def _extract_current_results(data, curr, data_time):
grid = data['models']['simulationGrid']
plate_spacing = _meters(grid['plate_spacing'])
zmesh = np.linspace(0, plate_spacing, grid['num_z'] + 1) #holds the z-axis grid points in an array
beam = data['models']['beam']
if data.models.simulationGrid.simulation_mode == '3d':
cathode_area = _meters(grid['channel_width']) * _meters(grid['channel_height'])
else:
cathode_area = _meters(grid['channel_width'])
RD_ideal = sources.j_rd(beam['cathode_temperature'], beam['cathode_work_function']) * cathode_area
JCL_ideal = sources.cl_limit(beam['cathode_work_function'], beam['anode_work_function'], beam['anode_voltage'], plate_spacing) * cathode_area
if beam['currentMode'] == '2' or (beam['currentMode'] == '1' and beam['beam_current'] >= JCL_ideal):
curr2 = np.full_like(zmesh, JCL_ideal)
y2_title = 'Child-Langmuir cold limit'
else:
curr2 = np.full_like(zmesh, RD_ideal)
y2_title = 'Richardson-Dushman'
return {
'title': 'Current for Time: {:.4e}s'.format(data_time),
'x_range': [0, plate_spacing],
'y_label': 'Current [A]',
'x_label': 'Z [m]',
'points': [
curr.tolist(),
curr2.tolist(),
],
'x_points': zmesh.tolist(),
'y_range': [min(np.min(curr), np.min(curr2)), max(np.max(curr), np.max(curr2))],
'y1_title': 'Current',
'y2_title': y2_title,
}
def _extract_current_results(data, curr, data_time):
grid = data['models']['simulationGrid']
plate_spacing = _meters(grid['plate_spacing'])
zmesh = np.linspace(0, plate_spacing, grid['num_z'] + 1) #holds the z-axis grid points in an array
beam = data['models']['beam']
if _SIM_DATA.warpvnd_is_3d(data):
cathode_area = _meters(grid['channel_width']) * _meters(grid['channel_height'])
else:
cathode_area = _meters(grid['channel_width'])
RD_ideal = sources.j_rd(beam['cathode_temperature'], beam['cathode_work_function']) * cathode_area
JCL_ideal = sources.cl_limit(beam['cathode_work_function'], beam['anode_work_function'], beam['anode_voltage'], plate_spacing) * cathode_area
if beam['currentMode'] == '2' or (beam['currentMode'] == '1' and beam['beam_current'] >= JCL_ideal):
curr2 = np.full_like(zmesh, JCL_ideal)
y2_title = 'Child-Langmuir cold limit'
else:
curr2 = np.full_like(zmesh, RD_ideal)
y2_title = 'Richardson-Dushman'
return {
'title': 'Current for Time: {:.4e}s'.format(data_time),
'x_range': [0, plate_spacing],
'y_label': 'Current [A]',
'x_label': 'Z [m]',
'points': [
curr.tolist(),
curr2.tolist(),
],
'x_points': zmesh.tolist(),
'y_range': [min(np.min(curr), np.min(curr2)), max(np.max(curr), np.max(curr2))],
'y1_title': 'Current',
'y2_title': y2_title,
}
def main(x_struts, y_struts, V_grid, grid_height, strut_width, strut_height,
rho_ew, T_em, phi_em, T_coll, phi_coll, rho_cw, gap_distance, rho_load,
run_id, channel_width=100e-9,
injection_type=2, magnetic_field=0.0, random_seed=True, install_grid=True, max_wall_time=1e9,
particle_diagnostic_switch=False, field_diagnostic_switch=False, lost_diagnostic_switch=False):
"""
Run a simulation of a gridded TEC.
Args:
x_struts: Number of struts that intercept the x-axis.
y_struts: Number of struts that intercept the y-axis
V_grid: Voltage to place on the grid in Volts.
grid_height: Distance from the emitter to the grid normalized by gap_distance, unitless.
strut_width: Transverse extent of the struts in meters.
strut_height: Longitudinal extent of the struts in meters.
rho_ew: Emitter side wiring resistivity, ohms*cm.
T_em: Emitter temperature, kelvin.
phi_em: Emitter work function, eV.
T_coll: Collector termperature, kelvin.
phi_coll: Collector work function, eV.
rho_cw: Collector side wiring resistivity, ohms*cm.
gap_distance: Distance from emitter to collector, meters.
rho_load: Load resistivity, ohms*cm.
run_id: Run ID. Mainly used for parallel optimization.
injection_type: 1: For constant current emission with only thermal velocity spread in z and CL limited emission.
2: For true thermionic emission. Velocity spread along all axes.
random_seed: True/False. If True, will force a random seed to be used for emission positions.
install_grid: True/False. If False the grid will not be installed. Results in simple parallel plate setup.
If False then phi_em - phi_coll specifies the voltage on the collector.
max_wall_time: Wall time to allow simulation to run for. Simulation is periodically checked and will halt if it
appears the next segment of the simulation will exceed max_wall_time. This is not guaranteed to
work since the guess is based on the run time up to that point.
Intended to be used when running on system with job manager.
particle_diagnostic_switch: True/False. Use openPMD compliant .h5 particle diagnostics.
field_diagnostic_switch: True/False. Use rswarp electrostatic .h5 field diagnostics (Maybe openPMD compliant?).
lost_diagnostic_switch: True/False. Enable collection of lost particle coordinates
with rswarp.diagnostics.parallel.save_lost_particles.
"""
# record inputs and set parameters
run_attributes = deepcopy(locals())
for key in run_attributes:
if key in efficiency.tec_parameters:
efficiency.tec_parameters[key][0] = run_attributes[key]
# set new random seed
if random_seed:
top.seedranf(randint(1, 1e9))
# Control for printing in parallel
if comm_world.size != 1:
synchronizeQueuedOutput_mpi4py(out=True, error=True)
if particle_diagnostic_switch or field_diagnostic_switch:
# Directory paths
diagDir = 'diags_id{}/hdf5/'.format(run_id)
field_base_path = 'diags_id{}/fields/'.format(run_id)
diagFDir = {'magnetic': 'diags_id{}/fields/magnetic'.format(run_id),
'electric': 'diags_id{}/fields/electric'.format(run_id)}
# Cleanup command if directories already exist
if comm_world.rank == 0:
cleanupPrevious(diagDir, diagFDir)
load_balance = LoadBalancer()
######################
# DOMAIN/GEOMETRY/MESH
######################
# Dimensions
X_MAX = +channel_width / 2.
X_MIN = -X_MAX
Y_MAX = +channel_width / 2.
Y_MIN = -Y_MAX
Z_MAX = gap_distance
Z_MIN = 0.
# TODO: cells in all dimensions reduced by 10x for testing, will need to verify if this is reasonable (TEMP)
# Grid parameters
dx_want = 5e-9
dy_want = 5e-9
dz_want = 5e-9
NUM_X = int(round(channel_width / dx_want)) # 20 #128 #10
NUM_Y = int(round(channel_width / dy_want)) # 20 #128 #10
NUM_Z = int(round(gap_distance / dz_want))
# mesh spacing
dz = (Z_MAX - Z_MIN) / NUM_Z
dx = channel_width / NUM_X
dy = channel_width / NUM_Y
print "Channel width: {}, DX = {}".format(channel_width, dx)
print "Channel width: {}, DY = {}".format(channel_width, dy)
print "Channel length: {}, DZ = {}".format(gap_distance, dz)
# Solver Geometry and Boundaries
# Specify solver geometry
w3d.solvergeom = w3d.XYZgeom
# Set field boundary conditions
w3d.bound0 = neumann
w3d.boundnz = dirichlet
w3d.boundxy = periodic
# Particles boundary conditions
top.pbound0 = absorb
top.pboundnz = absorb
top.pboundxy = periodic
# Set mesh boundaries
w3d.xmmin = X_MIN
w3d.xmmax = X_MAX
w3d.ymmin = Y_MIN
w3d.ymmax = Y_MAX
w3d.zmmin = 0.
w3d.zmmax = Z_MAX
# Set mesh cell counts
w3d.nx = NUM_X
w3d.ny = NUM_Y
w3d.nz = NUM_Z
#############################
# PARTICLE INJECTION SETTINGS
#############################
# Cathode and anode settings
EMITTER_TEMP = T_em
EMITTER_PHI = phi_em # work function in eV
COLLECTOR_PHI = phi_coll # Can be used if vacuum level is being set
ACCEL_VOLTS = V_grid # ACCEL_VOLTS used for velocity and CL calculations
collector_voltage = phi_em - phi_coll
# Emitted species
background_beam = Species(type=Electron, name='background')
measurement_beam = Species(type=Electron, name='measurement')
# Emitter area and position
SOURCE_RADIUS_1 = 0.5 * channel_width # a0 parameter - X plane
SOURCE_RADIUS_2 = 0.5 * channel_width # b0 parameter - Y plane
Z_PART_MIN = dz / 1000. # starting particle z value
# Compute cathode area for geomtry-specific current calculations
if (w3d.solvergeom == w3d.XYZgeom):
# For 3D cartesion geometry only
cathode_area = 4. * SOURCE_RADIUS_1 * SOURCE_RADIUS_2
else:
# Assume 2D XZ geometry
cathode_area = 2. * SOURCE_RADIUS_1 * 1.
