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The human factor in licensing and operating the next generation of nuclear plants
As human factors specialists working at the intersection of human performance and nuclear operations, we are witnessing one of the nuclear sector’s most significant transitions in decades. The emergence of small modular reactors, microreactors, and other advanced designs is reshaping the industry’s landscape. Digital instrumentation and controls, passive safety systems, and increased automation are creating opportunities for greater safety margins and more flexible operation. These same features also fundamentally redefine what it means to “operate” a nuclear plant. Interactions among human roles, automation, and passive systems shape how people maintain awareness, exercise judgment, and intervene when necessary. These developments affect both operational realities and the regulatory foundations on which nuclear safety is built.
Hangbok Choi, Myunghee Choi, Ryan Hon
Nuclear Technology | Volume 205 | Number 3 | March 2019 | Pages 486-505
Technical Paper | doi.org/10.1080/00295450.2018.1495001
Articles are hosted by Taylor and Francis Online.
Calculations have been conducted for the KRITZ-2 (KRITZ-LWR-RESR-001/002/003) and the Fast Flux Test Facility (FFTF) (FFTF-LMFR-RESR-001) Nuclear Energy Agency benchmark problems using the PARCS reactor simulation code with lattice parameters generated by the DRAGON reactor physics code and with the MCNP6 Monte Carlo code. The benchmark analyses examined the DRAGON cross-section library, PARCS energy group structure, DRAGON fuel assembly modeling, and nuclide self-shielding effect. For KRITZ-2, the PARCS 2-group core calculations with a DRAGON 361-group library based on ENDF/B-VII.1 reproduced the benchmark keff with a root-mean-square (rms) error of 0.19% δk. DRAGON/PARCS also predicted the fission rates within 5%. The MCNP results are consistent with the DRAGON/PARCS results but with a small underestimation when compared to the benchmark value. For FFTF, the PARCS 33-group core calculations underpredicted the benchmark keff by 0.19% δk while the MCNP calculation overpredicted the benchmark keff by 0.23% δk. The neutron spectrum distributions calculated by PARCS and MCNP are consistent with measured data. Since the energy boundary values of the measured neutron spectrum are not available, the calculated spectra could not be directly compared to the measured value. The DRAGON/PARCS solution to a numerical benchmark of a gas-cooled fast reactor (GFR), i.e., the Energy Multiplier Module, predicted the keff and assembly power with 0.46% δk and 3.7% rms error, respectively, when compared to the MCNP simulation. The benchmark calculations of the selected thermal and fast reactors have shown that DRAGON/PARCS simulates small reactor cores with good accuracy.
