圆环旋转黏性液体射流破碎液滴粒径与速度数量密度分布相关性研究

压力旋流喷嘴被广泛应用于航空发动机、船用发动机、车用汽油缸内直喷发动机、燃气轮机等动力机械的燃油喷射系统中.以压力旋流喷嘴射流为研究对象,开展了圆环旋转黏性液体射流破碎液滴粒径与速度数量密度分布相关性问题研究.对于液体射流,以往的研究往往对破碎液滴粒径数量密度分布或速度数量密度分布进行单独研究,对于这两种数量密度分布之间关系的研究较少;从相关性的角度对圆环旋转黏性液体射流破碎液滴粒径与速度数量密度分布之间的关系进行研究.采用最大熵原理方法建立了圆环旋转黏性液体射流破碎液滴粒径与速度联合概率密度函数.对圆环旋转黏性液体射流破碎液滴粒径与速度联合概率密度函数进行了讨论,对圆环旋转黏性液体射流破碎液滴粒径数量密度分布与速度数量密度分布的相关性问题进行了研究.研究结果表明,为了给出正确的圆环旋转黏性液体射流破碎液滴粒径与速度联合概率密度函数,射流守恒约束条件中必须同时包括质量守恒定律、动量守恒定律以及能量守恒定律;破碎液滴粒径的数量密度分布与速度数量密度分布密切相关;射流旋转强度对破碎液滴粒径数量密度与速度数量密度分布结构影响不大,对破碎液滴粒径数量密度和速度数量密度的分布区域影响较大.      

基于重力场特征参数信息熵的适配区选择方法

针对当前重力统计特征参数类别繁多,选择标准复杂而导致错选有效匹配区域的问题,利用信息熵具有能够整合多种统计参数且算法计算量小的特点,提出了一种基于特征参数信息熵的重力辅助导航适配区的选择方法。首先,在DTU10模型下将该方法与传统单一特征参数的方法进行比较,确定了传统方法的确会错误选择可匹配区,从而也反映了所提出方法的优越性;其次,在该方法划分出的匹配区和非匹配区中分别设计了8条仿真航线,匹配区中仿真航线的匹配效果明显优于非匹配区中的匹配效果。仿真结果表明了该方法的有效性。     

Entropy,Fisher Information and Variance with Frost-Musulin Potenial

This study presents the Shannon and Renyi information entropy for both position and momentum space and the Fisher information for the position-dependent mass Schrödinger equation with the Frost-Musulin potential. The analysis of the quantum mechanical probability has been obtained via the Fisher information. The variance information of this potential is equally computed. This controls both the chemical properties and physical properties of some of the molecular systems. We have observed the behaviour of the Shannon entropy. Renyi entropy, Fisher information and variance with the quantum number n respectively.     

Research on the Longitudinal Section of River Restoration Using Probabilistic Theory

Since the 1960s, many rivers have been destroyed as a consequence of the process of rapid urbanization. As accurate figures are important to repair rivers, there have been many research reports on methods to obtain the exact river slope and elevation. Until now, many research efforts have analyzed the river using measured river topographic factors, but when the flow velocity changes rapidly, such as during a flood, surveying is not easy; and due to cost, frequent measurements are difficult. Previous research has focused on the cross section of the river, so the information on the river longitudinal profile is insufficient. In this research, using informational entropy theory, equations are presented that can calculate the average river slope, river slope, and river longitudinal elevation for a river basin in real time. The applicability was analyzed through a comparison with the measured data of river characteristic factors obtained from the river plan. The parameters were calculated using informational entropy theory and nonlinear regression analysis using actual data, and then the longitudinal elevation entropy equation for each river and the average river slope were calculated. As a result of analyzing the applicability of the equations presented in this study by R² and Root Mean Square Error, all R² values were over 0.80, while RMSE values were analyzed to be between 0.54 and 2.79. Valid results can be obtained by calculating river characteristic factors.     

Phase-space relative Rényi entropy in density functional theory

The phase-space relative Rényi entropy is introduced using the information theoretical and thermodynamic view of density functional theory. In the special case of constant inverse temperature the phase-space relative Rényi entropy is a sum of the position-space relative Rényi entropy and a term arising from the momentum space. This quantity can be considered as a measure of similarity. It includes more information than the position-space measures, since it also incorporates momentum-space knowledge.     

The criteria for selecting a method for unfolding neutron spectra based on the information entropy theory

To further expand the application of an artificial neural network in the field of neutron spectrometry, the criteria for choosing between an artificial neural network and the maximum entropy method for the purpose of unfolding neutron spectra was presented. The counts of the Bonner spheres for IAEA neutron spectra were used as a database, and the artificial neural network and the maximum entropy method were used to unfold neutron spectra; the mean squares of the spectra were defined as the differences between the desired and unfolded spectra. After the information entropy of each spectrum was calculated using information entropy theory, the relationship between the mean squares of the spectra and the information entropy was acquired. Useful information from the information entropy guided the selection of unfolding methods. Due to the importance of the information entropy, the method for predicting the information entropy using the Bonner spheres' counts was established. The criteria based on the information entropy theory can be used to choose between the artificial neural network and the maximum entropy method unfolding methods. The application of an artificial neural network to unfold neutron spectra was expanded.     

A class of third-order nonlinear evolution equations admitting invariant subspaces and associated reductions

With the aid of symbolic computation by Maple, a class of third-order nonlinear evolution equations admitting invariant subspaces generated by solutions of linear ordinary differential equations of order less than seven is analyzed. The presented equations are either solved exactly or reduced to finite-dimensional dynamical systems. A number of concrete examples admitting invariant subspaces generated by power, trigonometric and exponential functions are computed to illustrate the resulting theory.