Large error at boudaries while calculating first and second derivative using Finite Difference
up vote
3
down vote
favorite
I am calculating first and second derivatives of a function a(x,y,z) stored in a 3d array a(n1,n2,n3) using Finite Difference. Here the functional value at boundaries is zero. here is the code in Fortran:
implicit none
integer i1,i2,i3
integer, parameter :: n1 = 33
integer, parameter :: n2 = 33
integer, parameter :: n3 = 32
real*8 pi, a(n1,n2,n3), a2(n1,n2,n3), z(n3),x(n1),y(n2),h,a1(n1,n2,n3)
real*8 num(n1,n2,n3),deno(n1,n2,n3),diff(n1,n2,1),A0,dx,dy
pi=3.14159265358979323846d0
dx=2.0d0*pi/(n1-1)
dy=4.0d0*pi/(n2-1)
do i1=1,n1
x(i1)=-pi+(i1-1)*dx
do i2=1,n2
y(i2)=-2.0d0*pi+(i2-1)*dy
do i3=1,n3
z(i3)=(i3-1)*2.0d0*pi/n3
a(i1,i2,1)= dcos(x(i1)/2.0d0) * dcos(y(i2)/4.0d0) !input array
a1(i1,i2,1)= - 0.25d0*dcos(x(i1)/2.0d0) * dsin(y(i2)/4.0d0) !analytical expression of first order y-derivative
enddo
enddo
enddo
do i1=1,n1
do i2=1,n2
write(20,*)x(i1),y(i2),a(i1,i2,1)
enddo
enddo
call d1y(n1,n2,n3,a,a2)
do i1=1,n1
do i2=1,n2
num(i1,i2,1)=(a2(i1,i2,1)-a1(i1,i2,1)) !numerator of error calculation
deno(i1,i2,1)=a2(i1,i2,1) !denomenator of error calculation
if (dabs(deno(i1,i2,1)) .lt. 1e-10)deno(i1,i2,1)=1.0d0
diff(i1,i2,1)=dabs(num(i1,i2,1))/dabs(deno(i1,i2,1)) !relative error in 1st order derivative calculation
write(21,*)x(i1),y(i2),a(i1,i2,1),a2(i1,i2,1),diff(i1,i2,1),a1(i1,i2,1)
write(21,*)
enddo
enddo
end
subroutine d1y(n1,n2,n3,a,a2)
implicit none
integer n1, n2, n3, i1, i2, i3
real*8 pi, a(n1,n2,n3), a2(n1,n2,n3), z(n3),x(n1),y(n2),h,a1(n1,n2,n3)
pi=3.14159265358979323846d0
h=4.0d0*pi/(n2-1)
do i1=1,n1
do i3=1,n3
do i2=1,n2
if(i2 .eq. 1)then
a2(i1,i2,i3)=( -3.0d0*a(i1,i2,i3) + 4.0d0*a(i1,i2+1,i3) - a(i1,i2+2,i3) )/ (2.0d0*h)
else if(i2 .eq. n2)then
a2(i1,i2,i3)=( 3.0d0*a(i1,i2,i3) - 4.0d0*a(i1,i2-1,i3) + a(i1,i2-2,i3) )/ (2.0d0*h)
else
a2(i1,i2,i3)=( a(i1,i2+1,i3) - a(i1,i2-1,i3) )/ (2.0d0*h)
endif
enddo
enddo
enddo
end subroutine
My input function a(i1,i2,1)= dcos(x(i1)/2.0d0) * dcos(y(i2)/4.0d0), so the sample input data (for grid no 17*17*16)
-3.1415926535897931 -6.2831853071795862 3.7493994566546440E-033
-3.1415926535897931 -5.4977871437821380 1.1945836920083898E-017
-3.1415926535897931 -4.7123889803846897 2.3432602026631496E-017
-3.1415926535897931 -3.9269908169872414 3.4018865378450254E-017
-3.1415926535897931 -3.1415926535897931 4.3297802811774670E-017
-3.1415926535897931 -2.3561944901923448 5.0912829964730140E-017
-3.1415926535897931 -1.5707963267948966 5.6571305614385013E-017
-3.1415926535897931 -0.78539816339744828 6.0055777714832775E-017
-3.1415926535897931 0.0000000000000000 6.1232339957367660E-017
-3.1415926535897931 0.78539816339744828 6.0055777714832775E-017
-3.1415926535897931 1.5707963267948966 5.6571305614385013E-017
-3.1415926535897931 2.3561944901923439 5.0912829964730146E-017
-3.1415926535897931 3.1415926535897931 4.3297802811774670E-017
-3.1415926535897931 3.9269908169872423 3.4018865378450242E-017
-3.1415926535897931 4.7123889803846897 2.3432602026631496E-017
-3.1415926535897931 5.4977871437821371 1.1945836920083910E-017
-3.1415926535897931 6.2831853071795862 3.7493994566546440E-033
-2.7488935718910690 -6.2831853071795862 1.1945836920083898E-017
-2.7488935718910690 -5.4977871437821380 3.8060233744356645E-002
-2.7488935718910690 -4.7123889803846897 7.4657834050342639E-002
-2.7488935718910690 -3.9269908169872414 0.10838637566236967
-2.7488935718910690 -3.1415926535897931 0.13794968964147156
-2.7488935718910690 -2.3561944901923448 0.16221167441072892
-2.7488935718910690 -1.5707963267948966 0.18023995550173702
-2.7488935718910690 -0.78539816339744828 0.19134171618254495
-2.7488935718910690 0.0000000000000000 0.19509032201612833
-2.7488935718910690 0.78539816339744828 0.19134171618254495
-2.7488935718910690 1.5707963267948966 0.18023995550173702
-2.7488935718910690 2.3561944901923439 0.16221167441072895
-2.7488935718910690 3.1415926535897931 0.13794968964147156
-2.7488935718910690 3.9269908169872423 0.10838637566236962
-2.7488935718910690 4.7123889803846897 7.4657834050342639E-002
-2.7488935718910690 5.4977871437821371 3.8060233744356686E-002
-2.7488935718910690 6.2831853071795862 1.1945836920083898E-017
-2.3561944901923448 -6.2831853071795862 2.3432602026631496E-017
-2.3561944901923448 -5.4977871437821380 7.4657834050342639E-002
-2.3561944901923448 -4.7123889803846897 0.14644660940672630
-2.3561944901923448 -3.9269908169872414 0.21260752369181418
-2.3561944901923448 -3.1415926535897931 0.27059805007309856
-2.3561944901923448 -2.3561944901923448 0.31818964514320852
-2.3561944901923448 -1.5707963267948966 0.35355339059327384
-2.3561944901923448 -0.78539816339744828 0.37533027751786530
-2.3561944901923448 0.0000000000000000 0.38268343236508984
-2.3561944901923448 0.78539816339744828 0.37533027751786530
-2.3561944901923448 1.5707963267948966 0.35355339059327384
-2.3561944901923448 2.3561944901923439 0.31818964514320858
-2.3561944901923448 3.1415926535897931 0.27059805007309856
-2.3561944901923448 3.9269908169872423 0.21260752369181410
-2.3561944901923448 4.7123889803846897 0.14644660940672630
-2.3561944901923448 5.4977871437821371 7.4657834050342722E-002
-2.3561944901923448 6.2831853071795862 2.3432602026631496E-017
-1.9634954084936207 -6.2831853071795862 3.4018865378450254E-017
-1.9634954084936207 -5.4977871437821380 0.10838637566236967
-1.9634954084936207 -4.7123889803846897 0.21260752369181418
-1.9634954084936207 -3.9269908169872414 0.30865828381745519
-1.9634954084936207 -3.1415926535897931 0.39284747919355117
-1.9634954084936207 -2.3561944901923448 0.46193976625564342
-1.9634954084936207 -1.5707963267948966 0.51327996715933677
-1.9634954084936207 -0.78539816339744828 0.54489510677581865
-1.9634954084936207 0.0000000000000000 0.55557023301960229
-1.9634954084936207 0.78539816339744828 0.54489510677581865
-1.9634954084936207 1.5707963267948966 0.51327996715933677
-1.9634954084936207 2.3561944901923439 0.46193976625564348
-1.9634954084936207 3.1415926535897931 0.39284747919355117
-1.9634954084936207 3.9269908169872423 0.30865828381745508
-1.9634954084936207 4.7123889803846897 0.21260752369181418
-1.9634954084936207 5.4977871437821371 0.10838637566236978
-1.9634954084936207 6.2831853071795862 3.4018865378450254E-017
-1.5707963267948966 -6.2831853071795862 4.3297802811774670E-017
-1.5707963267948966 -5.4977871437821380 0.13794968964147156
-1.5707963267948966 -4.7123889803846897 0.27059805007309856
-1.5707963267948966 -3.9269908169872414 0.39284747919355117
-1.5707963267948966 -3.1415926535897931 0.50000000000000011
-1.5707963267948966 -2.3561944901923448 0.58793780120967942
-1.5707963267948966 -1.5707963267948966 0.65328148243818829
-1.5707963267948966 -0.78539816339744828 0.69351992266107376
-1.5707963267948966 0.0000000000000000 0.70710678118654757
-1.5707963267948966 0.78539816339744828 0.69351992266107376
-1.5707963267948966 1.5707963267948966 0.65328148243818829
-1.5707963267948966 2.3561944901923439 0.58793780120967942
-1.5707963267948966 3.1415926535897931 0.50000000000000011
-1.5707963267948966 3.9269908169872423 0.39284747919355101
-1.5707963267948966 4.7123889803846897 0.27059805007309856
-1.5707963267948966 5.4977871437821371 0.13794968964147170
-1.5707963267948966 6.2831853071795862 4.3297802811774670E-017
-1.1780972450961724 -6.2831853071795862 5.0912829964730140E-017
-1.1780972450961724 -5.4977871437821380 0.16221167441072892
-1.1780972450961724 -4.7123889803846897 0.31818964514320852
-1.1780972450961724 -3.9269908169872414 0.46193976625564342
-1.1780972450961724 -3.1415926535897931 0.58793780120967942
-1.1780972450961724 -2.3561944901923448 0.69134171618254492
-1.1780972450961724 -1.5707963267948966 0.76817775671141630
-1.1780972450961724 -0.78539816339744828 0.81549315684891710
-1.1780972450961724 0.0000000000000000 0.83146961230254524
-1.1780972450961724 0.78539816339744828 0.81549315684891710
-1.1780972450961724 1.5707963267948966 0.76817775671141630
-1.1780972450961724 2.3561944901923439 0.69134171618254503
-1.1780972450961724 3.1415926535897931 0.58793780120967942
-1.1780972450961724 3.9269908169872423 0.46193976625564326
-1.1780972450961724 4.7123889803846897 0.31818964514320852
-1.1780972450961724 5.4977871437821371 0.16221167441072909
-1.1780972450961724 6.2831853071795862 5.0912829964730140E-017
-0.78539816339744828 -6.2831853071795862 5.6571305614385013E-017
-0.78539816339744828 -5.4977871437821380 0.18023995550173702
-0.78539816339744828 -4.7123889803846897 0.35355339059327384
-0.78539816339744828 -3.9269908169872414 0.51327996715933677
-0.78539816339744828 -3.1415926535897931 0.65328148243818829
-0.78539816339744828 -2.3561944901923448 0.76817775671141630
-0.78539816339744828 -1.5707963267948966 0.85355339059327373
-0.78539816339744828 -0.78539816339744828 0.90612744635288778
-0.78539816339744828 0.0000000000000000 0.92387953251128674
-0.78539816339744828 0.78539816339744828 0.90612744635288778
-0.78539816339744828 1.5707963267948966 0.85355339059327373
-0.78539816339744828 2.3561944901923439 0.76817775671141642
-0.78539816339744828 3.1415926535897931 0.65328148243818829
-0.78539816339744828 3.9269908169872423 0.51327996715933655
-0.78539816339744828 4.7123889803846897 0.35355339059327384
-0.78539816339744828 5.4977871437821371 0.18023995550173721
-0.78539816339744828 6.2831853071795862 5.6571305614385013E-017
-0.39269908169872414 -6.2831853071795862 6.0055777714832775E-017
-0.39269908169872414 -5.4977871437821380 0.19134171618254495
-0.39269908169872414 -4.7123889803846897 0.37533027751786530
-0.39269908169872414 -3.9269908169872414 0.54489510677581865
-0.39269908169872414 -3.1415926535897931 0.69351992266107376
-0.39269908169872414 -2.3561944901923448 0.81549315684891710
-0.39269908169872414 -1.5707963267948966 0.90612744635288778
-0.39269908169872414 -0.78539816339744828 0.96193976625564337
-0.39269908169872414 0.0000000000000000 0.98078528040323043
-0.39269908169872414 0.78539816339744828 0.96193976625564337
-0.39269908169872414 1.5707963267948966 0.90612744635288778
-0.39269908169872414 2.3561944901923439 0.81549315684891721
-0.39269908169872414 3.1415926535897931 0.69351992266107376
-0.39269908169872414 3.9269908169872423 0.54489510677581843
-0.39269908169872414 4.7123889803846897 0.37533027751786530
-0.39269908169872414 5.4977871437821371 0.19134171618254514
-0.39269908169872414 6.2831853071795862 6.0055777714832775E-017
0.0000000000000000 -6.2831853071795862 6.1232339957367660E-017
0.0000000000000000 -5.4977871437821380 0.19509032201612833
0.0000000000000000 -4.7123889803846897 0.38268343236508984
0.0000000000000000 -3.9269908169872414 0.55557023301960229
0.0000000000000000 -3.1415926535897931 0.70710678118654757
0.0000000000000000 -2.3561944901923448 0.83146961230254524
0.0000000000000000 -1.5707963267948966 0.92387953251128674
0.0000000000000000 -0.78539816339744828 0.98078528040323043
0.0000000000000000 0.0000000000000000 1.0000000000000000
0.0000000000000000 0.78539816339744828 0.98078528040323043
0.0000000000000000 1.5707963267948966 0.92387953251128674
0.0000000000000000 2.3561944901923439 0.83146961230254535
0.0000000000000000 3.1415926535897931 0.70710678118654757
0.0000000000000000 3.9269908169872423 0.55557023301960207
0.0000000000000000 4.7123889803846897 0.38268343236508984
0.0000000000000000 5.4977871437821371 0.19509032201612853
0.0000000000000000 6.2831853071795862 6.1232339957367660E-017
0.39269908169872414 -6.2831853071795862 6.0055777714832775E-017
0.39269908169872414 -5.4977871437821380 0.19134171618254495
0.39269908169872414 -4.7123889803846897 0.37533027751786530
0.39269908169872414 -3.9269908169872414 0.54489510677581865
0.39269908169872414 -3.1415926535897931 0.69351992266107376
0.39269908169872414 -2.3561944901923448 0.81549315684891710
0.39269908169872414 -1.5707963267948966 0.90612744635288778
0.39269908169872414 -0.78539816339744828 0.96193976625564337
0.39269908169872414 0.0000000000000000 0.98078528040323043
0.39269908169872414 0.78539816339744828 0.96193976625564337
0.39269908169872414 1.5707963267948966 0.90612744635288778
0.39269908169872414 2.3561944901923439 0.81549315684891721
0.39269908169872414 3.1415926535897931 0.69351992266107376
0.39269908169872414 3.9269908169872423 0.54489510677581843
0.39269908169872414 4.7123889803846897 0.37533027751786530
0.39269908169872414 5.4977871437821371 0.19134171618254514
0.39269908169872414 6.2831853071795862 6.0055777714832775E-017
0.78539816339744828 -6.2831853071795862 5.6571305614385013E-017
0.78539816339744828 -5.4977871437821380 0.18023995550173702
0.78539816339744828 -4.7123889803846897 0.35355339059327384
0.78539816339744828 -3.9269908169872414 0.51327996715933677
0.78539816339744828 -3.1415926535897931 0.65328148243818829
0.78539816339744828 -2.3561944901923448 0.76817775671141630
0.78539816339744828 -1.5707963267948966 0.85355339059327373
0.78539816339744828 -0.78539816339744828 0.90612744635288778
0.78539816339744828 0.0000000000000000 0.92387953251128674
0.78539816339744828 0.78539816339744828 0.90612744635288778
0.78539816339744828 1.5707963267948966 0.85355339059327373
0.78539816339744828 2.3561944901923439 0.76817775671141642
0.78539816339744828 3.1415926535897931 0.65328148243818829
0.78539816339744828 3.9269908169872423 0.51327996715933655
0.78539816339744828 4.7123889803846897 0.35355339059327384
0.78539816339744828 5.4977871437821371 0.18023995550173721
0.78539816339744828 6.2831853071795862 5.6571305614385013E-017
1.1780972450961720 -6.2831853071795862 5.0912829964730146E-017
1.1780972450961720 -5.4977871437821380 0.16221167441072895
