Effect of Plasma Fluctuations on In-Flight Particle Parameters
Bibliographic record
Abstract
Abstract The influence of arc root fluctuations in DC plasma spraying on the physical state of the particle jet is investigated by correlating individual in-flight particle temperature and velocity measurements with the instantaneous voltage difference between the electrodes. In-flight diagnostics with the DPV-2000 sensing device involves two-color pyrometry and time-of-flight technique for the determination of temperature and velocity. Synchronization of particle diagnostics with the torch voltage fluctuations is performed using an electronic circuit that generates a pulse when the voltage reaches some specific level; this pulse, that can be shifted by an arbitrary period of time, is used to trigger the acquisition of the pyrometric signals. Unlike what has been predicted by numerical modeling, time-dependent particle temperature and velocity due to power fluctuations induced by the arc movement can be very important. Periodic variations of the mean particle temperature and velocity, reaching ΔT= 600°C and Δv = 200m/s, are recorded during a voltage cycle. Moreover, very few particles are detected during some part of the cycle. The existence of quiet periods suggests that particles that are injected at some specific moments in the plasma are neither heated nor accelerated efficiently. To our knowledge, this is the first time large time-dependent effects of the arc root fluctuations on the particle state (temperature and velocity) are experimentally demonstrated with quantitative measurements.
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 | 0.002 |
| 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 | 0.000 | 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".