CFD, Thermal and Stress Analysis for daVinci X-Prize Manned Space Mission: Computational Design Analysis - Part 1 (Keynote Lecture)
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. The da Vinci Project has an objective of launching the first private sub-orbital manned space flight by the end of 2004. Under X Prize rules the vehicle must be reusable, built to hold 3 people and complete 2 flights within a 2 week period. The basic launch configuration is to lift the rocket using a reusable helium balloon to a launch altitude of 24,400 meters (80,000 feet). The paper describes in detail the various major subsystem components of the Rocket, Balloon Launch Platform, Ground Operations and Logistics and the multidisciplinary approach to arrive at a viable and safe design. A state of the art software and engineering data management methodology is described. All parameters that effect the design are ported through a data management software linking, CFD, FEA and Flight Simulation software’s directly to the primary CAD platform resulting in direct updates to the CAD model. This process allows an iterative design to develop rapidly, multiple configurations to be assessed and a final design output in the shortest overall timeframe. The data management software contains every parameter in the rockets design including all rocket engine performance criteria. Running a variation on the engine lsp (Specific Impulse) results in the CAD drawings updated with new tank sizes etc, yet still driven by constrictions such as maximum diameter of the vehicle. Lowering of the Isp in such case would result in the need for more fuel to reach the minimum assigned altitude of 115 Km. The rockets length would automatically be increased, given a diameter restriction. CG and CP values for instance are automatically recalculated for stability analysis.
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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 | 0.001 |
| 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".