Real Experiment-MCBEND Simulation

  • #1
gxa
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TL;DR Summary
Normalization for simulation results
Do I need to apply normalization these two graphs to make them similar to each other and if so, how can I do this? I would be very grateful if you can help me if these results are consistent. I compared the results of the experiment and the results of the mcbend simulation.
 

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  • #2
gxa said:
TL;DR Summary: Normalization for simulation results

Do I need to apply normalization these two graphs to make them similar to each other and if so, how can I do this? I would be very grateful if you can help me if these results are consistent. I compared the results of the experiment and the results of the mcbend simulation.
It's best not to post documents that can contain macros, like Word or Excel documents. It's better to upload a PDF or JPEG image of your work to help folks understand your question.

Is this for MCNP work? If so, I can move the thread to the Nuclear Engineering forum, where such questions usually go. Finally, is this for schoolwork? What is the context of the question? Thanks.
 
  • #3
Got it, thanks. Can you move my question to nuclear engineering section and this is not a homework. Also, I added the necessary graphics for my question as a picture again.
 

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  • #4
What do you want to learn from your data? Where does it come from?

It's possible the simulation hasn't been scaled to match the expected experimental result. In that case a normalization is useful. If you want to normalize to the same integral, or same integral of a peak, or something else, will depend on your experiment and your data analysis goal.
 
  • #5
yes, I did not scale the data. what I want to do is to make the graphs similar. But I don't know exactly how to make normalization
 
  • #6
gxa said:
what I want to do is to make the graphs similar.
Similar in what way, for what purpose?

Your experimental graph doesn't have the same shape as the theoretical one so they won't match up nicely no matter what you do.
 
  • #7
I think my code is the same as the environment I have set up. I am investigating the accuracy of the simulation. These are the measurements of my detector and I added my code
1692791605783.png
 

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FAQ: Real Experiment-MCBEND Simulation

What is Real Experiment-MCBEND Simulation?

Real Experiment-MCBEND Simulation refers to the use of the MCBEND software to simulate and analyze nuclear experiments. MCBEND is a Monte Carlo code used for neutron and gamma transport calculations, often applied in nuclear reactor physics, radiation shielding, and dosimetry.

How accurate are MCBEND simulations compared to real experiments?

MCBEND simulations are highly accurate when provided with correct input data and parameters. However, the accuracy can vary based on the complexity of the experiment, the quality of the nuclear data libraries used, and the precision of the input geometries and materials. Validation against real experimental results is often necessary to ensure reliability.

What are the primary applications of MCBEND simulations?

MCBEND simulations are primarily used in nuclear reactor design, radiation shielding analysis, radiation protection, medical physics, and nuclear criticality safety. They help in predicting radiation transport and interactions in various materials and geometries, thereby aiding in the design and safety assessments of nuclear systems.

What are the main challenges in performing MCBEND simulations?

The main challenges include the need for detailed and accurate input data, significant computational resources for complex simulations, and the expertise required to interpret the results correctly. Additionally, ensuring that the simulations are validated against experimental data can be challenging and time-consuming.

How can I validate the results of an MCBEND simulation?

Validation of MCBEND simulation results can be done by comparing them with experimental data or results from other validated computational methods. Benchmarking against well-documented experiments, using peer-reviewed nuclear data libraries, and performing sensitivity analyses are common practices to ensure the accuracy and reliability of the simulations.

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