def AlphaCalculate(fade_rate,
amplitude,
dadn,
dk,
kc,
a,
a_range,
a_amplitude,
stress_ratio,
numofaverage=7,
error_ratio=0.2):
# 放弃
# 移动平均
kc_Manual_averaged2, dk_Manual_averaged2 = \
DoubleDataSelectMovingAverage(data=kc, reference=dk,
ratio=error_ratio, numofaverage=numofaverage)
_, a_Manual_averaged2 = \
DoubleDataSelectMovingAverage(data=kc, reference=a,
ratio=error_ratio, numofaverage=numofaverage)
_, dadn_Manual_averaged2 = \
DoubleDataSelectMovingAverage(data=kc, reference=dadn,
ratio=error_ratio, numofaverage=numofaverage)
# 数据筛选a_range
dadn_selecting_average2, dk_selecting_averaged2, kc_selecting_averaged2, a_selecting_averaged2 = \
mts_analysis.FCGDataSelectByThreshold(dadn=dadn_Manual_averaged2, dk=dk_Manual_averaged2,
n=kc_Manual_averaged2, a=a_Manual_averaged2,
threshold=a_range[0], target='a', keepbigger=1)
dadn_selected_averaged2, dk_selected_averaged2, kc_selected_averaged2, a_selected_averaged2 = \
mts_analysis.FCGDataSelectByThreshold(dadn=dadn_selecting_average2, dk=dk_selecting_averaged2,
n=kc_selecting_averaged2, a=a_selecting_averaged2,
threshold=a_range[1], target='a', keepbigger=0)
# 拟合数据准备
kmin_selected_averaged2 = dk_selected_averaged2 * (
stress_ratio / (1 - stress_ratio)) # Kmin计算
kmax_selected_averaged2 = dk_selected_averaged2 * (1 / (1 - stress_ratio)
) # Kmax计算
c_p, m_p = paris_and_walker.ParisParameter(r=stress_ratio) # Paris参数读取
betas = (a_selected_averaged2 - a_amplitude) / a_amplitude # 裂纹长度无量纲量
# 计算参数alpha
alpha_e = []
for beta in betas:
if beta < 0:
alpha_e.append(2 / np.pi)
else:
alpha_e.append(amplitude * np.exp(-fade_rate * beta))
alpha_e = np.array(alpha_e)
# 基于参数计算有效应力强度因子
dkeff_alpha_e = kmax_selected_averaged2 - alpha_e * kc_selected_averaged2 - (
1 - alpha_e) * kmin_selected_averaged2
# 计算dadn
dadn_alpha_e = mts_analysis.ParisCalculating(c=c_p,
m=m_p,
dk=dkeff_alpha_e)
return dk_selected_averaged2, dkeff_alpha_e, dadn_alpha_e, alpha_e, betas, a_selected_averaged2
# 实验参数
sequence = ["yang-baoban_Lu-420-06"] # Graph Saving Sequence
pol = 4000
r = 0.1
threshold = 0
aol_hold = 12.0057 # 高载长度：按Hold时记录的裂纹长度
aol_maxrate = 12.0057 # 高载长度：对应FCGRate最大时的裂纹长度
aol_minrate = 12.7927 # 高载长度：对应FCGRate最小时的裂纹长度
# 程序参数
show = 1 # 绘图开关
# 裂纹扩展公式数据库
c_w, m_w, gamma = paris_and_walker.WalkerParameter()
c_p, m_p = paris_and_walker.ParisParameter(r=r)
# 读取数据并完成基本的FCG处理
specimen, width, notch_length, thickness, elastic_modulus, yield_strength, precrack_length = \
read_data.ReadTestInf(sequence=sequence[0])
cycles, cracklength, kmax, kmin, pmax, pmin, kclosure, closureload = \
read_data.ReadOriginResult(sequence=sequence[0], closure=True, cod=False)
dadn_Manual, n_Manual, dk_Manual, a_Manual, kc_Manual = \
experiment_calculation.FCGRandDKbyOriginalData(b=thickness, w=width, n=cycles, pmax=pmax, pmin=pmin,
a=cracklength,
ys=yield_strength, r=r, kclosure=kclosure,
threshold=threshold)
# Kmax at Overload计算
k_max_ol = mts_analysis.DeltaKCalculating(b=thickness,
w=width,
eta = -3 # Modified-wheeler模型修正
kappa = 100
plz_factor = 'Xiaoping'
# 类建立
specimen = OverloadSpecimen(name=sequence)
