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Program Foucault
IMPLICIT NONE
REAL,DIMENSION(:),ALLOCATABLE :: t, x,y
REAL,PARAMETER :: pi=3.14159265358979323846, g=9.81
REAL :: L, vitessea, lat, h, omega, beta
INTEGER :: i , zeta
zeta=1000
Allocate(x(zeta),y(zeta),t(zeta))
L=67.
lat=49/180*pi
omega=sqrt(g/L)
h=0.01
Do i= 1,zeta
IF(i==1 .OR. i==2) THEN
t(1)=0.0
t(2)=0.0
x(1)=0.1
x(2)=1
y(1)=0.0
y(2)=0.0
ELSE
t(i+1)=real(i)*h
x(i+1)=(-omega**2*x(i)+2.0*((y(i)-y(i-1))/h)*latang(lat))*h**2+2.0*x(i)-x(i-1)
y(i+1)=(-omega**2*y(i)-2.0*((x(i)-x(i-1))/h)*latang(lat))*h**2+2.0*y(i)-y(i-1)
END IF
WRITE(40,*) t(i), x(i)
WRITE(60,*) t(i), y(i)
WRITE(50,*) x(i), y(i)
END DO
Contains
REAL Function latang(alpha)
REAL, INTENT(IN) :: alpha
REAL :: sol
latang=2*pi*sin(alpha)/86400
END FUNCTION
End Program Foucault
I'm trying to code the original Foucault Pendulum in Paris. My code seems to be working but so far, I could only get the below right graphic, "the flower" evolution. Therefore, I changed my parameters constantly to get the left graphic but I couldn't.
I took parameters of Foucault Pendulum installed in Paris with L=67, angular velocity of earth =2*pi/86400 and latitude of 49/180*pi.
My initial conditions are as written in the code. I tried a way range of parameters varying all of my initial conditions, my latitude and angular velocity but i couldn't get the left desired results.
I used Foucault differential equations as below : i coded them with Finite difference method (more simple than Runge-Kutta) by replacing the 2nd order derivation by its central finite difference. And the first order one by it's backward finite difference. By then, i build my loop by isolating x(i+1) and y(i+1) in both equations.
My code is very sensitive to parameters such as h (=derivation step), earth angular velocity and latitude (which is normal). I tried to change a way big range of parameters from a big h step to a small one, to a minimal and high latitude, initial conditions...etc but i couldn't ever get the left graphic which i rather need.
What could be made to get the left one ?
I was able to get the two charts, by speeding up the earth's rotation 120× fold, and allowing the simulation to run for 32 swings of the pendulum. Also, I noticed that Euler integration added energy to the system making for bad results, so I reverted to a standard RK4 implementation.
and here is the code I used to solve this ODE:
program FoucaultOde
implicit none
integer, parameter :: sp = kind(1.0), dp = kind(1d0)
! Constants
real, parameter :: g=9.80665, pi =3.1415926536
! Variables
real, allocatable :: y(:,:), yp(:), k0(:),k1(:),k2(:),k3(:)
real :: lat, omega, h, L, earth, period
real :: t0,x0,y0,vx0,vy0
integer :: i, zeta, f1, swings
! Code starts here
swings = 32
zeta = 400*swings
L = 67
lat = 49*pi/180
period = 24*60*60 ! period = 86400
earth = (2*pi*sin(lat)/period)*120 !120 multiplier for roation
omega = sqrt(g/L)
allocate(y(5,zeta))
allocate(yp(5), k0(5),k1(5),k2(5),k3(5))
! make pendulum complete 'swings' cycles in 'zeta' steps
h = swings*2*pi/(omega*zeta)
t0 = 0
x0 = 0.5 ! Initial displacement
y0 = 0
vx0 = 0
vy0 = 0
! Initial conditions in the state vector Y
Y(:,1) = [t0,x0,y0,vx0,vy0]
do i=2, zeta
! Euler method (single step)
