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  • 2D Wave Equation (2)

    • 2维波动方程初边值问题:
      2维波动方程如下,
      egin{equation}
      frac{partial^2u}{partial t^2} = Dleft(frac{partial^2u}{partial x^2} + frac{partial^2 u}{partial y^2} ight), quad (x, y) in Omega = [-L_x. L_x] imes[-L_y, L_y], tin[0, T]
      label{eq_1}
      end{equation}
      初始条件如下,
      egin{equation}
      left{
      egin{split}
      &u(x, y, 0) = u_0(x, y),quad &(x, y) in Omega \
      &u_t(x, y, 0) = 0,quad &(x, y) in Omega
      end{split}
      ight.
      label{eq_2}
      end{equation}
      边界条件如下,
      egin{equation}
      u(x, y, t) = 0, quad (x, y) in partial Omega, t in [0, T]
      label{eq_3}
      end{equation}
    • 连续型系统的离散化:
      本文拟降阶时间微分项, 令
      egin{equation}
      frac{partial u}{partial t} = v
      label{eq_4}
      end{equation}
      式$eqref{eq_1}$转化如下,
      egin{equation}
      left{
      egin{split}
      frac{partial u}{partial t} &= v \
      frac{partial v}{partial t} &= Dleft( frac{partial^2 u}{partial x^2} + frac{partial^2 u}{partial y^2} ight) \
      end{split}
      ight .
      label{eq_5}
      end{equation}
      式$eqref{eq_2}$转化如下,
      egin{equation}
      left{
      egin{split}
      u(x, y, 0) &= u_0(x, y) \
      v(x, y, 0) &= 0 \
      end{split}
      ight.
      label{eq_6}
      end{equation}
      本文拟采用中心差分处理空间微分项, 式$eqref{eq_5}$转换如下,
      egin{equation}
      left{
      egin{split}
      frac{partial u_{i, j}}{partial t} &= v_{i, j} \
      frac{partial v_{i, j}}{partial t} &= Dleft( frac{u_{i+1,j} + u_{i-1, j} - 2u_{i,j}}{h_x^2} + frac{u_{i,j+1} + u_{i,j-1} - 2u_{i,j}}{h_y^2} ight)
      end{split}
      ight .
      label{eq_7}
      end{equation}
      令,
      egin{equation*}
      U = egin{bmatrix}
      u_{0,0} & cdots & u_{0,n_x} \
      vdots & ddots & vdots \
      u_{n_y,0} & cdots & u_{n_y,n_x} \
      end{bmatrix}quad
      V = egin{bmatrix}
      v_{0,0} & cdots & v_{0,n_x} \
      vdots & ddots & vdots \
      v_{n_y,0} & cdots & v_{n_y,n_x} \
      end{bmatrix}quad
      A_x = egin{bmatrix}
      -2 & 1 & & & \
      1 & -2 & 1 & & \
      & ddots & ddots & ddots & \
      & & 1 & -2 & 1 \
      & & & 1 & -2 \
      end{bmatrix}quad
      A_y = egin{bmatrix}
      -2 & 1 & & & \
      1 & -2 & 1 & & \
      & ddots & ddots & ddots & \
      & & 1 & -2 & 1 \
      & & & 1 & -2 \
      end{bmatrix}
      end{equation*}
      式$eqref{eq_7}$转换如下,
      egin{equation}
      left{
      egin{split}
      frac{partial U}{partial t} &= V \
      frac{partial V}{partial t} &= Dleft( frac{UA_x}{h_x^2} + frac{A_yU}{h_y^2} ight)
      end{split}
      ight.
