def __init__(self, nspts, cfg):
self.nspts = nspts
self.cfg = cfg
self.order = cfg.getint('solver', 'order')
self.antialias = cfg.get('solver', 'anti-alias', 'none')
self.antialias = {s.strip() for s in self.antialias.split(',')}
self.antialias.discard('none')
if self.antialias - {'flux', 'div-flux', 'surf-flux'}:
raise ValueError('Invalid anti-alias options')
self.ubasis = get_polybasis(self.name, self.order + 1, self.upts)
if nspts:
self.nsptsord = nsptord = self.order_from_nspts(nspts)
self.sbasis = get_polybasis(self.name, nsptord, self.spts)
# Basis for free-stream metric
# We need p-th order pseudo grid points, which includes
# p-th order points on faces.
# It guarantees th q-th order collocation projection on the face
# on the both adjacent cells.
# Ref. 1 JCP 281, 28-54, Sec 4.2
# Ref. 2 JSC 26(3), 301-327, Definition 1
self.mbasis = get_polybasis(self.name, self.order + 1, self.mpts)
def __init__(self, nspts, cfg):
self.nspts = nspts
self.cfg = cfg
self.order = cfg.getint('solver', 'order')
self.antialias = cfg.get('solver', 'anti-alias', 'none')
self.antialias = {s.strip() for s in self.antialias.split(',')}
self.antialias.discard('none')
if self.antialias - {'flux', 'surf-flux'}:
raise ValueError('Invalid anti-alias options')
self.ubasis = get_polybasis(self.name, self.order + 1, self.upts)
if nspts:
self.nsptsord = nsptord = self.order_from_nspts(nspts)
self.sbasis = get_polybasis(self.name, nsptord, self.spts)
# Basis for free-stream metric
# We need p-th order pseudo grid points, which includes
# p-th order points on faces.
# It guarantees th q-th order collocation projection on the face
# on the both adjacent cells.
# Ref. 1 JCP 281, 28-54, Sec 4.2
# Ref. 2 JSC 26(3), 301-327, Definition 1
self.mbasis = get_polybasis(self.name, max(self.order + 1, 2),
self.mpts)
def __init__(self, nspts, cfg):
self._nspts = nspts
self._cfg = cfg
self._order = cfg.getint('solver', 'order')
self.ubasis = get_polybasis(self.name, self._order + 1, self.upts)
if nspts:
self._nsptsord = nsptord = self.order_from_nspts(nspts)
self.sbasis = get_polybasis(self.name, nsptord, self.spts)
def _find_curved_eles(etype, eles, tol=1e-5):
nspts, neles, ndims = eles.shape
shape = subclass_where(BaseShape, name=etype)
order = shape.order_from_nspts(nspts)
basis = get_polybasis(etype, order, shape.std_ele(order - 1))
lmodes = get_polybasis(etype, 2).degrees
hmodes = [i for i, j in enumerate(basis.degrees) if j not in lmodes]
ehmodes = basis.invvdm.T[hmodes] @ eles.reshape(nspts, -1)
ehmodes = ehmodes.reshape(-1, neles, ndims)
return np.any(np.abs(ehmodes) > tol, axis=(0, 2))
def __init__(self, nspts, cfg):
self.nspts = nspts
self.cfg = cfg
self.order = cfg.getint('solver', 'order')
self.antialias = cfg.get('solver', 'anti-alias', 'none')
self.antialias = {s.strip() for s in self.antialias.split(',')}
self.antialias.discard('none')
if self.antialias - {'flux', 'div-flux', 'surf-flux'}:
raise ValueError('Invalid anti-alias options')
self.ubasis = get_polybasis(self.name, self.order + 1, self.upts)
if nspts:
self.nsptsord = nsptord = self.order_from_nspts(nspts)
self.sbasis = get_polybasis(self.name, nsptord, self.spts)
def _fbasis_coeffs_for(self, ftype, fproj, fdjacs, nffpts):
# Suitable quadrature rules for various face types
qrule_map = {
'line': ('gauss-legendre', self._order + 1),
'quad': ('gauss-legendre', (self._order + 1)**2),
'tri': ('williams-shunn', 36)
}
# Obtain a quadrature rule for integrating on the face
qrule = get_quadrule(ftype, *qrule_map[ftype])
# Project the rule points onto the various faces
proj = fproj(*np.atleast_2d(qrule.np_points.T))
qfacepts = np.vstack(list(np.broadcast(*p)) for p in proj)
# Obtain a nodal basis on the reference face
fname = self._cfg.get('solver-interfaces-' + ftype, 'flux-pts')
ffpts = get_quadrule(ftype, fname, nffpts)
nodeb = get_polybasis(ftype, self._order + 1, ffpts.np_points)
L = nodeb.nodal_basis_at(qrule.np_points)
M = self.ubasis.ortho_basis_at(qfacepts)
M = M.reshape(-1, len(proj), len(qrule.np_points))
