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Comparison and Application on the Aero-acoustics Numerical Computing Methods

XU Jun-wei;WU Ya-feng;CHEN Geng

2012, 32 (4): 6-10. DOI: 10.3969/j.issn.1006-1355.2012.04.002

Abstract （2365）

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The current aerodynamic noise problems have become more common and prominent. Although Lighthill created a pneumatic acoustics for more than half a century, but because of the complexity of the its equation, so over a long period of time it was unable to realize the accurate calculation of aerodynamic noise. With the maturation of the calculation method for computational fluid dynamics and acoustics, numerical computing is becoming the main tool to solve aerodynamic noise problems. This article, from the basic theory of pneumatic acoustics, the existing three pneumatic noise numerical calculation methods and their applicability were introduced. And through the application examples, their respective advantages and disadvantages and solving process were descibed. This may provide certain reference to the pneumatic noise forecast.

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《Resumption of Missingdata in Testing Based on Parameter Model》

GAO Shu-yuan;WU Ya-feng;ZHAI Wei-lei

2010, 30 (3): 23-26. DOI: 10.3969/j.issn.1006-1355.2010.03.007

Abstract （1780）

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Datamissing phenomena may happen in engineering testing due to the mismatch of component parts or breakdown of the testing system. In this paper, a parameter model is proposed to recover the missingdata. Through analyzing the correct data, the structure, the best order and the specific parameters of the model can be determined, and be further tested and optimized, so that the best model can be obtained. Finally, as an example, an AR model is applied to resume the missing data in the testing of an actual flight. The results show that the method is feasible and effective.

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《Application of EEMD to Suppression of Mode Mixing in Oscillation Signals》

QI Tian;QIU Yan;WU Ya-feng

2010, 30 (2): 103-106. DOI: 10.3969/j.issn.1006-1355.2010.02.103

Abstract （2655）

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Mode mixing is one of the drawbacks in empirical mode decomposition (EMD). It makes the IMF component to lose its original physical meaning. Ensemble empirical mode decomposition (EEMD) is a new method which applies the noise assisted data analysis to EMD. It can improve the EMD decomposition and suppress mode mixing phenomena. Application of EEMD in suppression of mode mixing for mode extraction from oscillation signals has made a better result for engineering projects.