def initialize_ode_solver(self, y_0, yd_0, t_0):
model = Implicit_Problem(self.residual, y_0, yd_0, t_0)
model.handle_result = self.handle_result
solver = IDA(model)
solver.rtol = self.solver_rtol
solver.atol = self.solver_atol # * np.array([100, 10, 1e-4, 1e-4])
solver.inith = 0.1 # self.wind.R_g / const.C
solver.maxh = self.dt * self.wind.R_g / const.C
solver.report_continuously = True
solver.display_progress = False
solver.verbosity = 50 # 50 = quiet
solver.num_threads = 3
# solver.display_progress = True
return solver
def main():
res_class = eval(inputs.test_type)
# plt.close('all')
t_count = time.time()
SV_0 = sol_init.SV_0
SV_dot_0 = np.zeros_like(SV_0)
t_0 = 0.
t_f = 3600. / inputs.C_rate #63006.69900049 93633
algvar = sol_init.algvar
atol = np.ones_like(SV_0) * 1e-6
atol[cat.ptr_vec['eps_S8']] = 1e-15
atol[cat.ptr_vec['eps_Li2S']] = 1e-15
atol[cat.ptr_vec['rho_k_el']] = 1e-16 # 1e-16 for Bessler
rtol = 1e-6
sim_output = 50
rtol_ch = 1e-6
atol_ch = np.ones_like(SV_0) * 1e-6
atol_ch[cat.ptr_vec['eps_S8']] = 1e-15
atol_ch[cat.ptr_vec['eps_Li2S']] = 1e-15
atol_ch[cat.ptr_vec['rho_k_el']] = 1e-30
rate_tag = str(inputs.C_rate) + "C"
# if 'cascade' in inputs.ctifile:
# ncols = 2 + inputs.flag_req
# else:
ncols = 1 + inputs.flag_req
fig, axes = plt.subplots(sharey="row",
figsize=(9, 12),
nrows=3,
ncols=(ncols) * inputs.n_cycles,
num=1)
plt.subplots_adjust(wspace=0.15, hspace=0.4)
fig.text(0.35,
0.85,
rate_tag,
fontsize=20,
bbox=dict(facecolor='white', alpha=0.5))
# Set up user function to build figures based on inputs
"----------Equilibration----------"
print('\nEquilibrating...')
# Set external current to 0 for equilibration
cat.set_i_ext(0)
# cat.nucleation_flag = 1
# Create problem object
bat_eq = res_class(res_class.res_fun, SV_0, SV_dot_0, t_0)
bat_eq.external_event_detection = True
bat_eq.algvar = algvar
# Create simulation object
sim_eq = IDA(bat_eq)
sim_eq.atol = atol
sim_eq.rtol = rtol
sim_eq.verbosity = sim_output
sim_eq.make_consistent('IDA_YA_YDP_INIT')
t_eq, SV_eq, SV_dot_eq = sim_eq.simulate(t_f)
# Put solution into pandas dataframe with labeled columns
SV_eq_df = label_columns(t_eq, SV_eq, an.npoints, sep.npoints, cat.npoints)
# Obtain tag strings for dataframe columns
tags = tag_strings(SV_eq_df)
# plot_sim(tags, SV_eq_df, 'Equilibrating', 0, fig, axes)
t_equilibrate = time.time() - t_count
print("Equilibration time = ", t_equilibrate, '\n')
print('Done equilibrating\n')
for cycle_num in np.arange(0, inputs.n_cycles):
"------------Discharging-------------"
print('Discharging...')
# cat.np_L = inputs.np_Li2S_init
cat.nucleation_flag = np.zeros((inputs.npoints_cathode, 1))
# New initial conditions from previous simulation
if cycle_num == 0:
# SV_0 = SV_0
SV_0 = SV_eq[-1, :]
# SV_dot_0 = SV_dot_0
SV_dot_0 = SV_dot_eq[-1, :]
else:
SV_0 = SV_0_cycle
SV_dot_0 = SV_dot_0_cycle
# Set external current
cat.set_i_ext(cat.i_ext_amp)
# Update problem instance initial conditions
bat_dch = res_class(res_class.res_fun, SV_0, SV_dot_0, t_0)
bat_dch.external_event_detection = True
bat_dch.algvar = algvar
# Re-initialize simulation object
sim_dch = IDA(bat_dch)
sim_dch.atol = atol
sim_dch.rtol = rtol
# sim_dch.maxh = 57
sim_dch.verbosity = sim_output
sim_dch.make_consistent('IDA_YA_YDP_INIT')
t_dch, SV_dch, SV_dot_dch = sim_dch.simulate(131865.32)
SV_dch_df = label_columns(t_dch, SV_dch, an.npoints, sep.npoints,
cat.npoints)
# Obtain tag strings for dataframe columns
tags = tag_strings(SV_dch_df)
plot_sim(tags, SV_dch_df, 'Discharging', 0 + 2 * cycle_num, fig, axes)
# plot_meanPS(SV_dch_df, tags, 'Discharging')
print('Done Discharging\n')
"--------Re-equilibration---------"
if inputs.flag_req == 1:
print('Re-equilibrating...')
