Burgers方程解决方案迅速增加
我在实现一个简单的子程序来解决Burgers方面遇到了问题 方程：
边界条件x = [0,5] t = [0,1]
问题是代码很好地预测了函数f(x,U(time,x))中结果向量的所有值，如果我改变时间步长，域的大小等等...向量的最后4个点（矩阵U）的列超出正常溶液的范围（通常快速增加）
这是代码（模块子程序和简单主程序）：
MODULE PARAM
IMPLICIT NONE
INTEGER, PARAMETER :: SP = KIND(1.0)
INTEGER, PARAMETER :: DP = KIND(1.D0)
END MODULE
!-----------------------------------------------------------------------
MODULE BURGER_2
USE PARAM
IMPLICIT NONE
REAL(DP), PARAMETER :: x0=0. , xF= 5.
REAL(DP), PARAMETER :: t0=0. , tF= 1.0
REAL(DP), PARAMETER :: dx=0.01, dt=0.1
INTEGER , PARAMETER :: N= CEILING((xF-x0)/dx)
INTEGER , PARAMETER :: M= CEILING((tF-t0)/dt)
REAL(DP), PARAMETER :: nu = 1D-4
REAL(DP), PARAMETER :: pi=3.14159265358979323846
CONTAINS
!-----------------------------------------------------------------------
SUBROUTINE BURGER_EXPLICIT (x,t,u)
USE PARAM
REAL(DP), DIMENSION(N+1) :: x
REAL(DP), DIMENSION(M+1) :: t
REAL(DP), DIMENSION(N+1,M+1) :: U
REAL(DP) :: r,k
INTEGER :: i,j,stat
x(1:N+1) = (/ ( x0+dx*(i-1) , i=1,N+1 ) /)
t(1:M+1) = (/ (t0+ dt*(j-1) , j=1,M+1 ) /)
U(1:N+1,1:M+1) = RESHAPE((/(((sin(2*pi*(i-1)/N)), i=1,N+1), j=1,M+1)/),(/N+1,M+1/))
U(1,:) = x0
U(N+1,:) = xF
U(:,1) = t0
U(:,M+1) = tF
r = nu*DT/DX**2
k = DT/(2*DX)
DO J=2,M
DO I=2,N
U(i,j+1) = r*u(i+1,j)+(1-2*r)*u(i,j)+r*u(i-1,j) - k*u(i,j)*(u(i+1,j)-u(i-1,j))
END DO
END DO
!DO j=1,M+1
OPEN(10,file='bur1.dat',STATUS='UNKNOWN',IOSTAT=stat)
IF (STAT .NE. 0) THEN
WRITE(6,FMT='(A)')'Error opening File ''bur1.dat'' (EXIT1)'
STOP
ELSE
WRITE(10,FMT='(2F20.5)') (x(i), u(i,9), i=1,N) !((u(i,j) , i=1,N+1), J=1,M+1)
!WRITE(10,*)
!WRITE(10,FMT='(2F20.5)') (x(i), u(i,2), i=1,N) !((u(i,j) , i=1,N+1), J=1,M+1)
WRITE(6,'(50("-"))')
!END DO
END IF
RETURN
END SUBROUTINE
!-----------------------------------------------------------------------
!-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
END MODULE
主程序是：
PROGRAM main
USE BURGER_2
IMPLICIT NONE
REAL(DP) , DIMENSION (N+1) :: xx
REAL(DP) , DIMENSION (M+1) :: tt
REAL(DP) , DIMENSION (n+1,M+1) :: UU
CALL burger_EXPLICIT(xx,tt,UU)
STOP
END
我想获得一个矩阵（写入文件）用于绘制带有gnuplot的3d表面..但我不知道写这些数据的正确方法
这里举例说明程序为 u（i，9）i = 1，N + 1
编写的向量 0.00000 0.00000
0.01000 0.00637
0.02000 0.01275
0.03000 0.01912
0.04000 0.02550
0.05000 0.03187
0.06000 0.03825
0.07000 0.04462
0.08000 0.05099
0.09000 0.05736
0.10000 0.06374
0.11000 0.07011
0.12000 0.07648
0.13000 0.08285
0.14000 0.08921
0.15000 0.09558
0.16000 0.10195
0.17000 0.10831
0.18000 0.11467
0.19000 0.12104
0.20000 0.12740
0.21000 0.13375
0.22000 0.14011
0.23000 0.14647
0.24000 0.15282
0.25000 0.15917
0.26000 0.16552
0.27000 0.17187
0.28000 0.17822
0.29000 0.18456
0.30000 0.19090
0.31000 0.19724
0.32000 0.20358
0.33000 0.20991
0.34000 0.21624
0.35000 0.22257
