1#@ s*: Label=FastTest 2#@ *: DakotaConfig=HAVE_NPSOL 3# DAKOTA Input File: dakota_pcbdo_short_column.in 4# Optimization under uncertainty using polynomial chaos methods within a 5# nested OUU environment. The test problem is the "short_column" problem from 6# Kuschel and Rackwitz, 1997. 7 8environment, 9 method_pointer = 'SBLO' 10 11method, 12 id_method = 'SBLO' 13 surrogate_based_local 14 model_pointer = 'OPTIM_M' 15 approx_method_pointer = 'OPTIM' 16 max_iterations = 50 17 soft_convergence_limit = 2 18 trust_region 19 initial_size = 1.0 20 contraction_factor = 0.5 21 expansion_factor = 1.50 22 output verbose 23 24########################### 25# begin opt specification # 26########################### 27method, 28 id_method = 'OPTIM' 29 npsol_sqp 30 convergence_tolerance = 1.e-6 31 output verbose 32 33model, 34 id_model = 'OPTIM_M' 35 surrogate local taylor_series 36 variables_pointer = 'OPTIM_V' 37 responses_pointer = 'OPTIM_R_SURR' 38 actual_model_pointer = 'OPTIM_TRUTH' 39 40variables, 41 id_variables = 'OPTIM_V' 42 continuous_design = 2 43 initial_point 10. 15. 44 lower_bounds 5. 15. 45 upper_bounds 15. 25. 46 descriptors 'b' 'h' 47 48responses, 49# minimize b*h 50# s.t. beta >= 2.5 51# NOTE: This specifies the TOTAL RESPONSE for the optimization, 52# which is a combination of nested & interface responses. 53 id_responses = 'OPTIM_R_SURR' 54 objective_functions = 1 55 nonlinear_inequality_constraints = 1 56 nonlinear_inequality_lower_bounds = 2.5 57 nonlinear_inequality_upper_bounds = 1.e+50 58 analytic_gradients 59 no_hessians 60 61########################## 62# begin TS specification # 63########################## 64model, 65 id_model = 'OPTIM_TRUTH' 66 nested 67 variables_pointer = 'OPTIM_V' 68 sub_method_pointer = 'UQ' 69 responses_pointer = 'OPTIM_R_TRUTH' 70# use projection of analytic PCE moments: constrain beta 71 primary_response_mapping = 1. 0. 0. 0. 0. 72 secondary_response_mapping = 0. 0. 0. 0. 1. 73 74responses, 75# NOTE: This specifies the TOTAL RESPONSE from the nested mapping 76# used for constructing the surrogate. 77 id_responses = 'OPTIM_R_TRUTH' 78 objective_functions = 1 79 nonlinear_inequality_constraints = 1 80 analytic_gradients 81 no_hessians #s0,#s2 82# quasi_hessians sr1 #s1,#s3 83 84########################## 85# begin UQ specification # 86########################## 87method, 88 id_method = 'UQ' 89 model_pointer = 'UQ_M' 90 polynomial_chaos askey 91 expansion_order = 2 #s0,#s1 92 collocation_ratio = 2. #s0,#s1 93 seed = 12347 fixed_seed #s0,#s1 94# quadrature_order = 3 95# sparse_grid_level = 2 non_nested #s2,#s3 96 num_response_levels = 0 1 97 response_levels = 0.0 98 compute reliabilities 99 cumulative distribution 100 101model, 102 id_model = 'UQ_M' 103 single 104 variables_pointer = 'UQ_V' 105 interface_pointer = 'UQ_I' 106 responses_pointer = 'UQ_R' 107 108variables, 109 id_variables = 'UQ_V' 110 continuous_design = 2 111 normal_uncertain = 2 112 means = 500.0 2000.0 113 std_deviations = 100.0 400.0 114 descriptors = 'P' 'M' 115 lognormal_uncertain = 1 116 means = 5.0 117 std_deviations = 0.5 118 descriptors = 'Y' 119 uncertain_correlation_matrix = 1 0.5 0 120 0.5 1 0 121 0 0 1 122 123interface, 124 id_interface = 'UQ_I' 125 direct 126 analysis_driver = 'short_column' 127 128responses, 129 id_responses = 'UQ_R' 130 response_functions = 2 131 analytic_gradients 132 no_hessians 133