Describing cosmic particles in mcnp6

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When I used PAR=-CR of mcnp6 to describe cosmic particles
When I used PAR=-CR of mcnp6 to describe cosmic particles, there was an error:"Expire parameter is too many cases of erg > emax,bad trouble in subroutine startp of mcrun,"Any idea on how to resolve this problem?
 
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So CR is a lot of different particles, the error suggests the energy set is too high for many of them. What energy are you specifying and can you reduce it?

Also is the advanced physics on or off?
 
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Alex A said:
So CR is a lot of different particles, the error suggests the energy set is too high for many of them. What energy are you specifying and can you reduce it?

Also is the advanced physics on or off?
Thanks for your reply! I want to calculate μ, but it's not clear how much to limit the energy to by phys cards, or to limit the energy of other particles. I know that the particles in mcnp have the highest energy limit. Is it possible that the energy of cosmic rays exceeds the limit? Does the manual have the energy limit table for various particles? Also, how do I know if advanced physics is on or off?
 
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I don't know much about simulating cosmic rays. I think it's normally thought of as a fluka problem rather than an mcnp one. Some answers should be in the output file, if you can share that adding .txt to the filename and attaching it to a post would be helpful.

Oh and welcome to physicsforums!
 
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What are cosmic particles and how are they simulated in MCNP6?

Cosmic particles, also known as cosmic rays, are high-energy particles that originate from outer space and interact with the Earth's atmosphere. These particles primarily consist of protons, with a smaller amount of heavier nuclei and other subatomic particles. In MCNP6, a general-purpose Monte Carlo N-Particle code, cosmic particles are simulated by modeling their interactions with various materials and the atmosphere. This involves detailed geometrical and material descriptions to accurately predict the transport and interaction of these particles.

How does MCNP6 handle the geometry necessary for cosmic particle simulations?

MCNP6 is equipped with powerful geometric modeling capabilities that allow it to define complex 3D environments. For cosmic particle simulations, users can create detailed models of the Earth’s atmosphere and other relevant geometries. MCNP6 uses a combination of combinatorial and constructive solid geometry to define the spatial layout and materials necessary for accurate simulation of cosmic particle interactions.

What types of interactions can MCNP6 simulate for cosmic particles?

MCNP6 can simulate a wide range of interactions for cosmic particles, including scattering, absorption, and nuclear reactions. This is crucial for understanding the behavior of cosmic rays as they penetrate the Earth's atmosphere and interact with its constituents. The software includes models for electromagnetic interactions, such as pair production and Bremsstrahlung, as well as hadronic interactions important for simulating the nuclear components of cosmic rays.

Can MCNP6 be used to assess the radiation risk from cosmic particles?

Yes, MCNP6 can be used to assess the radiation risk from cosmic particles. By simulating the interaction of cosmic rays with the Earth's atmosphere and other materials, MCNP6 can help in calculating dose rates and understanding the potential radiation exposure to humans and electronics, both on the ground and in flight. This application is particularly important for aviation safety and space missions, where exposure to cosmic rays is significant.

What are the challenges in simulating cosmic particles with MCNP6?

Simulating cosmic particles with MCNP6 presents several challenges. One major challenge is the need for extensive computational resources due to the complex interactions and large geometrical areas involved. Additionally, accurately modeling the spectrum and composition of cosmic rays, which vary over time and with solar activity, requires up-to-date data and sophisticated models. There is also the challenge of integrating the simulation results with experimental data to validate and refine the models used in MCNP6.

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