纯电动汽车磷酸铁锂电池组的建模及优化

鉴于传统神经网络和支持向量机机理复杂、计算量大的缺陷,很难实时跟踪磷酸铁锂电池组复杂快速的内部反应,影响电池荷电状态的估算精度,提出应用一种简单、有效的极限学习机对一额定容量为100Ah、额定电压为72V的纯电动汽车磷酸铁锂电池组建模,并分别与BP神经网络、RBF神经网络、支持向量机进行对比。随后,以学习时间和泛化性能为优化目标,应用粒子群方法寻找最佳隐层节点个数。结果表明,基于极限学习机的磷酸铁锂电池组模型的学习时间、泛化性能优于BP神经网络、RBF神经网络、支持向量机;隐层节点优化后,模型的学习时间和泛化性能达到最优。     

大规模声学边界元法的GPU并行计算

提出一种大规模声学边界元法的高效率、高精度GPU并行计算方法.基于Burton-Miller边界积分方程,推导适于GPU的并行计算格式并实现了传统边界元法的GPU加速算法.为提高原型算法的效率,研究GPU数据缓存优化方法.由于GPU的双精度浮点运算能力较低,为了降低数值误差,研究基于单精度浮点运算实现的doublesingle精度算法.数值算例表明,改进的算法实现了最高89.8%的GPU使用效率,且数值精度与直接使用双精度数相当,而计算时间仅为其1/28,显存消耗也仅为其一半.该方法可在普通PC机(8GB内存,NVIDIA Ge Force 660 Ti显卡)上快速完成自由度超过300万的大规模声学边界元分析,计算速度和内存消耗均优于快速边界元法.     

二冷配水优化建模与混合自适应PSO算法求解

针对连铸二冷区生产环境复杂且存在着大量水雾干扰的情况,建立了连铸水量优化模型并提出了一种混合的自适应粒子群算法来求解连铸二冷水优化问题。依据冶金过程中的工艺要求建立了二冷水量优化模型,并在经典的PSO算法基础上提出了适合该问题求解了混合自适应PSO算法。由于连铸过程存在着偏微分方程约束,传统的优化方法容易陷入局部最优解,不能达到很好的动态优化效果。研究了粒子群算法,基于种群的多样性,不断的自适应的更新粒子群算法中参数,将禁忌搜索的方法和传统的粒子群算法结合,增强了算法的局部搜索能力和全局寻找全局最优的能力。将该算法应用到连铸二冷水动态优化中,实验结果表面该算法能够快速有效的求解该优化问题。该方法用于连铸二冷水优化是可行的、有效的。     

IGCC空气侧整体综合优化的研究

主要符号表X。。氮气回注系数如空分整体化系数E。；c燃气轮机压气机效率G。l回注氮气流量万燃气轮机透平前温尼。”系统相对效率G。。煤气氮气掺混流量Ea；，c空气专用压缩机效率N。”系统相对功率1前言整体煤气化联合循环（IGCC）系统模拟与优化逐步受到重视，开展了许多相关研究［‘－‘］。但是，现有的方法［‘，‘］一个突出的问题，它是在设定的具体流程方案下的数值分析，这常常导致落入“局部优化掩盖了整体最优”的误区。本文是研究以空分系统为中心的空气侧子系统的整体优化，阐述基本概念，尝试新方法，并结合大型商业化装…     

混合制冷剂循环液化天然气流程的优化分析

在液化天然气流程中,混合制冷剂循环液化天然气流程由于其机组设备少、流程简单、管理方便等优点而备受国内外关注。本文对两种混合制冷剂循环液化天然气流程分别以流程中压缩机耗功最小、压缩机耗功与丙烷预冷量之和最小为目标函数进行优化,得到了最优流程参数及相应的流程性能参数;并对计算结果进行分析。     

8T���߹��͸��³�������ĵ���Ż������

介绍了一种强磁场高温超导螺线管磁体电磁性能的优化方法。通过编写Matlab数据处理程序优化了一个中心磁感应强度为8T、内径为20cm的磁体。利用有限元分析法分析已优化磁体的磁场分布,得到磁体在垂直方向的最大磁感应强度值与Matlab数据处理程序计算的结果基本吻合。     

