summaryrefslogtreecommitdiff
path: root/src/simple.py
blob: 0b5436ac71ef6606abba3d35ae51734a949fb960 (plain)
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
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
import numpy as np


class SIMPLE:
    def __init__(self, shape, bfs_shape, step, Re, alpha_p=0.8, alpha_uv=0.5):
        np.set_printoptions(precision=2, floatmode="maxprec", suppress=True)

        self.Re = Re
        self.nu = 1 / Re
        self.alpha_p = alpha_p
        self.alpha_uv = alpha_uv

        self.step = step
        self.bfs_shape = bfs_shape

        # Allocations
        self.u = np.zeros(shape=(shape[0], shape[1] + 1), dtype=float)
        self.u_star = np.zeros(shape=(shape[0], shape[1] + 1), dtype=float)

        self.v = np.zeros(shape=(shape[0] + 1, shape[1]), dtype=float)
        self.v_star = np.zeros(shape=(shape[0] + 1, shape[1]), dtype=float)

        self.p = np.zeros(shape=shape, dtype=float)
        self.p_star = np.random.rand(*shape)
        self.p_prime = np.zeros(shape=shape, dtype=float)

        self.d_e = np.zeros(shape=self.u.shape, dtype=float)
        self.d_n = np.zeros(shape=self.v.shape, dtype=float)
        self.b = np.zeros(shape=shape, dtype=float)

    def assert_positive(self, value):
        '''Assert that the value is nearly positive'''
        assert value > -0.01, f'WARNING: Value must be positive: {value}'
        return value

    def solve_momentum_equations(self):
        # Momentum along X direction
        for i in range(1, self.u.shape[0] - 1):
            for j in range(1, self.u.shape[1] - 1):
                if i >= self.bfs_shape[0] or j >= self.bfs_shape[1]:
                    u_W = 0.5 * (self.u[i][j] + self.u[i][j - 1])
                    u_E = 0.5 * (self.u[i][j] + self.u[i][j + 1])

                    v_S = 0.5 * (self.v[i][j - 1] + self.v[i][j])
                    v_N = 0.5 * (self.v[i + 1][j - 1] + self.v[i + 1][j])

                    a_E = self.assert_positive(-0.5 * u_E * self.step + self.nu)
                    a_W = self.assert_positive(+0.5 * u_W * self.step + self.nu)
                    a_N = self.assert_positive(-0.5 * v_N * self.step + self.nu)
                    a_S = self.assert_positive(+0.5 * v_S * self.step + self.nu)

                    a_e = 0.5 * self.step * (u_E - u_W + v_N - v_S) + 4 * self.nu
                    A_e = self.step

                    self.d_e[i][j] = A_e / a_e

                    self.u_star[i][j] = (
                        a_E * self.u[i][j + 1] +
                        a_W * self.u[i][j - 1] +
                        a_N * self.u[i + 1][j] +
                        a_S * self.u[i - 1][j] +
                        self.b[i][j - 1]
                    ) / a_e + self.d_e[i][j] * (self.p_star[i][j - 1] - self.p_star[i][j]) # p - p_e

        # Momentum along Y direction
        for i in range(1, self.v.shape[0] - 1):
            for j in range(1, self.v.shape[1] - 1):
                if i >= self.bfs_shape[0] or j >= self.bfs_shape[1]:
                    u_W = 0.5 * (self.u[i - 1][j] + self.u[i][j])
                    u_E = 0.5 * (self.u[i - 1][j + 1] + self.u[i][j + 1])

                    v_N = 0.5 * (self.v[i][j] + self.v[i + 1][j])
                    v_S = 0.5 * (self.v[i][j] + self.v[i - 1][j])

                    a_E = self.assert_positive(-0.5 * u_E * self.step + self.nu)
                    a_W = self.assert_positive(+0.5 * u_W * self.step + self.nu)
                    a_N = self.assert_positive(-0.5 * v_N * self.step + self.nu)
                    a_S = self.assert_positive(+0.5 * v_S * self.step + self.nu)

                    a_n = 0.5 * self.step * (u_E - u_W + v_N - v_S) + 4 * self.nu
                    A_n = self.step

                    self.d_n[i][j] = A_n / a_n

                    self.v_star[i][j] = (
                        a_E * self.v[i][j + 1] +
                        a_W * self.v[i][j - 1] +
                        a_N * self.v[i + 1][j] +
                        a_S * self.v[i - 1][j] +
                        self.b[i - 1][j]
                    ) / a_n + self.d_n[i][j] * (self.p_star[i - 1][j] - self.p_star[i][j]) # p - p_n

