I generally code in MATLAB, but for some reasons I decided to switch to a JAVA approach.
The question is quite easy: I'd like understanding how to translate the following MATLAB code into a working JAVA's one.
Within MATLAB I have a target function called findZ0:
function F = findZ0(V, Z, Latitude, TI, x)
%%% Inputs
% V = Average Wind Speed at Hub Height
% Z = Hub Height;
% Latitude = Specific Site Latitude (default value equal to 50 deg);
% x = Tryout Roughness length;
% TI = Target Turbulent Intensity;
%%% Outputs
% F = Roughness Length tuned to match Target Turbulent Intensity
Latitude = deg2rad(Latitude);
omega = 72.9E-06;
f = 2*omega*sin(Latitude);
ustar = ( 0.4*V - 34.5*f*Z)/log(Z/x);
mu = 1 - ((6*f*Z)/(ustar));
p = mu^(16);
sigmaTarget = (V*TI)/100;
F = sigmaTarget - (( 7.5*mu*ustar*((0.538 + .09*log(Z/x))^p) )/(1 + .156*log(ustar/(f*x))));
end
I then called this lines:
Uhub = 8;
HubHt = 90;
Latitude = 50;
x_trial = 0.01;
TI_target = 24;
find_z0 = @(x) findZ0(Uhub,HubHt,Latitude,TI_target, x);
z0 = fsolve(find_z0,x_trial,{'fsolve','Jacobian','on','levenberg-marquardt',.005,'MaxIter',15000,'TolX',1e-07,'TolFun',1E-07,'Display','off'});
I am aware that Fortran packages have been imported in Java, but I don't really have a clue how to achieve my goal by applying the mentioned tools. Hence, I'd welcome any suggestion on how to overcome this problem.
I'd suggest using an existing solution like Apache Commons - it's a robust library containing a lot of tools you may find helpful.
The LM optimization method is implemented by this class - you can either use it directly or just look at it for inspiration.