Modeling a Galfenol based stress sensor capable of sensing up to three axial stresses
Bibliographic record
Abstract
A three dimensional rate equation model can be used to calculate the magnetization response in a Galfenol sample under the application of any or all components of stress (axial and shear) [P. Weetman and G. Akhras, J. Appl. Phys. 109, 043902 (2011)]. For a Galfenol based stress sensor, one is essentially interested in the inverse of that calculation: from magnetization measurements, determine which stresses are acting on the system. A conceptual design of a Galfenol based three dimensional dynamical sensor is presented. One assumes the time-varying magnetization and its time derivative in all three directions can be measured for different external magnetic bias fields at different points in time. It is shown that the rate equation model can be used to calculate all the stresses acting on the system from knowledge of the magnetization and the time derivative of magnetization. The necessary calculations are presented and then applied to a sample set of magnetization values, which were generated from a benchmarked sensing model that used up to three axial stresses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 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".