def get_iec_frequencies(use_alpha, temperature, use_maxwellian=False):
electron_mass = PhysicalConstants.electron_mass
deuterium_mass = 2.01410178 * UnitConversions.amu_to_kg
deuterium_tritium_mass = 5.0064125184e-27
alpha_mass = 3.7273 * UnitConversions.amu_to_kg
alpha_species = ChargedParticle(alpha_mass, 2 * PhysicalConstants.electron_charge)
dt_species = ChargedParticle(deuterium_tritium_mass, 2 * PhysicalConstants.electron_charge)
background_species = ChargedParticle(electron_mass, -PhysicalConstants.electron_charge)
n_background = 1e20
dt_velocity = np.sqrt(2 * 50e3 * PhysicalConstants.electron_charge / dt_species.m)
alpha_velocity = np.sqrt(2 * 3.5e6 * PhysicalConstants.electron_charge / alpha_species.m)
beam_species = alpha_species if use_alpha else dt_species
beam_velocity = alpha_velocity if use_alpha else dt_velocity
collision = CoulombCollision(beam_species, background_species, 1.0, beam_velocity)
relaxation_process = MaxwellianRelaxationProcess(collision)
if use_maxwellian:
v_K = relaxation_process.numerical_kinetic_loss_maxwellian_frequency(n_background, temperature, beam_velocity)
v_P = relaxation_process.numerical_kinetic_loss_maxwellian_frequency(n_background, temperature, beam_velocity)
return v_K, v_P
# maxwellian_frequency = relaxation_process.maxwellian_collisional_frequency(n_background, temperature, beam_velocity)
# return maxwellian_frequency, maxwellian_frequency
else:
stationary_kinetic_frequency = relaxation_process.kinetic_loss_stationary_frequency(n_background, temperature, beam_velocity)
stationary_momentum_frequency = relaxation_process.momentum_loss_stationary_frequency(n_background, temperature, beam_velocity)
return stationary_kinetic_frequency, stationary_momentum_frequency
def get_maxwellian_collisional_frequencies():
# Generate charged particles
alpha = ChargedParticle(6.64424e-27, PhysicalConstants.electron_charge * 2)
electron = ChargedParticle(9.014e-31, -PhysicalConstants.electron_charge)
impact_parameter_ratio = 1.0
beam_velocity = np.sqrt(2 * 3.5e6 * PhysicalConstants.electron_charge /
alpha.m)
n = 1e20
temperature = 20e3 * UnitConversions.eV_to_K
print(n, temperature, beam_velocity)
# Get reactant collision frequency and energy loss rate
collision = CoulombCollision(electron, alpha, impact_parameter_ratio,
beam_velocity)
relaxation = MaxwellianRelaxationProcess(collision)
numerical_v_P = relaxation.momentum_loss_maxwellian_frequency(
n, temperature, beam_velocity)
theoretical_v_P = 2 / (3 * np.sqrt(
2 * np.pi)) * relaxation.momentum_loss_stationary_frequency(
n, temperature, beam_velocity)
print("Numerical Momentum Loss Frequency: {}".format(numerical_v_P))
print("Theoretical Momentum Loss Frequency: {}".format(theoretical_v_P))
print("Numerical to theoretical momentum loss ratio: {}\n".format(
numerical_v_P / theoretical_v_P))
numerical_v_K = relaxation.kinetic_loss_maxwellian_frequency(
n, temperature, beam_velocity)
print("Numerical Kinetic Loss Frequency: {}".format(numerical_v_K))
print("Numerical kinetic to momentum loss ratio: {}".format(numerical_v_K /
numerical_v_P))
def plot_collisional_frequencies():
"""
Plot the variation of collisional frequency with density, and temperature for different
collisions
"""
deuterium = ChargedParticle(2.01410178 * 1.66054e-27,
PhysicalConstants.electron_charge)
electron = ChargedParticle(9.014e-31, -PhysicalConstants.electron_charge)
impact_parameter_ratio = 1.0 # Is not necessary for this analysis
velocities = np.logspace(4, 6, 100)
number_density = np.logspace(20, 30, 100)
VEL, N = np.meshgrid(velocities, number_density, indexing='ij')
collision_pairs = [["Deuterium-Deuterium", deuterium, deuterium],
["Electron-Deuterium", electron, deuterium],
["Electron-Electron", electron, electron]]
num_temp = 3
temperatures = np.logspace(3, 5, num_temp) * UnitConversions.eV_to_K
for j, pair in enumerate(collision_pairs):
name = pair[0]
p_1 = pair[1]
p_2 = pair[2]
fig, ax = plt.subplots(2, num_temp, figsize=(15, 20))
fig.suptitle("Collisional Frequencies for {} Relaxation".format(name))
for i, temp in enumerate(temperatures):
collision = CoulombCollision(p_1, p_2, impact_parameter_ratio, VEL)
relaxation_process = RelaxationProcess(collision)
kinetic_frequency = relaxation_process.kinetic_loss_stationary_frequency(
N, temp, VEL)
momentum_frequency = relaxation_process.momentum_loss_stationary_frequency(
N, temp, VEL, first_background=True)
im = ax[0, i].contourf(np.log10(VEL), np.log10(N),
np.log10(kinetic_frequency), 100)
ax[0, i].set_title("Kinetic: T = {}".format(
temp * UnitConversions.K_to_eV))
ax[0, i].set_xlabel("Velocity (ms-1)")
ax[0, i].set_ylabel("Number density (m-3)")
fig.colorbar(im, ax=ax[0, i])
im = ax[1, i].contourf(np.log10(VEL), np.log10(N),
np.log10(momentum_frequency), 100)
ax[1, i].set_title("Momentum: T = {}".format(
temp * UnitConversions.K_to_eV))
ax[1, i].set_xlabel("Velocity (ms-1)")
ax[1, i].set_ylabel("Number density (m-3)")
fig.colorbar(im, ax=ax[1, i])
plt.show()
def compare_maxwellian_and_stationary_frequencies_against_temperature(
number_density, temperature):
# Generate charged particles
alpha = ChargedParticle(6.64424e-27, PhysicalConstants.electron_charge * 2)
electron = ChargedParticle(9.014e-31, -PhysicalConstants.electron_charge)
# Get deuterium and alpha velocities from product and reactant beam
# energies
e_alpha = 1e3 * PhysicalConstants.electron_charge
v_alpha = np.sqrt(2 * e_alpha / alpha.m)
# Get product collision frequency and energy loss rate
impact_parameter_ratio = 1.0 # Is not necessary for this analysis
product_collision = CoulombCollision(alpha, electron,
impact_parameter_ratio, v_alpha)
product_relaxation = MaxwellianRelaxationProcess(product_collision)
alpha_stationary_momentum_frequency = product_relaxation.momentum_loss_stationary_frequency(
number_density, temperature, v_alpha)
maxwellian_frequency = np.zeros(temperature.shape)
alpha_maxwellian_momentum_frequency = np.zeros(temperature.shape)
for i, T in enumerate(temperature):
# Get maxwellian frequencies
v = product_relaxation.maxwellian_collisional_frequency(
number_density, T, v_alpha)
maxwellian_frequency[i] = v
v_P = product_relaxation.numerical_momentum_loss_maxwellian_frequency(
number_density, T, v_alpha, epsrel=1e-8)
