Development of an implosion-driven hypervelocity launcher for orbital debris impact simulation
Bibliographic record
Abstract
The ability to soft-launch projectiles to velocities exceeding 10 km/s is of interest for a number of scientific fields, including orbital debris impact testing. Current soft-launch technologies have reached a performance plateau below this operating range. In the implosion-driven launcher (IDL) concept, explosives are used to linearly implode a pressurized steel tube, thereby dynamically compressing a light driver gas to significantly higher pressures and temperatures than typical light-gas launchers. As a result, the IDL has the potential to significantly outperform current state of the art hypervelocity launchers. This work will focus on establishing an understanding of the critical design parameters of the IDL with the goal of improving the velocity potential of the launcher. For this purpose, a computational gasdynamics solver capable of simulating the internal ballistics of the IDL has been developed. The elevated pressure and temperature in the driver gas lead to a number of non-ideal effects during the launch cycle, including expansion of the launcher walls, convective heat transfer, and gas leakage, which have a significant effect on launcher performance. These effects have been simulated by coupling the gasdynamics solver to loss models. Specifically, a structural hydrocode has been developed to provide a realistic model of reservoir and launch tube expansion, which has been identified as the main source of performance loss in the launch cycle. The complete internal ballistics solver will be used in conjunction with classical internal ballistics theory and experimental results, in order to gain valuable understanding of the key design parameters of the launcher and improve the design of the McGill IDL. This analysis has led to the development of an IDL capable of launching a 0.1-g projectile to 9.1 km/s.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".