Electromagnetic acoustic transducer analysis by the finite element method
Bibliographic record
Abstract
Electromagnetic acoustic transducers (EMATs) are non-contact, non-destructive testing devices for conducting specimens. The efficiency of EMATs is inherently low and methods for increasing it have been the subject of intense research. In publications, the finite element (FE) technique has been applied to the diffusion equation in order to model EMATs. However, electromagnetic theory indicates that the diffusion equation must be solved for the magnetic vector potential (MVP) in conjunction with an equation for the total current. An existing FE formulation considering the two equations becomes unstable when modeling transient inputs in EMAT transmitters. The limitation posed by the instability prompted the development of an improved FE formulation, which solves the two equations and is suitable for the modeling of EMATs. The objective of the research work described in this thesis is to properly and accurately model EMATs, so that their efficiency could be improved at the design stage. The thesis describes the governing equations of EMATs, improved techniques for their solution, and the application of these techniques to the analysis of such devices. The improved techniques are used for modeling EMATs for both non-ferromagnetic and ferromagnetic specimens. The following aspects are developed: (a) Derivation of various types of forces and current densities in EMATs. (b) Computation of the MVP in EMAT transmitters by an improved FE formulation. (c) Accurate computation of the induced voltage in EMAT receivers by an improved method. (d) Derivation of analytical solutions to validate the FE solutions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.011 | 0.004 |
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".