CFD, Thermal and Stress Analysis for daVinci X-Prize Manned Space Mission: Part 2 (Keynote)
Bibliographic record
Abstract
In the present article we review engineering and research efforts conducted by a group of volunteers with the help of advanced engineering commercial software (CFD-ACE+, ANSYS, CFD-FASTRAN, Matlab/Simulink, Autodesk Inventor, Maple) in support of the da Vinci Project, the first Canadian competitor in the International X Prize Competition. Full account of these activities was presented at the da Vinci Space Project Technical Conference Program [1]. Announced in 1996 to promote the development and flight of spacecraft for low-cost commercial transport of humans into space, the international X-Prize Foundation is providing a purse of US$10 million to the first competitor who can safely launch and land a manned spacecraft to an altitude of 100 kilometers (the international border of space), twice in a two-week period. The first Canadian entry in this competition, the fully volunteer da Vinci Project (a wholly owned by ORVA Space Corp.) has put years of engineering research, design and developmental testing into the vehicle design, propulsion and flight guidance system. A full-scale flight-engineering prototype of the manned rocket has been constructed. Detailed engineering and fabrication of the full-scaled manned rocket named Wild Fire Mk VI is currently underway. Flight-testing of the manned rocket and X-Prize competition flights are targeted to continue throughout 2004. For R&D efforts on the project, a wide range of engineering software was utilized for CAD, basic engineering calculations, trajectory analysis, dynamics and mission control, supersonic external aerodynamics, and internal heat flow. Part 1 of this lecture appearing in Volume 1 describes space mission, thermal and CFD analyses and CAD integration. This installment describes how ANSYS and LSDYNA software packages were utilized to perform stress analysis of the space capsule and the rocket block.
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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".