0.36000 0.22890
0.37000 0.23522
0.38000 0.24154
0.39000 0.24786
0.40000 0.25417
0.41000 0.26049
0.42000 0.26679
0.43000 0.27310
0.44000 0.27940
0.45000 0.28570
0.46000 0.29199
0.47000 0.29828
0.48000 0.30457
0.49000 0.31085
0.50000 0.31713
0.51000 0.32340
0.52000 0.32967
0.53000 0.33594
0.54000 0.34220
0.55000 0.34846
0.56000 0.35471
0.57000 0.36095
0.58000 0.36720
0.59000 0.37344
0.60000 0.37967
0.61000 0.38590
0.62000 0.39212
0.63000 0.39833
0.64000 0.40455
0.65000 0.41075
0.66000 0.41695
0.67000 0.42315
0.68000 0.42934
0.69000 0.43552
0.70000 0.44169
0.71000 0.44786
0.72000 0.45403
0.73000 0.46018
0.74000 0.46634
0.75000 0.47248
0.76000 0.47862
0.77000 0.48475
0.78000 0.49087
0.79000 0.49698
0.80000 0.50309
0.81000 0.50919
0.82000 0.51528
0.83000 0.52137
0.84000 0.52745
0.85000 0.53351
0.86000 0.53957
0.87000 0.54563
0.88000 0.55167
0.89000 0.55770
0.90000 0.56373
0.91000 0.56975
0.92000 0.57575
0.93000 0.58175
0.94000 0.58774
0.95000 0.59372
0.96000 0.59969
0.97000 0.60565
0.98000 0.61159
0.99000 0.61753
1.00000 0.62346
1.01000 0.62937
1.02000 0.63528
1.03000 0.64117
1.04000 0.64706
1.05000 0.65293
1.06000 0.65878
1.07000 0.66463
1.08000 0.67047
1.09000 0.67629
1.10000 0.68210
1.11000 0.68789
1.12000 0.69367
1.13000 0.69944
1.14000 0.70520
1.15000 0.71094
1.16000 0.71667
1.17000 0.72238
1.18000 0.72808
1.19000 0.73376
1.20000 0.73943
1.21000 0.74508
1.22000 0.75072
1.23000 0.75634
1.24000 0.76194
1.25000 0.76752
1.26000 0.77309
1.27000 0.77864
1.28000 0.78418
1.29000 0.78969
1.30000 0.79519
1.31000 0.80067
1.32000 0.80613
1.33000 0.81157
1.34000 0.81699
1.35000 0.82238
1.36000 0.82776
1.37000 0.83312
1.38000 0.83845
1.39000 0.84377
1.40000 0.84906
1.41000 0.85432
1.42000 0.85957
1.43000 0.86479
1.44000 0.86998
1.45000 0.87515
1.46000 0.88030
1.47000 0.88542
1.48000 0.89051
1.49000 0.89558
1.50000 0.90062
1.51000 0.90563
1.52000 0.91061
1.53000 0.91556
1.54000 0.92048
1.55000 0.92537
1.56000 0.93023
1.57000 0.93506
1.58000 0.93986
1.59000 0.94462
1.60000 0.94935
1.61000 0.95404
1.62000 0.95870
1.63000 0.96332
1.64000 0.96790
1.65000 0.97244
1.66000 0.97695
1.67000 0.98141
1.68000 0.98583
1.69000 0.99020
1.70000 0.99453
1.71000 0.99882
1.72000 1.00305
1.73000 1.00724
1.74000 1.01137
1.75000 1.01545
1.76000 1.01948
1.77000 1.02344
1.78000 1.02734
1.79000 1.03118
1.80000 1.03495
1.81000 1.03864
1.82000 1.04226
1.83000 1.04580
1.84000 1.04925
1.85000 1.05261
1.86000 1.05588
1.87000 1.05903
1.88000 1.06208
1.89000 1.06501
1.90000 1.06780
1.91000 1.07046
1.92000 1.07296
1.93000 1.07531
1.94000 1.07747
1.95000 1.07945
1.96000 1.08122
1.97000 1.08276
1.98000 1.08407
1.99000 1.08511
2.00000 1.08586
2.01000 1.08631
2.02000 1.08643
2.03000 1.08618
2.04000 1.08554
