Rickover Fellowship Program Qualified Academic Areas

Rickover Fellows must be enrolled in an academic course of study and pursue research applicable to the science and engineering programs for the Rickover Fellowship Program in Nuclear Engineering. A Fellow’s academic program must be structured so that it supports one of the following research areas, or a closely related area of study:

Reactor Physics

  • Research on data for modeling nuclear phenomena including their improvement and assessment against worldwide experiments
  • Development of advanced Monte Carlo techniques to solve the neutron transport equation for complex material arrangements in three-dimensional geometries using novel variance reduction procedures
  • Improvements in methods using the diffusion approximation for calculating core neutronic behavior with burn up in the design of reactors
  • Development and application of accurate and efficient deterministic methods for solution of the neutron transport equation for realistic, three-dimensional reactor core geometries.
  • Investigation of procedures with improved accuracy and efficiency for evaluation of important reactor design parameters
  • Development of advanced experimental techniques (e.g., measurement of sub-criticality, determination of fissile content in spent fuel)
  • Development of advanced or innovative reactor design concepts

Thermal Hydraulics and Computational Fluid Dynamics

  • Measurements and modeling of the characteristics of thin liquid films in two-phase flow
  • Measurements and modeling of void fraction, velocity and interfacial area in two- phase flow regimes under a wide range of conditions
  • Mechanistic modeling of critical heat flux in the nucleate boiling and departure from nucleate boiling (DNB) regime
  • Direct measurement and modeling of wall shear and pressure drop in two-phase flow
  • Measurement and modeling of the size of liquid droplets and entrainment rates in annular two-phase flow
  • Investigation of the calculational stability of various two-phase flow source terms
  • Measurements and modeling of transient two-phase flow
  • Development of a single-phase and/or two-phase Computational Fluid Dynamics (CFD) validation, uncertainty quantification and best-estimate plus uncertainty design methods
  • Measurement of single-phase and/or two-phase flow field quantities required to validate CFD methods
  • Development of new turbulence models for internal, anisotropic flows for application to CFD

Materials Science

  • Performance prediction of nuclear fuels
  • Advanced materials for use in neutron environments
  • Corrosion in nuclear environments
  • Fission product attack of materials
  • Instrumentation for in-core measurements
  • Fundamental studies of neutron and fission fragment damage to materials
  • Computational material science studies
  • Advanced failure / damage mechanics analyses of nuclear materials
  • Development of constitutive models or use in finite element analyses (FEA) for deformation and failure
  • Advanced computational methods for analysis and prediction of mechanical behavior


  • Improved parallel efficiency in deterministic transport calculations
  • Discontinuous mesh computations for large 3D problems
  • Application of Monte Carlo to large scale shielding problems
  • Hybrid Monte Carlo/deterministic shielding methods

Acoustic Technology

  • Noise source identification, including advanced measurement techniques and advanced signal processing for airborne, fluidborne, and structureborne applications
  • Flow-induced noise and vibration, including testing and analysis
  • Noise control and reduction, including active and passive noise control, advanced materials and treatments, advanced control systems and isolation device design
  • Advanced computational methods, including computational aeroacoustics, fluid-structure interaction and stability, and structural acoustics
  • Turbomachinery noise and vibration control including analytical methods, fundamental testing, and noise source identification

Artificial Intelligence

  • Artificial Intelligence (AI) algorithm and application development
  • AI design applications, digital twinning, AI based signal processing, and AI manufacturing applications
  • Large data set quality assurance and cleaning
  • Natural language processing, generative adversarial networks, neural networks, and image analysis