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Nuclear Engineering Sample Classes and Course Descriptions

The Interdisciplinary Engineering Degrees (MSE or MS) requires 30 credit hours total with at least 18 hours in engineering and 18 hours from Purdue. Courses can count in either or both categories and most are 3 credit hours.  You may add a concentration to your degree but a concentration is not required.  If chosen, the concentration requirements must be met in addition to the regular degree requirements.  A concentration is chosen on the e-POS form and shows on the transcript, not the diploma.

The list of courses offered via distance may be sorted by semester/year or school.  Please keep in mind that this list is subject to change and you will need to review it frequently throughout your degree program.  Changes may be made to your POS in the future if they meet the degree and concentration requirements.

Total Credits: 30

  • The below 3 Nuclear Engineering Courses are required. Total Credits: 9
    • NUCL 50100 - Nuclear Engineering Principles - The first course for graduate students desiring a nuclear engineering sequence and an elective for students in science or engineering. The course is structured in four parts: Nuclear structure and radiation, biological effects and medical applications of radiation; basics of neutron and reactor physics, neutron diffusion and reactor criticality; nuclear materials and waste; and reactor systems and safety.
    • NUCL 51000 - Nuclear Reactor Theory I - This course teaches the methodologies of neutron flux calculations, diffusion and slowing down theory, flux separation, material buckling, resonance absorption, Doppler effect, 2-group and multi-group theories, and reactivity balances for design and operation. It provides an introduction to reactor kinetics, delayed neutrons, point reactor kinetics, transient behavior, load changes, reactivity feedback, and safety implications.
    • NUCL 55100 - Mass, Momentum, and Energy Transfer in Systems - This course covers formulations for analyzing complicated thermal-hydraulic phenomena in energy systems. Course topics include Derivation of two-phase flow field equations and constitutive relations; Thermal-hydraulic modeling of nuclear reactor systems; Analyses of nuclear reactor safety-related phenomena based on conservation principles.
  • The remaining 21 credits can be in any 500 and 600 levels courses in engineering, science, and mathematics. Please visit the courses by school page, for a full list of Purdue's online engineering graduate courses.

More Online Nuclear Engineering Courses

Please visit the courses by school page for continuous updates on new courses offered.

  • NUCL50200 Nuclear Engineering Systems - A second course for graduate students desiring a nuclear engineering sequence and an elective for students in science or engineering. Course topics include principles and practice of nuclear power plant systems with design applications, reactor kinetics, reactor control, radiation protection, shielding, nuclear fuels, fuel cycles, waste management, thermal cycles, heat transport, thermal hydraulics, reactor accidents, and safety analysis.
  • NUCL57000 Fuzzy Approaches in Engineering - Course topics include Intellectual Framework, basics, Fuzzy Models and Formal Structures, Fuzzy Control, General Principles of Rule-Based Systems Development and Limitations, and Advanced Topics.
  • NUCL57500 - Neural Computer in Engineering - The course presents the mathematical fundamentals of computing with neural networks and a survey of engineering applications. Computational metaphors from biological neurons serve as the basis for artificial neural networks modeling complex, non-linear and ill-posed problems. Applications emphasize the engineering utilization of neural computing to diagnostics, control, safety, and decision-making problems. Course topics include: Basics, Backpropagation, and Related Training Algorithms, Feedback and Other Special Neural Networks, Dynamic Neural Networks and Control Systems, Practical Aspects of Using Neural Networks, and Advanced Topics
  • NUCL 59700 Big Data and Machine Learning in Engineering - This course familiarizes students with key information technologies and their underlying methods and techniques that are used to store, manipulate, analyze and exploit large volumes of data with an emphasis in engineering applications and particularly nuclear data.