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Implemented new friction factor calculation spanning entire flow regime #767

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6fd1663
Started implementation of churchill friction factor
cvTHM Nov 13, 2025
bc27ce2
Implemented friction factor calculation by Churchill (1977)
cvTHM Nov 14, 2025
2762c36
correction in return statement of function calc_lambda
cvTHM Nov 14, 2025
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2 changes: 1 addition & 1 deletion src/pandapipes/converter/stanet/preparing_steps.py
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Original file line number Diff line number Diff line change
Expand Up @@ -171,7 +171,7 @@ def get_net_params(net, stored_data):
:return: net parameters
:rtype:
"""
known_friction_models = {1: "swamee-jain", 3: "nikuradse", 5: "colebrook"}
known_friction_models = {1: "swamee-jain", 3: "nikuradse", 5: "colebrook", 7:"churchill"}
compressibility_models = {0: "linear", 1: "AGA", 2: "GERG-88"}
net_params = dict()
net_data = stored_data["net_parameters"]
Expand Down
29 changes: 29 additions & 0 deletions src/pandapipes/pf/derivative_calculation.py
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Expand Up @@ -199,6 +199,13 @@ def calc_lambda(m, eta, d, k, gas_mode, friction_model, lengths, options, area):
# 1.325 instead of 0.25???
lambda_swamee_jain = 0.25 / ((np.log10(k / (3.7 * d) + 5.74 / (re ** 0.9))) ** 2)
return lambda_swamee_jain, re

elif friction_model == "churchill":
paramA = (-2.457*np.log((7/re)**0.9 + 0.27*k/d))**16
paramB = (37530/re)**16
lambda_churchill = 8*((8/re)**12 + 1/(paramA+paramB)**1.5)**(1/12)
return lambda_churchill, re

else:
# lambda_tot = np.where(re > 2300, lambda_laminar + lambda_nikuradse, lambda_laminar)
lambda_tot = lambda_laminar + lambda_nikuradse
Expand Down Expand Up @@ -256,6 +263,23 @@ def calc_der_lambda(m, eta, d, k, friction_model, lambda_pipe, area, re, lengths
lambda_der[pos] = 0.5 * np.log(10) ** 2 / (np.log(param) ** 3) / param * 5.166 * (
(eta[pos] * area[pos]) / (d[pos])) ** 0.9 * np.abs(m[pos]) ** -1.9
return lambda_der

elif friction_model == "churchill":
param = (7*eta[pos]*area[pos]/(m[pos]*d[pos]))**0.9 + 0.27*k[pos]/d[pos]
partial_dparamdm = -0.9*7**0.9 * (d[pos]/(eta[pos] * area[pos]))**(-0.9) * m[pos]**(-1.9)

paramsAB = ((-2.457*np.log((7*eta[pos]*area[pos]/(m[pos] * d[pos]))**0.9 + 0.27*k[pos]/d[pos]))**16 + (37530*eta[pos]*area[pos]/(m[pos] * d[pos]))**16)
paramC = (8*eta[pos]*area[pos]/(m[pos]*d[pos]))**12 + paramsAB**(-1.5)

partial_dAdm = 16*(2.457*np.log(param))**15 * 2.457*param**(-1) * partial_dparamdm
partial_dBdm = -16*37530**16*(eta[pos]*area[pos]/(m[pos]*d[pos]))**17

partial_dCdm = -12*(8*eta[pos]*area[pos]/(m[pos]*d[pos]))**11 * (8*eta[pos]*area[pos]/(m[pos]**2*d[pos])) - paramsAB**(-3)*1.5*paramsAB**0.5 * (partial_dAdm + partial_dBdm)

lambda_der[pos] = 2/3* paramC**(-11/12) * partial_dCdm

return lambda_der

else:
lambda_der[pos] = -(64 * eta[pos] * area[pos]) / (m[pos] ** 2 * d[pos])
return lambda_der
Expand Down Expand Up @@ -309,3 +333,8 @@ def cw_derivative(lambda_cb, re_nz, k_nz, d_nz):
converged = np.all(res.converged)

return converged, lambda_res