Investigation of flow structures involved in sound generation by two- and three-dimensional cavity flows

Proper Orthogonal Decomposition and Stochastic Estimation are combined to shed some light on the link between organized flow structures and noise generation by turbulent flows. Proper Orthogonal Decomposition (POD) is firstly used to extract selected flow events. Based on the knowledge of these structures, the Quadratic Stochastic Estimation of the acoustic pressure field is secondly performed. Both procedures are successively applied to two- and three-dimensional numerical databases of a flow over a cavity. It is demonstrated that POD can extract selected aerodynamic events which can be associated with selected frequencies in the acoustic spectra. Reconstructed acoustic fields also indicate the aerodynamic events which are responsible of the main energy of the noise emission. Such mathematical tools offer new perspectives in analysing flow structures involved in sound generation by turbulent flows and in the experimental design of a flow control strategy.     

Choice of generalized linear mixed models using predictive crossvalidation

The choice of generalized linear mixed models is difficult, because it involves the selection of both fixed and random effects. Classical criteria like Akaike’s information criterion (AIC) are often not suitable for the latter task, and others which are useful in linear mixed models are difficult to extend to the generalized case, especially for overdispersed data. A predictive leave-one-out crossvalidation approach is suggested that can be applied for choosing both fixed and random effects, even in models with overdispersion, and is based on proper scoring rules. An attractive feature of this approach is the fact that the model has to be fitted just once to the data set, which makes computations fast and convenient. As the calculation of the leave-one-out predictive distribution is not possible analytically, it is shown how an iteratively weighted least squares algorithm combined with some analytic approximations can be used for this task. A simulation study and two applications of the methodology to binary and count data are provided, as well as comparisons with two other methods.     

一种低压低速多支路永磁同步电动机设计

提出了一种低压低速多支路永磁同步电动机的设计方案.给出了确定电机定、转子尺寸、绕组安排和转子磁路结构的方法.通过计算机辅助设计,得出电机的设计方案,应用Ansoft软件对电机进行二维建模仿真分析,结果表明了其结构设计的合理性.     

Homomorphisms of modules associated with polynomial matrices with infinite elementary divisors

If the inverse of a nonsingular polynomial matrix L has a polynomial part then one can associate with L a module over the ring of proper rational functions, which is related to the structure of L at infinity. In this paper we characterize homomorphisms of such modules.     

Computing the Karhunen-Loève dimension of an extensively chaotic flow field given a finite amount of data

The use of Karhunen-Loève decomposition (KLD) to explore the complex fluid flows that are common in engineering applications is increasing and has yielded new physical insights. However, for most engineering systems the dimension of the dynamics is expected to be very large yet the flow field data is available only for a finite time. In this context, it is important to establish the amount of data required to compute the asymptotic value of the Karhunen-Loève dimension given a finite amount of data. Using direct numerical simulations of Rayleigh-Bénard convection in a finite cylindrical geometry we compute the asymptotic value of the Karhunen-Loève dimension. The amount of time required for the Karhunen-Loève dimension to reach a steady value is very slow in comparison with the time scale of the convection rolls. We show that the asymptotic value of the Karhunen-Loève dimension can be determined using much less data if one uses the azimuthal symmetry of the governing equations prior to performing a KLD. The Karhunen-Loève dimension is found to be extensive as the system size is increased and for a dimension measurement that captures 90% of the variance in the data the Karhunen-Loève dimension is approximately 20 times larger than the Lyapunov dimension.     

Fast Prediction Method for Steady‐State Heat Convection

A reduced model by proper orthogonal decomposition (POD) and Galerkin projection methods for steady‐state heat convection is established on a nonuniform grid. It was verified by thousands of examples that the results are in good agreement with the results obtained from the finite volume method. This model can also predict the cases where model parameters far exceed the sample scope. Moreover, the calculation time needed by the model is much shorter than that needed for the finite volume method. Thus, the nonuniform POD‐Galerkin projection method exhibits high accuracy, good suitability, and fast computation. It has universal significance for accurate and fast prediction. Also, the methodology can be applied to more complex modeling in chemical engineering and technology, such as reaction and turbulence.     

POD原理解析及在结构风工程中的几点应用

本征正交分解(POD)是压缩数据和提取随机场本质特征的有效技术.介绍了POD的基本原理及其在结构风工程研究方面的主要优点,这些优点包括仅使用少量的本征模态就可很好的重建风压场;预测风压场时可提高测压点的分辨率,根据已有的风压数据获得没有布置测压点处的风压时间序列.以双坡屋盖为例,利用风洞试验同步测量的风压数据对以上几方面基本内容进行讨论,包括风压场的重建和预测分析.通过在时域和频域与实验结果对比表明POD技术的有效性和实用性.     

EMD多尺度分析的湍流数据缩减算法研究

基于经验模态分解(EMD)多尺度分析方法,结合统计检验理论,提出一种相对合理的确定湍流数据适宜采样尺度的缩减算法。以湍动能为统计特征量,对风机试验数据进行湍流数据的适宜采样尺度分析,结果验证了采用该算法确定湍流数据适宜采样尺度的合理性和可行性。进一步分析表明,相对于传统算法,采用算法缩减所得的适宜采样尺度湍流数据更能合理反映原始湍流数据的流动特征;相对于小波缩减算法,算法无需先验基函数即可获得包含足够丰富湍流信息量的缩减湍流数据。     

Proper Generalized Decomposition based dynamic data driven inverse identification

Dynamic Data-Driven Application Systems—DDDAS—appear as a new paradigm in the field of applied sciences and engineering, and in particular in Simulation-based Engineering Sciences. By DDDAS we mean a set of techniques that allow the linkage of simulation tools with measurement devices for real-time control of systems and processes. One essential feature of DDDAS is the ability to dynamically incorporate additional data into an executing application, and in reverse, the ability of an application to dynamically control the measurement process. DDDAS need accurate and fast simulation tools using if possible off-line computations to limit as much as possible the on-line computations. With this aim, efficient solvers can be constructed by introducing all the sources of variability as extra-coordinates in order to solve the model off-line only once. This way, its most general solution is obtained and therefore it can be then considered in on-line purposes. So to speak, we introduce a physics-based meta-modeling technique without the need for prior computer experiments. However, such models, that must be solved off-line, are defined in highly multidimensional spaces suffering the so-called curse of dimensionality. We proposed recently a technique, the Proper Generalized Decomposition—PGD—able to circumvent the redoubtable curse of dimensionality. The marriage of DDDAS concepts and tools and PGD off-line computations could open unimaginable possibilities in the field of dynamic data-driven application systems. In this work we explore some possibilities in the context of on-line parameter estimation.