若干高维可积和不可积模型的严格解

可积和不可积模型可以描述自然科学中的诸多现象, 寻找高维非线性模型的严格解已成为可积系统的一个重要研究内容. 结合达布变换法和多线性分离变量法, 可以得到多个(2+1)维非线性模型包含任意函数的严格解, 通过选取不同的任意函数, 构造这些非线性模型新的相互激发模式. 进一步推广了形变映射理论, 建立了变系数 场和sine-Gordon以及双sine-Gordon场的形变映射关系, 从而得到高维不可积模型包含任意函数的新严格解. 对任意函数的不同选择, 构造了sine-Gordon和双sine-Gordon可积模型丰富的局域解和周期解, 如多solitoff解及其周期波推广、周期形变的蛇形孤波解以及变模的拟周期解等.     

非齐次动力学方程的一种精细积分单步方法

针对非齐次动力学方程■,结合精细积分法和微分求积法,利用同阶的显式龙格-库塔法对计算过程中待求的v_(k+i/s)(i=1,2,…,s)进行预估,提出了一种避免状态矩阵求逆的高效精细积分单步方法。该方法采用精细积分法计算e~(Ht),而Duhamel积分项采用s级s阶的时域微分求积法,计算格式统一且易于编程,可灵活实现变阶变步长。仿真结果表明,与其他单步法及预估校正-辛时间子域法进行数值比较,该方法具有高精度、高效率及良好的稳定性,在求解大规模动力系统时间响应问题中具有较大的优势。     

Neural network representations of quantum many-body states

Machine learning is currently the most active interdisciplinary field having numerous applications;additionally,machine-learning techniques are used to research quantum many-body problems.In this study,we first propose neural network quantum states(NNQSs)with general input observables and explore a few related properties,such as the tensor product and local unitary operation.Second,we determine the necessary and sufficient conditions for the representability of a general graph state using normalized NNQS.Finally,to quantify the approximation degree of a given pure state,we define the best approximation degree using normalized NNQSs.Furthermore,we observe that some 7V-qubit states can be represented by a normalized NNQS,such as separable pure states,Bell states and GHZ states.     

基于流量测点的热网变动阻力系数优化辨识

随着供热计量系统的普及，系统可以根据负荷的变化进行调整，管网的阻力系数随即发生变化。对变动阻力系数进行优化辨识是了解供热管网实时运行状况的有效手段。提出一种基于流量监测数据的供热管网变动阻力系数优化辨识方法，并利用遗传算法进行求解。对洛阳市某小区供暖季管网实际运行数据进行验证，辨识结果的相对误差在5%以内。结果表明：该方法可以在只有流量观测数据时得到精度较高的供热管网变动阻力系数，为供热系统的运行调节提供指导。     

变刚度层叠梁的力学性能研究

为满足不同场景下的功能需求，变刚度结构得到越来越广泛的应用．以机器人手臂为例，在执行操作时，需要其手臂的结构刚度足够大，避免出现过大的扭曲和变形，而在与人交互时，又需要其结构足够柔软，以保证在此过程中与人交互的安全性．该类变刚度结构可根据需求通过外部激励在柔性和刚性状态之间自由切换．在该文章中，研究分析了层叠梁结构，通过理论推导和数值模拟，对其力学性能做出了很好的预测，同时为此类结构的研究提供了有效可靠的思路和方法．     

基于不确定控制理论的最优策略研究

本文在不确定理论的框架下,研究一类带背景状态变量的最优控制模型.在乐观值准则下,利用不确定动态规划的方法,证明了不确定最优性原则,得到最优性方程.作为应用,求解一个固定缴费(DC)型养老金的最优投资策略问题,在乐观值准则下,以工资变量为背景状态变量,建立养老金模型.通过求解不确定最优性方程得到最优投资策略和最优支付率.     

Application of DEM modified with enlarged particle model to simulation of bead motion in a bead mill

We applied the discrete element method （DEM） of simulation modified by an enlarged particle model to simulate bead motion in a large bead mill. The stainless-steel bead mill has inner diameter of 102 mm and mill length of 198 mm. The bead diameter and filling ratio were fixed respectively at 0.5 mm and 85%. The agitator rotational speed was changed from 1863 to 3261 rpm. The bead motion was monitored experimentally using a high-speed video camera through a transparent mill body. For the simulation, enlarged particle sizes were set as 3-6 mm in diameter. With the DEM modified by the enlarged particle model, the motion of enlarged particles in a mill was simulated.The velocity data of the simulated enlarged particles were compared with those obtained in the experiment. The simulated velocity of the enlarged particles depends on the virtual frictional coefficient in the DEM model. The optimized value of the virtual frictional coefficient can be determined by considering the accumulated mean value. Results show that the velocity of the enlarged particles simulated increases with an increase in the optimum virtual frictional coefficient, but the simulated velocity agrees well with that determined experimentally by optimizing the virtual frictional coefficient in the simulation. The computing time in the simulation decreases with increased particle size.