Rapid noise prediction models for serrated leading and trailing edges
Rapid noise prediction models for serrated leading and trailing edges
复制标题
锯齿状前缘和后缘的快速噪声预测模型
DOI:
10.1016/j.jsv.2019.115136
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发表时间:
2019
影响因子:
4.7
通讯作者:
Lorna J. Ayton
中科院分区:
文献类型:
--
作者:
B. Lyu;Lorna J. Ayton
Leading- and trailing-edge serrations have been widely used to reduce the leading- and trailing-edge noise in applications such as contra-rotating fans and large wind turbines. Recent studies show that these two noise problems can be modelled analytically using the Wiener-Hopf method. However, the resulting models involve infinite-interval integrals that cannot be evaluated analytically, and consequently implementing them poses practical difficulty. This paper develops easily-implementable noise prediction models for flat plates with serrated leading and trailing edges, respectively. By exploiting the fact that high-order modes are cut-off and adjacent modes do not interfere in the far field except at sufficiently high frequencies, an infinite-interval integral involving two infinite sums is approximated by a single straightforward sum. Numerical comparison shows that the resulting models serve as excellent approximations to the original models. Good agreement is also achieved when the leading-edge model predictions are compared with experimental results for sawtooth serrations of various root-to-tip amplitudes, whereas a qualitative evaluation of TE noise model shows that an accurate characterization of the wall pressure statistics beneath turbulent boundary layers is crucial for an accurate TE noise prediction. Importantly, the models developed in this paper can be evaluated robustly in a very efficient manner. For example, a typical far-field noise spectrum can be calculated within milliseconds for both the trailing- and leading-edge noise models on a standard desktop computer. Due to their efficiency and ease of numerical implementation, these models are expected to be of particular importance in applications where a numerical optimization is likely to be needed.
影响因子:
3.7
作者:
Lorna J. Ayton
通讯作者:
Lorna J. Ayton
影响因子:
3.7
作者:
L. E. Jones;R. Sandberg
通讯作者:
L. E. Jones;R. Sandberg
影响因子:
4.7
作者:
Lau, Alex S. H.;Haeri, Sina;Kim, Jae Wook
通讯作者:
Kim, Jae Wook