Experimental Validation of Nonlinear Compliant Contact Force Models
Bibliographic record
Abstract
Contact dynamics modeling continues to be an intensive research area with new applications of contact dynamics simulation arising in engineering practice. One approach to normal contact force modeling that has gained significant popularity is the compliant model in which the contact force between two objects is defined explicitly as a function of local deformation and its rate. Probably the most well-known model in this category is the Hunt and Crossley model, which employs a nonlinear damping term to model the energy dissipation during contact, with the damping coefficient related to the coefficient of restitution. This model prompted several investigations on how to evaluate the damping coefficient, in turn resulting in several variations on the original Hunt-Crossley model. In this paper, the authors aim to experimentally validate the Hunt-Crossley type of nonlinear contact force models and furthermore, to compare the experimental results to the model predictions obtained with different values of the damping coefficient. The paper reports our findings from the sphere to plate impact experiments, conducted for a range of initial impacting velocities, with measurements of impact forces and accelerations. The experimental forces are compared to those predicted from the contact dynamics simulation of the experimental scenario. The experiments, in addition to generating novel impact measurements, provide a number of insights into both the study of impact and the impact response.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".