# If using the XZ geometry, set so injection uses the same geometry
top.linj_rectangle = (w3d.solvergeom == w3d.XZgeom or w3d.solvergeom == w3d.XYZgeom)
# Returns velocity beam_beta (in units of beta) for which frac of emitted particles have v < beam_beta * c
beam_beta = sources.compute_cutoff_beta(EMITTER_TEMP, frac=0.99)
PTCL_PER_STEP = 100
if injection_type == 1:
CURRENT_MODIFIER = 0.5 # Factor to multiply CL current by when setting beam current
# Constant current density - beam transverse velocity fixed to zero, very small longitduinal velocity
# Set injection flag
top.inject = 1 # 1 means constant; 2 means space-charge limited injection;# 6 means user-specified
top.npinject = PTCL_PER_STEP
beam_current = 4. / 9. * eps0 * sqrt(2. * echarge / background_beam.mass) \
* ACCEL_VOLTS ** 1.5 / gap_distance ** 2 * cathode_area
background_beam.ibeam = beam_current * CURRENT_MODIFIER
background_beam.a0 = SOURCE_RADIUS_1
background_beam.b0 = SOURCE_RADIUS_2
background_beam.ap0 = .0e0
background_beam.bp0 = .0e0
w3d.l_inj_exact = True
# Initial velocity settings (5% of c)
vrms = np.sqrt(1 - 1 / (0.05 / 511e3 + 1) ** 2) * 3e8
top.vzinject = vrms
if injection_type == 2:
# True Thermionic injection
top.inject = 1
# Set both beams to same npinject to keep weights the same
background_beam.npinject = PTCL_PER_STEP
measurement_beam.npinject = PTCL_PER_STEP
w3d.l_inj_exact = True
# Specify thermal properties
background_beam.vthz = measurement_beam.vthz = np.sqrt(EMITTER_TEMP * kb_J / background_beam.mass)
background_beam.vthperp = measurement_beam.vthperp = np.sqrt(EMITTER_TEMP * kb_J / background_beam.mass)
top.lhalfmaxwellinject = 1 # inject z velocities as half Maxwellian
beam_current = sources.j_rd(EMITTER_TEMP, EMITTER_PHI) * cathode_area # steady state current in Amps
print('beam current expected: {}, current density {}'.format(beam_current, beam_current / cathode_area))
jcl = 4. / 9. * eps0 * sqrt(2. * echarge / background_beam.mass) \
* ACCEL_VOLTS ** 1.5 / gap_distance ** 2 * cathode_area
print('child-langmuir limit: {}, current density {}'.format(jcl, jcl / cathode_area))
background_beam.ibeam = measurement_beam.ibeam = beam_current
background_beam.a0 = measurement_beam.a0 = SOURCE_RADIUS_1
background_beam.b0 = measurement_beam.b0 = SOURCE_RADIUS_2
background_beam.ap0 = measurement_beam.ap0 = .0e0
background_beam.bp0 = measurement_beam.bp0 = .0e0
derivqty()
##############
# FIELD SOLVER
##############
# Add Uniform B_z field if turned on
if magnetic_field:
bz = np.zeros([w3d.nx, w3d.ny, w3d.nz])
bz[:, :, :] = magnetic_field
z_start = w3d.zmmin
z_stop = w3d.zmmax
top.ibpush = 2
addnewbgrd(z_start, z_stop, xs=w3d.xmmin, dx=(w3d.xmmax - w3d.xmmin), ys=w3d.ymmin, dy=(w3d.ymmax - w3d.ymmin),
nx=w3d.nx, ny=w3d.ny, nz=w3d.nz, bz=bz)
# Set up fieldsolver
f3d.mgtol = 1e-6
solverE = MultiGrid3D()
registersolver(solverE)
########################
# CONDUCTOR INSTALLATION
########################
if install_grid:
accel_grid, gl = create_grid(x_struts, y_struts, V_grid,
grid_height * gap_distance, strut_width, strut_height,
channel_width)
accel_grid.voltage = V_grid
# --- Anode Location
zplate = Z_MAX
# Create source conductors
if install_grid:
source = ZPlane(zcent=w3d.zmmin, zsign=-1., voltage=0., condid=2)
else:
source = ZPlane(zcent=w3d.zmmin, zsign=-1., voltage=0.)
# Create ground plate
total_rho = efficiency.tec_parameters['rho_load'][0]
if install_grid:
plate = ZPlane(zcent=zplate, condid=3)
circuit = ExternalCircuit(top, solverE, total_rho, collector_voltage, cathode_area * 1e4, plate, debug=False)
plate.voltage = circuit
# plate.voltage = collector_voltage
else:
plate = ZPlane(zcent=zplate)
circuit = ExternalCircuit(top, solverE, total_rho, collector_voltage, cathode_area * 1e4, plate, debug=False)
plate.voltage = circuit
# plate.voltage = collector_voltage
if install_grid:
installconductor(accel_grid)
installconductor(source, dfill=largepos)
installconductor(plate, dfill=largepos)
scraper = ParticleScraper([accel_grid, source, plate],
lcollectlpdata=True,
lsaveintercept=True)
scraper_dictionary = {'grid': 1, 'source': 2, 'collector': 3}
else:
installconductor(source, dfill=largepos)
installconductor(plate, dfill=largepos)
scraper = ParticleScraper([source, plate],
lcollectlpdata=True,
lsaveintercept=True)
scraper_dictionary = {'source': 1, 'collector': 2}
#############
# DIAGNOSTICS
#############
# Particle/Field diagnostic options
if particle_diagnostic_switch:
particleperiod = 250 # TEMP
particle_diagnostic_0 = ParticleDiagnostic(period=particleperiod, top=top, w3d=w3d,
species={species.name: species
for species in listofallspecies},
# if species.name == 'measurement'}, # TEMP
comm_world=comm_world, lparallel_output=False,
write_dir=diagDir[:-5])
installafterstep(particle_diagnostic_0.write)
if field_diagnostic_switch:
fieldperiod = 1000
efield_diagnostic_0 = FieldDiagnostic.ElectrostaticFields(solver=solverE, top=top, w3d=w3d,
comm_world=comm_world,
period=fieldperiod)
installafterstep(efield_diagnostic_0.write)
# Set externally derived parameters for efficiency calculation
efficiency.tec_parameters['A_em'][0] = cathode_area * 1e4 # cm**2
if install_grid:
efficiency.tec_parameters['occlusion'][0] = efficiency.calculate_occlusion(**efficiency.tec_parameters)
else:
efficiency.tec_parameters['occlusion'][0] = 0.0
##########################
# SOLVER SETTINGS/GENERATE
##########################
# prevent gist from starting upon setup
top.lprntpara = false
top.lpsplots = false
top.verbosity = -1 # Reduce solver verbosity
solverE.mgverbose = -1 # further reduce output upon stepping - prevents websocket timeouts in Jupyter notebook
init_iters = 20000
regular_iters = 200
init_tol = 1e-6
regular_tol = 1e-6
# Time Step
# Determine an appropriate time step based upon estimated final velocity
if install_grid:
vz_accel = sqrt(2. * abs(V_grid) * np.abs(background_beam.charge) / background_beam.mass)
else:
vz_accel = sqrt(2. * abs(collector_voltage) * np.abs(background_beam.charge) / background_beam.mass)
vzfinal = vz_accel + beam_beta * c
dt = dz / vzfinal
top.dt = dt
solverE.mgmaxiters = init_iters
solverE.mgtol = init_tol
package("w3d")
generate()
solverE.mgtol = regular_tol
solverE.mgmaxiters = regular_iters
print("weights (background) (measurement): {}, {}".format(background_beam.sw, measurement_beam.sw))
# Use rnpinject to set number of macroparticles emitted
background_beam.rnpinject = PTCL_PER_STEP
measurement_beam.rnpinject = 0 # measurement beam is off at start
##################
# CONTROL SEQUENCE
##################
# Run until steady state is achieved (flat current profile at collector) (measurement species turned on)
# Record data for effiency calculation
# Switch off measurement species and wait for simulation to clear (background species is switched on)
early_abort = 0 # If true will flag output data to notify
startup_time = 4 * gap_distance / vz_accel # ~4 crossing times to approach steady-state with external circuit
crossing_measurements = 10 # Number of crossing times to record for
steps_per_crossing = int(gap_distance / vz_accel / dt)
ss_check_interval = int(steps_per_crossing / 2.)