! Yp = ode(Y(:,i-1))
! Runge-Kutta method (four steps)
k0 = ode(Y(:,i-1))
k1 = ode(Y(:,i-1) + h/2*k0)
k2 = ode(Y(:,i-1) + h/2*k1)
k3 = ode(Y(:,i-1) + h*k2)
Yp = (k0+2*k1+2*k2+k3)/6
! Take a step
Y(:,i) = Y(:,i-1) + h*Yp
end do
open( newunit=f1, file='results.csv', status = 'replace', pad='no')
! write header
write (f1, '(a15,a,a15,a,a15,a,a15,a,a15)') 't',',', 'x',',','y',',', 'vx',',','vy'
! write rows of data, comma-separated
do i=1, zeta
write (f1, '(g,a,g,a,g,a,g,a,g)') y(1,i),',',y(2,i),',',y(3,i),',',y(4,i),',',y(5,i)
end do
close(f1)
contains
function ode(Y) result(Yp)
real, intent(in) :: Y(5)
real :: Yp(5), t,px,py,vx,vy,ax,ay
! Read state vector Y to component values
t = Y(1)
px = Y(2)
py = Y(3)
vx = Y(4)
vy = Y(5)
! Reference paper:
! http://www.legi.grenoble-inp.fr/people/Achim.Wirth/final_version.pdf
ax = -(omega**2)*px + 2*vy*earth ! (equation 53)
ay = -(omega**2)*py - 2*vx*earth ! (equation 54)
! State vector rate. Note, rate of time is aways 1.0
Yp = [1.0, vx, vy, ax, ay]
end function
end program FoucaultOde
The resulting file results.csv looks like this for me (for checking)
t, x, y, vx, vy
.000000 , 5.000000 , .000000 , .000000 , .000000
.4105792E-01, 4.999383 , .1112020E-06, -.3004657E-01, .8124921E-05
.8211584E-01, 4.997533 , .8895339E-06, -.6008571E-01, .3249567E-04
.1231738 , 4.994450 , .3001796E-05, -.9011002E-01, .7310022E-04
.1642317 , 4.990134 , .7114130E-05, -.1201121 , .1299185E-03
.2052896 , 4.984587 , .1389169E-04, -.1500844 , .2029225E-03
.2463475 , 4.977810 , .2399832E-04, -.1800197 , .2920761E-03
.2874054 , 4.969805 , .3809619E-04, -.2099106 , .3973353E-03
...
from which I plotted the 2nd and 3rd columns in one chart, and the 4th and 5th for the second chart.
There is one thing that may be wrong depending on how you manage different step sizes, and an observation on the physics of the real-world example. With the initialization of the arrays, you imply an initial velocity of about 0.9/0.01=90 [m/s] in x direction away from the center. To get compatible results for different step sizes, you would need to adapt the calculation of x(2). However, in the graphs the plot starts from a point with zero velocity. This you can implement to first order by setting x(2)=x(1)=1. As the used integration method is also first order, this is sufficient.
For the second point, note that one can write the system using complex coordinates z=x+iy as
z'' = -w^2*z - 2*i*E*z', E = Omega*sin(theta)
This is a linear ODE with constant coefficients, the solution of it is
z(t) = exp(-i*E*t) * (A*cos(w1*t)+B*sin(w1*t)), w1 = sqrt(w^2+E^2)
This describes a pendulum motion of frequency w1 whose plane rotates with frequency E clockwise. The grand rotation has period T=2*pi/E, during which w1*T/(2*pi)=w1/E pendulum swings occur.
Now insert your numbers, w=sqrt(g/L)=0.383 and E=2*pi*sin(49°)/86400=5.49e-05, so that essentially w1=w. The number of pendulum cycles per full rotation is w/E=6972, so that you can expect a densely filled circle in the plot. Or a very narrow double wedge if only a few cycles are plotted. As each cycle takes 2*pi/w=16.4 [s], and the integration goes 1000 steps of step size 0.01, in the plot as it is you can expect a swing forth and part of the swing back.
