Stability Analysis of Real‐Time Hybrid Simulation with an Inerter‐Type Experimental Substructure
Bibliographic record
Abstract
ABSTRACT Although integrating inerters with conventional passive vibration control systems has shown enhanced performance in various studies, experimental investigations remain limited. Real‐time hybrid simulation (RTHS) models the well‐understood portion of a structure as the numerical substructure (NS) while physically testing the structural component of interest as the experimental substructure (ES). Testing the inerter as the ES in RTHS is considered cost‐effective and less facility demanding. However, RTHS experiences time delay induced by actuator dynamics, risking instability if not managed effectively. Consequently, stability analysis is crucial for the successful implementation of RTHS. Previous studies primarily focused on RTHS stability, including a stiffness‐type ES. The RTHS stability with an inerter‐type ES, characterized by a large mass ratio relative to the NS, remains underexplored. To address this gap, this study analyzes the RTHS stability, including an inerter‐type ES, implemented through various direct integration algorithms. Augmented state‐space equations are employed to solve the roots of the discrete RTHS system considering different values of time delay. Virtual RTHSs are performed to validate the analytical investigation. The time delay is found to increase the order of the discrete RTHS system, yielding more spurious roots. Moreover, the time delay in RTHS with a stiffness‐type ES primarily increases the magnitude of principal roots, whereas in RTHS with an inerter‐type ES, it mainly amplifies the magnitude of spurious roots, potentially inducing instability. Both analytical and simulation results show that the spurious root‐induced instability can be effectively mitigated by numerical damping.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".