class ADERDGBase(ABC):
def __init__(self, order, multipleSimulations, matricesDir):
self.order = order
self.alignStride = lambda name: True
if multipleSimulations > 1:
self.alignStride = lambda name: name.startswith('fP')
transpose = multipleSimulations > 1
self.transpose = lambda name: transpose
self.t = (lambda x: x[::-1]) if transpose else (lambda x: x)
self.db = parseXMLMatrixFile('{}/matrices_{}.xml'.format(
matricesDir, self.numberOf3DBasisFunctions()),
transpose=self.transpose,
alignStride=self.alignStride)
clonesQP = {'v': ['evalAtQP'], 'vInv': ['projectQP']}
self.db.update(
parseXMLMatrixFile('{}/plasticity_ip_matrices_{}.xml'.format(
matricesDir, order),
clonesQP,
transpose=self.transpose,
alignStride=self.alignStride))
qShape = (self.numberOf3DBasisFunctions(), self.numberOfQuantities())
self.Q = OptionalDimTensor('Q',
's',
multipleSimulations,
0,
qShape,
alignStride=True)
self.I = OptionalDimTensor('I',
's',
multipleSimulations,
0,
qShape,
alignStride=True)
Aplusminus_spp = self.flux_solver_spp()
self.AplusT = Tensor('AplusT',
Aplusminus_spp.shape,
spp=Aplusminus_spp)
self.AminusT = Tensor('AminusT',
Aplusminus_spp.shape,
spp=Aplusminus_spp)
Tshape = (self.numberOfExtendedQuantities(),
self.numberOfExtendedQuantities())
trans_spp = self.transformation_spp()
self.T = Tensor('T', trans_spp.shape, spp=trans_spp)
trans_inv_spp = self.transformation_inv_spp()
self.Tinv = Tensor('Tinv', trans_inv_spp.shape, spp=trans_inv_spp)
godunov_spp = self.godunov_spp()
self.QgodLocal = Tensor('QgodLocal',
godunov_spp.shape,
spp=godunov_spp)
self.QgodNeighbor = Tensor('QgodNeighbor',
godunov_spp.shape,
spp=godunov_spp)
self.oneSimToMultSim = Tensor('oneSimToMultSim', (self.Q.optSize(), ),
spp={(i, ): '1.0'
for i in range(self.Q.optSize())})
def numberOf2DBasisFunctions(self):
return self.order * (self.order + 1) // 2
def numberOf3DBasisFunctions(self):
return self.order * (self.order + 1) * (self.order + 2) // 6
def numberOf3DQuadraturePoints(self):
return (self.order + 1)**3
def godunov_spp(self):
shape = (self.numberOfQuantities(), self.numberOfQuantities())
return np.ones(shape, dtype=bool)
def flux_solver_spp(self):
shape = (self.numberOfQuantities(), self.numberOfExtendedQuantities())
return np.ones(shape, dtype=bool)
def transformation_spp(self):
shape = (self.numberOfExtendedQuantities(),
self.numberOfExtendedQuantities())
return np.ones(shape, dtype=bool)
def transformation_inv_spp(self):
return self.godunov_spp()
@abstractmethod
def numberOfQuantities(self):
pass
@abstractmethod
def numberOfExtendedQuantities(self):
pass
@abstractmethod
def extendedQTensor(self):
pass
@abstractmethod
def starMatrix(self, dim):
pass
def addInit(self, generator):
fluxScale = Scalar('fluxScale')
computeFluxSolverLocal = self.AplusT['ij'] <= fluxScale * self.Tinv[
'ki'] * self.QgodLocal['kq'] * self.starMatrix(
0)['ql'] * self.T['jl']
generator.add('computeFluxSolverLocal', computeFluxSolverLocal)
computeFluxSolverNeighbor = self.AminusT[
'ij'] <= fluxScale * self.Tinv['ki'] * self.QgodNeighbor[
'kq'] * self.starMatrix(0)['ql'] * self.T['jl']
generator.add('computeFluxSolverNeighbor', computeFluxSolverNeighbor)
QFortran = Tensor(
'QFortran',
(self.numberOf3DBasisFunctions(), self.numberOfQuantities()))
multSimToFirstSim = Tensor('multSimToFirstSim', (self.Q.optSize(), ),
spp={(0, ): '1.0'})
if self.Q.hasOptDim():
copyQToQFortran = QFortran[
'kp'] <= self.Q['kp'] * multSimToFirstSim['s']
else:
copyQToQFortran = QFortran['kp'] <= self.Q['kp']
generator.add('copyQToQFortran', copyQToQFortran)
@abstractmethod
def addLocal(self, generator):
pass
@abstractmethod
def addNeighbor(self, generator):
pass
@abstractmethod
def addTime(self, generator):
pass
class ADERDGBase(ABC):
def __init__(self, order, multipleSimulations, matricesDir):
self.order = order
self.alignStride = lambda name: True
if multipleSimulations > 1:
