The analysis of emergency situations related to fires at nuclear power plants

Introduction. The article provides a system and statistical analysis of emergency situations associated with fires at nuclear power plants (NPPs) in various countries of the world for the period from 1955 to 2019. The countries, where fires occurred at nuclear power plants, were identified (the USA, Great Britain, Switzerland, the USSR, Germany, Spain, Japan, Russia, India and France). Facilities, exposed to fires, are identified; causes of fires are indicated. The types of reactors where accidents and incidents, accompanied by large fires, have been determined.The analysis of major emergency situations at nuclear power plants accompanied by large fires. During the period from 1955 to 2019, 27 large fires were registered at nuclear power plants in 10 countries. The largest number of major fires was registered in 1984 (three fires), all of them occurred in the USSR. Most frequently, emergency situations occurred at transformers and cable channels — 40 %, nuclear reactor core — 15 %, reactor turbine — 11 %, reactor vessel — 7 %, steam pipeline systems, cooling towers — 7 %. The main causes of fires were technical malfunctions — 33 %, fires caused by the personnel — 30 %, fires due to short circuits — 18 %, due to natural disasters (natural conditions) — 15 % and unknown reasons — 4 %. A greater number of fires were registered at RBMK — 6, VVER — 5, BWR — 3, and PWR — 3 reactors.Conclusions. Having analyzed accidents, involving large fires at nuclear power plants during the period from 1955 to 2019, we come to the conclusion that the largest number of large fires was registered in the USSR. Nonetheless, to ensure safety at all stages of the life cycle of a nuclear power plant, it is necessary to apply such measures that would prevent the occurrence of severe fires and ensure the protection of personnel and the general public from the effects of a radiation accident.

Application of genetic algorithms in optimization of SFR nuclear reactor design

This work presents a demonstrational application of genetic algorithms (GAs) to solve sample optimization problems in the generation IV nuclear reactor core design. The new software was developed implementing novel GAs, and it was applied to show their capabilities by presenting an example solution of two selected problems to check whether GAs can be used successfully in reactor engineering as an optimization tool. The 3600 MWth oxide core, which was based on the OECD/NEA sodium-cooled fast reactor (SFR) benchmark, was used a reference design [1]. The first problem was the optimization of the fuel isotopic inventory in terms of minimizing the volume share of long-lived actinides, while maximizing the effective neutron multiplication factor. The second task was the optimization of the boron shield distribution around the reactor core to minimize the sodium void reactivity effect (SVRE). Neutron transport and fuel depletion simulations were performed using Monte Carlo neutron transport code SERPENT2. The simulation resulted in an optimized fuel mixture composition for the selected parameters, which demonstrates the functionality of the algorithm. The results show the efficiency and universality of GAs in multidimensional optimization problems in nuclear engineering.

AI-based design of a nuclear reactor core

The authors developed an artificial intelligence (AI)-based algorithm for the design and optimization of a nuclear reactor core based on a flexible geometry and demonstrated a 3× improvement in the selected performance metric: temperature peaking factor. The rapid development of advanced, and specifically, additive manufacturing (3-D printing) and its introduction into advanced nuclear core design through the Transformational Challenge Reactor program have presented the opportunity to explore the arbitrary geometry design of nuclear-heated structures. The primary challenge is that the arbitrary geometry design space is vast and requires the computational evaluation of many candidate designs, and the multiphysics simulation of nuclear systems is very time-intensive. Therefore, the authors developed a machine learning-based multiphysics emulator and evaluated thousands of candidate geometries on Summit, Oak Ridge National Laboratory’s leadership class supercomputer. The results presented in this work demonstrate temperature distribution smoothing in a nuclear reactor core through the manipulation of the geometry, which is traditionally achieved in light water reactors through variable assembly loading in the axial direction and fuel shuffling during refueling in the radial direction. The conclusions discuss the future implications for nuclear systems design with arbitrary geometry and the potential for AI-based autonomous design algorithms.

Axionlike particles searches in reactor experiments

Reactor neutrino experiments provide a rich environment for the study of axionlike particles (ALPs). Using the intense photon flux produced in the nuclear reactor core, these experiments have the potential to probe ALPs with masses below 10 MeV. We explore the feasibility of these searches by considering ALPs produced through Primakoff and Compton-like processes as well as nuclear transitions. These particles can subsequently interact with the material of a nearby detector via inverse Primakoff and inverse Compton-like scatterings, via axio-electric absorption, or they can decay into photon or electron-positron pairs. We demonstrate that reactor-based neutrino experiments have a high potential to test ALP-photon couplings and masses, currently probed only by cosmological and astrophysical observations, thus providing complementary laboratory-based searches. We furthermore show how reactor facilities will be able to test previously unexplored regions in the ∼MeV ALP mass range and ALP-electron couplings of the order of gaee ∼ 10−8 as well as ALP-nucleon couplings of the order of $$ {g}_{ann}^{(1)} $$ g ann 1 ∼ 10−9, testing regions beyond TEXONO and Borexino limits.

UNCERTAINTY CHARACTERIZATION FRAMEWORK FOR STEADY-STATE AND TRANSIENT NEUTRONICS SIMULATIONS OF A CANDU REACTOR

This paper reports on the development and testing of a comprehensive few-group cross section input uncertainty library for the NESTLE-C nodal diffusion-based nuclear reactor core simulator. This library represents the first milestone of a first-of-a-kind framework for the integrated characterization of uncertainties in steady-state and transient CANDU reactor simulations. The objective of this framework is to propagate, prioritize and devise a mapping capability for uncertainties in support of model validation of best-estimate calculations. A complete framework would factor both input and modeling uncertainty contributions. The scope of the present work is limited to the propagation of multi-group cross-section uncertainties through lattice physics calculations down to the few-group format, representing the input to the NESTLE-C core simulator, and finally to core responses of interest.