Nuclear Reactor Physics

UNIST CORE (COmputational Reactor physics and Experiment laboratory) is an academic laboratory dedicated to the research in nuclear reactor physics. The laboratory actively develops theoretical methods and computer codes to tackle neutron transport and diffusion theory in all its aspects: collision with medium, slowing-down, scattering, absorption, nuclear fission, chain reaction, secondary particle production and nuclide transmutation. The laboratory manages the knowledge required for reactor core design, analysis and operation and conducts studies in the fields of criticality, reactivity feedback and reactivity control, reactor kinetics, nuclear fuel depletion, perturbation theory, deep-penetration shielding, steady-state and transient simulations, reactor design and safety analysis, and many more.

유니스트 원자로물리연구실은 원자로물리에 대하여 연구하고 있습니다. 원자로물리란 중성자 수송이론 및 확산이론을 기초로 원자로 내에서 중성자의 거동 즉, 매질과 충돌, 산란, 흡수, 핵분열 및 연쇄 반응 등을 연구하고, 중성자의 시공간 및 에너지 분포를 예측 및 제어하는 방법을 연구하는 공학의 한 분야입니다. 임계, 반응도 제어, 중성자 감속, 중성자 확산이론, 중성자 수송이론, 동특성, 반응도 궤환 등 원자로 설계, 해석 및 운전에 필요한 지식을 다룹니다. [네이버 지식백과] 원자로물리학 [Nuclear reactor physics] (학문명백과 : 공학, 형설출판사)


  • Code Development
  • Methodology Development
  • Nuclear Reactor Design
  • Machine Learning and Artificial Intelligence
  • Multi-Physics with Accuracy and Uncertainty (BEPU)

Research Projects

  • Advanced Reactor Analysis Computer Codes Developments

    Advanced Reactor Analysis Computer Codes Developments

    Reactor analysis code

    developing_code Monte Carlo Code Development – MCS Neutron Transport Code Development – STREAM MC-MoC Hybrid Methodology Development Resonance Treatment Method of Characteristics (MoC) Solver CMFD implementation, Linear source approximation, Memory optimization Future study: Depletion, Adjoint flux (perturbation), 2-D/1-D coupling, Parallelization of 2-D/1-D, MoC at transient state



    MCS - Monte Carlo Code


    MCS: Large scale reactor analysis with accelerated Monte Carlo simulation BEAVRS benchmark analysis results

  • Methodology Development

    Methodology Development

    Methodology development of reactor physics

    Monte Carlo Methods Method of Characteristics Unified Nodal Method Acceleration Techniques

  • Nuclear Reactor Design

    Nuclear Reactor Design

    SM-SFR, PWR, and MSR

    Design study of Ultra-long Cycle Fast Reactor UCFR 1000MWe and short-term deployable 100MWe design Strategies: Breed-and burn, power flattening, inherent safety Fuel study: LEU zoning, blanket breeding, PWR spent fuel loading, uranium-thorium mixed fuel performance, metallic fuel analysis Safety analysis: Inherent safety, temperature coefficient, sodium void worth, control rod worth, fast neutron fluence Design of SM-SFR Small modular fast reactor with breed-and burn strategy and liquid metal coolant Feasible design criteria study for nuclear power plant type and components Take advantage of the merit of SMR and SFR simultaneously Benchmark-Modeling for Calculating Neutronic Parameters To Solve Benchmark Problems with MC code / MoC and Nodal code KUCA Kyoto university critical assembly Molten Salt Reactor Design Molten salt reactor (MSR) design and code development GEN-III+ PWR Burnable absorper design for low-boron operation Reflector design

  • Multi-Physics Multi-Scale Simulations with Accuracy and Uncertainty (BEPU)

    Multi-Physics Multi-Scale Simulations with Accuracy and Uncertainty (BEPU)

    Multy-Physics Coupling, Uncertainty Quantification, Accuracy Improvements

    Worldwide attention from nuclear researchers are poured into the calculation of more realistic results in terms of reactor core safety margins against critical core conditions. The analysis of non-quantified uncertainties on account of multi-physics phenomena involves the coupled modeling of neutron kinetics, coolant thermal-hydraulics and nuclear fuel performance using the numerical integration methods with built-in precision and accuracy control.

  • Machine Learning and Artificial Intelligence

    Machine Learning and Artificial Intelligence

    Cross-section Generation, Core Diagnostics System, Surrogate Model for Core Parameters

    Application of CNN, ANN in predicting the core parameters, cross-section generation for nodal code, calculation acceleration for transport code, data generation and management for training and testing of developed networks. AI-based diagnostic for reactor operation and Simulated Annealing based algorithm for loading pattern optimization are also being conducted.

Laboratory Resource

  • LINUX Cluster

    LINUX Cluster





  • Retained Codes & Nuclear Data Libraries

    Retained Codes & Nuclear Data Libraries

    Reactor Analysis Computer


    Nuclear Data Library