Sound radiation from a perforated panel set in a baffle with a different perforation ratio

A finite flexible perforated panel set in a differently perforated rigid baffle is considered. The radiation efficiency from such a panel is derived using a 2-D wavenumber domain formulation. This generalization is later used to represent a more practical case of a perforated panel fixed in an unperforated baffle. The perforations are in the form of an array of uniformly distributed circular holes. A complex impedance model for the holes available in the literature is used. An averaged fluid particle velocity is derived using the continuity equation and the surface pressure is derived using an appropriate momentum equation. The discontinuity in the perforate impedance (due to different hole dimensions or perforation ratio) at the panel-baffle interface is carefully taken into account. It is found that there exists a `coupling' of different wavenumbers of the spatially mean fluid particle velocity field. The change in the resonance frequencies and the modeshapes of the panel due to the perforations is taken into account using the Receptance method. Analytical expressions for the radiated power and radiation efficiency are derived in an integral form and numerical results are presented. Several comparisons are made to understand the radiation efficiency curves. Since both the resistive and reactive components of the hole impedance are taken into account, the model is directly applicable to micro-perforated panels also. (C) 2016 Elsevier Ltd. All rights reserved.

LEVEL SET输运方程的求解方法及其对气-液两相流运动界面数值模拟的影响

用数值模拟方法来研究气-液两相流动与传热现象是当今多相流领域的一个热门课题.由于两相流固有的复杂性,气-液两相流界面迁移现象的数值模拟一直是两相流研究中的一大难点.本文介绍了捕捉气-液两相流相界面运动的水平集方法(Level Set)及其研究进展,介绍了求解Level Set输运方程的3种方法,即一般差分格式、Superbee-TVD格式和Runge-Kutta法-5阶WENO组合格式.结合主流场的求解,分别用这3种方法对4种典型相界面在5种流场中的迁移特性进行了模拟计算,并对计算结果进行了比较和分析.结果表明,Runge-Kutta法-5阶WENO组合格式求解Level Set输运方程的效果最好,在以后的计算中将主要采用这种组合格式来进行气-液相界面输运方程的求解.

The Relationship between Decision Trees and the Scale of Train Data Set

Decision Trees need train samples in the train data set to get classification rules. If the number of train data was too small, the important information might be missed and thus the model could not explain the classification rules of data. While it is not affirmative that large scale of train data set can get well model. This Paper analysis the relationship between decision trees and the train data scale. We use nine decision tree algorithms to experiment the accuracy, complexity and robustness of decision tree algorithms. Some results are demonstrated.