ss_max_checks = 8 # Maximum number of of times to run steady-state check procedure before aborting
times = [] # Write out timing of cycle steps to file
clock = 0 # clock tracks the current, total simulation-runtime
# Run initial block of steps
record_time(stept, times, startup_time)
clock += times[-1]
stop_initialization = top.it # for diag file
print("Completed Initialization on Step {}\nInitialization run time: {}".format(top.it, times[-1]))
# Start checking for Steady State Operation
tol = 0.01
ss_flag = 0
check_count = 0 # Track number of times steady-state check performed
while ss_flag != 1 and check_count < ss_max_checks:
if (max_wall_time - clock) < times[-1]:
early_abort = 1
break
record_time(step, times, ss_check_interval*4)
clock += times[-1]
tstart = (top.it - ss_check_interval) * top.dt
_, current1 = plate.get_current_history(js=None, l_lost=1, l_emit=0,
l_image=0, tmin=tstart, tmax=None, nt=1)
current = np.sum(current1)
if np.abs(current) < 0.5 * efficiency.tec_parameters['occlusion'][0] * beam_current:
# If too little current is getting through run another check cycle
check_count += 1
print("Completed check {}, insufficient current, running again for {} steps".format(check_count,
ss_check_interval))
continue
ss_flag = 1
# print np.abs(current), 0.5 * efficiency.tec_parameters['occlusion'][0] * beam_current
# try:
# # If steady_state check initialized no need to do it again
# steady_state
# except NameError:
# # If this is the first pass with sufficient current then initialize the check
# if check_count == 0:
# # If the initial period was long enough to get current on collector then use that
# steady_state = SteadyState(top, plate, steps_per_crossing)
# else:
# # If we had to run several steady state checks with no current then just use the period with current
# steady_state = SteadyState(top, plate, ss_check_interval)
#
# ss_flag = steady_state(steps_per_crossing)
check_count += 1
stop_ss_check = top.it # For diag file
# If there was a failure to reach steady state after specified number of checks then pass directly end
if check_count == ss_max_checks:
early_abort = -1
crossing_measurements = 0
print("Failed to reach steady state. Aborting simulation.")
else:
# Start Steady State Operation
print(" Steady State Reached.\nStarting efficiency "
"recording for {} crossing times.\nThis will be {} steps".format(crossing_measurements,
steps_per_crossing * crossing_measurements))
# particle_diagnostic_0.period = steps_per_crossing #TEMP commented out
# Switch to measurement beam species
measurement_beam.rnpinject = PTCL_PER_STEP
background_beam.rnpinject = 0
# Install Zcrossing Diagnostic
ZCross = ZCrossingParticles(zz=grid_height * gap_distance / 200., laccumulate=1)
emitter_flux = []
crossing_wall_time = times[-1] * steps_per_crossing / ss_check_interval # Estimate wall time for one crossing
print('crossing_wall_time estimate: {}, for {} steps'.format(crossing_wall_time, steps_per_crossing))
print('wind-down loop time estimate: {}, for {} steps'.format(crossing_wall_time *
steps_per_crossing / ss_check_interval,
ss_check_interval))
for sint in range(crossing_measurements):
# Kill the loop and proceed to writeout if we don't have time to complete the loop
if (max_wall_time - clock) < crossing_wall_time:
early_abort = 2
break
record_time(step, times, steps_per_crossing)
clock += times[-1]
# Re-evaluate time for next loop
crossing_wall_time = times[-1]
# Record velocities of emitted particles for later KE calculation
velocity_array = np.array([ZCross.getvx(js=measurement_beam.js),
ZCross.getvy(js=measurement_beam.js),
ZCross.getvz(js=measurement_beam.js)]).transpose()
# velocity_array = velocity_array[velocity_array[:, 2] >= 0.] # Filter particles moving to emitter
emitter_flux.append(velocity_array)
ZCross.clear() # Clear ZcrossingParticles memory
print("Measurement: {} of {} intervals completed. Interval run time: {} s".format(sint + 1,
crossing_measurements,
times[-1]))
stop_eff_calc = top.it # For diag file
# Run wind-down until measurement particles have cleared
measurement_beam.rnpinject = 0
background_beam.rnpinject = PTCL_PER_STEP
initial_population = measurement_beam.npsim[0]
measurement_tol = 0.03
# if particle_diagnostic_switch:
# particle_diagnostic_0.period = ss_check_interval
while measurement_beam.npsim[0] > measurement_tol * initial_population:
# Kill the loop and proceed to writeout if we don't have time to complete the loop
if (max_wall_time - clock) < crossing_wall_time * ss_check_interval / steps_per_crossing :
early_abort = 3
break
record_time(step, times, ss_check_interval)
clock += times[-1]
# Record velocities of emitted particles for later KE calculation
# Check is required here as measurement_beam particles will not always be passing through
if ZCross.getvx(js=measurement_beam.js).shape[0] > 0:
velocity_array = np.array([ZCross.getvx(js=measurement_beam.js),
ZCross.getvy(js=measurement_beam.js),
ZCross.getvz(js=measurement_beam.js)]).transpose()
print "Backwards particles: {}".format(np.where(velocity_array[:, 2] < 0.)[0].shape[0])
# velocity_array = velocity_array[velocity_array[:, 2] >= 0.] # Filter particles moving to emitter
emitter_flux.append(velocity_array)
ZCross.clear() # Clear ZcrossingParticles memory
print(" Wind-down: Taking {} steps, On Step: {}, {} Particles Left".format(ss_check_interval, top.it,
measurement_beam.npsim[0]))
stop_winddown = top.it # For diag file
######################
# CALCULATE EFFICIENCY
######################
try:
emitter_flux = np.vstack(emitter_flux)
except ValueError:
# If this triggered then measurement emission never took place
# Run took too long probably and abort took place
emitter_flux = np.array([[0., 0., 0.]])
# Find integrated charge on each conductor
surface_charge = analyze_scraped_particles(top, measurement_beam, solverE)
measured_charge = {}
for key in surface_charge:
# We can abuse the fact that js=0 for background species to filter it from the sum
measured_charge[key] = np.sum(surface_charge[key][:, 1] * surface_charge[key][:, 3])
# Set derived parameters from simulation
efficiency.tec_parameters['run_time'][0] = crossing_measurements * steps_per_crossing * dt
if crossing_measurements == 0:
# Set to large value to force all powers and currents to zero
efficiency.tec_parameters['run_time'][0] = 1e20
# Find total number of measurement particles that were emitted
total_macroparticles = measurement_beam.npsim[0] + np.sum([measured_charge[key] for key in surface_charge])
efficiency.tec_parameters['J_em'][0] = e * (total_macroparticles - measured_charge[scraper_dictionary['source']]) \
* measurement_beam.sw / \
efficiency.tec_parameters['run_time'][0] / efficiency.tec_parameters['A_em'][0]
# If grid isn't being used then J_grid will not be in scraper dict
try:
efficiency.tec_parameters['J_grid'][0] = e * measured_charge[scraper_dictionary['grid']] * measurement_beam.sw / \
efficiency.tec_parameters['run_time'][0] / \
(efficiency.tec_parameters['occlusion'][0] *
efficiency.tec_parameters['A_em'][0])
except KeyError:
efficiency.tec_parameters['J_grid'][0] = 0.0
efficiency.tec_parameters['J_ec'][0] = e * measured_charge[scraper_dictionary['collector']] * measurement_beam.sw / \
efficiency.tec_parameters['run_time'][0] / efficiency.tec_parameters['A_em'][0]
efficiency.tec_parameters['P_em'][0] = efficiency.calculate_power_flux(emitter_flux, measurement_beam.sw,
efficiency.tec_parameters['phi_em'][0],
**efficiency.tec_parameters)
# Efficiency calculation
print("Efficiency")
efficiency_result = efficiency.calculate_efficiency(**efficiency.tec_parameters)
print("Overall Efficiency: {}".format(efficiency_result['eta']))
print("Total steps: {}".format(top.it))
######################
# FINAL RUN STATISTICS
######################
if comm_world.rank == 0:
if not os.path.exists('diags_id{}'.format(run_id)):
os.makedirs('diags_id{}'.format(run_id))
np.save('iv_data.npy', np.array([circuit.current_history, circuit.voltage_history]))
write_parameter_file(run_attributes, filename='diags_id{}/'.format(run_id))
filename = 'efficiency_id{}.h5'.format(str(run_id))
with h5.File(os.path.join('diags_id{}'.format(run_id), filename), 'w') as h5file:
# TODO: Add current history
eff_group = h5file.create_group('/efficiency')
run_group = h5file.create_group('/attributes')
scrap_group = h5file.create_group('/scraper')
h5file.attrs['complete'] = early_abort
for key in efficiency_result:
eff_group.attrs[key] = efficiency_result[key]
for key in efficiency.tec_parameters:
eff_group.attrs[key] = efficiency.tec_parameters[key]
for key in run_attributes:
run_group.attrs[key] = run_attributes[key]
run_group.attrs['dt'] = top.dt
run_group.attrs['stop_initialization'] = stop_initialization
run_group.attrs['stop_ss_check'] = stop_ss_check
run_group.attrs['stop_eff_calc'] = stop_eff_calc
run_group.attrs['stop_winddown'] = stop_winddown
# for key, value in scraper_dictionary.iteritems():
# scrap_group.attrs[key] = measured_charge[value]
#
inv_scraper_dict = {value: key for key, value in scraper_dictionary.iteritems()}
for cond in solverE.conductordatalist:
cond_objs = cond[0]
scrap_group.attrs[inv_scraper_dict[cond_objs.condid]] = measured_charge[cond_objs.condid]
_, bckgrnd_current = cond_objs.get_current_history(js=0, l_lost=1, l_emit=0,
l_image=0, tmin=None, tmax=None, nt=top.it)
_, msrmnt_current = cond_objs.get_current_history(js=1, l_lost=1, l_emit=0,
l_image=0, tmin=None, tmax=None, nt=top.it)
scrap_group.create_dataset('{}_background'.format(inv_scraper_dict[cond_objs.condid]),
data=bckgrnd_current)
scrap_group.create_dataset('{}_measurement'.format(inv_scraper_dict[cond_objs.condid]),
data=msrmnt_current)
h5file.create_dataset('times', data=times)
beam_current = sources.cl_limit(CATHODE_PHI, ANODE_WF, GRID_BIAS,
PLATE_SPACING) * cathode_area
beam.ibeam = beam_current
beam.a0 = SOURCE_RADIUS_1
beam.b0 = SOURCE_RADIUS_2
w3d.l_inj_exact = True
elif USER_INJECT == 3:
#Thermionic injection
#Set injection flag
top.inject = 6 # 1 means constant; 2 means space-charge limited injection;# 6 means user-specified
beam_current = sources.j_rd(
CATHODE_TEMP,
CATHODE_PHI) * cathode_area #steady state current in Amps
beam.ibeam = beam_current
beam.a0 = SOURCE_RADIUS_1
beam.b0 = SOURCE_RADIUS_2
myInjector = injectors.injectorUserDefined(beam, CATHODE_TEMP,
CHANNEL_WIDTH, Z_PART_MIN,
PTCL_PER_STEP)
installuserinjection(myInjector.inject_thermionic)
# These must be set for user injection
top.ainject = 1.0
top.binject = 1.0
derivqty()
def main(injection_type,
cathode_temperature,
cathode_workfunction,
anode_workfunction,
anode_voltage,
gate_voltage,
lambdaR,
beta,
srefprob,
drefprob,
reflection_scheme,
gap_voltage,
dgap,
dt,
nsteps,
particles_per_step,
file_path,
fieldperiod=100,
particleperiod=1000,
reflections=True):
settings = deepcopy(locals())
############################
# Domain / Geometry / Mesh #
############################
# Grid geometry
w = 1.6e-3 # full hexagon width
a = w / 2.0 # inner wall width
b = 80e-6 # thickness
r = np.sqrt(3) / 2. * w # distance between two opposite walls
d = np.sqrt(b**2 / 4. + b**2) #
h = 0.2e-3 # height of grid (length in z)
PLATE_SPACING = dgap
CHANNEL_WIDTH_X = w + 2 * d + a
CHANNEL_WIDTH_Y = 2 * r + 2 * b
# Dimensions
X_MAX = +CHANNEL_WIDTH_X / 2.
