Quick Example

Here is an example run script to perform aerodynamic optimization. We’ll go into more detail later about how to set up a model, define the optimization problem, and postprocess results.

# docs checkpoint 0
import numpy as np

import openmdao.api as om

from openaerostruct.geometry.utils import generate_mesh
from openaerostruct.geometry.geometry_group import Geometry
from openaerostruct.aerodynamics.aero_groups import AeroPoint

# Create a dictionary to store options about the mesh
mesh_dict = {"num_y": 7, "num_x": 2, "wing_type": "CRM", "symmetry": True, "num_twist_cp": 5}

# Generate the aerodynamic mesh based on the previous dictionary
mesh, twist_cp = generate_mesh(mesh_dict)

# Create a dictionary with info and options about the aerodynamic
# lifting surface
surface = {
    # Wing definition
    "name": "wing",  # name of the surface
    "symmetry": True,  # if true, model one half of wing
    # reflected across the plane y = 0
    "S_ref_type": "wetted",  # how we compute the wing area,
    # can be 'wetted' or 'projected'
    "fem_model_type": "tube",
    "twist_cp": twist_cp,
    "mesh": mesh,
    # Aerodynamic performance of the lifting surface at
    # an angle of attack of 0 (alpha=0).
    # These CL0 and CD0 values are added to the CL and CD
    # obtained from aerodynamic analysis of the surface to get
    # the total CL and CD.
    # These CL0 and CD0 values do not vary wrt alpha.
    "CL0": 0.0,  # CL of the surface at alpha=0
    "CD0": 0.015,  # CD of the surface at alpha=0
    # Airfoil properties for viscous drag calculation
    "k_lam": 0.05,  # percentage of chord with laminar
    # flow, used for viscous drag
    "t_over_c_cp": np.array([0.15]),  # thickness over chord ratio (NACA0015)
    "c_max_t": 0.303,  # chordwise location of maximum (NACA0015)
    # thickness
    "with_viscous": True,  # if true, compute viscous drag
    "with_wave": False,  # if true, compute wave drag
}

# Create the OpenMDAO problem
prob = om.Problem()

# Create an independent variable component that will supply the flow
# conditions to the problem.
indep_var_comp = om.IndepVarComp()
indep_var_comp.add_output("v", val=248.136, units="m/s")
indep_var_comp.add_output("alpha", val=5.0, units="deg")
indep_var_comp.add_output("Mach_number", val=0.84)
indep_var_comp.add_output("re", val=1.0e6, units="1/m")
indep_var_comp.add_output("rho", val=0.38, units="kg/m**3")
indep_var_comp.add_output("cg", val=np.zeros((3)), units="m")

# Add this IndepVarComp to the problem model
prob.model.add_subsystem("prob_vars", indep_var_comp, promotes=["*"])

# Create and add a group that handles the geometry for the
# aerodynamic lifting surface
geom_group = Geometry(surface=surface)
prob.model.add_subsystem(surface["name"], geom_group)

# Create the aero point group, which contains the actual aerodynamic
# analyses
aero_group = AeroPoint(surfaces=[surface])
point_name = "aero_point_0"
prob.model.add_subsystem(
    point_name, aero_group, promotes_inputs=["v", "alpha", "Mach_number", "re", "rho", "cg"]
)

name = surface["name"]

# Connect the mesh from the geometry component to the analysis point
prob.model.connect(name + ".mesh", point_name + "." + name + ".def_mesh")

# Perform the connections with the modified names within the
# 'aero_states' group.
prob.model.connect(name + ".mesh", point_name + ".aero_states." + name + "_def_mesh")

prob.model.connect(name + ".t_over_c", point_name + "." + name + "_perf." + "t_over_c")

# Import the Scipy Optimizer and set the driver of the problem to use
# it, which defaults to an SLSQP optimization method
prob.driver = om.ScipyOptimizeDriver()
prob.driver.options["tol"] = 1e-9

# Setup problem and add design variables, constraint, and objective
prob.model.add_design_var("wing.twist_cp", lower=-10.0, upper=15.0)
prob.model.add_constraint(point_name + ".wing_perf.CL", equals=0.5)
prob.model.add_objective(point_name + ".wing_perf.CD", scaler=1e4)

# Set up and run the optimization problem
prob.setup()

prob.run_driver()

Optimization terminated successfully    (Exit mode 0)
            Current function value: 315.7580550695561
            Iterations: 18
            Function evaluations: 18
            Gradient evaluations: 18
Optimization Complete
-----------------------------------

# docs checkpoint 1

print(prob["aero_point_0.wing_perf.CD"][0])

0.031575805506955615

print(prob["aero_point_0.wing_perf.CL"][0])

0.499999999999899

print(prob["aero_point_0.CM"][1])

-2.1182832055054557