To be more realistic, set the initial velocity to zero, that is, the pendulum is taken to its start position and then let go. Also increase the time to 30 [s] to have more than one pendulum cycle in the plot.
It from this we can see that the solutions converge, and with some imagination, that they converge linearly.
To get a plot like in the cited images, one needs a much smaller fraction of w/E, counting the swings, it has to be around 15. Note that you can not get this ratio anywhere on earth with a realistically scaled pendulum. So set w=pi, E=pi/16 and integrate over 15 time units using the first order method.
This detoriorates really fast, even for the smallest step size with 40 points in a pendulum cycle.
For a better result, increase the local truncation order to the next higher by using the central difference in the first derivative approximation.
z(i+1) - 2*z(i) + z(i-1) = -w^2*z(i)*dt^2 - i*E*(z(i+1)-z(i-1))*dt
z(i+1) = ( 2*z(i) - z(i-1) - w^2*z(i)*dt^2 + i*E*z(i-1)*dt ) / (1+i*E*dt)
The division by the complex number can also be easily carried out in the real components of the trajectory,
! x(i+1)-2*x(i)+x(i-1) = h^2*(-omega**2*x(i)) + h*earth*(y(i+1)-y(i-1))
! y(i+1)-2*y(i)+y(i-1) = h^2*(-omega**2*y(i)) - h*earth*(x(i+1)-x(i-1))
t(i) = t(i-1) + h
cx = (2-(h*omega)**2)*x(i) - x(i-1) - h*earth*y(i-1)
cy = (2-(h*omega)**2)*y(i) - y(i-1) + h*earth*x(i-1)
den = 1+(h*earth)**2
x(i+1) = (cx + h*earth*cy)/den
y(i+1) = (cy - h*earth*cx)/den
Now to respect the increased order, also the initial points need to have an order of accuracy more, using again zero initial speed, this gives in the second order Taylor expansion
z(2) = z(1) - 0.5*w^2*z(1)*dt^2
All the step sizes that gave deviating and structurally deteriorating results in the first order method now give a visually identical, structurally stable results in this second order method.
I want to calculate z value as the coordinate in range of x:-50~50 and y:-50~50 like below code.
program test
implicit none
! --- [local entities]
real*8 :: rrr,th,U0,amp,alp,Ndiv
real*8 :: pi,alpR,NR,Rmin,Rmax,z
integer :: ir, i, j
do i=0, 50
do j=0, 50
th=datan2(i,j)
pi=datan(1.d0)*4.d0
!
Ndiv= 24.d0 !! Number of circumferential division
alp = 90.d0/180.d0*pi !! phase [rad]
U0 = 11.4d0 !! average velocity
amp = 0.5d0 !! amplitude of velocity
Rmin = 10 !! [m]
Rmax = 50 !! [m]
NR = 6.d0 !! Number of radial division
!
rrr=dsqrt(i**2+j**2)
ir=int((rrr-Rmin)/(Rmax-Rmin)*NR)
alpR=2.d0*pi/dble(Ndiv)*dble(mod(ir,2))
z=U0*(1.d0+amp*dsin(0.5d0*Ndiv*th+alp+alpR))
write(*,*) 'i, j, z'
write(*,*) i, j, z
end do
end do
stop
end program test
But I couldn't make it work like below error. I think because i, j are in datan(i,j). How should I change these code?