X_MIN = -X_MAX
Y_MAX = +CHANNEL_WIDTH_Y / 2.
Y_MIN = -Y_MAX
Z_MAX = PLATE_SPACING
Z_MIN = 0.
# Grid parameters
NUM_X = 100
NUM_Y = 100
NUM_Z = 25
# z step size
dx = (X_MAX - X_MIN) / NUM_X
dy = (Y_MAX - Y_MIN) / NUM_Y
dz = (Z_MAX - Z_MIN) / NUM_Z
print(" --- (xmin, ymin, zmin) = ({}, {}, {})".format(X_MIN, Y_MIN, Z_MIN))
print(" --- (xmax, ymax, zmax) = ({}, {}, {})".format(X_MAX, Y_MAX, Z_MAX))
print(" --- (dx, dy, dz) = ({}, {}, {})".format(dx, dy, dz))
# Solver Geometry and Boundaries
# Specify solver geometry
w3d.solvergeom = w3d.XYZgeom
# Set field boundary conditions
w3d.bound0 = dirichlet
w3d.boundnz = dirichlet
w3d.boundxy = periodic
# Particles boundary conditions
top.pbound0 = absorb
top.pboundnz = absorb
top.pboundxy = periodic
# Set mesh boundaries
w3d.xmmin = X_MIN
w3d.xmmax = X_MAX
w3d.ymmin = Y_MIN
w3d.ymmax = Y_MAX
w3d.zmmin = Z_MIN
w3d.zmmax = Z_MAX
# Set mesh cell counts
w3d.nx = NUM_X
w3d.ny = NUM_Y
w3d.nz = NUM_Z
################
# FIELD SOLVER #
################
magnetic_field = True
if magnetic_field:
bz = np.zeros([w3d.nx, w3d.ny, w3d.nz])
bz[:, :, :] = 200e-3
z_start = w3d.zmmin
z_stop = w3d.zmmax
top.ibpush = 2
addnewbgrd(z_start,
z_stop,
xs=w3d.xmmin,
dx=(w3d.xmmax - w3d.xmmin),
ys=w3d.ymmin,
dy=(w3d.ymmax - w3d.ymmin),
nx=w3d.nx,
ny=w3d.ny,
nz=w3d.nz,
bz=bz)
# Set up fieldsolver
f3d.mgtol = 1e-6
solverE = MultiGrid3D()
registersolver(solverE)
###############################
# PARTICLE INJECTION SETTINGS #
###############################
volts_on_conductor = gap_voltage
# INJECTION SPECIFICATION
USER_INJECT = injection_type
# Cathode and anode settings
CATHODE_TEMP = cathode_temperature
CATHODE_PHI = cathode_workfunction
ANODE_WF = anode_workfunction # Can be used if vacuum level is being set
CONDUCTOR_VOLTS = volts_on_conductor # ACCEL_VOLTS used for velocity and CL calculations
# Emitted species
background_beam = Species(type=Electron, name='background')
# Reflected species
reflected_electrons = Species(type=Electron, name='reflected')
# Emitter area and position
SOURCE_RADIUS_1 = 0.5 * CHANNEL_WIDTH_X # a0 parameter - X plane
SOURCE_RADIUS_2 = 0.5 * CHANNEL_WIDTH_Y # b0 parameter - Y plane
Z_PART_MIN = dz / 1000. # starting particle z value
# Compute cathode area for geomtry-specific current calculations
if (w3d.solvergeom == w3d.XYZgeom):
# For 3D cartesion geometry only
cathode_area = 4. * SOURCE_RADIUS_1 * SOURCE_RADIUS_2
else:
# Assume 2D XZ geometry
cathode_area = 2. * SOURCE_RADIUS_1 * 1.
# If using the XZ geometry, set so injection uses the same geometry
top.linj_rectangle = (w3d.solvergeom == w3d.XZgeom
or w3d.solvergeom == w3d.XYZgeom)
# Returns velocity beam_beta (in units of beta) for which frac of emitted particles have v < beam_beta * c
beam_beta = sources.compute_cutoff_beta(CATHODE_TEMP, frac=0.99)
PTCL_PER_STEP = particles_per_step
if USER_INJECT == 1:
CURRENT_MODIFIER = 0.5 # Factor to multiply CL current by when setting beam current
# Constant current density - beam transverse velocity fixed to zero, very small longitduinal velocity
# Set injection flag
top.inject = 1 # 1 means constant; 2 means space-charge limited injection;# 6 means user-specified
top.npinject = PTCL_PER_STEP
beam_current = 4. / 9. * eps0 * sqrt(2. * echarge / background_beam.mass) \
* CONDUCTOR_VOLTS ** 1.5 / PLATE_SPACING ** 2 * cathode_area
background_beam.ibeam = beam_current * CURRENT_MODIFIER
background_beam.a0 = SOURCE_RADIUS_1
background_beam.b0 = SOURCE_RADIUS_2
background_beam.ap0 = .0e0
background_beam.bp0 = .0e0
w3d.l_inj_exact = True
# Initial velocity settings (5% of c)
vrms = np.sqrt(1 - 1 / (0.05 / 511e3 + 1)**2) * 3e8
top.vzinject = vrms
if USER_INJECT == 2:
# SC limited Thermionic injection
top.inject = 2
# Set both beams to same npinject to keep weights the same
background_beam.npinject = PTCL_PER_STEP
top.finject = [1.0, 0.0]
w3d.l_inj_exact = True
# Specify thermal properties
background_beam.vthz = np.sqrt(CATHODE_TEMP * kb_J /
background_beam.mass)
background_beam.vthperp = np.sqrt(CATHODE_TEMP * kb_J /
background_beam.mass)
top.lhalfmaxwellinject = 1 # inject z velocities as half Maxwellian
beam_current = sources.j_rd(
CATHODE_TEMP,
CATHODE_PHI) * cathode_area # steady state current in Amps
print('beam current expected: {}, current density {}'.format(
beam_current, beam_current / cathode_area))
jcl = 0.
