Design, modeling, and simulation of a novel transducer for vibration energy recovery system of speed bump
Bibliographic record
Abstract
Based on the designed vibration energy recovery system of speed bump, the structure and principle of a novel transducer are introduced in this paper, which is an important part of the system. AMEsim simulation software was used to study the characteristics of the transducer, and the feasibility of the transducer design was verified through modeling and analysis of the dual-cylinder transducer. The coupling model of vehicle speed bump and transducer was analyzed, the dynamic differential equation was listed, and the decoupling operation was carried out. On the basis of the above, Matlab/Simulink was used to establish the model simulation, and the time-domain responses of displacement, velocity, and acceleration of the vibration of the car body, wheel, and transducer in the coupling model system were analyzed. The control variable method was used to further explore the time-domain response of vehicle acceleration in the coupling system only when the vehicle speed changes or the transducer damping changes. Finally, the vibration energy recovery system of the speed bump was built, and the preliminary test proved that the transducer can realize its function.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".