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ohspec2

PURPOSE ^

Calculates and plots a bimodal Ochi-Hubble spectral density

SYNOPSIS ^

[S, S1] = ohspec2(w,sdata,plotflag)

DESCRIPTION ^

  OHSPEC2 Calculates and plots a bimodal Ochi-Hubble spectral density
  
   CALL:  [S S1] = ohspec2(w,data,plotflag); 
 
         S    = a struct containing the total spectral density
         S1   = a 2D struct array containing the swell and wind generated 
                part of the spectral density
         w    = angular frequency (default linspace(0,3,257))
         data = [Hm0 def]
                Hm0 = significant wave height (default 7 (m))
                def = defines the parametrization (default 1)
                      1 : The most probable spectrum
                      2,3,...11 : gives 95% Confidence spectra
     plotflag = 0, do not plot the spectrum (default).
                1, plot the  spectrum.
 
   The OH spectrum is a six parameter spectrum, all functions of Hm0.
   The values of these parameters are determined from a analysis of data
   obtained in the North Atlantic. The source of the data is the same as
   that for the development of the Pierson-Moskowitz spectrum, but
   analysis is carried out on over 800 spectra including those in
   partially developed seas and those having a bimodal shape. From a
   statistical analysis of the data, a family of wave spectra consisting
   of 11 members is generated for a desired sea severity (Hm0) with the
   coefficient of 0.95. 
   A significant advantage of using a family of spectra for design  of
   marine systems is that one of the family members yields the largest
   response such as motions or wave induced forces for a specified sea
   severity, while another yields the smallest response with confidence
   coefficient of 0.95. 
 
  See also  ohspec, jonswap, torsethaugen

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

001 function [S, S1] = ohspec2(w,sdata,plotflag)
002 % OHSPEC2 Calculates and plots a bimodal Ochi-Hubble spectral density
003 % 
004 %  CALL:  [S S1] = ohspec2(w,data,plotflag); 
005 %
006 %        S    = a struct containing the total spectral density
007 %        S1   = a 2D struct array containing the swell and wind generated 
008 %               part of the spectral density
009 %        w    = angular frequency (default linspace(0,3,257))
010 %        data = [Hm0 def]
011 %               Hm0 = significant wave height (default 7 (m))
012 %               def = defines the parametrization (default 1)
013 %                     1 : The most probable spectrum
014 %                     2,3,...11 : gives 95% Confidence spectra
015 %    plotflag = 0, do not plot the spectrum (default).
016 %               1, plot the  spectrum.
017 %
018 %  The OH spectrum is a six parameter spectrum, all functions of Hm0.
019 %  The values of these parameters are determined from a analysis of data
020 %  obtained in the North Atlantic. The source of the data is the same as
021 %  that for the development of the Pierson-Moskowitz spectrum, but
022 %  analysis is carried out on over 800 spectra including those in
023 %  partially developed seas and those having a bimodal shape. From a
024 %  statistical analysis of the data, a family of wave spectra consisting
025 %  of 11 members is generated for a desired sea severity (Hm0) with the
026 %  coefficient of 0.95. 
027 %  A significant advantage of using a family of spectra for design  of
028 %  marine systems is that one of the family members yields the largest
029 %  response such as motions or wave induced forces for a specified sea
030 %  severity, while another yields the smallest response with confidence
031 %  coefficient of 0.95. 
032 %
033 % See also  ohspec, jonswap, torsethaugen
034 
035 % References:
036 % Ochi, M.K. and Hubble, E.N. (1976)
037 % 'On six-parameter wave spectra.'
038 % In proc. 15th Conf. Coastal Engng.,1,pp301-328
039 
040 % Tested on: matlab 5.2
041 % History:
042 % by pab 16.02.2000
043 
044 monitor=0;
045 
046 if nargin<3|isempty(plotflag)
047   plotflag=0;
048 end
049 if nargin<1|isempty(w)
050   w=linspace(0,3,257).';
051 end
052 sdata2=[7 1]; % default values
053 if nargin<2|isempty(sdata)
054 else
055  ns1=length(sdata);
056  k=find(~isnan(sdata(1:min(ns1,2))));
057  if any(k)
058    sdata2(k)=sdata(k);
059  end
060 end %
061 
062 
063 Hm0=sdata2(1);
064 def=sdata2(2);
065 
066 
067 switch def, % 95% Confidence spectra
068   case 2, hp=[0.84 0.54];wa=[.93 1.5]; wb=[0.056 0.046];Li=[3.00 2.77*exp(-0.112*Hm0)];
069   case 3, hp=[0.84 0.54];wa=[.41 .88]; wb=[0.016 0.026];Li=[2.55 1.82*exp(-0.089*Hm0)];
070   case 4, hp=[0.84 0.54];wa=[.74 1.3]; wb=[0.052 0.039];Li=[2.65 3.90*exp(-0.085*Hm0)];
071   case 5, hp=[0.84 0.54];wa=[.62 1.03];wb=[0.039 0.030];Li=[2.60 0.53*exp(-0.069*Hm0)];
072   case 6, hp=[0.95 0.31];wa=[.70 1.50];wb=[0.046 0.046];Li=[1.35 2.48*exp(-0.102*Hm0)];
073   case 7, hp=[0.65 0.76];wa=[.61 0.94];wb=[0.039 0.036];Li=[4.95 2.48*exp(-0.102*Hm0)];  
074   case 8, hp=[0.90 0.44];wa=[.81 1.60];wb=[0.052 0.033];Li=[1.80 2.95*exp(-0.105*Hm0)];  
075   case 9, hp=[0.77 0.64];wa=[0.54 .61];wb=[0.039 0.000];Li=[4.50 1.95*exp(-0.082*Hm0)];  
076   case 10,hp=[0.73 0.68];wa=[.70 0.99];wb=[0.046 0.039];Li=[6.40 1.78*exp(-0.069*Hm0)];    
077   case 11,hp=[0.92 0.39];wa=[.70 1.37];wb=[0.046 0.039];Li=[0.70 1.78*exp(-0.069*Hm0)];     
078   otherwise % The most probable spectrum
079     def=1;
080     hp=[0.84 0.54]; wa= [.7 1.15];wb=[0.046 0.039];  Li=[3 1.54*exp(-0.062*Hm0)];
081 end
082 Hm0i=hp*Hm0;
083 Tpi= 2*pi.*exp(wb*Hm0)./wa;
084 
085 for ix=1:2,
086   S1(ix)=ohspec(w,[Hm0i(ix), Tpi(ix),Li(ix)]);
087 end
088 S=S1(1);
089 S.S=S1(1).S+S1(2).S;
090 
091 %S.note=['Ochi-Hubble2, Hm0 = ' num2str(Hm0i)  ', Tp = ' num2str(Tpi) , ' L = ' num2str(Li)];
092 S.note=['Ochi-Hubble2, Hm0 = ' num2str(Hm0)  ', def = ' num2str(def) ];
093 if monitor
094   disp(['Hm0, Tp      = ' num2str([Hm0i Tpi])])
095 end
096 
097 if plotflag
098   ih=ishold;
099   wspecplot(S,plotflag)
100   hold on
101   wspecplot(S1,plotflag,'k--')
102   if ~ih,hold off,end
103 end
104

Mathematical Statistics
Centre for Mathematical Sciences
Lund University with Lund Institute of Technology

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