def _resid(self, dtau, x):
comm, rank, root = get_comm_rank_root()
# Get an errest kern to compute the square of the maximum residual
errest = self._get_errest_kerns()
# Prepare and run the kernel
self._prepare_reg_banks(x, x, x)
self._queue % errest(dtau, 0.0)
# L2 norm
if self._pseudo_norm == 'l2':
# Reduce locally (element types) and globally (MPI ranks)
res = np.array([sum(ev) for ev in zip(*errest.retval)])
comm.Allreduce(get_mpi('in_place'), res, op=get_mpi('sum'))
# Normalise and return
return np.sqrt(res / self._gndofs)
# L^∞ norm
else:
# Reduce locally (element types) and globally (MPI ranks)
res = np.array([max(ev) for ev in zip(*errest.retval)])
comm.Allreduce(get_mpi('in_place'), res, op=get_mpi('max'))
# Normalise and return
return np.sqrt(res)
def _errest(self, rcurr, rprev, rerr):
comm, rank, root = get_comm_rank_root()
errest = self._get_reduction_kerns(rcurr, rprev, rerr, method='errest',
norm=self._norm)
# Obtain an estimate for the squared error
self._queue.enqueue_and_run(errest, self._atol, self._rtol)
# L2 norm
if self._norm == 'l2':
# Reduce locally (element types + field variables)
err = np.array([sum(v for e in errest for v in e.retval)])
# Reduce globally (MPI ranks)
comm.Allreduce(get_mpi('in_place'), err, op=get_mpi('sum'))
# Normalise
err = math.sqrt(float(err) / self._gndofs)
# L^∞ norm
else:
# Reduce locally (element types + field variables)
err = np.array([max(v for e in errest for v in e.retval)])
# Reduce globally (MPI ranks)
comm.Allreduce(get_mpi('in_place'), err, op=get_mpi('max'))
# Normalise
err = math.sqrt(float(err))
return err if not math.isnan(err) else 100
def _errest(self, x, y, z):
comm, rank, root = get_comm_rank_root()
errest = self._get_errest_kerns()
# Obtain an estimate for the squared error
self._prepare_reg_banks(x, y, z)
self._queue % errest(self._atol, self._rtol)
# L2 norm
if self._norm == 'l2':
# Reduce locally (element types) and globally (MPI ranks)
rl = sum(errest.retval)
rg = comm.allreduce(rl, op=get_mpi('sum'))
# Normalise
err = math.sqrt(rg / self._gndofs)
# Uniform norm
else:
# Reduce locally (element types) and globally (MPI ranks)
rl = max(errest.retval)
rg = comm.allreduce(rl, op=get_mpi('max'))
# Normalise
err = math.sqrt(rg)
return err if not math.isnan(err) else 100
def _resid(self, rcurr, rold, dt_fac):
comm, rank, root = get_comm_rank_root()
# Get a reduction kern to compute the square of the maximum residual
resid = self._get_reduction_kerns(rcurr,
rold,
method='resid',
norm=self._pseudo_norm)
# Run the kernel
self._queue.enqueue_and_run(resid, dt_fac)
# L2 norm
if self._pseudo_norm == 'l2':
# Reduce locally (element types) and globally (MPI ranks)
res = np.array([sum(ev) for ev in zip(*[r.retval for r in resid])])
comm.Allreduce(get_mpi('in_place'), res, op=get_mpi('sum'))
# Normalise and return
return tuple(np.sqrt(res / self._gndofs))
# L^∞ norm
else:
# Reduce locally (element types) and globally (MPI ranks)
res = np.array([max(ev) for ev in zip(*[r.retval for r in resid])])
comm.Allreduce(get_mpi('in_place'), res, op=get_mpi('max'))
# Normalise and return
return tuple(np.sqrt(res))
def _errest(self, x, y, z):
comm, rank, root = get_comm_rank_root()
errest = self._get_errest_kerns()
# Obtain an estimate for the squared error
self._prepare_reg_banks(x, y, z)
self._queue % errest(self._atol, self._rtol)
# L2 norm
if self._norm == 'l2':
# Reduce locally (element types + field variables)
err = np.array([sum(v for e in errest.retval for v in e)])
# Reduce globally (MPI ranks)
comm.Allreduce(get_mpi('in_place'), err, op=get_mpi('sum'))
# Normalise
err = math.sqrt(float(err) / self._gndofs)
# L^∞ norm
else:
# Reduce locally (element types + field variables)
err = np.array([max(v for e in errest.retval for v in e)])
# Reduce globally (MPI ranks)
comm.Allreduce(get_mpi('in_place'), err, op=get_mpi('max'))
# Normalise
err = math.sqrt(float(err))
return err if not math.isnan(err) else 100