    def correct_pressure(self):
        self.p_prime = np.zeros(shape=self.p.shape, dtype=float)
        for i in range(self.p.shape[0]):
            for j in range(self.p.shape[1]):
                if i >= self.bfs_shape[0] or j >= self.bfs_shape[1]:
                    a_E = 0 if j == self.p.shape[1] - 1 else self.assert_positive(-self.d_e[i][j+1] * self.step)
                    a_W = 0 if j == 0 else self.assert_positive(-self.d_e[i][j] * self.step)
                    a_N = 0 if i == self.p.shape[0] - 1 else self.assert_positive(-self.d_n[i+1][j] * self.step)
                    a_S = 0 if i == 0 else self.assert_positive(-self.d_n[i][j] * self.step)
                    a_P = a_E + a_W + a_N + a_S

                    self.b[i][j] = self.step * (
                        (self.u_star[i][j+1] - self.u_star[i][j]) +
                        (self.v_star[i+1][j] - self.v_star[i][j])
                    )

                    if a_P != 0:
                        self.p_prime[i][j] = (
                            (a_E * self.p_prime[i][j+1] if a_E > 0 else 0) +
                            (a_W * self.p_prime[i][j-1] if a_W > 0 else 0) +
                            (a_N * self.p_prime[i+1][j] if a_N > 0 else 0) +
                            (a_S * self.p_prime[i-1][j] if a_S > 0 else 0) +
                            self.b[i][j]
                        ) / a_P

        self.p = self.p_star + self.p_prime * self.alpha_p
        self.p_star = self.p

    def correct_velocities(self):
        for i in range(self.u.shape[0]):
            for j in range(1, self.u.shape[1] - 1):
                self.u[i][j] = self.u_star[i][j] + self.alpha_uv * self.d_e[i][j] * (self.p_prime[i][j - 1] - self.p_prime[i][j])

        for i in range(1, self.v.shape[0] - 1):
            for j in range(self.v.shape[1]):
                self.v[i][j] = self.v_star[i][j] + self.alpha_uv * self.d_n[i][j] * (self.p_prime[i - 1][j] - self.p_prime[i][j])

    def iterate(self):
        self.solve_momentum_equations()

        # Boundary
        self.u_star[:, 0] = 2 - self.u_star[:, 1]
        self.v_star[:, 0] = 0

        self.v_star[-2, :] = -self.v_star[-1, :]
        self.v_star[1, :] = -self.v_star[0, :]

        self.v_star[self.bfs_shape[0], :self.bfs_shape[1]] = self.v_star[self.bfs_shape[0] - 1, :self.bfs_shape[1]]
        self.u_star[:self.bfs_shape[0], self.bfs_shape[1]] = self.u_star[:self.bfs_shape[0], self.bfs_shape[1] - 1]

        self.p_star[:self.bfs_shape[0], :self.bfs_shape[1]] = 0
        self.u_star[:, -1] = self.u_star[:, -2]

        self.correct_pressure()
        self.correct_velocities()

        # Boundary enforce
        self.u[:, 0] = 2 - self.u[:, 1]
        self.v[:, 0] = 0

        self.v[-2, :] = -self.v[-1, :]
        self.v[1, :] = -self.v[0, :]

        self.v[self.bfs_shape[0], :self.bfs_shape[1]] = self.v[self.bfs_shape[0] - 1, :self.bfs_shape[1]]
        self.u[:self.bfs_shape[0], self.bfs_shape[1]] = self.u[:self.bfs_shape[0], self.bfs_shape[1] - 1]

        self.p[:self.bfs_shape[0], :self.bfs_shape[1]] = 0
        self.u[:, -1] = self.u[:, -2]

    def avg_error(self):
        return np.absolute(self.b).sum()

    def save(self, path):
        print('SAVE', path)
        with open(path, 'wb') as file:
            np.save(file, self.u)
            np.save(file, self.v)
            np.save(file, self.p)
            np.save(file, self.b)

    def load(self, path):
        print('LOAD', path)
        with open(path, 'rb') as file:
            self.u = np.load(file)
            self.v = np.load(file)
            self.p = np.load(file)
            self.b = np.load(file)
            self.p_star = self.p