# Do the quadrature
S = np.einsum('i...,ik,jli->lkj', qrule.np_weights, L, M)
# Account for differing face areas
S *= np.asanyarray(fdjacs)[:,None,None]
return S.reshape(-1, self.nupts)
def __init__(self, nspts, cfg):
self.nspts = nspts
self.cfg = cfg
self.order = cfg.getint('solver', 'order')
self.antialias = cfg.get('solver', 'anti-alias', 'none')
self.antialias = {s.strip() for s in self.antialias.split(',')}
self.antialias.discard('none')
if self.antialias - {'flux', 'div-flux', 'surf-flux'}:
raise ValueError('Invalid anti-alias options')
self.ubasis = get_polybasis(self.name, self.order + 1, self.upts)
if nspts:
self.nsptsord = nsptord = self.order_from_nspts(nspts)
self.sbasis = get_polybasis(self.name, nsptord, self.spts)
def _linearise_eles(self, lintol):
lidx = {}
for etype, pent in self._elenodes:
if pent != self._felespent:
continue
# Elements and type information
elesix = self._elenodes[etype, pent]
petype, nnodes = self._etype_map[etype]
# Obtain the dimensionality of the element type
ndim = self._petype_ndim[petype]
# Node maps between input and PyFR orderings
itop = self._nodemaps[petype, nnodes]
ptoi = np.argsort(itop)
# Construct the element array
eles = self._nodepts[elesix[:, itop], :ndim].swapaxes(0, 1)
# Generate the associated polynomial bases
shape = subclass_where(BaseShape, name=petype)
order = shape.order_from_nspts(nnodes)
hbasis = get_polybasis(petype, order, shape.std_ele(order - 1))
lbasis = get_polybasis(petype, 2, shape.std_ele(1))
htol = hbasis.nodal_basis_at(lbasis.pts)
ltoh = lbasis.nodal_basis_at(hbasis.pts)
leles = (ltoh @ htol) @ eles.reshape(nnodes, -1)
leles = leles.reshape(nnodes, -1, ndim)
# Use this to determine which elements are linear
num = np.max(np.abs(eles - leles), axis=0)
den = np.max(eles, axis=0) - np.min(eles, axis=0)
lin = lidx[petype] = np.any(num / den < lintol, axis=1)
for ix in np.nonzero(lin)[0]:
self._nodepts[elesix[ix], :ndim] = leles[ptoi, ix]
return lidx
def facebases(self):
fb = {}
for kind in {k for k, p, n, a in self.faces}:
rule = self.cfg.get('solver-interfaces-' + kind, 'flux-pts')
npts = self.npts_for_face[kind](self.order)
pts = get_quadrule(kind, rule, npts).pts
fb[kind] = get_polybasis(kind, self.order + 1, pts)
return fb
def facebases(self):
fb = {}
for kind in {k for k, p, n in self.faces}:
rule = self.cfg.get(f'solver-interfaces-{kind}', 'flux-pts')
npts = self.npts_for_face[kind](self.order)
pts = get_quadrule(kind, rule, npts).pts
fb[kind] = get_polybasis(kind, self.order + 1, pts)
return fb
def fbasis_coeffs(self):
coeffs = []
rcache = {}
# Suitable quadrature rules for various face types
qrule_map = {
'line': ('gauss-legendre', self.order + 1),
'quad': ('gauss-legendre', (self.order + 1)**2),
'tri': ('williams-shunn', 36)
}
for kind, proj, norm, area in self.faces:
# Obtain a quadrature rule for integrating on the reference face
# and evaluate this rule at the nodal basis defined by the flux
# points
try:
qr, L = rcache[kind]
except KeyError:
qr = get_quadrule(kind, *qrule_map[kind])
rule = self.cfg.get('solver-interfaces-' + kind, 'flux-pts')
npts = self.npts_for_face[kind](self.order)
pts = get_quadrule(kind, rule, npts).np_points
ppb = get_polybasis(kind, self.order + 1, pts)
L = ppb.nodal_basis_at(qr.np_points)
rcache[kind] = (qr, L)
# Do the quadrature
M = self.ubasis.ortho_basis_at(_proj_rule_pts(proj, qr))
S = np.einsum('i...,ik,ji->kj', area*qr.np_weights, L, M)
coeffs.append(S)
return np.vstack(coeffs)
def fbasis_coeffs(self):
coeffs = []
rcache = {}
# Suitable quadrature rules for various face types
qrule_map = {
'line': ('gauss-legendre', self.order + 1),
'quad': ('gauss-legendre', (self.order + 1)**2),
'tri': ('williams-shunn', 36)
}
for kind, proj, norm, area in self.faces:
# Obtain a quadrature rule for integrating on the reference face
# and evaluate this rule at the nodal basis defined by the flux
# points
try:
qr, L = rcache[kind]
except KeyError:
qr = get_quadrule(kind, *qrule_map[kind])
rule = self.cfg.get('solver-interfaces-' + kind, 'flux-pts')
npts = self.npts_for_face[kind](self.order)
