{
 "metadata": {
  "name": "WAFO Chapter 4"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Chapter 4 Fatigue load analysis and rain-flow cycles\n",
      "=====================================================\n",
      "\n",
      "Section 4.3.1 Crossing intensity\n",
      "--------------------------------\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import wafo.data as wd\n",
      "import wafo.objects as wo\n",
      "\n",
      "printing=0\n",
      "xx_sea = wd.sea()  \n",
      "ts = wo.mat2timeseries(xx_sea)\n",
      "tp = ts.turning_points()\n",
      "mM = tp.cycle_pairs(kind='min2max')\n",
      "lc = mM.level_crossings(intensity=True)\n",
      "T_sea = ts.args[-1]-ts.args[0]\n",
      "\n",
      "subplot(1,2,1)\n",
      "lc.plot()\n",
      "subplot(1,2,2)\n",
      "lc.setplotter(plotmethod='step')\n",
      "lc.plot()\n",
      "show() \n",
      " \n",
      " \n",
      "m_sea = ts.data.mean() \n",
      "f0_sea = interp(m_sea, lc.args,lc.data)\n",
      "extr_sea = len(tp.data)/(2*T_sea)\n",
      "alfa_sea = f0_sea/extr_sea\n",
      "print('alfa = %g ' % alfa_sea )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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dywboLDA8PDxw6dIl1ePt27fDy5Kd5a0QEbB/P9C/v9SRWIlOncQFMg4dwogR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\nkT+tW9R/eaN3FgAAAABJRU5ErkJggg==\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "alfa = 0.491212 \n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Section 4.3.2 Extraction of rainflow cycles\n",
      "-------------------------------------------\n",
      "Min-max and rainflow cycle plots\n",
      "---------------------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mM_rfc = tp.cycle_pairs(h=0.3)\n",
      " \n",
      "clf()\n",
      "subplot(122), \n",
      "mM.plot() \n",
      "title('min-max cycle pairs')\n",
      "subplot(121), \n",
      "mM_rfc.plot()\n",
      "title('Rainflow filtered cycles')\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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nmzs3gFWhjY1llWgB5izXOl9X33NPqQ4rlUQI46mQtrSwC+Pdd927ufSUi8DX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\nAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Min-max and rainflow cycle distributions\n",
      "-------------------------------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import wafo.misc as wm\n",
      "ampmM_sea = mM.amplitudes()\n",
      "ampRFC_sea = mM_rfc.amplitudes()\n",
      "clf()\n",
      "subplot(121) \n",
      "wm.plot_histgrm(ampmM_sea,25)\n",
      "ylim = gca().get_ylim()\n",
      "title('min-max amplitude distribution')\n",
      "subplot(122)\n",
      "wm.plot_histgrm(ampRFC_sea,25)\n",
      "gca().set_ylim(ylim)\n",
      "title('Rainflow amplitude distribution')\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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IAAsCERHpsCAQEREAFgQiItJhQSAiIgAsCEREpMOCQEREAFgQiIhIhwWBiIgA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\nDgsCEREBYEEgIiIdFgQiIgIA/D/Pv8fbekijgAAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#!#! Section 4.3.3 Simulation of rainflow cycles\n",
      "#!#! Simulation of cycles in a Markov model\n",
      "n=41; param_m=[-1, 1, n]; param_D=[1, n, n];\n",
      "u_markov=levels(param_m);\n",
      "G_markov=mktestmat(param_m,[-0.2, 0.2],0.15,1);\n",
      "T_markov=5000;\n",