def __call__(self, intg):
# Return if no output is due
if intg.nsteps % self.nsteps:
return
# MPI info
comm, rank, root = get_comm_rank_root()
# Solution matrices indexed by element type
solns = dict(zip(intg.system.ele_types, intg.soln))
# Force vector
f = np.zeros(self.ndims)
for etype, fidx in self._m0:
# Get the interpolation operator
m0 = self._m0[etype, fidx]
nfpts, nupts = m0.shape
# Extract the relevant elements from the solution
uupts = solns[etype][..., self._eidxs[etype, fidx]]
# Interpolate to the face
ufpts = np.dot(m0, uupts.reshape(nupts, -1))
ufpts = ufpts.reshape(nfpts, self.nvars, -1)
ufpts = ufpts.swapaxes(0, 1)
# Compute the pressure
p = self.elementscls.conv_to_pri(ufpts, self.cfg)[-1]
# Get the quadrature weights and normal vectors
qwts = self._qwts[etype, fidx]
norms = self._norms[etype, fidx]
# Do the quadrature
f += np.einsum('i...,ij,jik', qwts, p, norms)
# Reduce and output if we're the root rank
if rank != root:
comm.Reduce(f, None, op=get_mpi('sum'), root=root)
else:
comm.Reduce(get_mpi('in_place'), f, op=get_mpi('sum'), root=root)
# Build the row
row = [intg.tcurr] + f.tolist()
# Write
print(','.join(str(r) for r in row), file=self.outf)
# Flush to disk
self.outf.flush()
def _get_gndofs(self):
comm, rank, root = get_comm_rank_root()
# Get the number of degrees of freedom in this partition
ndofs = sum(self.system.ele_ndofs)
# Sum to get the global number over all partitions
return comm.allreduce(ndofs, op=get_mpi('sum'))
def _get_gndofs(self):
comm, rank, root = get_comm_rank_root()
# Get the number of degrees of freedom in this partition
ndofs = sum(self.system.ele_ndofs)
# Sum to get the global number over all partitions
return comm.allreduce(ndofs, op=get_mpi('sum'))
def __call__(self, intg):
# If an output is due this step
if intg.nacptsteps % self.nsteps == 0 and intg.nacptsteps:
# MPI info
comm, rank, root = get_comm_rank_root()
# Previous and current solution
prev = self._prev
curr = intg.soln
# Square of the residual vector for each variable
resid = sum(
np.linalg.norm(p - c, axis=(0, 2))**2
for p, c in zip(prev, curr))
# Reduce and, if we are the root rank, output
if rank != root:
comm.Reduce(resid, None, op=get_mpi('sum'), root=root)
else:
comm.Reduce(get_mpi('in_place'),
resid,
op=get_mpi('sum'),
root=root)
# Normalise
resid = np.sqrt(resid) / (intg.tcurr - self._tprev)
# Build the row
row = [intg.tcurr] + resid.tolist()
# Write
print(','.join(str(r) for r in row), file=self.outf)
# Flush to disk
self.outf.flush()
del self._prev, self._tprev
# If an output is due next step
if (intg.nacptsteps + 1) % self.nsteps == 0:
self._prev = [s.copy() for s in intg.soln]
self._tprev = intg.tcurr
def __call__(self, intg):
# If an output is due this step
if intg.nacptsteps % self.nsteps == 0 and intg.nacptsteps:
# MPI info
comm, rank, root = get_comm_rank_root()
# Previous and current solution
prev = self._prev
curr = intg.soln
# Square of the residual vector for each variable
resid = sum(np.linalg.norm(p - c, axis=(0, 2))**2
for p, c in zip(prev, curr))
# Reduce and, if we are the root rank, output
if rank != root:
comm.Reduce(resid, None, op=get_mpi('sum'), root=root)
else:
comm.Reduce(get_mpi('in_place'), resid, op=get_mpi('sum'),
root=root)
# Normalise
resid = np.sqrt(resid) / (intg.tcurr - self._tprev)
# Build the row
row = [intg.tcurr] + resid.tolist()
# Write
print(','.join(str(r) for r in row), file=self.outf)
# Flush to disk
self.outf.flush()
del self._prev, self._tprev
# If an output is due next step
if (intg.nacptsteps + 1) % self.nsteps == 0:
self._prev = [s.copy() for s in intg.soln]
self._tprev = intg.tcurr
def __call__(self, intg):
if intg.nacptsteps % self.nsteps == 0:
# MPI info
comm, rank, root = get_comm_rank_root()
# Evaluate the integation expressions
iintex = self._eval_exprs(intg)
# Reduce and output if we're the root rank
if rank != root:
comm.Reduce(iintex, None, op=get_mpi('sum'), root=root)
