Numerical simulation of plume–surface interaction and lunar dust dispersion during lunar landing using four engines
Bibliographic record
Abstract
As the lander approaches the lunar surface, the engine plumes impinge on the lunar regolith and entrain lunar dust from the surface. This plume–surface interaction and the resulting dispersion of lunar dust form a multi-physics, multi-scale problem, which becomes even more complex under multi-engine conditions. This study employed the direct simulation Monte Carlo method to simulate the plume–surface interaction flow field of a four-engine lunar lander at various landing altitudes and lunar surface angles. Flow characteristics were analyzed, and the impact of the plume and backflow on the lander was assessed. Subsequently, lunar dust simulation was conducted using the plume field as a basis. The study determined the spatial distribution of particles with different diameters at various landing altitudes and surface angles, as well as their impact velocities on the lander. Furthermore, taking into account the variations in the lander's altitude and attitude, a dynamic simulation of lunar dust during the landing process was conducted. This process resulted in the dynamic distribution of lunar dust during landing, laying the groundwork for real-time simulation of lunar dust distribution and reliable visualization during landing simulations. These findings are valuable for assessing and mitigating the hazards posed by lunar dust.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".