      "#xxD_markov=mctpsim({G_markov [,]},T_markov);\n",
      "#xx_markov=[(1:T_markov)' u_markov(xxD_markov)'];\n",
      "#clf\n",
      "#plot(xx_markov(1:50,1),xx_markov(1:50,2))\n",
      "#title('Markov chain of turning points')\n",
      "#wafostamp([],'(ER)')\n",
      "#disp('Block 5'),pause(pstate)\n",
      "#\n",
      "#\n",
      "##!#! Rainflow cycles in a transformed Gaussian model\n",
      "##!#! Hermite transformed wave data and rainflow filtered turning points, h = 0.2.\n",
      "#me = mean(xx_sea(:,2));\n",
      "#sa = std(xx_sea(:,2));\n",
      "#Hm0_sea = 4*sa;\n",
      "#Tp_sea = 1/max(lc_sea(:,2));\n",
      "#spec = jonswap([],[Hm0_sea Tp_sea]);\n",
      "#\n",
      "#[sk, ku] = spec2skew(spec);\n",
      "#spec.tr = hermitetr([],[sa sk ku me]);\n",
      "#param_h = [-1.5 2 51];\n",
      "#spec_norm = spec;\n",
      "#spec_norm.S = spec_norm.S/sa^2;\n",
      "#xx_herm = spec2sdat(spec_norm,[2^15 1],0.1);\n",
      "##! ????? PJ, JR 11-Apr-2001\n",
      "##! NOTE, in the simulation program spec2sdat\n",
      "##!the spectrum must be normalized to variance 1 \n",
      "##! ?????\n",
      "#h = 0.2;\n",
      "#[dtp,u_herm,xx_herm_1]=dat2dtp(param_h,xx_herm,h);\n",
      "#clf\n",
      "#plot(xx_herm(:,1),xx_herm(:,2),'k','LineWidth',2); hold on;\n",
      "#plot(xx_herm_1(:,1),xx_herm_1(:,2),'k--','Linewidth',2);\n",
      "#axis([0 50 -1 1]), hold off;\n",
      "#title('Rainflow filtered wave data')\n",
      "#wafostamp([],'(ER)')\n",
      "#disp('Block 6'),pause(pstate)\n",
      "#\n",
      "##!#! Rainflow cycles and rainflow filtered rainflow cycles in the transformed Gaussian process.\n",
      "#tp_herm=dat2tp(xx_herm);\n",
      "#RFC_herm=tp2rfc(tp_herm);\n",
      "#mM_herm=tp2mm(tp_herm);\n",
      "#h=0.2;\n",
      "#[dtp,u,tp_herm_1]=dat2dtp(param_h,xx_herm,h);\n",
      "#RFC_herm_1 = tp2rfc(tp_herm_1);\n",
      "#clf\n",
      "#subplot(121), ccplot(RFC_herm)\n",
      "#title('h=0')\n",
      "#subplot(122), ccplot(RFC_herm_1)\n",
      "#title('h=0.2')\n",
      "#if (printing==1), print -deps ../bilder/fatigue_8.eps \n",
      "#end\n",
      "#wafostamp([],'(ER)')\n",
      "#disp('Block 7'),pause(pstate)\n",
      "#\n",
      "##!#! Section 4.3.4 Calculating the rainflow matrix\n",
      "#\n",
      "#\n",
      "#Grfc_markov=mctp2rfm({G_markov []});\n",
      "#clf\n",
      "#subplot(121), cmatplot(u_markov,u_markov,G_markov), axis('square')\n",
      "#subplot(122), cmatplot(u_markov,u_markov,Grfc_markov), axis('square')\n",
      "#wafostamp([],'(ER)')\n",
      "#disp('Block 8'),pause(pstate)\n",
      "#\n",
      "##!#! \n",
      "#clf\n",
      "#cmatplot(u_markov,u_markov,{G_markov Grfc_markov},3) \n",
      "#wafostamp([],'(ER)')\n",
      "#disp('Block 9'),pause(pstate)\t\n",
      "#\n",
      "##!#! Min-max-matrix and theoretical rainflow matrix for test Markov sequence.\n",
      "#cmatplot(u_markov,u_markov,{G_markov Grfc_markov},4)\n",
      "#subplot(121), axis('square'), title('min2max transition matrix')\n",
      "#subplot(122), axis('square'), title('Rainflow matrix')\n",
      "#if (printing==1), print -deps ../bilder/fatigue_9.eps \n",
      "#end\n",
      "#wafostamp([],'(ER)')\n",
      "#disp('Block 10'),pause(pstate)\n",
      "#\n",
      "##!#! Observed and theoretical rainflow matrix for test Markov sequence.\n",
      "#n=length(u_markov);\n",
      "#Frfc_markov=dtp2rfm(xxD_markov,n);\n",
