基于蒙特卡洛方法的采摘机械手工作空间分析

机械手的工作空间是一个重要的运动学指标.利用基于随机抽样的蒙特卡洛方法,对4自由度采摘机械手的工作空间进行了分析.根据分析过程编制算法程序,借助计算机图形显示功能,得到机械手工作空间仿真图形和主剖视图.结果显示,该机械手的工作空间紧凑,没有空腔和空洞,取得了较好的效果.     

黄河干流梯级水库补偿调节方案风险分析

本文以黄河干流梯级水库补偿调节为例,系统分析了补偿调节方案的主要风险因素,提出了多因素组合风险估计的蒙特卡罗(Monte-Carlo)随机模拟技术,并根据风险指标对补偿调节方案的风险做出评价。结果表明,研究建立的补偿调节方案风险模型是合理可行的。     

大体积混凝土稳定温度场的蒙特卡罗解

利用一种求偏微分方程的新方法蒙特卡罗算法通过不规则网格随机游动来求解大体积混凝土稳定温度场。应用这种方法可以在小分解总刚矩阵的情况下，求解各单个结点上的温度值，并可以通过随机抽样次数来控制精度。文中给出了相应的随机游动模型，并通过实例计算加以验证。计算表明，蒙特卡罗法的结果同其它方法结果吻合很好，新方法是可行的。     

基于Voronoi图的单粒播玉米产量效果的蒙特卡罗模拟

为了明确在单粒播种条件下田间出苗率对玉米产量的影响,以蒙特卡罗二项分布作缺苗模拟,以Voronoi图分配缺苗土地,通过对产量-密度方程进行数学恒等变换计算产量补偿,模拟了2种单粒播策略的玉米产量随出苗率(75%~95%)的变化。结果表明,缺苗斑数目与出苗率呈单峰曲线关系,缺苗斑大小随出苗率呈负指数下降;单粒播造成0.06%~16.78%的减产,且减产率随出苗率下降而增加;两种单粒播策略的产量大小和产量稳定性均有差异,且差异随出苗率下降而扩大。该研究推荐的玉米单粒播策略是:在千方百计提高出苗率基础上,播种的种子数目应当等于目标种植密度除以出苗率、保证出苗的植株数目达到推荐种植密度。该结论对于在玉米生产上广泛采用单粒精量播种具有重要参考价值。     

水电项目投资风险分析及管理方法研究

在分析了水电工程项目风险特性的基础上,论述了水电工程在施工过程中影响投资的主要风险。应用改进后的蒙特卡洛随机模拟方法进行投资风险分析,针对水电工程过程性和动态性的特点,研究了面向施工过程的动态投资风险管理方法,提出一个能够将风险管理与投资管理与控制有机结合的系统模型,并介绍了该模型的要点。然后在以上研究的基础上,分析并且提出了结合风险管理的水电工程投资动态管理与控制方法,绘制了其工作流程,并且论述其与传统的管理办法相比较具有的优势。最后进行了工程实例分析。     

考虑灌溉参数空间变异的区域畦灌模拟与验证

精确评估区域畦田灌水质量有助于提高农田灌水管理水平,而具有空间变异性的灌溉参数如何有效表征是影响区域畦田灌水质量精确模拟评价的关键因素。为此,该研究的目的在于借助Monte-Carlo抽样,建立考虑畦灌参数空间变异性的区域畦灌模拟方法。采用Monte-Carlo抽样将具有空间变异性的区域灌溉参数(如入畦单宽流量、土壤砂粒含量、黏粒含量、土壤容重等)离散表征为若干个灌溉参数样本,依次输入田块尺度畦灌地表水流-土壤水动力学耦合模型,以模拟评价区域畦灌过程。基于3次区域畦灌试验的实测数据和1个对比的确定性畦灌模拟方法,验证建立的模型的模拟效果。结果表明,所建模拟方法与确定性模拟方法模拟计算的灌水效率和灌水均匀具一定差异,所建模型的模拟值与实测值间的灌溉定额和田间水利用系数相对误差分别为9.95%~12.23%和8.39%~10.21%,而基于现有模型的相对误差则分别为14.15%~16.78%和13.87%~15.88%,畦田平均土壤含水率实测值与所建模型模拟值的累积分布趋势表现出良好的一致性。上述指标表明所建模拟方法有效缩小了区域灌溉参数空间均化处理所带来的模拟误差范围较大等问题,为区域畦田灌溉优化设计和管理提供了技术支撑。     

Analysis and simulation of land-use change in the central Arizona – Phoenix region,USA

To understand how urbanization has transformed the desert landscape in the central Arizona – Phoenix region of the United States, we conducted a series of spatial analyses of the land-use pattern from 1912–1995. The results of the spatial analysis show that the extent of urban area has increased exponentially for the past 83 years, and this urban expansion is correlated with the increase in population size for the same period of time. The accelerating urbanization process has increased the degree of fragmentation and structural complexity of the desert landscape. To simulate land-use change we developed a Markov-cellular automata model. Model parameters and neighborhood rules were obtained both empirically and with a modified genetic algorithm. Land-use maps for 1975 and 1995 were used to implement the model at two distinct spatial scales with a time step of one year. Model performance was evaluated using Monte-Carlo confidence interval estimation for selected landscape pattern indices. The coarse-scale model simulated the statistical patterns of the landscape at a higher accuracy than the fine-scale model. The empirically derived parameter set poorly simulated land-use change as compared to the optimized parameter set. In summary, our results showed that landscape pattern metrics (patch density, edge density, fractal dimension, contagion) together were able to effectively capture the trend in land-use associated with urbanization for this region. The Markov-cellular automata parameterized by a modified genetic algorithm reasonably replicated the change in land-use pattern.     

应用Monte-Carlo法计算输油管道腐蚀失效概率

在对埋地管道进行腐蚀失效风险分析的过程中,用传统的数学解析方法无法计算管道的失效概率,而应用Monte-Carlo方法无需知道腐蚀速率的具体值,只需知道腐蚀速率的概率分布,或根据统计数据求出腐蚀速率的概率分布,就可模拟腐蚀失效过程。借助MATLAB的强大数值计算功能,可以提高Monte-Carlo抽样模拟的效率。经对某一实际管道失效概率的计算,验证了Monte-Carlo方法的可行性。     

THE MONTE-CARLO METHOD FOR RELIABILITY EVALUATION OF LARGE SCALE COMPOSITE GENERETION-TRANSMISSION SYSTEMS

The paper presents a Monte-carlo method for reliability evaluation of large scale composite generation transmission systems. The method, which possesses coputational rapidity and sufficient accuracy,is based on combining the dispersed sampling techique with the analysis approach and the optimization model of power not supplied. Because the amount of calculation increases only linearly with, the number of elements, it is specially sui- table for large scale systems. The Monte-carlo sampling draws more near the real situation than determinate state enumeration in simulating occurrence of power system outages. Also, the method can be easily generalized to involve multi-states of elements, multi-levels and stochastic distribution of loads. The results of IEEE-RTS, as example, are given and compared with ones obtained by using the other two methods.