Efficiently Running Multiple MCNP Simulations with Variable Source Positions

In summary, the conversation discusses the use of mcnp to model a HPGe detector with a Co-60 source. The speaker is looking for a way to incorporate 50 different positions of the source into one input file instead of having to edit and run it 50 times separately. It is suggested to use a scripting language like bash or python to automate the process and potentially run the jobs in parallel.
  • #1
jamez_302
3
0
I'm currently working on a project using mcnp to model a HPGe detector with a Co-60 source. I have defined my cells and got my geometry spot on but the project requires me to move the source around about 50 different positions relative to the detector. I was wondering if there was any way to basically incorporate this into one input file to save me having to edit it 50 times and run it on 50 separate occasions? Thanks
 
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  • #2
Don't know about mcnp in particular but 50 input files and a scripting language like bash or python would make short work of it. You could even run the jobs in parallel if multiple machines are to be had.
 
  • #3
Paul Colby said:
Don't know about mcnp in particular but 50 input files and a scripting language like bash or python would make short work of it. You could even run the jobs in parallel if multiple machines are to be had.
I had the same thought this morning, but didn't respond. That's pretty much how folks do a series of computations, if not using Fortran or C++ with an interaction loop and 50 line input file.
 

1. What is MCNP modelling?

MCNP (Monte Carlo N-Particle) is a computer code used for simulating how particles interact with matter. It is commonly used in nuclear engineering and radiation physics to model the behavior of particles in various materials and environments.

2. What types of systems can be modelled with MCNP?

MCNP can be used to model a wide range of systems, from simple geometries such as spheres and cubes, to more complex systems like nuclear reactors or medical imaging devices. It can also model different materials and their interactions with particles.

3. How accurate is MCNP modelling?

The accuracy of MCNP modelling depends on several factors, such as the complexity of the system being modelled, the accuracy of the input data and assumptions made, and the computational power available. Generally, MCNP is considered to be a highly accurate tool for predicting particle behavior.

4. What are some common applications of MCNP modelling?

MCNP modelling has a wide range of applications, including nuclear reactor design, radiation shielding, medical imaging, and radiation therapy. It is also used in research and development for nuclear energy, nuclear medicine, and other areas of science and engineering.

5. How can I learn to use MCNP for modelling?

There are many resources available for learning MCNP, including online tutorials, textbooks, and training courses. It is recommended to have a strong background in physics, mathematics, and computer programming before attempting to use MCNP. Many universities also offer courses and workshops on MCNP modelling.

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