Active Control of Sound Radiation Into Enclosures Using Structural Error Sensors
Bibliographic record
Abstract
Active control of vibration and sound inside a structure-surrounded enclosure leads to many applications such as noise control inside vehicle cabins. Despite the extensive research carried out in the last two decades, ANVC technology is still in its infancy and has not yet been introduced massively in practical engineering applications. One of the problems to be resolved is that most of presently used techniques require the use of microphones inside the cavity, which is not practical in many situations. In addition, due to the coupling between the vibrating structure and the confined enclosure, demand for more robust control strategy is apparent. This paper tackles the aforementioned problem using a benchmark system in which only PVDF (Polymer polyvinylidene fluoride) sensors are used on the structural surface. A new method based on genetic algorithms is developed for sensor design. This design process ensures a proper consideration of the acoustic energy in the enclosure without the direct use of acoustic sensors inside the cavity. Roughly speaking, the sensor is designed to capture the most radiating motion of the structure via an automatic optimization process. In the proposed method, Genetic Algorithms and the least quadratic square optimal theory are organically combined together. For each configuration of error sensors, the amplitude of control forces, which can either be point forces or excitation generated by piezoceramic actuators, is first determined by minimizing the sum of the squared outputs of error sensors using the least quadratic square optimal theory. Then with the optimal amplitude of control forces, the acoustic potential energy of the sound cavity is computed and used as the evaluation criteria in the evolution process. Using Genetic Algorithms, the optimal configuration of the error sensors can be determined. A cylindrical shell with an internal floor partition is used as an example to illustrate the effectiveness of the proposed approach. To increase the computational efficiency, the structural surface is assumed to be covered with strip-typed PVDF sensors along both the circumferential and longitudinal directions. Both numerical and experimental results show the great effectiveness of the proposed GA-based design method. The sound reduction is achieved not only at the design frequency but also at most frequencies in the low frequency range. The proposed method demonstrates great merits in sensor design for complex structures.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".