组合转子同步电机稳定性探讨

组合转子同步电机由轴向叠片各向异性(ALA)磁阻段和面贴式永磁(SPM)段组合而成,磁路各自独立.为研究组合转子电机在开环V/f控制方式下的稳定性,采用小信号分析方法,探讨无惯性阻尼器和附加机械惯性阻尼器的组合式转子电机SPM段占转子全长之比k与电机稳定性的关系.研究结果表明,无惯性阻尼器的组合式转子电机,k值增大,稳定频率范围扩宽不明显;附加惯性阻尼器,选择合适的惯量盘惯量,调节阻尼器的粘性阻尼系数能明显扩宽稳定频率范围,且k和惯量盘惯量越大,阻尼器的粘性阻尼系数调节范围越宽,电机的稳定性越好.     

钢铁制造流程的能量流行为和能量流网络问题

为应对全球气候变化,钢铁工业在下一步技术转型中,应高度重视钢铁制造流程的能量流和能量流网络问题。本文从充分发挥钢厂三个功能的视角,阐述了钢铁制造流程中铁素物质流和碳素能量流的行为规律,提出了应注重与铁素物质流相关的碳素能量流的输入/输出特点和能量流网络构建以及相应的信息化集成调控的观点,剖析了钢铁工业节能减排的潜力,指出了提高钢厂综合竞争力和多方位服务于可持续发展的可能性,探讨了相关理论的建立、技术开发和工程化实施的策略等。     

Iterative Learning Control Algorithm with a Fixed Step

Iterative Learning Control (ILC) captures interests of many scholars because of its capability of high precision control implement without identifying plant mathematical models, and it is widely applied in control engineering. Presently, most ILC algorithms still follow the original ideas of ARIMOTO, in which the iterative-learning-rate is composed by the control error with its derivative and integral values. This kind of algorithms will result in inevitable problems such as huge computation, big storage capacity for algorithm data, and also weak robust. In order to resolve these problems, an improved iterative learning control algorithm with fixed step is proposed here which breaks the primary thought of ARIMOTO. In this algorithm, the control step is set only according to the value of the control error, which could enormously reduce the computation and storage size demanded, also improve the robust of the algorithm by not using the differential coefficient of the iterative learning error. In this paper, the convergence conditions of this proposed fixed step iterative learning algorithm is theoretically analyzed and testified. Then the algorithm is tested through simulation researches on a time-variant object with randomly set disturbance through calculation of step threshold value, algorithm robustness testing,and evaluation of the relation between convergence speed and step size. Finally the algorithm is validated on a valve-serving-cylinder system of a joint robot with time-variant parameters. Experiment results demonstrate the stability of the algorithm and also the relationship between step value and convergence rate. Both simulation and experiment testify the feasibility and validity of the new algorithm proposed here. And it is worth to noticing that this algorithm is simple but with strong robust after improvements, which provides new ideas to the research of iterative learning control algorithms.     

Material flow and core/multi-shell structures in a friction stir welded aluminum alloy with embedded copper markers

The objective of this study was to reveal the material flow and temperature distribution in a thick aluminum plate during friction stir welding and examine the subsequent microstructural change with particular attention to the reaction between copper marker and aluminum matrix. It is shown that the material adjacent to the threaded pin was transported from the top to bottom non-symmetrically, and then was forced to move upwards at a small distance from the pin due to the constraint of an extrusion-die-like configuration. The interaction between the copper marker and aluminum matrix led to the formation of (i) a unique core/multi-shell microstructure consisting of copper core, inner shell of AlCu and outer shell of Al₂Cu, and (ii) a composite band containing uniformly-distributed Al₂Cu particles with refined grains due to the lower stacking fault energy of copper and the pinning role of Al₂Cu particles.     

Apparent wind load effects on the tether of a kite power generator

This paper is devoted to analytical modeling of apparent wind load effects on a tether constraining a power kite performing a fast crosswind motion. The refined crosswind motion law generalized for accounting the kite control is derived in the case of equilibrium motion of the kite. The paper also presents the study of the effect of tether sag based on the mathematical model of a gently sloping tether line.     

25 m射电望远镜俯仰系统的全过程建模

最优控制可有效提高大射电望远镜的指向精度,但其控制器设计需要精确的被控对象模型。为此,文中依据其有限元模型,建立了俯仰系统从电机到副面运动的全过程模型。首先,应用模态坐标法建立了大齿轮和主反射面模型,体现了结构主体刚柔混合特性;然后,应用Euler-Bernoulli梁方程式推导了副面撑杆和副面模型,反映了结构末端柔性;最后,通过仿真分析验证了该建模方法的准确性和有效性。与目前已有的射电望远镜建模方法相比,该方法的优点在于在天线设计阶段就可模拟各部分的实际运转状况,可提前发现不足。     

Calibrating whole building energy models: An evidence-based methodology

This paper reviews existing case studies and methods for calibrating whole building energy models to measured data. This research describes a systematic, evidence-based methodology for the calibration of these models. Under this methodology, parameter values in the final calibrated model reference the source of information used to make changes to the initial model. Thus, the final model is based solely on evidence. Version control software stores a complete record of the calibration process, and the evidence on which the final model is based. Future users can review the changes made throughout the calibration process along with the supporting evidence. In addition to the evidence-based methodology, this paper also describes a new zoning process that represents the real building more closely than the typical core and four perimeter zone approach. Though the methodology is intended to apply to detailed calibration studies with high resolution measured data, the primary aspects of the methodology (evidence-based approach, version control, and zone-typing) are independent of the available measured data.     

提高弱电网中光伏并网逆变器稳定性的控制方法

随着电网中分布电源的增加,电网呈现出了弱电网特性。电网阻抗使得逆变器与电网之间存在相互作用,对逆变器的稳定性产生影响,威胁着系统的安全稳定运行。从相位角度分析了电网阻抗对系统稳定性的影响,电网阻抗的增加会导致系统相位降低,继而使系统进入不稳定状态。为增强系统在电网阻抗变化过程中的鲁棒性,提出一种基于神经网络的逆变器变增益控制方法。该方法采用一定的控制环节补偿系统相位,采用径向基函数神经网络建模的方法实时调整系统的开环截止频率,使相位裕度始终保持最大值。仿真结果验证了本文的理论分析。     

Neural Network–Based Intelligent Compaction Analyzer for Estimating Compaction Quality of Hot Asphalt Mixes

Continuous real-time estimating of compaction quality during the construction of a hot mix asphalt (HMA) pavement is addressed in this paper. The densification of asphalt pavements during construction usually is accomplished by using vibratory compactors. During compaction, the compactor and the asphalt mat form a coupled system whose dynamics are influenced by the changing stiffness of the mat. The measured vibrations of the compactor along with process parameters such as lift thickness, mix type, mix temperature, and compaction pressure can be used to predict the asphalt mat density. Contrary to existing techniques in the literature in which a model is developed to fit experimental data and to predict mat density, a neural network-based approach is adopted that is model-free and uses pattern-recognition techniques to estimate density. The neural network is designed to read the entire frequency spectrum of roller vibrations and to classify these vibrations into different levels. The intelligent asphalt compaction analyzer (IACA) is then trained to convert these vibration levels into a “number” indicative of the asphalt mat density at a given location. This two-step process eliminates the need for regression analysis and produces more accurate density measurements than those reported elsewhere in the literature. Compaction studies of HMA mixes on a stiff subgrade indicate that the changes in the vibration characteristics of the roller are attributable to an increased compaction of the HMA base. The results also show that, with the neural network working as a classifier, the IACA can estimate the density continuously, and in real time, with accuracy levels adequate for quality control in the field.