alpha_maxwellian_momentum_frequency[i] = v_P
print(T, v_P, v_P - alpha_stationary_momentum_frequency[i],
np.abs(v_P - v) / v)
fig, ax = plt.subplots()
temperature *= UnitConversions.K_to_eV
ax.loglog(temperature,
alpha_stationary_momentum_frequency,
label="Stationary")
ax.loglog(temperature, maxwellian_frequency, label="Maxwellian")
ax.loglog(temperature,
alpha_maxwellian_momentum_frequency,
label="Numerical Maxwellian")
ax.legend()
plt.show()
def run_sim(sim_type):
"""
Runs simulation of a single species in a uniform velocity distribution
sim_type: Instances of simulation class for coulomb collisions
"""
# Set seed
# for seed in range(5):
seed = 1
np.random.seed(seed)
# Set up simulation
particle = ChargedParticle(2.01410178 * 1.66054e-27,
PhysicalConstants.electron_charge)
n = 10000
weight = 1
weighted_particle = ChargedParticle(
2.01410178 * 1.66054e-27 * weight,
PhysicalConstants.electron_charge * weight)
sim = sim_type(n, particle, weight)
# Get initial uniform velocities
v_max = 1.0
velocities = np.random.uniform(-v_max, v_max, size=(n, 3))
# Get time step from collisional frequencies
c = CoulombCollision(weighted_particle, weighted_particle, 1.0,
2.0 * v_max)
process = RelaxationProcess(c)
v_K = process.kinetic_loss_stationary_frequency(n * weight, 1.0,
2.0 * v_max)
v_P = process.momentum_loss_stationary_frequency(n * weight, 1.0,
2.0 * v_max)
collision_time = 1.0 / max([v_K, v_P])
dt = 0.1 * collision_time
final_time = 2.5 * collision_time
t, v_results = sim.run_sim(velocities, dt, final_time)
post_process_results(t, v_results)
def plot_impact_parameter_variation_with_temperature():
"""
Simple function to see how the impact parameter varies with temperature
"""
temperatures = UnitConversions.eV_to_K * np.logspace(1, 4, 100)
deuterium = ChargedParticle(2.01410178 * 1.66054e-27,
PhysicalConstants.electron_charge)
electron = ChargedParticle(9.014e-31, -PhysicalConstants.electron_charge)
electron_b_90 = np.zeros(temperatures.shape)
deuterium_b_90 = np.zeros(temperatures.shape)
for i, temperature in enumerate(temperatures):
electron_velocity = np.sqrt(PhysicalConstants.boltzmann_constant *
temperature / electron.m)
deuterium_velocity = np.sqrt(PhysicalConstants.boltzmann_constant *
temperature / deuterium.m)
collision = CoulombCollision(deuterium, electron, 1.0,
electron_velocity)
b_90 = collision.b_90
electron_b_90[i] = b_90
collision = CoulombCollision(deuterium, electron, 1.0,
deuterium_velocity)
b_90 = collision.b_90
deuterium_b_90[i] = b_90
plt.figure()
plt.loglog(temperatures, electron_b_90, label="Electron Velocity")
plt.loglog(temperatures, deuterium_b_90, label="Deuterium Velocity")
plt.title(
"Impact Parameter vs Temperature for Deuterium-Electron binary collision"
)
plt.ylabel("Impact Parameter (m)")
plt.xlabel("Temperature (K)")
plt.legend()
plt.show()
def run_electon_beam_into_argon_gas_sim(sim_type):
"""
Run a simulation of an electron beam into a background gas at a
given temperature
"""
p_1 = ChargedParticle(PhysicalConstants.electron_mass,
-PhysicalConstants.electron_charge)
p_2 = ChargedParticle(PhysicalConstants.electron_mass,
-PhysicalConstants.electron_charge)
p_3 = ChargedParticle(39.948 * UnitConversions.amu_to_kg,
PhysicalConstants.electron_charge)
w_1 = 1
w_2 = 1000
w_3 = 1000
n = int(1e3)
if sim_type == "Nanbu":
sim = NanbuCollisionModel(np.asarray([n, n, n]),
np.asarray([p_1, p_2, p_3]),
np.asarray([w_1, w_2, w_3]),
coulomb_logarithm=10.0,
frozen_species=np.asarray(
[False, True, True]))
else:
raise ValueError("Invalid sim type")
# Set initial velocity conditions of beam
beam_velocity = np.sqrt(2 * 100 * PhysicalConstants.electron_charge /
p_1.m)
velocities = np.zeros((3 * n, 3))
velocities[:n, :] = np.asarray([0.0, 0.0, beam_velocity])
# Set initial velocity conditions of background
k_T = 2.0
sigma = np.sqrt(2 * k_T * PhysicalConstants.electron_charge / p_2.m)
electron_velocities = np.random.normal(
loc=0.0, scale=sigma, size=velocities[n:2 * n, :].shape) / np.sqrt(3)
velocities[n:2 * n, :] = electron_velocities
k_T = 0.02
sigma = np.sqrt(2 * k_T * PhysicalConstants.electron_charge / p_3.m)
ion_velocities = np.random.normal(
loc=0.0, scale=sigma, size=velocities[2 * n:, :].shape) / np.sqrt(3)
velocities[2 * n:, :] = ion_velocities
tau = get_relaxation_time(p_1, n * w_2, beam_velocity)
dt = 0.01 * tau
final_time = 1.0 * tau
t, v_results = sim.run_sim(velocities, dt, final_time)
t /= tau
v_z_estimate = 1.0 - 3 * t
v_ort_estimate = 3.98 * t
fig, ax = plt.subplots(2, figsize=(10, 10))
l_v_z = ax[0].plot(t,
np.mean(v_results[:n, 2, :], axis=0) / beam_velocity,
color="b",
label="<v_z>")
l_v_z_estimate = ax[0].plot(t,
v_z_estimate,
linestyle="--",
color="c",
label="<v_z_estimate>")
ax[0].set_xlim([0.0, t[-1]])
ax[0].set_xlabel("Timestep")
ax[0].set_ylabel("Velocities ms-1")
ax[0].set_ylim([0.0, 1.0])
ax[0].set_title("Beam Velocities")
# Twin axes for second plot to replicate paper set up
ax_twin = ax[0].twinx()
l_ort = ax_twin.plot(
t,
np.mean(v_results[:n, 0, :]**2 + v_results[:n, 1, :]**2, axis=0) /
beam_velocity**2,
color="r",
label="<v_ort^2>")
l_ort_estimate = ax_twin.plot(t,
v_ort_estimate,
linestyle="--",
color="y",
label="<v_ort_estimate>")
ax_twin.set_ylabel("Square of Velocities (ms-1)^2")
ax_twin.set_ylim([0.0, 0.33])
lines = l_v_z + l_v_z_estimate + l_ort + l_ort_estimate
labs = [l.get_label() for l in lines]
ax[0].legend(lines, labs)
ax[1].plot(t, np.mean(v_results[2 * n:, 0, :], axis=0), label="<v_x>")
ax[1].plot(t, np.mean(v_results[2 * n:, 1, :], axis=0), label="<v_y>")
ax[1].plot(t, np.mean(v_results[2 * n:, 2, :], axis=0), label="<v_z>")
ax[1].set_xlim([0.0, t[-1]])
ax[1].legend()
ax[1].set_xlabel("Timestep")
ax[1].set_ylabel("Velocities ms-1")
ax[1].set_title("Background Velocities")
plt.show()
energy = p_1.m * np.sum(v_results[:n, 0, :]**2 + v_results[:n, 1, :]**2 +
v_results[:n, 2, :]**2,
axis=0)
energy += p_2.m * np.sum(v_results[n:, 0, :]**2 + v_results[n:, 1, :]**2 +
v_results[n:, 2, :]**2,
axis=0)
x_mom = p_1.m * np.sum(v_results[:n, 0, :], axis=0) + p_2.m * np.sum(
v_results[n:, 0, :], axis=0)
y_mom = p_1.m * np.sum(v_results[:n, 1, :], axis=0) + p_1.m * np.sum(
v_results[n:, 1, :], axis=0)
z_mom = p_1.m * np.sum(v_results[:n, 2, :], axis=0) + p_1.m * np.sum(
v_results[n:, 2, :], axis=0)
