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Automotive interior noise prediction based on single sound cavity using statistical energy analysis method

机译:统计能量分析法基于单声腔的汽车内部噪声预测

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摘要

In order to predict car interior noises at the car design and development stage, the statistical energy analysis (SEA) method was used. All the input parameters - modal density (MD), damping loss factor (DLF) and coupling loss factor (CLF) were calculated with SEA principle. Meanwhile, the sound excitation was calculated with sound power experiment data of internal combustion engine given by the engine manufacturer and sound source radiation formula. Engine mount excitation was also computed through the acceleration at initiative side of the engine mount and the transmissibility. A car virtual prototype was built to calculate a car body suspension receiving excitation from road roughness. A computational fluid dynamics (CFD) model was also built up to analyze the wind excitation on the outside surfaces. The car interior noises were predicted by the SEA model with all of the parameters and excitations. A good agreement was indicated by comparing predicted results with measured ones. The maximum relative error between prediction and measurement results is less than 3%, and the maximum absolute error is less than 2.5 dB (A). The above predicted results satisfy engineering precision requirements and as well as showing that using SEA method to predict car internal noises is feasible. The acoustic sensitivity analysis was made at the end. The car internal noise prediction method presented in the paper can be used at car design and development stage.
机译:为了在汽车设计和开发阶段预测汽车内部噪音,使用了统计能量分析(SEA)方法。所有输入参数-模态密度(MD),阻尼损耗因子(DLF)和耦合损耗因子(CLF)均采用SEA原理进行计算。同时,利用发动机制造商给出的内燃机声功率实验数据和声源辐射公式计算出声激励。发动机悬置的激励也通过发动机悬置的主动侧的加速度和可传递性来计算。建立了汽车虚拟原型,以计算接收来自路面不平整的激励的车身悬架。还建立了计算流体动力学(CFD)模型来分析外表面的风激励。 SEA模型通过所有参数和激励来预测汽车内部噪声。通过将预测结果与实测结果进行比较,表明了良好的一致性。预测和测量结果之间的最大相对误差小于3%,最大绝对误差小于2.5 dB(A)。以上预测结果满足工程精度要求,表明采用SEA方法预测汽车内部噪声是可行的。最后进行声学灵敏度分析。本文提出的汽车内部噪声预测方法可以在汽车设计和开发阶段使用。

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