Thermopiezoelectric Response of a One-Dimensional Functionally Graded Piezoelectric Medium to a Moving Heat Source - A Review
Bibliographic record
Abstract
The multi-physics of piezoelectric materials under different environmental conditions has been an active research subject for a few decades. Particularly, the thermoelastic behaviour of smart materials and structures is of great importance to their reliability in different applications. Traditionally, the Fourier heat conduction theory was introduced in dealing with the thermoelastic reactions of smart materials and structures. This may lead to reasonable analyses and useful guidelines in design of smart structures, especially when no severe thermal gradient is involved. However, when a severe thermal gradient is indeed involved in the service environment of a smart structure, the analysing results based on the Fourier heat conduction theory is unrealistic and usually rendered useless. Non-Fourier heat conduction theories have been introduced in the thermoelastic analysis of smart materials and structures in recent years and resulted in reasonable results. In this paper, we review the recent results of a thermopiezoelectric problem of a one-dimensional (1-D), finite length, functionally graded medium excited by a moving heat source using both the Fourier and Non-Fourier heat conduction theories. Numerical examples are displayed to illustrate the effects of non-homogeneity index, length and thermal relaxation time on the results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".