DAVINCI Venus Entry, Descent, and Landing Modeling and Simulation
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-1165.vid The Deep Atmosphere Venus Investigation of Noble gases, Chemistry, and Imaging (DAVINCI) mission is scheduled to launch in June 2029 and explore Venus via two flybys and a probe descent scheduled for June 2031. The goals of the mission are to study the origin, evolution, and current state of Venus and to understand if it was habitable at a point in the past. The entry, descent, and landing (EDL) concept of operations of the probe leverages on the successful Pioneer Venus large probe mission. The science objectives of the mission levy certain requirements on the EDL system, such as landing in the scientifically important Alpha Regio Tessera and telemetering several gigabytes of instrumentation data to the orbiting relay spacecraft before the probe impacts the surface. In order to optimize the EDL sequence of the lander and to verify key driving requirements, a six degree of freedom EDL flight mechanics simulation has been created based on the best available aerodynamic and atmospheric models for Venus. This paper describes the EDL modeling and simulation and summarizes the current flight mechanics results for the mission.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".