Variation of Flow Field Around Twin Cylinders with and Without the Outer Perforated Cylinder ? Numerical Study

Presence of the outer perforated cylinder reduces the direct wave impact on the inner cylinder, which has been testified by many researchers. However, the force reduction mechanism, which is complicated due to the wave-porous structure interaction, needs to be addressed in detail. The present study explains the mechanism with the aid of the computational fluid dynamics (CFD) tool STAR CCM+. This package is chosen for its capabilities to simulate viscous and turbulence effects caused by passage of waves. For the present study, flow fields around the twin cylinders with different orientations are examined with and without the outer perforated cover. Mechanism contributing to the reduction of force on the existing structure is explained in physical terms, and force reduction is quantified. The present study has direct application in the retrofitting application of offshore members.     

新型射流式元件结构设计与试验研究

射流式液动锤由于其具有钻进效率高、钻进深度不受限制等优点被成功应用于油气钻井领域。然而由于射流元件在复杂受力条件下极易破损,严重制约了射流式液动锤的使用寿命。为解决此问题,对射流式液动锤射流元件受力情况进行了数值模拟分析,通过分析射流元件内部应力场分布情况,得出了射流元件破损机理,并设计了新型两体式射流元件。模拟分析研究表明,采用新型两体式射流元件可将元件内最大应力值降低一个数量级,使射流元件受力情况明显改善,可有效地提高其使用寿命。     

A land use/land cover change geospatial cyberinfrastructure to integrate big data and temporal topology

ABSTRACT

Big data have shifted spatial optimization from a purely computational-intensive problem to a data-intensive challenge. This is especially the case for spatiotemporal (ST) land use/land cover change (LUCC) research. In addition to greater variety, for example, from sensing platforms, big data offer datasets at higher spatial and temporal resolutions; these new offerings require new methods to optimize data handling and analysis. We propose a LUCC-based geospatial cyberinfrastructure (GCI) that optimizes big data handling and analysis, in this case with raster data. The GCI provides three levels of optimization. First, we employ spatial optimization with graph-based image segmentation. Second, we propose ST Atom Model to temporally optimize the image segments for LUCC. At last, the first two domain ST optimizations are supported by the computational optimization for big data analysis. The evaluation is conducted using DMTI (DMTI Spatial Inc.) Satellite StreetView imagery datasets acquired for the Greater Montreal area, Canada in 2006, 2009, and 2012 (534 GB, 60 cm spatial resolution, RGB image). Our LUCC-based GCI builds an optimization bridge among LUCC, ST modelling, and big data.     

Real-time genetic spatial optimization to improve forest fire spread forecasting in high-performance computing environments

Abstract

Forest fires are a kind of natural hazard with a high number of occurrences in southern European countries. To avoid major damages and to improve forest fire management, one can use forest fire spread simulators to predict fire behavior. When providing forest fire predictions, there are two main considerations: accuracy and computation time. In the context of natural hazards simulation, it is well known that part of the final forecast error comes from uncertainty in the input data. These data typically consist of a set of GIS files, which should be appropriately conflated. For this reason, several input data calibration methods have been developed by the scientific community. In this work, the Two-Stage calibration methodology, which has been shown to provide good results, is used. This calibration strategy is computationally intensive and time-consuming because it uses a Genetic Algorithm as a solution. Taking into account the aspect of urgency in forest fire spread prediction, it is necessary to maintain a balance between accuracy and the time needed to calibrate the input parameters. In order to take advantage of this technique, one must deal with the problem that some of the obtained solutions are impractical, since they involve simulation times that are too long, preventing the prediction system from being deployed at an operational level. A new method which finds the minimum resolution reduction for such long simulations, keeping accuracy loss to a known interval, is proposed. The proposed improvement is based on a time-aware core allocation policy that enables real-time forest fire spread forecasting. The final prediction system is a cyberinfrastructure, which enables forest fire spread prediction at real time.     

