specimen.dk_Manual))
'''
数据保存
'''
if filesave:
for specimen in specimen_data:
data = np.array([
specimen.n_Manual, specimen.a_Manual, specimen.dk_Manual,
specimen.dadn_Manual, specimen.kc_Manual
])
name = [
"Original_Cycles", "Original_CrackLength/mm",
"Original_SIFRange/MPam0.5", "Original_FCGRate/mm per cycle",
"Original_ClosureSIF/MPam0.5"
]
_ = write_data.SaveData(dataset=data, name=name, filename=specimen.name)
'''
绘图命令
'''
# 评估移动平均对dadN的修正效果
plt.figure(num=1, figsize=(10, 8))
for specimen in specimen_data:
plt.scatter(specimen.dk_Manual,
specimen.dadn_Manual,
label=specimen.name,
marker='.')
plt.plot(specimen.dk_Manual, specimen.dadn_Manual)
#plt.scatter(specimen.dk_MTS, specimen.dadn_MTS, label='$ExperimentDataByMTS$', color='orange', marker='.')
plt.plot(specimen.dk_Manual,
dadn_paris_Manual,
label='$Paris for CA$',
dadn_alpha_e,
label='$CrackClosure Alpha Method$',
marker='2')
plt.plot(dk_alpha_e, dadn_mixed_1, label='Mix Try 1', color='red', linewidth=2)
plt.title("FCG Rates - deltaK(Wheeler Model),OLR=" +
str(specimen.overload / specimen.maxload))
plt.ylabel("FCG Rates/mm per cycle")
plt.xlabel("DeltaSIF/MPa.m0.5")
plt.xscale('log')
plt.yscale('log')
plt.axis([
max(10, min(specimen.dk)),
max(20, max(specimen.dk)),
min(specimen.dadn) * 0.9,
max(specimen.dadn) * 1.1
])
plt.grid(which='minor', linestyle='--')
plt.grid(which='major', linestyle='--')
plt.legend()
if show:
plt.show()
if savedata:
data = np.array([specimen.dk, specimen.dadn, dk_alpha_e, dadn_mixed_1])
name = [
'dk_experiment', 'dadn_experiment', 'dk_ModifiedAlpha',
'dadn_ModifiedAlpha'
]
_ = write_data.SaveData(dataset=data,
name=name,
filename=specimen.name + "_modified_wheeler_alpha")
def WheelerFittingBaseParis2(a,
dadn,
dk,
rm,
aol,
rol,
c,
m,
eta=0,
savedata=0):
# usage: 由高载为起始点的数据，以Paris公式为基础裂纹扩展速率模型进行Wheeler模型拟合
# NOTE：数据的起始点必须是高载后！
# input parameter:
# a, dadn, dk, rm：FCG基础数据及对应时刻的塑性区尺寸rm
# aol，rol：高载时刻的裂纹长度aol和塑形区尺寸rol
# c，m：基础Paris公式参数
# eta：裂纹长度修正，相当于延长裂纹的长度（即等效裂纹概念），作用于Wheeler系数的分母，默认不修正
# return parameter:
# m1：Wheeler迟滞指数
left = a + rm + eta
right = aol + rol
print(aol, rol)
if left[-1] < right:
dadn_fitting = dadn
dk_fitting = dk
a_fitting = a
rm_fitting = rm
print("Wheeler Region Check: Fitting Region:dk_start=" +
str(dk_fitting[0]) + ",to the END.")
elif left[0] >= right:
print(
"ERRORFromWheelerFittingBaseParis2:No Wheeler Region, Check Input Parameter!"
