Aerodynamic Database Development for Mars Smart Lander Vehicle Configurations
Bibliographic record
Abstract
An aerodynamic database has been generated for the Mars Smart Lander Shelf-All configuration using computational fluid dynamics (CFD) simulations. Three different CFD codes, USM3D and FELISA, based on unstructured grid technology and LAURA, an established and validated structured CFD code, were used. As part of this database development, the results for the Mars continuum were validated with experimental data and comparisons made where applicable. The validation of USM3D and LAURA with the Unitary experimental data, the use of intermediate LAURA check analyses, as well as the validation of FELISA with the Mach 6 CF(sub 4) experimental data provided a higher confidence in the ability for CFD to provide aerodynamic data in order to determine the static trim characteristics for longitudinal stability. The analyses of the noncontinuum regime showed the existence of multiple trim angles of attack that can be unstable or stable trim points. This information is needed to design guidance controller throughout the trajectory.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".