基于扩展有限元法的黏聚性裂缝模型的混凝土梁断裂过程模拟

为研究混凝土梁的断裂过程,提出用基于二维扩展有限元法（eXtended Finite ElementMethod,XFEM）的黏聚性裂缝模型模拟混凝土简支梁在集中载荷作用下的断裂过程.推导考虑近裂尖奇异性的基于XFEM的黏聚性裂缝模型,得出裂缝开度随裂缝长度的变化曲线;对上述模型与相关文献用有限元结合节点释放技术对相同时间的裂缝扩展的计算结果进行比较,二者结果吻合良好,并与实际裂缝扩展过程相符.计算结果证实基于XFEM的黏聚性裂缝模型能有效进行混凝土梁的断裂过程模拟.     

优化的BP网络在抽油井故障诊断中的应用研究

抽油井故障诊断系统是油井系统产量的关键,为了更好更快地对当前油井系统进行诊断以保证石油的产量,人们利用各种各样的技术来完成这一目标。BP神经网络技术就是新兴的油井故障诊断手段之一,但是由于其容易陷于局部最优解、收敛速度慢以及泛化能力差等缺点,运用融入免疫算法浓度概念的遗传算法对BP神经网络进行优化,并将优化后的BP神经网络算法应用于抽油机现场的故障诊断过程中,结果显示优化后的BP算法有更高的诊断率,更快的运行速度,因而优化后的BP算法的寻找全局最优点的能力更强,收敛速度更快。     

Self-adaptive utility-based web session management

In the Internet, where millions of users are a click away from your site, being able to dynamically classify the workload in real time, and predict its short term behavior, is crucial for proper self-management and business efficiency. As workloads vary significantly according to current time of day, season, promotions and linking, it becomes impractical for some ecommerce sites to keep over-dimensioned infrastructures to accommodate the whole load. When server resources are exceeded, session-based admission control systems allow maintaining a high throughput in terms of properly finished sessions and QoS for a limited number of sessions; however, by denying access to excess users, the website looses potential customers.In the present study we describe the architecture of AUGURES, a system that learns to predict Web user’s intentions for visiting the site as well its resource usage. Predictions are made from information known at the time of their first request and later from navigational clicks. For this purpose we use machine learning techniques and Markov-chain models. The system uses these predictions to automatically shape QoS for the most profitable sessions, predict short-term resource needs, and dynamically provision servers according to the expected revenue and the cost to serve it. We test the AUGURES prototype on access logs from a high-traffic, online travel agency, obtaining promising results.     

基于工作流的高校科研项目管理Petri网建模

科研项目管理是高校科研信息系统的核心部分之一,利用Petri网,对复杂的科研项目管理工作流程建模,解决高校信息化中较为复杂的科研项目管理工作流问题。以高校科研项目申请流程为例,给出了相关定义,分析和解决建模中遇到的几个问题。     

基于DCC-GARCH的海上风电场出力空间相关性分析及预测

多座海上风电场出力之间存在一定的空间相关性,构建合适的风电出力相关性模型有助于提高风电出力的预测精度。针对空间相关性具有时变特性以及难以描述和衡量,提出基于动态条件相关广义自回归条件异方差（DCC-GARCH）模型的海上风电场出力相关性模型。利用多维正态分布和DCC-GARCH模型拟合多风电场的皮尔森相关系数,求解随时间变化的风电场出力空间相关系数,在准确表征空间相关性大小的同时体现空间相关性的时序变化特征。基于DCC-GARCH模型建立多座风电场出力动态空间相关性短期预测模型。基于江苏省盐城市海上风电场数据进行算例分析,结果验证了所提方法的合理性和有效性。     

弱电网下直驱风机网侧换流器建模及稳定运行控制策略分析

直驱风机网侧换流器可能因与弱电网动态交互引发系统失稳问题。为探究系统的交互机理,保证系统的稳定运行,首先对直驱风机并网模型进行了合理简化,建立了弱电网下直驱风机网侧换流器与电网交互的单输入单输出传递函数模型,并应用经典频域判据进行稳定性分析,探究电气与控制环节对于系统稳定性的影响。其次在分析锁相环导致系统失稳的原因基础上,提出了一种新型3阶锁相环控制结构设计方案,并对锁相环参数进行了多目标优化设计。结果表明,3阶锁相环具有更好的谐波衰减效果,在短路比为2的极弱电网下仍可以保持稳定运行。最后基于MATLAB/Simulink仿真平台验证了所提设计方案的有效性。     

