Parameter optimization of fractional Maxwell viscoelastic system based on RSM model
Bibliographic record
Abstract
Viscoelastic materials have been successfully used in shock absorbers of engineered vehicles because of their low cost and good cushioning effect. Aiming at the conundrums of excessive computational complexity and difficult parameter optimization of fractional order viscoelastic models, the response surface agent model is introduced to replace the displacement response of the Maxwell fractional order viscoelastic oscillator with fluid characteristics. Firstly, a fractional-order Maxwell viscoelastic model is established by integrating fractional order modeling with a viscoelastic oscillator. Secondly, the Latin hypercube method is used to collect samples, and the natural frequency, damping ratio, and order of the model are selected as design variables to build a secondary response surface proxy model. Finally, the displacement response of the model and the hysteresis area of the constitutive model are taken as the optimization objectives for multi-objective optimization. The results show that the displacement response decreases by approximately 32.6% and the hysteresis area increases by about 25% after the quadratic response surface proxy model is used to proxy the fractional order model and the parameters are optimized.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".