def __call__(self, t, x, profiles, HCoeffsTurb):
#n = profiles['default']
# Define the contributions to the H coefficients for the Shestakov Problem
H1 = np.ones_like(x)
H7 = shestakov_nonlinear_diffusion.H7contrib_Source(x)
HCoeffs = tango.multifield.HCoefficients(H1=H1, H7=H7)
HCoeffs = HCoeffs + HCoeffsTurb
return HCoeffs
#==============================================================================
# MAIN STARTS HERE
#==============================================================================
tlog.setup()
tlog.info("Initializing...")
L, N, dx, x, nL, n = initialize_shestakov_problem()
maxIterations, lmParams, tol = initialize_parameters()
fluxModel = shestakov_nonlinear_diffusion.AnalyticFluxModel(dx)
label = 'n'
turbHandler = tango.lodestro_method.TurbulenceHandler(dx, x, fluxModel)
compute_all_H_density = ComputeAllH()
lodestroMethod = tango.lodestro_method.lm(lmParams['EWMAParamTurbFlux'],
lmParams['EWMAParamProfile'],
lmParams['thetaParams'])
field0 = tango.multifield.Field(label=label,
rightBC=nL,
not the same, where the turbulent flux model has a buffer zone, and there is noise in the turbulent flux.
This example is similar to buffer_zone_and_extrapolation.py, but with noise added. This example shows that
convergence can be achieved in the Shestakov problem when both complications of noise and a buffer zone are
present.
"""
from __future__ import division, absolute_import
import numpy as np
import matplotlib.pyplot as plt
import h5py
from tango.extras import shestakov_nonlinear_diffusion, bufferzone, noisyflux
import tango
import tango.tango_logging as tlog
tlog.setup(parallel=False, rank=0, level=tlog.INFO)
def initialize_shestakov_problem():
# Problem Setup
L = 1 # size of domain
N = 300 # number of spatial grid points
dx = L / (N-1) # spatial grid size
x = np.arange(N)*dx # location corresponding to grid points j=0, ..., N-1
nL = 0.6 # right boundary condition
n_initialcondition = 1 - 0.5*x
return (L, N, dx, x, nL, n_initialcondition)
def initialize_parameters():
maxIterations = 2000
thetaparams = {'Dmin': 1e-5,
'Dmax': 1e13,
solver tango Solver (object)
"""
iterationNumber = solver.l
if iterationNumber == 50:
self.turbhandler.set_ewma_params(0.1, 0.1)
# run for 2.5/EWMA = 25 iterations
if iterationNumber == 75:
self.turbhandler.set_ewam_params(0.03, 0.03)
# run for 2.5/EWMA = 83 iterations
# ************************************************** #
#### START OF MAIN PROGRAM ####
# ************************************************** #
MPIrank = gene_tango.init_mpi()
tlog.setup(True, MPIrank, tlog.DEBUG)
(L, rTango, pressureRightBC, pressureICTango, maxIterations, tol,
geneFluxModel, turbhandler, compute_all_H, t_array) = problem_setup()
# seed the EWMA for the turbulent heat flux
heatFluxSeed = read_seed_turb_flux()
turbhandler.seed_EWMA_turb_flux(heatFluxSeed)
# set up FileHandlers
# GENE output to save periodically. For list of available, see handlers.py
diagdir = DIAGDIR
f1HistoryHandler = tango.handlers.SaveGeneOutputHandler('checkpoint_000',
iterationInterval=2,
diagdir=diagdir)
geneFilesToSave = [
This example is similar to buffer_zone_and_extrapolation.py, but with noise added. This example shows that
convergence can be achieved in the Shestakov problem when both complications of noise and a buffer zone are
present.
"""
from __future__ import division, absolute_import
import numpy as np
from tango.extras import shestakov_nonlinear_diffusion, bufferzone, noisyflux
import tango as tng
import matplotlib.pyplot as plt
from tango import analysis
import tango.tango_logging as tlog
tlog.setup(False, 0, tlog.INFO) # when in a serial environment
def initialize_shestakov_problem():
# Problem Setup
L = 1 # size of domain
N = 300 # number of spatial grid points
dx = L / (N - 1) # spatial grid size
x = np.arange(
N) * dx # location corresponding to grid points j=0, ..., N-1
nL = 0.6 # right boundary condition
n_initialcondition = 1 - 0.5 * x
return (L, N, dx, x, nL, n_initialcondition)
def initialize_parameters():