# New initial conditions from previous simulation
SV_0 = SV_dch[-1, :]
SV_dot_0 = SV_dot_dch[-1, :]
# Set external current
cat.set_i_ext(0)
# Update problem instance initial conditions
bat_req = res_class(res_class.res_fun, SV_0, SV_dot_0, t_0)
bat_req.external_event_detection = True
bat_req.algvar = algvar
# Re-initialize simulation object
sim_req = IDA(bat_req)
sim_req.atol = atol
sim_req.rtol = rtol
sim_req.verbosity = sim_output
sim_req.make_consistent('IDA_YA_YDP_INIT')
t_req, SV_req, SV_dot_req = sim_req.simulate(t_f)
SV_req_df = label_columns(t_req, SV_req, an.npoints, sep.npoints,
cat.npoints)
# plot_sim(tags, SV_req_df, 'Re-Equilibrating', 1, fig, axes)
print('Done re-equilibrating\n')
else:
SV_req = SV_dch
SV_dot_req = SV_dot_dch
"-----------Charging-----------"
if 'dog' in inputs.ctifile:
print('Charging...')
SV_0 = SV_req[-1, :]
SV_dot_0 = SV_dot_req[-1, :]
SV_0[cat.ptr_vec['eps_S8']] = cat.eps_cutoff
cat.set_i_ext(-cat.i_ext_amp)
# Update problem instance initial conditions
bat_ch = res_class(res_class.res_fun, SV_0, SV_dot_0, t_0)
bat_ch.external_event_detection = True
bat_ch.algvar = algvar
# Re-initialize simulation object
sim_ch = IDA(bat_ch)
sim_ch.atol = atol_ch
sim_ch.rtol = rtol_ch
if cycle_num > 0:
sim_ch.maxh = 0.1
sim_ch.verbosity = sim_output
sim_ch.make_consistent('IDA_YA_YDP_INIT')
t_ch, SV_ch, SV_dot_ch = sim_ch.simulate(t_f)
SV_ch_df = label_columns(t_ch, SV_ch, an.npoints, sep.npoints,
cat.npoints)
plot_sim(tags, SV_ch_df, 'Charging',
1 + inputs.flag_req + 2 * cycle_num, fig, axes)
plot_meanPS(SV_ch_df, tags, 'Charging')
SV_ch_df = 0
#
# print('Max S_8(e) concentration = ', max(SV_ch[:, 6]))
# SV_0_cycle = SV_ch[-1, :]
# SV_dot_0_cycle = SV_ch[-1, :]
print('Done Charging\n')
exp_data_01C = pd.read_csv(r'0.1C Data.csv', header=None)
exp_data_05C = pd.read_csv(r'0.5C Data.csv', header=None)
exp_data_1C = pd.read_csv(r'1C Data.csv', header=None)
Bessler = pd.read_csv(r'Bessler Dennis Data.csv', header=None)
SV_copy = SV_dch_df.copy()
SV_copy.loc[:, 'Time'] *= -cat.i_ext_amp * inputs.A_cat / 3600 / (
cat.m_S_tot_0)
"Set up your figure"
fig = plt.figure(3)
ax = fig.add_axes([0.2, 0.2, 0.6, 0.75])
fig.set_size_inches((10., 5.0))
# ax2 = ax.twinx()
"Formatting for the figure:"
fs = 20 #font size for plots
lw = 3.0 #line width for plots
# font = plt.matplotlib.font_manager.FontProperties(family='Times New Roman',size=fs-1)
for tick in ax.xaxis.get_major_ticks():
tick.label1.set_fontsize(fs)
tick.label1.set_fontname('Times New Roman')
for tick in ax.yaxis.get_major_ticks():
tick.label1.set_fontsize(fs)
tick.label1.set_fontname('Times New Roman')
color = matplotlib.cm.plasma(inputs.C_rate)
