The effects of measurement errors in the restoring force feedback during real-time hybrid simulations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Real-time hybrid simulation (RTHS) is a practical and economical experimental technique that integrates physical testing with computer simulation.In this method by dividing the structure into two parts, known as the experimental and analytical substructures, and synchronizing them, the equations of motion are solved in real-time.Thus, RTHS can capture the load-rate dependencies in an accurate manner.The implementation of RTHS involves challenges in accurate control of experimental substructure, execution of the testing algorithms in realtime as well as the synchronization of signals.One of these challenges is the measurement errors in restoring force feedback resulting from the random electrical noise that is usually inevitable in these testing platforms.Since the measured restoring force is used in command generation, RTHS suffers from error propagation affecting the accuracy and in some cases the stability of the simulation results.In this paper, using a recently developed user-reconfigurable computational/control platform at the University of Toronto, the effects of force feedback errors on the RTHS results will be investigated considering a wide range of experimental to analytical stiffness ratios.The accuracy of the RTHS results will be assessed using tracking indicators that reveal the phase and amplitude errors between the RTHS results and exact numerical solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it