Interface Models for Multirate Co-Simulation of Nonsmooth Multibody Systems
Bibliographic record
Abstract
Abstract Co-simulation techniques enable the coupling of physically diverse subsystems in an efficient and modular way. Complex engineering applications can be simulated in co-simulation setups, in which each subsystem is solved and integrated using numerical methods tailored to its physical behaviour. Co-simulation implies that the communication between subsystems takes place at discrete-time instants and is limited to a given set of coupling variables, while the internals of each subsystem are generally not accessible to the rest of the simulation environment. In non-iterative co-simulation schemes, this may lead to the instability of the integration. Increasingly demanding requirements in the simulation of machinery have led to the coupling, in real-time co-simulation setups, of multibody models of mechanical systems to computational representations of non-mechanical subsystems, such as hydraulics and electronics. Often, these feature faster dynamics than their mechanical counterparts, which leads to the use of multirate integration in non-iterative co-simulation environments. The stability of the integration in these cases can be enhanced using interface models, i.e., reduced representations of the multibody system, to provide meaningful input values to faster subsystems between communication points. This work describes such interface models that can be used to represent nonsmooth mechanical systems subjected to unilateral contact and friction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".