三维动态非结构重叠网格Navier-Stokes方程并行算法

 提出了一种三维动态非结构重叠网格Navier-Stokes(N-S)方程的并行计算方法。N-S方程的空间离散采用格点有限体积方法,时间离散采用隐式的双时间步长方法。应用一方程Spalart-Allmaras(S-A)模型来计算湍流黏性。并行计算采用动态的区域分裂方法,在每一物理时间步利用METIS网格分区系统对网格进行分区。为了实现各CPU之间的负载均衡,每块网格都按CPU个数进行分区并对活动节点和非活动节点进行了加权处理。最后,通过对外挂物投放无黏流动的数值模拟和内埋武器弹舱开启黏性流动的数值模拟,验证了该并行程序的准确性、高性能并行计算以及处理复杂几何外形的能力。     

企业信息系统领域的软件建筑模型

将软件建筑的基本概念以及研究成果引入到企业信息系统建设中,提出了企业信息系统领域的软件建筑模型.首先对软件工程方法学的发展做了简要评述,指出了软件建筑在软件过程中的重要性.随后提出了企业信息系统领域的基于软件建筑模型,包括软件建筑模型、参考需求、参考建筑等.描述了基于该模型的软件开发过程,讨论了若干相关研究工作.     

TiAl 与TC4 扩散焊接接头的组织与性能

主要对TiAl基金属间化合物与TC4的扩散连接技术进行了研究。通过对钛铝基合金进行热处理,获得了高温性能良好的全片层组织;通过对扩散连接后的接头进行组织结构、力学性能和断口的分析发现,在915℃/80 MPa/1 h的扩散连接条件下,TiAl基合金与TC4实现了冶金结合,在室温、400和500℃下,接头的抗拉强度分别达到了TiAl母材的91.77%、95.27%和99.21%。     

Forecasting space weather: Can new econometric methods improve accuracy?

Space weather forecasts are currently used in areas ranging from navigation and communication to electric power system operations. The relevant forecast horizons can range from as little as 24 h to several days. This paper analyzes the predictability of two major space weather measures using new time series methods, many of them derived from econometrics. The data sets are the A_p geomagnetic index and the solar radio flux at 10.7 cm. The methods tested include nonlinear regressions, neural networks, frequency domain algorithms, GARCH models (which utilize the residual variance), state transition models, and models that combine elements of several techniques. While combined models are complex, they can be programmed using modern statistical software. The data frequency is daily, and forecasting experiments are run over horizons ranging from 1 to 7 days. Two major conclusions stand out. First, the frequency domain method forecasts the A_p index more accurately than any time domain model, including both regressions and neural networks. This finding is very robust, and holds for all forecast horizons. Combining the frequency domain method with other techniques yields a further small improvement in accuracy. Second, the neural network forecasts the solar flux more accurately than any other method, although at short horizons (2 days or less) the regression and net yield similar results. The neural net does best when it includes measures of the long-term component in the data.     

振荡函数的移动最小二乘逼近方法

移动最小二乘法在求解偏微分方程数值解中得到广泛使用,但是用等距节点逼近振荡函数时逼近精度不高,针对这一问题,用适应函数变化的非均匀节点离散求解区域,与之相适应,移动最小二乘法中权函数的支撑域半径也随节点密度变化,这里用基于领域搜索算法确定支撑域半径。数值算例表明,在移动最小二乘法中,用适应节点变化的节点集及基于领域的搜索算法确定支撑域半径能够有效减小逼近误差。     

高动态下的星体目标质心提取方法

高动态情况下,星点像斑在星敏感器探测器上会呈现出拖尾现象,星点质心无法被准确提取。针对上述难点,本文提出了一种高动态条件下基于差异哈希算法的星点质心提取方法。该方法分为三步：第一,建立动态星斑的数学模型;第二,利用差异哈希算法和汉明距离实现星跟踪窗口内星体目标粗定位;第三,在粗定位区域使用阈值分割与连通域法提取星点质心。实验结果表明,该方法能够适应各种长度的曝光时间,并实现星敏感器在3（°）/s条件下的稳定跟踪。在曝光时间50ms,角速度3（°）/s的条件下,星对角距误差为13”,平均提取率为96%,相比于传统方法,分别提高了29.6%和22.9%。     

无界域上大规模凹二次规划的一个算法

本文给出了无界域上大规模凹二次规划的一种下逼近算法,并证明了算法的收敛性。     

基于贝叶斯网的业务端编程方法

针对现有的业务端编程方法缺乏对于没有软件开发背景的最终用户的指导机制,提出了一种基于贝叶斯网的业务端编程方法.首先提出了一种基于贝叶斯网的最终用户编程基本框架,然后基于贝叶斯网结构学习方法和过程日志建立了用来支撑业务端编程的领域专家系统,在此基础上提出了以领域专家系统为核心的启发式业务端编程方法.该方法在编程过程中依赖领域专家系统对最终用户推荐业务活动,提供实时指导,帮助最终用户逐步完成应用系统的构造.最后对该方法的有效性进行了定性的评估和分析,结果表明,基于贝叶斯网的业务端编程方法可以在一定程度上提高最终用户编程的效率.     

Combining frequency and time domain models to forecast space weather

Space weather series incorporate several distinct components, cycles at multiple frequencies, irregular trends, and nonlinear variability. The cycles are stochastic, i.e., the amplitude varies over time. Similarly, the trend is stochastic: the slope and direction of trending change repeatedly. This study sets out a combined model using both frequency and time domain methods, in two stages. In the first stage, a frequency domain algorithm is estimated and forecasted. In the second stage, the forecast is used as an input in a neural network. The combined model also includes a term enabling the model to react inversely to large deviations between the actual values and forecast. The models are evaluated using two data sets, the hemispheric power data obtained from the Polar Orbiting Environment satellites, and the Aa geomagnetic index. All the series are at a daily resolution. Forecasting experiments are run over horizons of 1–7 days. The models are estimated using a moving window or adaptive approach. The combined model consistently achieves the most accurate results. Among single equation methods, the frequency domain model is more accurate for the geomagnetic index because it is able to capture the underlying cycles more effectively. In the hemispheric power series, the cycles are less pronounced, so that time domain methods are more accurate, except at very short horizons. Nevertheless, in both data sets, the combined model works well because the frequency domain algorithm captures cyclical behavior, while the neural net is better able to capture short-term dependence and trending.     

结构动力学中的一种直接积分法

针对结构动力学求解问题,介绍了一种对时间的直接积分法,并将该方法与已知的纽马克法、威尔逊法、呼伯特法进行了比较,对其优缺点进行了分析。