AOA Sweep

Using the wing defined in the previous section, we now sweep the angle of attack.

# ----------------- AOA SWEEP --------------------------------------------------

# Sequence of sweeps to run
# NOTE: To help convergence and speed it up the sweep, we recommend starting
#       each sweep at 0deg AOA since the sweep steps over points using the last
#       converged solution as the initial guess
sweep1 = [0, 2.1, 4.2, 6.3, 8.4, 10.5]      # Same AOAs than Weber's experiment
sweep2 = range(0, -50, step=-0.5)           # Sweep from 0 into deep negative stall (-50deg)
sweep3 = range(0, 50, step=0.5)             # Sweep from 0 into deep positive stall (50deg)


@time wingpolar = pnl.run_polarsweep(ll,
                            magUinf, rho, X0, cref, b;
                            aoa_sweeps = (sweep1, sweep2, sweep3),
                            solver,
                            solver_optargs,
                            align_joints_with_Uinfs, 
                            use_Uind_for_force
                        )

(see the complete example under examples/liftingline_weber.jl to see how to postprocess the solution as plotted here below)

Spanwise loading distribution

Wing Polar

Notice that the spanwise drag distribution shows really good agreement at low angles of attack but it starts to deviate at the tip for $\alpha \geq 6.3^\circ$. This also seen in the polar curves. To get more accurate $C_D$ predictions (at the trade of a less accurate spanwise drag distribution), we recommend using use_Uind_for_force = false as follows:

    # ----------------- AOA SWEEP 2 ------------------------------------------------

    @time wingpolar2 = pnl.run_polarsweep(ll,
                                magUinf, rho, X0, cref, b;
                                use_Uind_for_force = false,
                                aoa_sweeps = (sweep1, sweep2, sweep3),
                                solver,
                                solver_optargs,
                                align_joints_with_Uinfs, 
                            )

Spanwise loading distribution

Wing Polar

We have added the option of superimpossing a dragging line in order to make the lifting line method more robust post-stall, which is activated using sigmafactor=-1.0. Re-running the sweep with that option increases the success rate from 47% to 63%. Furthermore, we can use align_joints_with_Uinfs = true to further increase the success rate to 78%.

Here are the polars zoomed out to post-stall using sigmafactor=-1.0 and align_joints_with_Uinfs=true:

Wing Polar post-stall

sigmafactor=-1.0

sigmafactor=-1.0, align_joints_with_Uinfs=true

import FLOWPanel as pnl

include(joinpath(pnl.examples_path, "liftingline_weber.jl"))