if gap_voltage > 0.:
jcl = 4. / 9. * eps0 * sqrt(2. * echarge / background_beam.mass) \
* CONDUCTOR_VOLTS ** 1.5 / PLATE_SPACING ** 2 * cathode_area
print('child-langmuir limit: {}, current density {}'.format(
jcl, jcl / cathode_area))
background_beam.ibeam = beam_current
background_beam.a0 = SOURCE_RADIUS_1
background_beam.b0 = SOURCE_RADIUS_2
background_beam.ap0 = .0e0
background_beam.bp0 = .0e0
if USER_INJECT == 4:
w3d.l_inj_exact = True
w3d.l_inj_user_particles_v = True
# Schottky model
top.inject = 1
top.ninject = 1
top.lhalfmaxwellinject = 1 # inject z velocities as half Maxwellian
top.zinject = np.asarray([Z_PART_MIN])
top.ainject = np.asarray([SOURCE_RADIUS_1])
top.binject = np.asarray([SOURCE_RADIUS_2])
top.finject = np.asarray([[1.0, 0.0]])
electric_field = 0
delta_w = np.sqrt(e**3 * electric_field / (4 * np.pi * eps0))
A0 = 1.20173e6
AR = A0 * lambdaR
background_beam.a0 = SOURCE_RADIUS_1
background_beam.b0 = SOURCE_RADIUS_2
background_beam.ap0 = .0e0
background_beam.bp0 = .0e0
background_beam.vthz = np.sqrt(CATHODE_TEMP * kb_J /
background_beam.mass)
background_beam.vthperp = np.sqrt(CATHODE_TEMP * kb_J /
background_beam.mass)
# use Richardson current to estimate particle weight
rd_current = AR * CATHODE_TEMP**2 * np.exp(
-(CATHODE_PHI * e) / (CATHODE_TEMP * k)) * cathode_area
electrons_per_second = rd_current / e
electrons_per_step = electrons_per_second * dt
background_beam.sw = electrons_per_step / PTCL_PER_STEP
def schottky_emission():
# schottky emission at cathode side
global num_particles_res
Ez = solverE.getez()
Ez_mean = np.mean(Ez[:, :, 0])
if w3d.inj_js == background_beam.js:
delta_w = 0.
if Ez_mean < 0.:
delta_w = np.sqrt(beta * e**3 * np.abs(Ez_mean) /
(4 * np.pi * eps0))
rd_current = AR * CATHODE_TEMP**2 * np.exp(
-(CATHODE_PHI * e - delta_w) /
(CATHODE_TEMP * k)) * cathode_area
electrons_per_second = rd_current / e
electrons_per_step = electrons_per_second * top.dt
float_num_particles = electrons_per_step / background_beam.sw
num_particles = int(float_num_particles + num_particles_res +
np.random.rand())
num_particles_res += float_num_particles - num_particles
# --- inject np particles of species electrons1
# --- Create the particles on the surface
x = -background_beam.a0 + 2 * background_beam.a0 * np.random.rand(
num_particles)
y = -background_beam.b0 + 2 * background_beam.b0 * np.random.rand(
num_particles)
vz = np.random.rand(num_particles)
vz = np.maximum(1e-14 * np.ones_like(vz), vz)
vz = background_beam.vthz * np.sqrt(-2.0 * np.log(vz))
vrf = np.random.rand(num_particles)
vrf = np.maximum(1e-14 * np.ones_like(vrf), vrf)
vrf = background_beam.vthz * np.sqrt(-2.0 * np.log(vrf))
trf = 2 * np.pi * np.random.rand(num_particles)
vx = vrf * np.cos(trf)
vy = vrf * np.sin(trf)
# --- Setup the injection arrays
w3d.npgrp = num_particles
gchange('Setpwork3d')
# --- Fill in the data. All have the same z-velocity, vz1.
w3d.xt[:] = x
w3d.yt[:] = y
w3d.uxt[:] = vx
w3d.uyt[:] = vy
w3d.uzt[:] = vz
installuserparticlesinjection(schottky_emission)
derivqty()
print("weight:", background_beam.sw)
##########################
# CONDUCTOR INSTALLATION #
##########################
install_conductor = True
# --- Anode Location
zplate = Z_MAX
# Create source conductors
if install_conductor:
emitter = ZPlane(zcent=w3d.zmmin, zsign=-1., voltage=0., condid=2)
else:
emitter = ZPlane(zcent=w3d.zmmin, zsign=-1., voltage=0.)
# Create collector
if install_conductor:
collector = ZPlane(voltage=gap_voltage, zcent=zplate, condid=3)
else:
collector = ZPlane(voltage=gap_voltage, zcent=zplate)
# Create grid
if install_conductor:
# Offsets to make sure grid is centered after install
# Because case089 was not made with a hexagon at (0., 0.) it must be moved depending on STL file used and
# version of STLconductor
cxmin, cymin, czmin = -0.00260785012506, -0.00312974047847, 0.000135000009323
cxmax, cymax, czmax = 0.00339215015993, 0.00287025980651, 0.000335000018822
conductor = STLconductor(
"honeycomb_case0.89t_xycenter_zcen470.stl",
xcent=cxmin + (cxmax - cxmin) / 2.,
ycent=cymin + (cymax - cymin) /
2., #zcent=czmin + (czmax - czmin) / 2., disp=(0.,0.,1e-6),
verbose="on",
voltage=gate_voltage,
normalization_factor=dz,
condid=1)
if install_conductor:
installconductor(conductor, dfill=largepos)
installconductor(emitter, dfill=largepos)
installconductor(collector, dfill=largepos)
scraper_diode = ParticleScraper([emitter, collector],
lcollectlpdata=True,
lsaveintercept=True)
scraper_gate = ParticleScraper([conductor],
lcollectlpdata=True,
lsaveintercept=False)
scraper_dictionary = {1: 'grid', 2: 'emitter', 3: 'collector'}
else:
installconductor(emitter, dfill=largepos)
installconductor(collector, dfill=largepos)
scraper = ParticleScraper([emitter, collector],
lcollectlpdata=True,
lsaveintercept=True)
scraper_dictionary = {1: 'emitter', 2: 'collector'}
########################
# Hacked Grid Scraping #
########################
# Implements boxes with a reflection probability based on transparency attribute
# used to crudely emulate particles reflected from the STLconductor honeycomb which cannot provide
# scraper positions needed for reflection calculation
grid_reflections = True
if grid_reflections:
grid_front = Box(xcent=0.,
ycent=0.,
zcent=0.135e-3 - dz / 2.,
xsize=2 * (X_MAX - X_MIN),
ysize=2 * (Y_MAX - Y_MIN),
zsize=dz)
grid_back = Box(xcent=0.,
ycent=0.,
zcent=0.335e-3 + dz / 2,
xsize=2 * (X_MAX - X_MIN),
ysize=2 * (Y_MAX - Y_MIN),
zsize=dz)
scraper_front = ParticleScraperGrid(grid_front,
lcollectlpdata=True,
lsaveintercept=True)
scraper_back = ParticleScraperGrid(grid_back,
lcollectlpdata=True,
lsaveintercept=True)
# Fraction of particles expected to pass through the conductor (assuming they cross it in a single step)
scraper_front.transparency = 0.80
scraper_back.transparency = 0.80
# Directional scraper to prevent particles from being scraped coming out of honeycomb interior
scraper_front.directional_scraper = -1
scraper_back.directional_scraper = +1
#####################
# Diagnostics Setup #
#####################
efield_diagnostic_0 = FieldDiagnostic.ElectrostaticFields(
solver=solverE,
top=top,
w3d=w3d,
comm_world=comm_world,
period=fieldperiod,
write_dir=os.path.join(file_path, 'fields'))
installafterstep(efield_diagnostic_0.write)
particle_diagnostic_0 = ParticleDiagnostic(
period=particleperiod,
top=top,
w3d=w3d,
species={species.name: species
for species in listofallspecies},
comm_world=comm_world,
lparallel_output=False,
write_dir=file_path)
installafterstep(particle_diagnostic_0.write)
####################
# CONTROL SEQUENCE #
####################
# prevent gist from starting upon setup
top.lprntpara = false
top.lpsplots = false
top.verbosity = 1 # Reduce solver verbosity
solverE.mgverbose = 1 # further reduce output upon stepping - prevents websocket timeouts in Jupyter notebook
init_iters = 2000
regular_iters = 50
init_tol = 1e-5
regular_tol = 1e-6
# Time Step
top.dt = dt
# Define and install particle reflector
if reflections:
collector_reflector = ParticleReflector(scraper=scraper_diode,
conductor=collector,
spref=reflected_electrons,
srefprob=srefprob,
drefprob=drefprob,
refscheme=reflection_scheme)
installparticlereflector(collector_reflector)
print("reflection_scheme = " + reflection_scheme)
else:
print("reflections: off")
if grid_reflections:
reflector_front = ParticleReflector(scraper=scraper_front,
conductor=grid_front,
spref=reflected_electrons,
srefprob=srefprob,
drefprob=0.75,
refscheme=reflection_scheme)
installparticlereflector(reflector_front)
reflector_back = ParticleReflector(scraper=scraper_back,
conductor=grid_back,
spref=reflected_electrons,
srefprob=srefprob,
drefprob=0.75,
refscheme=reflection_scheme)
installparticlereflector(reflector_back)
# initialize field solver and potential field
solverE.mgmaxiters = init_iters
solverE.mgtol = init_tol
package("w3d")
generate()
# Specify particle weight for reflected_electrons
reflected_electrons.sw = background_beam.sw
print("weight: background_beam = {}, reflected = {}".format(
background_beam.sw, reflected_electrons.sw))
solverE.mgmaxiters = regular_iters
solverE.mgtol = regular_tol
step(nsteps)
####################
# Final Output #
####################
surface_charge = analyze_collected_charge(top, solverE)
if reflections:
reflected_charge = analyze_reflected_charge(top, [collector_reflector],
comm_world=comm_world)
if comm_world.rank == 0:
filename = os.path.join(
file_path, "all_charge_anodeV_{}.h5".format(anode_voltage))
diag_file = h5.File(filename, 'w')
# Write simulation parameters
for key, val in settings.items():
diag_file.attrs[key] = val
for dom_attr in [
'xmmin', 'xmmax', 'ymmin', 'ymmax', 'zmmin', 'zmmax', 'nx',
'ny', 'nz'
]:
diag_file.attrs[dom_attr] = eval('w3d.' + dom_attr)
# Record scraped particles into scraper group of file
scraper_data = diag_file.create_group('scraper')
for key, val in scraper_dictionary.items():
scraper_data.attrs[val] = key
for condid, cond_data in surface_charge.items():
cond_group = scraper_data.create_group('{}'.format(
scraper_dictionary[condid]))
for i, spec_dat in enumerate(cond_data):
cond_group.create_dataset(listofallspecies[i].name,
data=spec_dat)
# Record reflected particles into reflector group
if reflections:
reflector_data = diag_file.create_group('reflector')
for key in reflected_charge:
reflector_data.attrs[scraper_dictionary[key]] = key
for condid, ref_data in reflected_charge.items():
refl_group = reflector_data.create_group('{}'.format(
scraper_dictionary[condid]))
refl_group.create_dataset('reflected', data=ref_data)
diag_file.close()
def main(x_struts,
y_struts,
V_grid,
grid_height,
strut_width,
strut_height,
rho_ew,
T_em,
phi_em,
T_coll,
phi_coll,
rho_cw,
gap_distance,
rho_load,
run_id,
channel_width=100e-9,
injection_type=2,
magnetic_field=0.0,
random_seed=True,
install_grid=True,
install_circuit=True,
max_wall_time=1e9,
particle_diagnostic_switch=False,
field_diagnostic_switch=False,
lost_diagnostic_switch=False):
"""
Run a simulation of a gridded TEC.