p1, = plt.plot(SV_copy.loc[:, 'Time'],
SV_copy.loc[:, 'Phi_ed1'],
'k-',
linewidth=lw)
# p2, = plt.plot(exp_data_01C.iloc[:,0], exp_data_01C.iloc[:,1], 'ro')
# p3, = plt.plot(exp_data_05C.iloc[:,0], exp_data_05C.iloc[:,1], 'co')
# p4, = plt.plot(exp_data_1C.iloc[:,0], exp_data_1C.iloc[:,1], 'ko')
p5, = plt.plot(Bessler.iloc[:, 0], Bessler.iloc[:, 1], 'mo', ms=4)
plt.xlim((0, 1700))
plt.xticks([0, 400, 800, 1200, 1600])
plt.ylim((1.8, 2.6))
plt.ylabel(r'Cell Voltage $[\mathrm{V}]$',
fontstyle='normal',
fontname='Times new Roman',
fontsize=fs + 2,
labelpad=5.0)
plt.xlabel(
r'Capacity $[\mathrm{Ah} \hspace{0.5} \mathrm{kg}^{-1}_{\mathrm{sulfur}}]$',
fontstyle='normal',
fontname='Times new Roman',
fontsize=fs + 2,
labelpad=5.0)
# plt.legend(["Discharge", "Charge"])
file_name_dch = 'dch' + str(inputs.C_rate) + "C_" + inputs.mech + '.csv'
SV_dch = SV_dch_df.copy()
SV_dch.loc[:,
'Time'] *= -cat.i_ext_amp * inputs.A_cat / 3600 / (cat.m_S_0 +
cat.m_S_el)
SV_dch.to_csv(file_name_dch, index=False, header=True)
t_elapsed = time.time() - t_count
print('t_cpu=', t_elapsed, '\n')
return SV_eq_df, SV_dch_df, SV_ch_df, tags
# Volume fraction of SEI per cell
res[-1] = SV_dot[-1]
return res
"""------------------------------------------------------------------------"""
# Set up problem instance
SEI_1D = Implicit_Problem(residual, SV_0, SV_dot_0, t_0)
# Define simulation parameters
simulation = IDA(SEI_1D) # Create simulation instance
simulation.atol = 1e-6 # Solver absolute tolerance
simulation.rtol = 1e-6 # Solver relative tolerance
simulation.maxh = 0.1 # Solver max step size
# Set simulation end time, slope flag (for anode voltage cycle), and run simulation
t_f = ((phi_bounds[1] - phi_anode_0)/R)*5
#ncp_list = np.arange(0, t_f, 0.15)
#ncp = 10000
# Run simulation
t, SV, SV_dot = simulation.simulate(t_f)
# %% Organize data and plot
SV_df = pd.DataFrame(SV)
def main():
res_class = eval(inputs.test_type)
# plt.close('all')
t_count = time.time()
SV_0 = sol_init.SV_0
SV_dot_0 = np.zeros_like(SV_0)
t_0 = 0.
t_f = 3600. / inputs.C_rate
algvar = sol_init.algvar
atol = np.ones_like(SV_0) * 1e-5
atol[cat.ptr_vec['eps_S8']] = 1e-25
atol[cat.ptr_vec['eps_Li2S']] = 1e-25
atol[cat.ptr_vec['rho_k_el']] = 1e-25
# atol = 1e-30;
rtol = 1e-4
sim_output = 50
rate_tag = str(inputs.C_rate) + "C"
fig, axes = plt.subplots(sharey="row", figsize=(9, 12), nrows=3, ncols=1)
plt.subplots_adjust(wspace=0.15, hspace=0.4)
fig.text(0.15,
0.8,
rate_tag,
fontsize=20,
bbox=dict(facecolor='white', alpha=0.5))
# Set up user function to build figures based on inputs
"----------Equilibration----------"
print('\nEquilibrating...')
