Closed-Loop Precision Throttling of a Hybrid Rocket Motor
Bibliographic record
Abstract
A closed-loop throttle controller for a laboratory-scale and hydroxyl-terminated polybutadiene hybrid rocket motor is presented. Closed-loop throttling was achieved using commercial off-the-shelf valve hardware and a commercially available motor case adapted for hybrid rocket testing. Multiple and open- and closed-loop tests were performed to demonstrate that closed-loop control can significantly reduce the run-to-run burn variability typical of hybrid rocket motors. Closed-loop proportional/integral control algorithms featuring thrust or pressure feedback were used to track prescribed step and linear ramp profiles. Because the relationship between the selected throttle control valve position and the effective valve flow area was highly nonlinear, the effect of valve position on motor thrust/chamber pressure was measured open loop and curve fit to allow direct command of either total thrust or chamber pressure. Control law gains were tuned a priori using a numerical model and then adjusted using the actual test hardware. Response profiles were optimized according the integral absolute error criterion. Control law tuning examples are presented. Test results indicate that, to a 95% confidence level, closed-loop throttling significantly reduces the mean run-to-run thrust variability from to less than . When effects of nozzle erosion are accounted for, the closed-loop thrust variability reduces to .
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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".