fig, ax = plt.subplots(2)
ax[0].plot(t, energy)
ax[0].set_title("Conservation of Energy")
ax[1].plot(t, x_mom, label="x momentum")
ax[1].plot(t, y_mom, label="y momentum")
ax[1].plot(t, z_mom, label="z momentum")
ax[1].legend()
ax[1].set_title("Conservation of Momentum")
fig.suptitle("Conservation Plots")
plt.show()
return t, v_results
def generate_sim_results(number_densities, T):
# Set simulation independent parameters
N = int(1e4)
dt_factor = 0.01
w_1 = int(1)
# Generate result lists
energy_results = dict()
velocity_results = dict()
t_halves = dict()
t_theory = dict()
# Run simulations
names = ["reactant", "product"]
for j, name in enumerate(names):
t_halves[name] = np.zeros(number_densities.shape)
t_theory[name] = np.zeros(number_densities.shape)
energy_results[name] = []
velocity_results[name] = []
for i, n in enumerate(number_densities):
# Set up beam sim
if "product" == name:
p_1 = ChargedParticle(6.64424e-27, 2 * PhysicalConstants.electron_charge)
energy = 3.5e6 * PhysicalConstants.electron_charge
elif "reactant" == name:
p_1 = ChargedParticle(2.014102 * UnitConversions.amu_to_kg, PhysicalConstants.electron_charge)
energy = 50e3 * PhysicalConstants.electron_charge
else:
raise ValueError()
beam_velocity = np.sqrt(2 * energy / p_1.m)
p_2 = ChargedParticle(2.014102 * UnitConversions.amu_to_kg, PhysicalConstants.electron_charge)
# Instantiate simulation
w_2 = int(n / N)
sim = AbeCoulombCollisionModel(N, p_1, w_1=w_1, N_2=N, particle_2=p_2, w_2=w_2, freeze_species_2=False)
# Set up velocities
velocities = np.zeros((2 * N, 3))
velocities[:N, :] = np.asarray([0.0, 0.0, beam_velocity])
velocities[N:] = np.asarray([0.0, 0.0, 0.0])
# Get approximate time scale
impact_parameter_ratio = 1.0 # Is not necessary for this analysis
reactant_collision = CoulombCollision(p_1, p_2,
impact_parameter_ratio,
beam_velocity)
reactant_relaxation = RelaxationProcess(reactant_collision)
deuterium_kinetic_frequency = reactant_relaxation.kinetic_loss_stationary_frequency(N * w_2, T, beam_velocity)
tau = 1.0 / deuterium_kinetic_frequency
dt = dt_factor * tau
final_time = 4.0 * tau
t, v_results = sim.run_sim(velocities, dt, final_time)
# Get salient results
velocities = np.mean(np.sqrt(v_results[:N, 0, :] ** 2 + v_results[:N, 1, :] ** 2 + v_results[:N, 2, :] ** 2), axis=0)
energies = 0.5 * p_1.m * velocities ** 2
velocity_results[name].append(velocities)
energy_results[name].append(velocities)
# Get energy half times
energy_time_interpolator = interp1d(energies / energy, t)
t_half = energy_time_interpolator(0.5)
t_halves[name][i] = t_half
t_theory[name][i] = tau
# Save results
np.savetxt("stationary_{}_{}_{}_half_times".format(name, N, dt_factor), t_halves[name], fmt='%s')
np.savetxt("stationary_{}_{}_{}_velocities".format(name, N, dt_factor), velocity_results[name], fmt='%s')
np.savetxt("stationary_{}_{}_{}_energies".format(name, N, dt_factor), energy_results[name], fmt='%s')
# Plot results
plt.figure()
for name in names:
plt.loglog(number_densities, t_halves[name], label="{}_Simulation".format(name))
plt.loglog(number_densities, t_theory[name], label="{}_Theoretical values".format(name))
plt.title("Comparison of thermalisation rates of products and reactants")
plt.legend()
plt.savefig("energy_half_times")
plt.show()
def run_sim():
p_1 = ChargedParticle(PhysicalConstants.electron_mass,
-PhysicalConstants.electron_charge)
p_2 = ChargedParticle(39.948 * UnitConversions.amu_to_kg,
PhysicalConstants.electron_charge)
n = int(1e4)
w = int(1e17)
sim = NanbuCollisionModel(np.asarray([n, n]),
np.asarray([p_1, p_2]),
np.asarray([w, w]),
coulomb_logarithm=15.9,
frozen_species=np.asarray([False, True]))
# Set initial velocity conditions of background
velocities = np.zeros((2 * n, 3))
k_T = 1e3
sigma = np.sqrt(2 * k_T * PhysicalConstants.electron_charge / p_1.m)
electron_velocities = np.random.normal(
loc=0.0, scale=sigma, size=velocities[:n, :].shape) / np.sqrt(3)
velocities[:n, :] = electron_velocities
sigma = np.sqrt(2 * k_T * PhysicalConstants.electron_charge / p_2.m)
ion_velocities = np.random.normal(
loc=0.0, scale=sigma, size=velocities[:n, :].shape) / np.sqrt(3)
velocities[n:, :] = ion_velocities
V = np.sqrt(8.0 * k_T / (np.pi * p_1.m))
num_periods = 50.0
frequencies = [1e4, 1e5]
v_squared_results = []
for i, f in enumerate(frequencies):
t_p = 1 / f
omega = 2 * np.pi * f
dt = t_p / 10.0
dt = min(dt, 2.5e-7)
t = 0.0
n = 1
times = [t]
v_squared_results.append([0.0])
I = np.zeros((n, 3))
I[:, :] = np.asarray([1.0, 0.0, 0.0])
new_vel = np.copy(velocities)
while t < num_periods * t_p:
I_results = []
v_means = []
while t < n * t_p:
print(t / t_p)
# Accelerate particles due to field
new_vel[:n, :] -= I * V * np.sin(omega * t)
# Carry out coulomb collisions
new_vel = sim.single_time_step(new_vel, dt)
# Calculate I
v_mean = np.mean(
np.sqrt(new_vel[:n, 0]**2 + new_vel[:n, 1]**2 +
new_vel[:n, 2]**2))
current = v_mean**2 * dt
v_means.append(v_mean)
I_results.append(current)
# Correct velocities
new_vel[:n, :] += I * V * np.sin(omega * t)
t += dt
# plt.figure()
# plt.plot(v_means)
# plt.show()
v = np.sum(np.asarray(I_results)) / t_p
v_squared_results[i].append(v / V**2)
n += 1
# Plot results
plt.figure()
for v_squared in v_squared_results:
plt.plot(np.asarray(v_squared)**2)
plt.scatter(range(len(v_squared)), np.asarray(v_squared)**2)
plt.show()
# Save results
v_squared_results = np.asarray(v_squared_results)
np.savetxt("v_squared_results", v_squared_results)
def compare_product_and_reactant_energy_loss_rates(
number_density,
reaction_name,
plot_results=True,
plot_energy_frequencies=True):
# Generate charged particles
alpha = ChargedParticle(6.64424e-27, PhysicalConstants.electron_charge * 2)
electron = ChargedParticle(9.014e-31, -PhysicalConstants.electron_charge)
if reaction_name == "DT":
reactant = ChargedParticle(5.0064125184e-27,
2 * PhysicalConstants.electron_charge)
e_alpha = 3.5e6 * PhysicalConstants.electron_charge
e_reactant = 30e3 * PhysicalConstants.electron_charge
Z = 2
elif reaction_name == "pB":
reactant = ChargedParticle((10.81 + 1) * UnitConversions.amu_to_kg,
PhysicalConstants.electron_charge * 12)
e_alpha = 8.6e6 / 3 * PhysicalConstants.electron_charge
e_reactant = 500e3 * PhysicalConstants.electron_charge
Z = 6
else:
raise ValueError("Name is invalid!")
