Modeling of an Unconstrained Flexible Rocket Using Finite-Elements and the LISA Method
Bibliographic record
Abstract
This paper describes the production of a finite-element based unconstrained motion model for a flexible rocket. The main contribution is a demonstration of the LISA Method, a procedure for transforming a detailed finite-element (small-motion) model directly into a large-motion model with all of its requisite inertia terms. The work is targeted at the problem of modeling flight vehicles, whether aircraft or spacecraft, but is applicable to any large-motion flexible structure modeling application. This methodology promotes a “model it as it is” approach that bypasses the traditional practice of structural simplification to beams or other degenerate structural forms when an unconstrained flexible model is required. The sample problem used to demonstrate the modeling methodology employs the SPHADS-1 rocket vehicle developed at Ryerson University. A source finite-element model of the vehicle was developed in ANSYS and the assembled mass and stiffness matrices exported. The LISA Method was applied to produce a full-detail, full-order large-motion model. To render it more amenable to simulation, a reduced-order dynamics model was then derived from the full-order model. The application of this model in a limited study of aeroelastic stability is described briefly.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".