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change include_bl_data to a class-level parameter, not a run-level …
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…parameter. Will allow more future flexibility with output handling
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@peterdsharpe
peterdsharpe committed Feb 26, 2024
1 parent b579063 commit 89cf212
Showing 1 changed file with 71 additions and 77 deletions.
148 changes: 71 additions & 77 deletions aerosandbox/aerodynamics/aero_2D/xfoil.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,6 +49,7 @@ def __init__(self,
max_iter: int = 100,
xfoil_command: str = "xfoil",
xfoil_repanel: bool = True,
include_bl_data: bool = False,
verbose: bool = False,
timeout: Union[float, int, None] = 30,
working_directory: Union[Path, str] = None,
Expand Down Expand Up @@ -117,6 +118,11 @@ def __init__(self,
xfoil_repanel: Controls whether to allow XFoil to repanel your airfoil using its internal methods (PANE,
with default settings, 160 nodes). Boolean, defaults to True.
include_bl_data: Controls whether or not to include boundary layer data in the output. If this is True,
the functions `alpha()` and `cl()` will return a dictionary with an additional key, "bl_data",
which contains the boundary layer data in the form of a pandas DataFrame. Results in slightly higher
runtime, mostly due to file I/O bottleneck. Defaults to False.
verbose: Controls whether or not XFoil output is printed to command line. Defaults to False.
timeout: Controls how long any individual XFoil run (i.e. alpha sweep) is allowed to run before the
Expand All @@ -141,6 +147,7 @@ def __init__(self,
self.max_iter = max_iter
self.xfoil_command = xfoil_command
self.xfoil_repanel = xfoil_repanel
self.include_bl_data = include_bl_data
self.verbose = verbose
self.timeout = timeout

Expand Down Expand Up @@ -434,97 +441,72 @@ def str_to_float(s: str) -> float:

# Read the BL data
if read_bl_data_from is not None:
if read_bl_data_from == "alpha":
dump_filenames = directory.glob("dump_a_*.txt")
cpwr_filenames = directory.glob("cpwr_a_*.txt")
import pandas as pd
bl_datas: List[pd.DataFrame] = []

if read_bl_data_from == "alpha":
alpha_to_dump_mapping = {
float(dump_filename.stem.split("_")[-1]): dump_filename
for dump_filename in dump_filenames
for dump_filename in directory.glob("dump_a_*.txt")
}
alpha_to_cpwr_mapping = {
float(cpwr_filename.stem.split("_")[-1]): cpwr_filename
for cpwr_filename in cpwr_filenames
}

bl_data_for_each_alpha = []

for alpha in output["alpha"]:
dump_filename = alpha_to_dump_mapping[
min(alpha_to_dump_mapping.keys(), key=lambda x: abs(x - alpha))
]
cpwr_filename = alpha_to_cpwr_mapping[
min(alpha_to_cpwr_mapping.keys(), key=lambda x: abs(x - alpha))
]

import pandas as pd
dump_data = pd.read_csv(
dump_filename,
sep="\s+",
names=["s", "x", "y", "Ue/Vinf", "Dstar", "Theta", "Cf", "H"],
skiprows=1,
)
cpwr_data = pd.read_csv(
cpwr_filename,
sep="\s+",
names=["x", "y", "Cp"],
skiprows=3,
bl_datas.append(
pd.read_csv(
dump_filename,
sep="\s+",
names=["s", "x", "y", "ue/vinf", "dstar", "theta", "cf", "H"],
skiprows=1,
)
)

dump_data["Cp"] = cpwr_data["Cp"]

bl_data_for_each_alpha.append(dump_data)

output["bl_data"] = np.fromiter(bl_data_for_each_alpha, dtype="O")

elif read_bl_data_from == "cl":
dump_filenames = directory.glob("dump_cl_*.txt")
cpwr_filenames = directory.glob("cpwr_cl_*.txt")

cl_to_dump_mapping = {
float(dump_filename.stem.split("_")[-1]): dump_filename
for dump_filename in dump_filenames
}
cl_to_cpwr_mapping = {
float(cpwr_filename.stem.split("_")[-1]): cpwr_filename
for cpwr_filename in cpwr_filenames
for dump_filename in directory.glob("dump_cl_*.txt")
}

bl_data_for_each_cl = []

for cl in output["CL"]:
dump_filename = cl_to_dump_mapping[
min(cl_to_dump_mapping.keys(), key=lambda x: abs(x - cl))
]
cpwr_filename = cl_to_cpwr_mapping[
min(cl_to_cpwr_mapping.keys(), key=lambda x: abs(x - cl))
]

import pandas as pd
dump_data = pd.read_csv(
dump_filename,
sep="\s+",
names=["s", "x", "y", "Ue/Vinf", "Dstar", "Theta", "Cf", "H"],
skiprows=1,
)
cpwr_data = pd.read_csv(
cpwr_filename,
sep="\s+",
names=["x", "y", "Cp"],
skiprows=3,
bl_datas.append(
pd.read_csv(
dump_filename,
sep="\s+",
names=["s", "x", "y", "ue/vinf", "dstar", "theta", "cf", "H"],
skiprows=1,
)
)

dump_data["Cp"] = cpwr_data["Cp"]
else:
raise ValueError("The `read_bl_data_from` parameter must be 'alpha', 'cl', or None.")

