optimization_result.out - main - starlv/Fatigue_plan_optimizer_cpp

optimization_result.out
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Agamirov L.V.
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22 май 2026, 18:40
22 май 2026, 18:401f16d79
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TWO-STAGE FATIGUE TEST PLANNING OPTIMIZATION
Stage 1: Theoretical optimization (based on Fisher information matrix)
Stage 2: Monte Carlo refinement
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Problem parameters:
  sigma_inf = 250 MPa
  C = 800, m = 0.12
  a = 0.08, alpha = 0.8
  lgN levels (5 pcs): 5 5.5 6 6.5 7 
  C3/C1 = 1
  eps = 0.2, gamma = 0.1, t = 2
  delta_target = 0.01000
  f = 1000.00000 Hz
  n_simulations = 700

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OPTIMIZATION FOR N0 = 1e+05
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  Theoretical optimization for N0=1e+05...

    Variant                                    n     delta      cost nu1...nun
    ---------------------------------------- ---- ---------- ---------- --------------------------------------------------
    Uniform                                   58   0.00991     106.5   [0.200, 0.200, 0.200, 0.200, 0.200] OK
    Right shift p=0.500000                   239   0.00998     525.5   [0.031, 0.159, 0.223, 0.272, 0.314] OK
    Right shift p=1.000000                   300   0.01438     728.2   [0.004, 0.102, 0.200, 0.298, 0.396] NO
    Right shift p=1.500000                   300   0.25000     784.3   [0.000, 0.061, 0.167, 0.305, 0.467] NO
    Right shift p=2.000000                   300   0.25000     827.6   [0.000, 0.035, 0.135, 0.300, 0.530] NO
    Right shift p=2.500000                   300   0.25000     871.1   [0.000, 0.020, 0.106, 0.288, 0.586] NO
    Right shift p=3.000000                   300   0.25000     908.9   [0.000, 0.011, 0.082, 0.271, 0.636] NO
    Right shift p=4.000000                   300   0.25000     964.6   [0.000, 0.003, 0.047, 0.231, 0.719] NO
    Right shift p=5.000000                   300   0.25000    1002.4   [0.000, 0.001, 0.026, 0.189, 0.784] NO
    Right shift p=7.000000                   300   0.25000    1057.3   [0.000, 0.000, 0.007, 0.119, 0.873] NO
    Right shift p=10.000000                  300   0.25000    1097.8   [0.000, 0.000, 0.001, 0.055, 0.944] NO
    Left shift p=0.500000                     39   0.00987      50.8   [0.314, 0.272, 0.223, 0.159, 0.031] OK
    Left shift p=1.000000                     32   0.00986      40.3   [0.396, 0.298, 0.200, 0.102, 0.004] OK
    Left shift p=1.500000                     27   0.00996      33.9   [0.467, 0.305, 0.167, 0.061, 0.000] OK
    Left shift p=2.000000                     24   0.00994      29.4   [0.530, 0.300, 0.135, 0.035, 0.000] OK
    Left shift p=2.500000                     22   0.00990      27.1   [0.586, 0.288, 0.106, 0.020, 0.000] OK
    Left shift p=3.000000                    300   0.25000     322.0   [0.636, 0.271, 0.082, 0.011, 0.000] NO
    Left shift p=4.000000                    300   0.25000     316.8   [0.719, 0.231, 0.047, 0.003, 0.000] NO
    Left shift p=5.000000                    300   0.25000     314.6   [0.784, 0.189, 0.026, 0.001, 0.000] NO
    Left shift p=7.000000                    300   0.25000     311.8   [0.873, 0.119, 0.007, 0.000, 0.000] NO
    Left shift p=10.000000                   300   0.25000     310.4   [0.944, 0.055, 0.001, 0.000, 0.000] NO
    Symmetric (0.3,0.1,0.1,0.1,0.3)           37   0.00996      75.7   [0.333, 0.111, 0.111, 0.111, 0.333] OK
    Symmetric (0.25,0.15,0.1,0.15,0.25)       43   0.00998      84.8   [0.278, 0.167, 0.111, 0.167, 0.278] OK
    Symmetric (0.2,0.2,0.2,0.2,0.2)           58   0.00991     106.5   [0.200, 0.200, 0.200, 0.200, 0.200] OK
    Symmetric (0.15,0.25,0.2,0.25,0.15)       72   0.00999     124.1   [0.150, 0.250, 0.200, 0.250, 0.150] OK
    Symmetric (0.1,0.3,0.2,0.3,0.1)           99   0.00997     161.5   [0.100, 0.300, 0.200, 0.300, 0.100] OK
    Symmetric (0.05,0.35,0.2,0.35,0.05)      164   0.01000     250.8   [0.050, 0.350, 0.200, 0.350, 0.050] OK
    Extreme right (0.05,0.05,0.1,0.3,0.5)    213   0.01000     570.8   [0.050, 0.050, 0.100, 0.300, 0.500] OK
    Extreme left (0.5,0.3,0.1,0.05,0.05)      25   0.00993      30.5   [0.500, 0.300, 0.100, 0.050, 0.050] OK

    ★ Best theoretical plan (accuracy achieved):
      Left shift p=2.500000
      nu = [0.5862, 0.2879, 0.1062, 0.0197, 0.0000]
      n = 22 -> distribution: [12, 6, 2, 1, 1]
      delta = 0.00990 (target <= 0.01000)
      cost = 27.1

  Stage 2: Monte Carlo refinement (simulations=700)...
    Theoretical plan: n=22, delta=0.01301
    Accuracy not achieved, increasing n...
    Found n = 34, delta = 0.00951

    ★ Final plan (Monte Carlo):
      nu = [0.5862, 0.2879, 0.1062, 0.0197, 0.0000]
      n = 34 -> distribution: [18, 10, 4, 1, 1]
      delta = 0.00951 (target <= 0.01000)
      cost = 40.1

    Execution time: 64.7 sec

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SUMMARY TABLE OF RESULTS
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N0             Stage          n     delta     cost      Distribution (n1...nn)
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N0_100000      Theoretical        22   0.00990      27.1 [12, 6, 2, 1, 1]
               Monte Carlo        34   0.00951      40.1 [18, 10, 4, 1, 1]


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RESULTS SAVED TO FILE: optimization_result.out
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