test.f90:10.16:
th=datan2(i,j)
1
Error: 'y' argument of 'datan2' intrinsic at (1) must be REAL
test.f90:21.16:
rrr=dsqrt(i**2+j**2)
1
Error: 'x' argument of 'dsqrt' intrinsic at (1) must be REAL
Inspired by the comments of #Rodrigo Rodrigues, #Ian Bush, and #Richard, here is a suggested rewrite of the code segment from #SW. Kim
program test
use, intrinsic :: iso_fortran_env, only : real64
implicit none
! --- [local entities]
! Determine the kind of your real variables (select one):
! for specifying a given numerical precision
integer, parameter :: wp = selected_real_kind(15, 307) !15 digits, 10**307 range
! for specifying a given number of bits
! integer, parameter :: wp = real64
real(kind=wp), parameter :: pi = atan(1._wp)*4._wp
real(kind=wp) :: rrr, th, U0, amp, alp, Ndiv
real(kind=wp) :: alpR, NR, Rmin, Rmax, z
integer :: ir, i, j
do i = 0, 50
do j = 0, 50
th = atan2(real(i, kind=wp), real(j, kind=wp))
!
Ndiv= 24._wp !! Number of circumferential division
alp = 90._wp/180._wp*pi !! phase [rad]
U0 = 11.4_wp !! average velocity
amp = 0.5_wp !! amplitude of velocity
Rmin = 10 !! [m]
Rmax = 50 !! [m]
NR = 6._wp !! Number of radial division
!
rrr = sqrt(real(i, kind=wp)**2 + real(j, kind=wp)**2)
ir = int((rrr - Rmin) / (Rmax - Rmin) * NR)
alpR = 2._wp * pi / Ndiv * mod(ir, 2)
z = U0 * (1._wp + amp * sin(0.5_wp * Ndiv * th + alp + alpR))
!
write(*,*) 'i, j, z'
write(*,*) i, j, z
end do
end do
stop
end program test
Specifically, the following changes were made with respect to the original code posted:
Minimum change to answer the question: casting integer variables i and j to real values for using them in the real valued functions datan and dsqrt.
Using generic names for intrinsic procedures, i.e sqrt instead of dsqrt, atan instead of datan, and sin instead of dsin. One benefit of this approach, is that the kind of working precision wp can be changed in one place, without requiring explicit changes elsewhere in the code.
Defining the kind of real variables and calling it wp. Extended discussion of this topic, its implications and consequences can be found on this site, for example here and here. Also #Steve Lionel has an in depth post on his blog, where his general advice is to use selected_real_kind.
Defining pi as a parameter calculating its value once, instead of calculating the same value repeatedly within the nested for loops.
I am trying to write a FORTRAN code to evaluate the fast Fourier transform of the Gaussian function f(r)=exp(-(r^2)) using FFTW3 library. As everyone knows, the Fourier transform of the Gaussian function is another Gaussian function.
I consider evaluating the Fourier-transform integral of the Gaussian function in the spherical coordinate.
Hence the resulting integral can be simplified to be integral of [r*exp(-(r^2))*sin(kr)]dr.
I wrote the following FORTRAN code to evaluate the discrete SINE transform DST which is the discrete Fourier transform DFT using a PURELY real input array. DST is performed by C_FFTW_RODFT00 existing in FFTW3, taking into account that the discrete values in position space are r=i*delta (i=1,2,...,1024), and the input array for DST is the function r*exp(-(r^2)) NOT the Gaussian. The sine function in the integral of [r*exp(-(r^2))*sin(kr)]dr resulting from the INTEGRATION over the SPHERICAL coordinates, and it is NOT the imaginary part of exp(ik.r) that appears when taking the analytic Fourier transform in general.
However, the result is not a Gaussian function in the momentum space.
