Optimum design of a non-conventional multiple tuned mass damper for a complex power plant structure
Bibliographic record
Abstract
This paper implemented an optimal design of non-conventional multiple tuned mass damper (NC-MTMD) system for coal scuttles in power plant. The NC-MTMD was introduced using base isolation for the coal scuttles. Mathematical models of the NC-MTMD were derived by considering multiple three-dimensional (3D) modes. Base seismic excitation is represented by 3D Kanai-Tajimi filtered white noise. Properties of the base isolation (stiffness, damping) were calibrated through global optimisation using base shear and torsion as the objective function. Derivative (e.g. Global Search, Multi Start), or derivative free (e.g. Pattern Search, Genetic Algorithm and Simulated Annealing) algorithms were considered. Pattern Search algorithm showed better convergence, efficiency and stability performance. A comparative study was performed for single- and multi-mode cases. Robustness of the optimum design was investigated quantitatively. Design parameter variation and mass variation (due to different levels of coal storages) were considered and their influences were studied.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".