Bibliographic record
Abstract
Abstract The friction model is a crucial element in the detailed analysis of the dynamics of mechanical systems across various industries. Currently, the Coulomb friction model is commonly employed to simulate tube-support interactions in steam generators within the nuclear industry. However, this model is very basic and cannot capture all aspects of friction behavior, particularly the transition from static friction to dynamic friction. In previous work, the parameters for different friction models were identified and validated using FEM (Finite Element Method). Several issues were identified, such as the discontinuity in the Stribeck or decay friction models. In this paper, we analyze this discontinuity and propose improvements by introducing a new ’ Continuous Decay/Stribeck Friction Model’ model. The identification of model parameters, their sensitivity, and their effects on the frictional behavior and dynamic response of the system are also discussed. Additionally, the limitations of this model are addressed. The experimental results presented in the works of Baumberger et al. are used for model testing and validation. Using the experimental data, the parameter identification for the Stribeck and decay friction models, as well as the new proposed friction model is done.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".