Module FFTW3
use, intrinsic :: iso_c_binding
include 'fftw3.f03'
end module
program sine_FFT_transform
use FFTW3
implicit none
integer, parameter :: dp=selected_real_kind(8)
real(kind=dp), parameter :: pi=acos(-1.0_dp)
integer, parameter :: n=1024
real(kind=dp) :: delta, k
real(kind=dp) :: numerical_F_transform
integer :: i
type(C_PTR) :: my_plan
real(C_DOUBLE), dimension(1024) :: y
real(C_DOUBLE), dimension(1024) :: yy, yk
integer(C_FFTW_R2R_KIND) :: C_FFTW_RODFT00
my_plan= fftw_plan_r2r_1d(1024,y,yy,FFTW_FORWARD, FFTW_ESTIMATE)
delta=0.0125_dp
do i=1, n !inserting the input one-dimension position function
y(i)= 2*(delta)*(i-1)*exp(-((i-1)*delta)**2)
! I multiplied by 2 due to the definition of C_FFTW_RODFT00 in FFTW3
end do
call fftw_execute_r2r(my_plan, y,yy)
do i=2, n
k = (i-1)*pi/n/delta
yk(i) = 4*pi*delta*yy(i)/2 !I divide by 2 due to the definition of
!C_FFTW_RODFT00
numerical_F_transform=yk(i)/k
write(11,*) i,k,numerical_F_transform
end do
call fftw_destroy_plan(my_plan)
end program
Executing the previous code gives the following plot which is not for Gaussian function.
Can anyone help me understand what the problem is? I guess the problem is mainly due to FFTW3. Maybe I did not use it properly especially concerning the boundary conditions.
Looking at the related pages in the FFTW site (Real-to-Real Transforms, transform kinds, Real-odd DFT (DST)) and the header file for Fortran, it seems that FFTW expects FFTW_RODFT00 etc rather than FFTW_FORWARD for specifying the kind of
real-to-real transform. For example,
! my_plan= fftw_plan_r2r_1d( n, y, yy, FFTW_FORWARD, FFTW_ESTIMATE )
my_plan= fftw_plan_r2r_1d( n, y, yy, FFTW_RODFT00, FFTW_ESTIMATE )
performs the "type-I" discrete sine transform (DST-I) shown in the above page. This modification seems to fix the problem (i.e., makes the Fourier transform a Gaussian with positive values).
The following is a slightly modified version of OP's code to experiment the above modification:
! ... only the modified part is shown...
real(dp) :: delta, k, r, fftw, num, ana
integer :: i, j, n
type(C_PTR) :: my_plan
real(C_DOUBLE), allocatable :: y(:), yy(:)
delta = 0.0125_dp ; n = 1024 ! rmax = 12.8
! delta = 0.1_dp ; n = 128 ! rmax = 12.8
! delta = 0.2_dp ; n = 64 ! rmax = 12.8
! delta = 0.4_dp ; n = 32 ! rmax = 12.8
allocate( y( n ), yy( n ) )
! my_plan= fftw_plan_r2r_1d( n, y, yy, FFTW_FORWARD, FFTW_ESTIMATE )
my_plan= fftw_plan_r2r_1d( n, y, yy, FFTW_RODFT00, FFTW_ESTIMATE )
! Loop over r-grid
do i = 1, n
r = i * delta ! (2-a)
y( i )= r * exp( -r**2 )
end do
call fftw_execute_r2r( my_plan, y, yy )
! Loop over k-grid
do i = 1, n
! Result of FFTW
k = i * pi / ((n + 1) * delta) ! (2-b)
fftw = 4 * pi * delta * yy( i ) / k / 2 ! the last 2 due to RODFT00
! Numerical result via quadrature
num = 0
do j = 1, n
r = j * delta
num = num + r * exp( -r**2 ) * sin( k * r )
enddo
num = num * 4 * pi * delta / k
! Analytical result
ana = sqrt( pi )**3 * exp( -k**2 / 4 )
! Output
write(10,*) k, fftw
write(20,*) k, num
write(30,*) k, ana
end do
Compile (with gfortran-8.2 + FFTW3.3.8 + OSX10.11):
$ gfortran -fcheck=all -Wall sine.f90 -I/usr/local/Cellar/fftw/3.3.8/include -L/usr/local/Cellar/fftw/3.3.8/lib -lfftw3
If we use FFTW_FORWARD as in the original code, we get
which has a negative lobe (where fort.10, fort.20, and fort.30 correspond to FFTW, quadrature, and analytical results). Modifying the code to use FFTW_RODFT00 changes the result as below, so the modification seems to be working (but please see below for the grid definition).
