def get_run_data(
Re
): # Get the data for an XFoil alpha sweep at one specific Re.
run_data = XFoil(airfoil=self, Re=Re,
**xfoil_kwargs).alpha(alphas)
run_data["Re"] = Re * np.ones_like(run_data["alpha"])
return run_data # Data is a dict where keys are figures of merit [str] and values are 1D ndarrays.
def plot_winds_at_altitude(altitude=18000):
fig, ax = plt.subplots()
day_of_years = np.linspace(0, 365, 150)
latitudes = np.linspace(-80, 80, 120)
Day_of_years, Latitudes = np.meshgrid(day_of_years, latitudes)
winds = wind_speed_world_95(
altitude=altitude * np.ones_like(Latitudes.flatten()),
latitude=Latitudes.flatten(),
day_of_year=Day_of_years.flatten(),
).reshape(Latitudes.shape)
args = [day_of_years, latitudes, winds]
levels = np.arange(0, 80.1, 5)
CS = plt.contour(*args,
levels=levels,
linewidths=0.5,
colors="k",
alpha=0.7)
CF = plt.contourf(*args,
levels=levels,
cmap='viridis_r',
alpha=0.7,
extend="max")
cbar = plt.colorbar(label="Wind Speed [m/s]", extendrect=True)
ax.clabel(CS, inline=1, fontsize=9, fmt="%.0f m/s")
plt.xticks(
np.linspace(0, 365, 13)[:-1],
("Jan. 1", "Feb. 1", "Mar. 1", "Apr. 1", "May 1", "June 1",
"July 1", "Aug. 1", "Sep. 1", "Oct. 1", "Nov. 1", "Dec. 1"),
rotation=40)
lat_label_vals = np.arange(-80, 80.1, 20)
lat_labels = []
for lat in lat_label_vals:
if lat >= 0:
lat_labels.append(f"{lat:.0f}N")
else:
lat_labels.append(f"{-lat:.0f}S")
plt.yticks(lat_label_vals, lat_labels)
show_plot(
f"95th-Percentile Wind Speeds at {altitude / 1e3:.0f} km Altitude",
xlabel="Day of Year",
ylabel="Latitude",
)
def spy(
matrix,
show=True,
):
"""
Plots the sparsity pattern of a matrix.
:param matrix: The matrix to plot the sparsity pattern of. [2D ndarray or CasADi array]
:param show: Whether or not to show the sparsity plot. [boolean]
:return: The figure to be plotted [go.Figure]
"""
try:
matrix = matrix.toarray()
except:
pass
abs_m = np.abs(matrix)
sparsity_pattern = abs_m >= 1e-16
matrix[sparsity_pattern] = np.log10(abs_m[sparsity_pattern] + 1e-16)
j_index_map, i_index_map = np.meshgrid(np.arange(matrix.shape[1]),
np.arange(matrix.shape[0]))
i_index = i_index_map[sparsity_pattern]
j_index = j_index_map[sparsity_pattern]
val = matrix[sparsity_pattern]
val = np.ones_like(i_index)
fig = go.Figure(
data=go.Heatmap(
y=i_index,
x=j_index,
z=val,
# type='heatmap',
colorscale='RdBu',
showscale=False,
), )
fig.update_layout(
plot_bgcolor="black",
xaxis=dict(showgrid=False, zeroline=False),
yaxis=dict(showgrid=False,
zeroline=False,
autorange="reversed",
scaleanchor="x",
scaleratio=1),
width=800,
height=800 * (matrix.shape[0] / matrix.shape[1]),
)
if show:
fig.show()
return fig
def plot_winds_at_day(day_of_year=0):
fig, ax = plt.subplots()
altitudes = np.linspace(0, 30000, 150)
latitudes = np.linspace(-80, 80, 120)
Altitudes, Latitudes = np.meshgrid(altitudes, latitudes)
winds = wind_speed_world_95(
altitude=Altitudes.flatten(),
latitude=Latitudes.flatten(),
day_of_year=day_of_year * np.ones_like(Altitudes.flatten()),
).reshape(Altitudes.shape)
args = [altitudes / 1e3, latitudes, winds]
levels = np.arange(0, 80.1, 5)
CS = plt.contour(*args,
levels=levels,
linewidths=0.5,
colors="k",
alpha=0.7)
CF = plt.contourf(*args,
levels=levels,
cmap='viridis_r',
alpha=0.7,
extend="max")
cbar = plt.colorbar(label="Wind Speed [m/s]", extendrect=True)
ax.clabel(CS, inline=1, fontsize=9, fmt="%.0f m/s")
lat_label_vals = np.arange(-80, 80.1, 20)
lat_labels = []
for lat in lat_label_vals:
if lat >= 0:
lat_labels.append(f"{lat:.0f}N")
else:
lat_labels.append(f"{-lat:.0f}S")
plt.yticks(lat_label_vals, lat_labels)
show_plot(
f"95th-Percentile Wind Speeds at Day {day_of_year:.0f}",
xlabel="Altitude [km]",
ylabel="Latitude",
)
data = {k: np.array([d[k] for d in data]) for k in data[0].keys()}
order = np.argsort(data['mach'])
data = {k: v[order] for k, v in data.items()}
return data
machs = np.linspace(0, 1.3, 500)
airfoil = asb.Airfoil("rae2822")
airfoil.generate_polars(cache_filename="./cache/rae2822.json")
aerobuildup_data = {
"mach": machs,
"CL": airfoil.CL_function(1, 6.5e6, machs, 0) * np.ones_like(machs),
"CD": airfoil.CD_function(1, 6.5e6, machs, 0) * np.ones_like(machs),
"CM": airfoil.CM_function(1, 6.5e6, machs, 0) * np.ones_like(machs),
}
datas = {
"XFoil v6 (P-G)": get_data(data_folder / "xfoil6.csv"),
"MSES (Euler + IBL)": get_data(data_folder / "mses.csv"),
"SU2 (RANS)": get_data(data_folder / "su2.csv"),
"ASB AeroBuildup": aerobuildup_data,
}
import matplotlib.pyplot as plt
import aerosandbox.tools.pretty_plots as p
fig, ax = plt.subplots()
def beta(mach):
return np.sqrt(1 - mach**2)
machs_to_fit = np.linspace(0.001, 0.999, 500)
def sigmoid(x):
return 1 / (1 + np.exp(x))
def inv_sigmoid(x):
return np.log(1 / x - 1)
weights = np.ones_like(machs_to_fit)
weights[0] = 1000
weights[machs_to_fit > 0.9] = 0.5
weights[machs_to_fit > 0.95] = 0.25
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={
X, Y = np.meshgrid(
np.linspace(-2, 2, 50),
np.linspace(-2, 2, 50),
)
X = X.flatten()
Y = Y.flatten()
x_panels = np.array([1, -1, -1, 1, 1])
y_panels = np.array([1, 1, -1, -1, 1])
U, V = calculate_induced_velocity_line_singularities(
x_field=X,
y_field=Y,
x_panels=x_panels,
y_panels=y_panels,
gamma=1 * np.ones_like(x_panels),
sigma=1 * np.ones_like(x_panels))
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(palette=sns.color_palette("husl"))
fig, ax = plt.subplots(1, 1, figsize=(6, 6), dpi=200)
plt.quiver(X, Y, U, V, (U**2 + V**2)**0.5, scale=10)
plt.axis("equal")
plt.xlabel(r"$x$")
plt.ylabel(r"$z$")
plt.title(r"Linear-Strength Vortex: Induced Velocity")
plt.tight_layout()
# plt.savefig("C:/Users/User/Downloads/temp.svg")