else:
comm.Reduce(get_mpi('in_place'),
iintex,
op=get_mpi('sum'),
root=root)
# Write
print(intg.tcurr, *iintex, sep=',', file=self.outf)
# Flush to disk
self.outf.flush()
def __init__(self, intg, cfgsect, suffix):
super().__init__(intg, cfgsect, suffix)
# Underlying elements class
self.elementscls = intg.system.elementscls
# Output frequency
self.nsteps = self.cfg.getint(cfgsect, 'nsteps')
# List of points to be sampled and format
self.pts = ast.literal_eval(self.cfg.get(cfgsect, 'samp-pts'))
self.fmt = self.cfg.get(cfgsect, 'format', 'primitive')
# MPI info
comm, rank, root = get_comm_rank_root()
# MPI rank responsible for each point and rank-indexed info
self._ptsrank = ptsrank = []
self._ptsinfo = ptsinfo = [[] for i in range(comm.size)]
# Physical location of the solution points
plocs = [p.swapaxes(1, 2) for p in intg.system.ele_ploc_upts]
for p in self.pts:
# Find the nearest point in our partition
cp = _closest_upt(intg.system.ele_types, plocs, p)
# Reduce over all partitions
mcp, mrank = comm.allreduce(cp, op=get_mpi('minloc'))
# Store the rank responsible along with the info
ptsrank.append(mrank)
ptsinfo[mrank].append(mcp[1:])
# If we're the root rank then open the output file
if rank == root:
# Determine the file path
fname = self.cfg.get(cfgsect, 'file')
# Append the '.csv' extension
if not fname.endswith('.csv'):
fname += '.csv'
# Open for appending
self.outf = open(fname, 'a')
# Output a header if required
if (os.path.getsize(fname) == 0 and
self.cfg.getbool(cfgsect, 'header', True)):
print(self._header, file=self.outf)
def __init__(self, intg, cfgsect, suffix):
super().__init__(intg, cfgsect, suffix)
# Underlying elements class
self.elementscls = intg.system.elementscls
# Output frequency
self.nsteps = self.cfg.getint(cfgsect, 'nsteps')
# List of points to be sampled and format
self.pts = ast.literal_eval(self.cfg.get(cfgsect, 'samp-pts'))
self.fmt = self.cfg.get(cfgsect, 'format', 'primitive')
# MPI info
comm, rank, root = get_comm_rank_root()
# MPI rank responsible for each point and rank-indexed info
self._ptsrank = ptsrank = []
self._ptsinfo = ptsinfo = [[] for i in range(comm.size)]
# Physical location of the solution points
plocs = [p.swapaxes(1, 2) for p in intg.system.ele_ploc_upts]
for p in self.pts:
# Find the nearest point in our partition
cp = _closest_upt(intg.system.ele_types, plocs, p)
# Reduce over all partitions
mcp, mrank = comm.allreduce(cp, op=get_mpi('minloc'))
# Store the rank responsible along with the info
ptsrank.append(mrank)
ptsinfo[mrank].append(mcp[1:])
# If we're the root rank then open the output file
if rank == root:
# Determine the file path
fname = self.cfg.get(cfgsect, 'file')
# Append the '.csv' extension
if not fname.endswith('.csv'):
fname += '.csv'
# Open for appending
self.outf = open(fname, 'a')
# Output a header if required
if (os.path.getsize(fname) == 0 and
self.cfg.getbool(cfgsect, 'header', True)):
print(self._header, file=self.outf)
def __call__(self, intg):
# If an output is due this step
if intg.nacptsteps % self.nsteps == 0 and intg.nacptsteps:
# MPI info
comm, rank, root = get_comm_rank_root()
# Previous and current solution
prev = self._prev
curr = intg.soln
# Square of the residual vector for each variable
resid = sum(
np.linalg.norm(p - c, axis=(0, 2))**2
for p, c in zip(prev, curr))
# Reduce and, if we are the root rank, output
if rank != root:
comm.Reduce(resid, None, op=get_mpi('sum'), root=root)
else:
comm.Reduce(get_mpi('in_place'),
resid,
op=get_mpi('sum'),
root=root)
# Normalise
resid = np.sqrt(resid) / (intg.tcurr - self._tprev)
# Write
print(intg.tcurr, *resid, sep=',', file=self.outf)
# Flush to disk
self.outf.flush()
del self._prev, self._tprev
# Prep work if an output is due next step
self._prep_next_output(intg)
def setup_dataframe(self):
self.elementscls = self.solver.system.elementscls
# List of points to be sampled and format
file = open(self.base_dir + '/samp_pts.txt', 'r')