Args:
x_struts: Number of struts that intercept the x-axis.
y_struts: Number of struts that intercept the y-axis
V_grid: Voltage to place on the grid in Volts.
grid_height: Distance from the emitter to the grid normalized by gap_distance, unitless.
strut_width: Transverse extent of the struts in meters.
strut_height: Longitudinal extent of the struts in meters.
rho_ew: Emitter side wiring resistivity, ohms*cm.
T_em: Emitter temperature, kelvin.
phi_em: Emitter work function, eV.
T_coll: Collector termperature, kelvin.
phi_coll: Collector work function, eV.
rho_cw: Collector side wiring resistivity, ohms*cm.
gap_distance: Distance from emitter to collector, meters.
rho_load: Load resistivity, ohms*cm.
run_id: Run ID. Mainly used for parallel optimization.
injection_type: 1: For constant current emission with only thermal velocity spread in z and CL limited emission.
2: For true thermionic emission. Velocity spread along all axes.
random_seed: True/False. If True, will force a random seed to be used for emission positions.
install_grid: True/False. If False the grid will not be installed. Results in simple parallel plate setup.
If False then phi_em - phi_coll specifies the voltage on the collector.
install_circuit: True/False. Include external circuit that will modulate gap voltage based on
current flow and contact potential between cathode/anode.
max_wall_time: Wall time to allow simulation to run for. Simulation is periodically checked and will halt if it
appears the next segment of the simulation will exceed max_wall_time. This is not guaranteed to
work since the guess is based on the run time up to that point.
Intended to be used when running on system with job manager.
particle_diagnostic_switch: True/False. Use openPMD compliant .h5 particle diagnostics.
field_diagnostic_switch: True/False. Use rswarp electrostatic .h5 field diagnostics (Maybe openPMD compliant?).
lost_diagnostic_switch: True/False. Enable collection of lost particle coordinates
with rswarp.diagnostics.parallel.save_lost_particles.
"""
# record inputs and set parameters
run_attributes = deepcopy(locals())
for key in run_attributes:
if key in efficiency.tec_parameters:
efficiency.tec_parameters[key][0] = run_attributes[key]
# set new random seed
if random_seed:
top.seedranf(randint(1, 1e9))
# Control for printing in parallel
if comm_world.size != 1:
synchronizeQueuedOutput_mpi4py(out=True, error=True)
if particle_diagnostic_switch or field_diagnostic_switch:
# Directory paths
diagDir = 'diags_id{}/hdf5/'.format(run_id)
field_base_path = 'diags_id{}/fields/'.format(run_id)
diagFDir = {
'magnetic': 'diags_id{}/fields/magnetic'.format(run_id),
'electric': 'diags_id{}/fields/electric'.format(run_id)
}
# Cleanup command if directories already exist
if comm_world.rank == 0:
cleanupPrevious(diagDir, diagFDir)
load_balance = LoadBalancer()
######################
# DOMAIN/GEOMETRY/MESH
######################
# Dimensions
X_MAX = +channel_width / 2.
X_MIN = -X_MAX
Y_MAX = +channel_width / 2.
Y_MIN = -Y_MAX
Z_MAX = gap_distance
Z_MIN = 0.
# TODO: cells in all dimensions reduced by 10x for testing, will need to verify if this is reasonable (TEMP)
# Grid parameters
dx_want = 5e-9
dy_want = 5e-9
dz_want = 5e-9
NUM_X = int(round(channel_width / dx_want)) # 20 #128 #10
NUM_Y = int(round(channel_width / dy_want)) # 20 #128 #10
NUM_Z = int(round(gap_distance / dz_want))
# mesh spacing
dz = (Z_MAX - Z_MIN) / NUM_Z
dx = channel_width / NUM_X
dy = channel_width / NUM_Y
print "Channel width: {}, DX = {}".format(channel_width, dx)
print "Channel width: {}, DY = {}".format(channel_width, dy)
print "Channel length: {}, DZ = {}".format(gap_distance, dz)
# Solver Geometry and Boundaries
# Specify solver geometry
w3d.solvergeom = w3d.XYZgeom
# Set field boundary conditions
w3d.bound0 = neumann
w3d.boundnz = dirichlet
w3d.boundxy = periodic
# Particles boundary conditions
top.pbound0 = absorb
top.pboundnz = absorb
top.pboundxy = periodic
# Set mesh boundaries
w3d.xmmin = X_MIN
w3d.xmmax = X_MAX
w3d.ymmin = Y_MIN
w3d.ymmax = Y_MAX
w3d.zmmin = 0.
w3d.zmmax = Z_MAX
# Set mesh cell counts
w3d.nx = NUM_X
w3d.ny = NUM_Y
w3d.nz = NUM_Z
#############################
# PARTICLE INJECTION SETTINGS
#############################
# Cathode and anode settings
EMITTER_TEMP = T_em
EMITTER_PHI = phi_em # work function in eV
COLLECTOR_PHI = phi_coll # Can be used if vacuum level is being set
ACCEL_VOLTS = V_grid # ACCEL_VOLTS used for velocity and CL calculations
collector_voltage = phi_em - phi_coll
# Emitted species
background_beam = Species(type=Electron, name='background')
measurement_beam = Species(type=Electron, name='measurement')
# Emitter area and position
SOURCE_RADIUS_1 = 0.5 * channel_width # a0 parameter - X plane
SOURCE_RADIUS_2 = 0.5 * channel_width # b0 parameter - Y plane
Z_PART_MIN = dz / 1000. # starting particle z value
# Compute cathode area for geomtry-specific current calculations
if (w3d.solvergeom == w3d.XYZgeom):
# For 3D cartesion geometry only
cathode_area = 4. * SOURCE_RADIUS_1 * SOURCE_RADIUS_2
else:
# Assume 2D XZ geometry
cathode_area = 2. * SOURCE_RADIUS_1 * 1.
