The code below is used to generate a data set (qtot_eV) by varying only the gap between the two stacks. However, I would like to modify the code so as to generate data set by varying the thicknsess of each layers. I already modified the functions that contain the layer thickness variations for each stacks as pasted below the main code. I need help on how to accurately generate dataset by varying the layer thicknesses and gap between them. I need the data for further optimization in deep learning. Thanks
%Main code
deld=(80E-9-10E-9)/5;
d=[10E-9:deld:80E-9]; % Variation of the distance between the hot and cold stacks
q=1.602E-19;
hbar=1.054571596E-34;
hbar_eV=hbar/q; %eV s
kb=1.3806503E-23;
kb_eV=kb/q;
h=2*pi*hbar;
h_eV=h/q; % eVs
T1=1500; %Emitter temperature, in K
T2=300; %TPV cell temperature, in K
c=2.9979E8; %speed of light
%Wavelength over which the near field heat transfer spectrum is calculated.
lambda_min1=10000E-6;
lambda_max1=2.5E-6;
lambda_max2=1E-6;
%number frequency points in the two calculation ranges
res1=200; %between lambda_min1 and lambda_max1
res2=200; %between lambda_max1 and lambda_max2
%wavelength to frequency conversion, do not touch
w_min1=2*pi*3E8/lambda_min1;
w_max1=2*pi*3E8/lambda_max1;
del1=(w_max1-w_min1)/res1;
w_min2=w_max1+del1;
w_max2=2*pi*3E8/lambda_max2;
del2=(w_max2-w_min2)/res2;
w=[(w_min1:del1:w_max1) (w_min2:del2:w_max2)];
lambda=2*pi*c./w;
w_ev=w/1.5193E15;
qevan_p=zeros(length(w),length(d)); %vector initialization
qevan_s=zeros(length(w),length(d));
qprop_p=zeros(length(w),length(d));
qprop_s=zeros(length(w),length(d));
for k = 1:length(d)
for a=1:length(w)
betaevanmax=10/d(k); %upper limit of the parallel wavevector for evanescent wave integration
betaevanmin=w(a)/c; %lower limit
gap=d(k);
qevan_p(a,k)=(Theta(w(a), T1,0)-Theta(w(a), T2))*1/pi^2*integral(@(beta)sevan_p(w(a),beta, d(k)),betaevanmin,betaevanmax,'Reltol',1E-6,'AbsTol',1E-10); %default abstol = 1E-10 reltol 1E-6
qevan_s(a,k)=(Theta(w(a), T1,0)-Theta(w(a), T2))*1/pi^2*integral(@(beta)sevan_s(w(a),beta, d(k,t)),betaevanmin,betaevanmax,'Reltol',1E-6,'AbsTol',1E-10);
qprop_p(a,k)=(Theta(w(a), T1,0)-Theta(w(a), T2)).*1/pi^2.*integral(@(beta)(sprop_p(w(a),beta,d(k))),0,w(a)/c);
qprop_s(a,k)=(Theta(w(a), T1,0)-Theta(w(a), T2)).*1/pi^2.*integral(@(beta)(sprop_s(w(a),beta,d(k))),0,w(a)/c);
a/length(w) % progress indicator
end
end
qtot=qevan_p+qevan_s+qprop_p+qprop_s; %total intensity spectum
qtot_eV=qtot*1.5193E15/10000; %in w/cm2/eV
qprop=(qprop_p+qprop_s)*1.5193E15/10000; %prop waves only, in w/cm2/eV
qevan=(qevan_p+qevan_s)*1.5193E15/10000; %evan waves only, in w/cm2/eV
bb=blackb(w,T1)*1.5193E15/10000; %in w/cm2/eV
%Function for first stack
function [t,n]=stack1(lambda)
t=[linspace(10E-9,150E-9, 5) linspace(10E-9,100E-9, 5)]; %layer thicknesses
n=ones((length(t)+2),length(lambda));
n(1,:)=1*n(1,:);
n(2,:)=sqrt(epsilon_ITO( lambda ));
n(3,:)=sqrt(epsilon_SiC( lambda ));
n(4,:)=sqrt(epsilon_w(lambda));
%Function for stack2
function [t,n]=stack2(lambda)
t=[linspace(10E-9,150E-9, 5) linspace(500E-9,1E-6, 5)];
n=ones((length(t)+2),length(lambda)); %intializes a 2D matrix of wavelenght-dependant refractive indices
n(1,:)=1*n(1,:);
n(2,:)=sqrt(epsilon_tisi2(lambda));
n(3,:)=3.5*n(1,:);
n(4,:)=sqrt(epsilon_Al( lambda )); %cell back electrode