1.1780972450961720 -4.7123889803846897 0.31818964514320858
1.1780972450961720 -3.9269908169872414 0.46193976625564348
1.1780972450961720 -3.1415926535897931 0.58793780120967942
1.1780972450961720 -2.3561944901923448 0.69134171618254503
1.1780972450961720 -1.5707963267948966 0.76817775671141642
1.1780972450961720 -0.78539816339744828 0.81549315684891721
1.1780972450961720 0.0000000000000000 0.83146961230254535
1.1780972450961720 0.78539816339744828 0.81549315684891721
1.1780972450961720 1.5707963267948966 0.76817775671141642
1.1780972450961720 2.3561944901923439 0.69134171618254503
1.1780972450961720 3.1415926535897931 0.58793780120967942
1.1780972450961720 3.9269908169872423 0.46193976625564331
1.1780972450961720 4.7123889803846897 0.31818964514320858
1.1780972450961720 5.4977871437821371 0.16221167441072912
1.1780972450961720 6.2831853071795862 5.0912829964730146E-017
1.5707963267948966 -6.2831853071795862 4.3297802811774670E-017
1.5707963267948966 -5.4977871437821380 0.13794968964147156
1.5707963267948966 -4.7123889803846897 0.27059805007309856
1.5707963267948966 -3.9269908169872414 0.39284747919355117
1.5707963267948966 -3.1415926535897931 0.50000000000000011
1.5707963267948966 -2.3561944901923448 0.58793780120967942
1.5707963267948966 -1.5707963267948966 0.65328148243818829
1.5707963267948966 -0.78539816339744828 0.69351992266107376
1.5707963267948966 0.0000000000000000 0.70710678118654757
1.5707963267948966 0.78539816339744828 0.69351992266107376
1.5707963267948966 1.5707963267948966 0.65328148243818829
1.5707963267948966 2.3561944901923439 0.58793780120967942
1.5707963267948966 3.1415926535897931 0.50000000000000011
1.5707963267948966 3.9269908169872423 0.39284747919355101
1.5707963267948966 4.7123889803846897 0.27059805007309856
1.5707963267948966 5.4977871437821371 0.13794968964147170
1.5707963267948966 6.2831853071795862 4.3297802811774670E-017
1.9634954084936211 -6.2831853071795862 3.4018865378450242E-017
1.9634954084936211 -5.4977871437821380 0.10838637566236962
1.9634954084936211 -4.7123889803846897 0.21260752369181410
1.9634954084936211 -3.9269908169872414 0.30865828381745508
1.9634954084936211 -3.1415926535897931 0.39284747919355101
1.9634954084936211 -2.3561944901923448 0.46193976625564326
1.9634954084936211 -1.5707963267948966 0.51327996715933655
1.9634954084936211 -0.78539816339744828 0.54489510677581843
1.9634954084936211 0.0000000000000000 0.55557023301960207
1.9634954084936211 0.78539816339744828 0.54489510677581843
1.9634954084936211 1.5707963267948966 0.51327996715933655
1.9634954084936211 2.3561944901923439 0.46193976625564331
1.9634954084936211 3.1415926535897931 0.39284747919355101
1.9634954084936211 3.9269908169872423 0.30865828381745491
1.9634954084936211 4.7123889803846897 0.21260752369181410
1.9634954084936211 5.4977871437821371 0.10838637566236972
1.9634954084936211 6.2831853071795862 3.4018865378450242E-017
2.3561944901923448 -6.2831853071795862 2.3432602026631496E-017
2.3561944901923448 -5.4977871437821380 7.4657834050342639E-002
2.3561944901923448 -4.7123889803846897 0.14644660940672630
2.3561944901923448 -3.9269908169872414 0.21260752369181418
2.3561944901923448 -3.1415926535897931 0.27059805007309856
2.3561944901923448 -2.3561944901923448 0.31818964514320852
2.3561944901923448 -1.5707963267948966 0.35355339059327384
2.3561944901923448 -0.78539816339744828 0.37533027751786530
2.3561944901923448 0.0000000000000000 0.38268343236508984
2.3561944901923448 0.78539816339744828 0.37533027751786530
2.3561944901923448 1.5707963267948966 0.35355339059327384
2.3561944901923448 2.3561944901923439 0.31818964514320858
2.3561944901923448 3.1415926535897931 0.27059805007309856
2.3561944901923448 3.9269908169872423 0.21260752369181410
2.3561944901923448 4.7123889803846897 0.14644660940672630
2.3561944901923448 5.4977871437821371 7.4657834050342722E-002
2.3561944901923448 6.2831853071795862 2.3432602026631496E-017
2.7488935718910685 -6.2831853071795862 1.1945836920083910E-017
2.7488935718910685 -5.4977871437821380 3.8060233744356686E-002
2.7488935718910685 -4.7123889803846897 7.4657834050342722E-002
2.7488935718910685 -3.9269908169872414 0.10838637566236978
2.7488935718910685 -3.1415926535897931 0.13794968964147170
2.7488935718910685 -2.3561944901923448 0.16221167441072909
2.7488935718910685 -1.5707963267948966 0.18023995550173721
2.7488935718910685 -0.78539816339744828 0.19134171618254514
2.7488935718910685 0.0000000000000000 0.19509032201612853
2.7488935718910685 0.78539816339744828 0.19134171618254514
2.7488935718910685 1.5707963267948966 0.18023995550173721
2.7488935718910685 2.3561944901923439 0.16221167441072912
2.7488935718910685 3.1415926535897931 0.13794968964147170
2.7488935718910685 3.9269908169872423 0.10838637566236972
2.7488935718910685 4.7123889803846897 7.4657834050342722E-002
2.7488935718910685 5.4977871437821371 3.8060233744356721E-002
2.7488935718910685 6.2831853071795862 1.1945836920083910E-017
3.1415926535897931 -6.2831853071795862 3.7493994566546440E-033
3.1415926535897931 -5.4977871437821380 1.1945836920083898E-017
3.1415926535897931 -4.7123889803846897 2.3432602026631496E-017
3.1415926535897931 -3.9269908169872414 3.4018865378450254E-017
3.1415926535897931 -3.1415926535897931 4.3297802811774670E-017
3.1415926535897931 -2.3561944901923448 5.0912829964730140E-017
3.1415926535897931 -1.5707963267948966 5.6571305614385013E-017
3.1415926535897931 -0.78539816339744828 6.0055777714832775E-017
3.1415926535897931 0.0000000000000000 6.1232339957367660E-017
3.1415926535897931 0.78539816339744828 6.0055777714832775E-017
3.1415926535897931 1.5707963267948966 5.6571305614385013E-017
3.1415926535897931 2.3561944901923439 5.0912829964730146E-017
3.1415926535897931 3.1415926535897931 4.3297802811774670E-017
3.1415926535897931 3.9269908169872423 3.4018865378450242E-017
3.1415926535897931 4.7123889803846897 2.3432602026631496E-017
3.1415926535897931 5.4977871437821371 1.1945836920083910E-017
3.1415926535897931 6.2831853071795862 3.7493994566546440E-033
Input and output functions are as shown in the figure below.
As the output function is the same as - 0.25d0*dcos(x(i1)/2.0d0) * dsin(y(i2)/4.0d0), this shows derivative calculation is correct. Here in subroutine 'd1y', I have used forward and backward finite difference formula for calculation of derivative at boundaries and the central difference for points lying between two boundaries. Then I have calculated the relative error. I got an error of 0.003 at the boundary of y axis and 0.0015 at the points in between boundaries as shown in fig below
.
I calculated 2nd derivative using same technique. The subroutine is given below:
`subroutine d2y(n1,n2,n3,a,a2)
implicit none
integer n1, n2, n3, i1, i2, i3
real*8 pi, a(n1,n2,n3), a2(n1,n2,n3), z(n3),x(n1),y(n2),h
pi=3.14159265358979323846d0
h=4.0d0*pi/(n2-1)
a2=0.0d0
i3 =1
do i1=1,n1
do i2=1,n2
if(i2 == 1)then
a2(i1,i2,i3)=( 2.0d0*a(i1,i2,i3) - 5.0d0*a(i1,i2+1,i3) + 4.0d0*a(i1,i2+2,i3) - a(i1,i2+3,i3))/(h*h)
else if( i2 == n2)then
a2(i1,i2,i3)=( 2.0d0*a(i1,i2,i3) - 5.0d0*a(i1,i2-1,i3) + 4.0d0*a(i1,i2-2,i3) - a(i1,i2-3,i3))/(h*h)
else
a2(i1,i2,i3)= ( a(i1,i2+1,i3) - 2.0d0*a(i1,i2,i3) + a(i1,i2-1,i3) )/(h*h)
endif
enddo
enddo
! enddo
end subroutine
Here also the error is large at boundaries, in fact, the error is very large compared to first order derivative as shown in figure:
.
Why this large error in boundary? Please explain.
`
fortran numerical-methods derivative
edited Nov 11 at 14:05
b-fg
1,64811422
asked Nov 10 at 12:41
Jagannath Mahapatra
286
|
show 2 more comments
up vote
3
down vote
favorite
I am calculating first and second derivatives of a function a(x,y,z) stored in a 3d array a(n1,n2,n3) using Finite Difference. Here the functional value at boundaries is zero. here is the code in Fortran:
implicit none
integer i1,i2,i3
integer, parameter :: n1 = 33
integer, parameter :: n2 = 33
integer, parameter :: n3 = 32
real*8 pi, a(n1,n2,n3), a2(n1,n2,n3), z(n3),x(n1),y(n2),h,a1(n1,n2,n3)
real*8 num(n1,n2,n3),deno(n1,n2,n3),diff(n1,n2,1),A0,dx,dy
pi=3.14159265358979323846d0
dx=2.0d0*pi/(n1-1)
dy=4.0d0*pi/(n2-1)
do i1=1,n1
x(i1)=-pi+(i1-1)*dx
do i2=1,n2
y(i2)=-2.0d0*pi+(i2-1)*dy
do i3=1,n3
z(i3)=(i3-1)*2.0d0*pi/n3
a(i1,i2,1)= dcos(x(i1)/2.0d0) * dcos(y(i2)/4.0d0) !input array
a1(i1,i2,1)= - 0.25d0*dcos(x(i1)/2.0d0) * dsin(y(i2)/4.0d0) !analytical expression of first order y-derivative
enddo
enddo
enddo
do i1=1,n1
do i2=1,n2
write(20,*)x(i1),y(i2),a(i1,i2,1)
enddo
enddo
call d1y(n1,n2,n3,a,a2)
do i1=1,n1
do i2=1,n2
num(i1,i2,1)=(a2(i1,i2,1)-a1(i1,i2,1)) !numerator of error calculation
deno(i1,i2,1)=a2(i1,i2,1) !denomenator of error calculation
if (dabs(deno(i1,i2,1)) .lt. 1e-10)deno(i1,i2,1)=1.0d0
diff(i1,i2,1)=dabs(num(i1,i2,1))/dabs(deno(i1,i2,1)) !relative error in 1st order derivative calculation
write(21,*)x(i1),y(i2),a(i1,i2,1),a2(i1,i2,1),diff(i1,i2,1),a1(i1,i2,1)
write(21,*)
enddo
enddo
end
subroutine d1y(n1,n2,n3,a,a2)
implicit none
integer n1, n2, n3, i1, i2, i3
real*8 pi, a(n1,n2,n3), a2(n1,n2,n3), z(n3),x(n1),y(n2),h,a1(n1,n2,n3)
pi=3.14159265358979323846d0
h=4.0d0*pi/(n2-1)
do i1=1,n1
do i3=1,n3
do i2=1,n2
if(i2 .eq. 1)then
a2(i1,i2,i3)=( -3.0d0*a(i1,i2,i3) + 4.0d0*a(i1,i2+1,i3) - a(i1,i2+2,i3) )/ (2.0d0*h)
else if(i2 .eq. n2)then
a2(i1,i2,i3)=( 3.0d0*a(i1,i2,i3) - 4.0d0*a(i1,i2-1,i3) + a(i1,i2-2,i3) )/ (2.0d0*h)
else
a2(i1,i2,i3)=( a(i1,i2+1,i3) - a(i1,i2-1,i3) )/ (2.0d0*h)
endif
enddo
enddo
enddo
end subroutine
My input function a(i1,i2,1)= dcos(x(i1)/2.0d0) * dcos(y(i2)/4.0d0), so the sample input data (for grid no 17*17*16)
-3.1415926535897931 -6.2831853071795862 3.7493994566546440E-033
-3.1415926535897931 -5.4977871437821380 1.1945836920083898E-017
-3.1415926535897931 -4.7123889803846897 2.3432602026631496E-017
-3.1415926535897931 -3.9269908169872414 3.4018865378450254E-017
-3.1415926535897931 -3.1415926535897931 4.3297802811774670E-017
-3.1415926535897931 -2.3561944901923448 5.0912829964730140E-017
-3.1415926535897931 -1.5707963267948966 5.6571305614385013E-017
-3.1415926535897931 -0.78539816339744828 6.0055777714832775E-017
-3.1415926535897931 0.0000000000000000 6.1232339957367660E-017
-3.1415926535897931 0.78539816339744828 6.0055777714832775E-017
-3.1415926535897931 1.5707963267948966 5.6571305614385013E-017
-3.1415926535897931 2.3561944901923439 5.0912829964730146E-017
-3.1415926535897931 3.1415926535897931 4.3297802811774670E-017
-3.1415926535897931 3.9269908169872423 3.4018865378450242E-017
-3.1415926535897931 4.7123889803846897 2.3432602026631496E-017
-3.1415926535897931 5.4977871437821371 1.1945836920083910E-017
-3.1415926535897931 6.2831853071795862 3.7493994566546440E-033
-2.7488935718910690 -6.2831853071795862 1.1945836920083898E-017
-2.7488935718910690 -5.4977871437821380 3.8060233744356645E-002
-2.7488935718910690 -4.7123889803846897 7.4657834050342639E-002
-2.7488935718910690 -3.9269908169872414 0.10838637566236967
-2.7488935718910690 -3.1415926535897931 0.13794968964147156
-2.7488935718910690 -2.3561944901923448 0.16221167441072892
-2.7488935718910690 -1.5707963267948966 0.18023995550173702
-2.7488935718910690 -0.78539816339744828 0.19134171618254495
-2.7488935718910690 0.0000000000000000 0.19509032201612833
-2.7488935718910690 0.78539816339744828 0.19134171618254495
-2.7488935718910690 1.5707963267948966 0.18023995550173702
-2.7488935718910690 2.3561944901923439 0.16221167441072895
-2.7488935718910690 3.1415926535897931 0.13794968964147156
-2.7488935718910690 3.9269908169872423 0.10838637566236962
-2.7488935718910690 4.7123889803846897 7.4657834050342639E-002
-2.7488935718910690 5.4977871437821371 3.8060233744356686E-002
-2.7488935718910690 6.2831853071795862 1.1945836920083898E-017
-2.3561944901923448 -6.2831853071795862 2.3432602026631496E-017
-2.3561944901923448 -5.4977871437821380 7.4657834050342639E-002
-2.3561944901923448 -4.7123889803846897 0.14644660940672630
-2.3561944901923448 -3.9269908169872414 0.21260752369181418
-2.3561944901923448 -3.1415926535897931 0.27059805007309856
-2.3561944901923448 -2.3561944901923448 0.31818964514320852
-2.3561944901923448 -1.5707963267948966 0.35355339059327384
-2.3561944901923448 -0.78539816339744828 0.37533027751786530
-2.3561944901923448 0.0000000000000000 0.38268343236508984
-2.3561944901923448 0.78539816339744828 0.37533027751786530
-2.3561944901923448 1.5707963267948966 0.35355339059327384
-2.3561944901923448 2.3561944901923439 0.31818964514320858
-2.3561944901923448 3.1415926535897931 0.27059805007309856
-2.3561944901923448 3.9269908169872423 0.21260752369181410
-2.3561944901923448 4.7123889803846897 0.14644660940672630
-2.3561944901923448 5.4977871437821371 7.4657834050342722E-002
-2.3561944901923448 6.2831853071795862 2.3432602026631496E-017
-1.9634954084936207 -6.2831853071795862 3.4018865378450254E-017
-1.9634954084936207 -5.4977871437821380 0.10838637566236967
-1.9634954084936207 -4.7123889803846897 0.21260752369181418
-1.9634954084936207 -3.9269908169872414 0.30865828381745519
-1.9634954084936207 -3.1415926535897931 0.39284747919355117
-1.9634954084936207 -2.3561944901923448 0.46193976625564342
-1.9634954084936207 -1.5707963267948966 0.51327996715933677
-1.9634954084936207 -0.78539816339744828 0.54489510677581865
-1.9634954084936207 0.0000000000000000 0.55557023301960229
-1.9634954084936207 0.78539816339744828 0.54489510677581865
-1.9634954084936207 1.5707963267948966 0.51327996715933677
-1.9634954084936207 2.3561944901923439 0.46193976625564348
-1.9634954084936207 3.1415926535897931 0.39284747919355117
-1.9634954084936207 3.9269908169872423 0.30865828381745508
