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! $Header: /home/cvsroot/LMDZ4/libf/phylmd/clouds_gno.F,v 1.2 2004/11/09 16:55:40 lmdzadmin Exp $ |
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C |
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C================================================================================ |
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C |
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SUBROUTINE CLOUDS_GNO(klon,ND,R,RS,QSUB,PTCONV,RATQSC,CLDF) |
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IMPLICIT NONE |
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C |
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C-------------------------------------------------------------------------------- |
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C |
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C Inputs: |
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C |
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C ND----------: Number of vertical levels |
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C R--------ND-: Domain-averaged mixing ratio of total water |
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C RS-------ND-: Mean saturation humidity mixing ratio within the gridbox |
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C QSUB-----ND-: Mixing ratio of condensed water within clouds associated |
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C with SUBGRID-SCALE condensation processes (here, it is |
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C predicted by the convection scheme) |
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C Outputs: |
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C |
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C PTCONV-----ND-: Point convectif = TRUE |
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C RATQSC-----ND-: Largeur normalisee de la distribution |
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C CLDF-----ND-: Fraction nuageuse |
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C |
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C-------------------------------------------------------------------------------- |
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INTEGER klon,ND |
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REAL R(klon,ND), RS(klon,ND), QSUB(klon,ND) |
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LOGICAL PTCONV(klon,ND) |
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REAL RATQSC(klon,ND) |
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REAL CLDF(klon,ND) |
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c -- parameters controlling the iteration: |
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c -- nmax : maximum nb of iterations (hopefully never reached) |
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c -- epsilon : accuracy of the numerical resolution |
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c -- vmax : v-value above which we use an asymptotic expression for ERF(v) |
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INTEGER nmax |
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PARAMETER ( nmax = 10) |
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REAL epsilon, vmax0, vmax(klon) |
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PARAMETER ( epsilon = 0.02, vmax0 = 2.0 ) |
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REAL min_mu, min_Q |
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PARAMETER ( min_mu = 1.e-12, min_Q=1.e-12 ) |
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INTEGER i,K, n, m |
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REAL mu(klon), qsat(klon), delta(klon), beta(klon) |
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real zu2(klon),zv2(klon) |
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REAL xx(klon), aux(klon), coeff(klon), block(klon) |
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REAL dist(klon), fprime(klon), det(klon) |
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REAL pi, u(klon), v(klon), erfcu(klon), erfcv(klon) |
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REAL xx1(klon), xx2(klon) |
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real erf,kkk |
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real sqrtpi,sqrt2,zx1,zx2,exdel |
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c lconv = true si le calcul a converge (entre autre si qsub < min_q) |
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LOGICAL lconv(klon) |
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cym |
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cldf(:,:)=0.0 |
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pi = ACOS(-1.) |
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sqrtpi=sqrt(pi) |
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sqrt2=sqrt(2.) |
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ptconv=.false. |
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ratqsc=0. |
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DO 500 K = 1, ND |
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do i=1,klon ! vector |
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mu(i) = R(i,K) |
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mu(i) = MAX(mu(i),min_mu) |
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qsat(i) = RS(i,K) |
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qsat(i) = MAX(qsat(i),min_mu) |
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delta(i) = log(mu(i)/qsat(i)) |
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enddo ! vector |
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C |
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C *** There is no subgrid-scale condensation; *** |
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C *** the scheme becomes equivalent to an "all-or-nothing" *** |
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C *** large-scale condensation scheme. *** |
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C |
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C |
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C *** Some condensation is produced at the subgrid-scale *** |
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C *** *** |
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C *** PDF = generalized log-normal distribution (GNO) *** |
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C *** (k<0 because a lower bound is considered for the PDF) *** |
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C *** *** |
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C *** -> Determine x (the parameter k of the GNO PDF) such *** |
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C *** that the contribution of subgrid-scale processes to *** |
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C *** the in-cloud water content is equal to QSUB(K) *** |
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C *** (equations (13), (14), (15) + Appendix B of the paper) *** |
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C *** *** |
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C *** Here, an iterative method is used for this purpose *** |
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C *** (other numerical methods might be more efficient) *** |
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C *** *** |
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C *** NB: the "error function" is called ERF *** |
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C *** (ERF in double precision) *** |
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C |
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c On commence par eliminer les cas pour lesquels on n'a pas |
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c suffisamment d'eau nuageuse. |
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do i=1,klon ! vector |
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IF ( QSUB(i,K) .lt. min_Q ) THEN |
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ptconv(i,k)=.false. |
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ratqsc(i,k)=0. |
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lconv(i) = .true. |
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c Rien on a deja initialise |
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ELSE |
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lconv(i) = .FALSE. |
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vmax(i) = vmax0 |
