Bibliographic record
Abstract
Contact dynamics modeling remains an intensive area of research with new applications emerging in robotics, biomechanics and multibody dynamics areas. Many formulations for contact dynamics problem have been proposed. The two most prominent categories include the discrete approach, which employs the impulse-momentum relations, and the continuous approach, which requires integration of dynamics equations through the contact phase. A number of methods in the latter category are based on an explicit compliant model for the contact force. One such model was developed by Hunt and Crossley three decades ago who introduced a nonlinear damping term of the form λxnx˙ into the contact force model. In addition to proposing the general form of this damping component of the contact force, Hunt and Crossley derived a simple expression for relating the damping coefficient λ to the coefficient of restitution e. This model gained considerable popularity due to its simplicity and realistic physics. It also spurred new research in the area, specifically on how to evaluate the damping coefficient λ. Subsequently, several authors put forward different approximations for λ, however, without clearly revealing the range of validity of their simplifying assumptions or the accuracy limitations of the resulting contact force models. The authors of this paper analyze the various approaches employed to derive the damping coefficient. We also evaluate and compare performance of the corresponding models by using a meaningful measure for their accuracy. A new derivation is proposed to calculate more precisely the damping coefficient for the nonlinear complaint contact model. Numerical results comparing all models are presented for a sphere dropping on a stationary surface.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".