Virtual analog of uranium-water subcritical assembly. (July 2022)

• Record Type: Journal Article
• Authors: Kiryukhin, P.K.; Romanenko, V.I.; Khomyakov, D.A.; Shcherbakov, A.A.; Pugachev, P.A.; Yushin, I.M.; Ashraf, O.; Tikhomirov, G.V.
• Is Part Of: Annals of nuclear energy. Volume 172(2022)
• Publication Date: 2022-07
• Subjects: Virtual reality -- UWSA -- Radiation dose rate -- Serpent -- MCU; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2022.109058
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: The optimal uranium–water ratio for the UWSA in VR was determined. Unreal Engine 4 was used to create the virtual analog. The neutronics model of the UWSA was obtained by the MCU code. Operating experience feedback was introduced. Abstract: Virtual reality (VR) technology is now being adopted in many industries, including entertainment, medicine, science, and engineering. In the nuclear field, the primary purposes of VR are: reducing radiation dose rates, security of nuclear facilities, visualization of physical processes, and training of personnel. Additionally, VR is a much cheaper alternative to expensive and license-requiring experimental nuclear facilities. This work focuses on reconstructing the workroom with the Uranium-Water Subcritical Assembly (UWSA) located at the National Research Nuclear University MEPhI to determine the optimal uranium–water ratio associated with this assembly in virtual reality. The creation of the virtual analog using Unreal Engine 4 was introduced to integrate the physical model into the virtual environment. The neutronic model of the UWSA was obtained by the MCU code. A similar model was generated by the Serpent code for verification purposes. Additional functions such as neutron flux visualization, radiation dose rate distribution visualization, and dose accumulation mechanics were introduced into the project to improve the quality of education. Visualization of both neutron flux in the assembly and gamma radiation distribution in the workroom was performed using particle systems and volumetric fog based on calculated and experimental data. Operating experience feedback was introduced to prevent or minimize difficulties that may occur in the future by learning from events that have already occurred.