Why is Sobol null when I use polynomial chaos for sensitivity analysis?

[Ating24]

Here is the input file for my sensitivity analysis：

[StochasticTools]
[]
[Distributions]
  [k_dist]
    type = Uniform
    lower_bound = 0.8
    upper_bound = 1.2
  []
  [density_dist]
    type = Uniform
    lower_bound = 0.9
    upper_bound = 1.1
  []
  [cp_dist]
    type = Uniform
    lower_bound = 0.9
    upper_bound = 1.1
  []
  [fluxcoff_dist]
    type = Uniform
    lower_bound = 1200
    upper_bound = 1800
  []
[]

[Samplers]
  [sample]
    type = MonteCarlo
    num_rows = 10000
    distributions = 'k_dist density_dist cp_dist fluxcoff_dist'
    execute_on = initial
  []  
[]

[Surrogates]
  [poly_chaos_disp_x1]
    type = PolynomialChaos
    filename = 'poly_chaos_training_poly_chaos_disp_x1.rd'
  []
  [poly_chaos_disp_y1]
    type = PolynomialChaos
    filename = 'poly_chaos_training_poly_chaos_disp_y1.rd'
  []
  [poly_chaos_disp_z1]
    type = PolynomialChaos
    filename = 'poly_chaos_training_poly_chaos_disp_z1.rd'
  []
  [poly_chaos_disp_x2]
    type = PolynomialChaos
    filename = 'poly_chaos_training_poly_chaos_disp_x2.rd'
  []
  [poly_chaos_disp_y2]
    type = PolynomialChaos
    filename = 'poly_chaos_training_poly_chaos_disp_y2.rd'
  []
  [poly_chaos_disp_z2]
    type = PolynomialChaos
    filename = 'poly_chaos_training_poly_chaos_disp_z2.rd'
  []
  [poly_chaos_disp_x3]
    type = PolynomialChaos
    filename = 'poly_chaos_training_poly_chaos_disp_x3.rd'
  []
  [poly_chaos_disp_y3]
    type = PolynomialChaos
    filename = 'poly_chaos_training_poly_chaos_disp_y3.rd'
  []
  [poly_chaos_disp_z3]
    type = PolynomialChaos
    filename = 'poly_chaos_training_poly_chaos_disp_z3.rd'
  []
  [poly_chaos_disp_x4]
    type = PolynomialChaos
    filename = 'poly_chaos_training_poly_chaos_disp_x4.rd'
  []
  [poly_chaos_disp_y4]
    type = PolynomialChaos
    filename = 'poly_chaos_training_poly_chaos_disp_y4.rd'
  []
  [poly_chaos_disp_z4]
    type = PolynomialChaos
    filename = 'poly_chaos_training_poly_chaos_disp_z4.rd'
  []  [poly_chaos_disp_x5]
    type = PolynomialChaos
    filename = 'poly_chaos_training_poly_chaos_disp_x5.rd'
  []
  [poly_chaos_disp_y5]
    type = PolynomialChaos
    filename = 'poly_chaos_training_poly_chaos_disp_y5.rd'
  []
  [poly_chaos_disp_z5]
    type = PolynomialChaos
    filename = 'poly_chaos_training_poly_chaos_disp_z5.rd'
  []
  [poly_chaos_temperature1]
    type = PolynomialChaos
    filename = 'poly_chaos_training_poly_chaos_temperature1.rd'
  []
  [poly_chaos_temperature2]
    type = PolynomialChaos
    filename = 'poly_chaos_training_poly_chaos_temperature2.rd'
  []
  [poly_chaos_temperature3]
    type = PolynomialChaos
    filename = 'poly_chaos_training_poly_chaos_temperature3.rd'
  []
  [poly_chaos_temperature4]
    type = PolynomialChaos
    filename = 'poly_chaos_training_poly_chaos_temperature4.rd'
  []
  [poly_chaos_temperature5]
    type = PolynomialChaos
    filename = 'poly_chaos_training_poly_chaos_temperature5.rd'
  []
[]



[Reporters]
  [samp]
    type = EvaluateSurrogate
    model = 'poly_chaos_disp_x1 poly_chaos_disp_y1 poly_chaos_disp_z1 poly_chaos_disp_x2 poly_chaos_disp_y2 poly_chaos_disp_z2 poly_chaos_disp_x3 poly_chaos_disp_y3 poly_chaos_disp_z3 poly_chaos_disp_x4 poly_chaos_disp_y4 poly_chaos_disp_z4 poly_chaos_disp_x5 poly_chaos_disp_y5 poly_chaos_disp_z5 poly_chaos_temperature1 poly_chaos_temperature2 poly_chaos_temperature3 poly_chaos_temperature4 poly_chaos_temperature5'
    sampler = sample
    parallel_type = ROOT
  []
  [stats]
    type = PolynomialChaosReporter
    pc_name = 'poly_chaos_disp_x1 poly_chaos_disp_y1 poly_chaos_disp_z1 poly_chaos_disp_x2 poly_chaos_disp_y2 poly_chaos_disp_z2 poly_chaos_disp_x3 poly_chaos_disp_y3 poly_chaos_disp_z3 poly_chaos_disp_x4 poly_chaos_disp_y4 poly_chaos_disp_z4 poly_chaos_disp_x5 poly_chaos_disp_y5 poly_chaos_disp_z5 poly_chaos_temperature1 poly_chaos_temperature2 poly_chaos_temperature3 poly_chaos_temperature4 poly_chaos_temperature5'
    #statistics = 'mean stddev'
    #local_sensitivity_points = '1 1 1 1500; 1 1 1 1500;1 1 1 1500;1 1 1 1500;1 1 1 1500;1 1 1 1500; 1 1 1 1500;1 1 1 1500;1 1 1 1500;1 1 1 1500;1 1 1 1500; 1 1 1 1500;1 1 1 1500;1 1 1 1500;1 1 1 1500;1 1 1 1500; 1 1 1 1500;1 1 1 1500;1 1 1 1500;1 1 1 1500'
    include_sobol = True
  []
[]

[Outputs]
  [out]
    type = JSON
    execute_on = final
  []
[]

[Ating24]

The result is like this：

"stats": {
                "poly_chaos_disp_x1_SOBOL": {
                    "FIRST_ORDER": [
                        [
                            null,
                            null,
                            null,
                            null
                        ],
                        []
                    ],
                    "SECOND_ORDER": [
                        [
                            [
                                null,
                                null,
                                null,
                                null
                            ],
                            [
                                null,
                                null,
                                null,
                                null
                            ],
                            [
                                null,
                                null,
                                null,
                                null
                            ],
                            [
                                null,
                                null,
                                null,
                                null
                            ]
                        ],
                        []
                    ],
                    "TOTAL": [
                        [
                            null,
                            null,
                            null,
                            null
                        ],
                        []
                    ]
                },