pts = get_quadrule(kind, rule, npts).np_points
ppb = get_polybasis(kind, self.order + 1, pts)
L = ppb.nodal_basis_at(qr.np_points)
rcache[kind] = (qr, L)
# Do the quadrature
M = self.ubasis.ortho_basis_at(_proj_rule_pts(proj, qr))
S = np.einsum('i...,ik,ji->kj', area * qr.np_weights, L, M)
coeffs.append(S)
return np.vstack(coeffs)
def set_backend(self, backend, nscalupts, nonce, linoff):
super().set_backend(backend, nscalupts, nonce, linoff)
kernel = self._be.kernel
kprefix = 'pyfr.solvers.baseadvecdiff.kernels'
slicem = self._slice_mat
# Register our pointwise kernels
self._be.pointwise.register(f'{kprefix}.gradcoru')
self._be.pointwise.register(f'{kprefix}.gradcorulin')
# Mesh regions
regions = self._mesh_regions
self.kernels['_copy_fpts'] = lambda: kernel(
'copy', self._vect_fpts.slice(0, self.nfpts), self._scal_fpts)
self.kernels['tgradpcoru_upts'] = lambda uin: kernel(
'mul',
self.opmat('M4 - M6*M0'),
self.scal_upts[uin],
out=self._vect_upts)
self.kernels['tgradcoru_upts'] = lambda: kernel('mul',
self.opmat('M6'),
self._vect_fpts.slice(
0, self.nfpts),
out=self._vect_upts,
beta=1.0)
# Template arguments for the physical gradient kernel
tplargs = {
'ndims': self.ndims,
'nvars': self.nvars,
'nverts': len(self.basis.linspts),
'jac_exprs': self.basis.jac_exprs
}
if 'curved' in regions:
self.kernels['gradcoru_upts_curved'] = lambda: kernel(
'gradcoru',
tplargs=tplargs,
dims=[self.nupts, regions['curved']],
gradu=slicem(self._vect_upts, 'curved'),
smats=self.smat_at('upts', 'curved'),
rcpdjac=self.rcpdjac_at('upts', 'curved'))
if 'linear' in regions:
self.kernels['gradcoru_upts_linear'] = lambda: kernel(
'gradcorulin',
tplargs=tplargs,
dims=[self.nupts, regions['linear']],
gradu=slicem(self._vect_upts, 'linear'),
upts=self.upts,
verts=self.ploc_at('linspts', 'linear'))
def gradcoru_fpts():
nupts, nfpts = self.nupts, self.nfpts
vupts, vfpts = self._vect_upts, self._vect_fpts
# Exploit the block-diagonal form of the operator
muls = [
kernel('mul', self.opmat('M0'),
vupts.slice(i * nupts, (i + 1) * nupts),
vfpts.slice(i * nfpts, (i + 1) * nfpts))
for i in range(self.ndims)
]
return MetaKernel(muls)
self.kernels['gradcoru_fpts'] = gradcoru_fpts
if 'flux' in self.antialias:
def gradcoru_qpts():
nupts, nqpts = self.nupts, self.nqpts
vupts, vqpts = self._vect_upts, self._vect_qpts
# Exploit the block-diagonal form of the operator
muls = [
self._be.kernel('mul', self.opmat('M7'),
vupts.slice(i * nupts, (i + 1) * nupts),
vqpts.slice(i * nqpts, (i + 1) * nqpts))
for i in range(self.ndims)
]
return MetaKernel(muls)
self.kernels['gradcoru_qpts'] = gradcoru_qpts
# Shock capturing
shock_capturing = self.cfg.get('solver', 'shock-capturing', 'none')
if shock_capturing == 'artificial-viscosity':
tags = {'align'}
# Register the kernels
self._be.pointwise.register(
'pyfr.solvers.baseadvecdiff.kernels.shocksensor')
# Obtain the scalar variable to be used for shock sensing
shockvar = self.convarmap[self.ndims].index(self.shockvar)
# Obtain the name, degrees, and order of our solution basis
ubname = self.basis.ubasis.name
ubdegs = self.basis.ubasis.degrees
uborder = self.basis.ubasis.order
# Obtain the degrees of a basis whose order is one lower
lubdegs = get_polybasis(ubname, max(0, uborder - 1)).degrees
# Compute the intersection
ind_modes = [d not in lubdegs for d in ubdegs]
# Template arguments
tplargs_artvisc = dict(nvars=self.nvars,
nupts=self.nupts,
svar=shockvar,
c=self.cfg.items_as(
'solver-artificial-viscosity', float),
order=self.basis.order,
ind_modes=ind_modes,
invvdm=self.basis.ubasis.invvdm.T)
# Allocate space for the artificial viscosity vector
self.artvisc = self._be.matrix((1, self.neles),
extent=nonce + 'artvisc',
tags=tags)
# Apply the sensor to estimate the required artificial viscosity
self.kernels['shocksensor'] = lambda uin: self._be.kernel(
'shocksensor',
tplargs=tplargs_artvisc,
dims=[self.neles],
u=self.scal_upts[uin],
artvisc=self.artvisc)
elif shock_capturing == 'none':
self.artvisc = None
else:
raise ValueError('Invalid shock capturing scheme')