Enabling point pattern analysis on spatial big data using cloud computing: optimizing and accelerating Ripley’s K function

Performing point pattern analysis using Ripley’s K function on point events of large size is computationally intensive as it involves massive point-wise comparisons, time-consuming edge effect correction weights calculation, and a large number of simulations. This article presented two strategies to optimize the algorithm for point pattern analysis using Ripley’s K function and utilized cloud computing to further accelerate the optimized algorithm. The first optimization sorted the points on their x and y coordinates and thus narrowed the scope of searching for neighboring points down to a rectangular area around each point in estimating K function. Using the actual study area in computing edge effect correction weights is essential to estimate an unbiased K function, but is very computationally intensive if the study area is of complex shape. The second optimization reused the previously computed weights to avoid repeating expensive weights calculation. The optimized algorithm was then parallelized using Open Multi-Processing (OpenMP) and hybrid Message Passing Interface (MPI)/OpenMP on the cloud computing platform. Performance testing showed that the optimizations effectively accelerated point pattern analysis using K function by a factor of 8 using both the sequential version and the OpenMP-parallel version of the optimized algorithm. While the OpenMP-based parallelization achieved good scalability with respect to the number of CPU cores utilized and the problem size, the hybrid MPI/OpenMP-based parallelization significantly shortened the time for estimating K function and performing simulations by utilizing computing resources on multiple computing nodes. Computational challenge imposed by point pattern analysis tasks on point events of large size involving a large number of simulations can be addressed by utilizing elastic, distributed cloud resources.     

人口城镇化与土地城镇化协调性测度及优化——以南昌市为例

人口与土地是城镇化的重要组成部分,分析二者之间状态演化对促进城市健康发展具有重要意义。在界定人口城镇化与土地城镇化二者概念基础上,基于人口构成、人口素质、人口生活、城镇规模、城镇投入、城镇产出等构建评价指标体系,并通过耦合协调模型对省会型城市南昌市2002~2011年两者之间的协调关系进行测度和分析,结果表明：① 人口城镇化指数与土地城镇化指数呈波动变化并持续上涨,城镇发展由土地滞后型向人口滞后型转变;② 人口城镇化与土地城镇化的协调状态由失调衰退区向过渡协调区演进,发展类型则由高度不协调状态向勉强协调状态过渡。在此基础上分析了南昌市人口城镇化速度滞后于土地城镇化速度的原因,指出两者关系主要受到城市发展外在制度与内在基础的共同作用,并认为构建人口与土地城镇化双重优化路径是协调人口城镇化与土地城镇化同步发展的有效途径。     

伪卫星独立组网布站优化设计

本文针对伪卫星独立组网站址设计遍历搜索算法计算量过大的问题,梳理了顾及地形条件的伪卫星布站设计实施方案。采用逐点外推DEM格网可视域分析方法,避免了交叉点的重复计算问题。采用基于优选基础构型的分区搜索方法,避免了大量无效计算点。实际计算表明,分区搜索结合金字塔策略可以使计算量降低为原有计算量的8.43×10~(-7)%。     

改进的果蝇优化与Tikhonov正则化相结合的病态问题稳健解法

在对基本果蝇优化算法的优化流程进行深入分析的基础上,通过改变其随机搜索方向与增加搜索半径调整系数,给出了一种改进的果蝇优化算法(IFOA)。并在IFOA算法的目标函数中引入正则化项,提出了将IFOA算法与Tikhonov正则化方法进行结合以进行病态问题解算的方法。通过实例分析表明:该方法的解算精度要优于遗传算法和单一的Tikhonov正则化方法;在观测值含有粗差时,使用最小二乘法进行求解,其结果与真值的偏差会迅速增大,而此时本文方法的解算结果具有一定的稳健性。与以遗传算法为代表的智能搜索方法相比,本文方法具有参数设置少、计算速度快、寻优过程简单等特点,在病态问题解算中更具有实用性。     

侧扫声呐图像分割的中性集合与量子粒子群算法

针对现有的侧扫声呐图像分割方法存在分割准确率不高和效率偏低的问题,提出了一种基于中性集合和量子粒子群算法的侧扫声呐图像阈值分割方法。通过基于中性集合计算图像灰度共生矩阵,实现了侧扫声呐图像精细纹理的表达,提高了分割精度;基于二维最大熵理论,采用量子粒子群算法计算二维最优分割阈值向量,实现了分割阈值向量的快速准确获取,提高了分割效率和精度。最终实现了高噪声侧扫声呐图像目标的准确、高效分割。通过对含有不同目标的侧扫声呐图像的分割试验,验证了该算法的有效性。     

蚁群算法在 InSAR 相位解缠中的应用

相位解缠是InSAR信号处理的关键过程,针对传统的枝切法存在枝切线整体长度过长和容易产生解缠"孤岛"现象等问题,基于蚁群算法提出一种改进的枝切法,通过对传统的枝切线进行优化,有效地减少枝切线长度。采用伊朗Bam地区的InSAR图像数据对算法进行实验计算与分析,表明本文算法所需设置的枝切线整体长度较短,能获得更好的相位解缠效果。