# If using the XZ geometry, set so injection uses the same geometry
top.linj_rectangle = (w3d.solvergeom == w3d.XZgeom
or w3d.solvergeom == w3d.XYZgeom)
# Returns velocity beam_beta (in units of beta) for which frac of emitted particles have v < beam_beta * c
beam_beta = sources.compute_cutoff_beta(EMITTER_TEMP, frac=0.99)
PTCL_PER_STEP = 100
if injection_type == 1:
CURRENT_MODIFIER = 0.5 # Factor to multiply CL current by when setting beam current
# Constant current density - beam transverse velocity fixed to zero, very small longitduinal velocity
# Set injection flag
top.inject = 1 # 1 means constant; 2 means space-charge limited injection;# 6 means user-specified
top.npinject = PTCL_PER_STEP
beam_current = 4. / 9. * eps0 * sqrt(2. * echarge / background_beam.mass) \
* ACCEL_VOLTS ** 1.5 / gap_distance ** 2 * cathode_area
background_beam.ibeam = beam_current * CURRENT_MODIFIER
background_beam.a0 = SOURCE_RADIUS_1
background_beam.b0 = SOURCE_RADIUS_2
background_beam.ap0 = .0e0
background_beam.bp0 = .0e0
w3d.l_inj_exact = True
# Initial velocity settings (5% of c)
vrms = np.sqrt(1 - 1 / (0.05 / 511e3 + 1)**2) * 3e8
top.vzinject = vrms
if injection_type == 2:
# True Thermionic injection
top.inject = 1
# Set both beams to same npinject to keep weights the same
background_beam.npinject = PTCL_PER_STEP
measurement_beam.npinject = PTCL_PER_STEP
w3d.l_inj_exact = True
# Specify thermal properties
background_beam.vthz = measurement_beam.vthz = np.sqrt(
EMITTER_TEMP * kb_J / background_beam.mass)
background_beam.vthperp = measurement_beam.vthperp = np.sqrt(
EMITTER_TEMP * kb_J / background_beam.mass)
top.lhalfmaxwellinject = 1 # inject z velocities as half Maxwellian
beam_current = sources.j_rd(
EMITTER_TEMP,
EMITTER_PHI) * cathode_area # steady state current in Amps
print('beam current expected: {}, current density {}'.format(
beam_current, beam_current / cathode_area))
jcl = 4. / 9. * eps0 * sqrt(2. * echarge / background_beam.mass) \
* ACCEL_VOLTS ** 1.5 / gap_distance ** 2 * cathode_area
print('child-langmuir limit: {}, current density {}'.format(
jcl, jcl / cathode_area))
background_beam.ibeam = measurement_beam.ibeam = beam_current
background_beam.a0 = measurement_beam.a0 = SOURCE_RADIUS_1
background_beam.b0 = measurement_beam.b0 = SOURCE_RADIUS_2
background_beam.ap0 = measurement_beam.ap0 = .0e0
background_beam.bp0 = measurement_beam.bp0 = .0e0
derivqty()
##############
# FIELD SOLVER
##############
# Add Uniform B_z field if turned on
if magnetic_field:
bz = np.zeros([w3d.nx, w3d.ny, w3d.nz])
bz[:, :, :] = magnetic_field
z_start = w3d.zmmin
z_stop = w3d.zmmax
top.ibpush = 2
addnewbgrd(z_start,
z_stop,
xs=w3d.xmmin,
dx=(w3d.xmmax - w3d.xmmin),
ys=w3d.ymmin,
dy=(w3d.ymmax - w3d.ymmin),
nx=w3d.nx,
ny=w3d.ny,
nz=w3d.nz,
bz=bz)
# Set up fieldsolver
f3d.mgtol = 1e-6
solverE = MultiGrid3D()
registersolver(solverE)
########################
# CONDUCTOR INSTALLATION
########################
if install_grid:
accel_grid, gl = create_grid(x_struts, y_struts, V_grid,
grid_height * gap_distance, strut_width,
strut_height, channel_width)
accel_grid.voltage = V_grid
# --- Anode Location
zplate = Z_MAX
# Create source conductors
if install_grid:
source = ZPlane(zcent=w3d.zmmin, zsign=-1., voltage=0., condid=2)
else:
source = ZPlane(zcent=w3d.zmmin, zsign=-1., voltage=0.)
# Create ground plate
total_rho = efficiency.tec_parameters['rho_load'][0]
if install_grid:
plate = ZPlane(zcent=zplate, condid=3)
if install_circuit:
circuit = ExternalCircuit(top,
solverE,
total_rho,
collector_voltage,
cathode_area * 1e4,
plate,
debug=False)
plate.voltage = circuit
# plate.voltage = collector_voltage
else:
plate = ZPlane(zcent=zplate)
if install_circuit:
circuit = ExternalCircuit(top,
solverE,
total_rho,
collector_voltage,
cathode_area * 1e4,
plate,
debug=False)
plate.voltage = circuit
# plate.voltage = collector_voltage
if install_grid:
installconductor(accel_grid)
installconductor(source, dfill=largepos)
installconductor(plate, dfill=largepos)
scraper = ParticleScraper([accel_grid, source, plate],
lcollectlpdata=True,
lsaveintercept=True)
scraper_dictionary = {'grid': 1, 'source': 2, 'collector': 3}
else:
installconductor(source, dfill=largepos)
installconductor(plate, dfill=largepos)
scraper = ParticleScraper([source, plate],
lcollectlpdata=True,
lsaveintercept=True)
scraper_dictionary = {'source': 1, 'collector': 2}
#############
# DIAGNOSTICS
#############
# Particle/Field diagnostic options
if particle_diagnostic_switch:
particleperiod = 250 # TEMP
particle_diagnostic_0 = ParticleDiagnostic(
period=particleperiod,
top=top,
w3d=w3d,
species={species.name: species
for species in listofallspecies},
# if species.name == 'measurement'}, # TEMP
comm_world=comm_world,
lparallel_output=False,
write_dir=diagDir[:-5])
installafterstep(particle_diagnostic_0.write)
if field_diagnostic_switch:
fieldperiod = 1000
efield_diagnostic_0 = FieldDiagnostic.ElectrostaticFields(
solver=solverE,
top=top,
w3d=w3d,
comm_world=comm_world,
period=fieldperiod)
installafterstep(efield_diagnostic_0.write)
# Set externally derived parameters for efficiency calculation
efficiency.tec_parameters['A_em'][0] = cathode_area * 1e4 # cm**2
if install_grid:
efficiency.tec_parameters['occlusion'][
0] = efficiency.calculate_occlusion(**efficiency.tec_parameters)
else:
efficiency.tec_parameters['occlusion'][0] = 0.0
##########################
# SOLVER SETTINGS/GENERATE
##########################
# prevent gist from starting upon setup
top.lprntpara = false
top.lpsplots = false
top.verbosity = -1 # Reduce solver verbosity
solverE.mgverbose = -1 # further reduce output upon stepping - prevents websocket timeouts in Jupyter notebook
init_iters = 20000
regular_iters = 200
init_tol = 1e-6
regular_tol = 1e-6
# Time Step
# Determine an appropriate time step based upon estimated final velocity
if install_grid:
vz_accel = sqrt(2. * abs(V_grid) * np.abs(background_beam.charge) /
background_beam.mass)
else:
vz_accel = sqrt(2. * abs(collector_voltage) *
np.abs(background_beam.charge) / background_beam.mass)
vzfinal = vz_accel + beam_beta * c
dt = dz / vzfinal
top.dt = dt
solverE.mgmaxiters = init_iters
solverE.mgtol = init_tol
package("w3d")
generate()
solverE.mgtol = regular_tol
solverE.mgmaxiters = regular_iters
print("weights (background) (measurement): {}, {}".format(
background_beam.sw, measurement_beam.sw))
# Use rnpinject to set number of macroparticles emitted
background_beam.rnpinject = PTCL_PER_STEP
measurement_beam.rnpinject = 0 # measurement beam is off at start
##################
# CONTROL SEQUENCE
##################
# Run until steady state is achieved (flat current profile at collector) (measurement species turned on)
# Record data for effiency calculation
# Switch off measurement species and wait for simulation to clear (background species is switched on)
early_abort = 0 # If true will flag output data to notify
startup_time = 4 * gap_distance / vz_accel # ~4 crossing times to approach steady-state with external circuit
crossing_measurements = 10 # Number of crossing times to record for
steps_per_crossing = int(gap_distance / vz_accel / dt)
ss_check_interval = int(steps_per_crossing / 2.)
ss_max_checks = 8 # Maximum number of of times to run steady-state check procedure before aborting
times = [] # Write out timing of cycle steps to file
clock = 0 # clock tracks the current, total simulation-runtime
# Run initial block of steps
record_time(stept, times, startup_time)
clock += times[-1]
stop_initialization = top.it # for diag file
print("Completed Initialization on Step {}\nInitialization run time: {}".
format(top.it, times[-1]))
# Start checking for Steady State Operation
tol = 0.01
ss_flag = 0
check_count = 0 # Track number of times steady-state check performed
while ss_flag != 1 and check_count < ss_max_checks:
if (max_wall_time - clock) < times[-1]:
early_abort = 1
break
record_time(step, times, ss_check_interval * 4)
clock += times[-1]
tstart = (top.it - ss_check_interval) * top.dt
_, current1 = plate.get_current_history(js=None,
l_lost=1,
l_emit=0,
l_image=0,
tmin=tstart,
tmax=None,
nt=1)
current = np.sum(current1)
if np.abs(
current
) < 0.5 * efficiency.tec_parameters['occlusion'][0] * beam_current:
# If too little current is getting through run another check cycle
check_count += 1
print(
"Completed check {}, insufficient current, running again for {} steps"
.format(check_count, ss_check_interval))
continue
ss_flag = 1
# print np.abs(current), 0.5 * efficiency.tec_parameters['occlusion'][0] * beam_current
# try:
# # If steady_state check initialized no need to do it again
# steady_state
# except NameError:
# # If this is the first pass with sufficient current then initialize the check
# if check_count == 0:
# # If the initial period was long enough to get current on collector then use that
# steady_state = SteadyState(top, plate, steps_per_crossing)
# else:
# # If we had to run several steady state checks with no current then just use the period with current
# steady_state = SteadyState(top, plate, ss_check_interval)
#
# ss_flag = steady_state(steps_per_crossing)
check_count += 1
stop_ss_check = top.it # For diag file
# If there was a failure to reach steady state after specified number of checks then pass directly end
if check_count == ss_max_checks:
early_abort = -1
crossing_measurements = 0
print("Failed to reach steady state. Aborting simulation.")
