Synthetic turf finite element model development and validation
Bibliographic record
Abstract
Assessment of synthetic turf performance has been undertaken using a variety of experimental methods but is limited in understanding the complex physics of cleat-turf interaction. Computational models could provide insight, but there is currently no validated model of synthetic turf available. The scope of this study was to develop a Finite Element (FE) model of synthetic turf using a hierarchical approach and validate the model using independent test data. A physical third-generation synthetic turf comprising slit-film fibers with sand and rubber crumb infill was constructed. Experiments were conducted using a direct impact (Clegg) device and an artificial cleat-form. Material characterization tests were performed on the individual turf components, integrated into constitutive models, and a full turf FE model was constructed. The carpet was modeled with shell elements while the granular infill was modeled using smoothed-particle hydrodynamics (SPH) elements. A method to simulate the physical pre-conditioning performed on the experimental turf was developed. Both unconditioned and pre-conditioned turfs were assessed using Clegg tests. The re-created Clegg tests on the turf model demonstrated good agreement with the physical tests, with higher acceleration for the pre-conditioned turf. The turf model was validated using experiments with a turf test apparatus including dynamic translation and rotation of a cleat-form. The model predicted results in good agreement with the experiments (average CORA rating of 0.863) on pre-conditioned turf. The resulting model and methods can be expanded to synthetic turf of different constituent materials, to investigate their effects on cleat-surface interaction, optimizing performance, and reducing injury risk.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".