optvl 2.2.4

A modified version of Mark Drela and Harold Youngren's famous AVL code with a direct Python interface and gradient based optimization support.

AVL + python + optimization = OptVL

Documentation OptVL for AVL users

OptVL is a modified version of Mark Drela and Harold Youngren's famous AVL code with a python-wrapper and AD derivative routines for gradient-based optimization. Its key features are

• Python wrapper to easily conduct large parameter sweeps from an existing AVL geometry file or include a vortex lattice method in a larger model.
• Total derivatives of total forces, control surface derivatives, and stability derivatives with respect to geometric and aerodynamic variables for use with gradient-based optimization.
• Post-processing directly through Python or Paraview and Tecplot for flexibility.

Installation

The best way to install OptVL is through pip

pip install optvl

Windows, macOS, and Linux are all supported! For information on building locally see the developer docs

Basic usage

The API of OptVL was made to mirror the usage of AVL through its text interface. The user loads in a geometry file, adds constraints, and then executes analysis runs.

The AVL wrapper is implemented in the OVLSolver class. To use this wrapper, first one must initialize the OVLSolver object with a geometry file and optionally a mass file. After, the user can add constraints and then execute the run to generate data. Below is a basic example of this workflow and this page provides more information on building run scripts.

from optvl import OVLSolver

ovl = OVLSolver(geo_file="../geom_files/aircraft.avl", debug=False)

# look at the geometry to see that everything is right
ovl.plot_geom()

# set the angle of attack
ovl.set_variable("alpha", 1.23)
# modify the mach number
ovl.set_parameter("Mach", 0.3)

# set the deflection of the elevator to trim the pitching moment
ovl.set_constraint("Elevator", "Cm", 0.00)

# set the rudder deflection to set value
ovl.set_control_deflection("Rudder", 10.0)

# This is the method that acutally runs the analysis
ovl.execute_run()

# print data about the run
force_data = ovl.get_total_forces()
print(
    f'CL:{force_data["CL"]:10.6f}   CD:{force_data["CD"]:10.6f}   Cm:{force_data["Cm"]:10.6f}'
)

# lets look at the cp countours
ovl.plot_cp()

The plotting calls in this example use matplotlib to produce visualizations like this

Examples

• Eigen value analysis
• Modify leading edge position
• Twist optimization with Scipy
• Twist optimization with OpenMDAO
• Planform optimization with OpenMDAO

License

As stated, OptVL includes a modified version of the source code of AVL. AVL is distributed under the GLP version 2 license. As a consequence, OptVL is also distributed under a GLP license via the copyleft principle of the original license. Although, here we update the GLP license to version 3 as allowed by the original license.

This software includes portions of NetLib's LAPACK library. LAPACK is a software package provided by University of Tennessee, University of California Berkeley, University of Colorado Denver and NAG Ltd under the modified BSD license. See the license file in src/lapack/LICENSE.txt for more details.