specimen.status_input_from_database()
specimen.basic_result_calculate()
#specimen.a_ol_applied -= 1.5
specimen.a_dadn_min = 13.8055
specimen.a_kc_max = specimen.a_alpha_end
specimen.a_alpha_end += 2.5
specimen.specimen_print()
# paris参数
c_ca, m_ca = paris_and_walker.ParisParameter(r=specimen.stress_ratio)
# Overload PLZ相关参数，塑性区系数为Irwin
kol = mts_analysis.DeltaKCalculating(b=specimen.thickness,
w=specimen.width,
a=specimen.a_ol_applied,
pmax=specimen.overload,
pmin=0)
plz_ol = overload_analysis.PlasticZoneWithFactor(kmax=np.array([kol]),
ys=specimen.yield_stress,
factor=plz_factor,
t=specimen.thickness)
# 数据筛选（筛选出dadn_min后的数据，标记为1）
dadn_1, dk_1, kc_1, n_1, a_1 = specimen.data_select_by_cracklength(
lowerlimit=specimen.a_dadn_min, upperlimit=specimen.a_kc_max)
def Alpha2(sequence,
stress_ratio,
error_ratio,
numofaverage,
a_range,
amplitude=0,
fade_rate=0,
fitting=0,
model='paris'):
# usage: 采用提出的Alpha方法先拟合出幅度系数M，再根据其计算dadn结果
# input parameter:
# sequence: 试件名（与MTS中一致，将直接读取文件）
# stress_ratio：实验应力比
# error_ratio：进行数据筛选的误差带半宽度，详细注释见函数DoubleDataSelectMovingAverage
# numofaverage：进行移动平均的数据个数，详细注释见函数DoubleDataSelectMovingAverage
# a_range：拟合的数据范围，格式为数组[a_begin, a_end]，a_begin推荐为dkeff_basic的谷值或dadn的谷值，
# a_end要注意截止在数据开始离散之前
# fade_rate：幂函数中的指数系数，直接指定，常见[5,8]
# amplitude: 幂函数中的幅度系数，可定2/pi
# 注：函数自动将对输入为0的项先拟合参数再进行计算，若均不为零则直接计算不进行拟合
# fitting: 若fitting为1，则输出拟合时对标的dkeff_experiment值，以用于计算lost
# model: 设定计算过程中采用的model，默认采用paris，可选walker
# return parameter：
# Ce1: 计算得到的幅度系数
# dk_selected_averaged2, dkeff_alpha_e, dadn_alpha_e：对应的裂纹扩展参数
# alpha_e：对应的参数alpha
# 数据读入
dadn_Manual, n_Manual, dk_Manual, a_Manual, kc_Manual = \
BasicDataDispose(sequence=sequence, r=stress_ratio)
if model == 'walker':
c_w, m_w, gamma = paris_and_walker.WalkerParameter(data=1)
else:
c_p, m_p = paris_and_walker.ParisParameter(r=stress_ratio)
# 移动平均
kc_Manual_averaged2, dk_Manual_averaged2 = \
DoubleDataSelectMovingAverage(data=kc_Manual, reference=dk_Manual,
ratio=error_ratio, numofaverage=numofaverage)
_, a_Manual_averaged2 = \
DoubleDataSelectMovingAverage(data=kc_Manual, reference=a_Manual,
ratio=error_ratio, numofaverage=numofaverage)
_, dadn_Manual_averaged2 = \
DoubleDataSelectMovingAverage(data=kc_Manual, reference=dadn_Manual,
ratio=error_ratio, numofaverage=numofaverage)
# 数据筛选a_range
dadn_selecting_average2, dk_selecting_averaged2, kc_selecting_averaged2, a_selecting_averaged2 = \
mts_analysis.FCGDataSelectByThreshold(dadn=dadn_Manual_averaged2, dk=dk_Manual_averaged2,
n=kc_Manual_averaged2, a=a_Manual_averaged2,
threshold=a_range[0], target='a', keepbigger=1)
dadn_selected_averaged2, dk_selected_averaged2, kc_selected_averaged2, a_selected_averaged2 = \
mts_analysis.FCGDataSelectByThreshold(dadn=dadn_selecting_average2, dk=dk_selecting_averaged2,
n=kc_selecting_averaged2, a=a_selecting_averaged2,
threshold=a_range[1], target='a', keepbigger=0)
# 拟合数据准备
kmin_selected_averaged2 = dk_selected_averaged2 * (
stress_ratio / (1 - stress_ratio)) # Kmin计算
kmax_selected_averaged2 = dk_selected_averaged2 * (1 / (1 - stress_ratio)
) # Kmax计算
