huigui

说明：  直流电机双闭环系统，实现转速和电流的反馈，根据给定的不同，电动机转速也会不同(DC motor double closed loop system)

说明：  转子系统启动时的瞬态动力学分析，有限元分析(Transient Dynamics Analysis of Rotor System at Start-up)

说明：  三个ANSYS瞬态分析的命令流：冲击载荷作用悬臂梁的阻尼振动分析；破碎锤的瞬态动力学分析；滑动摩擦接触分析(Three command streams of ANSYS transient analysis: damped vibration analysis of cantilever beam under impact load; transient dynamic analysis of crushing hammer; sliding friction contact analysis)

计算井筒注入或者生产过程中的摩阻系数，可用于井筒压力分布预测(Wellbore fraction coefficient calcuation method)

说明：  400KW光伏参与并网，4组光伏模组共同输出400KW的功率(400KW photovoltaic participates in grid connection, 4 sets of photovoltaic modules jointly output 400KW power)

说明：  Ei-centro地震波三向数据，可用于各大有限元数值仿真软件，输入地震加速度时程数据。(EI Centro seismic wave three-way data)

对等边直角三角形阵和立体三棱锥阵进行仿真建模，并进行了定向定位的误差分析，得到了相应的波形。(The simulation modeling of the equilateral triangle array and the stereoscopic three pyramid array is carried out, and the error analysis of directional positioning is carried out, and the corresponding waveforms are obtained.)

用晶闸管建立的12脉动特高压直流输电模型(12 pulse HVDC model based on thyristor)

说明：  关于3维应力应变的金属本构方程子程序代码显式vumat(On the Subprogram Code of 3-D Stress-Strain Constitutive Equation of Metals Explicit VUMA)

大坝变形的精确预测对其安全评价方面发挥着重要作用。为了进一步提高大坝变形预测精度，提出了一种改进的灰狼算法优化支持向量机的预测模型。通过引入非线性收敛因子和采用动态加权策略，提升灰狼算法优化支持向量机惩罚因子和核函数参数的能力，并以优化的参数来建立支持向量机大坝变形预测模型。选取实例数据，在与布谷鸟算法、差分进化算法、粒子群算法、基本灰狼算法优化的支持向量机预测比较中，实验结果表明，改进的灰狼算法对支持向量机参数的优化是有效的，可以提高大坝变形预测精度。(Accurate prediction of dam deformation plays an important role in its safety evaluation. In order to further improve the prediction accuracy of dam deformation, an improved grey Wolf algorithm is proposed to optimize the prediction model of support vector machine. By introducing the nonlinear convergence factor and using the dynamic weighting strategy, the gray wolf algorithm is improved to optimize the capacity of the support vector machine penalty factor and the kernel parameter, and the support vector machine dam deformation prediction model is established with the optimized parameters. The experimental results show that the Improved Grey Wolf algorithm is effective for the optimization of support vector machine parameters, and it can improve the accuracy of the deformation prediction of the dam.)