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.
A Cross-sectional Shape Initial Design Method for Automotive Frame Structure Using Genetic Algorithms
