by airborne particle probes in ice clouds. Environment Canada and the NASA Glenn Research Center (GRC) undertook efforts to modify and test probe tips in order to mitigate the effect of shattering on measurements. This work presents an overview of the results obtained during the design work on the particle probe arm tips. Even though this work was focused on the modifications of three of the probes—Particle Measuring Systems Inc. (PMS) Forward Scattering Spectrometer Probe and optical array probe, and Droplet Measurement Technologies (DMT) Cloud Imaging Probe—the outcomes of this work bear a general character and are applicable to other similar instruments. The results of the airflow analysis around the probe’s housing and the simulations of particle bouncing from the probe tips are discussed here. The originally designed and modified tips were tested in a high-speed wind tunnel in ice and liquid sprays. The ice particle bouncing processes as well as patterns of water shedding over the surface of the probes arms were studied with the help of a high-speed video camera. It was found that at aircraft speed, after bouncing from a solid surface, ice particles may travel several centimeters across the airflow and bounce forward up to 1 cm. For the first time it has been directly documented with high-speed video recording that the sample volumes of particle probes with the originally designed tips are contaminated by shattered and bounced particles. A set of recommendations on the existing modification and the design of future particle probe housings is presented.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".