Timing Delay Analysis of the Auxiliary Pump Technique to Improve the Performance of an Implosion-Driven Hypervelocity Launcher
Bibliographic record
Abstract
Abstract In order to understand the irreproducibility of the auxiliary pump technique, an interior ballistic solver taking into account reservoir collapse has been used to simulate the performance of launchers. Launchers with different detonation velocities, explosive lengths, and timing delays (the difference between the initiation time of the pump tube explosives and auxiliary pump explosives) of the auxiliary pump have been calculated. The effective timing delay region, which could achieve a velocity gain larger than 1.0 km/s, has been discussed. And its influence factors, such as the detonation velocity of auxiliary pump explosives and the inner-wall velocity of the reservoir, have been analyzed. Results show that the velocity gain decreases with an increase in the timing delay and increases with the increasing length of explosives. The effective timing delay region is about 2μs and depends weakly on the detonation velocity and the length of explosives when using the same explosives for the pump tube and the reservoir. Nevertheless, low detonation velocity of the reservoir explosives and high inner-wall velocity could improve the effective timing delay region, but the maximum effective timing delay region cannot exceed 10μs, which is not easily accomplished experimentally. Therefore, the auxiliary pump technique should not be a very reproducible technique.
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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 | 0.000 | 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) | 0.002 | 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".