fem

并联机构 正反解 六自由度并联机构空间运动结算 实例分析(DOF parallel mechanism inverse solution space motion parallel mechanism settled case analysis)

代数涡粘模型用于计算近壁湍流的速度场，1D 4阶RK 积分(Algebraic eddy viscosity model is used to calculate the near-wall turbulence velocity field, 1D 4-order RK integration)

采用数值模拟方法对风一车一桥耦合系统的车桥气动特性进行分析研究，模拟计算了不同工况下车 辆、桥梁的气动力系数．分析了车桥问相互的气动影响，研究结果表明．车桥耦合系统与桥梁和车辆各自单体 相比较．气动力系数差异较大．故建议进行风一车一桥系统耦合振动分析时。车桥气动力系数应考虑车桥问 的气动影响．(The numerical simulation method axle aerodynamic characteristics of the the wind car bridge coupling system analysis, simulation under different conditions of vehicles, bridges aerodynamic coefficients. Analysis of the influence of the mutual pneumatic axle Q, the study results show that. The axle coupling system with the respective bridges and vehicles monomer comparison. Aerodynamic coefficients quite different. It is suggested that wind car bridge system coupled vibration analysis. Axle aerodynamic coefficients should consider impact of axle ask pneumatic.)

牛顿迭代法，用于求解非线性方程，具体使用方法见程序内部说明。(Newton iteration for solving nonlinear equations, the specific use of the procedure see the internal note.)

电力系统谐波潮流计算程序，根据各个节点谐波电力发射值采用解耦算法计算各个节点谐波电压(Harmonic voltage power system harmonic flow calculation procedures decoupling algorithm based on the harmonic power emitted value of each node, each node)

平面单元四边形八结点单元的应力应变分析,考虑了应力应变梯度(the analysis of 2D plane including stress and strain)

编程序，按如下要求来求解n元一次线性方程组（假设方程组具有唯一解）。 （1）方程个数n之值由用户通过键盘输入； （2）方程组存放在“增广矩阵”A之中，而n行n+1列的A存储空间通过new来动态分配，且A的各元素值也由用户通过键盘输入 (Allocation procedures, the following requirements to solve the n-a system of linear equations (assuming a unique solution of equations). (1) the number n of the value equation by the user through the keyboard input (2) equations stored in the augmented matrix A, while n line n+ 1 column A storage space through the new dynamically allocated, and A The value of each element by the user through the keyboard input)

针对多径效应的影响，提出了一种基于矩阵束的MIMO 雷达低仰角快速估计方法。该方法同时考虑了发射多径信号和接收多径信号，采用单样本数信号矢量构造了一个前后向矩阵束，并利用两个酉矩阵对该矩阵束进行降维处理，最后采用广义特征值分解的总体最小二乘法来估计目标角度。算法不需要估计数据协方差矩阵，可在低 信噪比和单样本数情况下，有效地克服多径效应，实现同时多目标低仰角估计，相比最大似然算法，避免了谱峰搜索，计算量小。仿真结果验证了该算法的有效性。(To overcome the multipath effect, a fast algorithm for low elevation estimation for MIMO radar via matrix pencil is proposed. The signals both in transmit multipath and receive multipath are considered. Firstly, a forward-backward matrix pencil is formed based on the single-sampled vector. Secondly, the reduced-dimensional processing is applied to the matrix pencil via two unitary matrices. Finally, the generalized eigen-decomposition is employed to estimate the low elevation in multipath environments directly. Without the covariance matrix estimation, the proposed method can overcome multipath effect effectively in case of low SNR and estimate multi-target simultaneously. Compared with Maximum Likelihood (ML) method, it avoids spectrum peak searching and reduces the computational burden. Numerical results verify the effectiveness of this method)

计算牛顿和拉格朗日插值方法,用户输入插值结点(calculated Newton and Lagrange interpolation methods, user input interpolation nodes)

说明：  自己编写的期望最大化（EM）算法的MATLAB实现，里面有较为运行方法和程序说明，对新手有很好的帮助( I have written the expectation-maximization (EM) algorithm in MATLAB, which has run more methods and procedures described, there is a good help for the novice)