temperature = e_reactant
# Get deuterium and alpha velocities from product and reactant beam
# energies
v_alpha = np.sqrt(2 * e_alpha / alpha.m)
v_reactant = np.sqrt(2 * e_reactant / reactant.m)
# Get reactant collision frequency and energy loss rate
impact_parameter_ratio = 1.0 # Is not necessary for this analysis
reactant_collision = CoulombCollision(reactant, reactant,
impact_parameter_ratio, v_reactant)
reactant_relaxation = RelaxationProcess(reactant_collision)
deuterium_kinetic_frequency = reactant_relaxation.kinetic_loss_stationary_frequency(
Z * number_density, temperature, v_reactant)
deuterium_energy_loss_rates = reactant_relaxation.energy_loss_with_distance(
Z * number_density, temperature, v_reactant)
deuterium_momentum_frequency = reactant_relaxation.momentum_loss_stationary_frequency(
Z * number_density, temperature, v_reactant)
deuterium_momentum_loss_rates = deuterium_momentum_frequency * reactant.m
# Get product collision frequency and energy loss rate
product_collision = CoulombCollision(alpha, reactant,
impact_parameter_ratio, v_alpha)
product_relaxation = RelaxationProcess(product_collision)
alpha_kinetic_frequency = product_relaxation.kinetic_loss_stationary_frequency(
Z * number_density, temperature, v_alpha)
alpha_energy_loss_rates = product_relaxation.energy_loss_with_distance(
Z * number_density, temperature, v_alpha)
alpha_momentum_frequency = product_relaxation.momentum_loss_stationary_frequency(
Z * number_density, temperature, v_alpha)
alpha_momentum_loss_rates = alpha_momentum_frequency * alpha.m
# Compare velocities of particles
v_electron = np.sqrt(2 * PhysicalConstants.boltzmann_constant *
temperature / electron.m)
print(
"For stationary collisions to be a reasonable approximation, the beams need "
"to be travelling faster than the electron thermal velocity... ")
print("Electron Normalised Velocity: {}".format(v_electron / 3e8))
print("Alpha-Electron Velocity Ratio: {}".format(v_alpha / v_electron))
print("Deuterium-Electron Velocity Ratio: {}".format(v_reactant /
v_electron))
print(deuterium_energy_loss_rates * e_alpha / alpha_energy_loss_rates /
e_reactant)
deuterium_energy_distance_travelled = e_reactant / deuterium_energy_loss_rates
alpha_energy_distance_travelled = e_alpha / alpha_energy_loss_rates
if plot_results:
fig, ax = plt.subplots(2, 2, figsize=(7, 7))
if plot_energy_frequencies:
# energy results
ax[0, 0].loglog(number_density,
deuterium_kinetic_frequency,
label="Reactant Beam")
ax[0, 0].loglog(number_density,
alpha_kinetic_frequency,
label="Product Beam")
ax[0, 0].set_xlabel("Number density (m-3)")
ax[0, 0].set_ylabel("Energy Collision Frequency (s-1)")
ax[0, 0].set_title(
"Energy Collision Frequencies - {}".format(reactant_name))
ax[0, 0].legend()
ax[1, 0].loglog(number_density,
deuterium_energy_loss_rates,
label="Reactant Beam")
ax[1, 0].loglog(number_density,
alpha_energy_loss_rates,
label="Product Beam")
ax[1, 0].set_xlabel("Number density (m-3)")
ax[1, 0].set_ylabel("Energy Loss Rate per metre (Jm-1)")
ax[1, 0].set_title(
"Energy Loss Rates per metre - {}".format(reactant_name))
ax[1, 0].legend()
ax[0, 1].loglog(number_density,
deuterium_energy_loss_rates / e_reactant,
label="Reactant Beam")
ax[0, 1].loglog(number_density,
alpha_energy_loss_rates / e_alpha,
label="Product Beam")
ax[0, 1].axhline(0.5, linestyle="--")
ax[0, 1].set_xlabel("Number density (m-3)")
ax[0, 1].set_ylabel("Normalised Kinetic Loss Rate (m-1)")
ax[0, 1].set_title(
"Normalised Kinetic Loss Rate - {}".format(reactant_name))
ax[0, 1].legend()
ax[1, 1].loglog(number_density,
deuterium_energy_distance_travelled,
label="Reactant Beam")
ax[1, 1].loglog(number_density,
alpha_energy_distance_travelled,
label="Product Beam")
ax[1, 1].set_xlabel("Number density (m-3)")
ax[1, 1].set_ylabel("Distance traveller (m)")
ax[1, 1].set_title("Distance travelled - {}".format(reactant_name))
ax[1, 1].legend()
else:
# momentum results
ax[0, 0].loglog(number_density,
deuterium_momentum_frequency,
label="Reactant Beam")
ax[0, 0].loglog(number_density,
alpha_momentum_frequency,
label="Product Beam")
ax[0, 0].set_xlabel("Number density (m-3)")
ax[0, 0].set_ylabel("Momentum Collision Frequency (s-1)")
ax[0, 0].set_title(
"Momentum Collision Frequencies - {}".format(reactant_name))
ax[0, 0].legend()
ax[1, 0].loglog(number_density,
deuterium_momentum_loss_rates,
label="Reactant Beam")
ax[1, 0].loglog(number_density,
alpha_momentum_loss_rates,
label="Product Beam")
ax[1, 0].set_xlabel("Number density (m-3)")
ax[1, 0].set_ylabel("Momentum Loss Rate per metre (Jm-1)")
ax[1, 0].set_title(
"Momentum Loss Rates per metre - {}".format(reactant_name))
ax[1, 0].legend()
deuterium_momentum = reactant.m * v_reactant
alpha_momentum = alpha.m * v_alpha
ax[0, 1].loglog(number_density,
deuterium_momentum_loss_rates / deuterium_momentum,
label="Reactant Beam")
ax[0, 1].loglog(number_density,
alpha_momentum_loss_rates / alpha_momentum,
label="Product Beam")
ax[0, 1].axhline(0.1, linestyle="--")
ax[0, 1].set_xlabel("Number density (m-3)")
ax[0, 1].set_ylabel("Normalised Momentum Loss Rate (m-1)")
ax[0, 1].set_title(
"Normalised Momentum Loss Rate - {}".format(reactant_name))
ax[0, 1].legend()
ax[1, 1].loglog(number_density,
deuterium_momentum / deuterium_momentum_loss_rates,
label="Reactant Beam")
ax[1, 1].loglog(number_density,
alpha_momentum / alpha_momentum_loss_rates,
label="Product Beam")
ax[1, 1].set_xlabel("Number density (m-3)")
ax[1, 1].set_ylabel("Distance traveller (m)")
ax[1, 1].set_title("Distance travelled - {}".format(reactant_name))
ax[1, 1].legend()
# fig.suptitle("Comparison of product and reactant beam loss rates due to electron collisions")
fig.tight_layout()
plt.savefig("relaxation_rates_{}".format(reactant_name))
plt.show()
return deuterium_energy_distance_travelled, alpha_energy_distance_travelled
def generate_sim_results(number_densities, T):
# Set simulation independent parameters
N = int(1e3)
dt_factor = 0.01
w_1 = int(1)
# Make results directory if it does not exist
res_dir = "results"
if not os.path.exists(res_dir):
os.mkdir(res_dir)
# Generate result lists
energy_results = dict()
velocity_results = dict()
t_halves = dict()
t_theory = dict()
# Run simulations
names = ["reactant", "product"]
for j, name in enumerate(names):
t_halves[name] = np.zeros(number_densities.shape)
t_theory[name] = np.zeros(number_densities.shape)
