#=##############################################################################
DESCRIPTION
Simulation of a DJI 9443 rotor in hover (two-bladed rotor, 9.4 inches diameter). This example replicates the experiment described in Zawodny & Boyd (2016), "Acoustic Characterization and Prediction of Representative, Small-scale Rotary-wing Unmanned Aircraft System Components."
AUTHORSHIP
Author : Eduardo J. Alvarez (edoalvarez.com)
Email : Edo.AlvarezR@gmail.com
Created : Mar 2023
Last updated : Mar 2023
License : MIT
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#=
Use the following parameters to obtain the desired fidelity
---- MID-LOW FIDELITY —
n = 20 # Number of blade elements per blade
nsteps_per_rev = 36 # Time steps per revolution
p_per_step = 4 # Sheds per time step
sigma_rotor_surf= R/10 # Rotor-on-VPM smoothing radius
vpm_integration = vpm.euler # VPM time integration scheme
vpm_SFS = vpm.SFS_none # VPM LES subfilter-scale model
shed_starting = false # Whether to shed starting vortex
suppress_fountain = true # Suppress hub fountain effect
sigmafactor_vpmonvlm = 1.0 # Shrink particles by this factor when
# calculating VPM-on-VLM/Rotor induced velocities
---- MID-HIGH FIDELITY —
n = 50
nsteps_per_rev = 72
p_per_step = 2
sigma_rotor_surf= R/10
sigmafactor_vpmonvlm = 1.0
shed_starting = false
suppress_fountain = true
vpm_integration = vpm.rungekutta3
vpm_SFS = vpm.SFS_none
---- HIGH FIDELITY -----
n = 50
nsteps_per_rev = 360
p_per_step = 2
sigma_rotor_surf= R/80
sigmafactor_vpmonvlm = 5.5
shed_starting = true
suppress_fountain = false
vpm_integration = vpm.rungekutta3
vpm_SFS = vpm.DynamicSFS(vpm.Estr_fmm, vpm.pseudo3level_positive;
alpha=0.999, maxC=1.0,
clippings=[vpm.clipping_backscatter])
=#
import FLOWUnsteady as uns
import FLOWVLM as vlm
import FLOWVPM as vpm
run_name = “rotorhover-example” # Name of this simulation
save_path = run_name # Where to save this simulation
paraview = true # Whether to visualize with Paraview
Uncomment this to have the folder named after this file instead
save_path = String(split(@FILE, “.”)[1])
run_name = “singlerotor”
paraview = false
----------------- GEOMETRY PARAMETERS ----------------------------------------
Rotor geometry
rotor_file = “DJI9443.csv” # Rotor geometry
data_path = uns.def_data_path # Path to rotor database
pitch = 0.0 # (deg) collective pitch of blades
CW = false # Clock-wise rotation
xfoil = false # Whether to run XFOIL
read_polar = vlm.ap.read_polar2 # What polar reader to use
NOTE: If xfoil=true, XFOIL will be run to generate the airfoil polars used
by blade elements before starting the simulation. XFOIL is run
on the airfoil contours found in rotor_file at the corresponding
local Reynolds and Mach numbers along the blade.
Alternatively, the user can provide pre-computer airfoil polars using
xfoil=false and providing the polar files through rotor_file.
read_polar is the function that will be used to parse polar files. Use
vlm.ap.read_polar for files that are direct outputs of XFOIL (e.g., as
downloaded from www.airfoiltools.com). Use vlm.ap.read_polar2 for CSV
files.
Discretization
n = 20 # Number of blade elements per blade
r = 1/10 # Geometric expansion of elements
NOTE: Here a geometric expansion of 1/10 means that the spacing between the
tip elements is 1/10 of the spacing between the hub elements. Refine the
discretization towards the blade tip like this in order to better
resolve the tip vortex.