Additional notes
I have slightly modified the grid definition for r and k in my code (Lines (2-a) and (2-b)), which is found to improve the accuracy. But I'm still not sure whether the above definition matches the definition used by FFTW, so please read the manual for details...
The fftw3.f03 header file gives the interface for fftw_plan_r2r_1d
type(C_PTR) function fftw_plan_r2r_1d(n,in,out,kind,flags) bind(C, name='fftw_plan_r2r_1d')
import
integer(C_INT), value :: n
real(C_DOUBLE), dimension(*), intent(out) :: in
real(C_DOUBLE), dimension(*), intent(out) :: out
integer(C_FFTW_R2R_KIND), value :: kind
integer(C_INT), value :: flags
end function fftw_plan_r2r_1d
(Because of no Tex support, this part is very ugly...) The integral of 4 pi r^2 * exp(-r^2) * sin(kr)/(kr) for r = 0 -> infinite is pi^(3/2) * exp(-k^2 / 4) (obtained from Wolfram Alpha or by noting that this is actually a 3-D Fourier transform of exp(-(x^2 + y^2 + z^2)) by exp(-i*(k1 x + k2 y + k3 z)) with k =(k1,k2,k3)). So, although a bit counter-intuitive, the result becomes a positive Gaussian.
I guess the r-grid can be chosen much coarser (e.g. delta up to 0.4), which gives almost the same accuracy as long as it covers the frequency domain of the transformed function (here exp(-r^2)).
Of course there are negative components of the real part to the FFT of a limited Gaussian spectrum. You are just using the real part of the transform. So your plot is absolutely correct.
You seem to be mistaking the real part with the magnitude, which of course would not be negative. For that you would need to fftw_plan_dft_r2c_1d and then calculate the absolute values of the complex coefficients. Or you might be mistaking the Fourier transform with a limited DFT.
You might want to check here to convince yourself of the correctness of you calculation above:
http://docs.mantidproject.org/nightly/algorithms/FFT-v1.html
Please do keep in mind that the plots on the above page are shifted, so that the 0 frequency is in the middle of the spectrum.
Citing yourself, the nummeric integration of [r*exp(-(r^2))*sin(kr)]dr would have negative components for all k>1 if normalised to 0 for highest frequency.
TLDR: Your plot is absolute state of the art and inline with discrete and limited functional analysis.
I want to do an iteration of the strain and stress change in rock mechanics, but am stuck on the errors:
"real::STRAIN (1:N), SIGMA (1:N),DSIGMA (1:N),STRAIN (1:N)=0.0"
and
"real,Dimension(6)::CEL(1:N,1:N)!stiffness matrix"
!program elastic_plastic
implicit none
!define all parameter
integer :: i = 1.0,j,K,M,N,inc
real::STRAIN (1:N), SIGMA (1:N),DSIGMA (1:N),DSTRAIN (1:N)=0.0
real,Dimension(6)::CEL(1:N,1:N)!stiffness matrix
real:: YOUNG, NU, COHESION !rock properties
real::ALPHA, KAPPA! cohesion and frictional angle
real::F !function
real::FRICTION_DEG, FRICTION_RAD !friction angle
real::VARJ2 ,VARI1 !stress invariants (I1 and J2)(MPa)
real:: LAMBDA,GMODU !lames constant and shear modulus
