An Implicit Cycle Scale Integrator For Accelerated Fatigue Simulations
Bibliographic record
Abstract
The properties of polycrystalline materials are strongly influenced by the underlying microstructural features such as grain size, orientation, grain boundaries and crystal defects. Various methods such as Discrete Dislocation Dynamics (DDD), Crystal Plasticity Finite Element Method (CPFEM), Crystal Plasticity Fast Fourier Transform (CPFFT), etc. are used to simulate the mechanical behaviour of polycrystalline materials at the microstructural level; and numerous examples can be found in the literature which show the fidelity of these methods to capture microstructure sensitive grain-level phenomena in polycrystalline materials in a computationally efficient manner for problems with monotonic loading. However, very limited attempts can be seen for problems involving cyclic loading. This paper presents a comprehensive study using CPFEM to predict crack nucleation in polycrystalline materials under high cycle fatigue. However, due to the large number of cycles till crack nucleation, such analysis can be computationally exhaustive. So, an implicit multi-time scale method involving cyclic scale integration is used in this study. The implicit method used in this study has an advantage over other conventional techniques which assumes periodicity and can provide incorrect response due to the strong non-periodic temporal evolution of plastic variables and localization in the spatial domain. In this method, first fine scale cyclic integration is done for few number of cycles, which is then followed by coarse scale cyclic integration resulting in significant acceleration. A cubic polycrystalline domain is simulated using the method to demonstrate its accuracy and efficiency.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 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".