Coupled finite element modeling of piezothermoelastic materials
Bibliographic record
Abstract
The governing equations of piezo-thermoelastic materials show full coupling between mechanical, electric, and temperature fields. It is often assumed in the literature that in high-frequency oscillations, the coupling between the temperature and mechanical displacement and electric field is small and, therefore, can be neglected. A solution for the temperature field is then determined from an uncoupled equation. A finite element (FE) model that accounts for full coupling between the mechanical, electric, and thermal fields, nonlinear constitutive behavior and heat generation resulting from dielectric losses under alternating driving fields is under development. This paper presents a linear fully coupled model as an early development of the fully coupled nonlinear FE model. In the linear model, a solution for all field variables is obtained simultaneously and compared with the uncoupled solution. The finite element model is based on the weighted-residual principle and uses 2-D four-node isoparametric finite elements with four degrees of freedom per node. A thin piezoelectric square disk is modeled to obtain some preliminary understanding of the coupled fields in a piezoelectric stack actuator.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".