1#@ s*: Label=FastTest 2#@ *: DakotaConfig=HAVE_NPSOL 3# DAKOTA Input File: dakota_rbdo_cantilever_trsb.in 4# Trust-region surrogate-based RBDO using the cantilever test function. 5 6environment, 7 method_pointer = 'SBLO' 8 9method, 10 id_method = 'SBLO' 11 surrogate_based_local 12 model_pointer = 'OPTIM_M' 13 approx_method_pointer = 'OPTIM' 14# max_iterations = 50 15# soft_convergence_limit = 2 16 trust_region 17 initial_size = 0.2 18 contraction_factor = 0.5 19 expansion_factor = 1.50 20 output verbose 21 22########################### 23# begin opt specification # 24########################### 25method, 26 id_method = 'OPTIM' 27# dot_sqp 28 npsol_sqp 29 convergence_tolerance = 1.e-6 30 31model, 32 id_model = 'OPTIM_M' 33 surrogate local taylor_series 34 variables_pointer = 'OPTIM_V' 35 actual_model_pointer = 'OPTIM_TRUTH' 36 responses_pointer = 'OPTIM_R' 37 38variables, 39 id_variables = 'OPTIM_V' 40 continuous_design = 2 41 initial_point 2.5 2.5 42 upper_bounds 10.0 10.0 43 lower_bounds 1.0 1.0 44 descriptors 'w' 't' 45 46responses, 47# minimize mean Weight 48# s.t. p_S/D <= .00135 Cases 0,1,2,3 49# s.t. beta_S/D >= 3 Cases 4,5,6,7 50# s.t. z_S/D <= 0. Cases 8,9,10,11,12,13,14,15 51# 52# NOTE: This specifies the TOTAL RESPONSE for the optimization, 53# which is a combination of nested & interface responses. 54 id_responses = 'OPTIM_R' 55 objective_functions = 1 56 nonlinear_inequality_constraints = 2 57 nonlinear_inequality_upper_bounds = .00135 .00135 #s0,#s1 58# nonlinear_inequality_lower_bounds = 3. 3. #s2,#s3 59# nonlinear_inequality_upper_bounds = 1.e+50 1.e+50 #s2,#s3 60 analytic_gradients 61 no_hessians 62 63########################## 64# begin TS specification # 65########################## 66model, 67 id_model = 'OPTIM_TRUTH' 68 nested 69 variables_pointer = 'OPTIM_V' 70 sub_method_pointer = 'UQ' 71 primary_response_mapping = 1. 0. 0. 0. 0. 0. 0. 0. 72 secondary_response_mapping = 0. 0. 0. 0. 1. 0. 0. 0. 73 0. 0. 0. 0. 0. 0. 0. 1. 74 responses_pointer = 'OPTIM_R' 75 76########################## 77# begin UQ specification # 78########################## 79method, 80 id_method = 'UQ' 81 model_pointer = 'UQ_M' 82 local_reliability nip 83 mpp_search x_taylor_mpp #s0,#s2,#s4,#s6 84# mpp_search no_approx #s1,#s3,#s5,#s7 85 num_response_levels = 0 1 1 #s0,#s1,#s2,#s3 86 response_levels = 0.0 0.0 #s0,#s1,#s2,#s3 87# compute reliabilities #s2,#s3 88# num_probability_levels = 0 1 1 #s4,#s5 89# probability_levels = .00135 .00135 #s4,#s5 90# num_reliability_levels = 0 1 1 #s6,#s7 91# reliability_levels = 3. 3. #s6,#s7 92# g functions scaled using deterministic opt. conventions: 93# g<=0 is safe/feasible, g>0 is failed/violated. Therefore, 94# we desire a complementary cumulative reliability index. 95 complementary distribution 96 97model, 98 id_model = 'UQ_M' 99 single 100 variables_pointer = 'UQ_V' 101 interface_pointer = 'UQ_I' 102 responses_pointer = 'UQ_R' 103 104variables, 105 id_variables = 'UQ_V' 106# continuous_design is not required (OUU will augment 107# automatically), but it is good form 108 continuous_design = 2 109 normal_uncertain = 4 110 means = 40000. 29.E+6 500. 1000. 111 std_deviations = 2000. 1.45E+6 100. 100. 112 descriptors = 'R' 'E' 'X' 'Y' 113 114interface, 115 id_interface = 'UQ_I' 116 direct 117 analysis_driver = 'cantilever' 118# deactivate evaluation_cache restart_file 119 120responses, 121 id_responses = 'UQ_R' 122 response_functions = 3 123 analytic_gradients 124# numerical_gradients 125# method_source dakota 126# interval_type central 127# fd_gradient_step_size = 1.e-4 128 no_hessians 129