用Python计算速度场3D的散度

问题描述

我正在尝试计算多相流设置（固体浸入流体中）中3D速度场的发散度。如果我们假设u，v，w是三个3D numpy数组的速度分量（每个n x n x n），这是我计算散度的函数： / p>

def calc_divergence_velocity(df,h=0.025):
    """        
        @param df: A dataframe with the entire vector field with columns [x,y,z,u,v,w] with 
                   x,z indicating the 3D coordinates of each point in the field and u,w
                   the velocities in the x,z directions respectively.  
        @param h: This is the dimension of a single side of the 3D (uniform) grid. Used 
                  as input to numpy.gradient() function.
    """

    """
        Reshape dataframe columns to get 3D numpy arrays (dim = 80) so each u,w is a 
        80x80x80 ndarray.
    """ 
   
    u = df['u'].values.reshape((dim,dim,dim))
    v = df['v'].values.reshape((dim,dim))
    w = df['w'].values.reshape((dim,dim))
    
    #Supply x,z coordinates appropriately.
    
    #Note: Only a scalar `h` has been supplied to np.gradient because
    #the type of grid we are dealing with is a uniform grid with each
    #grid cell having the same dimensions in x,z directions.

    u_grad = np.gradient(u,h,axis=0)   #central diff. du_dx
    v_grad = np.gradient(v,axis=1)   #central diff. dv_dy
    w_grad = np.gradient(w,axis=2)   #central diff. dw_dz
    
    """
        The `mask` column in the dataframe is a binary column indicating the locations
        in the field where we are interested in measuring divergence.
        The problem I am looking at is multi-phase flow with solid particles and a fluid
        hence we are only interested in the fluid locations.
    """
    sdf = df['mask'].values.reshape((dim,dim))  
    

    div = (u_grad*sdf) + (v_grad*sdf) + (w_grad*sdf)

    return div

我遇到的问题是我看到的差异值太高。 例如，下面的图片展示了一个值在[-350,350]之间的分布，而在我的情况下，大多数值在技术上应该接近零，并且在[20，-20]之间。这告诉我我不正确地计算了偏差，并且我希望获得一些有关如何更正上述函数以正确计算偏差的指标。据我所知（如果我错了，请纠正我），我认为所做的工作类似于this赞成SO响应。预先感谢！

解决方法

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