Add turbulent wind

[1-Bart-1]

Different apparent wind for each panel derived from a turbulence model. @ufechner7 where can we find the code for such a turbulence model?

[ufechner7]

Well, this code should be implemented in https://github.com/aenarete/AtmosphericModels.jl and not be integrated in this project. When available in AtmosphericModels.jl it can be used here.

It works like this:

• run a script that creates a 4D wind field (x,y,z, time)
• this is stored in a (huge) file and than a lookup function provides an interpolated wind vector for the given four parameters

The code is not yet translated to Julia: https://github.com/aenarete/AtmosphericModels.jl/blob/main/src/windfield.jl

[ufechner7]

Related: OpenSourceAWE/AtmosphericModels.jl#4

[jellepoland]

There is a form of an implementation in the develop branch, which uses the turning rate, to determine what the spanwise variation is.

Described in WingAerodynamics.py

Python
`
@va.setter
def va(self, va, yaw_rate: float = 0.0):

    # # Removing old wake filaments
    # self.panels = Wake.remove_frozen_wake(self.panels)
    if isinstance(va, tuple) and len(va) == 2:
        va, yaw_rate = va

    self._va = np.array(va)

    if len(va) == 3 and yaw_rate == 0.0:
        va_distribution = np.repeat([va], len(self.panels), axis=0)
    elif len(va) == len(self.panels):
        va_distribution = va
    elif yaw_rate != 0.0 and len(va) == 3:
        va_distribution = []

        for wing in self.wings:
            # Create the spanwise positions array
            spanwise_positions = np.array(
                [panel.control_point[1] for panel in self.panels]
            )

            for i in range(wing.n_panels):
                yaw_rate_apparent_velocity = np.array(
                    [-yaw_rate * spanwise_positions[i], 0, 0]
                )

                # Append the current wing's velocities to the overall distribution
                va_distribution.append(yaw_rate_apparent_velocity + va)

        # Concatenate all wings' distributions into a single array
        va_distribution = np.vstack(va_distribution)

    else:
        raise ValueError(
            f"Invalid va distribution, len(va) :{len(va)} != len(self.panels):{len(self.panels)}"
        )
    # Update the va attribute of each panel
    for i, panel in enumerate(self.panels):
        panel.va = va_distribution[i]

    # Add the frozen wake elements based on the va distribution
    self.panels = Wake.frozen_wake(va_distribution, self.panels)

`