Reaction Efforts Associated with Non-Holonomic and Rheonomic Constraints in Index-3 Augmented Lagrangian Formulations
Bibliographic record
Abstract
Index-3 augmented Lagrangian formulations with projections of velocities and accelerations represent an efficient an d robust method to carry out the forward-dynamics simulation of multibody systems. They are currently used in a wide variety of applications, ranging from biomechanics to heavy machinery simulators. Existing formalisms, however, are only able to deal with kinematic constraints whose expression at the position level is known. When this expression is not available, e.g. when non-holonomic constraints enter the picture, the constraint reaction forces yielded by these alg orithms are not correct anymore because they are obtained as a function of the violation of the constraints at position level alon e. In this work, a method to determine the constraint reaction forces from the expression of the projection of velocities and accelerations is introduced. The method was tested in the forward-dynamics simulation of a set of simple examples. Results showed that the proposed strategy can be used to expand the capabilities of the index-3 augmented Lagrangian algorithms, making them able to tackle kinematic constraints defined at the velocity level and provide the correct react ion efforts when non-holonomic constraints are used to model a mechanical system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".