[docs]
class VlasovAmpereOneSpecies(StruphyModel):
"""Vlasov-Ampère system for a single kinetic species in an electric field.
Parameters
----------
base_units: BaseUnits
Base units for normalization (default: BaseUnits())
charge_number: int
Charge number (in units of the positive elementary charge) of the ion species (default: 1)
mass_number: float
Mass number (in units or Proton mass) of the ion species (default: 1.0)
alpha : float, optional
Dimensionless parameter: plasma frequency / cyclotron frequency.
If None, computed from units and charge/mass numbers.
epsilon : float, optional
Normalized cyclotron period: 1 / (cyclotron frequency × time unit).
If None, computed from units and charge/mass numbers.
with_B0: bool
Whether to include the effect of a background magnetic field B0 (default: True)
"""
@classmethod
def model_type(cls) -> LiteralOptions.ModelTypes:
return "Kinetic"
# species
class EMFields(FieldSpecies):
def __init__(self):
self.e_field = FEECVariable(space="Hcurl")
self.phi = FEECVariable(space="H1")
self.init_variables()
class KineticIons(ParticleSpecies):
def __init__(
self,
charge_number: int = 1,
mass_number: float = 1.0,
alpha: float = None,
epsilon: float = None,
):
self.var = PICVariable(space="Particles6D")
self.init_variables(
charge_number=charge_number,
mass_number=mass_number,
alpha=alpha,
epsilon=epsilon,
)
# propagators
class Propagators:
def __init__(self, with_B0: bool = True):
self.push_eta = PushEta()
if with_B0:
self.push_vxb = PushVxB()
self.coupling_va = VlasovAmpereCoupling()
# abstract methods
def __init__(
self,
base_units: BaseUnits = BaseUnits(),
charge_number: int = 1,
mass_number: float = 1.0,
alpha: float = None,
epsilon: float = None,
with_B0: bool = True,
):
self.with_B0 = with_B0
# 0. store input parameters
self.params = copy.deepcopy(locals())
# 1. instantiate all species
self.em_fields = self.EMFields()
self.kinetic_ions = self.KineticIons(
charge_number,
mass_number,
alpha,
epsilon,
)
# 2. derive units (must be done after instantiating species to access charge and mass numbers)
self.setup_equation_params(base_units=base_units)
# 3. instantiate all propagators
self.propagators = self.Propagators(with_B0=with_B0)
# 4. assign variables to propagators
self.propagators.push_eta.variables.var = self.kinetic_ions.var
if with_B0:
self.propagators.push_vxb.variables.ions = self.kinetic_ions.var
self.propagators.coupling_va.variables.e = self.em_fields.e_field
self.propagators.coupling_va.variables.ions = self.kinetic_ions.var
# 5. define scalars to be tracked during simulation
alpha = self.kinetic_ions.equation_params.alpha
epsilon = self.kinetic_ions.equation_params.epsilon
electric_energy = BilinearEnergyFEEC(self.em_fields.e_field)
kinetic_energy = KineticEnergyPIC(
self.kinetic_ions.var,
normalization=alpha**2,
)
total_energy = electric_energy + kinetic_energy
self.scalars = Scalars(
electric_energy=electric_energy,
kinetic_energy=kinetic_energy,
total_energy=total_energy,
)
# initial Poisson (not a propagator used in time stepping)
particles_to_grid = ParticlesToGrid(
self.kinetic_ions.var,
"H1",
Pyccelkernel(accum_kernels.charge_density_0form),
)
self.initial_poisson = PoissonSolve(
rho=particles_to_grid,
rho_coeffs=alpha**2 / epsilon,
)
self.initial_poisson.variables.phi = self.em_fields.phi
@property
def bulk_species(self):
return self.kinetic_ions
@property
def velocity_scale(self):
return "light"
def allocate_helpers(self):
"""Solve initial Poisson equation.
:meta private:
"""
logger.info("\nINITIAL POISSON SOLVE:")
# use control variate method (reset weights after Poisson solve)
particles = self.kinetic_ions.var.particles
particles.update_weights()
self.initial_poisson.allocate()
# Solve with dt=1. and compute electric field
logger.info("\nSolving initial Poisson problem...")
self.initial_poisson(1.0)
phi = self.initial_poisson.variables.phi.spline.vector
Propagator.derham.grad.dot(-phi, out=self.em_fields.e_field.spline.vector)
logger.info("... Done.")
