Numerical Analysis of Fretting-Wear With a Hybrid Elastoplastic Friction Model
Bibliographic record
Abstract
Fretting-wear is a common problem in different industries, especially when it comes to interactions between metallic components. Flow-induced excitation forces in heat exchangers for instance cause tube-support interactions. The long-term interaction is an important phenomenon, which may cause fretting-wear of the tubes. Experimental tests of the interaction show the occurrence of stick–slip intermittent behavior in the tube response. To precisely simulate the intermittent stick–slip behavior, it is crucial to refine the conceptual model of the coefficient of friction for the entire motion from absolute zero velocity to gross slip phase. The incorporated friction model plays an important role in the determination of the level of fretting-wear in the system. The friction model should satisfy two important criteria. The first important aspect is the strategy of the friction model to detect the cessation of sticking, the beginning of partial-slipping, and establishment of the sliding region. The second important aspect is defining a friction coefficient function for the entire system response to precisely represent the transient stick–slip regions. In the present work, the velocity-limited friction model was compared with the LuGre model, which is a rate-dependent friction model. The effect of varying the break-away force and Stribeck effect on the stick–slip region were also investigated. Furthermore, the criteria to demarcate the stick–slip region in the LuGre model are discussed, and a different method to incorporate the Stribeck effect and presliding damping in the Dahl friction model is proposed. Using the tangential stress distribution in the contact area, a new hybrid spring-damper friction model is developed. The model is able to estimate the elastic, plastic, and partial-slipping distances during the relative motion. The ability of the model to reproduce experimental tests is investigated in the present work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".