bl_data_for_each_cl.append(dump_data)
# Augment the output data for each BL
for bl_data in bl_datas:
# Get Cp via Karman-Tsien compressibility correction, same as XFoil
Cp_0 = (1 - bl_data["ue/vinf"] ** 2)
bl_data["Cp"] = (Cp_0 /
(
np.sqrt(1 - self.mach ** 2)
+ (
(self.mach ** 2)
/ (1 + np.sqrt(1 - self.mach ** 2))
* (Cp_0 / 2)
)

output["bl_data"] = np.fromiter(bl_data_for_each_cl, dtype="O")
)
)

else:
# Get Re_theta
bl_data["Re_theta"] = np.abs(bl_data["ue/vinf"]) * bl_data["theta"] * self.Re

raise ValueError(
"The `read_bl_data_from` parameter must be either 'alpha' or 'cl'."
)
output["bl_data"] = np.fromiter(bl_datas, dtype="O")

return output

Expand Down Expand Up @@ -568,7 +550,6 @@ def open_interactive(self) -> None:
def alpha(self,
alpha: Union[float, np.ndarray],
start_at: Union[float, None] = 0,
include_bl_data: bool = False,
) -> Dict[str, np.ndarray]:
"""
Execute XFoil at a given angle of attack, or at a sequence of angles of attack.
Expand Down Expand Up @@ -603,9 +584,16 @@ def schedule_run(alpha: float):
if self.hinge_point_x is not None:
commands.append("fmom")

if include_bl_data:
commands.append(f"dump dump_a_{alpha:.8f}.txt")
commands.append(f"cpwr cpwr_a_{alpha:.8f}.txt")
if self.include_bl_data:
commands.extend([
f"dump dump_a_{alpha:.8f}.txt",
# "vplo",
# "cd", # Dissipation coefficient
# f"dump cdis_a_{alpha:.8f}.txt",
# f"n", # Amplification ratio
# f"dump n_a_{alpha:.8f}.txt",
# "",
])

if (
len(alphas) > 1 and
Expand All @@ -628,7 +616,7 @@ def schedule_run(alpha: float):

output = self._run_xfoil(
"\n".join(commands),
read_bl_data_from="alpha" if include_bl_data else None
read_bl_data_from="alpha" if self.include_bl_data else None
)

sort_order = np.argsort(output['alpha'])
Expand All @@ -641,7 +629,6 @@ def schedule_run(alpha: float):
def cl(self,
cl: Union[float, np.ndarray],
start_at: Union[float, None] = 0,
include_bl_data: bool = False,
) -> Dict[str, np.ndarray]:
"""
Execute XFoil at a given lift coefficient, or at a sequence of lift coefficients.
Expand Down Expand Up @@ -676,9 +663,16 @@ def schedule_run(cl: float):
if self.hinge_point_x is not None:
commands.append("fmom")

if include_bl_data:
commands.append(f"dump dump_cl_{cl:.8f}.txt")
commands.append(f"cpwr cpwr_cl_{cl:.8f}.txt")
if self.include_bl_data:
commands.extend([
f"dump dump_cl_{cl:.8f}.txt",
# "vplo",
# "cd", # Dissipation coefficient
# f"dump cdis_cl_{cl:.8f}.txt",
# f"n", # Amplification ratio
# f"dump n_cl_{cl:.8f}.txt",
# "",
])

if (
len(cls) > 1 and
Expand All @@ -701,7 +695,7 @@ def schedule_run(cl: float):

output = self._run_xfoil(
"\n".join(commands),
read_bl_data_from="cl" if include_bl_data else None
read_bl_data_from="cl" if self.include_bl_data else None
)

sort_order = np.argsort(output['alpha'])
Expand All @@ -719,13 +713,13 @@ def schedule_run(cl: float):
airfoil=af,
Re=1e6,
hinge_point_x=0.75,
# verbose=True,
include_bl_data=True,
working_directory=str(Path.home() / "Downloads" / "test"),
)

result_at_single_alpha = xf.alpha(5, include_bl_data=True)
result_at_single_alpha = xf.alpha(5)

result_at_several_CLs = xf.cl([-0.1, 0.5, 0.7, 0.8, 0.9], include_bl_data=True)
result_at_several_CLs = xf.cl([-0.1, 0.5, 0.7, 0.8, 0.9])

result_at_multiple_alphas = xf.alpha([3, 5, 60], include_bl_data=True)
result_at_multiple_alphas = xf.alpha([3, 5, 60])
# Note: if a result does not converge (such as the 60 degree case here), it will not be included in the results.

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