-1.9634954084936207 4.7123889803846897 0.21260752369181418
-1.9634954084936207 5.4977871437821371 0.10838637566236978
-1.9634954084936207 6.2831853071795862 3.4018865378450254E-017
-1.5707963267948966 -6.2831853071795862 4.3297802811774670E-017
-1.5707963267948966 -5.4977871437821380 0.13794968964147156
-1.5707963267948966 -4.7123889803846897 0.27059805007309856
-1.5707963267948966 -3.9269908169872414 0.39284747919355117
-1.5707963267948966 -3.1415926535897931 0.50000000000000011
-1.5707963267948966 -2.3561944901923448 0.58793780120967942
-1.5707963267948966 -1.5707963267948966 0.65328148243818829
-1.5707963267948966 -0.78539816339744828 0.69351992266107376
-1.5707963267948966 0.0000000000000000 0.70710678118654757
-1.5707963267948966 0.78539816339744828 0.69351992266107376
-1.5707963267948966 1.5707963267948966 0.65328148243818829
-1.5707963267948966 2.3561944901923439 0.58793780120967942
-1.5707963267948966 3.1415926535897931 0.50000000000000011
-1.5707963267948966 3.9269908169872423 0.39284747919355101
-1.5707963267948966 4.7123889803846897 0.27059805007309856
-1.5707963267948966 5.4977871437821371 0.13794968964147170
-1.5707963267948966 6.2831853071795862 4.3297802811774670E-017
-1.1780972450961724 -6.2831853071795862 5.0912829964730140E-017
-1.1780972450961724 -5.4977871437821380 0.16221167441072892
-1.1780972450961724 -4.7123889803846897 0.31818964514320852
-1.1780972450961724 -3.9269908169872414 0.46193976625564342
-1.1780972450961724 -3.1415926535897931 0.58793780120967942
-1.1780972450961724 -2.3561944901923448 0.69134171618254492
-1.1780972450961724 -1.5707963267948966 0.76817775671141630
-1.1780972450961724 -0.78539816339744828 0.81549315684891710
-1.1780972450961724 0.0000000000000000 0.83146961230254524
-1.1780972450961724 0.78539816339744828 0.81549315684891710
-1.1780972450961724 1.5707963267948966 0.76817775671141630
-1.1780972450961724 2.3561944901923439 0.69134171618254503
-1.1780972450961724 3.1415926535897931 0.58793780120967942
-1.1780972450961724 3.9269908169872423 0.46193976625564326
-1.1780972450961724 4.7123889803846897 0.31818964514320852
-1.1780972450961724 5.4977871437821371 0.16221167441072909
-1.1780972450961724 6.2831853071795862 5.0912829964730140E-017
-0.78539816339744828 -6.2831853071795862 5.6571305614385013E-017
-0.78539816339744828 -5.4977871437821380 0.18023995550173702
-0.78539816339744828 -4.7123889803846897 0.35355339059327384
-0.78539816339744828 -3.9269908169872414 0.51327996715933677
-0.78539816339744828 -3.1415926535897931 0.65328148243818829
-0.78539816339744828 -2.3561944901923448 0.76817775671141630
-0.78539816339744828 -1.5707963267948966 0.85355339059327373
-0.78539816339744828 -0.78539816339744828 0.90612744635288778
-0.78539816339744828 0.0000000000000000 0.92387953251128674
-0.78539816339744828 0.78539816339744828 0.90612744635288778
-0.78539816339744828 1.5707963267948966 0.85355339059327373
-0.78539816339744828 2.3561944901923439 0.76817775671141642
-0.78539816339744828 3.1415926535897931 0.65328148243818829
-0.78539816339744828 3.9269908169872423 0.51327996715933655
-0.78539816339744828 4.7123889803846897 0.35355339059327384
-0.78539816339744828 5.4977871437821371 0.18023995550173721
-0.78539816339744828 6.2831853071795862 5.6571305614385013E-017
-0.39269908169872414 -6.2831853071795862 6.0055777714832775E-017
-0.39269908169872414 -5.4977871437821380 0.19134171618254495
-0.39269908169872414 -4.7123889803846897 0.37533027751786530
-0.39269908169872414 -3.9269908169872414 0.54489510677581865
-0.39269908169872414 -3.1415926535897931 0.69351992266107376
-0.39269908169872414 -2.3561944901923448 0.81549315684891710
-0.39269908169872414 -1.5707963267948966 0.90612744635288778
-0.39269908169872414 -0.78539816339744828 0.96193976625564337
-0.39269908169872414 0.0000000000000000 0.98078528040323043
-0.39269908169872414 0.78539816339744828 0.96193976625564337
-0.39269908169872414 1.5707963267948966 0.90612744635288778
-0.39269908169872414 2.3561944901923439 0.81549315684891721
-0.39269908169872414 3.1415926535897931 0.69351992266107376
-0.39269908169872414 3.9269908169872423 0.54489510677581843
-0.39269908169872414 4.7123889803846897 0.37533027751786530
-0.39269908169872414 5.4977871437821371 0.19134171618254514
-0.39269908169872414 6.2831853071795862 6.0055777714832775E-017
0.0000000000000000 -6.2831853071795862 6.1232339957367660E-017
0.0000000000000000 -5.4977871437821380 0.19509032201612833
0.0000000000000000 -4.7123889803846897 0.38268343236508984
0.0000000000000000 -3.9269908169872414 0.55557023301960229
0.0000000000000000 -3.1415926535897931 0.70710678118654757
0.0000000000000000 -2.3561944901923448 0.83146961230254524
0.0000000000000000 -1.5707963267948966 0.92387953251128674
0.0000000000000000 -0.78539816339744828 0.98078528040323043
0.0000000000000000 0.0000000000000000 1.0000000000000000
0.0000000000000000 0.78539816339744828 0.98078528040323043
0.0000000000000000 1.5707963267948966 0.92387953251128674
0.0000000000000000 2.3561944901923439 0.83146961230254535
0.0000000000000000 3.1415926535897931 0.70710678118654757
0.0000000000000000 3.9269908169872423 0.55557023301960207
0.0000000000000000 4.7123889803846897 0.38268343236508984
0.0000000000000000 5.4977871437821371 0.19509032201612853
0.0000000000000000 6.2831853071795862 6.1232339957367660E-017
0.39269908169872414 -6.2831853071795862 6.0055777714832775E-017
0.39269908169872414 -5.4977871437821380 0.19134171618254495
0.39269908169872414 -4.7123889803846897 0.37533027751786530
0.39269908169872414 -3.9269908169872414 0.54489510677581865
0.39269908169872414 -3.1415926535897931 0.69351992266107376
0.39269908169872414 -2.3561944901923448 0.81549315684891710
0.39269908169872414 -1.5707963267948966 0.90612744635288778
0.39269908169872414 -0.78539816339744828 0.96193976625564337
0.39269908169872414 0.0000000000000000 0.98078528040323043
0.39269908169872414 0.78539816339744828 0.96193976625564337
0.39269908169872414 1.5707963267948966 0.90612744635288778
0.39269908169872414 2.3561944901923439 0.81549315684891721
0.39269908169872414 3.1415926535897931 0.69351992266107376
0.39269908169872414 3.9269908169872423 0.54489510677581843
0.39269908169872414 4.7123889803846897 0.37533027751786530
0.39269908169872414 5.4977871437821371 0.19134171618254514
0.39269908169872414 6.2831853071795862 6.0055777714832775E-017
0.78539816339744828 -6.2831853071795862 5.6571305614385013E-017
0.78539816339744828 -5.4977871437821380 0.18023995550173702
0.78539816339744828 -4.7123889803846897 0.35355339059327384
0.78539816339744828 -3.9269908169872414 0.51327996715933677
0.78539816339744828 -3.1415926535897931 0.65328148243818829
0.78539816339744828 -2.3561944901923448 0.76817775671141630
0.78539816339744828 -1.5707963267948966 0.85355339059327373
0.78539816339744828 -0.78539816339744828 0.90612744635288778
0.78539816339744828 0.0000000000000000 0.92387953251128674
0.78539816339744828 0.78539816339744828 0.90612744635288778
0.78539816339744828 1.5707963267948966 0.85355339059327373
0.78539816339744828 2.3561944901923439 0.76817775671141642
0.78539816339744828 3.1415926535897931 0.65328148243818829
0.78539816339744828 3.9269908169872423 0.51327996715933655
0.78539816339744828 4.7123889803846897 0.35355339059327384
0.78539816339744828 5.4977871437821371 0.18023995550173721
0.78539816339744828 6.2831853071795862 5.6571305614385013E-017
1.1780972450961720 -6.2831853071795862 5.0912829964730146E-017
1.1780972450961720 -5.4977871437821380 0.16221167441072895
1.1780972450961720 -4.7123889803846897 0.31818964514320858
1.1780972450961720 -3.9269908169872414 0.46193976625564348
1.1780972450961720 -3.1415926535897931 0.58793780120967942
1.1780972450961720 -2.3561944901923448 0.69134171618254503
1.1780972450961720 -1.5707963267948966 0.76817775671141642
1.1780972450961720 -0.78539816339744828 0.81549315684891721
1.1780972450961720 0.0000000000000000 0.83146961230254535
1.1780972450961720 0.78539816339744828 0.81549315684891721
1.1780972450961720 1.5707963267948966 0.76817775671141642
1.1780972450961720 2.3561944901923439 0.69134171618254503
1.1780972450961720 3.1415926535897931 0.58793780120967942
1.1780972450961720 3.9269908169872423 0.46193976625564331
1.1780972450961720 4.7123889803846897 0.31818964514320858
1.1780972450961720 5.4977871437821371 0.16221167441072912
1.1780972450961720 6.2831853071795862 5.0912829964730146E-017
1.5707963267948966 -6.2831853071795862 4.3297802811774670E-017
1.5707963267948966 -5.4977871437821380 0.13794968964147156
1.5707963267948966 -4.7123889803846897 0.27059805007309856
1.5707963267948966 -3.9269908169872414 0.39284747919355117
1.5707963267948966 -3.1415926535897931 0.50000000000000011
1.5707963267948966 -2.3561944901923448 0.58793780120967942
1.5707963267948966 -1.5707963267948966 0.65328148243818829
1.5707963267948966 -0.78539816339744828 0.69351992266107376
1.5707963267948966 0.0000000000000000 0.70710678118654757
1.5707963267948966 0.78539816339744828 0.69351992266107376
1.5707963267948966 1.5707963267948966 0.65328148243818829
1.5707963267948966 2.3561944901923439 0.58793780120967942
1.5707963267948966 3.1415926535897931 0.50000000000000011
1.5707963267948966 3.9269908169872423 0.39284747919355101
1.5707963267948966 4.7123889803846897 0.27059805007309856
1.5707963267948966 5.4977871437821371 0.13794968964147170
1.5707963267948966 6.2831853071795862 4.3297802811774670E-017
1.9634954084936211 -6.2831853071795862 3.4018865378450242E-017
1.9634954084936211 -5.4977871437821380 0.10838637566236962
1.9634954084936211 -4.7123889803846897 0.21260752369181410
1.9634954084936211 -3.9269908169872414 0.30865828381745508
1.9634954084936211 -3.1415926535897931 0.39284747919355101
1.9634954084936211 -2.3561944901923448 0.46193976625564326
1.9634954084936211 -1.5707963267948966 0.51327996715933655
1.9634954084936211 -0.78539816339744828 0.54489510677581843
1.9634954084936211 0.0000000000000000 0.55557023301960207
1.9634954084936211 0.78539816339744828 0.54489510677581843
1.9634954084936211 1.5707963267948966 0.51327996715933655
1.9634954084936211 2.3561944901923439 0.46193976625564331
1.9634954084936211 3.1415926535897931 0.39284747919355101
1.9634954084936211 3.9269908169872423 0.30865828381745491
1.9634954084936211 4.7123889803846897 0.21260752369181410
1.9634954084936211 5.4977871437821371 0.10838637566236972
1.9634954084936211 6.2831853071795862 3.4018865378450242E-017
2.3561944901923448 -6.2831853071795862 2.3432602026631496E-017
2.3561944901923448 -5.4977871437821380 7.4657834050342639E-002
2.3561944901923448 -4.7123889803846897 0.14644660940672630
2.3561944901923448 -3.9269908169872414 0.21260752369181418
2.3561944901923448 -3.1415926535897931 0.27059805007309856
2.3561944901923448 -2.3561944901923448 0.31818964514320852
2.3561944901923448 -1.5707963267948966 0.35355339059327384
2.3561944901923448 -0.78539816339744828 0.37533027751786530
2.3561944901923448 0.0000000000000000 0.38268343236508984
2.3561944901923448 0.78539816339744828 0.37533027751786530
2.3561944901923448 1.5707963267948966 0.35355339059327384
2.3561944901923448 2.3561944901923439 0.31818964514320858
2.3561944901923448 3.1415926535897931 0.27059805007309856
2.3561944901923448 3.9269908169872423 0.21260752369181410
2.3561944901923448 4.7123889803846897 0.14644660940672630
2.3561944901923448 5.4977871437821371 7.4657834050342722E-002
2.3561944901923448 6.2831853071795862 2.3432602026631496E-017
2.7488935718910685 -6.2831853071795862 1.1945836920083910E-017
2.7488935718910685 -5.4977871437821380 3.8060233744356686E-002
2.7488935718910685 -4.7123889803846897 7.4657834050342722E-002
2.7488935718910685 -3.9269908169872414 0.10838637566236978
2.7488935718910685 -3.1415926535897931 0.13794968964147170
2.7488935718910685 -2.3561944901923448 0.16221167441072909
2.7488935718910685 -1.5707963267948966 0.18023995550173721
2.7488935718910685 -0.78539816339744828 0.19134171618254514
2.7488935718910685 0.0000000000000000 0.19509032201612853
2.7488935718910685 0.78539816339744828 0.19134171618254514
2.7488935718910685 1.5707963267948966 0.18023995550173721
2.7488935718910685 2.3561944901923439 0.16221167441072912
2.7488935718910685 3.1415926535897931 0.13794968964147170
2.7488935718910685 3.9269908169872423 0.10838637566236972
2.7488935718910685 4.7123889803846897 7.4657834050342722E-002
2.7488935718910685 5.4977871437821371 3.8060233744356721E-002
2.7488935718910685 6.2831853071795862 1.1945836920083910E-017
3.1415926535897931 -6.2831853071795862 3.7493994566546440E-033
3.1415926535897931 -5.4977871437821380 1.1945836920083898E-017
3.1415926535897931 -4.7123889803846897 2.3432602026631496E-017
3.1415926535897931 -3.9269908169872414 3.4018865378450254E-017
3.1415926535897931 -3.1415926535897931 4.3297802811774670E-017
3.1415926535897931 -2.3561944901923448 5.0912829964730140E-017
3.1415926535897931 -1.5707963267948966 5.6571305614385013E-017
3.1415926535897931 -0.78539816339744828 6.0055777714832775E-017
3.1415926535897931 0.0000000000000000 6.1232339957367660E-017
3.1415926535897931 0.78539816339744828 6.0055777714832775E-017
3.1415926535897931 1.5707963267948966 5.6571305614385013E-017
3.1415926535897931 2.3561944901923439 5.0912829964730146E-017
3.1415926535897931 3.1415926535897931 4.3297802811774670E-017
3.1415926535897931 3.9269908169872423 3.4018865378450242E-017
3.1415926535897931 4.7123889803846897 2.3432602026631496E-017
3.1415926535897931 5.4977871437821371 1.1945836920083910E-017
3.1415926535897931 6.2831853071795862 3.7493994566546440E-033
Input and output functions are as shown in the figure below.
