On the identification and interpretation of large amplitude oscillatory compression (LAOC) loadings
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-2124.vid Smart rheological materials, such as magneto-rheological elastomers (MREs), show highly nonlinear hysteresis response (e.g., non-elliptical stress-strain curve) at large deformation. Unlike the shear mode of operation, in which the stress-strain response is symmetric with respect to the direction of shear/shear rate loadings, the compression response becomes asymmetric, particularly at large deformation. Thus, the materials possess different elastic and viscous characteristics at the end of loading and unloading cycles, which may not be simply represented by the single linear elastic storage and loss moduli. The aim of this study is to characterize MREs under the LAOC regime. The inter-and intra-cycle stress-strain nonlinearities such as strain softening, and strain stiffening features were qualitatively and quantitively analyzed. A methodology on the basis of Fourier Transform (FT) rheology was introduced that permits the identification and interpretation of stress-strain nonlinearities at the LAOC regime. Results show that the ratio of even harmonics (I_2/I_1, I_4/I_1, I_6/I_1, and I_8/I_1) increased as the strain amplitude increases from 10% to 20%. This is consistently observed at all the other loading conditions considered. This can quantify the local inter-cycle strain stiffening phenomenon, which is noticeable only in qualitatively manner in hysteresis stress-strain responses. The local inter-cycle strain stiffening cannot be detected from first harmonic analysis. Results also suggest that the intra-cycle strain stiffening phenomenon can be interpreted by greater value of the I_2/I_1 harmonic as compared with the I_3/I_1 harmonic at each strain amplitude.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".