Certain Investigations on Soft Lander for Lunar Exploration
Bibliographic record
Abstract
As all expeditions on moon are of great importance and people are more curious to know more and more about it, a lunar module is required to carry instruments safely to lunar surface. The lunar module has to be landed on its legs and with minimal shock on all kinds of surface after being dropped from a height, be it a hard or soft surface. So simulations and experiments on lunar landers are indispensable for a successful launch. Based on a model of the lunar lander, a CAD model was designed using SolidWorks and was tested for kinematics, dynamics by simulating its free fall using ADAMS. With the insight gained, a scaled physical model of the lander was fabricated with two-wheeler rear wheel spring-dampers in primary struts and packaging foam in footpads as cost-effective shock absorbing mechanisms. With a focus on impact forces on footpads, being one of the most important parameters, interfacing Arduino-UNO-Wifi-board with load cells a force measurement and wireless data acquisition system was developed, calibrated and incorporated into the footpads. Drop test experiments and simulations were carried out for different drop heights and angles of landing surface and the impact forces on the footpads and soil penetration depths were measured, and a basic shock and energy analysis was carried out. Our results indicate that shocks are within limits up to a certain height and landings are stable for the anticipated landing surface angles for the chosen model parameters and surface conditions.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".