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
Shielding
- 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