Model-Based Co-Simulation of Flexible Mechanical Systems With Contacts Using Reduced Interface Models
Bibliographic record
Abstract
Co-simulation is a useful approach in the modelling of robotic systems composed of multiple parts. In co-simulation, the subsystems only exchange information at communication points. The time delay of information exchange may cause error and instability. Thus, an appropriate way to determine the interface variables between the communication points is essential for efficient and stable performance, especially for real-time applications. Reduced interface models (RIMs) can be used to represent the dynamic behaviour of the subsystems at the interface in co-simulation. Such a model-based co-simulation scheme was limited to systems consisting of rigid bodies in previous studies. In this work, we introduce the formulation of RIMs for flexible multibody systems and based on that propose a general co-simulation scheme for systems consisting of both rigid body components and elements with structural flexibility. A robotic model is employed as an example to demonstrate the co-simulation scheme, where a non-smooth subsystem with contact interactions is present. The advantages of constructing RIM using flexible mechanical system models over rigid body models are also addressed by comparing the effective mass properties and the simulation results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".