我只是在测试游戏中轨道动力学的几种整合方案。这里我使用了具有恒定和自适应步长的RK4方法，链接如下： http://www.physics.buffalo.edu/phy410-505/2011/topic2/app1/index.html
我将其与简单的Verlet整合法进行了比较（以及欧拉法，但其性能非常差）。似乎RK4与恒定步长并不比Verlet更好。使用自适应步长的RK4更好，但提升并不是很大。我想知道是否出了什么问题？或者在哪种意义上说RK4比Verlet要好得多？
关键在于每个RK4步骤需要评估4次力，而每个Verlet步骤只需要评估1次力。因此，为了获得相同的性能，我可以将时间步长设为Verlet的4倍小。使用4倍较小的时间步长，Verlet比具有恒定步长的RK4更精确，与具有自适应步长的RK4几乎相当。
请查看以下图像： https://lh4.googleusercontent.com/-I4wWQYV6o4g/UW5pK93WPVI/AAAAAAAAA7I/PHSsp2nEjx0/s800/kepler.png
10T表示10个轨道周期，以下数字48968、7920、48966是所需的力评估次数。
以下是使用pylab的Python代码：
from pylab import *
import math
G_m1_plus_m2 = 4 * math.pi**2
ForceEvals = 0
def getForce(x,y):
global ForceEvals
ForceEvals += 1
r = math.sqrt( x**2 + y**2 )
A = - G_m1_plus_m2 / r**3
return x*A,y*A
def equations(trv):
x = trv[0]; y = trv[1]; vx = trv[2]; vy = trv[3];
ax,ay = getForce(x,y)
flow = array([ vx, vy, ax, ay ])
return flow
def SimpleStep( x, dt, flow ):
x += dt*flow(x)
def verletStep1( x, dt, flow ):
ax,ay = getForce(x[0],x[1])
vx = x[2] + dt*ax; vy = x[3] + dt*ay;
x[0]+= vx*dt; x[1]+= vy*dt;
x[2] = vx; x[3] = vy;
def RK4_step(x, dt, flow): # replaces x(t) by x(t + dt)
k1 = dt * flow(x);
x_temp = x + k1 / 2; k2 = dt * flow(x_temp)
x_temp = x + k2 / 2; k3 = dt * flow(x_temp)
x_temp = x + k3 ; k4 = dt * flow(x_temp)
x += (k1 + 2*k2 + 2*k3 + k4) / 6
def RK4_adaptive_step(x, dt, flow, accuracy=1e-6): # from Numerical Recipes
SAFETY = 0.9; PGROW = -0.2; PSHRINK = -0.25; ERRCON = 1.89E-4; TINY = 1.0E-30
scale = flow(x)
scale = abs(x) + abs(scale * dt) + TINY
while True:
x_half = x.copy(); RK4_step(x_half, dt/2, flow); RK4_step(x_half, dt/2, flow)
x_full = x.copy(); RK4_step(x_full, dt , flow)
Delta = (x_half - x_full)
error = max( abs(Delta[:] / scale[:]) ) / accuracy
if error <= 1:
break;
dt_temp = SAFETY * dt * error**PSHRINK
if dt >= 0:
dt = max(dt_temp, 0.1 * dt)
else:
dt = min(dt_temp, 0.1 * dt)
if abs(dt) == 0.0:
raise OverflowError("step size underflow")
if error > ERRCON:
dt *= SAFETY * error**PGROW
else:
dt *= 5
x[:] = x_half[:] + Delta[:] / 15
return dt
def integrate( trv0, dt, F, t_max, method='RK4', accuracy=1e-6 ):
global ForceEvals
ForceEvals = 0
trv = trv0.copy()
step = 0
t = 0
print "integrating with method: ",method," ... "
while True:
if method=='RK4adaptive':
dt = RK4_adaptive_step(trv, dt, equations, accuracy)
elif method=='RK4':
RK4_step(trv, dt, equations)
elif method=='Euler':
SimpleStep(trv, dt, equations)
elif method=='Verlet':
verletStep1(trv, dt, equations)
step += 1
t+=dt
F[:,step] = trv[:]
if t > t_max:
break
print " step = ", step
# ============ MAIN PROGRAM BODY =========================
r_aphelion = 1
eccentricity = 0.95
a = r_aphelion / (1 + eccentricity)
T = a**1.5
vy0 = math.sqrt(G_m1_plus_m2 * (2 / r_aphelion - 1 / a))
print " Semimajor axis a = ", a, " AU"
print " Period T = ", T, " yr"
print " v_y(0) = ", vy0, " AU/yr"
dt = 0.0003
accuracy = 0.0001
# x y vx vy
trv0 = array([ r_aphelion, 0, 0, vy0 ])
def testMethod( trv0, dt, fT, n, method, style ):
print " "
F = zeros((4,n));
integrate(trv0, dt, F, T*fT, method, accuracy);
print "Periods ",fT," ForceEvals ", ForceEvals
plot(F[0],F[1], style ,label=method+" "+str(fT)+"T "+str( ForceEvals ) );
testMethod( trv0, dt, 10, 20000 , 'RK4', '-' )
testMethod( trv0, dt, 10, 10000 , 'RK4adaptive', 'o-' )
testMethod( trv0, dt/4, 10, 100000, 'Verlet', '-' )
#testMethod( trv0, dt/160, 2, 1000000, 'Euler', '-' )
legend();
axis("equal")
savefig("kepler.png")
show();