采用PEABr添加剂获得高效且稳定的碳基CsPbBr3太阳能电池

全无机CsPbBr₃钙钛矿材料因其本征稳定性好、成本低廉从而在光伏领域展现出巨大的应用潜力,但目前CsPbBr₃太阳能电池的光电转换效率仍远低于其他体系的钙钛矿太阳能电池.本文以无空穴传输层结构的碳基CsPbBr₃全无机钙钛矿电池作为研究对象,以多步旋涂法为基础,通过在PbBr₂(DMF)溶液中添加2-苯乙胺溴盐(PEABr)来调控CsPbBr₃薄膜的结晶质量,降低薄膜缺陷态密度,钝化晶粒间界,并对其中的关键工艺参数包括CsBr的用量(旋涂次数)、旋涂PbBr₂薄膜时的衬底预热温度以及退火温度进行了优化.最终在大气环境下获得了兼具稳定和高效的无空穴传输层结构的碳基CsPbBr₃太阳能电池,器件的光电转换效率达到8.25%,并在无封装条件下保存1500 h仍可保持90%以上的效率,对于进一步拓展CsPbBr₃钙钛矿电池的优化设计思路具有重要意义.     

叠加态下3色场波形优化增大谐波辐射截止能量及强度

在He~+离子1s态和2s态的叠加态作为初始态时,提出一种优化3束激光场来增大谐波截止能量和强度的方法.结果表明:在叠加初始态下,谐波强度要比基态初始态下增强3个数量级以上.随后,在3色场波形优化下,谐波截止能量又得到增大,进而获得一个高强度、高光子能量的谐波光谱连续区.通过叠加光谱连续区上最后100次的谐波可以获得一个27 as的孤立阿秒脉冲.     

New generalized zero forcing beamforming for serving more users in energy-harvesting enabled networks

This paper considers a multi-user wireless communication network supported by a multiple-antenna base station (BS), where the users who are located sufficiently close to the BS employ wireless energy harvesting (EH) to replenish their energy needs. The objective of this work is to design an efficient beamforming to maximize the minimum throughput among all the information users (IUs), subject to EH constraints. In this regard, transmit time-switching approach is employed, where energy and information are transmitted over different fractions of a time-slot. To achieve efficient EH, a conjugate beamforming (matched filtering) is applied. To design efficient information beamforming for max–min throughput optimization, conventional zero-forcing (ZF) beamforming can be adopted, however, it will not suppress multi-user interference if the number of users is greater than the number of antennas at the BS. To this end, different from the existing works which employ regularized zero-forcing (RZF) beamforming, this work proposes a new generalized zero-forcing (GZF) beamforming, which promises better max–min throughput compared to that achieved by the RZF beamforming. A new path-following algorithm is developed to achieve max–min throughput optimization by GZF beamforming, which is based on a simple convex quadratic program over each iteration.     

Throughput maximization of an IRS-assisted wireless powered network with interference: A deep unsupervised learning approach

We consider an intelligent reflecting surface (IRS)-assisted wireless powered communication network (WPCN) in which a multi antenna power beacon (PB) sends a dedicated energy signal to a wireless powered source. The source first harvests energy and then utilizing this harvested energy, it sends an information signal to destination where an external interference may also be present. For the considered system model, we formulated an analytical problem in which the objective is to maximize the throughput by jointly optimizing the energy harvesting (EH) time and IRS phase shift matrices. The optimization problem is high dimensional non-convex, thus a good quality solution can be obtained by invoking any state-of-the-art algorithm such as Genetic algorithm (GA). It is well-known that the performance of GA is generally remarkable, however it incurs a high computational complexity. To this end, we propose a deep unsupervised learning (DUL) based approach in which a neural network (NN) is trained very efficiently as time-consuming task of labeling a data set is not required. Numerical examples show that our proposed approach achieves a better performance–complexity trade-off as it is not only several times faster but also provides almost same or even higher throughput as compared to the GA.