https://github.com/landreman/stellopt_scenarios/tree/master/7DOF_varyAxisAndElongation_targetIotaAndQuasisymmetry
See that website for a detailed description of the problem.
"""
# This next line turns on detailed logging. It can be commented out if
# you do not want such verbose output.
log()
mpi = MpiPartition()
vmec = Vmec(
os.path.join(os.path.dirname(__file__), 'inputs',
'input.stellopt_scenarios_7dof'), mpi)
# We will optimize in the space of Garabedian coefficients:
surf = vmec.boundary.to_Garabedian()
vmec.boundary = surf
# Define parameter space:
surf.all_fixed()
surf.fixed_range(mmin=0, mmax=2, nmin=-1, nmax=1, fixed=False)
surf.set_fixed("Delta(1,0)") # toroidally-averaged major radius
surf.set_fixed("Delta(0,0)") # toroidally-averaged minor radius
# Define objective function:
boozer = Boozer(vmec, mpol=32, ntor=16)
qs = Quasisymmetry(
boozer,
1.0, # Radius to target
1,
0, # (M, N) you want in |B|
normalization="symmetric",
# In the next line, we can adjust how many groups the pool of MPI
# processes is split into.
mpi = MpiPartition(ngroups=1)
mpi.write()
# Start with a default surface, which is axisymmetric with major
# radius 1 and minor radius 0.1.
equil = Vmec(os.path.join(os.path.dirname(__file__), 'inputs',
'input.1DOF_Garabedian'),
mpi=mpi)
# We will optimize in the space of Garabedian coefficients rather than
# RBC/ZBS coefficients. To do this, we convert the boundary to the
# Garabedian representation:
surf = equil.boundary.to_Garabedian()
equil.boundary = surf
# VMEC parameters are all fixed by default, while surface parameters
# are all non-fixed by default. You can choose which parameters are
# optimized by setting their 'fixed' attributes.
surf.all_fixed()
surf.set_fixed('Delta(1,-1)', False)
# Each function we want in the objective function is then equipped
# with a shift and weight, to become a term in a least-squares
# objective function. A list of terms are combined to form a
# nonlinear-least-squares problem.
desired_iota = -0.41
prob = LeastSquaresProblem([(equil.iota_axis, desired_iota, 1)])
# Solve the minimization problem. We can choose whether to use a