Subcycled Hourglass Control for Explicit Time Integration of Dynamic Relaxation Equations
Bibliographic record
Abstract
Explicit methods, such as the central difference operator, rely on the economical evaluation of internal forces at each time step of a transient dynamic problem. One-point quadrature applied to the spatial discretization provides the greatest efficiency, but hourglass control is required to eliminate spurious zero energy modes. Computationally practical hourglass control methods involve considerable approximation in evaluating the internal force. Thus, a small additional approximation due to an alternative temporal integration of the hourglass force may not seriously affect the accuracy of the analysis. In particular, the possibility of evaluating the hourglass terms on a larger time interval than the usual stable time step could provide significant efficiencies. The proposed approach of subcycling the hourglass terms is examined in detail with respect to stability and accuracy. Implementation into an explicit finite element program is demonstrated on a three-dimensional example that involves several hourglass modes, and the new method proves to be beneficial for noninertial problems where artificial damping is used.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".