Vectorize python code for improved performance

I am writing a scientific code in python to calculate the energy of a system. Here is my function : cte1, cte2, cte3, cte4 are constants previously computed; pii is np.pi (calculated beforehand, since it slows the loop otherwise). I calculate the 3 components of the total energy, then sum them up.

def calc_energy(diam): 
    Energy1 = cte2*((pii*diam**2/4)*t)
    Energy2 = cte4*(pii*diam)*t
    d=diam/t
    u=np.sqrt((d)**2/(1+d**2))
    cc= u**2
    E = sp.special.ellipe(cc) 
    K = sp.special.ellipk(cc) 
    Id=cte3*d*(d**2+(1-d**2)*E/u-K/u)
    Energy3 = cte*t**3*Id
    total_energy = Energy1+Energy2+Energy3
    return (total_energy,Energy1)

My first idea was to simply loop over all values of the diameter :

start_diam, stop_diam, step_diam = 1e-10, 500e-6, 1e-9 #Diametre
diametres = np.arange(start_diam,stop_diam,step_diam)

for d in diametres:  
    res1,res2 = calc_energy(d)
    totalEnergy.append(res1)
    Energy1.append(res2)

In an attempt to speed up calculations, I decided to use numpy to vectorize, as shown below :

diams = diametres.reshape(-1,1) #If not reshaped, calculations won't run
r1 = np.apply_along_axis(calc_energy,1,diams)

However, the "vectorized" solution does not properly work. When timing I get 5 seconds for the first solution and 18 seconds for the second one.

I guess I'm doing something the wrong way but can't figure out what.