# reset particle weights
particles.weights = particles.weights_at_t0.copy()
## default parameters
def generate_default_parameter_file(self, path=None, prompt=True):
params_path = super().generate_default_parameter_file(path=path, prompt=prompt)
new_file = []
with open(params_path, "r") as f:
for line in f:
if "coupling_va.Options" in line:
new_file += [line]
new_file += ["model.initial_poisson.options = model.initial_poisson.Options()\n"]
elif "push_vxb.Options" in line:
new_file += ["if model.with_B0:\n"]
new_file += [" " + line]
elif "saving_params = " in line:
new_file += ["\nbinplot = BinningPlot(slice='e1', n_bins=128, ranges=(0.0, 1.0))\n"]
new_file += ["saving_params = SavingParameters(binning_plots=(binplot,))\n\n"]
else:
new_file += [line]
with open(params_path, "w") as f:
for line in new_file:
f.write(line)
## abstract methods for documentation
[docs]
@classmethod
def doc_pde(cls):
r"""**PDEs solved by model:**
Vlasov equation:
.. math::
\frac{\partial f}{\partial t} + \mathbf{v} \cdot \nabla f + \frac{1}{\varepsilon} \left( \mathbf{E} + \mathbf{v} \times \mathbf{B}_0 \right) \cdot \frac{\partial f}{\partial \mathbf{v}} = 0
Ampère's law:
.. math::
-\frac{\partial \mathbf{E}}{\partial t} = \frac{\alpha^2}{\varepsilon} \int_{\mathbb{R}^3} \mathbf{v} f \, \mathrm{d}^3 \mathbf{v}
Initial Poisson equation: At :math:`t=0`, solve weakly for the electric potential :math:`\phi`:
.. math::
\int_{\Omega} \nabla \psi^{\top} \cdot \nabla \phi \, \mathrm{d} \mathbf{x} &= \frac{\alpha^2}{\varepsilon} \int_{\Omega} \int_{\mathbb{R}^3} \psi \, (f - f_0) \, \mathrm{d}^3 \mathbf{v} \, \mathrm{d} \mathbf{x} \qquad \forall \ \psi \in H^1
\\[2mm]
\mathbf{E}(t=0) &= -\nabla \phi(t=0)
"""
[docs]
@classmethod
def doc_normalization(cls):
r"""Velocity and field normalizations:
.. math::
\hat{v} = c, \qquad \hat{E} = \hat{B} \hat{v}, \qquad \hat{\phi} = \hat{E} \hat{x}
Dimensionless parameters:
.. math::
\alpha = \frac{\hat{\omega}_\mathrm{p}}{\hat{\omega}_\mathrm{c}}, \qquad \varepsilon = \frac{1}{\hat{\omega}_\mathrm{c} \hat{t}}
where
.. math::
\hat{\omega}_\mathrm{p} = \sqrt{\frac{\hat{n} (Ze)^2}{\epsilon_0 (A m_\mathrm{H})}}, \qquad \hat{\omega}_\mathrm{c} = \frac{(Ze) \hat{B}}{(A m_\mathrm{H})}
For electrons: :math:`Z = -1`, :math:`A = 1/1836`."""
[docs]
@classmethod
def doc_scalar_quantities(cls):
r"""**The following scalars are tracked during simulation:**
- Electric field energy: :math:`E_E = \frac{1}{2} \int |\mathbf{E}|^2 \, \mathrm{d}V`
- Kinetic energy: :math:`E_f = \frac{\alpha^2}{2N} \sum_p w_p |\mathbf{v}_p|^2`
- Total energy: :math:`E_\mathrm{tot} = E_E + E_f`"""
[docs]
@classmethod
def doc_discretization(cls):
"""Time integration is performed by the following propagators (in sequence):
1. :class:`~struphy.propagators.push_eta.PushEta`
2. :class:`~struphy.propagators.push_vxb.PushVxB` (if :attr:`with_B0` is True)
3. :class:`~struphy.propagators.vlasov_ampere_coupling.VlasovAmpereCoupling`
"""
doc = rf"""Time integration is performed by the following propagators (in sequence):
**1. PushEta:**
{PushEta.__doc__}
**2. PushVxB:**
{PushVxB.__doc__}
**3. VlasovAmpereCoupling:**
{VlasovAmpereCoupling.__doc__}
"""
return doc
[docs]
@classmethod
def doc_long_description(cls):
r"""This model couples the kinetic Vlasov equation for the particle distribution function
with Ampère's law for the electric field evolution. It includes the effect of a static background
magnetic field :math:`\mathbf{B}_0` and solves the initial Poisson equation to satisfy Gauss's law
at :math:`t=0`. The model uses a particle-in-cell (PIC) method with finite element exterior
calculus (FEEC) for the electromagnetic fields.
All field variables are perturbations around a reference equilibrium distribution. The model enables
studies of kinetic instabilities, particle-wave interactions, and nonlinear plasma physics without
assuming a fluid approximation."""
[docs]
@classmethod
def doc_examples(cls):
r"""Create and initialize a Vlasov-Ampère model:
.. code-block:: python
from struphy.models import VlasovAmpereOneSpecies
model = VlasovAmpereOneSpecies()
# Access fields
model.em_fields.e_field
model.em_fields.phi
model.kinetic_ions.var
# Access tracked scalar quantities
model.scalars["electric_energy"]
model.scalars["kinetic_energy"]
model.scalars["total_energy"]
"""
[docs]
@classmethod
def doc_use_cases(cls):
r"""This model is appropriate for:
- studying kinetic instabilities in collisionless plasmas
- particle-wave interactions and nonlinear plasma physics
- initial value problems with prescribed equilibrium distributions
- benchmark problems for kinetic-field coupling schemes
- verification of PIC methods in simplified geometries"""
[docs]
@classmethod
def doc_cannot_be_used_for(cls):
r"""This model is not suitable for:
- multi-species systems (requires extension)
- collision operator effects
- electromagnetic wave propagation (no magnetic field evolution)
- drift-reduced or gyrokinetic approximations (full 6D Vlasov equation)"""
