Merge pull request #147 from abachma2/global

Global Sensitivity Analysis

input/haleu/sensitivity-analysis/global/README.md

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+# Global Sensitivity Analysis
+This directory contains files for performing global sensitivity 
+analysis on the transtion from US LWRs to advanced reactors. 
+There are three subdirectories, one for varying the build share 
+of each advanced reactor considered in this work. In each case 
+(i.e., subdirectory) the transition start time, LWR lifetime, 
+Xe-100 burnup, and MMR build share are varied, but the build share 
+of only advanced reactor is varied. The build share of only 
+one advanced reactor is varied at a time to prevent unphysical 
+combinations of advanced reactor build shares (e.g., 50% Xe-100, 
+50% MMR, and 50% VOYGR). 
+
+Each subdirectory contains two different Dakota input files:
+``list_sample.in`` and ``dakota.in``. The ``list_sample.in`` file 
+treats the MMR burnup as a continuous variable and the Xe-100 
+burnup as a discrete variable, with more points than previously 
+considered. This input file is run first, creating a ``.dat`` file 
+with data for the output metrics for each combination of input 
+parameters. This ``.dat`` file is then read by the ``dakota.in`` 
+input file to create a surrogate model of the data that treats all
+of the variables as continuous. The suurogate models created by 
+the ``dakota.in`` file are used for variance decomposition to 
+calculate Sobol' indices. Two surrogate model fits are used: quadratic 
+and gaussian. 

input/haleu/sensitivity-analysis/global/mmr_share/dakota.in

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+# Global analysis for variations in Xe-100 build share
+environment, 
+	tabular_data 
+		tabular_data_file = 'mmr_share_gaussian.dat'
+	output_file = 'mmr_share_gaussian.out'
+	write_restart = 'mmr_share_gaussian.rst'
+
+method,						
+    sampling
+        sample_type = lhs
+        variance_based_decomp
+	samples = 4000 # 100*parameters*(response+2)
+	seed = 112822
+
+model
+  id_model = 'SURR'
+  surrogate global
+  import_points_file = 'mmr_share_list.dat' annotated
+  #quadratic
+  gaussian_process surfpack
+    metrics = "root_mean_squared" "sum_abs" "rsquared"
+
+variables,					
+    active all
+    uniform_uncertain = 5
+    lower_bounds = 721  0     0            41           28
+    upper_bounds =  901   50    50           90           185
+    descriptors = 'ts' 'lwr' 'mmr_share' 'mmr_burnup' 'xe100_burnup'   
+
+interface,							
+	fork							
+	  asynchronous
+            evaluation_concurrency =  36
+          analysis_drivers = 'python mmr_share_driver.py'
+
+
+responses,				
+	descriptors = 'enr_u' 'haleu' 'swu' 'haleu_swu' 'waste' 'feed'
+	response_functions = 6
+	  scalar_responses = 6
+	no_gradients
+	no_hessians
+

input/haleu/sensitivity-analysis/global/mmr_share/list_sample.in

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+# Global analysis for variations in MMR build share
+environment, 
+	tabular_data 
+		tabular_data_file = 'mmr_share_list.dat'
+	output_file = 'mmr_share_list.out'
+	write_restart = 'mmr_share_list.rst'
+
+method,						
+  sampling 
+    sample_type = lhs
+    samples = 4000 
+    seed = 120622
+
+
+variables,
+    active all 
+    continuous_design = 4 
+      lower_bounds =  721   0     0            41
+      upper_bounds =  901   50    50           90 
+      descriptors = 'ts' 'lwr' 'mmr_share' 'mmr_burnup'
+    discrete_design_set 
+      integer = 1
+      elements_per_variable = 16 
+      elements = 28 32 48 56 64 72 84 96 112 120 128 140 151 160 168 185
+      descriptors = 'xe100_burnup'
+
+interface,							
+	fork							
+	  asynchronous
+            evaluation_concurrency = 44
+          analysis_drivers = 'python mmr_share_driver.py'
+
+
+responses,				
+	descriptors = 'enr_u' 'haleu' 'swu' 'haleu_swu' 'waste' 'feed'
+	response_functions = 6
+	  scalar_responses = 6
+	no_gradients
+	no_hessians
+