real::SIGMA_1,SIGMA_2,SIGMA_3 !principle stresses(MPa)
real::SHEAR_4,SHEAR_5,SHEAR_6 !shear stresses
real,parameter::DEG_2_RAD = 0.01745329
!INPUT
NU = 0.25
COHESION = 15 ! in MPa
YOUNG = 20 ! in GPa
FRICTION_DEG = 30.0d0
FRICTION_RAD = FRICTION_DEG *(DEG_2_RAD)
!perform calculations
KAPPA=6.*COHESION *cos(FRICTION_DEG*DEG_2_RAD)/(sqrt(3.)*(3.-sin(FRICTION_DEG*DEG_2_RAD)))
ALPHA=2.*sin(FRICTION_DEG*DEG_2_RAD)/(sqrt(3.)*(3.-sin(FRICTION_DEG*DEG_2_RAD)))
GMODU=YOUNG/2.*(1.+NU)
LAMBDA=NU*YOUNG/((1.+NU)*(1.-(2.*NU)))
!Set up elastic stiffness matrix (CEL)
CEL(1:N,1:N)=0.0
CEL (1,1)= LAMBDA-(2.*GMODU)
CEL (2,2)= LAMBDA-(2.*GMODU)
CEL (3,3)= LAMBDA-(2.*GMODU)
CEL (4,4)= 2.*GMODU
CEL (5,5)= 2.*GMODU
CEL (6,6)= 2.*GMODU
DO
inc = inc + 1
DSTRAIN(1)=0.00002
DSIGMA = matmul (CEL(1:N,1:N), DSTRAIN)
SIGMA =SIGMA +DSIGMA
STRAIN=STRAIN+DSTRAIN
!calculate I1 AND J2
VARI1=SIGMA_1+SIGMA_2+SIGMA_3
VARJ2=1./6.*((SIGMA_1-SIGMA_2)**2+(SIGMA_2-SIGMA_3)**2+(SIGMA_3- SIGMA_1)**2+SHEAR_4**2+SHEAR_5**2+SHEAR_6**2)
!Yield function (Drucker-prager)
F= ALPHA*VARI1+(sqrt(VARJ2)-KAPPA)
IF (F.LE.0.0d0)then !Elastic step (exit)
SIGMA =SIGMA
STRAIN=STRAIN
exit
endif
if (F.GT.0.0d0)then !Plastic step (continue)
goto 20
end if
20 continue
write(11,*)STRAIN,SIGMA,inc
END DO
end
You can't statically define an array with a variable. You must use a constant.
For example the following will work:
real::STRAIN (1:5), SIGMA (1:5),DSIGMA (1:5),DSTRAIN (1:5)=0.0
real,Dimension(6)::CEL(1:5,1:5)!stiffness matrix
If you don't know the size of the arrays at code time you can use the 'allocate' statement. This is known as 'dynamic storage allocation'. From 'Arrays and Parallel programming in Fortran 90/95':
The way to declare an allocatable array is as follows:
integer Nparticles ! number of particles
integer, parameter :: dim=3 ! dimensionality of space
...
real, allocatable :: charge(:) ! defines an array containing the charge of
! each particle
integer, allocatable :: xyz(:,:) ! coordinates of each particle
Once the actual number of particles in the simulation has been read, we can allocate these arrays:
read(*,*) Nparticles
allocate (charge(Nparticles),xyz(dim,Nparticles))
I am trying to write a programme to calculate an absorption band model for seismic waves. The whole calculation is based on 3 equations. If interested, see equations 3, 4, 5 on p.2 here:
http://www.eri.u-tokyo.ac.jp/people/takeuchi/publications/14EPSL-Iritani.pdf
However, I have debugged this programme several times now but I do not seem to get the expected answer. I am specifically trying to calculate Q_1 variable (seismic attenuation) in the following programme, which should be a REAL positive value on the order of 10^-3. However, I am getting negative values. I need a fresh pair of eyes to take a look at the programme and to check where I have done a mistake if any. Could someone please check? Many thanks !
PROGRAM absorp
! Calculate an absorption band model and output
! files for plotting.
! Ref. Iritani et al. (2014), EPSL, 405, 231-243.
