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Runge-Kutta:求解二阶微分方程时出错

[英]Runge-Kutta : error while solving a second order differential equation

I am trying to solve a third order non linear differential equation.我正在尝试求解三阶非线性微分方程。 I have tried to transform it and I've obtained this problem which is a second order problem:我试图改变它,我得到了这个问题,这是一个二阶问题:

要解决的主要问题

I am trying to implement a fourth order Range-Kutta algorithm in order to solve it by writing it like this:我正在尝试实现四阶 Range-Kutta 算法,以便通过这样编写来解决它:

龙格-库塔问题

Here is my code for the Range-Kutta algorithm:这是我的 Range-Kutta 算法代码:

import numpy as np
import matplotlib.pyplot as plt

''''X,Y = integrate(F,x,y,xStop,h).
4th-order Runge-Kutta method for solving the initial value problem {y}' = {F(x,{y})}, where {y} = {y[0],y[1],...,y[n-1]}.
x,y = initial conditions
xStop = terminal value of x 
h = increment of x used in integration
F = user-supplied function that returns the 
array F(x,y) = {y'[0],y'[1],...,y'[n-1]}.
'''

def integrate(F,x,y,xStop,h):
    
    def run_kut4(F,x,y,h):
        K0 = h*F(x,y)
        K1 = h*F(x + h/2.0, y + K0/2.0)
        K2 = h*F(x + h/2.0, y + K1/2.0)
        K3 = h*F(x + h, y + K2)
        return (K0 + 2.0*K1 + 2.0*K2 + K3)/6.0
    
    X =[]
    Y =[]
    X.append(x)
    Y.append(y)
    while x < xStop:
        h = min(h,xStop - x)
        y = y + run_kut4(F,x,y,h)
        x = x + h
        X.append(x)
        Y.append(y)
    return np.array(X),np.array(Y)

It works fine for other differential equations.它适用于其他微分方程。

In this case the function F is defined as:在这种情况下,function F 定义为:

F函数

And the main code is:主要代码是:

def F(x,y):
    F = np.zeros(2)
    F[0] = y[1]
    F[1] = (2*(1-x)/x**3)*y[0]**(-1/2)
    return F

x = 1.0
xStop = 20
y = np.array([0,0])
h = 0.2
X,Y = integrate(F,x,y,xStop,h)
plt.plot(X,Y)
plt.grid()
plt.show()

Unfortunately, I got this error:不幸的是,我收到了这个错误:

<ipython-input-8-8216949e6888>:4: RuntimeWarning: divide by zero encountered in power
  F[1] = (2*(1-x)/x**3)*y[0]**(-1/2)
<ipython-input-8-8216949e6888>:4: RuntimeWarning: divide by zero encountered in double_scalars
  F[1] = (2*(1-x)/x**3)*y[0]**(-1/2)

It's related to the fact that the initial value of the function is 0 but I don't know how to get rid of it in order to simplify the problem again...这与function的初始值为0有关,但我不知道如何摆脱它以再次简化问题......

Could someone help me to find an other alternative?有人可以帮我找到其他选择吗?

Thank you for your help,谢谢您的帮助,

you y is [0,0] and in y[0]**(-1/2) there is division operation with 0 in the denominator which is giving ZeroDivision warning and invalid value encountered in double_scalars is due to expression y[0]**(-1/2) changed to NaN .您的y[0,0]并且在y[0]**(-1/2)中存在分母中为0的除法运算,这会给出 ZeroDivision 警告,并且在 double_scalars 中遇到的无效值是由于表达式y[0]**(-1/2)更改为NaN however, those are warnings and F is returning value array([ 0., nan]) .但是,这些是警告, F正在返回值array([ 0., nan]) you need to replace y[0]**(-1/2) as negative powers of zero are undefined or you can use an extremely small value near zero if it suits your need.您需要替换y[0]**(-1/2)因为零的负幂是未定义的,或者如果适合您的需要,您可以使用接近零的极小值。 maybe your equation is not continuous at (1,0).也许您的方程在 (1,0) 处不连续。

You can test approximate solutions close to the initial point that are powers of (1-x) or power series in this difference.您可以测试接近初始点的近似解,即(1-x)的幂或此差异中的幂级数。 In the simplest case you get y(x)=(1-x)^2 for x <= 1 .在最简单的情况下,您会得到y(x)=(1-x)^2 for x <= 1 To get solutions for x>1 you need to take the other sign in the square root.要获得x>1的解决方案,您需要在平方根中取另一个符号。 In total this gives a far-field behavior of x(t)=1+c*exp(-t) .总的来说,这给出了x(t)=1+c*exp(-t)的远场行为。

Now you can follow two strategies,现在你可以遵循两种策略,

  • integrate the reduced equation from some initial point x=1-h with y(1-h)=h^2 , y'(1-h)=-2h , along with a time integration dt/dx=y(x)^(-1/2) where t(1-h) is arbitrary, or将某个初始点x=1-h的简化方程与y(1-h)=h^2 , y'(1-h)=-2h以及时间积分dt/dx=y(x)^(-1/2)积分dt/dx=y(x)^(-1/2)其中t(1-h)是任意的,或
  • integrate the original equation from some time t=T with conditions following the derivatives of x(t)=1+c*exp(Tt) , that is, x(T)=1+c with arbitrary small c , x'(t)=-c , x''(T)=c .将某个时间t=T的原始方程与x(t)=1+c*exp(Tt)的导数之后的条件积分,即x(T)=1+c与任意小的c , x'(t)=-c , x''(T)=c In theory they should be the same, practically with fixed-step RK4 in both cases there will be differences.理论上它们应该是相同的,实际上与固定步长RK4在两种情况下都会有差异。

Make a cut across all approaches.切入所有方法。 Close to the asymptote x(t)=1 the solution is monotonous and thus time can be expressed in terms of x-1 .接近渐近线x(t)=1的解是单调的,因此时间可以用x-1表示。 This means that the derivative can (probably) be expressed as power series in x-1这意味着导数可以(可能)表示为x-1中的幂级数

x'  (t) = c_1 * (x-1) + c_2 * (x-1)^2 + ...
x'' (t) = c_1 * x'(t) + 2c_2 * (x-1)*x' + ...
        = (c_1 + 2c_2*(x-1)+...)*(c_1+c_2*(x-1)+..)*(x-1)
        = c_1^2*(x-1)+3c_1c_2*(x-1)^2 + ...
x'''(t) = (c_1^2+6c_1c_2*(x-1)+...)*(c_1+c_2*(x-1)+..)*(x-1)
        = c_1^3*(x-1) + 7c_1^2c_2*(x-1)^2 + ...

and 

(1-x)/x^3 = -(x-1)*(1+(x-1))^(-3)=-(x-1)+3*(x-1)^2 + ...

so equating coefficients

c_1^3=-1 ==> c_1 = -1
7c_1^2c_2 = 3 ==> c_2 = 3/7

Given x(T) close enough to 1, the other initial values have to be 
x'(T)=-(x(T)-1) + 3/7*(x(T)-1)^2
x''(T)=x(T)-1 -9/7*(x(T)-1)^2

Then the far-field approximation due to these first two terms is the solution to那么由于前两项的远场近似是

x'(t) = -(x-1) + 3/7 * (x-1)^2

Substitute u(t) = (x-1)^(-1) - 3/7

u'(t) = u(t)

(x(t)-1)^(-1) - 3/7 = ((x(T)-1)^(-1) - 3/7) * exp(t-T)

x(t) = 1 + (x(T)-1)*exp(T-t) / ( 1 - 3/7*(x(T)-1)*(1-exp(T-t)) )

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