Automotive Glass Exciter Technology for Acoustic Application
Bibliographic record
Abstract
This paper discusses the modeling and analysis of a novel audio subwoofer system for automotive applications using the automobile windshield glass. The use of a piezo-electric actuator coupled with a mechanical amplifier linked to a large glass panel provides a highly efficient method of producing sound. The proposed subwoofer system has the advantage over existing conventional systems of not only reducing the weight of the automobile, but also a significant power savings resulting in an increase of expected fuel economy. Among various design challenges, the glass-sealing design is of huge importance, as it affects the system dynamic response and so the output sound characteristics. The main goal in this manuscript is to evaluate different glass-sealing design configurations by providing a comprehensive Finite Element model of the system. To do so, a comprehensive, yet simplified FE model is developed, and experimental studies are performed in the component level to fine-tune and verify the model. Harmonic response of the system for each sealing configuration design is obtained in the frequency range of 0–200 Hz, and the results are compared and discussed. The finite element model is also beneficial in preliminary design of other components as well as the exciter placement, and predicting the performance of the overall system.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".