可逆固体氧化物电池在低碳能源系统中的应用前景与挑战

允许可逆操作的固体氧化物电池因其同时拥有极高的发电和电解效率而备受关注。随着固体氧化物电池集成电站技术日趋成熟,对于SOC在能源系统中的应用模式尚缺少全面的讨论。围绕SOC在能源系统中的应用前景和挑战,从技术优势和潜在问题2个方面,分别总结了SOC在微网、输网和配网层面的应用现状和研究进展,同时结合能源互联网的发展理念和要求,对基于SOC电站和H2的新型能源利用模式进行了探讨和展望。结合未来高效的储氢、输氢、用氢模式,SOC在综合能源系统中的应用将极具前景。     

煤粉浓缩器在超细煤粉再燃系统中的应用

超细煤粉再燃降低氮氧化物排放技术具有较强的经济性和实用性,而超细煤粉的制备是这项技术实施的关键。采用离心式旋转煤粉浓缩器对三次风中的超细煤粉进行浓缩,弱化了三次风对机组运行的不良影响,同时解决了超细煤粉的来源问题,为再燃技术的实施奠定了基础。     

温家山隧道施工爆破设计

根据石头河水库防洪抢险道路温家山隧道围岩(f=6)较为破碎、裂隙发育等特性,通过分析计算确定了光爆参数、炮眼布置和炸药量的控制等,提出了较合理的全断面掘进光面爆破设计,可供同类工程参考。     

背面熔化对颗粒硅带表面质量的改善

衬底的表面质量对外延薄膜有着显著的影响。为获得高质量的衬底，我们改进了SSP的制备方式。将制备的SSP硅带进行背面熔化后，硅带背面的表面质量得到大幅改善，晶粒明显增大。通过SEM和金相显微镜观察，对两种SSP进行了比较，分析了产生这些变化的原因，并讨论了改进建议。     

指定压力边界条件在无限空间自然对流数值模拟中的应用

探讨了指定压力边界条件在无限空间自然对流模拟中的数值实施方法。指出了由边界的指定压力转化为速度的方法。以常壁温圆柱在无限空间的二维层流自然对流为例,实施了这种方法,计算结果与其它数值解、试验值和分析解的对比表明,本方法不但实施方便而且可以采用较小的计算区域而获得正确的结果,节省了计算时间和计算存储量。     

螺杆泵采油提产增效技术研究

通过统计大庆油田螺杆泵井和抽油机井近10年沉没度现状,发现螺杆泵井平均沉没度比抽油机井平均沉没度高212m。制约螺杆泵采油的4个主要影响因素分别是:在螺杆泵理论排量、定子管直径相同情况下,1:2型线结构螺杆泵因导程长易漏失,导致泵效降低,又因偏心距大,导致杆管偏磨能耗增加,严重时会导致杆管损坏检泵,降低产量;井下液体流动性影响泵的充满程度,导致转速上调但产量不增加;螺杆泵常规驱动装置传动效率低,参数调整不方便;生产运行参数不合理导致沉没度高,抑制产液。根据各项影响因素制定实施了相应解决措施,研制了3:4型线单螺杆泵、螺杆泵井下防偏磨装置、螺杆泵井除气降黏提液辅助装置、螺杆泵地面直接驱动装置,并应用了等排量对比法与弹性调参法。措施应用后,平均单井日产液量增加6.2t/d,系统效率提高2.5%。     

基于有限元仿真的载重子午线轮胎降噪方法研究

通过有限元仿真分析研究载重子午线轮胎结构对振动噪声的影响。建立了轮胎有限元分析模型并对其合理性进行了试验验证;在原轮胎结构的基础上,对带束层结构和胎面花纹进行优化设计,利用声学仿真软件进行仿真分析,得到了轮胎各设计方案的振动噪声。结果表明,带束层结构和胎面花纹设计均会影响轮胎振动噪声,带束层结构和胎面花纹优化后最大降噪量分别达到3.09和3.9dB。     

一种基于规则的软件体系结构层性能演化优化方法

目前基于规则的软件体系结构（Software Architecture,简记为SA）层性能优化方法大多未充分考虑优化过程中规则的使用次数和使用顺序的不确定性,导致了搜索空间受限而难以获取更优的性能改进方案.针对这一问题并以最小化系统响应时间为优化目标,文中首先定义一种基于规则的SA层性能优化模型RPOM,以将SA层性能优化抽象为求解最优规则序列的数学问题;然后设计一种支持SA层性能改进规则序列执行的框架RSEF;进一步提出一种采用约束检查、修复及统计学习机制的演化求解算法EA4PO;最后以Web应用为案例与已有方法进行实验对比.结果表明：（1）本文方法较已有方法可以获取更短的系统响应时间;（2） EA4PO所引入的统计学习机制可显著提高演化求解算法的收敛速度和解质量.     

Adaptive VM Management with Two Phase Power Consumption Cost Models in Cloud Datacenter

As cloud computing models have evolved from clusters to large-scale data centers, reducing the energy consumption, which is a large part of the overall operating expense of data centers, has received much attention lately. From a cluster-level viewpoint, the most popular method for an energy efficient cloud is Dynamic Right Sizing (DRS), which turns off idle servers that do not have any virtual resources running. To maximize the energy efficiency with DRS, one of the primary adaptive resource management strategies is a Virtual Machine (VM) migration which consolidates VM instances into as few servers as possible. In this paper, we propose a Two Phase based Adaptive Resource Management (TP-ARM) scheme that migrates VM instances from under-utilized servers that are supposed to be turned off to sustainable ones based on their monitored resource utilizations in real time. In addition, we designed a Self-Adjusting Workload Prediction (SAWP) method to improve the forecasting accuracy of resource utilization even under irregular demand patterns. From the experimental results using real cloud servers, we show that our proposed schemes provide the superior performance of energy consumption, resource utilization and job completion time over existing resource allocation schemes.     

SPSIC: Semi-Physical Simulation for IoT Clouds

Recent years have witnessed various successful demonstrations of the emerging IoT technologies, while the researchers still need to face a lot of technological challenges. It is necessary to verify and evaluate the relevant theories and calculations before the application of IoT, and that is why building a simulation platform for the IoT becomes so important, especially for a large scale IoT to meet the requirement of a scale perception in a large scope. The IoT which always contains a complicated network and communication system has made the network simulation software OPNET Modeler to be a good choice for it. Moreover, the IoT has transfer all kinds of “objects” that humans need into the form of data by various sensing equipment and intelligent devices, and those data will be stored, analyzed and processed by cloud computing finally. The paper presents an innovative method to establish an intelligent, independent and expandable data driven IoT service platform by OPNET’s Semi-Physical Simulation to combine the simulated network for IoT with the real Cloud computing(SPSIC) which applies real network to achieve the long-term surveillance, management, sharing and analysis of the collected data at any time.