self.alignStride = lambda name: name.startswith('fP')
transpose = multipleSimulations > 1
self.transpose = lambda name: transpose
self.t = (lambda x: x[::-1]) if transpose else (lambda x: x)
self.db = parseXMLMatrixFile('{}/matrices_{}.xml'.format(
matricesDir, self.numberOf3DBasisFunctions()),
transpose=self.transpose,
alignStride=self.alignStride)
clonesQP = {'v': ['evalAtQP'], 'vInv': ['projectQP']}
self.db.update(
parseXMLMatrixFile('{}/plasticity_ip_matrices_{}.xml'.format(
matricesDir, order),
clonesQP,
transpose=self.transpose,
alignStride=self.alignStride))
self.db.update(
parseJSONMatrixFile(
'{}/sampling_directions.json'.format(matricesDir)))
self.db.update(
parseJSONMatrixFile('{}/mass_{}.json'.format(matricesDir, order)))
qShape = (self.numberOf3DBasisFunctions(), self.numberOfQuantities())
self.Q = OptionalDimTensor('Q',
's',
multipleSimulations,
0,
qShape,
alignStride=True)
self.I = OptionalDimTensor('I',
's',
multipleSimulations,
0,
qShape,
alignStride=True)
Aplusminus_spp = self.flux_solver_spp()
self.AplusT = Tensor('AplusT',
Aplusminus_spp.shape,
spp=Aplusminus_spp)
self.AminusT = Tensor('AminusT',
Aplusminus_spp.shape,
spp=Aplusminus_spp)
Tshape = (self.numberOfExtendedQuantities(),
self.numberOfExtendedQuantities())
trans_spp = self.transformation_spp()
self.T = Tensor('T', trans_spp.shape, spp=trans_spp)
trans_inv_spp = self.transformation_inv_spp()
self.Tinv = Tensor('Tinv', trans_inv_spp.shape, spp=trans_inv_spp)
godunov_spp = self.godunov_spp()
self.QgodLocal = Tensor('QgodLocal',
godunov_spp.shape,
spp=godunov_spp)
self.QgodNeighbor = Tensor('QgodNeighbor',
godunov_spp.shape,
spp=godunov_spp)
self.oneSimToMultSim = Tensor('oneSimToMultSim', (self.Q.optSize(), ),
spp={(i, ): '1.0'
for i in range(self.Q.optSize())})
self.db.update(
parseJSONMatrixFile(
'{}/nodal/nodalBoundary_matrices_{}.json'.format(
matricesDir, self.order), {},
alignStride=self.alignStride,
transpose=self.transpose,
namespace='nodal'))
self.INodal = OptionalDimTensor(
'INodal',
's',
False, #multipleSimulations,
0,
(self.numberOf2DBasisFunctions(), self.numberOfQuantities()),
alignStride=True)
project2nFaceTo3m = tensor_collection_from_constant_expression(
base_name='project2nFaceTo3m',
expressions=lambda i: self.db.rDivM[i]['jk'] * self.db.V2nTo2m['kl'
],
group_indices=range(4),
target_indices='jl')
self.db.update(project2nFaceTo3m)
def numberOf2DBasisFunctions(self):
return self.order * (self.order + 1) // 2
def numberOf3DBasisFunctions(self):
return self.order * (self.order + 1) * (self.order + 2) // 6
def numberOf3DQuadraturePoints(self):
return (self.order + 1)**3
def godunov_spp(self):
shape = (self.numberOfQuantities(), self.numberOfQuantities())
return np.ones(shape, dtype=bool)
def flux_solver_spp(self):
shape = (self.numberOfQuantities(), self.numberOfExtendedQuantities())
return np.ones(shape, dtype=bool)
def transformation_spp(self):
shape = (self.numberOfExtendedQuantities(),
self.numberOfExtendedQuantities())
return np.ones(shape, dtype=bool)
def transformation_inv_spp(self):
return self.godunov_spp()
@abstractmethod
def numberOfQuantities(self):
pass
@abstractmethod
def numberOfExtendedQuantities(self):
pass
@abstractmethod
def extendedQTensor(self):
pass
@abstractmethod
def starMatrix(self, dim):
pass
def addInit(self, generator):
fluxScale = Scalar('fluxScale')
computeFluxSolverLocal = self.AplusT['ij'] <= fluxScale * self.Tinv[
'ki'] * self.QgodLocal['kq'] * self.starMatrix(
0)['ql'] * self.T['jl']
generator.add('computeFluxSolverLocal', computeFluxSolverLocal)
computeFluxSolverNeighbor = self.AminusT[
'ij'] <= fluxScale * self.Tinv['ki'] * self.QgodNeighbor[
'kq'] * self.starMatrix(0)['ql'] * self.T['jl']
generator.add('computeFluxSolverNeighbor', computeFluxSolverNeighbor)
QFortran = Tensor(
'QFortran',
(self.numberOf3DBasisFunctions(), self.numberOfQuantities()))
multSimToFirstSim = Tensor('multSimToFirstSim', (self.Q.optSize(), ),