      "#clf\n",
      "#cmatplot(u_markov,u_markov,{Frfc_markov Grfc_markov*T_markov/2},3) \n",
      "#subplot(121), axis('square'), title('Observed rainflow matrix')\n",
      "#subplot(122), axis('square'), title('Theoretical rainflow matrix')\n",
      "#if (printing==1), print -deps ../bilder/fatigue_10.eps \n",
      "#end\n",
      "#wafostamp([],'(ER)')\n",
      "#disp('Block 11'),pause(pstate)\n",
      "#\n",
      "##!#! Smoothed observed and calculated rainflow matrix for test Markov sequence.\n",
      "#tp_markov=dat2tp(xx_markov);\n",
      "#RFC_markov=tp2rfc(tp_markov);\n",
      "#h=1;\n",
      "#Frfc_markov_smooth=cc2cmat(param_m,RFC_markov,[],1,h);\n",
      "#clf\n",
      "#cmatplot(u_markov,u_markov,{Frfc_markov_smooth Grfc_markov*T_markov/2},4)\n",
      "#subplot(121), axis('square'), title('Smoothed observed rainflow matrix')\n",
      "#subplot(122), axis('square'), title('Theoretical rainflow matrix')\n",
      "#if (printing==1), print -deps ../bilder/fatigue_11.eps \n",
      "#end\n",
      "#wafostamp([],'(ER)')\n",
      "#disp('Block 12'),pause(pstate)\n",
      "#\n",
      "##!#! Rainflow matrix from spectrum\n",
      "#clf\n",
      "##!GmM3_herm=spec2mmtpdf(spec,[],'Mm',[],[],2);\n",
      "#GmM3_herm=spec2cmat(spec,[],'Mm',[],param_h,2);\n",
      "#pdfplot(GmM3_herm)\n",
      "#wafostamp([],'(ER)')\n",
      "#disp('Block 13'),pause(pstate)\n",
      "#\n",
      "#\n",
      "##!#! Min-max matrix and theoretical rainflow matrix for Hermite-transformed Gaussian waves.\n",
      "#Grfc_herm=mctp2rfm({GmM3_herm.f []});\n",
      "#u_herm=levels(param_h);\n",
      "#clf\n",
      "#cmatplot(u_herm,u_herm,{GmM3_herm.f Grfc_herm},4)\n",
      "#subplot(121), axis('square'), title('min-max matrix')\n",
      "#subplot(122), axis('square'), title('Theoretical rainflow matrix')\n",
      "#if (printing==1), print -deps ../bilder/fatigue_12.eps \n",
      "#end\n",
      "#wafostamp([],'(ER)')\n",
      "#disp('Block 14'),pause(pstate)\n",
      "#\n",
      "##!#!\n",
      "#clf\n",
      "#Grfc_direct_herm=spec2cmat(spec,[],'rfc',[],[],2);\n",
      "#subplot(121), pdfplot(GmM3_herm), axis('square'), hold on\n",
      "#subplot(122), pdfplot(Grfc_direct_herm), axis('square'), hold off\n",
      "#if (printing==1), print -deps ../bilder/fig_mmrfcjfr.eps\n",
      "#end\n",
      "#wafostamp([],'(ER)')\n",
      "#disp('Block 15'),pause(pstate)\n",
      "#\n",
      "#\n",
      "##!#! Observed smoothed and theoretical min-max matrix, \n",
      "##!#! (and observed smoothed and theoretical rainflow matrix for Hermite-transformed Gaussian waves).\n",
      "#tp_herm=dat2tp(xx_herm);\n",
      "#RFC_herm=tp2rfc(tp_herm);\n",
      "#mM_herm=tp2mm(tp_herm);\n",
      "#h=0.2;\n",
      "#FmM_herm_smooth=cc2cmat(param_h,mM_herm,[],1,h);\n",
      "#Frfc_herm_smooth=cc2cmat(param_h,RFC_herm,[],1,h);\n",
      "#T_herm=xx_herm(end,1)-xx_herm(1,1);\n",
      "#clf\n",
      "#cmatplot(u_herm,u_herm,{FmM_herm_smooth GmM3_herm.f*length(mM_herm) ; ...\n",
      "#      Frfc_herm_smooth Grfc_herm*length(RFC_herm)},4)\n",
      "#subplot(221), axis('square'), title('Observed smoothed min-max matrix')\n",
      "#subplot(222), axis('square'), title('Theoretical min-max matrix')\n",
      "#subplot(223), axis('square'), title('Observed smoothed rainflow matrix')\n",
      "#subplot(224), axis('square'), title('Theoretical rainflow matrix')\n",
