Lunar PAD Vacuum Flow Visualization Experiment for 3D Lunar and Planetary Landing Pads
Bibliographic record
Abstract
Repeated landings on regolith-covered bodies such as the Moon are known to cause plume surface interactions (PSIs) that can impinge on the environment around the landing site, resulting in potential hazards for the mission and potential crew. This project acts as a proof of concept test to show that it is possible to characterize the exhaust plume on a landing pad and as a test of different pad designs to mitigate and redirect the plume to minimize any potential hazards. Eight landing pad designs were 3D printed and tested in vacuum with a cold gas nozzle plume. Interaction of the plume with the landing pad was somewhat detectable using force sensor resistors (FSRs) but found to be much more quantitatively characterizable using the thermal camera. The landing pad geometries allowed a comparison between flat and vented designs in a variety of configurations. The thermal camera was able to identify a relatively repeatable plume coming off of the nozzle and specific differences in the temperature trend near the landing pad itself that appear to correlate to the impacts of different 3D printed design concepts on plume stagnation and redirection. This experiment setup was then simulated in computational fluid dynamics (CFD) software which generally shows similar trends. We propose that (1) vacuum testing in this manner is effective in helping bridge the gap between unvalidated CFD models and full-scale testing, (2) the effectiveness of various design features can be characterized quantitatively by using temperature as a measure of plume buildup, and (3) among other takeaways, vented landing pads demonstrate improvement over flat designs in terms of reducing plume buildup. Overall, landing pads are shown to be an effective technological solution for reducing harmful PSI effects and should be incorporated into lunar missions to enable safe, repeatable landings without damage to surface infrastructure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".