      label{eq_8}
      end{equation}
      注意, 上式$eqref{eq_8}$中$U$之边界值需以边界条件式$eqref{eq_3}$填充. 本文拟采用Runge-Kutta方法求解上式$eqref{eq_8}$, 则有,
      egin{equation}
      egin{bmatrix}U \ Vend{bmatrix}^{k+1}
      = egin{bmatrix}U \ Vend{bmatrix}^k + frac{1}{6}left(vec{K}_1 + 2vec{K}_2 + 2vec{K}_3 + vec{K}_4 ight)h_t
      label{eq_9}
      end{equation}
      注意, 上式$eqref{eq_9}$中$U$、$V$之初始值需以初始条件式$eqref{eq_6}$填充. 其中,
      egin{gather*}
      vec{F}(U, V) = egin{bmatrix} V \ Dleft( frac{UA_x}{h_x^2} + frac{A_yU}{h_y^2} ight) end{bmatrix} =
      egin{bmatrix} F_1 \ F_2 end{bmatrix}\
      vec{K}_1 = vec{F}(U, V) \
      vec{K}_2 = vec{F}(U + frac{h_t}{2}F_1^{(1)}, V+frac{h_t}{2}F_2^{(1)}) \
      vec{K}_3 = vec{F}(U + frac{h_t}{2}F_1^{(2)}, V+frac{h_t}{2}F_2^{(2)}) \
      vec{K}_4 = vec{F}(U + h_tF_1^{(3)}, V + h_tF_2^{(3)}) \
      end{gather*}
      其中, 上标$^{(1)}$、$^{(2)}$、$^{(3)}$分别代表相应分量来自于$vec{K}_1$、$vec{K}_2$、$vec{K}_3$. 由此, 根据式$eqref{eq_9}$即可完成式$eqref{eq_1}$2维波动方程的数值求解.
    • 代码实现:
      Part Ⅰ:
      现以如下初始条件为例进行算法实施,
      egin{equation*}
      u_0(x,y) = 0.1mathrm{e}^{-((x-x_c)^2 + (y-y_c)^2)/0.0005}
      end{equation*}
      Part Ⅱ:
      利用finite difference求解2D Wave equation, 实现如下,
        1 # 2D Wave equation之求解 - 有限差分法
        2 
        3 import numpy
        4 from matplotlib import pyplot as plt
        5 from matplotlib import animation
        6 
        7 
        8 class WaveEq(object):
        9     
       10     def __init__(self, nx=300, ny=300, nt=1000, Lx=1, Ly=1, Lt=6):
       11         self.__nx = nx                        # x轴子区间划分数
       12         self.__ny = ny                        # y轴子区间划分数
       13         self.__nt = nt                        # t轴子区间划分数
       14         self.__Lx = Lx                        # x轴半长
       15         self.__Ly = Ly                        # y轴半长
       16         self.__Lt = Lt                        # t轴全长
       17         self.__D = 0.1
       18         
       19         self.__hx = 2 * Lx / nx
       20         self.__hy = 2 * Ly / ny
       21         self.__ht = Lt / nt
       22         
       23         self.__X, self.__Y = self.__build_gridPoints()
       24         self.__T = numpy.linspace(0, Lt, nt + 1)
       25         self.__Ax, self.__Ay = self.__build_2ndOrdMat()
       26         
       27         
       28     def get_solu(self):
       29         '''
       30         数值求解
       31         '''
       32         UList = list()
       33         
       34         U0 = self.__get_initial_U0()
       35         V0 = self.__get_initial_V0()
       36         self.__fill_boundary_U(U0)
       37         UList.append(U0)
       38         for t in self.__T[:-1]:
       39             # print(t, numpy.max(U0))
       40             Uk, Vk = self.__calc_Uk_Vk(t, U0, V0)
       41             UList.append(Uk)
       42             U0, V0 = Uk, Vk
       43         
       44         return self.__X, self.__Y, self.__T, UList
       45             
       46     
       47     def __calc_Uk_Vk(self, t, U0, V0):
       48         K1 = self.__calc_F(U0, V0)
       49         tmpU, tmpV = U0 + self.__ht / 2 * K1[0], V0 + self.__ht / 2 * K1[1]
       50         self.__fill_boundary_U(tmpU)
       51         
       52         K2 = self.__calc_F(tmpU, tmpV)
       53         tmpU, tmpV = U0 + self.__ht / 2 * K2[0], V0 + self.__ht / 2 * K2[1]
       54         self.__fill_boundary_U(tmpU)
       55         
       56         K3 = self.__calc_F(tmpU, tmpV)
       57         tmpU, tmpV = U0 + self.__ht * K3[0], V0 + self.__ht * K3[1]
       58         self.__fill_boundary_U(tmpU)
       59         
       60         K4 = self.__calc_F(tmpU, tmpV)
       61         
       62         Uk = U0 + (K1[0] + 2 * K2[0] + 2 * K3[0] + K4[0]) / 6 * self.__ht
       63         Vk = V0 + (K1[1] + 2 * K2[1] + 2 * K3[1] + K4[1]) / 6 * self.__ht
       64         self.__fill_boundary_U(Uk)
       65         return Uk, Vk
