A Procedure for Modeling Multibody Systems Using Subsystem Models
Bibliographic record
Abstract
With the increasing use of microprocessors to control multibody systems, the inclusion of both analogue and digital electronic components in multibody formulations has become one of the challenges facing the multibody community. Models of mechanical systems that incorporate these types of components are referred to as "mechatronic" systems, while multibody systems incorporating only analogue components are dubbed "electromechanical" systems. Traditional approaches to modeling such systems can be very time-intensive and result in extremely complex equations. The following article proposes a method for efficiently generating the governing symbolic equations for an electromechanical multibody system. The key to the proposed approach lies in exploiting the topology of a given system by applying subsystems derived using a newly developed extension to linear graph theory. Exploiting the topology in this manner accommodates parallel formulation strategies and helps to clarify and organize the system level models, thereby increasing the efficiency of the modeling process and subsequent numerical simulations. In addition, because the subsystem models are developed using a linear graph formulation, it is shown that they naturally combine with graph models of electrical subsystems to model electromechanical systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.010 | 0.003 |
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".