self.pts = eval(file.read())
self.fmt = 'not primitive' # all the configs had this as primitive but i dont think it was used
# Define directory where solution snapshots should be saved
self.save_dir = 'sol_data'
if self.baseline_file is not None:
f = h5py.File(self.baseline_file, 'r')
self.goal_state = np.array(f['sol_data'])
else:
self.goal_state = None
# Initial omega
self.solver.system.omega = 0
# MPI info
comm, rank, root = get_comm_rank_root()
# MPI rank responsible for each point and rank-indexed info
self._ptsrank = ptsrank = []
self._ptsinfo = ptsinfo = [[] for i in range(comm.size)]
# Physical location of the solution points
plocs = [p.swapaxes(1, 2) for p in self.solver.system.ele_ploc_upts]
# Load map from point to index
with open(self.base_dir + '/loc_to_idx.json') as loc_to_idx:
loc_to_idx_str = json.load(loc_to_idx, )
self.loc_to_idx = dict()
for key in loc_to_idx_str:
self.loc_to_idx[int(key)] = loc_to_idx_str[key]
# Locate the closest solution points in our partition
closest = _closest_upts(self.solver.system.ele_types, plocs, self.pts)
# Process these points
for cp in closest:
# Reduce over the distance
_, mrank = comm.allreduce((cp[0], rank), op=get_mpi('minloc'))
# Store the rank responsible along with its info
ptsrank.append(mrank)
ptsinfo[mrank].append(
comm.bcast(cp[1:] if rank == mrank else None, root=mrank))
def __init__(self, backend, systemcls, rallocs, mesh, initsoln, cfg):
self.backend = backend
self.rallocs = rallocs
self.cfg = cfg
# Sanity checks
if self._controller_needs_errest and not self._stepper_has_errest:
raise TypeError('Incompatible stepper/controller combination')
# Start time
self.tstart = cfg.getfloat('solver-time-integrator', 't0', 0.0)
# Output times
self.tout = sorted(range_eval(cfg.get('soln-output', 'times')))
self.tend = self.tout[-1]
# Current time; defaults to tstart unless resuming a simulation
if initsoln is None or 'stats' not in initsoln:
self.tcurr = self.tstart
else:
stats = Inifile(initsoln['stats'])
self.tcurr = stats.getfloat('solver-time-integrator', 'tcurr')
# Cull already written output times
self.tout = [t for t in self.tout if t > self.tcurr]
# Ensure no time steps are in the past
if self.tout[0] < self.tcurr:
raise ValueError('Output times must be in the future')
# Determine the amount of temp storage required by thus method
nreg = self._stepper_nregs
# Construct the relevant mesh partition
self.system = systemcls(backend, rallocs, mesh, initsoln, nreg, cfg)
# Extract the UUID of the mesh (to be saved with solutions)
self._mesh_uuid = mesh['mesh_uuid']
# Get a queue for subclasses to use
self._queue = backend.queue()
# Get the number of degrees of freedom in this partition
ndofs = sum(self.system.ele_ndofs)
comm, rank, root = get_comm_rank_root()
# Sum to get the global number over all partitions
self._gndofs = comm.allreduce(ndofs, op=get_mpi('sum'))
def _resid(self, x, y):
comm, rank, root = get_comm_rank_root()
# Get an errest kern to compute the square of the maximum residual
errest = self._get_errest_kerns()
# Prepare and run the kernel
self._prepare_reg_banks(x, y, y)
self._queue % errest(self._pseudo_aresid, self._pseudo_rresid)
# Reduce locally (element types) and globally (MPI ranks)
rl = max(errest.retval)
rg = comm.allreduce(rl, op=get_mpi('max'))
# Normalise
return math.sqrt(rg)
def _resid(self, x, y):
comm, rank, root = get_comm_rank_root()
# Get an errest kern to compute the square of the maximum residual
errest = self._get_errest_kerns()
# Prepare and run the kernel
self._prepare_reg_banks(x, y, y)
self._queue % errest(self._pseudo_aresid, self._pseudo_rresid)
# Reduce locally (element types) and globally (MPI ranks)
rl = max(errest.retval)
rg = comm.allreduce(rl, op=get_mpi('max'))
# Normalise
return math.sqrt(rg)
def __init__(self, backend, systemcls, rallocs, mesh, initsoln, cfg):
self.backend = backend
self.rallocs = rallocs