As the output function is the same as - 0.25d0*dcos(x(i1)/2.0d0) * dsin(y(i2)/4.0d0), this shows derivative calculation is correct. Here in subroutine 'd1y', I have used forward and backward finite difference formula for calculation of derivative at boundaries and the central difference for points lying between two boundaries. Then I have calculated the relative error. I got an error of 0.003 at the boundary of y axis and 0.0015 at the points in between boundaries as shown in fig below.
I calculated 2nd derivative using same technique. The subroutine is given below:
`subroutine d2y(n1,n2,n3,a,a2)
implicit none
integer n1, n2, n3, i1, i2, i3
real*8 pi, a(n1,n2,n3), a2(n1,n2,n3), z(n3),x(n1),y(n2),h
pi=3.14159265358979323846d0
h=4.0d0*pi/(n2-1)
a2=0.0d0
i3 =1
do i1=1,n1
do i2=1,n2
if(i2 == 1)then
a2(i1,i2,i3)=( 2.0d0*a(i1,i2,i3) - 5.0d0*a(i1,i2+1,i3) + 4.0d0*a(i1,i2+2,i3) - a(i1,i2+3,i3))/(h*h)
else if( i2 == n2)then
a2(i1,i2,i3)=( 2.0d0*a(i1,i2,i3) - 5.0d0*a(i1,i2-1,i3) + 4.0d0*a(i1,i2-2,i3) - a(i1,i2-3,i3))/(h*h)
else
a2(i1,i2,i3)= ( a(i1,i2+1,i3) - 2.0d0*a(i1,i2,i3) + a(i1,i2-1,i3) )/(h*h)
endif
enddo
enddo
! enddo
end subroutine
Here also the error is large at boundaries, in fact, the error is very large compared to first order derivative as shown in figure:.
Why this large error in boundary? Please explain.
`
fortran numerical-methods derivative
edited Nov 11 at 14:05
b-fg
1,64811422
asked Nov 10 at 12:41
Jagannath Mahapatra
286
1
Not sure if related, but in the first snippet you seem to be printinga(i1,i2,2)before it is assigned any value, in this line:write(20,*)x(i1),y(i2),a(i1,i2,2)
– Rodrigo Rodrigues
Nov 10 at 14:36
1
By the way, welcome to StackOverflow. Please take the tour and check How to Ask. Could you please add a sample of input data you used for your graphs and provide a Minimal, Complete, and Verifiable example, in order for us to be able to help you more effectively?
– Rodrigo Rodrigues
Nov 10 at 14:47
comment to your 1st question @RodrigoRodrigues, yes it should be a(i1,i2,1). I am using only one index as my input function is z-independent.
– Jagannath Mahapatra
Nov 11 at 4:08
Of course the error is bigger at the boundary. Forward and backward difference are 1st order accurate and central difference is 2nd order accurate. While you cannot use central difference on the boundary, there are 2 nd order formulas for boundaries. Use one of them.
– Dan Sp.
Nov 11 at 5:56
1
It is 1st order accuracy, Dan Sp. is correct. You should check the total global error, that one should be 2nd order because the boundary layer is getting smaller and smaller with grid refinement.
– Vladimir F
Nov 11 at 15:03
|
show 2 more comments
up vote
3
down vote
favorite
up vote
3
down vote
favorite
I am calculating first and second derivatives of a function a(x,y,z) stored in a 3d array a(n1,n2,n3) using Finite Difference. Here the functional value at boundaries is zero. here is the code in Fortran:
implicit none
integer i1,i2,i3
integer, parameter :: n1 = 33
integer, parameter :: n2 = 33
integer, parameter :: n3 = 32
real*8 pi, a(n1,n2,n3), a2(n1,n2,n3), z(n3),x(n1),y(n2),h,a1(n1,n2,n3)
real*8 num(n1,n2,n3),deno(n1,n2,n3),diff(n1,n2,1),A0,dx,dy
pi=3.14159265358979323846d0
dx=2.0d0*pi/(n1-1)
dy=4.0d0*pi/(n2-1)
do i1=1,n1
x(i1)=-pi+(i1-1)*dx
do i2=1,n2
y(i2)=-2.0d0*pi+(i2-1)*dy
do i3=1,n3
z(i3)=(i3-1)*2.0d0*pi/n3
a(i1,i2,1)= dcos(x(i1)/2.0d0) * dcos(y(i2)/4.0d0) !input array
a1(i1,i2,1)= - 0.25d0*dcos(x(i1)/2.0d0) * dsin(y(i2)/4.0d0) !analytical expression of first order y-derivative
enddo
enddo
enddo
do i1=1,n1
do i2=1,n2
write(20,*)x(i1),y(i2),a(i1,i2,1)
enddo
enddo
call d1y(n1,n2,n3,a,a2)
do i1=1,n1
do i2=1,n2
num(i1,i2,1)=(a2(i1,i2,1)-a1(i1,i2,1)) !numerator of error calculation
deno(i1,i2,1)=a2(i1,i2,1) !denomenator of error calculation
if (dabs(deno(i1,i2,1)) .lt. 1e-10)deno(i1,i2,1)=1.0d0
diff(i1,i2,1)=dabs(num(i1,i2,1))/dabs(deno(i1,i2,1)) !relative error in 1st order derivative calculation
write(21,*)x(i1),y(i2),a(i1,i2,1),a2(i1,i2,1),diff(i1,i2,1),a1(i1,i2,1)
write(21,*)
enddo
enddo
end
subroutine d1y(n1,n2,n3,a,a2)
implicit none
integer n1, n2, n3, i1, i2, i3
real*8 pi, a(n1,n2,n3), a2(n1,n2,n3), z(n3),x(n1),y(n2),h,a1(n1,n2,n3)
pi=3.14159265358979323846d0
h=4.0d0*pi/(n2-1)
do i1=1,n1
do i3=1,n3
do i2=1,n2
if(i2 .eq. 1)then
a2(i1,i2,i3)=( -3.0d0*a(i1,i2,i3) + 4.0d0*a(i1,i2+1,i3) - a(i1,i2+2,i3) )/ (2.0d0*h)
else if(i2 .eq. n2)then
a2(i1,i2,i3)=( 3.0d0*a(i1,i2,i3) - 4.0d0*a(i1,i2-1,i3) + a(i1,i2-2,i3) )/ (2.0d0*h)
else
a2(i1,i2,i3)=( a(i1,i2+1,i3) - a(i1,i2-1,i3) )/ (2.0d0*h)
endif
enddo
enddo
enddo
end subroutine
My input function a(i1,i2,1)= dcos(x(i1)/2.0d0) * dcos(y(i2)/4.0d0), so the sample input data (for grid no 17*17*16)
-3.1415926535897931 -6.2831853071795862 3.7493994566546440E-033
-3.1415926535897931 -5.4977871437821380 1.1945836920083898E-017
-3.1415926535897931 -4.7123889803846897 2.3432602026631496E-017
-3.1415926535897931 -3.9269908169872414 3.4018865378450254E-017
-3.1415926535897931 -3.1415926535897931 4.3297802811774670E-017
-3.1415926535897931 -2.3561944901923448 5.0912829964730140E-017
-3.1415926535897931 -1.5707963267948966 5.6571305614385013E-017
-3.1415926535897931 -0.78539816339744828 6.0055777714832775E-017
-3.1415926535897931 0.0000000000000000 6.1232339957367660E-017
-3.1415926535897931 0.78539816339744828 6.0055777714832775E-017
-3.1415926535897931 1.5707963267948966 5.6571305614385013E-017
-3.1415926535897931 2.3561944901923439 5.0912829964730146E-017
-3.1415926535897931 3.1415926535897931 4.3297802811774670E-017
-3.1415926535897931 3.9269908169872423 3.4018865378450242E-017
-3.1415926535897931 4.7123889803846897 2.3432602026631496E-017
-3.1415926535897931 5.4977871437821371 1.1945836920083910E-017
-3.1415926535897931 6.2831853071795862 3.7493994566546440E-033
-2.7488935718910690 -6.2831853071795862 1.1945836920083898E-017
-2.7488935718910690 -5.4977871437821380 3.8060233744356645E-002
-2.7488935718910690 -4.7123889803846897 7.4657834050342639E-002
-2.7488935718910690 -3.9269908169872414 0.10838637566236967
-2.7488935718910690 -3.1415926535897931 0.13794968964147156
-2.7488935718910690 -2.3561944901923448 0.16221167441072892
-2.7488935718910690 -1.5707963267948966 0.18023995550173702
-2.7488935718910690 -0.78539816339744828 0.19134171618254495
-2.7488935718910690 0.0000000000000000 0.19509032201612833
-2.7488935718910690 0.78539816339744828 0.19134171618254495
-2.7488935718910690 1.5707963267948966 0.18023995550173702
-2.7488935718910690 2.3561944901923439 0.16221167441072895
-2.7488935718910690 3.1415926535897931 0.13794968964147156
-2.7488935718910690 3.9269908169872423 0.10838637566236962
-2.7488935718910690 4.7123889803846897 7.4657834050342639E-002
-2.7488935718910690 5.4977871437821371 3.8060233744356686E-002
-2.7488935718910690 6.2831853071795862 1.1945836920083898E-017
-2.3561944901923448 -6.2831853071795862 2.3432602026631496E-017
-2.3561944901923448 -5.4977871437821380 7.4657834050342639E-002
-2.3561944901923448 -4.7123889803846897 0.14644660940672630
-2.3561944901923448 -3.9269908169872414 0.21260752369181418
-2.3561944901923448 -3.1415926535897931 0.27059805007309856
-2.3561944901923448 -2.3561944901923448 0.31818964514320852
-2.3561944901923448 -1.5707963267948966 0.35355339059327384
-2.3561944901923448 -0.78539816339744828 0.37533027751786530
-2.3561944901923448 0.0000000000000000 0.38268343236508984
-2.3561944901923448 0.78539816339744828 0.37533027751786530
-2.3561944901923448 1.5707963267948966 0.35355339059327384
-2.3561944901923448 2.3561944901923439 0.31818964514320858
-2.3561944901923448 3.1415926535897931 0.27059805007309856
-2.3561944901923448 3.9269908169872423 0.21260752369181410
-2.3561944901923448 4.7123889803846897 0.14644660940672630
-2.3561944901923448 5.4977871437821371 7.4657834050342722E-002
-2.3561944901923448 6.2831853071795862 2.3432602026631496E-017
-1.9634954084936207 -6.2831853071795862 3.4018865378450254E-017
-1.9634954084936207 -5.4977871437821380 0.10838637566236967
-1.9634954084936207 -4.7123889803846897 0.21260752369181418
-1.9634954084936207 -3.9269908169872414 0.30865828381745519
-1.9634954084936207 -3.1415926535897931 0.39284747919355117
-1.9634954084936207 -2.3561944901923448 0.46193976625564342
-1.9634954084936207 -1.5707963267948966 0.51327996715933677
-1.9634954084936207 -0.78539816339744828 0.54489510677581865
-1.9634954084936207 0.0000000000000000 0.55557023301960229
-1.9634954084936207 0.78539816339744828 0.54489510677581865
-1.9634954084936207 1.5707963267948966 0.51327996715933677
-1.9634954084936207 2.3561944901923439 0.46193976625564348
-1.9634954084936207 3.1415926535897931 0.39284747919355117
-1.9634954084936207 3.9269908169872423 0.30865828381745508
-1.9634954084936207 4.7123889803846897 0.21260752369181418
-1.9634954084936207 5.4977871437821371 0.10838637566236978
-1.9634954084936207 6.2831853071795862 3.4018865378450254E-017
-1.5707963267948966 -6.2831853071795862 4.3297802811774670E-017
-1.5707963267948966 -5.4977871437821380 0.13794968964147156
-1.5707963267948966 -4.7123889803846897 0.27059805007309856
-1.5707963267948966 -3.9269908169872414 0.39284747919355117
-1.5707963267948966 -3.1415926535897931 0.50000000000000011
-1.5707963267948966 -2.3561944901923448 0.58793780120967942
-1.5707963267948966 -1.5707963267948966 0.65328148243818829
-1.5707963267948966 -0.78539816339744828 0.69351992266107376
-1.5707963267948966 0.0000000000000000 0.70710678118654757
-1.5707963267948966 0.78539816339744828 0.69351992266107376
-1.5707963267948966 1.5707963267948966 0.65328148243818829
-1.5707963267948966 2.3561944901923439 0.58793780120967942
-1.5707963267948966 3.1415926535897931 0.50000000000000011
-1.5707963267948966 3.9269908169872423 0.39284747919355101
-1.5707963267948966 4.7123889803846897 0.27059805007309856
-1.5707963267948966 5.4977871437821371 0.13794968964147170
-1.5707963267948966 6.2831853071795862 4.3297802811774670E-017
-1.1780972450961724 -6.2831853071795862 5.0912829964730140E-017
-1.1780972450961724 -5.4977871437821380 0.16221167441072892
-1.1780972450961724 -4.7123889803846897 0.31818964514320852
-1.1780972450961724 -3.9269908169872414 0.46193976625564342
-1.1780972450961724 -3.1415926535897931 0.58793780120967942
-1.1780972450961724 -2.3561944901923448 0.69134171618254492
-1.1780972450961724 -1.5707963267948966 0.76817775671141630
-1.1780972450961724 -0.78539816339744828 0.81549315684891710
-1.1780972450961724 0.0000000000000000 0.83146961230254524
-1.1780972450961724 0.78539816339744828 0.81549315684891710
-1.1780972450961724 1.5707963267948966 0.76817775671141630
-1.1780972450961724 2.3561944901923439 0.69134171618254503
-1.1780972450961724 3.1415926535897931 0.58793780120967942
-1.1780972450961724 3.9269908169872423 0.46193976625564326
-1.1780972450961724 4.7123889803846897 0.31818964514320852
-1.1780972450961724 5.4977871437821371 0.16221167441072909
-1.1780972450961724 6.2831853071795862 5.0912829964730140E-017
-0.78539816339744828 -6.2831853071795862 5.6571305614385013E-017
-0.78539816339744828 -5.4977871437821380 0.18023995550173702
-0.78539816339744828 -4.7123889803846897 0.35355339059327384