2.05000 1.08448
2.06000 1.08297
2.07000 1.08096
2.08000 1.07843
2.09000 1.07532
2.10000 1.07161
2.11000 1.06725
2.12000 1.06219
2.13000 1.05639
2.14000 1.04980
2.15000 1.04237
2.16000 1.03405
2.17000 1.02479
2.18000 1.01454
2.19000 1.00325
2.20000 0.99087
2.21000 0.97735
2.22000 0.96264
2.23000 0.94670
2.24000 0.92948
2.25000 0.91095
2.26000 0.89106
2.27000 0.86979
2.28000 0.84710
2.29000 0.82298
2.30000 0.79740
2.31000 0.77037
2.32000 0.74187
2.33000 0.71191
2.34000 0.68050
2.35000 0.64766
2.36000 0.61343
2.37000 0.57783
2.38000 0.54092
2.39000 0.50275
2.40000 0.46338
2.41000 0.42289
2.42000 0.38134
2.43000 0.33884
2.44000 0.29548
2.45000 0.25135
2.46000 0.20656
2.47000 0.16123
2.48000 0.11548
2.49000 0.06941
2.50000 0.02316
2.51000 -0.02316
2.52000 -0.06941
2.53000 -0.11548
2.54000 -0.16123
2.55000 -0.20656
2.56000 -0.25135
2.57000 -0.29548
2.58000 -0.33884
2.59000 -0.38134
2.60000 -0.42289
2.61000 -0.46338
2.62000 -0.50275
2.63000 -0.54092
2.64000 -0.57784
2.65000 -0.61343
2.66000 -0.64767
2.67000 -0.68050
2.68000 -0.71191
2.69000 -0.74187
2.70000 -0.77037
2.71000 -0.79740
2.72000 -0.82298
2.73000 -0.84710
2.74000 -0.86979
2.75000 -0.89106
2.76000 -0.91095
2.77000 -0.92948
2.78000 -0.94670
2.79000 -0.96264
2.80000 -0.97735
2.81000 -0.99087
2.82000 -1.00325
2.83000 -1.01454
2.84000 -1.02479
2.85000 -1.03405
2.86000 -1.04237
2.87000 -1.04980
2.88000 -1.05639
2.89000 -1.06219
2.90000 -1.06725
2.91000 -1.07161
2.92000 -1.07532
2.93000 -1.07843
2.94000 -1.08096
2.95000 -1.08297
2.96000 -1.08448
2.97000 -1.08554
2.98000 -1.08618
2.99000 -1.08643
3.00000 -1.08631
3.01000 -1.08586
3.02000 -1.08511
3.03000 -1.08407
3.04000 -1.08276
3.05000 -1.08122
3.06000 -1.07945
3.07000 -1.07747
3.08000 -1.07531
3.09000 -1.07296
3.10000 -1.07046
3.11000 -1.06780
3.12000 -1.06501
3.13000 -1.06208
3.14000 -1.05903
3.15000 -1.05588
3.16000 -1.05261
3.17000 -1.04925
3.18000 -1.04580
3.19000 -1.04226
3.20000 -1.03864
3.21000 -1.03495
3.22000 -1.03118
3.23000 -1.02734
3.24000 -1.02344
3.25000 -1.01948
3.26000 -1.01545
3.27000 -1.01137
3.28000 -1.00724
3.29000 -1.00305
3.30000 -0.99882
3.31000 -0.99453
3.32000 -0.99020
3.33000 -0.98583
3.34000 -0.98141
3.35000 -0.97695
3.36000 -0.97244
3.37000 -0.96790
3.38000 -0.96332
3.39000 -0.95870
3.40000 -0.95404
3.41000 -0.94935
3.42000 -0.94462
3.43000 -0.93986
3.44000 -0.93506
3.45000 -0.93023
3.46000 -0.92537
3.47000 -0.92048
3.48000 -0.91556
3.49000 -0.91061
3.50000 -0.90563
3.51000 -0.90062
3.52000 -0.89558
3.53000 -0.89051
3.54000 -0.88542
3.55000 -0.88030
3.56000 -0.87515
3.57000 -0.86998
3.58000 -0.86479
3.59000 -0.85957
3.60000 -0.85432
3.61000 -0.84906
3.62000 -0.84377
3.63000 -0.83845
3.64000 -0.83312
3.65000 -0.82776
3.66000 -0.82238
