Design of sustainable energy harvesters using a 3D car suspension system model and half-width speed bump
Bibliographic record
Abstract
This study aims to not only design a vibration energy harvester integrated in a three-dimensional (3D) multi-particle system but also investigate the longitudinal and lateral body sway and the effect of front and back wheel interlocking caused by the eccentric loading of the vehicle. Using multidimensional vehicle suspension system vibration modes for analysis, this study evaluates the impact of lateral and longitudinal eccentricity on the displacement of the four-suspension system wheel axes. In addition, this study discusses the effect of discontinuous half-width sinusoidal speed bump on power generation and maximum power generation capacity under good ride comfort conditions. To maximize the efficient electrical power from the car's suspension system through the use of energy harvesters, we have employed the simulated annealing algorithm, a robust global optimization technique. The objective function chosen for this optimization is the root-mean-square (RMS) electrical power, represented as WTT(RMS). In addition to this, we have considered the comfort experienced by passengers, incorporating the ride comfort efficiency into the objective function during the optimization process. As a result of this optimization, a 3D car model equipped with four energy harvesters has been fine-tuned to achieve a significant increase in induced electrical power, ultimately reaching an impressive 0.2 W.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".