CCD互相关粒子图像测速及其在对抗流流场测量中的应用

在常规自相关粒子图像测速（ＰＩＶ）系统的基础上,发展了一个采用ＣＣＤ像机的互相关粒子图像测速系统。用这一新系统测量了对抗流流场的结果表明,这一技术克服了自相关粒子图像测速技术在测量具有滞止点的流场中的困难。实验说明,与自相关粒子图像测速技术相比,这一技术更快捷有效。     

The turbulent combustion of a propane-air mixture

This paper models the combustion of a turbulent homogeneous mixture of propane and air within a duct having a stationary one-dimensional mean flow. The Bray-Moss model is applied to the closure of the chemical production terms, using a probability density function (pdf) of the temperature which is chosen as the characteristic variable. Under the conditions chosen for the study, chemical kinetic factors are important and the conventional assumption, that heat release is controlled by turbulent mixing, is not valid. The chemical model of Edelman and Fortune for the combustion of hydrocarbons is used and simplifying assumptions are made which reduce the systems of unknowns to that of the temperature alone. This leads to the introduction of two chemical production terms which are defined respectively in a “delay zone”, where the heat release is modest, and a “combustion zone”. The required equations for the Favre-averaged temperature, turbulence kinetic energy and the mean square fluctuation of the temperature are solved numerically. In the delay zone, a comparison is made between a second order Borghi type closure and the pdf closure. Good agreement is found in the case of relatively small turbulence intensity. It is shown that the pdf formulation does not require the two zones to be spatially distinct. Differing chemical source terms can be discriminated instantaneously by the reaction progress variable and contributions to the average production terms appropriately apportioned by its pdf. Predictions are made of the profiles of mean temperature and mean square fluctuation under different initial turbulence levels.     

Selection of classification techniques for land use/land cover change investigation

The concerns over land use/land cover (LULC) change have emerged on the global stage due to the realisation that changes occurring on the land surface also influence climate, ecosystem and its services. As a result, the importance of accurate mapping of LULC and its changes over time is on the increase. Landsat satellite is a major data source for regional to global LULC analysis. The main objective of this study focuses on the comparison of three classification tools for Landsat images, which are maximum likelihood classification (MLC), support vector machine and artificial neural network (ANN), in order to select the best method among them. The classifiers algorithms are well optimized for the gamma, penalty, degree of polynomial in case of SVM, while for ANN minimum output activation threshold and RMSE are taken into account. The overall analysis shows that the ANN is superior to the kernel based SVM (linear, radial based, sigmoid and polynomial) and MLC. The best tool (ANN) is then applied on detecting the LULC change over part of Walnut Creek, Iowa. The change analysis of the multi temporal images indicates an increase in urban areas and a major shift in the agricultural practices.