极坐标图马格努斯效应未显示正确数据

基本问题是.quiver极坐标Axes对象的方法仍然需要笛卡尔坐标中的矢量分量，因此您需要自己将 theta 和径向分量转换为 x 和 y：

for r in range(len(radius)):
    for t in range(len(theta)):

        VelocityRadius = (1.0 - (RadiusColumn**2/radius[r]**2)) * VelocityInfinity * np.cos(theta[t])
        VelocityTheta = - (1.0 + (RadiusColumn**2/radius[r]**2))* VelocityInfinity * np.sin(theta[t]) - (VortexStrength/(2*np.pi*radius[r]))
        TotalVelocity = np.linalg.norm((VelocityRadius, VelocityTheta))

        ax.quiver(theta[t], radius[r],
                  VelocityRadius/TotalVelocity*np.cos(theta[t])
                  - VelocityTheta/TotalVelocity*np.sin(theta[t]),
                  VelocityRadius/TotalVelocity*np.sin(theta[t])
                  + VelocityTheta/TotalVelocity*np.cos(theta[t]))
    
plt.show()

However, you can improve your code a lot by making use of vectorization: you don't need to loop over each point to obtain what you need. So the equivalent of your code, but even clearer:

def pol2cart(th,v_th,v_r):
    """convert polar velocity components to Cartesian, return v_x,v_y"""

    return v_r*np.cos(th) - v_th*np.sin(th), v_r*np.sin(th) + v_th*np.cos(th)


theta = np.linspace(0, 2*np.pi, NumberThetas, endpoint=False)
radius = np.linspace(RadiusColumn, 10 * RadiusColumn, NumberRadii)[:,None]

f = plt.figure()
ax = f.add_subplot(111, polar=True)

VelocityRadius = (1.0 - (RadiusColumn**2/radius**2)) * VelocityInfinity * np.cos(theta)
VelocityTheta = - (1.0 + (RadiusColumn**2/radius**2))* VelocityInfinity * np.sin(theta) - (VortexStrength/(2*np.pi*radius))
TotalVelocity = np.linalg.norm([VelocityRadius, VelocityTheta],axis=0)

VelocityX,VelocityY = pol2cart(theta, VelocityTheta, VelocityRadius)

ax.quiver(theta, radius, VelocityX/TotalVelocity, VelocityY/TotalVelocity)

plt.show()

Few notable changes:

• I added endpoint=False to theta: since your function is periodic in 2*pi, you don't need to plot the endpoints twice. Note that this means that currently you have more visible arrows; if you want the original behaviour I suggest that you decrease NumberThetas by one.
• I added [:,None] to radius: this will make it a 2d array, so later operations in the definition of the velocities will give you 2d arrays: different columns correspond to different angles, different rows correspond to different radii. quiver is compatible with array-valued input, so a single call to quiver will do your work.
• Since the velocities are now 2d arrays, we need to call np.linalg.norm on essentially a 3d array, but this works as expected if we specify an axis to work over.
• I defined the pol2cart auxiliary function to do the conversion from polar to Cartesian components; this is not necessary but it seems clearer to me this way.

Final remark: I suggest choosing shorter variable names, and ones that don't have CamelCase. That would probably make your coding faster too.