-0.78539816339744828 -3.9269908169872414 0.51327996715933677
-0.78539816339744828 -3.1415926535897931 0.65328148243818829
-0.78539816339744828 -2.3561944901923448 0.76817775671141630
-0.78539816339744828 -1.5707963267948966 0.85355339059327373
-0.78539816339744828 -0.78539816339744828 0.90612744635288778
-0.78539816339744828 0.0000000000000000 0.92387953251128674
-0.78539816339744828 0.78539816339744828 0.90612744635288778
-0.78539816339744828 1.5707963267948966 0.85355339059327373
-0.78539816339744828 2.3561944901923439 0.76817775671141642
-0.78539816339744828 3.1415926535897931 0.65328148243818829
-0.78539816339744828 3.9269908169872423 0.51327996715933655
-0.78539816339744828 4.7123889803846897 0.35355339059327384
-0.78539816339744828 5.4977871437821371 0.18023995550173721
-0.78539816339744828 6.2831853071795862 5.6571305614385013E-017
-0.39269908169872414 -6.2831853071795862 6.0055777714832775E-017
-0.39269908169872414 -5.4977871437821380 0.19134171618254495
-0.39269908169872414 -4.7123889803846897 0.37533027751786530
-0.39269908169872414 -3.9269908169872414 0.54489510677581865
-0.39269908169872414 -3.1415926535897931 0.69351992266107376
-0.39269908169872414 -2.3561944901923448 0.81549315684891710
-0.39269908169872414 -1.5707963267948966 0.90612744635288778
-0.39269908169872414 -0.78539816339744828 0.96193976625564337
-0.39269908169872414 0.0000000000000000 0.98078528040323043
-0.39269908169872414 0.78539816339744828 0.96193976625564337
-0.39269908169872414 1.5707963267948966 0.90612744635288778
-0.39269908169872414 2.3561944901923439 0.81549315684891721
-0.39269908169872414 3.1415926535897931 0.69351992266107376
-0.39269908169872414 3.9269908169872423 0.54489510677581843
-0.39269908169872414 4.7123889803846897 0.37533027751786530
-0.39269908169872414 5.4977871437821371 0.19134171618254514
-0.39269908169872414 6.2831853071795862 6.0055777714832775E-017
0.0000000000000000 -6.2831853071795862 6.1232339957367660E-017
0.0000000000000000 -5.4977871437821380 0.19509032201612833
0.0000000000000000 -4.7123889803846897 0.38268343236508984
0.0000000000000000 -3.9269908169872414 0.55557023301960229
0.0000000000000000 -3.1415926535897931 0.70710678118654757
0.0000000000000000 -2.3561944901923448 0.83146961230254524
0.0000000000000000 -1.5707963267948966 0.92387953251128674
0.0000000000000000 -0.78539816339744828 0.98078528040323043
0.0000000000000000 0.0000000000000000 1.0000000000000000
0.0000000000000000 0.78539816339744828 0.98078528040323043
0.0000000000000000 1.5707963267948966 0.92387953251128674
0.0000000000000000 2.3561944901923439 0.83146961230254535
0.0000000000000000 3.1415926535897931 0.70710678118654757
0.0000000000000000 3.9269908169872423 0.55557023301960207
0.0000000000000000 4.7123889803846897 0.38268343236508984
0.0000000000000000 5.4977871437821371 0.19509032201612853
0.0000000000000000 6.2831853071795862 6.1232339957367660E-017
0.39269908169872414 -6.2831853071795862 6.0055777714832775E-017
0.39269908169872414 -5.4977871437821380 0.19134171618254495
0.39269908169872414 -4.7123889803846897 0.37533027751786530
0.39269908169872414 -3.9269908169872414 0.54489510677581865
0.39269908169872414 -3.1415926535897931 0.69351992266107376
0.39269908169872414 -2.3561944901923448 0.81549315684891710
0.39269908169872414 -1.5707963267948966 0.90612744635288778
0.39269908169872414 -0.78539816339744828 0.96193976625564337
0.39269908169872414 0.0000000000000000 0.98078528040323043
0.39269908169872414 0.78539816339744828 0.96193976625564337
0.39269908169872414 1.5707963267948966 0.90612744635288778
0.39269908169872414 2.3561944901923439 0.81549315684891721
0.39269908169872414 3.1415926535897931 0.69351992266107376
0.39269908169872414 3.9269908169872423 0.54489510677581843
0.39269908169872414 4.7123889803846897 0.37533027751786530
0.39269908169872414 5.4977871437821371 0.19134171618254514
0.39269908169872414 6.2831853071795862 6.0055777714832775E-017
0.78539816339744828 -6.2831853071795862 5.6571305614385013E-017
0.78539816339744828 -5.4977871437821380 0.18023995550173702
0.78539816339744828 -4.7123889803846897 0.35355339059327384
0.78539816339744828 -3.9269908169872414 0.51327996715933677
0.78539816339744828 -3.1415926535897931 0.65328148243818829
0.78539816339744828 -2.3561944901923448 0.76817775671141630
0.78539816339744828 -1.5707963267948966 0.85355339059327373
0.78539816339744828 -0.78539816339744828 0.90612744635288778
0.78539816339744828 0.0000000000000000 0.92387953251128674
0.78539816339744828 0.78539816339744828 0.90612744635288778
0.78539816339744828 1.5707963267948966 0.85355339059327373
0.78539816339744828 2.3561944901923439 0.76817775671141642
0.78539816339744828 3.1415926535897931 0.65328148243818829
0.78539816339744828 3.9269908169872423 0.51327996715933655
0.78539816339744828 4.7123889803846897 0.35355339059327384
0.78539816339744828 5.4977871437821371 0.18023995550173721
0.78539816339744828 6.2831853071795862 5.6571305614385013E-017
1.1780972450961720 -6.2831853071795862 5.0912829964730146E-017
1.1780972450961720 -5.4977871437821380 0.16221167441072895
1.1780972450961720 -4.7123889803846897 0.31818964514320858
1.1780972450961720 -3.9269908169872414 0.46193976625564348
1.1780972450961720 -3.1415926535897931 0.58793780120967942
1.1780972450961720 -2.3561944901923448 0.69134171618254503
1.1780972450961720 -1.5707963267948966 0.76817775671141642
1.1780972450961720 -0.78539816339744828 0.81549315684891721
1.1780972450961720 0.0000000000000000 0.83146961230254535
1.1780972450961720 0.78539816339744828 0.81549315684891721
1.1780972450961720 1.5707963267948966 0.76817775671141642
1.1780972450961720 2.3561944901923439 0.69134171618254503
1.1780972450961720 3.1415926535897931 0.58793780120967942
1.1780972450961720 3.9269908169872423 0.46193976625564331
1.1780972450961720 4.7123889803846897 0.31818964514320858
1.1780972450961720 5.4977871437821371 0.16221167441072912
1.1780972450961720 6.2831853071795862 5.0912829964730146E-017
1.5707963267948966 -6.2831853071795862 4.3297802811774670E-017
1.5707963267948966 -5.4977871437821380 0.13794968964147156
1.5707963267948966 -4.7123889803846897 0.27059805007309856
1.5707963267948966 -3.9269908169872414 0.39284747919355117
1.5707963267948966 -3.1415926535897931 0.50000000000000011
1.5707963267948966 -2.3561944901923448 0.58793780120967942
1.5707963267948966 -1.5707963267948966 0.65328148243818829
1.5707963267948966 -0.78539816339744828 0.69351992266107376
1.5707963267948966 0.0000000000000000 0.70710678118654757
1.5707963267948966 0.78539816339744828 0.69351992266107376
1.5707963267948966 1.5707963267948966 0.65328148243818829
1.5707963267948966 2.3561944901923439 0.58793780120967942
1.5707963267948966 3.1415926535897931 0.50000000000000011
1.5707963267948966 3.9269908169872423 0.39284747919355101
1.5707963267948966 4.7123889803846897 0.27059805007309856
1.5707963267948966 5.4977871437821371 0.13794968964147170
1.5707963267948966 6.2831853071795862 4.3297802811774670E-017
1.9634954084936211 -6.2831853071795862 3.4018865378450242E-017
1.9634954084936211 -5.4977871437821380 0.10838637566236962
1.9634954084936211 -4.7123889803846897 0.21260752369181410
1.9634954084936211 -3.9269908169872414 0.30865828381745508
1.9634954084936211 -3.1415926535897931 0.39284747919355101
1.9634954084936211 -2.3561944901923448 0.46193976625564326
1.9634954084936211 -1.5707963267948966 0.51327996715933655
1.9634954084936211 -0.78539816339744828 0.54489510677581843
1.9634954084936211 0.0000000000000000 0.55557023301960207
1.9634954084936211 0.78539816339744828 0.54489510677581843
1.9634954084936211 1.5707963267948966 0.51327996715933655
1.9634954084936211 2.3561944901923439 0.46193976625564331
1.9634954084936211 3.1415926535897931 0.39284747919355101
1.9634954084936211 3.9269908169872423 0.30865828381745491
1.9634954084936211 4.7123889803846897 0.21260752369181410
1.9634954084936211 5.4977871437821371 0.10838637566236972
1.9634954084936211 6.2831853071795862 3.4018865378450242E-017
2.3561944901923448 -6.2831853071795862 2.3432602026631496E-017
2.3561944901923448 -5.4977871437821380 7.4657834050342639E-002
2.3561944901923448 -4.7123889803846897 0.14644660940672630
2.3561944901923448 -3.9269908169872414 0.21260752369181418
2.3561944901923448 -3.1415926535897931 0.27059805007309856
2.3561944901923448 -2.3561944901923448 0.31818964514320852
2.3561944901923448 -1.5707963267948966 0.35355339059327384
2.3561944901923448 -0.78539816339744828 0.37533027751786530
2.3561944901923448 0.0000000000000000 0.38268343236508984
2.3561944901923448 0.78539816339744828 0.37533027751786530
2.3561944901923448 1.5707963267948966 0.35355339059327384
2.3561944901923448 2.3561944901923439 0.31818964514320858
2.3561944901923448 3.1415926535897931 0.27059805007309856
2.3561944901923448 3.9269908169872423 0.21260752369181410
2.3561944901923448 4.7123889803846897 0.14644660940672630
2.3561944901923448 5.4977871437821371 7.4657834050342722E-002
2.3561944901923448 6.2831853071795862 2.3432602026631496E-017
2.7488935718910685 -6.2831853071795862 1.1945836920083910E-017
2.7488935718910685 -5.4977871437821380 3.8060233744356686E-002
2.7488935718910685 -4.7123889803846897 7.4657834050342722E-002
2.7488935718910685 -3.9269908169872414 0.10838637566236978
2.7488935718910685 -3.1415926535897931 0.13794968964147170
2.7488935718910685 -2.3561944901923448 0.16221167441072909
2.7488935718910685 -1.5707963267948966 0.18023995550173721
2.7488935718910685 -0.78539816339744828 0.19134171618254514
2.7488935718910685 0.0000000000000000 0.19509032201612853
2.7488935718910685 0.78539816339744828 0.19134171618254514
2.7488935718910685 1.5707963267948966 0.18023995550173721
2.7488935718910685 2.3561944901923439 0.16221167441072912
2.7488935718910685 3.1415926535897931 0.13794968964147170
2.7488935718910685 3.9269908169872423 0.10838637566236972
2.7488935718910685 4.7123889803846897 7.4657834050342722E-002
2.7488935718910685 5.4977871437821371 3.8060233744356721E-002
2.7488935718910685 6.2831853071795862 1.1945836920083910E-017
3.1415926535897931 -6.2831853071795862 3.7493994566546440E-033
3.1415926535897931 -5.4977871437821380 1.1945836920083898E-017
3.1415926535897931 -4.7123889803846897 2.3432602026631496E-017
3.1415926535897931 -3.9269908169872414 3.4018865378450254E-017
3.1415926535897931 -3.1415926535897931 4.3297802811774670E-017
3.1415926535897931 -2.3561944901923448 5.0912829964730140E-017
3.1415926535897931 -1.5707963267948966 5.6571305614385013E-017
3.1415926535897931 -0.78539816339744828 6.0055777714832775E-017
3.1415926535897931 0.0000000000000000 6.1232339957367660E-017
3.1415926535897931 0.78539816339744828 6.0055777714832775E-017
3.1415926535897931 1.5707963267948966 5.6571305614385013E-017
3.1415926535897931 2.3561944901923439 5.0912829964730146E-017
3.1415926535897931 3.1415926535897931 4.3297802811774670E-017
3.1415926535897931 3.9269908169872423 3.4018865378450242E-017
3.1415926535897931 4.7123889803846897 2.3432602026631496E-017
3.1415926535897931 5.4977871437821371 1.1945836920083910E-017
3.1415926535897931 6.2831853071795862 3.7493994566546440E-033
Input and output functions are as shown in the figure below.
