车辆受到路面或轨道不平度激励会产生随机振动,对这类随机振动进行快速有效的分析对于提高车辆性能具有非常重要的意义.而这类随机振动的分析,历来都只能将车辆模拟为自由度很低的弹簧质量体系,其计算精度难于保证.特别在需要计算疲劳应力集中的情况,需要使用网格很密的有限元模型,计算困难就更为突出.此外,在考虑多个车轮受到路面随机激励时,就更难应用效率相对较高的频域分析来计算车辆的随机振动;而只能借助于一条或几条路面不平度样本,让车轮在上面移动,借助数值积分工具而粗略地得到随机响应的统计特性.效率很低而且精度不高.虚拟激励法对于克服上述困难具有很好的效果;目前已在我国的汽车、火车、磁浮列车等领域获得日益广泛的应用.该文对此作一概略的叙述.
Random vibration will take place for vehicles moving on uneven road or railway surfaces.Quick and reliable PSD(power spectral density) analysis for such random vibration is of great importance in order to improve the performance of vehicles.Due to the low efficiency and precision of the conventional random vibration algorithm,previously the vehicles have to be modeled into spring-mass systems with very low degrees of freedom,and so the analysis precision cannot be ensured.Particularly if the fatigue stress concentration is computed,very refined finite element meshes must be adopted,the computational cost will be extremely high and prohibited.In addition,when several wheels are excited simultaneously on different locations of the surface,the MIMO(multi-input-multi-output) random vibration analysis will be even more difficult.Usually,only one or a few samples of the surface unevenness were taken for direct numerical integration,the results were then used to evaluate the statistical characteristics of the vehicle random responses.Clearly,both the precision and efficiency are quite limited.In recent years,the pseudo-excitation method has received much attention in overcoming the above difficulties,and are being used and further developed by many experts in the research fields of cars,trains or even maglevs,which are briefly summarized.