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beta(i) = QSUB(i,K)/mu(i) + EXP( -MIN(0.0,delta(i)) ) |
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c -- roots of equation v > vmax: |
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det(i) = delta(i) + vmax(i)**2. |
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if (det(i).LE.0.0) vmax(i) = vmax0 + 1.0 |
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det(i) = delta(i) + vmax(i)**2. |
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if (det(i).LE.0.) then |
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xx(i) = -0.0001 |
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else |
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zx1=-sqrt2*vmax(i) |
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zx2=SQRT(1.0+delta(i)/(vmax(i)**2.)) |
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xx1(i)=zx1*(1.0-zx2) |
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xx2(i)=zx1*(1.0+zx2) |
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xx(i) = 1.01 * xx1(i) |
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if ( xx1(i) .GE. 0.0 ) xx(i) = 0.5*xx2(i) |
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endif |
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if (delta(i).LT.0.) xx(i) = -0.5*SQRT(log(2.)) |
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ENDIF |
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enddo ! vector |
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c---------------------------------------------------------------------- |
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c Debut des nmax iterations pour trouver la solution. |
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c---------------------------------------------------------------------- |
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DO n = 1, nmax |
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do i=1,klon ! vector |
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if (.not.lconv(i)) then |
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u(i) = delta(i)/(xx(i)*sqrt2) + xx(i)/(2.*sqrt2) |
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v(i) = delta(i)/(xx(i)*sqrt2) - xx(i)/(2.*sqrt2) |
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IF ( v(i) .GT. vmax(i) ) THEN |
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IF ( ABS(u(i)) .GT. vmax(i) |
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: .AND. delta(i) .LT. 0. ) THEN |
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c -- use asymptotic expression of erf for u and v large: |
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c ( -> analytic solution for xx ) |
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exdel=beta(i)*EXP(delta(i)) |
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aux(i) = 2.0*delta(i)*(1.-exdel) |
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: /(1.+exdel) |
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if (aux(i).lt.0.) then |
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c print*,'AUX(',i,',',k,')<0',aux(i),delta(i),beta(i) |
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aux(i)=0. |
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endif |
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xx(i) = -SQRT(aux(i)) |
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block(i) = EXP(-v(i)*v(i)) / v(i) / sqrtpi |
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dist(i) = 0.0 |
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fprime(i) = 1.0 |
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ELSE |
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c -- erfv -> 1.0, use an asymptotic expression of erfv for v large: |
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erfcu(i) = 1.0-ERF(u(i)) |
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c !!! ATTENTION : rajout d'un seuil pour l'exponentiel |
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aux(i) = sqrtpi*erfcu(i)*EXP(min(v(i)*v(i),100.)) |
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coeff(i) = 1.0 - 1./2./(v(i)**2.) + 3./4./(v(i)**4.) |
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block(i) = coeff(i) * EXP(-v(i)*v(i)) / v(i) / sqrtpi |
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dist(i) = v(i) * aux(i) / coeff(i) - beta(i) |
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fprime(i) = 2.0 / xx(i) * (v(i)**2.) |
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: * ( coeff(i)*EXP(-delta(i)) - u(i) * aux(i) ) |
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: / coeff(i) / coeff(i) |
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ENDIF ! ABS(u) |
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ELSE |
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c -- general case: |
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erfcu(i) = 1.0-ERF(u(i)) |
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erfcv(i) = 1.0-ERF(v(i)) |
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block(i) = erfcv(i) |
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dist(i) = erfcu(i) / erfcv(i) - beta(i) |
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zu2(i)=u(i)*u(i) |
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zv2(i)=v(i)*v(i) |
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if(zu2(i).gt.20..or. zv2(i).gt.20.) then |
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c print*,'ATTENTION !!! xx(',i,') =', xx(i) |
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c print*,'ATTENTION !!! klon,ND,R,RS,QSUB,PTCONV,RATQSC,CLDF', |
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c .klon,ND,R(i,k),RS(i,k),QSUB(i,k),PTCONV(i,k),RATQSC(i,k), |
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c .CLDF(i,k) |
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c print*,'ATTENTION !!! zu2 zv2 =',zu2(i),zv2(i) |
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zu2(i)=20. |
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zv2(i)=20. |
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fprime(i) = 0. |
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else |
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fprime(i) = 2. /sqrtpi /xx(i) /erfcv(i)**2. |
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: * ( erfcv(i)*v(i)*EXP(-zu2(i)) |
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: - erfcu(i)*u(i)*EXP(-zv2(i)) ) |
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endif |
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ENDIF ! x |
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c -- test numerical convergence: |
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c print*,'avant test ',i,k,lconv(i),u(i),v(i) |
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if ( ABS(dist(i)/beta(i)) .LT. epsilon ) then |
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c print*,'v-u **2',(v(i)-u(i))**2 |
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c print*,'exp v-u **2',exp((v(i)-u(i))**2) |
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ptconv(i,K) = .TRUE. |
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lconv(i)=.true. |
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c borne pour l'exponentielle |
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ratqsc(i,k)=min(2.*(v(i)-u(i))**2,20.) |
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ratqsc(i,k)=sqrt(exp(ratqsc(i,k))-1.) |
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CLDF(i,K) = 0.5 * block(i) |
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else |
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xx(i) = xx(i) - dist(i)/fprime(i) |
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endif |
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c print*,'apres test ',i,k,lconv(i) |
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endif ! lconv |
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enddo ! vector |
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c---------------------------------------------------------------------- |
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c Fin des nmax iterations pour trouver la solution. |
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ENDDO ! n |
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c---------------------------------------------------------------------- |
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500 CONTINUE ! K |
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RETURN |
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END |
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