Read radius of this rotor and number of blades
R, B = uns.read_rotor(rotor_file; data_path=data_path)[[1,3]]
----------------- SIMULATION PARAMETERS --------------------------------------
Operating conditions
RPM = 5400 # RPM
J = 0.0001 # Advance ratio Vinf/(nD)
AOA = 0 # (deg) Angle of attack (incidence angle)
rho = 1.071778 # (kg/m^3) air density
mu = 1.85508e-5 # (kg/ms) air dynamic viscosity
speedofsound = 342.35 # (m/s) speed of sound
NOTE: For cases with zero freestream velocity, it is recommended that a
negligible small velocity is used instead of zero in order to avoid
potential numerical instabilities (hence, J here is negligible small
instead of zero)
magVinf = JRPM/60(2R)
Vinf(X, t) = magVinf[cos(AOApi/180), sin(AOApi/180), 0] # (m/s) freestream velocity vector
ReD = 2piRPM/60R * rho/mu * 2R # Diameter-based Reynolds number
Matip = 2piRPM/60 * R / speedofsound # Tip Mach number
println(“”"
RPM:
(
R
P
M
)
V
i
n
f
:
(RPM)Vinf:(Vinf(zeros(3), 0)) m/s
Matip:
(
r
o
u
n
d
(
M
a
t
i
p
,
d
i
g
i
t
s
=
3
)
)
R
e
D
:
(round(Matip,digits=3))ReD:(round(ReD, digits=0))
“”")
----------------- SOLVER PARAMETERS ------------------------------------------
Aerodynamic solver
VehicleType = uns.UVLMVehicle # Unsteady solver
VehicleType = uns.QVLMVehicle # Quasi-steady solver
const_solution = VehicleType==uns.QVLMVehicle # Whether to assume that the
# solution is constant or not
Time parameters
nrevs = 10 # Number of revolutions in simulation
nsteps_per_rev = 36 # Time steps per revolution
nsteps = const_solution ? 2 : nrevs*nsteps_per_rev # Number of time steps
ttot = nsteps/nsteps_per_rev / (RPM/60) # (s) total simulation time
VPM particle shedding
p_per_step = 4 # Sheds per time step
shed_starting = false # Whether to shed starting vortex
shed_unsteady = true # Whether to shed vorticity from unsteady loading
unsteady_shedcrit = 0.001 # Shed unsteady loading whenever circulation
# fluctuates by more than this ratio
max_particles = ((2*n+1)*B)nstepsp_per_step + 1 # Maximum number of particles
Regularization
sigma_rotor_surf= R/10 # Rotor-on-VPM smoothing radius
lambda_vpm = 2.125 # VPM core overlap
# VPM smoothing radius
sigma_vpm_overwrite = lambda_vpm * 2piR/(nsteps_per_rev*p_per_step)
sigmafactor_vpmonvlm= 1 # Shrink particles by this factor when
# calculating VPM-on-VLM/Rotor induced velocities
Rotor solver
vlm_rlx = 0.5 # VLM relaxation <-- this also applied to rotors
hubtiploss_correction = ((0.4, 5, 0.1, 0.05), (2, 1, 0.25, 0.05)) # Hub and tip correction
VPM solver
vpm_integration = vpm.euler # VPM temporal integration scheme
vpm_integration = vpm.rungekutta3
vpm_viscous = vpm.Inviscid() # VPM viscous diffusion scheme
vpm_viscous = vpm.CoreSpreading(-1, -1, vpm.zeta_fmm; beta=100.0, itmax=20, tol=1e-1)
vpm_SFS = vpm.SFS_none # VPM LES subfilter-scale model
vpm_SFS = vpm.SFS_Cd_twolevel_nobackscatter
vpm_SFS = vpm.SFS_Cd_threelevel_nobackscatter
vpm_SFS = vpm.DynamicSFS(vpm.Estr_fmm, vpm.pseudo3level_positive;
alpha=0.999, maxC=1.0,
clippings=[vpm.clipping_backscatter])
vpm_SFS = vpm.DynamicSFS(vpm.Estr_fmm, vpm.pseudo3level_positive;
alpha=0.999, rlxf=0.005, minC=0, maxC=1
clippings=[vpm.clipping_backscatter],
controls=[vpm.control_sigmasensor],
)
NOTE: In most practical situations, open rotors operate at a Reynolds number
high enough that viscous diffusion in the wake is actually negligible.
