PhD Thesis
UQ of Monte Carlo XS Generation for HTRs
Ann Arbor, MI
Advised by Prof. Thomas Downar
Aug 2023
- Thesis title: Quantification and Minimization the Uncertainty Propagation of Monte Carlo Cross Section Generation for HTR Applications
- Developed an analytic UQ methods based on perturbation theory for UQ of MC generated XS, implemented in AGREE
- Developed a stochastic UQ methods based on randomly sampling for UQ of MC generated XS, implemented with Dakota, coupled with AGREE and SAM
- Monte Carlo-Deterministic hybrid methods for HTR neutronics analysis, SAM system code for thermal-hydraulics simulation
- Methods were demonstrated on HTR-10 steady-state benchmark and gFHR steady-state / transient simulations
- UQ
- MC XS
- Hybrid method
- HTR
Experience
Argonne National Laboratory Summer Intern
Lemont, IL
Graduate Research Aid
May 2022 - Aug 2022
- SAM code development using C++ and MOOSE framework, implemented a control rod module with spacial effect
- Implemented a numerical ODE solver to solve xenon/iodine Bateman equations
- Created demo cases and unit tests for the newly implemented module/solver
- gFHR neutronics/thermo-fluids modeling and simulation
- C++
- MatLab
- Python
North Carolina State University Summer Intern
Raleigh, NC
Undergraduate Student Intern
Aug 2018 - Sept 2018
- Sensitivity analysis and uncertainty quantification https://github.com/jin-li/DLFR_SU
- Developed C++/Python scripts to calculate sensitivity and covariance data
- Developed MatLab scripts to quantify uncertainty and create plots
- SFR neutronics modeling and simulation
- C++
- MatLab
- Python
Research (Linear Solver)
RSOR - Linear Solver Preconditioner
- Implemented RSOR (Reduced Successive Over Relaxation) preconditioner for banded matrices, improving run time by ~25%
- The method was applied in the numerical library Futility to improve the performance of linear solvers
- C++
- Fortran
lpCMFD
- Implemented a linear prolongation approach to accelerate the convergence of CMFD (Coarse Mesh Finite Difference) solver by ~30% iterations, the stability was also improved
- The algorithm was applied in reactor simulation code MPACT
- Fortran
Projects
gFHR Modeling and Simulations
- Fluoride-cooled High-temperature Reactor (FHR), pebble-bed based design
- Monte Carlo - Deterministic Hybrid Method with Serpent 2 code and AGREE code
- Coupled neutronics / thermo-fluids simulation of the core and primary/secondary loops using system analysis code SAM
- Model development for the Digital Twin (DT) training
MSRE
- Molten Salt Reactor Experiment modeling and simulation
- Neutronics simulation using hybrid method with Serpent/Shift and PARCS
SPERT
- Development of the transient capability for the NEAMS neutronics code PROTEUS
- Modeling and Simulation of the SPERT experimental benchmark
Publications
- Jin Li, et al. “Uncertainty Propagation and Quantification of Monte Carlo Cross Section Generation for HTR Applications.” Nuclear Science and Engineering (submitted)
- Jin Li, et al. “Neutronics and Thermo-fluids Simulation of gFHR with Monte Carlo-Deterministic Hybrid Methods.” Nuclear Technology (submitted)
- Jin Li, et al. “Neutronics and Thermal-Hydraulics Simulation of Generic Pebble-Bed Fluoride-Salt-Cooled High-Temperature Reactor (gFHR).” PHYSOR (2022)
- Kaitlyn Barr, et al. “Verification of AGREE and Serpent for the Steady State HTR-10 Benchmark Problems.” PHYSOR (2022)
- Jin Li, et al. “Neutronics Simulation of the Molten Salt Reactor Experiment with Serpent/Shift and PARCS.” Mathematics & Computation (2021)
- Jin Li, et al. “Demonstration of a Linear Prolongation CMFD Method on MOC.” PHYSOR (2020)
- Yugao Ma, et al. “Neutronics and thermal-hydraulics coupling analysis in accelerator-driven subcritical system.” Progress in Nuclear Energy (2020)
- Won Sik Yang, et al. “Development of Transient Capabilities for the NEAMS Neutronics Code PROTEUS.” NEUP report DOE-PUR-0008561 (2019)
- Ishita Trivedi, et al. “Impact of Nuclear Data Uncertainties on Lead-cooled Fast Reactor Simulations.” Best Estimate Plus Uncertainty (2018)
- Yiran Wang, Jin Li. “Research on the Monte Carlo Simulation Methods of ADS MYRRHA.” Reactor Physics Asia (2017)
- Xiaotong Shang, et al. “Subcritical Multiplication Factor and Burnup Analysis of ADS with RMC.” ANS Winter Meeting (2017)
Jin Li
- +1 (734) 834-4826
- [email protected]
- https://jinli.io/en
- https://github.com/jin-li
- Ann Arbor, MI
Profile
New PhD grad (Aug 2023) major in nuclear engineering. Good at math, data analysis and programming, interested in advanced nuclear reactor design, modeling and simulation, multi-physics coupled analysis, reactor physics code development. Seeking positions for reactor core research and development.
Diplomas
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Doctor of Philosophy (2023)
- Nuclear Engineering and Radiological Sciences
- GPA = 3.926
- University of Michigan, Ann Arbor
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Bachelor of Engineering (2018)
- Engineering Physics
- Tsinghua University, China
Skills
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Reactor Simulation Code
- Serpent
- MCNP
- PARCS
- OpenMC
- SAM
- AGREE
- MPACT
- MOOSE Framework
-
Programming
- C++
- Python
- MATLAB
- Fortran
- C
- Bash/Zsh
-
Developer Tools
- Linux system
- Git
- Docker
- Conda
- CMake/XMake
-
Misc
- LaTeX
- Markdown
- Microsoft Office
- Adobe Tools
Interests
- Tennis
- Ski
- Cooking
- Building website