Introduction to OpenMC

OpenMC represents a modern approach to Monte Carlo neutron transport. Unlike traditional codes that rely on text input files, OpenMC uses Python as its primary interface. This means you can build models programmatically, automate parameter studies, and integrate seamlessly with data analysis tools.

Developed at MIT and now maintained by an active open-source community, OpenMC focuses on neutron criticality calculations for reactor physics. The code is completely free to use, modify, and distribute under the MIT license - no export controls or licensing headaches.

What really sets OpenMC apart is its Python-first design. Instead of learning another input format, you use familiar Python to create models. This makes complex tasks like parametric studies, optimization, and data processing much simpler than with traditional codes.

If you're comfortable with Python or want to learn modern computational physics, OpenMC offers significant advantages. The code excels at continuous-energy calculations, handles complex 3D geometries naturally, and includes built-in visualization tools.

OpenMC is particularly strong for research applications. The native Python interface makes it easy to integrate with machine learning workflows, optimization algorithms, and uncertainty quantification tools. You can also take advantage of the entire scientific Python ecosystem.

OpenMC is used extensively in nuclear engineering research and industry. Reactor designers use it to model advanced concepts like small modular reactors and Generation IV systems. The code's flexibility makes it ideal for studying complex geometries and novel fuel designs.

Research institutions rely on OpenMC for fuel cycle studies, nuclear data validation, and multi-physics simulations. The Python interface makes it particularly valuable for machine learning applications and automated design optimization.

This guide assumes you have basic Python knowledge but are new to Monte Carlo methods. We'll start with simple examples and gradually build complexity. By the end, you'll be able to model complex reactor systems and analyze results effectively.

Each section includes practical examples and working code. We recommend following the guide sequentially, as later concepts build on earlier foundations. Don't worry about memorizing everything - focus on understanding the workflow and building intuition.