! Variable Definition
! Corners - cf1, cf2
! Frequency range - [10^f_strt, 10^(f_end-f_strt)]
! Number of points to be sampled - n
! Angular frequency - w
! Frequency dependent Attenuation 1/Q - Q_1
! Relaxation times - tau1=1/(2*pi*cf1), tau2=1/(2*pi*cf2)
! Reference velocity - V0 (km/s)
! Attenuation (1/Q) at 1 Hz - Q1_1
! Frequency dependent peak Attenuation (1/Qm) - Qm_1
! Frequency dependent velocity - V_w
! D(omega) numerator - Dw1
! D(omega) denominator - Dw2
! D(omega) - D_w
! D(2pi) - D_2pi
IMPLICIT NONE
REAL :: cf1 = 2.0e0, cf2 = 1.0e+5
REAL, PARAMETER :: f_strt=-5, f_end=12
INTEGER :: indx
INTEGER, PARAMETER :: n=1e3
REAL, PARAMETER :: pi=4.0*atan(1.0)
REAL, DIMENSION(1:n) :: w, Q_1
REAL :: tau1, tau2, V0, freq, pow
REAL :: Q1_1=0.003, Qm_1
COMPLEX, DIMENSION(1:n) :: V_w
COMPLEX, PARAMETER :: i=(0.0,1.0)
COMPLEX :: D_2pi, D_w, Dw1, Dw2
! Reference Velocity km/s
V0 = 12.0
print *, "F1=", cf1, "F2=", cf2, "V0=",V0
! Relaxation times from corners
tau1 = 1.0/(2.0*pi*cf1)
tau2 = 1.0/(2.0*pi*cf2)
PRINT*, "tau1=",tau1, "tau2=",tau2
! Populate angular frequency array (non-linear)
DO indx = 1,n+1
pow = f_strt + f_end*REAL(indx-1)/n
freq=10**pow
w(indx) = 2*pi*freq
print *, w(indx)
END DO
! D(2pi) value
D_2pi = LOG((i*2.0*pi + 1/tau1)/(i*2.0*pi + 1/tau2))
! Calculate 1/Q from eq. 3 and 4
DO indx=1,n
!D(omega)
Dw1 = (i*w(indx) + 1.0/tau1)
Dw2 = (i*w(indx) + 1.0/tau2)
D_w = LOG(Dw1/Dw2)
!This is eq. 5 for 1/Qm
Qm_1 = 2.0*pi*Q1_1*IMAG(D_w)/ &
((Q1_1**2-4)*IMAG(D_w)**2 &
+ 4*Q1_1*IMAG(D_w)*REAL(D_w))
!This is eq. 3 for Alpha(omega)
V_w(indx) = V0*(SQRT(1.0 + 2.0/pi*Qm_1*D_w)/ &
REAL(SQRT(1.0 + 2.0/pi*Qm_1*D_2pi)))
!This is eq. 4 for 1/Q
Q_1(indx) = 2*IMAG(V_w(indx))/REAL(V_w(indx))
PRINT *, w(indx)/(2.0*pi), (V_w(indx)), Q_1(indx)
END DO
! write the results out
100 FORMAT(F12.3,3X,F7.3,3X,F8.5)
OPEN(UNIT=1, FILE='absorp.txt', STATUS='replace')
DO indx=1,n
WRITE(UNIT=1,FMT=100), w(indx)/(2.0*pi), REAL(V_w(indx)), Q_1(indx)
END DO
CLOSE(UNIT=1)
END PROGRAM
More of an extended comment with formatting than an answer ...
I haven't checked the equations you refer to, and I'm not going to, but looking at your code makes me suspect misplaced brackets as a likely cause of errors. The code, certainly as you've shown it here, isn't well formatted to reveal its logical structure. Whatever you do next invest in some indents and some longer lines to avoid breaking too frequently.
Personally I'm suspicious in particular of
!This is eq. 5 for 1/Qm
Qm_1 = 2.0*pi*Q1_1*IMAG(D_w)/ &
((Q1_1**2-4)*IMAG(D_w)**2 &
+ 4*Q1_1*IMAG(D_w)*REAL(D_w))