def model(m, p):
return np.blend(
p["trans_str"] * (m - p["m_trans"]), p["pc_sup"] +
p["a"] * np.exp(-(p["scale_sup"] * (m - p["center_sup"]))**2),
p["pc_sub"])
fit = asb.FittedModel(model=model,
x_data=data[:, 0],
y_data=data[:, 1],
parameter_guesses={
"a": 0.2,
"scale_sup": 1,
"center_sup": 1,
"m_trans": 1.,
"trans_str": 5,
"pc_sub": 0.16,
"pc_sup": 0.05,
},
parameter_bounds={
"trans_str": (0, 10),
},
verbose=False)
# def model(m, p):
# beta_squared_ideal = 1 - m ** 2
#
# beta_squared = np.softmax(
# beta_squared_ideal,
# -beta_squared_ideal,
# hardness=p["pg_hardness"]
def model(x, p):
return sigmoid(p["p5"] * (x - p["o5"])**5 + p["p3"] * (x - p["o3"])**4 +
p["p1"] * (x - p["o1"]))
fit = asb.FittedModel(
model=model,
x_data=machs_to_fit,
y_data=beta(machs_to_fit),
parameter_guesses={
"p1": 4.6,
"o1": 0.89,
"p3": 140,
"o3": 0.69,
"p5": 120,
"o5": 0.79,
},
parameter_bounds={
"o3": (0, 1),
"o5": (0, 1),
},
weights=weights,
# residual_norm_type="deviation"
)
error = fit(machs_to_fit) - beta(machs_to_fit)
error = np.array(error)
machs_to_plot = np.linspace(-0.5, 1.5, 500)
fig, ax = plt.subplots(1, 1, figsize=(8, 7), dpi=200)
import aerosandbox as asb
import aerosandbox.numpy as np
masses = np.load("masses.npy")
spans = np.load("spans.npy")
spar_masses = np.load("spar_masses.npy")
def spar_mass_model(x, p):
return p["c"] * (x["span"] / 40)**p["span_exp"] * (x["mass"] /
300)**p["mass_exp"]
fit = asb.FittedModel(model=spar_mass_model,
x_data={
"mass": masses.reshape(-1),
"span": spans.reshape(-1),
},
y_data=spar_masses.reshape(-1),
parameter_guesses={
"c": 1,
"span_exp": 1,
"mass_exp": 1,
},
put_residuals_in_logspace=True,
verbose=False)
print("\nMODEL\n-----")
for k, v in fit.parameters.items():
print(f"{k} = {v}")
print("spar_mass = c * (span / 40) ** span_exp * (mass_eff / 300) ** mass_exp")
eta = data['eta'].values
def model(x, p):
return 1 - p["1scl"] / (x - p["1cen"]) - (p["2scl"] / (x - p["2cen"]))**2
fit = asb.FittedModel(model=model,
x_data=fineness,
y_data=eta,
parameter_guesses={
"1scl": 1,
"1cen": -25,
"2scl": -1,
"2cen": -25,
},
parameter_bounds={
"1scl": (0, None),
"1cen": (None, 0),
"2scl": (0, None),
"2cen": (None, 0),
},
residual_norm_type="L2",
verbose=False)
print(fit.parameters)
from aerosandbox.tools.pretty_plots import plt, show_plot
fig, ax = plt.subplots()
plt.plot(fineness, eta, ".k")
fineness_plot = np.linspace(0, 40, 500)
As the fineness ratio goes to 0, the drag-divergent Mach should go to some reasonable value in the range of 0 to
1, probably around 0.5? Certainly no more than 0.6, I imagine. (intuition)
"""
return 1 - (p["a"] / (fr + p["b"]))**p["c"]
fit = asb.FittedModel(model=model,
x_data=np.concatenate([sub[:, 0], sup[:, 0]]),
y_data=np.concatenate([sub[:, 1], sup[:, 1]]),
weights=np.concatenate([
np.ones(len(sub)) / len(sub),
np.ones(len(sup)) / len(sup),
]),
parameter_guesses={
"a": 0.5,
"b": 3,
"c": 1,
},
parameter_bounds={
"a": (0, None),
"b": (0, None),
"c": (0, None)
},
residual_norm_type="L2")
plt.plot(fr, fit(fr), "-k", label="Fit")
p.show_plot(
"Drag-Divergent Mach Number for Generic\nFuselage of Varying Fineness Ratio",
"$2L_n / d$", r"$\mathrm{Mach}_{DD}$ [-]")
print(fit.parameters)
p["cd_sub"] + np.exp(p["a_sub"] + p["s_sub"] * (m - p["trans"])))
fit = asb.FittedModel(
model=model,
x_data=data[:, 0],
y_data=data[:, 1],
parameter_guesses={
"trans": 1,
"trans_str": 10,
"cd_sub": 1.2,
"cd_sup": 1.2,
"a_sub": -0.4,
"a_sup": -0.4,
"s_sub": 4.6,
"s_sup": -1.5,
},
parameter_bounds={
"s_sub": (0, None),
"s_sup": (None, 0),
# "cd_sub": (1.2, 1.2),
"trans": (0.9, 1.1),
"trans_str": (0, None),
},
weights=1 + 5 * ((data[:, 0] > 0.9) & (data[:, 0] < 1.1)),
verbose=False,
# residual_norm_type="LInf",
put_residuals_in_logspace=True)
from pprint import pprint
fit = asb.FittedModel(
model=model,
x_data={
"lats_scaled": lats_scaled,
"alts_scaled": alts_scaled
},
y_data=speeds_scaled,
parameter_guesses={
"agc": -0.5363,
"agh": 1.957,
"ags": 0.1459,
"aqc": -1.465,
"c0": -0.517,
"c12": 0.08495,
"c21": -0.0252,
"c4a": 0.02259,
"c4c": 1.028,
"cg": 0.8051,
"cgc": 0.2787,
"cqa": 0.1866,
"cql": 0.01651,
"cqla": -0.1362,
"lgc": 0.6944,
"lgh": 2.078,
"lgs": 0.9806,
"lqc": 4.036,
},
# parameter_bounds={
# "agc" : (-0.5363, -0.5363),
# "agh" : (1.957, 1.957),
# "ags" : (0.1459, 0.1459),
# "aqc" : (-1.465, -1.465),
# "c0" : (-0.517, -0.517),
# "c12" : (0.08495, 0.08495),
# "c21" : (-0.0252, -0.0252),
# "c4a" : (0.02259, 0.02259),
# "c4c" : (1.028, 1.028),
# "cg" : (0.8051, 0.8051),
# "cgc" : (0.2787, 0.2787),
# "cqa" : (0.1866, 0.1866),
# "cql" : (0.01651, 0.01651),
# "cqla": (-0.1362, -0.1362),
# "lgc" : (0.6944, 0.6944),
# "lgh" : (2.078, 2.078),
# "lgs" : (0.9806, 0.9806),
# "lqc" : (4.036, 4.036),
# },
weights=weights,
# put_residuals_in_logspace=True
)