       66         
       67         
       68     def __calc_F(self, U0, V0):
       69         F1 = V0
       70         term0 = numpy.matmul(U0, self.__Ax) / self.__hx ** 2
       71         term1 = numpy.matmul(self.__Ay, U0) / self.__hy ** 2
       72         F2 = self.__D * (term0 + term1)
       73         return F1, F2
       74     
       75         
       76     def __fill_boundary_U(self, U):
       77         '''
       78         填充边界条件
       79         '''
       80         U[0, :] = 0
       81         U[-1, :] = 0
       82         U[:, 0] = 0
       83         U[:, -1] = 0
       84         
       85         
       86     def __get_initial_V0(self):
       87         '''
       88         获取V之初始条件
       89         '''
       90         V0 = numpy.zeros(self.__X.shape)
       91         return V0
       92         
       93         
       94     def __get_initial_U0(self):
       95         '''
       96         获取U之初始条件
       97         '''
       98         xc = 0
       99         yc = 0
      100         U0 = 0.1 * numpy.exp(-((self.__X - xc) ** 2 + (self.__Y - yc) ** 2) / 0.0005)
      101         return U0
      102         
      103         
      104     def __build_2ndOrdMat(self):
      105         '''
      106         构造2阶微分算子的矩阵形式
      107         '''
      108         Ax = self.__build_AMat(self.__nx + 1)
      109         Ay = self.__build_AMat(self.__ny + 1)
      110         return Ax, Ay
      111         
      112         
      113     def __build_AMat(self, n):
      114         term0 = numpy.identity(n) * -2
      115         term1 = numpy.eye(n, n, 1)
      116         term2 = numpy.eye(n, n, -1)
      117         AMat = term0 + term1 + term2
      118         return AMat
      119         
      120         
      121     def __build_gridPoints(self):
      122         '''
      123         构造网格节点
      124         '''
      125         X = numpy.linspace(-self.__Lx, self.__Lx, self.__nx + 1)
      126         Y = numpy.linspace(-self.__Ly, self.__Ly, self.__ny + 1)
      127         X, Y = numpy.meshgrid(X, Y)
      128         return X, Y
      129         
      130         
      131 class WavePlot(object):
      132     
      133     fig = None
      134     ax1 = None
      135     line = None
      136     X, Y, T, Z = None, None, None, None
      137     
      138     @classmethod
      139     def plot_ani(cls, waveObj):
      140         cls.X, cls.Y, cls.T, cls.Z = waveObj.get_solu()
      141         
      142         cls.fig = plt.figure(figsize=(10, 10), dpi=40)
      143         cls.ax1 = plt.subplot()
      144         cls.line = cls.ax1.pcolormesh(cls.X, cls.Y, cls.Z[0][:-1, :-1], cmap="jet")
      145         
      146         ani = animation.FuncAnimation(cls.fig, cls.update, frames=range(1, len(cls.T), 25), blit=True, interval=5, repeat=True)
      147         
      148         ani.save("plot_ani.gif", writer="PillowWriter", fps=200)
      149         plt.close()
      150         
      151     
      152     @classmethod
      153     def update(cls, frame):
      154         cls.line.set_array(cls.Z[frame][:-1, :-1])
      155         cls.line.set_norm(norm=None)
      156         return cls.line,
      157         
      158         
      159 if __name__ == "__main__":
      160     nx = 300
      161     ny = 300
      162     nt = 4000
      163     
      164     Lx = 0.5
      165     Ly = 0.5
      166     Lt = 10
      167     waveObj = WaveEq(nx, ny, nt, Lx, Ly, Lt)
      168     # waveObj.get_solu()
      169     
      170     WavePlot.plot_ani(waveObj)
      View Code
    • 结果展示:
      注意, 以上为幅值分布情况, 为加强比对, 每一帧之色彩分布均是相对的.
    • 使用建议:
      ①. 利用变数变换降阶处理高阶PDE, 转换为低阶PDE系统便利离散化.
    • 参考文档:
      吴一东. 数值方法7:偏微分方程5:双曲型微分方程. bilibili, 2020.
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  • 原文地址:https://www.cnblogs.com/xxhbdk/p/14614327.html
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