A Discontinuous Galerkin solver oriented toward prototyping and education
Project description
Hyperbolic Educational Discontinuous Galerkin Equation Solver
This repository contains a Discontinuous Galerkin Python solver for 1D Hyperbolic PDEs. It is intended to be used primarily for education and as such prioritizes clarity in the codebase over computational efficiency or cleverness. It is designed to be extensible and modular so that support for new hyperbolic systems is easy to implement, and comes out of the box with an example for the 1D shallow water equations (SWE) with variable bathymetry, prescribed inflow, and free outflow.
Usage
Once you've installed the dependencies listed in requirements.txt
(pip install -r requirements.txt
), running main.py
will generate a full
animation of the SWE for several different variations on Gaussian inflows:
python main.py
Each simulation result is saved in a sub-directory of the out
folder according to the format
<epoch timestamp>_<six-digit random hex>
; the simulation data itself is saved in solution.csv
,
the parameters for the simulation in parameters.csv
, and a GIF of the solution animation is
saved at swe_1d.gif
.
The main script (main.py
) instantiates several instances of simulation.SWEFlowRunner
, which is
configured specifically for running simultaneous simulations for prescribed inflow and transmissive
outflow for the Shallow Water Equations. While that class may be useful as a reference
implementation for running several simulations, the following is a full example of
running a single simulation for the purpose of experimentation/modification:
#!/usr/bin/env python
# coding: utf-8
# # 1D Discontinuous Galerkin Shallow Water Solver
#
# We solve the 1D Shallow Water Equations in conservative form:
#
# \begin{align*}
# h_t + q_x &= 0 \\
# q_t + \left[ \frac{q^2}{h} + \frac{1}{2} g h^2 \right]_x &= -g h b_x - C_f \left(\frac{q}{h}\right)^2
# \end{align*}
#
# We neglect friction so that $C_f = 0$.
#
import numpy as np
import matplotlib.pyplot as plt
import hedges.bc as bc
import hedges.fluxes as fluxes
import hedges.quadrature as quadrature
import hedges.rk as rk
import hedges.swe_1d.pde as pde
# Physical parameters
#
g = 1.0 # gravity
# Domain
#
tspan = (0.0, 4.0)
xspan = (-1, 1)
# Bathymetry parameters
#
b_smoothness = 0.1
b_amplitude = 0.02
b_slope = 0.05
assert(b_smoothness > 0)
# Inflow parameters
#
inflow_amplitude = 0.05
inflow_smoothness = 1.0
inflow_peak_time = 2.0
assert(inflow_amplitude > 0)
# Initial waveheight
#
h0 = 0.2
assert(h0 > 0)
def swe_bathymetry(x):
"""
Describes bathymetry with an upslope which is perturbed by a hyperbolic tangent function.
"""
return b_slope * x + b_amplitude * np.arctan(x / b_smoothness)
def swe_bathymetry_derivative(x):
"""
Derivative of swe_bathymetry
"""
return b_slope + b_amplitude / (
b_smoothness * (1 + np.square(x / b_smoothness))
)
def q_bc(t):
"""
Describes a Gaussian inflow, where the function transitions to a constant value upon attaining
its maximum value.
:param t: Time
:return:
"""
t_np = np.array(t)
return inflow_amplitude * np.exp(
-np.square(
np.minimum(t_np, inflow_peak_time * np.ones(t_np.shape)) - inflow_peak_time
) / (2 * np.square(inflow_smoothness))
)
def initial_condition(x):
"""
Creates initial conditions for (h, uh).
:param x: Computational domain
:return:
"""
initial_height = h0 * np.ones(x.shape) - swe_bathymetry(x) # horizontal water surface
initial_flow = q_bc(0) * np.ones(x.shape) # Start with inflow BC
initial_values = np.array((
initial_height,
initial_flow,
))
# Verify consistency of initial condition
#
if not np.allclose(initial_values[1][0], q_bc(0)):
raise ValueError('Initial flow condition must match prescribed inflow.')
return initial_values
# Plot bathymetry and ICs
#
xl, xr = xspan
t0, tf = tspan
xx = np.linspace(xl, xr, num=100)
tt = np.linspace(t0, tf, num=100)
fig, (h_ax, hv_ax, q_bc_ax) = plt.subplots(3, 1)
ic = initial_condition(xx)
bb = swe_bathymetry(xx)
qq_bc = q_bc(tt)
# Plot initial wave height and bathymetry
#
h_ax.plot(xx, ic[0] + bb)
h_ax.plot(xx, bb)
h_ax.set_title('Initial wave height $h(x, 0)$')
# Plot initial flow rate
#
hv_ax.plot(xx, ic[1])
hv_ax.set_title('Initial flow rate $q(x, 0)$')
# Plot flow rate at left boundary over simulation time
#
q_bc_ax.plot(tt, qq_bc)
q_bc_ax.set_title('Boundary flow rate $q({}, t)$'.format(xl))
plt.tight_layout()
plt.show()
# Instantiate solver with bathymetry
#
solver = pde.ShallowWater1D(
b=swe_bathymetry,
b_x=swe_bathymetry_derivative,
gravity=g
)
t_interval_ms = 20
dt = t_interval_ms / 1000
surface_flux = fluxes.lax_friedrichs_flux
print('Integrating ODE system...')
solution = solver.solve(
tspan=tspan,
xspan=xspan,
cell_count=16,
polydeg=4,
initial_condition=initial_condition,
intercell_flux=surface_flux,
left_boundary_flux=pde.ShallowWater1D.bc_prescribed_inflow(
q_bc,
gravity=g,
surface_flux=surface_flux,
),
right_boundary_flux=bc.transmissive_boundary(
surface_flux=surface_flux,
direction=bc.Direction.DOWNSTREAM,
),
quad_rule=quadrature.gll,
**{
'method': rk.SSPRK33,
't_eval': np.arange(tspan[0], tspan[1], dt),
'max_step': dt, # max time step for ODE solver
'rtol': 1.0e-6,
'atol': 1.0e-6,
}
)
# Plot solution animation
#
ani, plt = solver.plot_animation(solution, frame_interval=t_interval_ms)
# Save animation to file
#
movie_name = 'swe_1d.gif'
print('Writing movie to {}...'.format(movie_name))
ani.save(movie_name, progress_callback=lambda i, n: print(
f'Saving animation frame {i + 1}/{n}'
) if i % 50 == 0 else None)
print('Animation written to {}.'.format(movie_name))
plt.show()
The plot_animation
function of the hedges.hyperbolic_solver_1d.Hyperbolic1DSolver
base class returns
a tuple of the form
(FuncAnimation,
matplotlib) which
may be used to save individual frames or modify plots as desired. Subclass implementations may
optionally override the default plotting behavior, with a reference implementation provided in
hedges.swe_1d.ShallowWater1D
.
A Jupyter notebook is provided in this repository at hedges/main.ipynb
, and may be viewed (and
interacted with) at
BinderHub.
Extending the Solver
Currently, only the 1D Shallow Water equations are implemented. However, support can easily be added
for other 1D hyperbolic PDE systems by simply subclassing hedges.hyperbolic_solver_1d.Hyperbolic1DSolver
and implementing the flux F(u)
, source S(u)
, and flux Jacobian F'(u)
terms for the desired
system in conservative form. See hedges.swe_1d.ShallowWater1D
for a reference implementation.
A local Lax-Friedrichs flux is used as the approximate Riemann solver between cell
interfaces, but the solve
method of the hedges.hyperbolic_solver_1d.Hyperbolic1DSolver
class accepts
a function reference which can be used to implement e.g. HLL flux (or exact Riemann solvers).
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.