Energy-based quasi-static modeling of the actuation and sensing behavior of single-crystal iron-gallium alloys
Bibliographic record
Abstract
An energy based model [W. D. Armstrong, J. Appl. Phys. 81, 2321 (1997); J. Atulasimha, Ph.D. thesis, Aerospace Engineering, University of Maryland, College Park, MD, 2006] is employed to predict the actuation (λ−H and B−H at various compressive stresses) and sensing behavior (B−σ and ε−σ at various bias fields) of single-crystal FeGa alloys. The significant feature of this formulation is that, in addition to modeling actuation behavior, the sensing behavior can be predicted based on parameters estimated from the actuator characteristics. These predictions are then validated against experimental data for furnace cooled 19 at. % [100] oriented single-crystal FeGa alloys. Furthermore, an attempt is made to couple the energy-based sensing model with a lumped-parameter model that simulates the magnetic interaction between the magnetostrictive specimen and the magnetic circuit comprising the transducer. This enables a prediction of the variation in field through the sample due to changes in reluctance of the magnetostrictive sample with stress, as well as the impact of this variation in field on the B−σ and ε−σ curves. These predictions are benchmarked against experimental data, wherein the bias field varies due to change in sample reluctance with application of compressive stress while the drive current to the transducer is maintained constant.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".