Modeling and Simulation for PVDF-based Pyroelectric Energy Harvester
Bibliographic record
Abstract
Energy harvesting technology allows the capturing of unused ambient energy such as solar, wind, thermal, strain and kinetic, energy of gas and liquid flows which is then converted into another form of usable energy. This paper focuses on the thermal-electrical energy harvesting based on pyroelectric effect. Pyroelectric materials generate a voltage, when subjected temperature variation. The pyroelectric polyvinylidene fluoride (PVDF) films were fabricated and characterized for pyroelectric and dielectric parameters. Using the foregoing parameters, the energy-harvesting capacity has been theoretically explored by capturing thermal energy available in the environment of Huntsville (pavement), Saudi Arabia (ambient) and MARS (ambient). The predicted maximum cumulative voltage by the end of a 300 hours cycle is approximately 0.13, 0.7 and 7.7 volts for Huntsville and Saudi Arabia and MARS, respectively for the PVDF based 10 cm2 pyro-elements. The results indicate that the electrical energy harvesting via pyroelectricity holds promise for powering autonomous low-duty electric devices. Furthermore, the mathematical modeling and numerical simulations can be helpful in designing of pyroelectric micro-power generators.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".