def execute(self):
V = self.values["V"]
A = self.values["A"]
K = self.values["K"]
c_in = self.values["c_in"]
D = self.values["D"]
L = self.values["L"]
num_elements = self.values["num_elements"]
maxt = self.values["maxt"]
outputfreq = self.values["outputfreq"]
time, uptake, c_in = dogbone(V, A, K, c_in, D, L, num_elements, maxt, outputfreq)
return pd.DataFrame({"time": time, "uptake": uptake, "c_in": c_in})
def dogbone_SS(x,V,A,c_in,t_data,y_data):
"""
Return the sum of square difference between
model output and y_data for times t_data
Args:
x a tuple containing K,D
V volume
A area
c_in initial concentration inside
L length
t_data time data points
y_data uptake data points
"""
lnK,lnD,lnL = x
K = np.exp(lnK)
D = np.exp(lnD)
L = np.exp(lnL)
times, y_model, _ = dogbone(V,A,K,c_in,D,L,times_to_save=t_data)
return np.sum((np.array(y_model)-np.array(y_data))**2)
V = 1000000.
A = 0.001
Bi = 1e-12
L = 1.
D = 1e-5
c_L = 1.
c_inf = 0.
maxt = 100000
dt = 1000
num_elements = 200
times, numeric_uptake, _ = dogbone(V,A,1.,c_L,D,L,num_elements,maxt,dt)
x = np.linspace(0.,L,num_elements)
analytic_uptake = []
for t in times:
analytic = Slab(Bi,L,D,c_L,c_inf)
analytic_c = analytic.evaluate(x,t)
analytic_uptake.append(np.trapz(analytic_c,x))
print A*np.array(analytic_uptake)
print numeric_uptake
plt.plot(times,A*np.array(analytic_uptake),'-')
plt.plot(times,np.array(numeric_uptake),'.')
plt.show()
