A Component-Based Parametric Reduced-Order Modeling Technique and Its Application to Probabilistic Vibration Analysis and Design Optimization
Bibliographic record
Abstract
In this paper, a component-based parametric reduced-order modeling (PROM) technique for vibration analysis of complex structures is presented, and applications to both structural design optimization and uncertainty analysis are shown. In structural design optimization, design parameters are allowed to vary in the feasible design space. In probabilistic analysis, selected model parameters are assumed to have predefined probability distributions. For both cases, each realization corresponding to a specific set of parameter values could be evaluated accurately based on the exact modes for the system with those parametric values. However, as the number of realizations increases, this approach becomes prohibitively expensive, especially for largescale finite element models. Recently, a PROM method that employs a fixed projection basis was introduced to avoid the eigenanalysis for each variation while retaining good accuracy. The fixed basis is comprised of a combination of selected mode sets of the full model calculated at only a few sampling points in the parameter space. However, the preparation for the basis may still be cumbersome, and the simulation cost and the model size increase rapidly as the number of parameters increases. In this work, a component-based approach is taken to improve the efficiency and effectiveness of the PROM technique. In particular, a component mode synthesis method is employed so that the parameter changes are captured at the substructure level and the analysis procedure is accelerated. Numerical results are presented for two example problems, a design optimization of a pickup truck and a probabilistic analysis of a simple L-shaped plate. It is shown that the new component-based approach significantly improves the efficiency of the PROM technique.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".