)
else:
for seq, _ in enumerate(left):
if left[seq] >= right:
dadn_fitting = dadn[0:seq]
dk_fitting = dk[0:seq]
a_fitting = a[0:seq]
rm_fitting = rm[0:seq]
print("Wheeler Region Check: Fitting Region:dk_start=" +
str(dk_fitting[0]) + ",dk_final=" + str(dk_fitting[-1]))
break
# 根据Wheeler公式筛选迟滞参数适用区域（确定拟合采用的数据）
X = []
Y = []
for seq, _ in enumerate(dadn_fitting):
y = np.log(dadn_fitting[seq]) - m * np.log(dk_fitting[seq]) - np.log(c)
Y.append(y)
x = np.log(rm_fitting[seq]) - np.log(aol + rol - a_fitting[seq] - eta)
X.append(x)
X = np.array(X).reshape(-1, )
Y = np.array(Y)
def residual_m_wheeler(p):
m_wheeler = p
return Y - m_wheeler * X
k = opt.leastsq(residual_m_wheeler, np.array([1]))
m1 = k[0]
print("Wheeler Model Fitting Result: m1=", m1)
# Wheeler模型 m1系数拟合
'''
绘图显示拟合效果
'''
plt.figure(num=3, figsize=(8, 6))
plt.scatter(X, Y, label='Data')
plt.plot(X, m1 * X, label='Fitting')
plt.scatter(0, 0)
plt.xlabel('log(dadn(OL)) - log(dadn(CA))')
plt.ylabel('log(r,i)-log(a_OL + r_OL - a,i)')
plt.legend()
'''
绘图数据输出
'''
if savedata:
data = np.array([X, Y, m1 * X])
name = ['X', 'Y(experiment data)', 'fitting curve(m1*X)']
_ = write_data.SaveData(dataset=data, name=name)
return m1
max(dadn_Manual), 6))
plt.xticks(np.linspace(min(dk_Manual) * 0.9, max(dk_Manual), 6))
plt.title("FCG Rates - Delta SIF_" + sequence)
plt.ylabel("FCG Rates/ mm per cycle")
plt.xlabel("DeltaK/ MPa.m0.5")
plt.xscale('log')
plt.yscale('log')
plt.legend()
plt.grid(which='minor', linestyle='--')
plt.grid(which='major', linestyle='--')
# 显示图像
if show:
plt.show()
'''
文件输出
'''
if datasave:
data = np.array([
dk_Manual, dadn_Manual, dk_selected_averaged2, dadn_alpha_e,
dk_Manual_averaged1_selected, dadn_paris_dkeff_bac_selected,
a_Manual_averaged1, dk_Manual_averaged0_selected,
dadn_paris_dkeff_2pi_selected, a_Manual_averaged0, dk_Manual,
dadn_paris_Manual
])
name = [
'dK_exp', 'dadn_exp', 'dk_alpha', 'dadn_alpha', 'dk_bac', 'dadn_bac',
'a_bac', 'dk_2pi', 'dadn_2pi', 'a_2pi', 'dk_CA', 'dadn_CA'
]
_ = write_data.SaveData(dataset=data, name=name)
label='Modified Wheeler-Alpha Model',
color='red',
linewidth=2)
plt.title("FCG Rates - deltaK(Modified Wheeler-Alpha Model),Data:" +
specimen.name)
plt.ylabel("FCG Rates/mm per cycle")
plt.xlabel("DeltaSIF/MPa.m0.5")
plt.xscale('log')
plt.yscale('log')
plt.axis([
max(10, min(specimen.dk)),
max(20, max(specimen.dk)),
min(specimen.dadn) * 0.9,
max(specimen.dadn) * 1.1
])
plt.grid(which='minor', linestyle='--')
plt.grid(which='major', linestyle='--')
plt.legend()
if show:
plt.show()
if savedata:
data = np.array([specimen.dk, specimen.dadn, dk_alpha_e, dadn_mixed_1])
name = [
'dk_experiment', 'dadn_experiment', 'dk_ModifiedAlpha',
'dadn_ModifiedAlpha'
]
_ = write_data.SaveData(dataset=data,
name=name,
filename=sequence + "_modified_wheeler_alpha")