Nonlocal strain gradient modeling of vibration energy harvesting in fluid-immersed bimorph sandwich nanoplates under thermal environment
Bibliographic record
Abstract
This research details a method for mathematically simulating and assessing thermal vibration energy harvesting in laminated bimorph nanoplates in fluid contact. The model uses the piezoelectric characteristics of the outer layers and the functionally graded (FG) core material to transform thermal stresses into electrical energy efficiently. Nanostructures' size effects and nonclassical behavior are captured by the nonlocal strain gradient theory (NSGT). Combining the Navier–Stokes equations with the electromechanical equations obtained from Hamilton's principle, first-order shear deformation theory (FSDT), and Gauss's law yields an advanced multi-physics model. The FG core exhibits variations by the power law principle and is composed of both ceramic and metal components. Analytical solutions are obtained for the frequency response functions that relate the electrical power output to the external circuit load resistance by solving the coupled electromechanical-fluid equations. A thorough investigation is conducted to analyze how different elements impact energy harvesting performance using parametric studies. These factors include the configuration of the harvester (either parallel or series piezoelectric connections), nonlocal and strain gradient effects, temperature gradients, fluid depth, electrical load, geometric dimensions, and the material properties of the piezoelectric layers, and functionally graded core.
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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".