input/haleu/sensitivity-analysis/global/mmr_share/mmr_share_driver.py

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+import numpy as np
+import subprocess
+import dakota.interfacing as di
+import sys
+import os
+from turtle import up
+sys.path.append('../../../../../scripts')
+import create_AR_DeployInst as cdi
+import output_metrics as oup
+import dakota_input as inp
+# ----------------------------
+# Parse Dakota parameters file
+# ----------------------------
+
+params, results = di.read_parameters_file()
+
+# -------------------------------
+# Convert and send to Cyclus
+# -------------------------------
+
+# Edit Cyclus input file
+cyclus_template = 'mmr_share_input.xml.in'
+scenario_name = ('ts_' + str(int(params['ts'])) +
+                '_lwr_' + str(int(params['lwr'])) + 
+                '_mmr_share_' + str(int(params['mmr_share'])) +
+                '_xe100_burnup_' + str(int(params['xe100_burnup'])) +
+                '_mmr_burnup_' + str(int(params['mmr_burnup'])))
+variable_dict = {'handle': scenario_name,
+                 'ts': str(int(params['ts'])),
+                 'lwr': str(int(params['lwr'])),
+                 'mmr_share': str(int(params['mmr_share'])),
+                 'mmr_burnup':str(int(params['mmr_burnup'])),
+                 'xe100_burnup':str(int(params['xe100_burnup']))}
+output_xml = "./cyclus-files/" + scenario_name + ".xml"
+output_sqlite = './cyclus-files/' + scenario_name + '.sqlite'
+
+mmr_lifetime = int(np.round(params['mmr_burnup'],2)*1331.73/15/30)
+mmr_burnup_dict = {'mmr_lifetime':mmr_lifetime}
+inp.render_input('../mmr_burnup_input.xml.in', 
+                 mmr_burnup_dict, 
+                 "./cyclus-files/mmr_" + str(int(params['mmr_burnup'])) + ".xml")
+
+xe100_cycles = {28:(1,7), 32:(1,8), 48:(2,6), 56:(2,7), 64:(2,8),
+                72:(3,6), 84:(3,7), 96:(3,8), 112:(4,7), 128:(4,8),
+                120:(5,6), 140:(5,7), 160:(5,8), 151:(6,6), 168:(6,7),
+                185:(6,8)}
+xe100_burnup_dict = {'xe100_cycle':xe100_cycles[int(params['xe100_burnup'])][1],
+                     'xe100_n_assem':xe100_cycles[int(params['xe100_burnup'])][0],
+                     'xe100_assem':1675.44/xe100_cycles[int(params['xe100_burnup'])][0]
+                    }
+inp.render_input("../xe100_burnup_input.xml.in",
+                 xe100_burnup_dict,
+                 "./cyclus-files/xe100_" + str(int(params['xe100_burnup'])) +
+                 ".xml")
+
+inp.render_input(cyclus_template, variable_dict, output_xml)
+
+# Create DeployInst for LWRs
+DI_dict = cdi.write_lwr_deployinst(
+    params['lwr'],
+    "../../../inputs/united_states/buildtimes/" +
+    "UNITED_STATES_OF_AMERICA/deployinst.xml",
+    "../../../../../database/lwr_power_order.txt")
+cdi.write_deployinst(DI_dict, './cyclus-files/' + 
+                              scenario_name +
+                              '_deployinst.xml')
+
+# Create DeployInst for advanced reactors
+duration = 1500
+reactor_prototypes = {'Xe-100': (76, 720), 
+                      'MMR': (5, mmr_lifetime), 
+                      'VOYGR': (73, 720)}
+demand_equation = np.zeros(duration)
+demand_equation[int(params['ts']):] = 87198.156
+lwr_DI = cdi.convert_xml_to_dict('./cyclus-files/' + 
+                              scenario_name +
+                              '_deployinst.xml')
+deploy_schedule = cdi.write_AR_deployinst(
+    lwr_DI,
+    "../../../inputs/united_states/reactors/",
+    duration,
+    reactor_prototypes,
+    demand_equation,
+    {'MMR':int(params['mmr_share'])})
+cdi.write_deployinst(deploy_schedule, "./cyclus-files/AR_DeployInst_" + 
+                                      scenario_name +
+                                      ".xml")
+
+# Run Cyclus with edited input file
+oup.run_cyclus(output_sqlite, output_xml)
+
+# ----------------------------
+# Return the results to Dakota
+# ----------------------------
+results = oup.get_all_results(results, output_sqlite)
+
+os.system('rm ' + output_sqlite)