As the output function is the same as - 0.25d0*dcos(x(i1)/2.0d0) * dsin(y(i2)/4.0d0), this shows derivative calculation is correct. Here in subroutine 'd1y', I have used forward and backward finite difference formula for calculation of derivative at boundaries and the central difference for points lying between two boundaries. Then I have calculated the relative error. I got an error of 0.003 at the boundary of y axis and 0.0015 at the points in between boundaries as shown in fig below.
I calculated 2nd derivative using same technique. The subroutine is given below:
`subroutine d2y(n1,n2,n3,a,a2)
implicit none
integer n1, n2, n3, i1, i2, i3
real*8 pi, a(n1,n2,n3), a2(n1,n2,n3), z(n3),x(n1),y(n2),h
pi=3.14159265358979323846d0
h=4.0d0*pi/(n2-1)
a2=0.0d0
i3 =1
do i1=1,n1
do i2=1,n2
if(i2 == 1)then
a2(i1,i2,i3)=( 2.0d0*a(i1,i2,i3) - 5.0d0*a(i1,i2+1,i3) + 4.0d0*a(i1,i2+2,i3) - a(i1,i2+3,i3))/(h*h)
else if( i2 == n2)then
a2(i1,i2,i3)=( 2.0d0*a(i1,i2,i3) - 5.0d0*a(i1,i2-1,i3) + 4.0d0*a(i1,i2-2,i3) - a(i1,i2-3,i3))/(h*h)
else
a2(i1,i2,i3)= ( a(i1,i2+1,i3) - 2.0d0*a(i1,i2,i3) + a(i1,i2-1,i3) )/(h*h)
endif
enddo
enddo
! enddo
end subroutine
Here also the error is large at boundaries, in fact, the error is very large compared to first order derivative as shown in figure:.
Why this large error in boundary? Please explain.
`
fortran numerical-methods derivative
edited Nov 11 at 14:05
b-fg
1,64811422
asked Nov 10 at 12:41
Jagannath Mahapatra
286
I am calculating first and second derivatives of a function a(x,y,z) stored in a 3d array a(n1,n2,n3) using Finite Difference. Here the functional value at boundaries is zero. here is the code in Fortran:
implicit none
integer i1,i2,i3
integer, parameter :: n1 = 33
integer, parameter :: n2 = 33
integer, parameter :: n3 = 32
real*8 pi, a(n1,n2,n3), a2(n1,n2,n3), z(n3),x(n1),y(n2),h,a1(n1,n2,n3)
real*8 num(n1,n2,n3),deno(n1,n2,n3),diff(n1,n2,1),A0,dx,dy
pi=3.14159265358979323846d0
dx=2.0d0*pi/(n1-1)
dy=4.0d0*pi/(n2-1)
do i1=1,n1
x(i1)=-pi+(i1-1)*dx
do i2=1,n2
y(i2)=-2.0d0*pi+(i2-1)*dy
do i3=1,n3
z(i3)=(i3-1)*2.0d0*pi/n3
a(i1,i2,1)= dcos(x(i1)/2.0d0) * dcos(y(i2)/4.0d0) !input array
a1(i1,i2,1)= - 0.25d0*dcos(x(i1)/2.0d0) * dsin(y(i2)/4.0d0) !analytical expression of first order y-derivative
enddo
enddo
enddo
do i1=1,n1
do i2=1,n2
write(20,*)x(i1),y(i2),a(i1,i2,1)
enddo
enddo
call d1y(n1,n2,n3,a,a2)
do i1=1,n1
do i2=1,n2
num(i1,i2,1)=(a2(i1,i2,1)-a1(i1,i2,1)) !numerator of error calculation
deno(i1,i2,1)=a2(i1,i2,1) !denomenator of error calculation
if (dabs(deno(i1,i2,1)) .lt. 1e-10)deno(i1,i2,1)=1.0d0
diff(i1,i2,1)=dabs(num(i1,i2,1))/dabs(deno(i1,i2,1)) !relative error in 1st order derivative calculation
write(21,*)x(i1),y(i2),a(i1,i2,1),a2(i1,i2,1),diff(i1,i2,1),a1(i1,i2,1)
write(21,*)
enddo
enddo
end
subroutine d1y(n1,n2,n3,a,a2)
implicit none
integer n1, n2, n3, i1, i2, i3
real*8 pi, a(n1,n2,n3), a2(n1,n2,n3), z(n3),x(n1),y(n2),h,a1(n1,n2,n3)
pi=3.14159265358979323846d0
h=4.0d0*pi/(n2-1)
do i1=1,n1
do i3=1,n3
do i2=1,n2
if(i2 .eq. 1)then
a2(i1,i2,i3)=( -3.0d0*a(i1,i2,i3) + 4.0d0*a(i1,i2+1,i3) - a(i1,i2+2,i3) )/ (2.0d0*h)
else if(i2 .eq. n2)then
a2(i1,i2,i3)=( 3.0d0*a(i1,i2,i3) - 4.0d0*a(i1,i2-1,i3) + a(i1,i2-2,i3) )/ (2.0d0*h)
else
a2(i1,i2,i3)=( a(i1,i2+1,i3) - a(i1,i2-1,i3) )/ (2.0d0*h)
endif
enddo
enddo
enddo
end subroutine
My input function a(i1,i2,1)= dcos(x(i1)/2.0d0) * dcos(y(i2)/4.0d0), so the sample input data (for grid no 17*17*16)
-3.1415926535897931 -6.2831853071795862 3.7493994566546440E-033
-3.1415926535897931 -5.4977871437821380 1.1945836920083898E-017
-3.1415926535897931 -4.7123889803846897 2.3432602026631496E-017
-3.1415926535897931 -3.9269908169872414 3.4018865378450254E-017
-3.1415926535897931 -3.1415926535897931 4.3297802811774670E-017
-3.1415926535897931 -2.3561944901923448 5.0912829964730140E-017
-3.1415926535897931 -1.5707963267948966 5.6571305614385013E-017
-3.1415926535897931 -0.78539816339744828 6.0055777714832775E-017
-3.1415926535897931 0.0000000000000000 6.1232339957367660E-017
-3.1415926535897931 0.78539816339744828 6.0055777714832775E-017
-3.1415926535897931 1.5707963267948966 5.6571305614385013E-017
-3.1415926535897931 2.3561944901923439 5.0912829964730146E-017
-3.1415926535897931 3.1415926535897931 4.3297802811774670E-017
-3.1415926535897931 3.9269908169872423 3.4018865378450242E-017
-3.1415926535897931 4.7123889803846897 2.3432602026631496E-017
-3.1415926535897931 5.4977871437821371 1.1945836920083910E-017
-3.1415926535897931 6.2831853071795862 3.7493994566546440E-033
-2.7488935718910690 -6.2831853071795862 1.1945836920083898E-017
-2.7488935718910690 -5.4977871437821380 3.8060233744356645E-002
-2.7488935718910690 -4.7123889803846897 7.4657834050342639E-002
-2.7488935718910690 -3.9269908169872414 0.10838637566236967
-2.7488935718910690 -3.1415926535897931 0.13794968964147156
-2.7488935718910690 -2.3561944901923448 0.16221167441072892
-2.7488935718910690 -1.5707963267948966 0.18023995550173702
-2.7488935718910690 -0.78539816339744828 0.19134171618254495
-2.7488935718910690 0.0000000000000000 0.19509032201612833
-2.7488935718910690 0.78539816339744828 0.19134171618254495
-2.7488935718910690 1.5707963267948966 0.18023995550173702
-2.7488935718910690 2.3561944901923439 0.16221167441072895
-2.7488935718910690 3.1415926535897931 0.13794968964147156
-2.7488935718910690 3.9269908169872423 0.10838637566236962
-2.7488935718910690 4.7123889803846897 7.4657834050342639E-002
-2.7488935718910690 5.4977871437821371 3.8060233744356686E-002
-2.7488935718910690 6.2831853071795862 1.1945836920083898E-017
-2.3561944901923448 -6.2831853071795862 2.3432602026631496E-017
-2.3561944901923448 -5.4977871437821380 7.4657834050342639E-002
-2.3561944901923448 -4.7123889803846897 0.14644660940672630
-2.3561944901923448 -3.9269908169872414 0.21260752369181418
-2.3561944901923448 -3.1415926535897931 0.27059805007309856
-2.3561944901923448 -2.3561944901923448 0.31818964514320852
-2.3561944901923448 -1.5707963267948966 0.35355339059327384
-2.3561944901923448 -0.78539816339744828 0.37533027751786530
-2.3561944901923448 0.0000000000000000 0.38268343236508984
-2.3561944901923448 0.78539816339744828 0.37533027751786530
-2.3561944901923448 1.5707963267948966 0.35355339059327384
-2.3561944901923448 2.3561944901923439 0.31818964514320858
-2.3561944901923448 3.1415926535897931 0.27059805007309856
-2.3561944901923448 3.9269908169872423 0.21260752369181410
-2.3561944901923448 4.7123889803846897 0.14644660940672630
-2.3561944901923448 5.4977871437821371 7.4657834050342722E-002
-2.3561944901923448 6.2831853071795862 2.3432602026631496E-017
-1.9634954084936207 -6.2831853071795862 3.4018865378450254E-017
-1.9634954084936207 -5.4977871437821380 0.10838637566236967
-1.9634954084936207 -4.7123889803846897 0.21260752369181418
-1.9634954084936207 -3.9269908169872414 0.30865828381745519
-1.9634954084936207 -3.1415926535897931 0.39284747919355117
-1.9634954084936207 -2.3561944901923448 0.46193976625564342
-1.9634954084936207 -1.5707963267948966 0.51327996715933677
-1.9634954084936207 -0.78539816339744828 0.54489510677581865
-1.9634954084936207 0.0000000000000000 0.55557023301960229
-1.9634954084936207 0.78539816339744828 0.54489510677581865
-1.9634954084936207 1.5707963267948966 0.51327996715933677
-1.9634954084936207 2.3561944901923439 0.46193976625564348
-1.9634954084936207 3.1415926535897931 0.39284747919355117
-1.9634954084936207 3.9269908169872423 0.30865828381745508
-1.9634954084936207 4.7123889803846897 0.21260752369181418
-1.9634954084936207 5.4977871437821371 0.10838637566236978
-1.9634954084936207 6.2831853071795862 3.4018865378450254E-017
-1.5707963267948966 -6.2831853071795862 4.3297802811774670E-017
-1.5707963267948966 -5.4977871437821380 0.13794968964147156
-1.5707963267948966 -4.7123889803846897 0.27059805007309856
-1.5707963267948966 -3.9269908169872414 0.39284747919355117
-1.5707963267948966 -3.1415926535897931 0.50000000000000011
-1.5707963267948966 -2.3561944901923448 0.58793780120967942
-1.5707963267948966 -1.5707963267948966 0.65328148243818829
-1.5707963267948966 -0.78539816339744828 0.69351992266107376
-1.5707963267948966 0.0000000000000000 0.70710678118654757
-1.5707963267948966 0.78539816339744828 0.69351992266107376
-1.5707963267948966 1.5707963267948966 0.65328148243818829
-1.5707963267948966 2.3561944901923439 0.58793780120967942
-1.5707963267948966 3.1415926535897931 0.50000000000000011
-1.5707963267948966 3.9269908169872423 0.39284747919355101
-1.5707963267948966 4.7123889803846897 0.27059805007309856
-1.5707963267948966 5.4977871437821371 0.13794968964147170
-1.5707963267948966 6.2831853071795862 4.3297802811774670E-017
-1.1780972450961724 -6.2831853071795862 5.0912829964730140E-017
-1.1780972450961724 -5.4977871437821380 0.16221167441072892
-1.1780972450961724 -4.7123889803846897 0.31818964514320852
-1.1780972450961724 -3.9269908169872414 0.46193976625564342
-1.1780972450961724 -3.1415926535897931 0.58793780120967942
-1.1780972450961724 -2.3561944901923448 0.69134171618254492
-1.1780972450961724 -1.5707963267948966 0.76817775671141630
-1.1780972450961724 -0.78539816339744828 0.81549315684891710
-1.1780972450961724 0.0000000000000000 0.83146961230254524
-1.1780972450961724 0.78539816339744828 0.81549315684891710
-1.1780972450961724 1.5707963267948966 0.76817775671141630
-1.1780972450961724 2.3561944901923439 0.69134171618254503
-1.1780972450961724 3.1415926535897931 0.58793780120967942
-1.1780972450961724 3.9269908169872423 0.46193976625564326
-1.1780972450961724 4.7123889803846897 0.31818964514320852
-1.1780972450961724 5.4977871437821371 0.16221167441072909
-1.1780972450961724 6.2831853071795862 5.0912829964730140E-017
-0.78539816339744828 -6.2831853071795862 5.6571305614385013E-017
-0.78539816339744828 -5.4977871437821380 0.18023995550173702
-0.78539816339744828 -4.7123889803846897 0.35355339059327384
-0.78539816339744828 -3.9269908169872414 0.51327996715933677
-0.78539816339744828 -3.1415926535897931 0.65328148243818829
-0.78539816339744828 -2.3561944901923448 0.76817775671141630
-0.78539816339744828 -1.5707963267948966 0.85355339059327373
-0.78539816339744828 -0.78539816339744828 0.90612744635288778
-0.78539816339744828 0.0000000000000000 0.92387953251128674
-0.78539816339744828 0.78539816339744828 0.90612744635288778
-0.78539816339744828 1.5707963267948966 0.85355339059327373
-0.78539816339744828 2.3561944901923439 0.76817775671141642
-0.78539816339744828 3.1415926535897931 0.65328148243818829
-0.78539816339744828 3.9269908169872423 0.51327996715933655
-0.78539816339744828 4.7123889803846897 0.35355339059327384
-0.78539816339744828 5.4977871437821371 0.18023995550173721
-0.78539816339744828 6.2831853071795862 5.6571305614385013E-017
-0.39269908169872414 -6.2831853071795862 6.0055777714832775E-017
-0.39269908169872414 -5.4977871437821380 0.19134171618254495
-0.39269908169872414 -4.7123889803846897 0.37533027751786530
-0.39269908169872414 -3.9269908169872414 0.54489510677581865
-0.39269908169872414 -3.1415926535897931 0.69351992266107376
-0.39269908169872414 -2.3561944901923448 0.81549315684891710
-0.39269908169872414 -1.5707963267948966 0.90612744635288778
-0.39269908169872414 -0.78539816339744828 0.96193976625564337
-0.39269908169872414 0.0000000000000000 0.98078528040323043
-0.39269908169872414 0.78539816339744828 0.96193976625564337
-0.39269908169872414 1.5707963267948966 0.90612744635288778
-0.39269908169872414 2.3561944901923439 0.81549315684891721
-0.39269908169872414 3.1415926535897931 0.69351992266107376
-0.39269908169872414 3.9269908169872423 0.54489510677581843
-0.39269908169872414 4.7123889803846897 0.37533027751786530
-0.39269908169872414 5.4977871437821371 0.19134171618254514
-0.39269908169872414 6.2831853071795862 6.0055777714832775E-017
0.0000000000000000 -6.2831853071795862 6.1232339957367660E-017
0.0000000000000000 -5.4977871437821380 0.19509032201612833
