LIANG Jingang
Associate Professor of Nuclear Science and Engineering
Ph.D. adviser/Master’s adviser
jingang@tsinghua.edu.cn
(+86 10) 6278-4836
Education
PhD, Nuclear Science and Engineering, Tsinghua University, 2015
B.S., Nuclear Science and Engineering, Tsinghua University, 2010
Working experiences
06/2021-present, Associate Professor, Institute of Nuclear and New Energy Technology, Tsinghua University, China
10/2019-05/2021, Assistant Professor, Institute of Nuclear and New Energy Technology, Tsinghua University, China
10/2015-07/2019, Postdoctoral Associate, Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, USA
Awards
Science and Technology Progress Award of SPIC (Third Prize), 2020
Excellent Paper Award, Reactor Physics Asia 2015 Conference (RPHA15), 2015
National Scholarship for Distinguished Doctorates, 2014
Research
Prof. Liang’s research interests are in the areas of nuclear reactor safety, computational reactor physics, radiation transport, scientific machine learning, high-performance computing, etc. Liang has been involved in various research projects, including:
1. high-fidelity radiation transport with Monte Carlo and hybrid methods
2. multi-physics simulation methods and tools development (i.e. RMC and OpenMC)
3. design and deployment of advanced reactor systems (i.e. high temperature reactors)
4. intelligent decision support in nuclear power plant for nuclear emergency
5. innovative nuclear data representations for efficient temperature treatment
6. accelerated simulation with next-generation computing architectures and algorithms
Intelligent decision support in nuclear power plant：
The nuclear emergency is the last barrier for nuclear safety defense. Nuclear emergency decision support technology is an important part of the emergency preparedness and response capacity for nuclear facilities, which includes accident diagnosis, source term estimation, accident consequence assessment and protective action recommendations. We proposed a research framework for nuclear emergency decision making based on the idea of domain knowledge intelligentization. In the framework, existing knowledge about the nuclear power plants (NPPs) including system design, accident analysis, probabilistic risk assessment and source term estimation is utilized to the greatest extent to construct the diagnosis and prediction model in nuclear emergency decision making. Intelligent technologies, like Bayesian network method, are made full use of to enable the calculations in the model to be implemented automatically.
High fidelity reactor simulation with Monte Carlo methods：
With the increasing requirements for the safety and economy of nuclear reactors as well as the developments of new types of nuclear systems, traditional methods and tools for reactor neutronics analysis are challenged. New high-fidelity methods are needed for the design and analysis of nuclear reactor cores. Monte Carlo (MC) method is advantageous for its exact representation of both geometry and physical phenomena that are important for reactor analysis. Historically, MC is computationally too costly and its applications have been limited to only small-scale simulations. Recent advances in computing power bring these methods closer to performing realistic core analysis and design. We are focusing on the development of novel algorithms for improving the efficiency of MC and aim to achieving large scale full-core reactor analysis on modern computing architecture.
Research Projects
Research on Advanced Nuclear Safety Analysis and Risk Assessment Technology, National Natural Science Foundation of China
PRA Based intelligent Risk Monitoring Method to Support Nuclear Emergency Response Decision Making, CNNC fund
Multi-physics Coupling Simulation Methods for XARP and HTGRs, Tsinghua Fund
Teaching
Principles of Nuclear Engineering
Advanced Reactor Physics (****0133-0)
Introduction to Monte Carlo Particle Transport Methods (in preparation)
Services
Guest Editor for Journals:
Frontiers in Energy Research (ISSN: 2296-598X)
Energies (ISSN: 1996-1073)
Journal Reviewer：
Annals of Nuclear Energy
Energies
Frontiers in Energy Research
Acta Polytechnica
F1000Research
Physics Letters A
AIMS Energy
Nuclear Science and Engineering
Recent Publications
B. Qi, L. Zhang, J. Liang*, J. Tong. Combinatorial Techniques for Fault Diagnosis in Nuclear Power Plants Based on Bayesian Neural Network and Simplified Bayesian Network-Artificial Neural Network. Frontiers in Energy Research. 2022, 10.
R. Li, Z. Liu, Z. Feng, J. Liang* , L. Zhang. High-fidelity MC-DEM Modeling and Uncertainty Analysis of HTR-PM First Criticality. Frontiers in Energy Research. 2022.
Z. Feng, N. An, J. Liang*, K. Wang. ODR-VS method for high packing fraction of dispersed TRISO particles. Annals of Nuclear Energy. 2022, 166. S. Kumar, J. Liang*, B. Forget, K. Smith. “BEAVRS: An integral full core multi-physics PWR benchmark with measurements and uncertainties”. Progress in Nuclear Energy. 129. 2020.
J. Liang*, K. Wang, Y. Qiu, X. Chai, S. Qiang. “Domain decomposition strategy for pin-wise full-core Monte Carlo depletion calculation with the reactor Monte Carlo code”. Nuclear Engineering and Technology. 2016, 48(3): 635-641.
J. Liang, X. Peng, et al. “Processing of a Comprehensive Windowed Multipole Library via Vector Fitting”. PHYSOR 2018: Reactor Physics paving the way towards more efficient systems. Cancun, Mexico, April 22-26, 2018.
J. Liang, S. Kumar, B. Forget, K. Smith. “Quantifying Uncertainty in the BEAVRS Benchmark”. M&C 2017 - International Conference on Mathematics & Computational Methods Applied to Nuclear Science & Engineering, Jeju, Korea, April 16-20, 2017, on USB (2017).
J. Liang, Z. Wu*, H Abdel-Kalik. “Nuclear Data Sensitivity Analysis in OpenMC Using the GPT-Free Method”. Transactions of the American Nuclear Society, 2018, 118: 921-924.
J. Liang, P. Ducru, et al, “Target Velocity Sampling for Resonance Elastic Scattering Using Windowed Multipole Cross Section Data”, Transactions of the American Nuclear Society, 2019, 119: 1163-1166.
X. Wang, J. Liang, Y. Li, Q. Zhang*. Hybrid Monte Carlo methods for Geant4-based nuclear well logging implementation. Annals of Nuclear Energy. 2022, 169.
S. Liu, J. Liang, K. Wang, Y. Chen. “Development of the integrated parallelism strategy for large scale depletion calculation in the Monte Carlo code RMC”. Annals of Nuclear Energy. 135: 106941. 2020.
X. Peng, J. Liang, et al. “Calculation of adjoint-weighted reactor kinetics parameters in OpenMC”. Annals of Nuclear Energy. 2019. 128: 231-235.
Z. Wu, J. Liang, et al. “GPT-Free Sensitivity Analysis for Monte Carlo Models”. Nuclear Technology. 2019: 1-16.
X. Peng*, J. Liang, et al. “Development of continuous-energy sensitivity analysis capability in OpenMC”. Annals of Nuclear Energy. 2017. 110: 362-383.
S. Liu, J. Liang, Q. Wu, J. Guo, S. Huang*, X. Tang, Z. Li, K. Wang. “BEAVRS full core burnup calculation in hot full power condition by RMC code”. Annals of Nuclear Energy. 2017. 101: 434-446.