self.cfg = cfg
self.isrestart = initsoln is not None
# Sanity checks
if self._controller_needs_errest and not self._stepper_has_errest:
raise TypeError('Incompatible stepper/controller combination')
# Start time
self.tstart = cfg.getfloat('solver-time-integrator', 'tstart', 0.0)
self.tend = cfg.getfloat('solver-time-integrator', 'tend')
# Current time; defaults to tstart unless restarting
if self.isrestart:
stats = Inifile(initsoln['stats'])
self.tcurr = stats.getfloat('solver-time-integrator', 'tcurr')
else:
self.tcurr = self.tstart
self.tlist = deque([self.tend])
# Determine the amount of temp storage required by thus method
nreg = self._stepper_nregs
# Construct the relevant mesh partition
self.system = systemcls(backend, rallocs, mesh, initsoln, nreg, cfg)
# Extract the UUID of the mesh (to be saved with solutions)
self.mesh_uuid = mesh['mesh_uuid']
# Get a queue for subclasses to use
self._queue = backend.queue()
# Get the number of degrees of freedom in this partition
ndofs = sum(self.system.ele_ndofs)
comm, rank, root = get_comm_rank_root()
# Sum to get the global number over all partitions
self._gndofs = comm.allreduce(ndofs, op=get_mpi('sum'))
def __init__(self, intg, cfgsect, suffix):
super().__init__(intg, cfgsect, suffix)
# Underlying elements class
self.elementscls = intg.system.elementscls
# Output frequency
self.nsteps = self.cfg.getint(cfgsect, 'nsteps')
# List of points to be sampled and format
self.pts = self.cfg.getliteral(cfgsect, 'samp-pts')
self.fmt = self.cfg.get(cfgsect, 'format', 'primitive')
# MPI info
comm, rank, root = get_comm_rank_root()
# MPI rank responsible for each point and rank-indexed info
self._ptsrank = ptsrank = []
self._ptsinfo = ptsinfo = [[] for i in range(comm.size)]
# Physical location of the solution points
plocs = [p.swapaxes(1, 2) for p in intg.system.ele_ploc_upts]
# Locate the closest solution points in our partition
closest = _closest_upts(intg.system.ele_types, plocs, self.pts)
# Process these points
for cp in closest:
# Reduce over the distance
_, mrank = comm.allreduce((cp[0], rank), op=get_mpi('minloc'))
# Store the rank responsible along with its info
ptsrank.append(mrank)
ptsinfo[mrank].append(
comm.bcast(cp[1:] if rank == mrank else None, root=mrank)
)
# If we're the root rank then open the output file
if rank == root:
self.outf = init_csv(self.cfg, cfgsect, self._header)
def __init__(self, intg, cfgsect, suffix):
super().__init__(intg, cfgsect, suffix)
# Underlying elements class
self.elementscls = intg.system.elementscls
# Output frequency
self.nsteps = self.cfg.getint(cfgsect, 'nsteps')
# List of points to be sampled and format
self.pts = self.cfg.getliteral(cfgsect, 'samp-pts')
self.fmt = self.cfg.get(cfgsect, 'format', 'primitive')
# MPI info
comm, rank, root = get_comm_rank_root()
# MPI rank responsible for each point and rank-indexed info
self._ptsrank = ptsrank = []
self._ptsinfo = ptsinfo = [[] for i in range(comm.size)]
# Physical location of the solution points
plocs = [p.swapaxes(1, 2) for p in intg.system.ele_ploc_upts]
# Locate the closest solution points in our partition
closest = _closest_upts(intg.system.ele_types, plocs, self.pts)
# Process these points
for cp in closest:
# Reduce over the distance
_, mrank = comm.allreduce((cp[0], rank), op=get_mpi('minloc'))
# Store the rank responsible along with its info
ptsrank.append(mrank)
ptsinfo[mrank].append(
comm.bcast(cp[1:] if rank == mrank else None, root=mrank))
# If we're the root rank then open the output file
if rank == root:
self.outf = init_csv(self.cfg, cfgsect, self._header)
def __init__(self, intg, cfgsect, suffix):
super().__init__(intg, cfgsect, suffix)
# Underlying elements class
self.elementscls = intg.system.elementscls
# Output frequency
self.nsteps = self.cfg.getint(cfgsect, 'nsteps')
# List of points to be sampled and format
self.pts = ast.literal_eval(self.cfg.get(cfgsect, 'samp-pts'))
