def interpolateDesignVec(orig_filter, orig_vec, new_filter, new_vec):
"""
This function interpolates a design vector from the original design space defined
on an OctForest or QuadForest and interpolates it to a new OctForest or QuadForest.
This function is used after a mesh adaptation step to get the new design space.
Args:
orig_filter (OctForest or QuadForest): Original filter Oct or QuadForest object
orig_vec (PVec): Design variables on the original mesh in a ParOpt.PVec
new_filter (OctForest or QuadForest): New filter Oct or QuadForest object
new_vec (PVec): Design variables on the new mesh in a ParOpt.PVec (set on ouput)
"""
# Convert the PVec class to TACSBVec
orig_x = TMR.convertPVecToVec(orig_vec)
if orig_x is None:
raise ValueError(
'Original vector must be generated by TMR.TopoProblem')
new_x = TMR.convertPVecToVec(new_vec)
if new_x is None:
raise ValueError('New vector must be generated by TMR.TopoProblem')
if orig_x.getVarsPerNode() != new_x.getVarsPerNode():
raise ValueError('Number of variables per node must be consistent')
orig_varmap = orig_x.getVarMap()
new_varmap = new_x.getVarMap()
vars_per_node = orig_x.getVarsPerNode()
# Create the interpolation class
interp = TACS.VecInterp(orig_varmap, new_varmap, vars_per_node)
new_filter.createInterpolation(orig_filter, interp)
interp.initialize()
# Perform the interpolation
interp.mult(orig_x, new_x)
return
# Set parameters
opt.setMaxMajorIterations(args.max_opt_iters)
opt.setHessianResetFreq(args.hessian_reset)
problem.setIterationCounter(args.max_opt_iters * ite)
opt.setAbsOptimalityTol(args.opt_abs_tol)
opt.setBarrierFraction(args.opt_barrier_frac)
opt.setBarrierPower(args.opt_barrier_power)
opt.setOutputFrequency(args.output_freq)
opt.setOutputFile(
os.path.join(args.prefix, 'paropt_output%d.out' % (ite)))
# If the old filter exists, we're on the second iteration
if old_filtr:
# Create the interpolation
interp = TACS.VecInterp(old_varmap, varmap)
filtr.createInterpolation(old_filtr, interp)
interp.initialize()
# Get the optimization variables for the new optimizer
x, z, zw, zl, zu = opt.getOptimizedPoint()
# Do not try to estimate the new multipliers
opt.setInitStartingPoint(0)
# Set the values of the new mass constraint multipliers
z[0] = old_z
# Do the interpolation
old_vec = problem.convertPVecToVec(old_dvs)
x_vec = problem.convertPVecToVec(x)
# Set parameters for ParOpt in the subproblem
opt.setMaxMajorIterations(500)
opt.setAbsOptimalityTol(1e-6)
# Don't update the quasi-Newton method
opt.setQuasiNewton(qn)
opt.setUseQuasiNewtonUpdates(0)
# Set the design variable bounds
filename = os.path.join(args.prefix, 'paropt%d.out' % (ite))
opt.setOutputFile(filename)
# If the old filter exists, we're on the second iteration
if old_filtr:
# Create the interpolation
interp = TACS.VecInterp(old_varmap, varmap)
filtr.createInterpolation(old_filtr, interp)
interp.initialize()
# Get the optimization variables for the new optimizer
x, z, zw, zl, zu = opt.getOptimizedPoint()
# Do the interpolation
old_vec = problem.convertPVecToVec(old_x)
x_vec = problem.convertPVecToVec(x)
interp.mult(old_vec, x_vec)
# Set the new design variables
problem.setInitDesignVars(x)
# Initialize the problem
for i in range(len(octants)):
if i % 7 == 0:
refine[i] = 2
elif i % 5 == 0:
refine[i] = -1
# Make the coarse tree finer than the fine tree for testing purposes
coarse = fine.duplicate()
coarse.refine(refine)
coarse.balance(0)
coarse.repartition()
coarse.setMeshOrder(2, TMR.GAUSS_LOBATTO_POINTS)
coarse.createNodes()
coarse_range = coarse.getNodeRange()
nc = coarse_range[comm.rank + 1] - coarse_range[comm.rank]
coarse_map = TACS.VarMap(comm, nc)
fine_range = fine.getNodeRange()
nf = fine_range[comm.rank + 1] - fine_range[comm.rank]
fine_map = TACS.VarMap(comm, nf)
# Create the two interpolations fine -> coarse and coarse -> fine
interp = TACS.VecInterp(coarse_map, fine_map, 1)
fine.createInterpolation(coarse, interp)
interp.initialize()
interp2 = TACS.VecInterp(fine_map, coarse_map, 1)
coarse.createInterpolation(fine, interp2)
interp2.initialize()
