Cyril de Walque, Ji Woo Yoo, ChanHee Jeong, Taesik Kong
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引用次数: 0
摘要
基于有限元(FE)的全修整车身模拟是汽车行业预测和了解整车噪声、振动和声振粗糙度(NVH)行为的关键工具。虽然结构和声学传递函数如今可以直接从此类模型中获得,但要全面了解整车的内在行为却较为复杂,尤其是当涉及到每个子部件对整体响应的贡献时。本文提出了一个完整的目标级联过程,首先评估汽车的哪个子部件对车内噪声的贡献最大,然后将总的结构声学传递函数分解为多个中间传递函数,从而更好地理解局部设计变更的影响。这种传递函数分解为逐级实现全车目标(通常包括实现车厢内的最大噪音水平)和部件级目标打开了大门。我们在一个工业 FE 模型的底板上演示了这一过程,该模型的结构和声学传递函数已根据测量结果进行了广泛验证。对几种备选设计的中间传递函数进行了计算和比较。最后,同样的过程被应用于简化模型,即只考虑楼板和声学装饰。这些简化模型可以更快地进行设计迭代,并能可靠地预测趋势。
Definition and Application of a Target Cascading Process on a Fully Trimmed Body, from Vehicle Objectives to Component Objectives
Finite element (FE) based simulations for fully trimmed bodies are a key tool in the automotive industry to predict and understand the Noise, Vibration and Harshness (NVH) behavior of a complete car. While structural and acoustic transfer functions are nowadays straightforward to obtain from such models, the comprehensive understanding of the intrinsic behavior of the complete car is more complex to achieve, in particular when it comes to the contribution of each sub-part to the global response. This paper proposes a complete target cascading process, which first assesses which sub-part of the car is the most contributing to the interior noise, then decomposes the total structure-borne acoustic transfer function into several intermediate transfer functions, allowing to better understand the effect of local design changes. This transfer functions decomposition opens the door to cascading full-vehicle objectives, which typically consists of achieving a maximal noise level in the cabin, to component-level objectives. This process is demonstrated on the floor panel of an industrial FE model for which both the structural and acoustic transfer functions have been extensively validated against measurements. Intermediate transfer functions are computed and compared for several alternative designs. The same process is finally applied on reduced models, which consider only the floor panels and acoustic trims. Those reduced models allow much faster design iterations and prove to be reliably predicting trends.