else:
# Start Steady State Operation
print(
" Steady State Reached.\nStarting efficiency "
"recording for {} crossing times.\nThis will be {} steps".format(
crossing_measurements,
steps_per_crossing * crossing_measurements))
# particle_diagnostic_0.period = steps_per_crossing #TEMP commented out
# Switch to measurement beam species
measurement_beam.rnpinject = PTCL_PER_STEP
background_beam.rnpinject = 0
# Install Zcrossing Diagnostic
ZCross = ZCrossingParticles(zz=grid_height * gap_distance / 200.,
laccumulate=1)
emitter_flux = []
crossing_wall_time = times[
-1] * steps_per_crossing / ss_check_interval # Estimate wall time for one crossing
print('crossing_wall_time estimate: {}, for {} steps'.format(
crossing_wall_time, steps_per_crossing))
print('wind-down loop time estimate: {}, for {} steps'.format(
crossing_wall_time * steps_per_crossing / ss_check_interval,
ss_check_interval))
for sint in range(crossing_measurements):
# Kill the loop and proceed to writeout if we don't have time to complete the loop
if (max_wall_time - clock) < crossing_wall_time:
early_abort = 2
break
record_time(step, times, steps_per_crossing)
clock += times[-1]
# Re-evaluate time for next loop
crossing_wall_time = times[-1]
# Record velocities of emitted particles for later KE calculation
velocity_array = np.array([
ZCross.getvx(js=measurement_beam.js),
ZCross.getvy(js=measurement_beam.js),
ZCross.getvz(js=measurement_beam.js)
]).transpose()
# velocity_array = velocity_array[velocity_array[:, 2] >= 0.] # Filter particles moving to emitter
emitter_flux.append(velocity_array)
ZCross.clear() # Clear ZcrossingParticles memory
print(
"Measurement: {} of {} intervals completed. Interval run time: {} s"
.format(sint + 1, crossing_measurements, times[-1]))
stop_eff_calc = top.it # For diag file
# Run wind-down until measurement particles have cleared
measurement_beam.rnpinject = 0
background_beam.rnpinject = PTCL_PER_STEP
initial_population = measurement_beam.npsim[0]
measurement_tol = 0.03
# if particle_diagnostic_switch:
# particle_diagnostic_0.period = ss_check_interval
while measurement_beam.npsim[0] > measurement_tol * initial_population:
# Kill the loop and proceed to writeout if we don't have time to complete the loop
if (max_wall_time - clock
) < crossing_wall_time * ss_check_interval / steps_per_crossing:
early_abort = 3
break
record_time(step, times, ss_check_interval)
clock += times[-1]
# Record velocities of emitted particles for later KE calculation
# Check is required here as measurement_beam particles will not always be passing through
if ZCross.getvx(js=measurement_beam.js).shape[0] > 0:
velocity_array = np.array([
ZCross.getvx(js=measurement_beam.js),
ZCross.getvy(js=measurement_beam.js),
ZCross.getvz(js=measurement_beam.js)
]).transpose()
print "Backwards particles: {}".format(
np.where(velocity_array[:, 2] < 0.)[0].shape[0])
# velocity_array = velocity_array[velocity_array[:, 2] >= 0.] # Filter particles moving to emitter
emitter_flux.append(velocity_array)
ZCross.clear() # Clear ZcrossingParticles memory
print(" Wind-down: Taking {} steps, On Step: {}, {} Particles Left".
format(ss_check_interval, top.it, measurement_beam.npsim[0]))
stop_winddown = top.it # For diag file
######################
# CALCULATE EFFICIENCY
######################
try:
emitter_flux = np.vstack(emitter_flux)
except ValueError:
# If this triggered then measurement emission never took place
# Run took too long probably and abort took place
emitter_flux = np.array([[0., 0., 0.]])
# Find integrated charge on each conductor
surface_charge = analyze_scraped_particles(top, measurement_beam, solverE)
measured_charge = {}
for key in surface_charge:
# We can abuse the fact that js=0 for background species to filter it from the sum
measured_charge[key] = np.sum(surface_charge[key][:, 1] *
surface_charge[key][:, 3])
# Set derived parameters from simulation
efficiency.tec_parameters['run_time'][
0] = crossing_measurements * steps_per_crossing * dt
if crossing_measurements == 0:
# Set to large value to force all powers and currents to zero
efficiency.tec_parameters['run_time'][0] = 1e20
# Find total number of measurement particles that were emitted
total_macroparticles = measurement_beam.npsim[0] + np.sum(
[measured_charge[key] for key in surface_charge])
efficiency.tec_parameters['J_em'][0] = e * (total_macroparticles - measured_charge[scraper_dictionary['source']]) \
* measurement_beam.sw / \
efficiency.tec_parameters['run_time'][0] / efficiency.tec_parameters['A_em'][0]
# If grid isn't being used then J_grid will not be in scraper dict
try:
efficiency.tec_parameters['J_grid'][0] = e * measured_charge[scraper_dictionary['grid']] * measurement_beam.sw / \
efficiency.tec_parameters['run_time'][0] / \
(efficiency.tec_parameters['occlusion'][0] *
efficiency.tec_parameters['A_em'][0])
except KeyError:
efficiency.tec_parameters['J_grid'][0] = 0.0
efficiency.tec_parameters['J_ec'][0] = e * measured_charge[scraper_dictionary['collector']] * measurement_beam.sw / \
efficiency.tec_parameters['run_time'][0] / efficiency.tec_parameters['A_em'][0]
efficiency.tec_parameters['P_em'][0] = efficiency.calculate_power_flux(
emitter_flux, measurement_beam.sw,
efficiency.tec_parameters['phi_em'][0], **efficiency.tec_parameters)
# Efficiency calculation
print("Efficiency")
efficiency_result = efficiency.calculate_efficiency(
**efficiency.tec_parameters)
print("Overall Efficiency: {}".format(efficiency_result['eta']))
print("Total steps: {}".format(top.it))
######################
# FINAL RUN STATISTICS
######################
if comm_world.rank == 0:
if not os.path.exists('diags_id{}'.format(run_id)):
os.makedirs('diags_id{}'.format(run_id))
np.save('iv_data.npy',
np.array([circuit.current_history, circuit.voltage_history]))
write_parameter_file(run_attributes,
filename='diags_id{}/'.format(run_id))
filename = 'efficiency_id{}.h5'.format(str(run_id))
with h5.File(os.path.join('diags_id{}'.format(run_id), filename),
'w') as h5file:
# TODO: Add current history
eff_group = h5file.create_group('/efficiency')
run_group = h5file.create_group('/attributes')
scrap_group = h5file.create_group('/scraper')
h5file.attrs['complete'] = early_abort
for key in efficiency_result:
eff_group.attrs[key] = efficiency_result[key]
for key in efficiency.tec_parameters:
eff_group.attrs[key] = efficiency.tec_parameters[key]
for key in run_attributes:
run_group.attrs[key] = run_attributes[key]
run_group.attrs['dt'] = top.dt
run_group.attrs['stop_initialization'] = stop_initialization
run_group.attrs['stop_ss_check'] = stop_ss_check
run_group.attrs['stop_eff_calc'] = stop_eff_calc
run_group.attrs['stop_winddown'] = stop_winddown
# for key, value in scraper_dictionary.iteritems():
# scrap_group.attrs[key] = measured_charge[value]
#
inv_scraper_dict = {
value: key
for key, value in scraper_dictionary.iteritems()
}
for cond in solverE.conductordatalist:
cond_objs = cond[0]
scrap_group.attrs[inv_scraper_dict[
cond_objs.condid]] = measured_charge[cond_objs.condid]
_, bckgrnd_current = cond_objs.get_current_history(js=0,
l_lost=1,
l_emit=0,
l_image=0,
tmin=None,
tmax=None,
nt=top.it)
_, msrmnt_current = cond_objs.get_current_history(js=1,
l_lost=1,
l_emit=0,
l_image=0,
tmin=None,
tmax=None,
nt=top.it)
scrap_group.create_dataset('{}_background'.format(
inv_scraper_dict[cond_objs.condid]),
data=bckgrnd_current)
scrap_group.create_dataset('{}_measurement'.format(
inv_scraper_dict[cond_objs.condid]),
data=msrmnt_current)
h5file.create_dataset('times', data=times)
beam_current = sources.cl_limit(CATHODE_PHI, ANODE_WF, GRID_BIAS, PLATE_SPACING)*cathode_area
beam.ibeam = beam_current
beam.a0 = SOURCE_RADIUS_1
beam.b0 = SOURCE_RADIUS_2
w3d.l_inj_exact = True
elif USER_INJECT == 3:
#Thermionic injection
#Set injection flag
top.inject = 6 # 1 means constant; 2 means space-charge limited injection;# 6 means user-specified
beam_current = sources.j_rd(CATHODE_TEMP,CATHODE_PHI)*cathode_area #steady state current in Amps
beam.ibeam = beam_current
beam.a0 = SOURCE_RADIUS_1
beam.b0 = SOURCE_RADIUS_2
myInjector = injectors.injectorUserDefined(beam, CATHODE_TEMP, CHANNEL_WIDTH, Z_PART_MIN, PTCL_PER_STEP)
installuserinjection(myInjector.inject_thermionic)
# These must be set for user injection
top.ainject = 1.0
top.binject = 1.0
derivqty()