Modeling and application of non-uniform engineering structures coupled with FGM and piezoelectric materials in stability enhancement and energy harvesting
Bibliographic record
Abstract
Dynamic analysis of non-uniform beams with tapered geometry and functionally graded material properties to achieve a better design for stability enhancement and energy harvesting applications is the main interest and focus of this thesis. A powerful and reliable theoretical model to derive the vibration response of nonlinearly tapered beams with axially functionally graded material properties within the framework of classical Euler–Bernoulli beam theory is developed and presented. The effect of geometry and material properties variation for different nonlinear profiles is comprehensively studied. It is demonstrated that piezoelectric layers and their coupling effect in addition to the non-uniform geometry, significantly enhance the stability of the smart non-uniform beam. The effects of compressive follower force, geometry taper ratio, boundary condition, and external piezoelectric voltage on flutter and buckling capacities of the non-uniform beam are examined. In addition, the model is also employed to present an analytical approach for the development of a non-uniform piezoelectric energy harvester. It is applied to surface bonded piezoelectric beams with non-uniform geometry and material variation profiles to derive the dynamic response of the structure to external environmental excitations and efficiently harvest the subsequent mechanical vibration energy. It is proved that the non-uniform configuration improves the electromechanical outputs. Additionally, an array of non-uniform harvesters is deployed to design a wideband piezoelectric energy harvesting system. It is shown that with the proposed formation, the system can optimally function over a wide frequency domain. Lastly, two new energy harvester configurations by using piezoelectric stacks are analytically developed. By benefiting from in-plane piezoelectric polarization, electrical outputs compared to a conventional harvester are considerably improved. At the end, an initial optimization model by using simulation-based optimization technique and machine learning algorithms is presented.
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 | 0.000 | 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 | 0.000 |
| 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".