Hence, it does not make much of a difference whether we run the
simulation with viscous diffusion enabled or not. On the other hand,
such high Reynolds numbers mean that the wake quickly becomes turbulent
and it is crucial to use a subfilter-scale (SFS) model to accurately
capture the turbulent decay of the wake (turbulent diffusion).
if VehicleType == uns.QVLMVehicle
# Mute warnings regarding potential colinear vortex filaments. This is
# needed since the quasi-steady solver will probe induced velocities at the
# lifting line of the blade
uns.vlm.VLMSolver._mute_warning(true)
end
----------------- WAKE TREATMENT ---------------------------------------------
NOTE: It is known in the CFD community that rotor simulations with an
impulsive RPM start (i.e., 0 to RPM in the first time step, as opposed
to gradually ramping up the RPM) leads to the hub “fountain effect”,
with the root wake reversing the flow near the hub.
The fountain eventually goes away as the wake develops, but this happens
very slowly, which delays the convergence of the simulation to a steady
state. To accelerate convergence, here we define a wake treatment
procedure that suppresses the hub wake for the first three revolutions,
avoiding the fountain effect altogether.
This is especially helpful in low and mid-fidelity simulations.
suppress_fountain = true # Toggle
Supress wake shedding on blade elements inboard of this r/R radial station
no_shedding_Rthreshold = suppress_fountain ? 0.35 : 0.0
Supress wake shedding for this many time steps
no_shedding_nstepsthreshold = 3*nsteps_per_rev
omit_shedding = [] # Index of blade elements to supress wake shedding
Function to suppress or activate wake shedding
function wake_treatment_supress(sim, args…; optargs…)
# Case: start of simulation -> suppress shedding if sim.nt == 1 # Identify blade elements on which to suppress shedding for i in 1:vlm.get_m(rotor) HS = vlm.getHorseshoe(rotor, i) CP = HS[5] if uns.vlm.norm(CP - vlm._get_O(rotor)) <= no_shedding_Rthreshold*R push!(omit_shedding, i) end end end # Case: sufficient time steps -> enable shedding if sim.nt == no_shedding_nstepsthreshold # Flag to stop suppressing omit_shedding .= -1 end return false
end
----------------- 1) VEHICLE DEFINITION --------------------------------------
println(“Generating geometry…”)
Generate rotor
rotor = uns.generate_rotor(rotor_file; pitch=pitch,
n=n, CW=CW, blade_r=r,
altReD=[RPM, J, mu/rho],
xfoil=xfoil,
read_polar=read_polar,
data_path=data_path,
verbose=true,
plot_disc=true
);
println(“Generating vehicle…”)
Generate vehicle
system = vlm.WingSystem() # System of all FLOWVLM objects
vlm.addwing(system, “Rotor”, rotor)
rotors = [rotor]; # Defining this rotor as its own system
rotor_systems = (rotors, ); # All systems of rotors
wake_system = vlm.WingSystem() # System that will shed a VPM wake
# NOTE: Do NOT include rotor when using the quasi-steady solver
if VehicleType != uns.QVLMVehicle
vlm.addwing(wake_system, “Rotor”, rotor)
end
vehicle = VehicleType( system;
rotor_systems=rotor_systems,
wake_system=wake_system
);
------------- 2) MANEUVER DEFINITION -----------------------------------------
Non-dimensional translational velocity of vehicle over time
Vvehicle(t) = zeros(3)
Angle of the vehicle over time
anglevehicle(t) = zeros(3)
RPM control input over time (RPM over RPMref)
RPMcontrol(t) = 1.0
angles = () # Angle of each tilting system (none)
RPMs = (RPMcontrol, ) # RPM of each rotor system
maneuver = uns.KinematicManeuver(angles, RPMs, Vvehicle, anglevehicle)
------------- 3) SIMULATION DEFINITION ---------------------------------------
Vref = 0.0 # Reference velocity to scale maneuver by
RPMref = RPM # Reference RPM to scale maneuver by
Vinit = VrefVvehicle(0) # Initial vehicle velocity
Winit = pi/180(anglevehicle(1e-6) - anglevehicle(0))/(1e-6*ttot) # Initial angular velocity
simulation = uns.Simulation(vehicle, maneuver, Vref, RPMref, ttot;
Vinit=Vinit, Winit=Winit);
Restart simulation
restart_file = nothing
NOTE: Uncomment the following line to restart a previous simulation.
