Implementation, Verification and Application of Real-time Hybrid Simulation
Bibliographic record
Abstract
This dissertation presents the implementation, verification and application of a state of the art dynamic testing method, namely the real-time hybrid simulation (RTHS) technique, using two cost-effective and reliable computational/control platforms. RTHS is a practical and economical experimental technique that complements the realism of physical testing with the power of numerical 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 capturing the load-rate dependencies in an accurate manner. \nIn the first part of the dissertation, the RTHS method is implemented using a small-scale shaker available for research and education. The implementation of the method is followed by the verification experiments to assure the reliability of the RTHS platform. As an application example, this platform is utilized to investigate the dynamic behavior of structures equipped with tuned liquid dampers (TLDs) in small scale.\nThe second part of the dissertation presents a user re-configurable computational/control platform developed to conduct RTHS. The architecture of this platform is based upon the integration of a real-time controller and a field programmable gate array (FPGA). This not only enables the user to apply user-defined control laws to control the experimental substructures, but also provides ample computational resources to run the integration and analytical substructure state determination algorithms in real-time. After the presentation of the hardware design and software development details, the platform is validated experimentally. The effect of force measurement errors on the RTHS results is the next topic discussed in this dissertation. An application example of the developed platform that investigates the performance of different structural systems equipped with large-scale TLDs is presented. Additionally, further developments on the inner loop control as well as outer loop computations are accomplished in collaboration with other researchers.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".