基于请求应答方式消息传递机制有色Petri网模型

分布式系统的对等模型中,各个节点从功能上看是对等关系,相互之间进行消息的请求和响应。讨论了一种将广播请求和点到点应答两种通信方式相结合的消息传递机制FMPRR(Fast Message Passing based on Request Response mode)的实现,这种机制采用wormhole寻径流控策略。用有色Petri网来形式化描述和分析FMPRR,通过模型状态空间分析结果,对FMPRR消息传递机制进行验证。     

一种基于标签的抗虫洞攻击MMCL算法

针对移动传感器网络节点定位算法的安全性问题,分析了虫洞攻击对MMCL算法的影响.为抵御虫洞攻击,改善定位性能,提出了一种基于标签的抗虫洞MMCL算法LB-MMCL(Label-Based Multi-hop Monte Carlo Localization).该算法基于节点邻居列表中的异常,依据距离约束机制和消息唯一性原则,标记并区分虫洞两端受攻击的节点,断开虚假链路,以减轻虫洞攻击的影响,达到抵御攻击的目的.仿真结果表明,在无需额外硬件辅助下,LB-MMCL算法能有效抵御虫洞攻击,改善节点定位精度和定位率.     

基于分步算法的裂缝性碳酸盐岩油藏大尺度酸化数值模拟

碳酸盐岩储层非均质性强,基质酸化时常形成酸蚀蚓孔。蚓孔作为优势通道能够有效沟通裂缝系统、增加酸化作用距离。限于计算精度和模型收敛条件,多数学者往往在厘米尺度对裂缝性地层酸化进行小规模研究。但酸化尤其是深部酸化的作用距离通常在米级,油田现场亟需一种适合大尺度地层的高效酸化模拟方法。基于经典的双尺度连续模型,利用离散裂缝网络模型,求解了径向流裂缝性地层蚓孔扩展问题;提出的分步算法可忽略近井已酸化区域的溶蚀扩孔过程,仅需考虑不同酸化阶段对蚓孔产生实际影响的裂缝,并可追踪主蚓孔的生长动态。研究表明,有限的计算域边界将为模拟结果带来较大误差,在酸化区域外设置一定的渗流阻力区,可以有效模拟蚓孔在无限大地层中的生长过程;同时流场分析表明,裂缝周围存在局部控制域,控制域尺寸与裂缝在酸液注入方向上的投影有关,与裂缝位置无关;蚓孔的生长轨迹由控制域覆盖蚓孔尖端的裂缝决定,与其他裂缝无关;入口压力和主蚓孔位置的酸液浓度与注酸时间的对数成线性关系;提出的分步算法具有较高的计算效率,可逐步模拟蚓孔在大尺度、复杂裂缝地层中的生长过程。     

Cold heavy oil production with sand simulation including wormhole propagation and foamy oil behaviour

Cold heavy oil production with sand (CHOPS) is a primary production method used for heavy oil reservoirs with no requirements of external energy consumption. A new model for CHOPS is simulated by incorporating wormhole propagation and foamy oil behaviour mechanisms to evaluate the recovery of an extra-heavy oil reservoir of the Athabasca region. The most critical mechanisms of CHOPS, wormhole propagation and foamy oil behaviour, are captured by using Wang's model for wormhole propagation and Uddin's model of gas exsolution. After 120 months of simulation, 53.38%, 99.76%, and 100% of wormhole propagation were achieved during three time steps of 12, 48, and 60 months, respectively, toward the sides of the studied reservoir section. The propagation was achieved within all directions of the investigated section compatible with the erratic nature of wormhole propagation. Furthermore, the application of Uddin's model was incorporated into production by reducing the fluid viscosity as a result of the foamy oil behaviour. Over 3% of the recovery was achieved at the end of the primary CHOPS phase simulation by taking into account that we modelled the recovery based on highly viscous oil with API 7.5. Finally, the model can provide an improved understanding of the primary CHOPS process by considering its two significant mechanisms.     

A deep learning based latency aware predictive routing model for network-on-chip architectures

Network on Chip (NoC) is an evolving platform for communications related applications, which are executed on a single silicon chip. There are several routing models in NoC architectures, but the accuracy of these models is limited, and the existing models are degraded because of over and under fitting issues. This research introduces the new deep learning-based latency aware predictive routing model for on-chip networks to route packets with better performance and power efficiency. The deep learning model used in this research is a new convolutional residual gated recurrent unit (CRGRU) with queuing theory. Moreover, the source and channel queuing delay is comprised of features to learn spatial and sequential information that improves the overall prediction accuracy. This router is modified by the intrusion of the Router States Monitor unit and the CRGRU hardware engine. The work is executed using the Xilinx platform, and the performance measures like latency and throughput are obtained by varying the network size as $4\times 4$, $8\times 8$, and $12\times 12$ and also varying the buffer space and length as $L=4,B=9$, $L=9,B=4$, and $L=14,B=3$, respectively. In addition, the squared correlation coefficient (SCC) and normalized root mean square error (NRMSE) are evaluated and compared with existing learning models to validate the proposed model.