Identification of higher-order elastic constants for grain assemblies based upon granular micromechanics
Bibliographic record
Abstract
Macroscale behavior of granular media is characterized by the significant effects of grain-pair interactions and the microstructure of each grain neighborhood.From a continuum viewpoint, granular materials may be modeled as micromorphic media to account for their complex grain-scale (microscale) kinematics.To this end we express the grain displacement in terms of the neighboring grain displacements utilizing the Taylor series expansion.The introduced gradients in the Taylor series are identified in terms of the macroscale deformation measures introduced in microstructural elasticity and micromorphic mechanics.As a result, a continuum model of the granular media is derived enriched by nonclassical terms, including terms that model grain displacement fluctuations and higher gradients of displacements.In the derived model, the continuum stiffness tensors are obtained in terms of grain-pair stiffness coefficients and fabric parameters defining the geometry of grains and their contacts.To identify the elastic constants of the enhanced continuum model, we perform numerical experiments on grain assemblies using discrete simulations subjected to relevant boundary conditions.The need for additional macroscale deformation measures for the continuum modeling of granular materials becomes evident in this identification process.The obtained elastic constants are then used to determine the microscale (or grain-pair) stiffness coefficients applicable to the continuum model.These grain-scale stiffness coefficients are found to be affected by the heterogeneity of microstructure.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".