if model == 'walker':
dkeff_experiment_averaged2 = (
(dadn_selected_averaged2 / c_w)**(1 / m_w)) / (
(1 - stress_ratio)**(gamma - 1))
else:
dkeff_experiment_averaged2 = (dadn_selected_averaged2 / c_p)**(
1 / m_p) # 拟合时的真值dkeff，由Paris给出
beta = (a_selected_averaged2 - a_range[0]) / a_range[0] # 裂纹长度无量纲量
# 拟合，得到幅度或衰减系数
if fade_rate != 0 and amplitude == 0:
def residual_alpha_e(p):
Ce = p
return dkeff_experiment_averaged2 - (
kmax_selected_averaged2 -
(Ce * np.exp(-fade_rate * beta)) * kc_selected_averaged2 -
(1 -
(Ce * np.exp(-fade_rate * beta))) * kmin_selected_averaged2)
k = opt.leastsq(residual_alpha_e, np.array([1]))
Ce1 = k[0]
elif amplitude != 0 and fade_rate == 0:
def residual_alpha_e(p):
n = p
return dkeff_experiment_averaged2 - (
kmax_selected_averaged2 -
(amplitude * np.exp(-n * beta)) * kc_selected_averaged2 -
(1 -
(amplitude * np.exp(-n * beta))) * kmin_selected_averaged2)
k = opt.leastsq(residual_alpha_e, np.array([1]))
fade_rate = k[0]
Ce1 = amplitude
elif amplitude != 0 and fade_rate != 0:
Ce1 = amplitude
elif amplitude == 0 and fade_rate == 0:
def residual_alpha_e(p):
Ce, n = p
return dkeff_experiment_averaged2 - (
kmax_selected_averaged2 -
(Ce * np.exp(-n * beta)) * kc_selected_averaged2 -
(1 - (Ce * np.exp(-n * beta))) * kmin_selected_averaged2)
k = opt.leastsq(residual_alpha_e, np.array([1, 1]))
Ce1, fade_rate = k[0]
# 计算参数alpha
alpha_e = Ce1 * np.exp(-fade_rate * beta)
print("alpha幂函数拟合:试件名:", sequence, "幅度:", Ce1, "衰减速率:", fade_rate)
# 计算dkeff_alpha和对应的dadn
dkeff_alpha_e = kmax_selected_averaged2 - alpha_e * kc_selected_averaged2 - (
1 - alpha_e) * kmin_selected_averaged2
if model == 'walker':
dadn_alpha_e = mts_analysis.WalkerCalculating(c0=c_w,
gamma=gamma,
m0=m_w,
r=stress_ratio,
dk=dkeff_alpha_e)
else:
dadn_alpha_e = mts_analysis.ParisCalculating(c=c_p,
m=m_p,
dk=dkeff_alpha_e)
if fitting:
return Ce1, fade_rate, dk_selected_averaged2, dkeff_alpha_e, dadn_alpha_e, alpha_e, dkeff_experiment_averaged2
else:
return Ce1, fade_rate, dk_selected_averaged2, dkeff_alpha_e, dadn_alpha_e, alpha_e
from FCGAnalysisLib import closureanalysis
import numpy as np
import matplotlib.pyplot as plt
# 实验参数
sequence = [
"yang-baoban_Lu-420-13", "yang-baoban_Lu-420-15", "yang-baoban_Lu-420-16",
"yang-baoban_Lu-420-07", "yang-baoban_Lu-420-10"
]
stress_ratio = [0.5, 0.5, 0.5, 0.5, 0.5]
# 程序参数
show = 1 # 绘图开关
# paris参数读取，以stress_ratio中第一组的应力比为准
c_CA, m_CA = paris_and_walker.ParisParameter(r=stress_ratio[0])
# seqence中数据的读取和处理，未对SIFclosure进行读取(记为-)
dadn_Manual1, n_Manual1, dk_Manual1, a_Manual1, _ = \
closureanalysis.BasicDataDispose(sequence=sequence[0], r=stress_ratio[0])
dadn_Manual2, n_Manual2, dk_Manual2, a_Manual2, _ = \
closureanalysis.BasicDataDispose(sequence=sequence[1], r=stress_ratio[1])
dadn_Manual3, n_Manual3, dk_Manual3, a_Manual3, _ = \
closureanalysis.BasicDataDispose(sequence=sequence[2], r=stress_ratio[2])
dadn_Manual4, n_Manual4, dk_Manual4, a_Manual4, _ = \
closureanalysis.BasicDataDispose(sequence=sequence[3], r=stress_ratio[3])
dadn_Manual5, n_Manual5, dk_Manual5, a_Manual5, _ = \