energy_results[name] = []
velocity_results[name] = []
for i, n in enumerate(number_densities):
print("Number density: {}".format(n))
# Set up beam sim
if "product" == name:
p_1 = ChargedParticle(6.64424e-27,
2 * PhysicalConstants.electron_charge)
energy = 3.5e6 * PhysicalConstants.electron_charge
elif "reactant" == name:
p_1 = ChargedParticle(2.014102 * UnitConversions.amu_to_kg,
PhysicalConstants.electron_charge)
energy = 50e3 * PhysicalConstants.electron_charge
else:
raise ValueError()
beam_velocity = np.sqrt(2 * energy / p_1.m)
p_2 = ChargedParticle(2.014102 * UnitConversions.amu_to_kg,
PhysicalConstants.electron_charge)
# Instantiate simulation
w_2 = int(n / N)
particle_numbers = np.asarray([N, N])
sim = NanbuCollisionModel(particle_numbers,
np.asarray([p_1, p_2]),
np.asarray([w_1, w_2]),
coulomb_logarithm=10.0,
frozen_species=np.asarray([False,
False]))
# Set up velocities
velocities = np.zeros((2 * N, 3))
velocities[:N, :] = np.asarray([0.0, 0.0, beam_velocity])
k_T = T * PhysicalConstants.boltzmann_constant
sigma = np.sqrt(2 * k_T / p_2.m)
background_velocities = np.random.normal(
loc=0.0, scale=sigma,
size=velocities[N:2 * N, :].shape) / np.sqrt(3)
velocities[N:2 * N, :] = background_velocities
# Get approximate time scale
impact_parameter_ratio = 1.0 # Is not necessary for this analysis
reactant_collision = CoulombCollision(p_1, p_2,
impact_parameter_ratio,
beam_velocity)
reactant_relaxation = MaxwellianRelaxationProcess(
reactant_collision)
deuterium_kinetic_frequency = reactant_relaxation.numerical_kinetic_loss_maxwellian_frequency(
N * w_2, T, beam_velocity)
tau = 1.0 / deuterium_kinetic_frequency
dt = dt_factor * tau
final_time = 4.0 * tau
t, v_results = sim.run_sim(velocities, dt, final_time)
# Get salient results
velocities = np.mean(
np.sqrt(v_results[:N, 0, :]**2 + v_results[:N, 1, :]**2 +
v_results[:N, 2, :]**2),
axis=0)
energies = 0.5 * p_1.m * velocities**2
velocity_results[name].append(velocities)
energy_results[name].append(energies)
velocities_deuterium = np.mean(
np.sqrt(v_results[N:2 * N, 0, :]**2 +
v_results[N:2 * N, 1, :]**2 +
v_results[N:2 * N, 2, :]**2),
axis=0)
energies_deuterium = 0.5 * p_2.m * np.mean(
np.sqrt(v_results[N:2 * N, 0, :]**2 +
v_results[N:2 * N, 1, :]**2 +
v_results[N:2 * N, 2, :]**2),
axis=0)
# Plot results
fig, ax = plt.subplots(2)
ax[0].plot(t, velocities_deuterium, label="ions")
ax[0].plot(t, velocities, label="beam")
ax[1].plot(t, energies_deuterium, label="ions")
ax[1].plot(t, energies, label="beam")
ax[1].plot(t, energies + energies_deuterium, label="total")
plt.legend()
plt.savefig(
os.path.join(
res_dir,
"stationary_{}_{}_{}_{}.png".format(name, N, dt_factor,
n)))
# Get energy half times
energy_time_interpolator = interp1d(energies / energy, t)
t_half = energy_time_interpolator(0.5)
t_halves[name][i] = t_half
t_theory[name][i] = tau
# Save results
np.savetxt(os.path.join(
res_dir,
"stationary_{}_{}_{}_half_times".format(name, N, dt_factor)),
t_halves[name],
fmt='%s')
np.savetxt(os.path.join(
res_dir,
"stationary_{}_{}_{}_velocities".format(name, N, dt_factor)),
velocity_results[name],
fmt='%s')
np.savetxt(os.path.join(
res_dir, "stationary_{}_{}_{}_energies".format(name, N,
dt_factor)),
energy_results[name],
fmt='%s')
# Plot results
plt.figure()
for name in names:
plt.loglog(number_densities,
t_halves[name],
label="{}_Simulation".format(name))
plt.loglog(number_densities,
t_theory[name],
label="{}_Theoretical values".format(name))
plt.title("Comparison of thermalisation rates of products and reactants")
plt.legend()
plt.savefig(os.path.join(res_dir, "energy_half_times"))
plt.show()
def run_electron_thermal_relaxation_sim(sim_type):
"""
Run a simulation of single species electron thermal relaxation
"""
# Set seed
np.random.seed(1)
p_1 = ChargedParticle(PhysicalConstants.electron_mass, -PhysicalConstants.electron_charge)
n = int(1e4)
sim = sim_type(n, p_1, 1, coulomb_logarithm=10.0)
T_y = 11500.0
T_factor = 1.3
T_e = (T_factor * T_y + 2 * T_y) / 3.0
velocities = np.zeros((n, 3))
sigma_ort = np.sqrt(PhysicalConstants.boltzmann_constant * T_y / p_1.m)
sigma_x = np.sqrt(PhysicalConstants.boltzmann_constant * T_factor * T_y / p_1.m)
velocities[:, 0] = np.random.normal(loc=0.0, scale=sigma_x, size=velocities[:, 0].shape)
velocities[:, 1] = np.random.normal(loc=0.0, scale=sigma_ort, size=velocities[:, 1].shape)
velocities[:, 2] = np.random.normal(loc=0.0, scale=sigma_ort, size=velocities[:, 2].shape)
# Get simulation time
tau = get_relaxation_time(p_1, n, T_e)
dt = 0.02 * tau
final_time = 12.0 * tau
T_x = np.std(velocities[:, 0], axis=0) ** 2 * p_1.m / (PhysicalConstants.boltzmann_constant)
T_y = np.std(velocities[:, 1], axis=0) ** 2 * p_1.m / (PhysicalConstants.boltzmann_constant)
T_z = np.std(velocities[:, 2], axis=0) ** 2 * p_1.m / (PhysicalConstants.boltzmann_constant)
t, v_results = sim.run_sim(velocities, dt, final_time)
t /= tau
dT_0 = (T_factor - 1.0) * T_y
dT = dT_0 * np.exp(-8.0 * t / (5.0 * np.sqrt(2.0 * np.pi)))
T_x_theory = 1.0 + dT / T_e * 2.0 / 3.0
T_ort_theory = 1.0 - dT / T_e / 3.0
fig, ax = plt.subplots(1, figsize=(10, 10))
T_x = np.std(v_results[:, 0, :], axis=0) ** 2 * p_1.m / (PhysicalConstants.boltzmann_constant)
T_y = np.std(v_results[:, 1, :], axis=0) ** 2 * p_1.m / (PhysicalConstants.boltzmann_constant)
T_z = np.std(v_results[:, 2, :], axis=0) ** 2 * p_1.m / (PhysicalConstants.boltzmann_constant)
ax.plot(t, T_x / T_e, label="T_x")
ax.plot(t, T_y / T_e, label="T_y")
ax.plot(t, T_z / T_e, label="T_z")
ax.plot(t, T_x_theory, label="T_x_num", linestyle="--")
ax.plot(t, T_ort_theory, label="T_ort_num", linestyle="--")
ax.set_xlim([0.0, t[-1]])
ax.legend()
ax.set_xlabel("Timestep")
ax.set_ylabel("Temperature")
ax.set_title("Electron thermal relaxation")
plt.savefig("electron_thermal_relaxation")
plt.show()
# Plot velocity histograms
fig, ax = plt.subplots(2, 3, figsize=(10, 10))
ax[0, 0].hist(v_results[:, 0, 0], 100)
ax[0, 1].hist(v_results[:, 1, 0], 100)
ax[0, 2].hist(v_results[:, 2, 0], 100)
ax[1, 0].hist(v_results[:, 0, -1], 100)
ax[1, 1].hist(v_results[:, 1, -1], 100)
ax[1, 2].hist(v_results[:, 2, -1], 100)
plt.savefig("electron_thermal_relaxation_velocity_distributions")
plt.show()
# Plot energy conservation
energy = np.sum(v_results[:, 0, :] ** 2 + v_results[:, 1, :] ** 2 + v_results[:, 2, :] ** 2, axis=0)
x_mom = np.sum(v_results[:, 0, :], axis=0)