self.fmt = self.cfg.get(cfgsect, 'format', 'primitive')
# MPI info
comm, rank, root = get_comm_rank_root()
# MPI rank responsible for each point and rank-indexed info
self._ptsrank = ptsrank = []
self._ptsinfo = ptsinfo = [[] for i in range(comm.size)]
# Physical location of the solution points
plocs = [p.swapaxes(1, 2) for p in intg.system.ele_ploc_upts]
for p in self.pts:
# Find the nearest point in our partition
cp = _closest_upt(intg.system.ele_types, plocs, p)
# Reduce over all partitions
mcp, mrank = comm.allreduce(cp, op=get_mpi('minloc'))
# Store the rank responsible along with the info
ptsrank.append(mrank)
ptsinfo[mrank].append(mcp[1:])
# If we're the root rank then open the output file
if rank == root:
self.outf = init_csv(self.cfg, cfgsect, self._header)
def __call__(self, intg):
# Return if no output is due
if intg.nacptsteps % self.nsteps:
return
# MPI info
comm, rank, root = get_comm_rank_root()
# Solution matrices indexed by element type
solns = dict(zip(intg.system.ele_types, intg.soln))
ndims, nvars = self.ndims, self.nvars
# Force vector
f = np.zeros(2*ndims if self._viscous else ndims)
for etype, fidx in self._m0:
# Get the interpolation operator
m0 = self._m0[etype, fidx]
nfpts, nupts = m0.shape
# Extract the relevant elements from the solution
uupts = solns[etype][..., self._eidxs[etype, fidx]]
# Interpolate to the face
ufpts = np.dot(m0, uupts.reshape(nupts, -1))
ufpts = ufpts.reshape(nfpts, nvars, -1)
ufpts = ufpts.swapaxes(0, 1)
# Compute the pressure
p = self.elementscls.conv_to_pri(ufpts, self.cfg)[-1]
# Get the quadrature weights and normal vectors
qwts = self._qwts[etype, fidx]
norms = self._norms[etype, fidx]
# Do the quadrature
f[:ndims] += np.einsum('i...,ij,jik', qwts, p, norms)
if self._viscous:
# Get operator and J^-T matrix
m4 = self._m4[etype]
rcpjact = self._rcpjact[etype, fidx]
# Transformed gradient at solution points
tduupts = np.dot(m4, uupts.reshape(nupts, -1))
tduupts = tduupts.reshape(ndims, nupts, nvars, -1)
# Physical gradient at solution points
duupts = np.einsum('ijkl,jkml->ikml', rcpjact, tduupts)
duupts = duupts.reshape(ndims, nupts, -1)
# Interpolate gradient to flux points
dufpts = np.array([np.dot(m0, duupts[i]) for i in range(ndims)])
dufpts = dufpts.reshape(ndims, nfpts, nvars, -1).swapaxes(1, 2)
# Viscous stress
vis = self.stress_tensor(ufpts, dufpts)
# Do the quadrature
f[ndims:] += np.einsum('i...,klij,jil', qwts, vis, norms)
# Reduce and output if we're the root rank
if rank != root:
comm.Reduce(f, None, op=get_mpi('sum'), root=root)
else:
comm.Reduce(get_mpi('in_place'), f, op=get_mpi('sum'), root=root)
# Build the row
row = [intg.tcurr] + f.tolist()
# Write
print(','.join(str(r) for r in row), file=self.outf)
# Flush to disk
self.outf.flush()
def __call__(self, intg):
# MPI info
comm, rank, root = get_comm_rank_root()
if not self.hasinlet:
intg.system.mdot = comm.allreduce(0.0, op=get_mpi('sum'))
else:
# Solution matrices indexed by element type
solns = dict(zip(intg.system.ele_types, intg.soln))
ndims, nvars = self.ndims, self.nvars
# Force vector
rhou = np.zeros(ndims)
area = np.zeros(ndims)
for etype, fidx in self._m0:
# Get the interpolation operator
m0 = self._m0[etype, fidx]
nfpts, nupts = m0.shape
# Extract the relevant elements from the solution
uupts = solns[etype][..., self._eidxs[etype, fidx]]
# Interpolate to the face
ufpts = np.dot(m0, uupts.reshape(nupts, -1))
ufpts = ufpts.reshape(nfpts, nvars, -1)
ufpts = ufpts.swapaxes(0, 1)
# Compute the U-momentum
ruidx = 1
ru = ufpts[ruidx]
ones = np.ones(np.shape(ru))
# Get the quadrature weights and normal vectors
qwts = self._qwts[etype, fidx]
norms = self._norms[etype, fidx]
# Do the quadrature
rhou[:ndims] += np.einsum('i...,ij,jik', qwts, ru, norms)
area[:ndims] += -np.einsum('i...,ij,jik', qwts, ones, norms)
self.mdot = abs(
(area[0] / self.area) * rhou[0] / area[0]
) # Negative since rhou_in normal points outwards, normalized by portion of total inlet area
