摘要
为了降低车室低频噪声,采用对声学贡献较大的车室地板、后地板、前围板、顶棚、前车门内板及后车门内板的厚度参数为因子,以车身质量、车身模态频率、驾驶员头部处声压峰值和声压均方根值为响应,采用最优拉丁超立方试验设计方法采集样本数据进行因子空间设计。利用径向基神经网络方法,建立了4个响应关于6个因子的误差小、精度高的近似模型,并对所建立的近似模型进行误差分析。以驾驶员头部处声压峰值最小为目标函数,板件厚度参数为自变量,驾驶员头部处声压均方根值、车身质量和车身模态频率为约束条件。采用自适应模拟退火算法对板件厚度进行优化设计,其优化结果表明,驾驶员头部处最大声压峰值所在的频率158 Hz处的声压降低了4.45 d B,134 Hz处的声压峰值降低了5.47 d B,在其他声压峰值较高的频率处,测点声压均有不同程度降低,说明在满足约束条件同时,通过优化有效地降低车室空腔噪声,提高车辆的声学舒适性。
The problem of reduction of the low frequency noise of a vehicle compartment is studied.The thickness parameters of the panels,which have large acoustic contribution,are considered as the influencing factors,and the vehicle weight,the seventh-order modal frequency of vehicle body,the sound pressure peak and the root-mean-square value of the sound pressure near the driver’s head are considered as the responses.The optimal Latin hypercubic sampling method is applied to perform the experimental design for sampling in the factorial design space.By using the RBF(radial basis function)neutral-network method,an approximate model of four responses about six factors is established.Then,error analysis is performed on the approximate model.An optimization model is set up with minimizing the peak sound pressure near driver’s head as the object function,the thickness parameters of panels as design variable,the sound pressure rootmean-square value,the vehicle weight and the seventh-order modal frequency of the vehicle body as the constraint conditions.The adaptive simulated annealing algorithm is applied to optimize the panel’s thickness to improve the vehicle compartment acoustic environment.Optimization results show that the peak sound pressure near driver’s head is reduced by4.45dB at158Hz frequency and by5.47dB at134Hz frequency respectively.The sound pressures at the measurement points are significantly reduced.The results indicate that through the optimization the vehicle interior cavity noise is reduced effectively,and the acoustical comfort of the vehicle is improved significantly.
作者
胡启国
王宇谦
李苏平
HU Qi-guo;WANG Yu-qian;LI Su-ping(School of Mechanotronics & Vehicle Engineering, Chongqing Jiaotong University,Chongqing 400074, China;College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China)
出处
《噪声与振动控制》
CSCD
2017年第5期97-102,共6页
Noise and Vibration Control
基金
国家自然科学基金资助项目(51375519)
重庆市基础科学与研究专项重点资助项目(cstc2015jcyj BX0133)
关键词
声学
低频噪声
最优拉丁超立方设计
径向基神经网络近似模型
自适应模拟退火算法
声学优化
acoustics
low frequency noise
optimal Latin hypercubic design
RBF neutral- network approximate model
adaptive simulated annealing algorithm
acoustic optimization
