codes/SourceTermFit.py at master · meierms1/codes · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jul  4 08:57:32 2022

@author: maycon
"""

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from scipy.integrate import simpson as sp

p = [0.1, 1.0, 2.0, 6.0, 10.0] ## MPa Units, Accepts: [0.1, 1, 2, 6, 10]
peak = []
def fitline(P, xx, low, high):
    k1 = 0.46 * P + 0.42 - 1
    k2 = 1.14 * P + 0.323 - 1
    k3 = 1.0

    a = 1.0

    max_fun = -0.0990568*P**2 + 2.79722*P + 0.322469 #1.713 * P + 1.98

    k4 = np.log(max_fun - k2/2 - k1/2) / (0.5*(1-0.5))

    phi = np.linspace(0,1, 101)

    fun = lambda i:  (k1) * i + (k2) * (1-i) + k3 * np.exp(k4 * i * (1 - i) / a)

    y = [fun(i) for i in phi]

    z = []
    for i in xx:
        val = i
        if i < low:
            phi = 1
        elif i > high:
            phi = 0
        else:
            phi = 1 - (( low - val ) * (1 - 0) / (low - high) )
        z.append(fun(phi))

    return y, z

df = pd.read_csv("heatfluxdata2.csv", dtype=np.float64, skiprows=1)

df_1atm_x =   df.values[:108,8]
df_1atm_y =   df.values[:108,9]

df_10atm_x =  df.values[:50,6]
df_10atm_y =  df.values[:50,7]

df_20atm_x =  df.values[:91,4]
df_20atm_y =  df.values[:91,5]

df_60atm_x =  df.values[:113,2]
df_60atm_y =  df.values[:113,3]

df_100atm_x = df.values[:171,0]
df_100atm_y = df.values[:171,1]

a = 1.0
c = ['lightseagreen','yellowgreen','gold','darkorange','orangered']
v = 0
for P in p:

    if P == 10:
        xx = df_100atm_x
        yy = df_100atm_y
        low  = 200 - 10 * a**2.6
        high = 200 + 10 * a**2.6
    elif P == 6:
        xx = df_60atm_x
        yy = df_60atm_y
        low  = 200 - 13 *a**2
        high = 200 + 10 * a**2
    elif P == 2:
        xx = df_20atm_x
        yy = df_20atm_y
        low = 180
        high = 220
    elif P == 1:
        xx = df_10atm_x
        yy = df_10atm_y
        low = 165
        high = 225
    elif P == 0.1:
        xx = df_1atm_x
        yy = df_1atm_y
        low = 190
        high = 210

    y,z = fitline(P, xx, low, high)
    peak.append(max(y))
    plt.plot(xx, yy, label = str(P)+" MPa (Data)",ls = '--', c = c[v])
    plt.plot(xx, z, label = str(P)+" MPa (Fit)" , ls = "-", c=c[v])
    v += 1

plt.legend(prop={'size':9})
plt.xlabel("Spatial Variable" )
plt.ylabel("Heat Flux  [kW / cm$^2$]" )
#plt.title("Heat Flux Data Fitting")
plt.grid()

print(p)
print(peak)
pout = ""
for i,j in zip(p,peak):
    pout += f"({i},{j}),"
print(pout)
plt.figure()
plt.plot(p, peak)
plt.show()
#plt.savefig("/home/mmeierdo/Paper_Folder/EpsFit1.pdf", format = "pdf" )


'''
int_exp = sp(yy,xx)
int_mod = sp(z,xx)
error = 100 * abs(int_exp - int_mod) / int_exp
print("Data Integral: "  + str(int_exp))
print("Model Integral: " + str(int_mod))
print("%Error: " + str(error))

plt.figure()
plt.plot(xx, yy, label = "experiment", c = "black" )
plt.plot(xx, z, label = "Model", c = "red" , ls = "-.")
plt.legend()
plt.xlabel("Spacial Variable" )
plt.ylabel("Heat Flux - [kW / cm^2]" )
plt.title("Model Fit" )
plt.figtext(0.14, 0.7, "Integral %Error " + str(round(error, 2)))
'''