intg.system.mdot = comm.allreduce(self.mdot, op=get_mpi('sum'))
if rank == root:
# Body forcing term added to maintain constant mass inflow -> weight by portion of total area for parallel runs
ruf = intg.system.rhouforce + (1.0 / intg._dt) * (
self.mdotstar - 2. * intg.system.mdot + intg.system.mdotold)
if (intg.system.mdot / self.mdotstar > 1.1
or intg.system.mdot / self.mdotstar < 0.9):
print('Mass flow rate exceeds 10%% error: ',
intg.system.mdot / self.mdotstar)
# Broadcast to all ranks
intg.system.rhouforce = float(comm.bcast(ruf, root=root))
intg.system.mdotold = float(comm.bcast(intg.system.mdot,
root=root))
else:
intg.system.rhouforce = float(comm.bcast(None, root=root))
intg.system.mdotold = float(comm.bcast(None, root=root))
def __init__(self, intg, cfgsect, suffix):
super().__init__(intg, cfgsect, suffix)
# Underlying elements class
self.elementscls = intg.system.elementscls
# Output frequency
self.nsteps = self.cfg.getint(cfgsect, 'nsteps')
# List of points to be sampled and format
self.pts = self.cfg.getliteral(cfgsect, 'samp-pts')
self.fmt = self.cfg.get(cfgsect, 'format', 'primitive')
# MPI info
comm, rank, root = get_comm_rank_root()
# MPI rank responsible for each sample point
if rank == root:
ptsrank = []
# Sample points we're responsible for, grouped by element type
elepts = [[] for i in range(len(intg.system.ele_map))]
# Search locations in transformed and physical space
tlocs, plocs = self._search_pts(intg)
# For each sample point find our nearest search location
closest = _closest_pts(plocs, self.pts)
# Process these points
for i, (dist, etype, (uidx, eidx)) in enumerate(closest):
# Reduce over the distance
_, mrank = comm.allreduce((dist, rank), op=get_mpi('minloc'))
# If we have the closest point then save the relevant info
if rank == mrank:
elepts[etype].append((i, eidx, tlocs[etype][uidx]))
# Note what rank is responsible for the point
if rank == root:
ptsrank.append(mrank)
# Refine
self._ourpts = ourpts = self._refine_pts(intg, elepts)
# Send the refined sample locations to the root rank
ptsplocs = comm.gather([pl for et, ei, pl, op in ourpts], root=root)
if rank == root:
nvars = self.nvars
# Allocate a buffer to store the sampled points
self._ptsbuf = ptsbuf = np.empty((len(self.pts), self.nvars))
# Tally up how many points each rank is responsible for
nptsrank = [len(ploc) for ploc in ptsplocs]
# Compute the counts and displacements, sans nvars
ptscounts = np.array(nptsrank, dtype=np.int32)
ptsdisps = np.cumsum([0] + nptsrank[:-1], dtype=np.int32)
# Apply the displacements to each ranks points
miters = [
enumerate(ploc, start=pdisp)
for ploc, pdisp in zip(ptsplocs, ptsdisps)
]
# With this form the final point (offset, location) list
self._ptsinfo = [next(miters[pr]) for pr in ptsrank]
# Form the MPI Gatherv receive buffer tuple
self._ptsrecv = (ptsbuf, (nvars * ptscounts, nvars * ptsdisps))
# Open the output file
self.outf = init_csv(self.cfg, cfgsect, self._header)
else:
self._ptsrecv = None
def __call__(self, intg):
# Return if no output is due
if intg.nacptsteps % self.nsteps:
return
# MPI info
comm, rank, root = get_comm_rank_root()
# Solution matrices indexed by element type
solns = dict(zip(intg.system.ele_types, intg.soln))
ndims, nvars = self.ndims, self.nvars
# Force vector
f = np.zeros(2*ndims if self._viscous else ndims)
for etype, fidx in self._m0:
# Get the interpolation operator
m0 = self._m0[etype, fidx]
nfpts, nupts = m0.shape
# Extract the relevant elements from the solution
uupts = solns[etype][..., self._eidxs[etype, fidx]]
# Interpolate to the face
ufpts = np.dot(m0, uupts.reshape(nupts, -1))
ufpts = ufpts.reshape(nfpts, nvars, -1)
ufpts = ufpts.swapaxes(0, 1)
# Compute the pressure
p = self.elementscls.con_to_pri(ufpts, self.cfg)[-1]
# Get the quadrature weights and normal vectors
qwts = self._qwts[etype, fidx]
norms = self._norms[etype, fidx]