      "#if (printing==1), print -deps ../bilder/fatigue_13.eps \n",
      "#end\n",
      "#wafostamp([],'(ER)')\n",
      "#disp('Block 16'),pause(pstate)\n",
      "#   \n",
      "##!#! Section 4.3.5 Simulation from crossings and rainflow structure\n",
      "#\n",
      "##!#! Crossing spectrum (smooth curve) and obtained spectrum (wiggled curve)\n",
      "##!#! for simulated process with irregularity factor 0.25.\n",
      "#clf\n",
      "#cross_herm=dat2lc(xx_herm);\n",
      "#alpha1=0.25;\n",
      "#alpha2=0.75;\n",
      "#xx_herm_sim1=lc2sdat(cross_herm,500,alpha1);\n",
      "#cross_herm_sim1=dat2lc(xx_herm_sim1);\n",
      "#subplot(211)\n",
      "#plot(cross_herm(:,1),cross_herm(:,2)/max(cross_herm(:,2)))\n",
      "#hold on\n",
      "#stairs(cross_herm_sim1(:,1),...\n",
      "#    cross_herm_sim1(:,2)/max(cross_herm_sim1(:,2)))\n",
      "#hold off\n",
      "#title('Crossing intensity, \\alpha = 0.25')\n",
      "#subplot(212)\n",
      "#plot(xx_herm_sim1(:,1),xx_herm_sim1(:,2))\n",
      "#title('Simulated load, \\alpha = 0.25')\n",
      "#if (printing==1), print -deps ../bilder/fatigue_14_25.eps \n",
      "#end\n",
      "#wafostamp([],'(ER)')\n",
      "#disp('Block 16'),pause(pstate)\n",
      "#\n",
      "##!#! Crossing spectrum (smooth curve) and obtained spectrum (wiggled curve)\n",
      "##!#! for simulated process with irregularity factor 0.75.\n",
      "#xx_herm_sim2=lc2sdat(cross_herm,500,alpha2);\n",
      "#cross_herm_sim2=dat2lc(xx_herm_sim2);\n",
      "#subplot(211)\n",
      "#plot(cross_herm(:,1),cross_herm(:,2)/max(cross_herm(:,2)))\n",
      "#hold on\n",
      "#stairs(cross_herm_sim2(:,1),...\n",
      "#    cross_herm_sim2(:,2)/max(cross_herm_sim2(:,2)))\n",
      "#hold off\n",
      "#title('Crossing intensity, \\alpha = 0.75')\n",
      "#subplot(212)\n",
      "#plot(xx_herm_sim2(:,1),xx_herm_sim2(:,2))\n",
      "#title('Simulated load, \\alpha = 0.75')\n",
      "#if (printing==1), print -deps ../bilder/fatigue_14_75.eps \n",
      "#end\n",
      "#wafostamp([],'(ER)')\n",
      "#disp('Block 17'),pause(pstate)\n",
      "#\n",
      "##!#! Section 4.4 Fatigue damage and fatigue life distribution\n",
      "##!#! Section 4.4.1 Introduction\n",
      "#beta=3.2; gam=5.5E-10; T_sea=xx_sea(end,1)-xx_sea(1,1);\n",
      "#d_beta=cc2dam(RFC_sea,beta)/T_sea;\n",
      "#time_fail=1/gam/d_beta/3600      #!in hours of the specific storm\n",
      "#disp('Block 18'),pause(pstate)\n",
      "#\n",
      "##!#! Section 4.4.2 Level crossings\n",
      "##!#! Crossing intensity as calculated from the Markov matrix (solid curve) and from the observed rainflow matrix (dashed curve).\n",
      "#clf\n",
      "#mu_markov=cmat2lc(param_m,Grfc_markov);\n",
      "#muObs_markov=cmat2lc(param_m,Frfc_markov/(T_markov/2));\n",
      "#clf\n",
      "#plot(mu_markov(:,1),mu_markov(:,2),muObs_markov(:,1),muObs_markov(:,2),'--')\n",
      "#title('Theoretical and observed crossing intensity ')\n",
      "#if (printing==1), print -deps ../bilder/fatigue_15.eps \n",
      "#end\n",
      "#wafostamp([],'(ER)')\n",
      "#disp('Block 19'),pause(pstate)\n",
      "#\n",
      "##!#! Section 4.4.3 Damage\n",
      "##!#! Distribution of damage from different RFC cycles, from calculated theoretical and from observed rainflow matrix.\n",
      "#beta = 4;\n",
      "#Dam_markov = cmat2dam(param_m,Grfc_markov,beta)\n",
      "#DamObs1_markov = cc2dam(RFC_markov,beta)/(T_markov/2)\n",