考虑单峰分布的电热综合能源系统风险调度

不确定可再生能源并入电热综合能源系统后将引发系统运行风险,而通过合理量化风险,实现风险可控性和调度经济性平衡是电热综合能源系统风险调度的关键。依据随机风电功率的单峰分布特性构建矩模糊集并以此刻画风电功率的不确定性,同时采用分布鲁棒机会约束方法来刻画系统的运行风险,进而建立了电热综合能源系统的分布鲁棒风险调度模型。在此基础上,从风险发生概率和经济成本的角度,提出了评估不确定风电功率引发运行风险的量化指标：均值风险概率和风险调度成本。最后,以改进的巴厘岛电热综合能源系统为例进行算例分析,验证了所提模型及其风险评估指标的可行性和有效性,并给出了电热综合能源系统的最优调度决策。     

变压器绕组松动故障的混沌特征分析方法

为了更加有效地对变压器绕组松动故障进行监测与识别,提出了一种变压器绕组松动故障的混沌特征分析方法。首先,针对振动信号的混沌动力学特性,采用互信息量法和G-P算法分别确定延迟时间和嵌入维数,对变压器振动信号进行相空间重构;其次,通过判断最大Lyapunov指数是否为正,进而证明变压器振动信号的混沌特性,在此基础上分析不同程度的绕组松动故障对相空间轨迹变化的影响;最后,将关联维数、Kolmogorov熵和最大Lyapunov指数作为一组混沌特征用以量化变压器绕组发生松动故障前后振动信号的混沌特性。结果表明：变压器振动信号的最大Lyapunov指数均大于0,证实了其具有混沌特性,所得到的混沌特征能够有效反映变压器绕组松动故障。研究结果为变压器绕组松动状态监测提供了一种理论依据。     

Adaptive multi-mode switching strategy for the spherical underwater robot with hybrid thrusters

The conventional autonomous cruise and tracking system cannot transition and change its motion mode according to the underwater environment during operation, so it is of great significance to study the multi-mode switching of cruise control for Autonomous Underwater Vehicles (AUVs). In this paper, a Multi-Mode Adaptive Switching (MMAS) strategy for the Spherical Underwater Robot with Hybrid Thruster (HPSUR) was proposed, which provided the possibility for the robot to choose optimal control mode according to the unpredictable operating environment. Then, the dynamic and force of the hybrid thruster are analyzed to improve the accuracy of multi-mode switching, and the MMAS strategy is developed linking the attitude adjustment and switching problems. Furthermore, a series of multi-mode switching experiments were conducted using water-jet mode, propeller mode and hybrid mode. Finally, the experimental result was discussed, which verified the effectiveness and accuracy of the proposed MMAS strategy. The proposed control strategy has a certain reference value for the multi-mode switching of propulsion devices.     

Coordinated scheduling of production and logistics for large-scale closed-loop manufacturing using Benders decomposition optimization

This paper studies coordinated scheduling of production and logistics for a large-scale closed-loop manufacturing system by integrating its manufacturing and recycling process. In addition to the forward manufacturing process, different recycling units in reverse recycling process are also studied. A decentralized network is designed to formulate the coordinated scheduling problem as a mixed integer programming model with both binary and integer variables. As the problem for closed-loop manufacturing is large-scale and computational-consuming in nature, the model is divided into integer variable sub-models and complex binary variable sub-models for preprocessing and reprocessing respectively. An iterative solution approach by Benders decomposition is developed to accelerate the solving efficiency in large-scale case by updating custom constraints. A case study is conducted to investigate the managerial implications of the decentralized network for the closed-loop manufacturing system. Computational experiments demonstrate the validity and efficiency of the proposed iterative solution approach for the large-scale scenarios.