# Set external current to 0 for equilibration
cat.set_i_ext(0)
# Create problem object
bat_eq = res_class(res_class.res_fun, SV_0, SV_dot_0, t_0)
bat_eq.external_event_detection = True
bat_eq.algvar = algvar
# Create simulation object
sim_eq = IDA(bat_eq)
sim_eq.atol = atol
sim_eq.rtol = rtol
sim_eq.verbosity = sim_output
sim_eq.make_consistent('IDA_YA_YDP_INIT')
t_eq, SV_eq, SV_dot_eq = sim_eq.simulate(t_f)
# Put solution into pandas dataframe with labeled columns
SV_eq_df = label_columns(t_eq, SV_eq, an.npoints, sep.npoints, cat.npoints)
# SV_eq_df = []
# Obtain tag strings for dataframe columns
tags = tag_strings(SV_eq_df)
# plot_sim(tags, SV_eq_df, 'Equilibrating', 0, fig, axes)
# print(SV_eq_df[tags['rho_el'][4:10]].iloc[-1])
print('Done equilibrating\n')
"------------Discharging-------------"
print('Discharging...')
# New initial conditions from previous simulation
SV_0 = SV_eq[-1, :]
SV_dot_0 = SV_dot_eq[-1, :]
# Set external current
cat.set_i_ext(cat.i_ext_amp)
# Update problem instance initial conditions
bat_dch = res_class(res_class.res_fun, SV_0, SV_dot_0, t_0)
bat_dch.external_event_detection = True
bat_dch.algvar = algvar
# Re-initialize simulation object
sim_dch = IDA(bat_dch)
sim_dch.atol = atol
sim_dch.rtol = rtol
sim_dch.maxh = 5
sim_dch.verbosity = sim_output
sim_dch.make_consistent('IDA_YA_YDP_INIT')
t_dch, SV_dch, SV_dot_dch = sim_dch.simulate(t_f)
# if hasattr(cathode, 'get_tflag'):
# t_flag_ch = cathode.get_tflag
SV_dch_df = label_columns(t_dch, SV_dch, an.npoints, sep.npoints,
cat.npoints)
# SV_dch_df = []
# Obtain tag strings for dataframe columns
# tags = tag_strings(SV_ch_df)
plot_sim(tags, SV_dch_df, 'Discharging', 1, fig, axes)
plot_meanPS(SV_dch_df, tags)
print('Done Discharging\n')
"--------Re-equilibration---------"
# if inputs.flag_req == 1:
#
# print('Re-equilibrating...')
#
# # New initial conditions from previous simulation
# SV_0 = SV_ch[-1, :]
# SV_dot_0 = SV_dot_ch[-1, :]
#
# # Set external current
# cat.set_i_ext(0)
#
# # Update problem instance initial conditions
# bat_req = res_class(res_class.res_fun, SV_0, SV_dot_0, t_0)
# bat_req.external_event_detection = True
# bat_req.algvar = algvar
#
# # Re-initialize simulation object
# sim_req = IDA(bat_req)
# sim_req.atol = atol
# sim_req.rtol = rtol
# sim_req.verbosity = sim_output
# sim_req.make_consistent('IDA_YA_YDP_INIT')
#
# t_req, SV_req, SV_dot_req = sim_req.simulate(t_f)
#
# SV_req_df = label_columns(t_req, SV_req, an.npoints, sep.npoints, cat.npoints)
#
## plot_sim(tags, SV_req_df, 'Re-Equilibrating', 2-1, fig, axes)
#
# print('Done re-equilibrating\n')
# else:
# SV_req = SV_ch
# SV_dot_req = SV_dot_ch
"-----------Charging-----------"
# print('Charging...')
#
# SV_0 = SV_req[-1, :]
# SV_dot_0 = SV_dot_req[-1, :]
#
# cat.set_i_ext(-cat.i_ext_amp)
#
# # Update problem instance initial conditions
# bat_dch = res_class(res_class.res_fun, SV_0, SV_dot_0, t_0)
# bat_dch.external_event_detection = True
# bat_dch.algvar = algvar
#
# # Re-initialize simulation object
# sim_dch = IDA(bat_dch)
# sim_dch.atol = atol
# sim_dch.rtol = rtol
# sim_dch.verbosity = sim_output
# sim_dch.make_consistent('IDA_YA_YDP_INIT')
#
# t_dch, SV_dch, SV_dot_dch = sim_dch.simulate(t_f)
#
## if hasattr(cathode, 'get_tflag'):
## t_flag_dch = cathode.get_tflag
#
# SV_dch_df = label_columns(t_dch, SV_dch, an.npoints, sep.npoints, cat.npoints)
#
# plot_sim(tags, SV_dch_df, 'Charging', 3, fig, axes)
#
# print('Done Charging\n')
t_elapsed = time.time() - t_count
print('t_cpu=', t_elapsed, '\n')
return SV_eq_df, SV_dch_df, tags #SV_eq_df, SV_req_df #, SV_dch_df