# Do the quadrature
f[:ndims] += np.einsum('i...,ij,jik', qwts, p, norms)
if self._viscous:
# Get operator and J^-T matrix
m4 = self._m4[etype]
rcpjact = self._rcpjact[etype, fidx]
# Transformed gradient at solution points
tduupts = np.dot(m4, uupts.reshape(nupts, -1))
tduupts = tduupts.reshape(ndims, nupts, nvars, -1)
# Physical gradient at solution points
duupts = np.einsum('ijkl,jkml->ikml', rcpjact, tduupts)
duupts = duupts.reshape(ndims, nupts, -1)
# Interpolate gradient to flux points
dufpts = np.array([np.dot(m0, du) for du in duupts])
dufpts = dufpts.reshape(ndims, nfpts, nvars, -1)
dufpts = dufpts.swapaxes(1, 2)
# Viscous stress
vis = self.stress_tensor(ufpts, dufpts)
# Do the quadrature
f[ndims:] += np.einsum('i...,klij,jil', qwts, vis, norms)
# Reduce and output if we're the root rank
if rank != root:
comm.Reduce(f, None, op=get_mpi('sum'), root=root)
else:
comm.Reduce(get_mpi('in_place'), f, op=get_mpi('sum'), root=root)
# Build the row
row = [intg.tcurr] + f.tolist()
# Write
print(','.join(str(r) for r in row), file=self.outf)
# Flush to disk
self.outf.flush()
def _check_abort(self):
comm, rank, root = get_comm_rank_root()
if comm.allreduce(self.abort, op=get_mpi('lor')):
# Ensure that the callbacks registered in atexit
# are called only once if stopping the computation
sys.exit(1)
def __call__(self, intg):
# Return if no output is due
if intg.nacptsteps % self.nsteps:
return
# MPI info
comm, rank, root = get_comm_rank_root()
# Solution matrices indexed by element type
solns = dict(zip(intg.system.ele_types, intg.soln))
ndims, nvars, mcomp = self.ndims, self.nvars, self._mcomp
# Force and moment vectors
fm = np.zeros((2 if self._viscous else 1, ndims + mcomp))
for etype, fidx in self._m0:
# Get the interpolation operator
m0 = self._m0[etype, fidx]
nfpts, nupts = m0.shape
# Extract the relevant elements from the solution
uupts = solns[etype][..., self._eidxs[etype, fidx]]
# Interpolate to the face
ufpts = m0 @ uupts.reshape(nupts, -1)
ufpts = ufpts.reshape(nfpts, nvars, -1)
ufpts = ufpts.swapaxes(0, 1)
# Compute the pressure
pidx = 0 if self._ac else -1
p = self.elementscls.con_to_pri(ufpts, self.cfg)[pidx]
# Get the quadrature weights and normal vectors
qwts = self._qwts[etype, fidx]
norms = self._norms[etype, fidx]
# Do the quadrature
fm[0, :ndims] += np.einsum('i...,ij,jik', qwts, p, norms)
if self._viscous:
# Get operator and J^-T matrix
m4 = self._m4[etype]
rcpjact = self._rcpjact[etype, fidx]
# Transformed gradient at solution points
tduupts = m4 @ uupts.reshape(nupts, -1)
tduupts = tduupts.reshape(ndims, nupts, nvars, -1)
# Physical gradient at solution points
duupts = np.einsum('ijkl,jkml->ikml', rcpjact, tduupts)
duupts = duupts.reshape(ndims, nupts, -1)
# Interpolate gradient to flux points
dufpts = np.array([m0 @ du for du in duupts])
dufpts = dufpts.reshape(ndims, nfpts, nvars, -1)
dufpts = dufpts.swapaxes(1, 2)
# Viscous stress
if self._ac:
vis = self.ac_stress_tensor(dufpts)
else:
vis = self.stress_tensor(ufpts, dufpts)
# Do the quadrature
fm[1, :ndims] += np.einsum('i...,klij,jil', qwts, vis, norms)
if self._mcomp:
# Get the flux points positions relative to the moment origin
rfpts = self._rfpts[etype, fidx]
# Do the cross product with the normal vectors
rcn = np.atleast_3d(np.cross(rfpts, norms))
# Pressure force moments
fm[0, ndims:] += np.einsum('i...,ij,jik->k', qwts, p, rcn)
if self._viscous:
# Normal viscous force at each flux point
viscf = np.einsum('ijkl,lkj->lki', vis, norms)
# Normal viscous force moments at each flux point
rcf = np.atleast_3d(np.cross(rfpts, viscf))
# Do the quadrature
fm[1, ndims:] += np.einsum('i,jik->k', qwts, rcf)
# Reduce and output if we're the root rank
if rank != root:
comm.Reduce(fm, None, op=get_mpi('sum'), root=root)
else:
comm.Reduce(get_mpi('in_place'), fm, op=get_mpi('sum'), root=root)
# Write
print(intg.tcurr, *fm.ravel(), sep=',', file=self.outf)
# Flush to disk
self.outf.flush()
