MCNP macro definition modeling

[chengmo]

TL;DR Summary

The results I modeled using the macro definition can be viewed with vised but vised doesn't display all of them and doesn't report errors. Why

C
1 1 -19.35 -7 1 -2
2 1 -19.35 -8 3 -4
3 1 -19.35 -5:-6
4 0 5 6 #1 #2

C
1 RCC 0 -10 0 0 -10 10 2
2 RCC 0 -10 0 0 -10 10 5
3 RCC 0 10 0 0 10 10 2
4 RCC 0 10 0 0 10 10 5
5 RPP 2 5 -10 10 0 10
6 RPP -3 0 -15 15 0 10
7 RPP 0 5 -15 -10 0 10
8 RPP 0 5 10 15 0 10

C
M1 074184 1

 

[member38644]

1 -19.35 -7 1 -2
M2 074185 2 1 -19.35 -8 3 -4
M3 074186 3 1 -19.35 -5:-6
M4 074187 4 0 5 6 #1 #2

MCNP macro definition modeling is a useful tool for creating complex geometries in Monte Carlo N-Particle (MCNP) simulations. In this example, the macro definitions are used to define various regions of interest in the model, such as RCC (right circular cylinder) and RPP (rectangular parallelepiped) shapes.

The first set of macro definitions (C1-C4) defines four RCC regions with different dimensions and positions. These regions can represent different materials or structures in the model, and the numbers following the RCC command specify the dimensions of the cylinder. The numbers following the RCC dimensions represent the coordinates of the center of the cylinder, as well as the rotation angles around the x, y, and z axes.

The next set of macro definitions (C5-C8) defines three RPP regions with different dimensions and positions. These regions can represent volumes within the RCC regions, allowing for more detailed modeling of the geometry. The numbers following the RPP command specify the dimensions of the parallelepiped, and the numbers following the dimensions represent the coordinates of the corners of the parallelepiped.

Finally, the last set of macro definitions (M1-M4) combines the previously defined regions to create the final model. The numbers following the M commands correspond to the number of the macro definition, allowing for easy identification and organization of the model.

Overall, MCNP macro definition modeling allows for efficient and flexible creation of complex geometries in MCNP simulations, making it a valuable tool in nuclear and radiation physics research.