[CAD] Cascade Tank Pressurization Method for Satellite-Delivering Rockets (Canadian Applied Physics Journal / 2025)
Bibliographic record
Abstract
Enclosed is the CAD files and information of a new method to pressurize the tanks of a rocket that does not require computer control. Below is the abstract and link to the paper. I hope this is of interest to all in aerospace !---------------------------------------------------------------------------------------------------------------------------------------------------------Satellite-delivering rockets share a common problem --- the dependency of pneumatic systems on electric networks driven by onboard computers that can fail and cripple/render the rocket inert. One such example is the propellant tank's pressurization system. This article presents a novel approach entitled the Cascade Tank Pressurization Method, that solves this problem by iteratively discharging in a domino effect a cluster of composite overwrapped pressure vessels triggered in series. A Piping & Instrumentation Diagram is available. One-dimensional pneumatic analysis conducted on various size clusters show a fine control --- of ullage pressure to a predefined target bandwidth --- without any computer controls. Ultimately, a disruptive fully-pneumatic self-regulated tank pressurization system is presented, enabling the option for a more computer-decoupled rocket architecture. The article was recently published in the open-source Canadian Applied Physics Research: https://doi.org/10.5539/apr.v17n2p40-------------------------------------------------------------------------For more public data, please visit my Figshare profile: https://figshare.com/authors/Luis_Teia/10811244
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.041 | 0.008 |
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".