Before I post my new graphs I'd like to hopefully clear up some misconceptions I might have about Kirchhoff-Fraunhofer (far field) and Kirchhoff-Fresnel diffraction (near field).
My understanding is that in any sort of Kirchhoff approach first and foremost the dimensions of the aperture must be greater than the original wavelength. Let ##a## be the radius of the aperture
##a \gg \lambda \Rightarrow k_0 a \gg 1##
Furthermore the distance from the aperture to the field point (call it ##r##) must be greater than both the radius of the aperture and the wavelength.
##r \gg a \gg \lambda##
The funny thing is my numerical ##z-\text{flux}## pattern has a strong agreement with known far field and near field solutions when I set ##k_0 a = 15## and a very poor agreement when ##k_0 a = 1000##. If anything you would think the opposite to be true with the stated conditions above.
This is how I set my range of values. As we know a higher Fresnel Number ##F_1 = \frac{a^2}{\lambda r_{min}}## corresponds to the near field and a lower Fresnel Number ##F_2 = \frac{a^2}{\lambda r_{max}}## corresponds to the Far Field
First choice of ##k_0 a = 15##
##k_0 a = 15##
##a = 10 \pi##
##k_0 = \frac{k_0 a}{a}##
##\lambda = \frac{2 \pi}{k_0}##
##F_1 = 1##
##F_2 = 0.05##
##r_{min} = \frac{a^2}{\lambda F_1} = 2.4a##
##r_{max} = \frac{a^2}{\lambda F_2} \approx 50a##
Second choice of ##k_0 a = 1000##
##k_0 a = 1000##
##a = 10 \pi##
##k_0 = \frac{k_0 a}{a}##
##\lambda = \frac{2 \pi}{k_0}##
##F_1 = 1##
##F_2 = 0.05##
##r_{min} = \frac{a^2}{\lambda F_1} \approx 160a##
##r_{max} = \frac{a^2}{\lambda F_2} \approx 3182a##
When I compare against known solutions (##S_z## flux) in the far field ##r_{max}## for ##k_0 a = 15## values I get the following percent error 3.5% error max
When I compare against known solutions (##S_z## flux) in the far field ##r_{max}## for ##k_0 a = 1000## values I get the following percent error ##2.5 \times 10^9##% error max (huge difference)
Why does my program work better for ##k_0 a = 15##? You would think it would work better for ##k_0a = 1000##Another concern I have is that depending on how many grid points I use to integrate and generate the points on the graph I can get very different numbers. When using a ##10 \times 10 \times 10## grid I get up to ##3 \times## more power than when i use a ##20 \times 20 \times 20## grid. Conventional wisdom says more points makes for a more accurate integral but how do I know when I'm getting close? BTW I used trapezoidal integration.
Here is the near field calculated power for ##k_0 a = 15## for ##20 \times 20 \times 20## 3D integration grid
Here is the near field calculated power for ##k_0 a = 15## for ##10 \times 10 \times 10## 3D integration grid
The former has 4 times as many points (surface integral ##n \times n## as opposed to ##n \times n \times n##) and roughly ##\frac{1}{3}##'rd power.