3.67000 -0.81699
3.68000 -0.81157
3.69000 -0.80613
3.70000 -0.80067
3.71000 -0.79519
3.72000 -0.78969
3.73000 -0.78418
3.74000 -0.77864
3.75000 -0.77309
3.76000 -0.76752
3.77000 -0.76194
3.78000 -0.75634
3.79000 -0.75072
3.80000 -0.74508
3.81000 -0.73943
3.82000 -0.73376
3.83000 -0.72808
3.84000 -0.72238
3.85000 -0.71667
3.86000 -0.71094
3.87000 -0.70520
3.88000 -0.69944
3.89000 -0.69367
3.90000 -0.68789
3.91000 -0.68210
3.92000 -0.67629
3.93000 -0.67047
3.94000 -0.66463
3.95000 -0.65878
3.96000 -0.65293
3.97000 -0.64706
3.98000 -0.64117
3.99000 -0.63528
4.00000 -0.62937
4.01000 -0.62346
4.02000 -0.61753
4.03000 -0.61159
4.04000 -0.60565
4.05000 -0.59969
4.06000 -0.59372
4.07000 -0.58774
4.08000 -0.58175
4.09000 -0.57575
4.10000 -0.56975
4.11000 -0.56373
4.12000 -0.55770
4.13000 -0.55167
4.14000 -0.54563
4.15000 -0.53957
4.16000 -0.53351
4.17000 -0.52745
4.18000 -0.52137
4.19000 -0.51528
4.20000 -0.50919
4.21000 -0.50309
4.22000 -0.49698
4.23000 -0.49087
4.24000 -0.48475
4.25000 -0.47862
4.26000 -0.47248
4.27000 -0.46634
4.28000 -0.46018
4.29000 -0.45403
4.30000 -0.44786
4.31000 -0.44169
4.32000 -0.43552
4.33000 -0.42934
4.34000 -0.42315
4.35000 -0.41695
4.36000 -0.41075
4.37000 -0.40455
4.38000 -0.39833
4.39000 -0.39212
4.40000 -0.38590
4.41000 -0.37967
4.42000 -0.37344
4.43000 -0.36720
4.44000 -0.36095
4.45000 -0.35471
4.46000 -0.34846
4.47000 -0.34220
4.48000 -0.33594
4.49000 -0.32967
4.50000 -0.32340
4.51000 -0.31713
4.52000 -0.31085
4.53000 -0.30457
4.54000 -0.29828
4.55000 -0.29199
4.56000 -0.28570
4.57000 -0.27940
4.58000 -0.27310
4.59000 -0.26679
4.60000 -0.26049
4.61000 -0.25417
4.62000 -0.24786
4.63000 -0.24154
4.64000 -0.23522
4.65000 -0.22890
4.66000 -0.22257
4.67000 -0.21624
4.68000 -0.20991
4.69000 -0.20358
4.70000 -0.19724
4.71000 -0.19090
4.72000 -0.18456
4.73000 -0.17822
4.74000 -0.17187
4.75000 -0.16552
4.76000 -0.15917
4.77000 -0.15282
4.78000 -0.14647
4.79000 -0.14011
4.80000 -0.13375
4.81000 -0.12740
4.82000 -0.12104
4.83000 -0.11467
4.84000 -0.10831
4.85000 -0.10195
4.86000 -0.09558
4.87000 -0.08921
4.88000 -0.08285
4.89000 -0.07648
4.90000 -0.07011
4.91000 -0.06374
4.92000 -0.05736
4.93000 -0.05099
4.94000 -0.04068
4.95000 -0.21859
4.96000 -539175687.87944
4.97000********************
4.98000********************
4.99000********************
5.00000********************
5.01000 5.00000
看看最后6点
0 个答案:
没有答案
相关问题
最新问题
- 我写了这段代码，但我无法理解我的错误
- 我无法从一个代码实例的列表中删除 None 值，但我可以在另一个实例中。为什么它适用于一个细分市场而不适用于另一个细分市场？
- 是否有可能使 loadstring 不可能等于打印？卢阿
- java中的random.expovariate()
- Appscript 通过会议在 Google 日历中发送电子邮件和创建活动
- 为什么我的 Onclick 箭头功能在 React 中不起作用？
- 在此代码中是否有使用“this”的替代方法？
- 在 SQL Server 和 PostgreSQL 上查询，我如何从第一个表获得第二个表的可视化
- 每千个数字得到
- 更新了城市边界 KML 文件的来源？