def parameter_sweep(sweep_input, debug=True):
start = time.time()
if sweep_input['m_dot'] > 0.0 and sweep_input['r_f'] > 0.0:
# create system
inputs = ICAES2.get_default_inputs()
# uncertainty parameters - general
inputs['T_grad_m'] = sweep_input['T_grad_m'] # [C/km]
inputs['p_hydro_grad'] = sweep_input['p_hydro_grad'] # [MPa/km]
inputs['p_frac_grad'] = sweep_input['p_frac_grad'] # [MPa/km]
inputs['loss_m_air'] = sweep_input['loss_m_air'] # [-]
# uncertainty parameters - location-specifc
inputs['depth'] = sweep_input['depth_m'] # [m]
inputs['h'] = sweep_input['thickness_m'] # [m]
inputs['phi'] = sweep_input['porosity'] # [-]
inputs['k'] = sweep_input['permeability_mD'] # [mD]
# machinery polytropic index
inputs['n_cmp1'] = sweep_input['n_cmp1']
inputs['n_exp1'] = sweep_input['n_exp1']
# design inputs
inputs['m_dot'] = sweep_input['m_dot'] # [kg/s]
inputs['r_f'] = sweep_input['r_f'] # [m]
# all other parameters - taken as default
system = ICAES2(inputs=inputs)
# run single cycle and analyze
try:
system.single_cycle()
results = system.analyze_performance()
end = time.time()
results['solve_time'] = end - start
# print out RTE
print(results['RTE'])
# combine inputs and results to return in single series
single_output = pd.concat([sweep_input, results])
except:
single_output = sweep_input
else:
single_output = sweep_input
return single_output
def sensitivity(sensitivity_input):
start = time.time()
# create system
inputs = ICAES2.get_default_inputs()
for variable in sensitivity_input.index:
inputs[variable] = sensitivity_input[variable]
system = ICAES2(inputs=inputs)
# run single cycle and analyze
system.single_cycle()
results = system.analyze_performance()
end = time.time()
results['solve_time'] = end - start
# combine inputs and results to return in single series
single_output = pd.concat([sensitivity_input, results])
return single_output
k = 236.1424 # [mD]
capacity = 200 # [MW]
duration = 24 # [hr]
r_w = 0.41 / 2.0 # [m]
m_dot = 619.6078491 # mass flow rate [kg/s]
r_f = 82.64452305 # formation radius [m]
timesteps_to_try = [
1, 2, 5, 10, 50, 75, 100, 125, 200, 300, 400, 500, 600, 700, 800, 900, 1000
]
df = pd.DataFrame()
for timesteps in timesteps_to_try:
# create system
inputs = ICAES2.get_default_inputs()
inputs['depth'] = depth
inputs['h'] = h
inputs['phi'] = phi
inputs['k'] = k
inputs['r_f'] = r_f
inputs['r_w'] = r_w
inputs['m_dot'] = m_dot
inputs['steps'] = timesteps
system = ICAES2(inputs=inputs)
# run single cycle and analyze
system.single_cycle()
results = system.analyze_performance()
RTE = results['RTE']
def parameter_sweep(sweep_input, debug=True):
start = time.time()
# convert inputs to model units
kW_out = sweep_input['capacity_MW'] * 1e3
kWh_out = sweep_input['capacity_MW'] * sweep_input['duration_hr'] * 1e3
if debug:
print('\nStarting sizing')
print("kW_out (desired) : " + str(round(kW_out, 3)))
print("kWh_out (desired): " + str(round(kWh_out, 3)))
print("\nDepth (m) : " + str(sweep_input['depth_m']))
print("Thickness (m) : " + str(sweep_input['thickness_m']))
print("Porosity (-) : " + str(sweep_input['porosity']))
print("Permeability (mD): " + str(sweep_input['permeability_mD']))
print("Well radius (m) : " + str(sweep_input['r_w']))
# allowable calculation error
error = 1e-6
# maximum number of iterations
count_max = 200
# initial guesses
m_dot = 10.0
r_f = 10.0
# initial results
kW_out_actual = 0.0
kWh_out_actual = 0.0
count = 0
while abs(kW_out_actual - kW_out) / kW_out + abs(
kWh_out_actual - kWh_out) / kWh_out > error:
if debug:
print("\nIteration : " + str(count))
print("m_dot : " + str(round(m_dot, 3)))
print("r_f : " + str(round(r_f, 3)))
# create system
inputs = ICAES2.get_default_inputs()
# user inputs
inputs['depth'] = sweep_input['depth_m'] # porosity depth [m]
inputs['h'] = sweep_input['thickness_m'] # porosity thickness [m]
inputs['phi'] = sweep_input['porosity'] # formation porosity [-]
inputs['k'] = sweep_input[
'permeability_mD'] # formation permeability [mD]
inputs['r_w'] = sweep_input['r_w'] # well radius [m]
# machinery polytropic index
inputs['n_cmp1'] = sweep_input['n_cmp1']
inputs['n_exp1'] = sweep_input['n_exp1']
# current guess/iteration
inputs['m_dot'] = m_dot # [kg/s]
inputs['r_f'] = r_f # [m]
system = ICAES2(inputs=inputs)
# run single cycle and analyze
system.single_cycle()
results = system.analyze_performance()
# extract results of interest
kW_out_actual = results['kW_out_avg']
kWh_out_actual = results['kWh_out']
# update guesses
tau = 0.5 # solver time constant
m_dot = m_dot * (1.0 + tau * (kW_out - kW_out_actual) / kW_out
) # m_dot linearly linked to kW
r_f = r_f * (1.0 + tau**2 * (kWh_out - kWh_out_actual) / kWh_out_actual
) # r_f exponentially linked to kWh
count = count + 1
if count > count_max: # sizing unsuccessful
results['errors'] = True
break
if debug:
print("MW_out_avg : " + str(round(kW_out_actual / 1e3, 3)))
print("MWh_out : " + str(round(kWh_out_actual / 1e3, 3)))
end = time.time()
results['solve_time'] = end - start
results['iterations'] = count
results['m_dot'] = m_dot
results['r_f'] = r_f
# print out RTE
print(results['RTE'])
# combine inputs and results to return in single series
single_output = pd.concat([sweep_input, results])
return single_output