y_mom = np.sum(v_results[:, 1, :], axis=0)
z_mom = np.sum(v_results[:, 2, :], axis=0)
fig, ax = plt.subplots(2)
ax[0].plot(t, energy)
ax[0].set_title("Conservation of Energy")
ax[1].plot(t, x_mom, label="x momentum")
ax[1].plot(t, y_mom, label="y momentum")
ax[1].plot(t, z_mom, label="z momentum")
ax[1].legend()
ax[1].set_title("Conservation of Momentum")
fig.suptitle("Conservation Plots")
plt.show()
def generate_sim_results(number_densities,
reactant_name,
plot_individual_sims=False):
# Set simulation independent parameters
N = int(1e4)
dt_factor = 0.01
# Generate result lists
energy_results = dict()
velocity_results = dict()
t_halves = dict()
t_theory = dict()
# Make results directory if it does not exist
res_dir = os.path.join("results", reactant_name)
if not os.path.exists(res_dir):
os.mkdir(res_dir)
# Run simulations
names = ["reactant", "product"]
for j, name in enumerate(names):
t_halves[name] = np.zeros(number_densities.shape)
t_theory[name] = np.zeros(number_densities.shape)
energy_results[name] = []
velocity_results[name] = []
for i, n in enumerate(number_densities):
# Set temperature of background - this is assumed to be lower than the energy of the beam
T_b = 1e3 if reactant_name is "DT" else 10e3
T_b *= UnitConversions.eV_to_K
# Set up beam sim
if "product" == name:
p_1 = ChargedParticle(6.64424e-27,
2 * PhysicalConstants.electron_charge)
if reactant_name == "DT":
p_2 = ChargedParticle(
5.0064125184e-27,
2 * PhysicalConstants.electron_charge)
energy = 3.5e6 * PhysicalConstants.electron_charge
Z = 2
# Get number density of products from reactivity
T = 50
reaction = DTReaction()
reactivity_fit = BoschHaleReactivityFit(reaction)
reactivity = reactivity_fit.get_reactivity(T)
n_1 = n * reactivity
elif reactant_name == "pB":
p_2 = ChargedParticle(
(10.81 + 1) * UnitConversions.amu_to_kg,
PhysicalConstants.electron_charge * 12)
energy = 8.6e6 / 3.0 * PhysicalConstants.electron_charge
Z = 6
# Get number density of products from reactivity
T = 500
reaction = pBReaction()
reactivity_fit = BoschHaleReactivityFit(reaction)
reactivity = reactivity_fit.get_reactivity(T)
n_1 = n * reactivity
else:
raise ValueError("Invalid reactant")
elif "reactant" == name:
n_1 = n
if reactant_name == "DT":
p_1 = ChargedParticle(
5.0064125184e-27,
2 * PhysicalConstants.electron_charge)
p_2 = ChargedParticle(
5.0064125184e-27,
2 * PhysicalConstants.electron_charge)
energy = 50e3 * PhysicalConstants.electron_charge
Z = 2
elif reactant_name == "pB":
p_1 = ChargedParticle(
(10.81 + 1) * UnitConversions.amu_to_kg,
PhysicalConstants.electron_charge * 12)
p_2 = ChargedParticle(
(10.81 + 1) * UnitConversions.amu_to_kg,
PhysicalConstants.electron_charge * 12)
energy = 500e3 * PhysicalConstants.electron_charge
Z = 6
else:
raise ValueError("Invalid reactant")
else:
raise ValueError()
beam_velocity = np.sqrt(2 * energy / p_1.m)
electron = ChargedParticle(9.014e-31,
-PhysicalConstants.electron_charge)
# Instantiate simulation
w_b = long(n / N)
w_1 = long(n_1 / N)
w_1 = w_1 if w_1 > 0 else long(1)
print(w_1, w_b)
particle_numbers = np.asarray([N, N, N])
sim = NanbuCollisionModel(particle_numbers,
np.asarray([p_1, p_2, electron]),
np.asarray([w_1, w_b, Z * w_b]),
coulomb_logarithm=10.0,
frozen_species=np.asarray(
[False, False, False]),
include_self_collisions=True)
# Set up velocities
velocities = np.zeros((np.sum(particle_numbers), 3))
velocities[:N, :] = np.asarray([0.0, 0.0, beam_velocity])
# Small maxwellian distribution used for background species
k_T = T_b * PhysicalConstants.boltzmann_constant
sigma = np.sqrt(2 * k_T / p_2.m)
p_2_velocities = np.random.normal(
loc=0.0, scale=sigma,
size=velocities[N:2 * N, :].shape) / np.sqrt(3)
velocities[N:2 * N, :] = p_2_velocities
sigma = np.sqrt(2 * k_T / electron.m)
electron_velocities = np.random.normal(
loc=0.0, scale=sigma,
size=velocities[2 * N:, :].shape) / np.sqrt(3)
velocities[2 * N:, :] = electron_velocities
# Get approximate time scale
impact_parameter_ratio = 1.0 # Is not necessary for this analysis
tau = sys.float_info.max
for background_particle in [p_2]:
reactant_collision = CoulombCollision(p_1, background_particle,
impact_parameter_ratio,
beam_velocity)
relaxation = MaxwellianRelaxationProcess(reactant_collision)
kinetic_frequency = relaxation.numerical_kinetic_loss_maxwellian_frequency(
N * w_b, T_b, beam_velocity)
tau = min(tau, 1.0 / kinetic_frequency)
dt = dt_factor * tau
final_time = 4.0 * tau
t, v_results = sim.run_sim(velocities, dt, final_time)
# Get salient results
velocities = np.mean(
np.sqrt(v_results[:N, 0, :]**2 + v_results[:N, 1, :]**2 +
v_results[:N, 2, :]**2),
axis=0)
energies = 0.5 * p_1.m * np.mean(
v_results[:N, 0, :]**2 + v_results[:N, 1, :]**2 +
v_results[:N, 2, :]**2,
axis=0)
total_energies = 0.5 * p_1.m * np.sum(
v_results[:N, 0, :]**2 + v_results[:N, 1, :]**2 +
v_results[:N, 2, :]**2,
axis=0)
total_energy = N * w_1 * energy
assert np.isclose(energies[0], energy,
atol=0.01 * energy), "{} != {}".format(
energies[0], w_1 * N * energy)
assert np.isclose(total_energies[0] * w_1,
total_energy,
atol=0.01 * total_energy), "{} != {}".format(
total_energies[0], total_energy)
velocity_results[name].append(velocities)
energy_results[name].append(energies)
velocities_p_2 = np.mean(np.sqrt(v_results[N:2 * N, 0, :]**2 +
v_results[N:2 * N, 1, :]**2 +
v_results[N:2 * N, 2, :]**2),
axis=0)
energies_p_2 = 0.5 * p_2.m * np.mean(
v_results[N:2 * N, 0, :]**2 + v_results[N:2 * N, 1, :]**2 +
v_results[N:2 * N, 2, :]**2,
axis=0)
velocities_electron = np.mean(np.sqrt(v_results[2 * N:, 0, :]**2 +
v_results[2 * N:, 1, :]**2 +
v_results[2 * N:, 2, :]**2),
axis=0)
energies_electron = 0.5 * electron.m * np.mean(
v_results[2 * N:, 0, :]**2 + v_results[2 * N:, 1, :]**2 +
v_results[2 * N:, 2, :]**2,
axis=0)
# Plot total velocity and energy results
fig, ax = plt.subplots(2, sharex=True)
ax[0].plot(t, velocities_p_2, label="ions")
ax[0].plot(t, velocities_electron, label="electron")
ax[0].plot(t, velocities, label="beam")
ax[0].set_ylabel("Velocities [$ms^{-1}$]")
ax[1].plot(t, energies_p_2, label="ions")
ax[1].plot(t, energies_electron, label="electron")
ax[1].plot(t, energies, label="beam")
ax[1].plot(t,
energies + energies_electron + energies_p_2,
label="total")
ax[1].set_ylabel("Energies [J]")
ax[1].set_xlabel("Time [s]")
plt.legend()
plt.savefig(
os.path.join(
res_dir,
"{}_{}_{}_{}_{}.png".format(name, N, dt_factor, n,
