Practical Large-Motion Modeling of Geometrically Complex Flexible Vehicles
Bibliographic record
Abstract
This paper introduces a powerful convenience method for automatically building largemotion models of flexible bodies of general structural form (not just beams). The starting point is the assembled standard mass matrix of a small-motion finite-element model. The goal point is a fully-coupled, all-terms-included large motion model that incorporates the same arbitrarily detailed structural geometry as the source finite-element model. An explicit straightforward procedure is provided for constructing both full and reduced-order models of flexible bodies that are able to undergo arbitrary displacement and rotation. The body translational and angular velocities are allowed to be large. Within the context of small deformations, all terms of the equations of motion are included. A standard finite-element small (absolute) motion model is used as the base model from which the equations of motion of the unrestrained body are derived. A fully consistent inertia formulation is assumed and encouraged; rotational coordinates are naturally admitted; and no low-level knowledge of the finite-element nodal shape functions is required. Lumped-mass formulations are included as a special sub-class. Automatic generic approximation is used for certain shape function products that cannot be directly extracted from the mass matrix of the small motion model. The critical strategy used to produce the approximations is called the “LISA Method” (Localized Implicit Shape-Function Approximation). The LISA Method has been demonstrated to reproduce exact results for a number of practical element types.
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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".