0.0000000000000000 -4.7123889803846897 0.38268343236508984
0.0000000000000000 -3.9269908169872414 0.55557023301960229
0.0000000000000000 -3.1415926535897931 0.70710678118654757
0.0000000000000000 -2.3561944901923448 0.83146961230254524
0.0000000000000000 -1.5707963267948966 0.92387953251128674
0.0000000000000000 -0.78539816339744828 0.98078528040323043
0.0000000000000000 0.0000000000000000 1.0000000000000000
0.0000000000000000 0.78539816339744828 0.98078528040323043
0.0000000000000000 1.5707963267948966 0.92387953251128674
0.0000000000000000 2.3561944901923439 0.83146961230254535
0.0000000000000000 3.1415926535897931 0.70710678118654757
0.0000000000000000 3.9269908169872423 0.55557023301960207
0.0000000000000000 4.7123889803846897 0.38268343236508984
0.0000000000000000 5.4977871437821371 0.19509032201612853
0.0000000000000000 6.2831853071795862 6.1232339957367660E-017
0.39269908169872414 -6.2831853071795862 6.0055777714832775E-017
0.39269908169872414 -5.4977871437821380 0.19134171618254495
0.39269908169872414 -4.7123889803846897 0.37533027751786530
0.39269908169872414 -3.9269908169872414 0.54489510677581865
0.39269908169872414 -3.1415926535897931 0.69351992266107376
0.39269908169872414 -2.3561944901923448 0.81549315684891710
0.39269908169872414 -1.5707963267948966 0.90612744635288778
0.39269908169872414 -0.78539816339744828 0.96193976625564337
0.39269908169872414 0.0000000000000000 0.98078528040323043
0.39269908169872414 0.78539816339744828 0.96193976625564337
0.39269908169872414 1.5707963267948966 0.90612744635288778
0.39269908169872414 2.3561944901923439 0.81549315684891721
0.39269908169872414 3.1415926535897931 0.69351992266107376
0.39269908169872414 3.9269908169872423 0.54489510677581843
0.39269908169872414 4.7123889803846897 0.37533027751786530
0.39269908169872414 5.4977871437821371 0.19134171618254514
0.39269908169872414 6.2831853071795862 6.0055777714832775E-017
0.78539816339744828 -6.2831853071795862 5.6571305614385013E-017
0.78539816339744828 -5.4977871437821380 0.18023995550173702
0.78539816339744828 -4.7123889803846897 0.35355339059327384
0.78539816339744828 -3.9269908169872414 0.51327996715933677
0.78539816339744828 -3.1415926535897931 0.65328148243818829
0.78539816339744828 -2.3561944901923448 0.76817775671141630
0.78539816339744828 -1.5707963267948966 0.85355339059327373
0.78539816339744828 -0.78539816339744828 0.90612744635288778
0.78539816339744828 0.0000000000000000 0.92387953251128674
0.78539816339744828 0.78539816339744828 0.90612744635288778
0.78539816339744828 1.5707963267948966 0.85355339059327373
0.78539816339744828 2.3561944901923439 0.76817775671141642
0.78539816339744828 3.1415926535897931 0.65328148243818829
0.78539816339744828 3.9269908169872423 0.51327996715933655
0.78539816339744828 4.7123889803846897 0.35355339059327384
0.78539816339744828 5.4977871437821371 0.18023995550173721
0.78539816339744828 6.2831853071795862 5.6571305614385013E-017
1.1780972450961720 -6.2831853071795862 5.0912829964730146E-017
1.1780972450961720 -5.4977871437821380 0.16221167441072895
1.1780972450961720 -4.7123889803846897 0.31818964514320858
1.1780972450961720 -3.9269908169872414 0.46193976625564348
1.1780972450961720 -3.1415926535897931 0.58793780120967942
1.1780972450961720 -2.3561944901923448 0.69134171618254503
1.1780972450961720 -1.5707963267948966 0.76817775671141642
1.1780972450961720 -0.78539816339744828 0.81549315684891721
1.1780972450961720 0.0000000000000000 0.83146961230254535
1.1780972450961720 0.78539816339744828 0.81549315684891721
1.1780972450961720 1.5707963267948966 0.76817775671141642
1.1780972450961720 2.3561944901923439 0.69134171618254503
1.1780972450961720 3.1415926535897931 0.58793780120967942
1.1780972450961720 3.9269908169872423 0.46193976625564331
1.1780972450961720 4.7123889803846897 0.31818964514320858
1.1780972450961720 5.4977871437821371 0.16221167441072912
1.1780972450961720 6.2831853071795862 5.0912829964730146E-017
1.5707963267948966 -6.2831853071795862 4.3297802811774670E-017
1.5707963267948966 -5.4977871437821380 0.13794968964147156
1.5707963267948966 -4.7123889803846897 0.27059805007309856
1.5707963267948966 -3.9269908169872414 0.39284747919355117
1.5707963267948966 -3.1415926535897931 0.50000000000000011
1.5707963267948966 -2.3561944901923448 0.58793780120967942
1.5707963267948966 -1.5707963267948966 0.65328148243818829
1.5707963267948966 -0.78539816339744828 0.69351992266107376
1.5707963267948966 0.0000000000000000 0.70710678118654757
1.5707963267948966 0.78539816339744828 0.69351992266107376
1.5707963267948966 1.5707963267948966 0.65328148243818829
1.5707963267948966 2.3561944901923439 0.58793780120967942
1.5707963267948966 3.1415926535897931 0.50000000000000011
1.5707963267948966 3.9269908169872423 0.39284747919355101
1.5707963267948966 4.7123889803846897 0.27059805007309856
1.5707963267948966 5.4977871437821371 0.13794968964147170
1.5707963267948966 6.2831853071795862 4.3297802811774670E-017
1.9634954084936211 -6.2831853071795862 3.4018865378450242E-017
1.9634954084936211 -5.4977871437821380 0.10838637566236962
1.9634954084936211 -4.7123889803846897 0.21260752369181410
1.9634954084936211 -3.9269908169872414 0.30865828381745508
1.9634954084936211 -3.1415926535897931 0.39284747919355101
1.9634954084936211 -2.3561944901923448 0.46193976625564326
1.9634954084936211 -1.5707963267948966 0.51327996715933655
1.9634954084936211 -0.78539816339744828 0.54489510677581843
1.9634954084936211 0.0000000000000000 0.55557023301960207
1.9634954084936211 0.78539816339744828 0.54489510677581843
1.9634954084936211 1.5707963267948966 0.51327996715933655
1.9634954084936211 2.3561944901923439 0.46193976625564331
1.9634954084936211 3.1415926535897931 0.39284747919355101
1.9634954084936211 3.9269908169872423 0.30865828381745491
1.9634954084936211 4.7123889803846897 0.21260752369181410
1.9634954084936211 5.4977871437821371 0.10838637566236972
1.9634954084936211 6.2831853071795862 3.4018865378450242E-017
2.3561944901923448 -6.2831853071795862 2.3432602026631496E-017
2.3561944901923448 -5.4977871437821380 7.4657834050342639E-002
2.3561944901923448 -4.7123889803846897 0.14644660940672630
2.3561944901923448 -3.9269908169872414 0.21260752369181418
2.3561944901923448 -3.1415926535897931 0.27059805007309856
2.3561944901923448 -2.3561944901923448 0.31818964514320852
2.3561944901923448 -1.5707963267948966 0.35355339059327384
2.3561944901923448 -0.78539816339744828 0.37533027751786530
2.3561944901923448 0.0000000000000000 0.38268343236508984
2.3561944901923448 0.78539816339744828 0.37533027751786530
2.3561944901923448 1.5707963267948966 0.35355339059327384
2.3561944901923448 2.3561944901923439 0.31818964514320858
2.3561944901923448 3.1415926535897931 0.27059805007309856
2.3561944901923448 3.9269908169872423 0.21260752369181410
2.3561944901923448 4.7123889803846897 0.14644660940672630
2.3561944901923448 5.4977871437821371 7.4657834050342722E-002
2.3561944901923448 6.2831853071795862 2.3432602026631496E-017
2.7488935718910685 -6.2831853071795862 1.1945836920083910E-017
2.7488935718910685 -5.4977871437821380 3.8060233744356686E-002
2.7488935718910685 -4.7123889803846897 7.4657834050342722E-002
2.7488935718910685 -3.9269908169872414 0.10838637566236978
2.7488935718910685 -3.1415926535897931 0.13794968964147170
2.7488935718910685 -2.3561944901923448 0.16221167441072909
2.7488935718910685 -1.5707963267948966 0.18023995550173721
2.7488935718910685 -0.78539816339744828 0.19134171618254514
2.7488935718910685 0.0000000000000000 0.19509032201612853
2.7488935718910685 0.78539816339744828 0.19134171618254514
2.7488935718910685 1.5707963267948966 0.18023995550173721
2.7488935718910685 2.3561944901923439 0.16221167441072912
2.7488935718910685 3.1415926535897931 0.13794968964147170
2.7488935718910685 3.9269908169872423 0.10838637566236972
2.7488935718910685 4.7123889803846897 7.4657834050342722E-002
2.7488935718910685 5.4977871437821371 3.8060233744356721E-002
2.7488935718910685 6.2831853071795862 1.1945836920083910E-017
3.1415926535897931 -6.2831853071795862 3.7493994566546440E-033
3.1415926535897931 -5.4977871437821380 1.1945836920083898E-017
3.1415926535897931 -4.7123889803846897 2.3432602026631496E-017
3.1415926535897931 -3.9269908169872414 3.4018865378450254E-017
3.1415926535897931 -3.1415926535897931 4.3297802811774670E-017
3.1415926535897931 -2.3561944901923448 5.0912829964730140E-017
3.1415926535897931 -1.5707963267948966 5.6571305614385013E-017
3.1415926535897931 -0.78539816339744828 6.0055777714832775E-017
3.1415926535897931 0.0000000000000000 6.1232339957367660E-017
3.1415926535897931 0.78539816339744828 6.0055777714832775E-017
3.1415926535897931 1.5707963267948966 5.6571305614385013E-017
3.1415926535897931 2.3561944901923439 5.0912829964730146E-017
3.1415926535897931 3.1415926535897931 4.3297802811774670E-017
3.1415926535897931 3.9269908169872423 3.4018865378450242E-017
3.1415926535897931 4.7123889803846897 2.3432602026631496E-017
3.1415926535897931 5.4977871437821371 1.1945836920083910E-017
3.1415926535897931 6.2831853071795862 3.7493994566546440E-033
Input and output functions are as shown in the figure below.
As the output function is the same as - 0.25d0*dcos(x(i1)/2.0d0) * dsin(y(i2)/4.0d0), this shows derivative calculation is correct. Here in subroutine 'd1y', I have used forward and backward finite difference formula for calculation of derivative at boundaries and the central difference for points lying between two boundaries. Then I have calculated the relative error. I got an error of 0.003 at the boundary of y axis and 0.0015 at the points in between boundaries as shown in fig below.
I calculated 2nd derivative using same technique. The subroutine is given below:
`subroutine d2y(n1,n2,n3,a,a2)
implicit none
integer n1, n2, n3, i1, i2, i3
real*8 pi, a(n1,n2,n3), a2(n1,n2,n3), z(n3),x(n1),y(n2),h
pi=3.14159265358979323846d0
h=4.0d0*pi/(n2-1)
a2=0.0d0
i3 =1
do i1=1,n1
do i2=1,n2
if(i2 == 1)then
a2(i1,i2,i3)=( 2.0d0*a(i1,i2,i3) - 5.0d0*a(i1,i2+1,i3) + 4.0d0*a(i1,i2+2,i3) - a(i1,i2+3,i3))/(h*h)
else if( i2 == n2)then
a2(i1,i2,i3)=( 2.0d0*a(i1,i2,i3) - 5.0d0*a(i1,i2-1,i3) + 4.0d0*a(i1,i2-2,i3) - a(i1,i2-3,i3))/(h*h)
else
a2(i1,i2,i3)= ( a(i1,i2+1,i3) - 2.0d0*a(i1,i2,i3) + a(i1,i2-1,i3) )/(h*h)
endif
enddo
enddo
! enddo
end subroutine
Here also the error is large at boundaries, in fact, the error is very large compared to first order derivative as shown in figure:.
Why this large error in boundary? Please explain.
`
fortran numerical-methods derivative
fortran numerical-methods derivative
edited Nov 11 at 14:05
b-fg
1,64811422
asked Nov 10 at 12:41
Jagannath Mahapatra
286
edited Nov 11 at 14:05
b-fg
1,64811422
asked Nov 10 at 12:41
Jagannath Mahapatra
286
edited Nov 11 at 14:05
b-fg
1,64811422
edited Nov 11 at 14:05
b-fg
1,64811422
edited Nov 11 at 14:05
b-fg
1,64811422
1,64811422
asked Nov 10 at 12:41
Jagannath Mahapatra
286
asked Nov 10 at 12:41
Jagannath Mahapatra
286
asked Nov 10 at 12:41
Jagannath Mahapatra
286
286
1
Not sure if related, but in the first snippet you seem to be printinga(i1,i2,2)before it is assigned any value, in this line:write(20,*)x(i1),y(i2),a(i1,i2,2)
– Rodrigo Rodrigues
Nov 10 at 14:36
1
By the way, welcome to StackOverflow. Please take the tour and check How to Ask. Could you please add a sample of input data you used for your graphs and provide a Minimal, Complete, and Verifiable example, in order for us to be able to help you more effectively?
– Rodrigo Rodrigues
Nov 10 at 14:47
comment to your 1st question @RodrigoRodrigues, yes it should be a(i1,i2,1). I am using only one index as my input function is z-independent.
– Jagannath Mahapatra
Nov 11 at 4:08
Of course the error is bigger at the boundary. Forward and backward difference are 1st order accurate and central difference is 2nd order accurate. While you cannot use central difference on the boundary, there are 2 nd order formulas for boundaries. Use one of them.