reactant_name)))
if plot_individual_sims:
plt.show()
plt.close()
# Plot velocities
fig, ax = plt.subplots(2, 2, figsize=(10, 10), sharex='col')
v_x_1 = np.mean(v_results[:N, 0, :], axis=0)
v_y_1 = np.mean(v_results[:N, 1, :], axis=0)
v_z_1 = np.mean(v_results[:N, 2, :], axis=0)
v_x_2 = np.mean(v_results[N:2 * N, 0, :], axis=0)
v_y_2 = np.mean(v_results[N:2 * N, 1, :], axis=0)
v_z_2 = np.mean(v_results[N:2 * N, 2, :], axis=0)
v_x_3 = np.mean(v_results[2 * N:, 0, :], axis=0)
v_y_3 = np.mean(v_results[2 * N:, 1, :], axis=0)
v_z_3 = np.mean(v_results[2 * N:, 2, :], axis=0)
ax[0, 0].plot(t, v_x_1, label="v_x_1")
ax[0, 0].plot(t, v_x_2, label="v_x_2")
ax[0, 0].plot(t, v_x_3, label="v_x_3")
ax[0, 0].legend()
ax[0, 1].plot(t, v_y_1, label="v_y_1")
ax[0, 1].plot(t, v_y_2, label="v_y_2")
ax[0, 1].plot(t, v_y_3, label="v_y_3")
ax[0, 1].legend()
ax[1, 0].plot(t, v_z_1, label="v_z_1")
ax[1, 0].plot(t, v_z_2, label="v_z_2")
ax[1, 0].plot(t, v_z_3, label="v_z_3")
ax[1, 0].legend()
ax[1, 1].plot(t, velocities, label="v_t_1")
ax[1, 1].plot(t, velocities_p_2, label="v_t_2")
ax[1, 1].plot(t, velocities_electron, label="v_t_3")
ax[1, 1].legend()
plt.savefig(
os.path.join(
res_dir, "species_velocities_{}_{}_{}_{}_{}.png".format(
name, N, dt_factor, n, reactant_name)))
if plot_individual_sims:
plt.show()
plt.close()
# Plot temperatures
fig, ax = plt.subplots(2,
2,
figsize=(10, 10),
sharex='col',
sharey='row')
velocities_total = np.sqrt(v_results[:, 0, :]**2 +
v_results[:, 1, :]**2 +
v_results[:, 2, :]**2)
T_x_1 = np.std(v_results[:N, 0, :], axis=0)**2 * p_1.m / (
PhysicalConstants.boltzmann_constant)
T_y_1 = np.std(v_results[:N, 1, :], axis=0)**2 * p_1.m / (
PhysicalConstants.boltzmann_constant)
T_z_1 = np.std(v_results[:N, 2, :], axis=0)**2 * p_1.m / (
PhysicalConstants.boltzmann_constant)
T_t_1 = np.std(velocities_total[:N, :], axis=0)**2 * p_1.m / (
PhysicalConstants.boltzmann_constant)
T_x_2 = np.std(v_results[N:2 * N, 0, :], axis=0)**2 * p_2.m / (
PhysicalConstants.boltzmann_constant)
T_y_2 = np.std(v_results[N:2 * N, 1, :], axis=0)**2 * p_2.m / (
PhysicalConstants.boltzmann_constant)
T_z_2 = np.std(v_results[N:2 * N, 2, :], axis=0)**2 * p_2.m / (
PhysicalConstants.boltzmann_constant)
T_t_2 = np.std(velocities_total[N:2 * N, :], axis=0)**2 * p_2.m / (
PhysicalConstants.boltzmann_constant)
T_x_3 = np.std(v_results[2 * N:, 0, :], axis=0)**2 * electron.m / (
PhysicalConstants.boltzmann_constant)
T_y_3 = np.std(v_results[2 * N:, 1, :], axis=0)**2 * electron.m / (
PhysicalConstants.boltzmann_constant)
T_z_3 = np.std(v_results[2 * N:, 2, :], axis=0)**2 * electron.m / (
PhysicalConstants.boltzmann_constant)
T_t_3 = np.std(velocities_total[2 * N:, :],
axis=0)**2 * electron.m / (
PhysicalConstants.boltzmann_constant)
ax[0, 0].plot(t, T_x_1, label="T_x_1")
ax[0, 0].plot(t, T_x_2, label="T_x_2")
ax[0, 0].plot(t, T_x_3, label="T_x_3")
ax[0, 0].legend()
ax[0, 1].plot(t, T_y_1, label="T_y_1")
ax[0, 1].plot(t, T_y_2, label="T_y_2")
ax[0, 1].plot(t, T_y_3, label="T_y_3")
ax[0, 1].legend()
ax[1, 0].plot(t, T_z_1, label="T_z_1")
ax[1, 0].plot(t, T_z_2, label="T_z_2")
ax[1, 0].plot(t, T_z_3, label="T_z_3")
ax[1, 0].legend()
ax[1, 1].plot(t, T_t_1, label="T_t_1")
ax[1, 1].plot(t, T_t_2, label="T_t_2")
ax[1, 1].plot(t, T_t_3, label="T_t_3")
ax[1, 1].legend()
plt.savefig(
os.path.join(
res_dir, "species_temperatures_{}_{}_{}_{}_{}.png".format(
name, N, dt_factor, n, reactant_name)))
if plot_individual_sims:
plt.show()
plt.close()
# Get energy half times - assuming beam has equilibrated at the end of the simulation
energies = energies - energies[-1]
energy_time_interpolator = interp1d(energies / energies[0], t)
t_half = energy_time_interpolator(0.5)
t_halves[name][i] = t_half
t_theory[name][i] = tau
# Plot results
plt.figure()
for name in names:
plt.loglog(number_densities,
t_halves[name],
label="{}_Simulation".format(name))
plt.loglog(number_densities,
t_theory[name],
label="{}_Theoretical values".format(name))
plt.title("Comparison of thermalisation rates of products and reactants")
plt.legend()
plt.savefig(
os.path.join(res_dir, "energy_half_times_{}".format(reactant_name)))
plt.show()
def plot_collisional_angles():
"""
Simple function to plot the scattering angles for different binary collisions at they vary with
impact parameter ratio
"""
impact_parameter_ratios = np.linspace(0.1, 10.0, 1000)
deuterium = ChargedParticle(2.01410178 * 1.66054e-27,
PhysicalConstants.electron_charge)
electron = ChargedParticle(9.014e-31, -PhysicalConstants.electron_charge)
temperature = 11600.0 # 1eV taken as base case
electron_velocity = np.sqrt(PhysicalConstants.boltzmann_constant *
temperature / electron.m)
deuterium_velocity = np.sqrt(PhysicalConstants.boltzmann_constant *
temperature / deuterium.m)
print("Electron Thermal Velocity at {}K: {}".format(
temperature, electron_velocity))
print("Ion Thermal Velocity at {}K: {}".format(temperature,
deuterium_velocity))
collision_pairs = [
["Deuterium-Deuterium", deuterium, deuterium, deuterium_velocity],
["Electron-Deuterium", electron, deuterium, electron_velocity],
["Electron-Electron", electron, electron, electron_velocity]
]
for j, pair in enumerate(collision_pairs):
name = pair[0]
p_1 = pair[1]
p_2 = pair[2]
velocity = pair[3]
chi = np.zeros(impact_parameter_ratios.shape)
cross_section = np.zeros(impact_parameter_ratios.shape)
for i, impact_parameter_ratio in enumerate(impact_parameter_ratios):
collision = CoulombCollision(p_1, p_2, impact_parameter_ratio,
velocity)
chi[i] = collision.chi
cross_section[i] = collision.differential_cross_section
collision = CoulombCollision(p_1, p_2, 1.0, velocity)
b_90 = collision.b_90
print("Impact Parameter for {} Collision: {}".format(name, b_90))
# Convert chi to degrees
chi /= np.pi
chi *= 180.0
# Plot chi vs.impact parameter
fig, ax = plt.subplots(2, figsize=(10, 10))
ax[0].plot(impact_parameter_ratios, chi)
ax[0].set_title(
"Scattering angle vs Impact parameter for {} Collision".format(
name))
ax[0].set_xlabel("Impact parameter ratio (b / b_90)")
ax[0].set_ylabel("Scattering angle (degrees)")
ax[1].semilogy(chi, cross_section)
ax[1].set_title(
"Cross Section vs Impact parameter for {} Collision".format(name))
ax[1].set_xlabel("Impact parameter ratio (b / b_90)")
ax[1].set_ylabel("Number of collisions at given solid angle")
plt.show()
def run_electron_beam_into_stationary_target_sim(sim_type):
