This course provides an introduction to computational methods and nuclear engineering principles, 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 for scientific computing
• Numpy and Matplotlib for data manipulation and visualization
• Numerical methods in engineering
• Lumped modeling for dynamic systems
• Nuclear reactor kinetics
• Neutron diffusion theory
• Applied Nuclear Safety and MELCOR code applications

Expected Outcomes:

Upon completion of the course, students will:

• Be introduced to core concepts in nuclear engineering and computational methods.
• Develop proficiency in Python for solving engineering problems.
• Learn how to effectively model and simulate complex nuclear systems using established algorithms.
• Acquire the skills to set up and solve problems using numerical techniques tailored to engineering applications.