      "#DamObs2_markov = cmat2dam(param_m,Frfc_markov,beta)/(T_markov/2)\n",
      "#disp('Block 20'),pause(pstate)\n",
      "#\n",
      "#Dmat_markov = cmat2dmat(param_m,Grfc_markov,beta);\n",
      "#DmatObs_markov = cmat2dmat(param_m,Frfc_markov,beta)/(T_markov/2); \n",
      "#clf\n",
      "#subplot(121), cmatplot(u_markov,u_markov,Dmat_markov,4)\n",
      "#title('Theoretical damage matrix') \n",
      "#subplot(122), cmatplot(u_markov,u_markov,DmatObs_markov,4)\n",
      "#title('Observed damage matrix') \n",
      "#if (printing==1), print -deps ../bilder/fatigue_16.eps \n",
      "#end\n",
      "#wafostamp([],'(ER)')\n",
      "#disp('Block 21'),pause(pstate)\n",
      "#\n",
      "#\n",
      "##!#!\n",
      "##!Damplus_markov = lc2dplus(mu_markov,beta)\n",
      "#pause(pstate)\n",
      "#\n",
      "##!#! Section 4.4.4 Estimation of S-N curve\n",
      "#\n",
      "##!#! Load SN-data and plot in log-log scale.\n",
      "#SN = load('sn.dat');\n",
      "#s = SN(:,1);\n",
      "#N = SN(:,2);\n",
      "#clf\n",
      "#loglog(N,s,'o'), axis([0 14e5 10 30])\n",
      "##!if (printing==1), print -deps ../bilder/fatigue_?.eps end\n",
      "#wafostamp([],'(ER)')\n",
      "#disp('Block 22'),pause(pstate)\n",
      "#\n",
      "#\n",
      "##!#! Check of S-N-model on normal probability paper.\n",
      "#\n",
      "#normplot(reshape(log(N),8,5))\n",
      "#if (printing==1), print -deps ../bilder/fatigue_17.eps \n",
      "#end\n",
      "#wafostamp([],'(ER)')\n",
      "#disp('Block 23'),pause(pstate)\n",
      "#\n",
      "##!#! Estimation of S-N-model on linear  scale.\n",
      "#clf\n",
      "#[e0,beta0,s20] = snplot(s,N,12);\n",
      "#title('S-N-data with estimated N(s)','FontSize',20)\n",
      "#set(gca,'FontSize',20)\n",
      "#if (printing==1), print -deps ../bilder/fatigue_18a.eps \n",
      "#end\n",
      "#wafostamp([],'(ER)')\n",
      "#disp('Block 24'),pause(pstate)\n",
      "#\n",
      "##!#! Estimation of S-N-model on log-log scale.\n",
      "#clf\n",
      "#[e0,beta0,s20] = snplot(s,N,14);\n",
      "#title('S-N-data with estimated N(s)','FontSize',20)\n",
      "#set(gca,'FontSize',20)\n",
      "#if (printing==1), print -deps ../bilder/fatigue_18b.eps \n",
      "#end\n",
      "#wafostamp([],'(ER)')\n",
      "#disp('Block 25'),pause(pstate)\n",
      "#\n",
      "##!#! Section 4.4.5 From S-N curve to fatigue life distribution\n",
      "##!#! Damage intensity as function of $\\beta$\n",
      "#beta = 3:0.1:8;\n",
      "#DRFC = cc2dam(RFC_sea,beta);\n",
      "#dRFC = DRFC/T_sea;\n",
      "#plot(beta,dRFC), axis([3 8 0 0.25])\n",
      "#title('Damage intensity as function of \\beta')\n",
      "#if (printing==1), print -deps ../bilder/fatigue_19.eps \n",
      "#end\n",
      "#wafostamp([],'(ER)')\n",
      "#disp('Block 26'),pause(pstate)\n",
      "#\n",
      "##!#! Fatigue life distribution with sea load.\n",
      "#dam0 = cc2dam(RFC_sea,beta0)/T_sea;\n",
      "#[t0,F0] = ftf(e0,dam0,s20,0.5,1);\n",
      "#[t1,F1] = ftf(e0,dam0,s20,0,1);\n",
      "#[t2,F2] = ftf(e0,dam0,s20,5,1);\n",
      "#plot(t0,F0,t1,F1,t2,F2)\n",
      "#title('Fatigue life distribution function')\n",
      "#if (printing==1), print -deps ../bilder/fatigue_20.eps \n",
      "#end\n",
      "#wafostamp([],'(ER)')\n",
      "#disp('Block 27, last block')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}