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Developing a highprecision Statistical Energy Analysis (SEA) model to predict vehicle wind noise response requires a significant amount of time and cost. In this paper, a method is proposed for rapidly constructing an equivalent SEA model for vehicle wind noise based on parameter identification, which simplifies the modeling process while ensuring prediction accuracy. An initial SEA model of the compartment is established according to the vehicle's body structure and dimensions, with the pressure fluctuation excitation on the side window surface and the actual wind tunnel response serving as the model's input and output, respectively. The Grey Wolf Optimizer (GWO) algorithm is employed to identify the acoustic cavity parameters of the model, resulting in an equivalent model that approximates the true wind noise response characteristics. Taking a prototype vehicle as an example, the equivalent wind noise SEA model is used to predict the wind noise response in the compartment under different design schemes. The average prediction error for the total sound pressure level is 1.47%, and the root mean square error of the spectrum is 1.23 dB. The results show that the equivalent model can accurately predict the invehicle wind noise response under different design schemes, thereby reducing the number of wind tunnel tests and having high engineering application value.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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