This course provides an introduction to computational methods for nuclear engineering, with a focus on practical application through Python programming. Students will gain proficiency in essential Python libraries, such as Numpy to support scientific computing. The course also covers fundamental numerical methods and their application to nuclear engineering problems, including lumped system modeling, nuclear reactor kinetics, the neutron diffusion equation, and Monte Carlo methods for particle transport.

Through a blend of theory and hands-on experience, students will learn how to formulate and solve engineering problems efficiently. The course aims to bridge the gap between mathematical modeling and real-world nuclear engineering challenges by equipping students with the necessary computational tools and methodologies.

Key Topics:

• Python fundamentals
• Linear Algebra Quick Reminders
• Integration of ODE Quick Reminders
• One-group neutron diffusion equation
• One-Group k-Eigenvalue Problems
• Two-Group k-Eigenvalue Problems
• Introduction to Monte Carlo Methods
• Multi-dimensional heat conduction
• Thermofluid Systems

Expected Outcomes:

• Acquire fundamental knowledge, skills, and attitudes for developing and using computational models in reactor physics and thermofluid systems
• Understand both perspectives:
  
  • Development of computational models
  • What is behind commercial or research codes embedding such models
• Recognize the essential role of numerical algorithms in obtaining quantitative, accurate (though approximate) results
• Build competence in handling these algorithms as a core engineering skill which is the essential prerequisite to move from theory to practical evaluation of real processes
• Refresh key concepts of numerical analysis as a foundation for advanced algorithms in computational nuclear engineering
• Develop the ability to propose and explore meaningful application examples to consolidate learning and inspire practical use