spp={(0, ): '1.0'})
if self.Q.hasOptDim():
copyQToQFortran = QFortran[
'kp'] <= self.Q['kp'] * multSimToFirstSim['s']
else:
copyQToQFortran = QFortran['kp'] <= self.Q['kp']
generator.add('copyQToQFortran', copyQToQFortran)
stiffnessTensor = Tensor('stiffnessTensor', (3, 3, 3, 3))
direction = Tensor('direction', (3, ))
christoffel = Tensor('christoffel', (3, 3))
computeChristoffel = christoffel[
'ik'] <= stiffnessTensor['ijkl'] * direction['j'] * direction['l']
generator.add('computeChristoffel', computeChristoffel)
@abstractmethod
def addLocal(self, generator, targets):
pass
@abstractmethod
def addNeighbor(self, generator, targets):
pass
@abstractmethod
def addTime(self, generator, targets):
pass
def add_include_tensors(self, include_tensors):
include_tensors.add(self.db.samplingDirections)
include_tensors.add(self.db.M2inv)
class ADERDGBase(ABC):
def __init__(self, order, multipleSimulations, matricesDir):
self.order = order
self.alignStride = lambda name: True
if multipleSimulations > 1:
self.alignStride = lambda name: name.startswith('fP')
transpose = multipleSimulations > 1
self.transpose = lambda name: transpose
self.t = (lambda x: x[::-1]) if transpose else (lambda x: x)
self.db = parseXMLMatrixFile('{}/matrices_{}.xml'.format(matricesDir, self.numberOf3DBasisFunctions()), transpose=self.transpose, alignStride=self.alignStride)
clonesQP = {
'v': [ 'evalAtQP' ],
'vInv': [ 'projectQP' ]
}
self.db.update( parseXMLMatrixFile('{}/plasticity_ip_matrices_{}.xml'.format(matricesDir, order), clonesQP, transpose=self.transpose, alignStride=self.alignStride))
qShape = (self.numberOf3DBasisFunctions(), self.numberOfQuantities())
self.Q = OptionalDimTensor('Q', 's', multipleSimulations, 0, qShape, alignStride=True)
self.I = OptionalDimTensor('I', 's', multipleSimulations, 0, qShape, alignStride=True)
Ashape = (self.numberOfQuantities(), self.numberOfExtendedQuantities())
self.AplusT = Tensor('AplusT', Ashape)
self.AminusT = Tensor('AminusT', Ashape)
Tshape = (self.numberOfExtendedQuantities(), self.numberOfExtendedQuantities())
self.T = Tensor('T', Tshape)
QgodShape = (self.numberOfQuantities(), self.numberOfQuantities())
self.Tinv = Tensor('Tinv', QgodShape)
self.QgodLocal = Tensor('QgodLocal', QgodShape)
self.QgodNeighbor = Tensor('QgodNeighbor', QgodShape)
self.oneSimToMultSim = Tensor('oneSimToMultSim', (self.Q.optSize(),), spp={(i,): '1.0' for i in range(self.Q.optSize())})
def numberOf2DBasisFunctions(self):
return self.order*(self.order+1)//2
def numberOf3DBasisFunctions(self):
return self.order*(self.order+1)*(self.order+2)//6
def numberOf3DQuadraturePoints(self):
return (self.order+1)**3
@abstractmethod
def numberOfQuantities(self):
pass
@abstractmethod
def numberOfExtendedQuantities(self):
pass
@abstractmethod
def extendedQTensor(self):
pass
@abstractmethod
def starMatrix(self, dim):
pass
def addInit(self, generator):
fluxScale = Scalar('fluxScale')
computeFluxSolverLocal = self.AplusT['ij'] <= fluxScale * self.Tinv['ki'] * self.QgodLocal['kq'] * self.db.star[0]['ql'] * self.T['jl']
generator.add('computeFluxSolverLocal', computeFluxSolverLocal)
computeFluxSolverNeighbor = self.AminusT['ij'] <= fluxScale * self.Tinv['ki'] * self.QgodNeighbor['kq'] * self.db.star[0]['ql'] * self.T['jl']
generator.add('computeFluxSolverNeighbor', computeFluxSolverNeighbor)
QFortran = Tensor('QFortran', (self.numberOf3DBasisFunctions(), self.numberOfQuantities()))
multSimToFirstSim = Tensor('multSimToFirstSim', (self.Q.optSize(),), spp={(0,): '1.0'})
if self.Q.hasOptDim():
copyQToQFortran = QFortran['kp'] <= self.Q['kp'] * multSimToFirstSim['s']
else:
copyQToQFortran = QFortran['kp'] <= self.Q['kp']
generator.add('copyQToQFortran', copyQToQFortran)
@abstractmethod
def addLocal(self, generator):
pass
@abstractmethod
def addNeighbor(self, generator):
pass
@abstractmethod
def addTime(self, generator):
pass