– Dan Sp.
Nov 11 at 5:56
1
It is 1st order accuracy, Dan Sp. is correct. You should check the total global error, that one should be 2nd order because the boundary layer is getting smaller and smaller with grid refinement.
– Vladimir F
Nov 11 at 15:03
|
show 2 more comments
1
Not sure if related, but in the first snippet you seem to be printinga(i1,i2,2)before it is assigned any value, in this line:write(20,*)x(i1),y(i2),a(i1,i2,2)
– Rodrigo Rodrigues
Nov 10 at 14:36
1
By the way, welcome to StackOverflow. Please take the tour and check How to Ask. Could you please add a sample of input data you used for your graphs and provide a Minimal, Complete, and Verifiable example, in order for us to be able to help you more effectively?
– Rodrigo Rodrigues
Nov 10 at 14:47
comment to your 1st question @RodrigoRodrigues, yes it should be a(i1,i2,1). I am using only one index as my input function is z-independent.
– Jagannath Mahapatra
Nov 11 at 4:08
Of course the error is bigger at the boundary. Forward and backward difference are 1st order accurate and central difference is 2nd order accurate. While you cannot use central difference on the boundary, there are 2 nd order formulas for boundaries. Use one of them.
– Dan Sp.
Nov 11 at 5:56
1
It is 1st order accuracy, Dan Sp. is correct. You should check the total global error, that one should be 2nd order because the boundary layer is getting smaller and smaller with grid refinement.
– Vladimir F
Nov 11 at 15:03
1
1
Not sure if related, but in the first snippet you seem to be printing
a(i1,i2,2) before it is assigned any value, in this line: write(20,*)x(i1),y(i2),a(i1,i2,2)– Rodrigo Rodrigues
Nov 10 at 14:36
Not sure if related, but in the first snippet you seem to be printing
a(i1,i2,2) before it is assigned any value, in this line: write(20,*)x(i1),y(i2),a(i1,i2,2)– Rodrigo Rodrigues
Nov 10 at 14:36
1
1
By the way, welcome to StackOverflow. Please take the tour and check How to Ask. Could you please add a sample of input data you used for your graphs and provide a Minimal, Complete, and Verifiable example, in order for us to be able to help you more effectively?
– Rodrigo Rodrigues
Nov 10 at 14:47
By the way, welcome to StackOverflow. Please take the tour and check How to Ask. Could you please add a sample of input data you used for your graphs and provide a Minimal, Complete, and Verifiable example, in order for us to be able to help you more effectively?
– Rodrigo Rodrigues
Nov 10 at 14:47
comment to your 1st question @RodrigoRodrigues, yes it should be a(i1,i2,1). I am using only one index as my input function is z-independent.
– Jagannath Mahapatra
Nov 11 at 4:08
comment to your 1st question @RodrigoRodrigues, yes it should be a(i1,i2,1). I am using only one index as my input function is z-independent.
– Jagannath Mahapatra
Nov 11 at 4:08
Of course the error is bigger at the boundary. Forward and backward difference are 1st order accurate and central difference is 2nd order accurate. While you cannot use central difference on the boundary, there are 2 nd order formulas for boundaries. Use one of them.
– Dan Sp.
Nov 11 at 5:56
Of course the error is bigger at the boundary. Forward and backward difference are 1st order accurate and central difference is 2nd order accurate. While you cannot use central difference on the boundary, there are 2 nd order formulas for boundaries. Use one of them.
– Dan Sp.
Nov 11 at 5:56
1
1
It is 1st order accuracy, Dan Sp. is correct. You should check the total global error, that one should be 2nd order because the boundary layer is getting smaller and smaller with grid refinement.
– Vladimir F
Nov 11 at 15:03
It is 1st order accuracy, Dan Sp. is correct. You should check the total global error, that one should be 2nd order because the boundary layer is getting smaller and smaller with grid refinement.
– Vladimir F
Nov 11 at 15:03
|
show 2 more comments
1 Answer
1
active
oldest
votes
up vote
2
down vote
accepted
First, I stand corrected. The names 'Forward Difference' and 'Backward Difference' usually refer to the standard 1st order formulas. You do have correct 2nd order, One-sided 1st derivative formulas.
The 2nd order, 1st derivative, Forward difference formula is derived with a Taylor series expression at x+h and at x+2h as follows:
f(x+h) = f(x) + h*f'(x) + h^2*f''(x)/2! + h^3*f'''(x)/3! ....
f(x+2h) = f(x) + 2h*f'(x) + 4h^2*f''(x)/2! + 8h^3*f'''(x)/3! ....
Now, take 4 times the 1st series and subtract the 2nd series and solve for f'(x). Note that the second derivative term disappears.
f'(x) = 3h/2 * f(x) - 2/h *f(x+h) + 1/(2h) * f(x+2h) + 0 - 4h^2*f'''(x)/3!
This is the formula you are using and it is 2nd order accurate. This does not mean that the accuracy is identical to the central difference accuracy, only that the order of the accuracy is the same.
If you look at the derivation of central difference while keeping the error term, you will find that the error term looks like:
-h^2*f'''(x)/6
Did you note that the error term in the forward difference formula has a constant in front of it. In general, the error will always be larger. But that is not what it means to be 2nd order accurate. The order of accuracy tells you how the error changes when you change h.
For example, if you halve h, you should get about 4 times the accuracy. That means in your example above, the central difference error will go from about 0.0015 to about 0.0004 and the boundary error will go from about 0.003 to about 0.0008
Final Takeaway: Your program is correct and your errors are correct!
answered Nov 14 at 6:08
Dan Sp.
9811317
Even second order formulas can produce first order local discretization errors at the boundary. And still the global discretization error can be second order, because the relative portion of the boundary region gets smaller with grid refinement.
– Vladimir F
Nov 14 at 9:56
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
2
down vote
accepted
First, I stand corrected. The names 'Forward Difference' and 'Backward Difference' usually refer to the standard 1st order formulas. You do have correct 2nd order, One-sided 1st derivative formulas.
The 2nd order, 1st derivative, Forward difference formula is derived with a Taylor series expression at x+h and at x+2h as follows:
f(x+h) = f(x) + h*f'(x) + h^2*f''(x)/2! + h^3*f'''(x)/3! ....
f(x+2h) = f(x) + 2h*f'(x) + 4h^2*f''(x)/2! + 8h^3*f'''(x)/3! ....
Now, take 4 times the 1st series and subtract the 2nd series and solve for f'(x). Note that the second derivative term disappears.
f'(x) = 3h/2 * f(x) - 2/h *f(x+h) + 1/(2h) * f(x+2h) + 0 - 4h^2*f'''(x)/3!
This is the formula you are using and it is 2nd order accurate. This does not mean that the accuracy is identical to the central difference accuracy, only that the order of the accuracy is the same.
If you look at the derivation of central difference while keeping the error term, you will find that the error term looks like:
-h^2*f'''(x)/6
Did you note that the error term in the forward difference formula has a constant in front of it. In general, the error will always be larger. But that is not what it means to be 2nd order accurate. The order of accuracy tells you how the error changes when you change h.
For example, if you halve h, you should get about 4 times the accuracy. That means in your example above, the central difference error will go from about 0.0015 to about 0.0004 and the boundary error will go from about 0.003 to about 0.0008
Final Takeaway: Your program is correct and your errors are correct!
answered Nov 14 at 6:08
Dan Sp.
9811317
Even second order formulas can produce first order local discretization errors at the boundary. And still the global discretization error can be second order, because the relative portion of the boundary region gets smaller with grid refinement.
– Vladimir F
Nov 14 at 9:56
add a comment |
up vote
2
down vote
accepted
First, I stand corrected. The names 'Forward Difference' and 'Backward Difference' usually refer to the standard 1st order formulas. You do have correct 2nd order, One-sided 1st derivative formulas.
The 2nd order, 1st derivative, Forward difference formula is derived with a Taylor series expression at x+h and at x+2h as follows:
f(x+h) = f(x) + h*f'(x) + h^2*f''(x)/2! + h^3*f'''(x)/3! ....
f(x+2h) = f(x) + 2h*f'(x) + 4h^2*f''(x)/2! + 8h^3*f'''(x)/3! ....
Now, take 4 times the 1st series and subtract the 2nd series and solve for f'(x). Note that the second derivative term disappears.
f'(x) = 3h/2 * f(x) - 2/h *f(x+h) + 1/(2h) * f(x+2h) + 0 - 4h^2*f'''(x)/3!
This is the formula you are using and it is 2nd order accurate. This does not mean that the accuracy is identical to the central difference accuracy, only that the order of the accuracy is the same.
If you look at the derivation of central difference while keeping the error term, you will find that the error term looks like:
-h^2*f'''(x)/6
Did you note that the error term in the forward difference formula has a constant in front of it. In general, the error will always be larger. But that is not what it means to be 2nd order accurate. The order of accuracy tells you how the error changes when you change h.
For example, if you halve h, you should get about 4 times the accuracy. That means in your example above, the central difference error will go from about 0.0015 to about 0.0004 and the boundary error will go from about 0.003 to about 0.0008
Final Takeaway: Your program is correct and your errors are correct!
answered Nov 14 at 6:08
Dan Sp.
9811317
Even second order formulas can produce first order local discretization errors at the boundary. And still the global discretization error can be second order, because the relative portion of the boundary region gets smaller with grid refinement.
– Vladimir F
Nov 14 at 9:56
add a comment |
up vote
2
down vote
accepted
up vote
2
down vote
accepted
First, I stand corrected. The names 'Forward Difference' and 'Backward Difference' usually refer to the standard 1st order formulas. You do have correct 2nd order, One-sided 1st derivative formulas.
The 2nd order, 1st derivative, Forward difference formula is derived with a Taylor series expression at x+h and at x+2h as follows:
f(x+h) = f(x) + h*f'(x) + h^2*f''(x)/2! + h^3*f'''(x)/3! ....
f(x+2h) = f(x) + 2h*f'(x) + 4h^2*f''(x)/2! + 8h^3*f'''(x)/3! ....
Now, take 4 times the 1st series and subtract the 2nd series and solve for f'(x). Note that the second derivative term disappears.
f'(x) = 3h/2 * f(x) - 2/h *f(x+h) + 1/(2h) * f(x+2h) + 0 - 4h^2*f'''(x)/3!
This is the formula you are using and it is 2nd order accurate. This does not mean that the accuracy is identical to the central difference accuracy, only that the order of the accuracy is the same.
If you look at the derivation of central difference while keeping the error term, you will find that the error term looks like:
-h^2*f'''(x)/6
Did you note that the error term in the forward difference formula has a constant in front of it. In general, the error will always be larger. But that is not what it means to be 2nd order accurate. The order of accuracy tells you how the error changes when you change h.
For example, if you halve h, you should get about 4 times the accuracy. That means in your example above, the central difference error will go from about 0.0015 to about 0.0004 and the boundary error will go from about 0.003 to about 0.0008
Final Takeaway: Your program is correct and your errors are correct!
answered Nov 14 at 6:08
Dan Sp.
9811317
First, I stand corrected. The names 'Forward Difference' and 'Backward Difference' usually refer to the standard 1st order formulas. You do have correct 2nd order, One-sided 1st derivative formulas.
The 2nd order, 1st derivative, Forward difference formula is derived with a Taylor series expression at x+h and at x+2h as follows:
f(x+h) = f(x) + h*f'(x) + h^2*f''(x)/2! + h^3*f'''(x)/3! ....
f(x+2h) = f(x) + 2h*f'(x) + 4h^2*f''(x)/2! + 8h^3*f'''(x)/3! ....
Now, take 4 times the 1st series and subtract the 2nd series and solve for f'(x). Note that the second derivative term disappears.
f'(x) = 3h/2 * f(x) - 2/h *f(x+h) + 1/(2h) * f(x+2h) + 0 - 4h^2*f'''(x)/3!
This is the formula you are using and it is 2nd order accurate. This does not mean that the accuracy is identical to the central difference accuracy, only that the order of the accuracy is the same.
If you look at the derivation of central difference while keeping the error term, you will find that the error term looks like:
-h^2*f'''(x)/6
Did you note that the error term in the forward difference formula has a constant in front of it. In general, the error will always be larger. But that is not what it means to be 2nd order accurate. The order of accuracy tells you how the error changes when you change h.
For example, if you halve h, you should get about 4 times the accuracy. That means in your example above, the central difference error will go from about 0.0015 to about 0.0004 and the boundary error will go from about 0.003 to about 0.0008
Final Takeaway: Your program is correct and your errors are correct!
answered Nov 14 at 6:08
Dan Sp.
9811317
answered Nov 14 at 6:08
Dan Sp.
9811317
answered Nov 14 at 6:08
Dan Sp.
9811317
answered Nov 14 at 6:08
Dan Sp.
9811317
9811317
Even second order formulas can produce first order local discretization errors at the boundary. And still the global discretization error can be second order, because the relative portion of the boundary region gets smaller with grid refinement.
– Vladimir F
Nov 14 at 9:56
add a comment |
Even second order formulas can produce first order local discretization errors at the boundary. And still the global discretization error can be second order, because the relative portion of the boundary region gets smaller with grid refinement.
– Vladimir F
Nov 14 at 9:56
Even second order formulas can produce first order local discretization errors at the boundary. And still the global discretization error can be second order, because the relative portion of the boundary region gets smaller with grid refinement.
– Vladimir F
Nov 14 at 9:56
Even second order formulas can produce first order local discretization errors at the boundary. And still the global discretization error can be second order, because the relative portion of the boundary region gets smaller with grid refinement.
– Vladimir F
Nov 14 at 9:56
add a comment |
draft saved
draft discarded
Thanks for contributing an answer to Stack Overflow!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
Some of your past answers have not been well-received, and you're in danger of being blocked from answering.
Please pay close attention to the following guidance:
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
draft saved
draft discarded
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53239057%2flarge-error-at-boudaries-while-calculating-first-and-second-derivative-using-fin%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
1
Not sure if related, but in the first snippet you seem to be printing
a(i1,i2,2)before it is assigned any value, in this line:write(20,*)x(i1),y(i2),a(i1,i2,2)– Rodrigo Rodrigues
Nov 10 at 14:36
1
By the way, welcome to StackOverflow. Please take the tour and check How to Ask. Could you please add a sample of input data you used for your graphs and provide a Minimal, Complete, and Verifiable example, in order for us to be able to help you more effectively?
– Rodrigo Rodrigues
Nov 10 at 14:47
comment to your 1st question @RodrigoRodrigues, yes it should be a(i1,i2,1). I am using only one index as my input function is z-independent.
– Jagannath Mahapatra
Nov 11 at 4:08
Of course the error is bigger at the boundary. Forward and backward difference are 1st order accurate and central difference is 2nd order accurate. While you cannot use central difference on the boundary, there are 2 nd order formulas for boundaries. Use one of them.
– Dan Sp.
Nov 11 at 5:56
1
It is 1st order accuracy, Dan Sp. is correct. You should check the total global error, that one should be 2nd order because the boundary layer is getting smaller and smaller with grid refinement.
– Vladimir F
Nov 11 at 15:03