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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.
Axel Hoefer, Laurent Coquard
Nuclear Technology | Volume 211 | Number 12 | December 2025 | Pages 3018-3029
Research Article | doi.org/10.1080/00295450.2025.2480984
Articles are hosted by Taylor and Francis Online.
We present a new methodology for the Improvement of RAdiological WAste Characterization with Bayesian MAchine Learning (IRAWAMA) using information from high-resolution gamma spectrometry with high-purity germanium (HPGe) detectors. As a real-life example, we consider the gamma spectrometry of a 200-L waste drum within the QUantitative ANalysis of TOxic and nontoxic Materials (QUANTOM®) device. To precisely represent the spatial material distribution of the waste matrix, the waste drum and the surrounding measuring arrangement of the QUANTOM device, and to correctly consider all relevant electromagnetic interactions (photoelectric absorption, Compton scattering, bremsstrahlung, pair production and annihilation), the Monte Carlo N-Particle Transport code MCNP 6.2 is used in combination with the ENDF/B VII.1 nuclear data library to compute the energy-dependent count rate measured by the HPGe detectors of the QUANTOM device. A large number of MCNP calculations are performed to calculate the HPGe detector signals for different combinations of Monte Carlo–sampled activities and spatial locations of the considered radionuclides and of the material density of the waste matrix. The resulting multivariate data set is used as training data for the applied Bayesian machine learning model. To quantify the performance of the trained model with respect to the prediction of activities and spatial locations of radionuclides within the waste matrix from the measured gamma spectra, the trained model is applied to a large number of simulated test cases. For each test case, the activities and spatial locations predicted from the gamma spectrometric data are compared to the true activities and spatial locations. Our test results show that the IRAWAMA methodology has the potential to significantly reduce the uncertainties of activities and spatial distributions of radionuclides within waste packages and to reliably quantify the related prediction errors. This is the case even if the density distribution within the waste matrix is unknown. The IRAWAMA methodology may, therefore, be of great benefit to producers of low- and intermediate-level waste intended for final disposal.
