pyflange.stats

The following example, creates a gap distribution and the calculates the 95% quantile of the gap height

from pyflange.gap import gap_height_distribution

D = 7.50      # Flange diameter in meters
u = 0.0014    # Flatness tolerance (non-dimensional)
L = 1.22      # Gap length
gap_dist = gap_height_distribution(D, u, L)     # a lognorm distribution object

u95 = gap_dist.ppf(0.95)    # PPF is the inverse of CDF. See scipy.stats.lognorm documentation.

The following example creates a Log-Normal distribution sampler and generates three realizations.

samp = lognorm_sampler(12.0, 0.25)  # log-normal sampler sampler with
                                    # mean value 12.0 and CoV = 0.25.

val1 = next(samp)   # A random value from the log-normal distribution
val2 = next(samp)   # Another random value from the log-normal distribution
val3 = next(samp)   # Yet another random value from the log-normal distribution

The following example creates a Normal distribution sampler and generates three realizations.

samp = norm_sampler(12.0, 0.25)   # normal sampler sampler with mean 12.0
                                  # and CoV = 0.25.

val1 = next(samp)   # A random value from the normal distribution
val2 = next(samp)   # Another random value from the normal distribution
val3 = next(samp)   # Yet another random value from the normal distribution

The following example creates a Normal distribution sampler and generates three realizations.

from scipy.stats import norm
ndist = norm(12.0, 2.0) # normal distribution with mean value 12 and standard deviation 2.

samp = sampler(ndist)   # sampler based on the ndist distribution

val1 = next(samp)       # A random value from ndist distribution
val2 = next(samp)       # Another random value from ndist distribution
val3 = next(samp)       # Yet another random value from ndist distribution

The following example creates a L-Flange segment sampler and generates three random flange segments.

from math import pi
from metrum.units import mm, um, kN

import pyflange.stats as stats
from pyflange.bolts import StandardMetricBolt, RoundNut
N_BOLTS = 156

fseg_samp = standard_PolynomialLFlangeSegment_sampler (
        a = 150*mm,        # distance between inner face of the flange and center of the bolt hole
        b = 122*mm,        # distance between center of the bolt hole and center-line of the shell
        s =  54*mm,        # shell thickness
        t = 172*mm,        # flange thickness
        R = 4000*mm,       # shell outer curvature radius
        central_angle = 2*pi / N_BOLTS,  # angle subtended by the flange segment

        Zg = -18044*kN / N_BOLTS,      # load applied to the flange segment shell at rest

        # Bolt object representing the flange segment bolt
        bolt = StandardMetricBolt("M80", "10.9", shank_length=160*mm,
                shank_diameter_ratio=76.1/80, stud=True),

        Do = 86*mm,               # Bolt hole diameter
        washer = None,            # Bolt washer
        nut = RoundNut("M80"),    # Bolt nut

        # bolt preload random sampler and preload losses parameters
        preload_sampler = stats.norm_sampler(2932.24*kN, 0.03), 
        ppl_sampler = stats.norm_sampler(0.03, 1/3),
        dft_sampler = stats.norm_sampler(250*um, 0.12),

        # gap object random sampler
        gap_sampler = stats.standard_gap_sampler(
                    flange_diameter = 8000*mm,
                    flange_flatness_tolerance = 0.0014,   # 1.4 mm/m
                    gap_angle_sampler = stats.lognorm_sampler(100*deg, 1.0),
                    gap_shape_factor_sampler = stats.norm_sampler(1.0, 0.15)
                ),

        s_ratio = 1.0,              # Ratio of bottom shell thickness over s.
        settlement_factor = 0.50    # Settlement reduction for tension tightening.
    )

fseg1 = next(fseg_samp)     # A random L-Flange segment object
fseg2 = next(fseg_samp)     # Another random L-Flange segment object
fseg3 = next(fseg_samp)     # Yet another random L-Flange segment object

The following example creates a standard gap-size sampler and generate three realizations.

# Gap size sampler for 7.5 m falnge with flatness tolerance 1.4 mm/m
samp = standard_gap_size_sampler(7.5, 0.0014)

ga1, gh1 = next(samp)  # A random gap-anlge, gap-height pair
ga2, gh2 = next(samp)  # Another random gap-anlge, gap-height pair
ga3, gh3 = next(samp)  # Yet another random gap-anlge, gap-height pair