"""
Run simulation of velocity relaxation of electron beam to stationary
background species
"""
p_1 = ChargedParticle(PhysicalConstants.electron_mass,
-PhysicalConstants.electron_charge)
p_2 = ChargedParticle(1e31, -PhysicalConstants.electron_charge)
w_1 = 1
w_2 = 10
n = int(1e3)
beam_velocity = 1.0
if sim_type == "Abe":
sim = AbeCoulombCollisionModel(n,
p_1,
w_1=w_1,
N_2=n,
particle_2=p_2,
w_2=w_2,
freeze_species_2=True)
elif sim_type == "Nanbu":
sim = NanbuCollisionModel(np.asarray([n, n]),
np.asarray([p_1, p_2]),
np.asarray([w_1, w_2]),
coulomb_logarithm=10.0,
frozen_species=[False, True])
else:
raise ValueError("Invalid sim type")
velocities = np.zeros((2 * n, 3))
velocities[:n, :] = np.asarray([0.0, 0.0, beam_velocity])
velocities[n:] = np.asarray([0.0, 0.0, 0.0])
tau = get_relaxation_time(p_1, n * w_2, beam_velocity)
dt = 0.05 * tau
final_time = 3.0 * tau
t, v_results = sim.run_sim(velocities, dt, final_time)
t /= tau
v_z_estimate = 1.0 - t
v_ort_estimate = 2 * t
fig, ax = plt.subplots(2, figsize=(10, 10))
ax[0].plot(
t,
np.mean(v_results[:n, 0, :]**2 + v_results[:n, 1, :]**2, axis=0) /
beam_velocity**2,
label="<v_ort^2>")
ax[0].plot(t, np.mean(v_results[:n, 2, :], axis=0), label="<v_z>")
ax[0].plot(t, v_z_estimate, linestyle="--", label="<v_z_estimate>")
ax[0].plot(t, v_ort_estimate, linestyle="--", label="<v_ort_estimate>")
ax[0].set_ylim([0.0, 1.0])
ax[0].set_xlim([0.0, t[-1]])
ax[0].legend()
ax[0].set_xlabel("Timestep")
ax[0].set_ylabel("Velocities ms-1")
ax[0].set_title("Beam Velocities")
ax[1].plot(t, np.mean(v_results[n:, 0, :], axis=0), label="<v_x>")
ax[1].plot(t, np.mean(v_results[n:, 1, :], axis=0), label="<v_y>")
ax[1].plot(t, np.mean(v_results[n:, 2, :], axis=0), label="<v_z>")
ax[1].legend()
ax[1].set_xlabel("Timestep")
ax[1].set_ylabel("Velocities ms-1")
ax[1].set_title("Background Velocities")
plt.show()
return t, v_results
def run_1d_electrostatic_well(radius, num_particles=int(1e4)):
"""
Function to run simulation - for this simulation, in a spherically symmetric potential well
radius: size of potential well
"""
# Define particles
number_density = 1e12
charge = 1.602e-19
weight = int(number_density / num_particles)
pic_particle = PICParticle(3.344496935079999e-27 * weight, charge * weight,
np.asarray([radius, 0.0, 0.0]),
np.asarray([0.0, 0.0, 0.0]))
collision_particle = ChargedParticle(3.344496935079999e-27 * weight,
charge * weight)
# Define fields
electron_charge_density = 1e20 * PhysicalConstants.electron_charge
def e_field(x):
E_field = np.zeros(x.shape)
for i in range(x.shape[0]):
r = magnitude(x[i])
if r <= radius:
e_field = electron_charge_density * r / (
3.0 * PhysicalConstants.epsilon_0)
else:
e_field = electron_charge_density * radius**3 / (
3.0 * PhysicalConstants.epsilon_0 * r**2)
e_field *= -normalise(x[i])
E_field[i, :] = e_field
return E_field
# Define time step and final time
total_V = radius**3 * electron_charge_density
total_V /= 2.0 * PhysicalConstants.epsilon_0 * radius
energy = total_V * pic_particle.charge
velocity = np.sqrt(2 * energy / pic_particle.mass)
dt = 0.01 * radius / velocity
final_time = 50.0 * radius / velocity
num_steps = int(final_time / dt)
# Define collision model
temperature = 298.3
thermal_velocity = np.sqrt(PhysicalConstants.boltzmann_constant *
temperature / pic_particle.mass)
c = CoulombCollision(collision_particle, collision_particle, 1.0, velocity)
debye_length = PhysicalConstants.epsilon_0 * temperature
debye_length /= number_density * PhysicalConstants.electron_charge**2
debye_length = np.sqrt(debye_length)
coulomb_logarithm = np.log(debye_length / c.b_90)
r = RelaxationProcess(c)
v_K = r.kinetic_loss_stationary_frequency(number_density, temperature,
velocity)
print("Thermal velocity: {}".format(thermal_velocity / 3e8))
print("Peak velocity: {}".format(velocity / 3e8))
print("Debye Length: {}".format(debye_length))
print("Coulomb Logarithm: {}".format(coulomb_logarithm))
print("Kinetic Loss Time: {}".format(1.0 / v_K))
print("Simulation Time Step: {}".format(dt))
assert dt < 0.01 * 1.0 / v_K
collision_model = AbeCoulombCollisionModel(
num_particles,
collision_particle,
weight,
coulomb_logarithm=coulomb_logarithm)
# Set up initial conditions
np.random.seed(1)
X = np.zeros((num_particles, 3))
for i in range(num_particles):
x_theta = np.random.uniform(0.0, 2 * np.pi)
y_theta = np.random.uniform(0.0, 2 * np.pi)
z_theta = np.random.uniform(0.0, 2 * np.pi)
X[i, :] = rotate_3d(np.asarray([1.0, 0.0, 0.0]),
np.asarray([x_theta, y_theta, z_theta]))
V = np.random.normal(0.0, thermal_velocity, size=X.shape)
V = np.zeros(X.shape)
# Run simulation
times = np.linspace(0.0, dt * num_steps, num_steps)
positions = np.zeros((times.shape[0], X.shape[0], X.shape[1]))
velocities = np.zeros((times.shape[0], X.shape[0], X.shape[1]))
for i, t in enumerate(times):
# Set first position and velocity
if i == 0:
positions[i, :, :] = X
velocities[i, :, :] = V
continue
# Update position and velocity due to field
dt = times[i] - times[i - 1]
x = X + V + 0.5 * dt if i == 1 else X + V * dt
E = e_field(x)
v = V + E * pic_particle.charge / pic_particle.mass * dt
# Update velocity due to collisions
new_v = collision_model.single_time_step(v, dt)
positions[i, :, :] = x
velocities[i, :, :] = new_v
X = x
V = new_v
print("Timestep: {}".format(i))
return times, positions, velocities
return 1.0 / stationary_frequency, 1.0 / theoretical_maxwellian_frequency, 1.0 / numerical_maxwellian_frequency, 1.0 / monte_carlo_maxwellian_frequency
if __name__ == '__main__':
# Define parameter space of scan
n = np.logspace(20, 30, 10)
T_keV = 10.0
T_K = T_keV * 1e3 * UnitConversions.eV_to_K
e = 1.5 * PhysicalConstants.boltzmann_constant * T_K
# Set up reaction
reaction_name = "pB"
if reaction_name == "DD":
reaction = DDReaction()
deuterium = ChargedParticle(2.01410178 * UnitConversions.amu_to_kg,
PhysicalConstants.electron_charge)
beam_velocity = np.sqrt(2 * e / deuterium.m)
beam_species = deuterium
background_species = deuterium
elif reaction_name == "DT":
reaction = DTReaction()
deuterium_tritium = ChargedParticle(
5.0064125184e-27, 2 * PhysicalConstants.electron_charge)
beam_velocity = np.sqrt(2 * e / deuterium_tritium.m)
beam_species = deuterium_tritium
background_species = deuterium_tritium
elif reaction_name == "pB":
reaction = pBReaction()
proton_boron = ChargedParticle((10.81 + 1) * UnitConversions.amu_to_kg,