STFT

fluent多相流DPM模型中使用用户自定义函数对颗粒进行统计(DPM fluent multiphase flow model using user-defined functions for particle statistics)

计算流体力学重要算法 simple算法计算顶盖驱动流动。(An example for using SIMPLE algorithm to computing lid driven flow in CFD)

A FORTRAN IV function to compute the probability of a standard normal deviate

可实现OFDM电力载波通信的前导序列检测，能进行连续多个前导检测(Can achieve OFDM power carrier communication leading sequence detection, can carry out a number of continuous preamble detection)

weno5求解sod激波管问题，是初学计算流编程时做作业的题目，供参考(sod shock problem solve by weno5 for reference)

大规模配电网118节点的潮流计算程序，辐射状网络，含原始数据。(Large-scale distribution network 118 nodes of the power flow calculation procedures, radial network, with the original data.)

采用动量梯度下降算法训练BP网络，采用两种训练方法，即 L-M 优化算法（trainlm）和贝叶斯正则化算法（trainbr），用以训练 BP 网络(Gradient descent algorithm using momentum BP network training, using two training methods, namely, LM optimization algorithm (trainlm) and Bayesian regularization algorithm (trainbr), to facilitate the training of BP network)

begining of UMAT in abaqus

说明：  常用的fluent边界条件的控制UDF，包括动网格UDF(Commonly used control UDF of fluent boundary conditions, including moving grid UDF)

说明：  共轭梯度法（Conjugate Gradient）是介于最速下降法与牛顿法之间的一个方法，它仅需利用一阶导数信息，但克服了最速下降法收敛慢的缺点，又避免了牛顿法需要存储和计算Hesse矩阵并求逆的缺点，共轭梯度法不仅是解决大型线性方程组最有用的方法之一，也是解大型非线性最优化最有效的算法之一。 在各种优化算法中，共轭梯度法是非常重要的一种。其优点是所需存储量小，具有步收敛性，稳定性高，而且不需要任何外来参数。(Conjugate gradient method Gradient) is a method between the steepest descent method and Newton's method. It only uses the first derivative information, but overcomes the disadvantage of slow convergence of steepest descent method, and avoids the disadvantage of storing and calculating Hesse matrix and solving inverse of Newton's method. Conjugate gradient method is not only one of the most useful methods to solve large-scale linear equations, but also the most effective method to solve large-scale nonlinear optimization One of the algorithms of. Among all kinds of optimization algorithms, conjugate gradient method is very important. It has the advantages of small storage, step convergence, high stability and no external parameters.)