Point it to a particle field file (with its full path) at a specific
time step, and run_simulation will start this simulation with the
particle field found in the restart simulation.
restart_file = “/path/to/a/previous/simulation/rotorhover-example_pfield.360”
------------- 4) MONITORS DEFINITIONS ----------------------------------------
Generate rotor monitor
monitor_rotor = uns.generate_monitor_rotors(rotors, J, rho, RPM, nsteps;
t_scale=RPM/60, # Scaling factor for time in plots
t_lbl=“Revolutions”, # Label for time axis
save_path=save_path,
run_name=run_name,
figname=“rotor monitor”,
)
Generate monitor of flow enstrophy (numerical stability)
monitor_enstrophy = uns.generate_monitor_enstrophy(;
save_path=save_path,
run_name=run_name,
figname=“enstrophy monitor”
)
Generate monitor of SFS model coefficient Cd
monitor_Cd = uns.generate_monitor_Cd(;
save_path=save_path,
run_name=run_name,
figname=“Cd monitor”
)
Concatenate monitors
monitors = uns.concatenate(monitor_rotor, monitor_enstrophy, monitor_Cd)
------------- 5) RUN SIMULATION ----------------------------------------------
println(“Running simulation…”)
Concatenate monitors and wake treatment procedure into one runtime function
runtime_function = uns.concatenate(monitors, wake_treatment_supress)
Run simulation
uns.run_simulation(simulation, nsteps;
# ----- SIMULATION OPTIONS -------------
Vinf=Vinf,
rho=rho, mu=mu, sound_spd=speedofsound,
# ----- SOLVERS OPTIONS ----------------
p_per_step=p_per_step,
max_particles=max_particles,
vpm_integration=vpm_integration,
vpm_viscous=vpm_viscous,
vpm_SFS=vpm_SFS,
sigma_vlm_surf=sigma_rotor_surf,
sigma_rotor_surf=sigma_rotor_surf,
sigma_vpm_overwrite=sigma_vpm_overwrite,
sigmafactor_vpmonvlm=sigmafactor_vpmonvlm,
vlm_rlx=vlm_rlx,
hubtiploss_correction=hubtiploss_correction,
shed_starting=shed_starting,
shed_unsteady=shed_unsteady,
unsteady_shedcrit=unsteady_shedcrit,
omit_shedding=omit_shedding,
extra_runtime_function=runtime_function,
# ----- RESTART OPTIONS -----------------
restart_vpmfile=restart_file,
# ----- OUTPUT OPTIONS ------------------
save_path=save_path,
run_name=run_name,
save_wopwopin=true, # <— Generates input files for PSU-WOPWOP noise analysis
save_code=@FILE
);
----------------- 6) VISUALIZATION -------------------------------------------
if paraview
println(“Calling Paraview…”)
# Files to open in Paraview files = joinpath(save_path, run_name*"_pfield...xmf;") for bi in 1:B global files files *= run_name*"_Rotor_Blade$(bi)_loft...vtk;" files *= run_name*"_Rotor_Blade$(bi)_vlm...vtk;" end # Call Paraview run(`paraview --data=$(files)`)
end
------------- 6) POSTPROCESSING ----------------------------------------------
Post-process monitor plots
include(joinpath(uns.examples_path, “rotorhover”, “rotorhover_postprocessing.jl”))
这是用Julia写的单个旋翼运动的文件，现在把他改成共轴旋翼运动的文件，上下两个旋翼参数相同，且间距为0.1D,D为旋翼直径
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博客分享了Gmail的小贴士，还给出相关链接http://g04.com/html/modules.php?name=News&new_topic=12 ，内容有趣。
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