Small Modular Reactor based on NuScale with Thorium base

Diego M. E. Gonçalves; Marcelo Vilela da Silva; C. J. C. M. R.  da Cunha; Giovanni L. Stefani

doi:10.15392/2319-0612.2025.2881

Autores/as

Diego M. E. Gonçalves a Nuclear Engineering Program / Universidade Federal do Rio de Janeiro / COPPE, Av. Horácio Macedo, 2030, Bloco G - Sala 206 - CT, University City, Rio de Janeiro, RJ, Brazil
Marcelo Vilela da Silva a Nuclear Engineering Program / Universidade Federal do Rio de Janeiro / COPPE, Av. Horácio Macedo, 2030, Bloco G - Sala 206 - CT, University City, Rio de Janeiro, RJ, Brazil
C. J. C. M. R. da Cunha b Centro Regional de Ciências Nucleares do Nordeste (CRCN-NE/CNEN), Av. Prof.Luis Freire N°200-Curado, Recife - PE, 50740-437, Brazil
Giovanni L. Stefani a Nuclear Engineering Program / Universidade Federal do Rio de Janeiro / COPPE, Av. Horácio Macedo, 2030, Bloco G - Sala 206 - CT, University City, Rio de Janeiro, RJ, Brazil

DOI:

https://doi.org/10.15392/2319-0612.2025.2881

Palabras clave:

Reactor Core, Fuel Cycle, NuScale, SMR, Nuclear fuel cycles

Resumen

This study proposes a novel approach to enhance the NuScale Small Modular Reactor (SMR) by incorporating mixed uranium-thorium (U-Th) oxide fuel, thereby increasing U-233 production, improving fuel use, and reducing radioactive waste. The research integrates advanced neutron transport simulations with optimization techniques to refine the reactor’s fuel design for greater sustainability and efficiency. The researchers modeled the reference NuScale reactor core using the SERPENT code, which relies on the Monte Carlo Method (MCM) to ensure exact neutron transport simulations. To meet substantial computational demands, they ran these simulations on the Lobo Carneiro supercomputer at NACAD/UFRJ. The team applied a Particle Swarm Optimization (PSO) algorithm to find the best seed-to-blanket volume ratio, thereby maximizing U-233 production and achieving a self-sustaining fuel cycle. Implemented in Python, the algorithm continuously adjusted reactor parameters, logged progress, and enabled ongoing monitoring and potential restarts. For the seed region, the researchers employed a 13x13 configuration and used a 19x19 configuration for the blanket. They evaluated the proposed core design against critical safety and performance metrics, including the Moderator Temperature Coefficient (MTC), Doppler Temperature Coefficient (DTC), boron worth coefficient (BWC). The team also conducted data analysis and visualization using SerpentTools in Python. The results show that integrating U-Th fuel into SMRs can boost reactor performance without compromising safety, thereby offering a promising path toward more sustainable, efficient, and scalable nuclear energy production. This approach can reshape next-generation nuclear reactors by addressing essential challenges related to fuel sustainability and waste management.

Descargas

Los datos de descarga aún no están disponibles.

Referencias

[1] AlRashidi, M. R. a. E.-H. M. E., 2009. A Survey of Particle Swarm Optimization Applications in Electric Power Systems. IEEE Transactions on Evolutionary Computation, pp. 913-918.

[2] AlRashidi, M. R. A. M. F. E.-H. M. E. &. E.-H. F., 2010. Applications of computational intelligence techniques for solving the revived optimal power flow problem. Electric Power Systems Research, pp. 694-702.

[3] Andrew Johnson, D. K. S. T. a. G. R., 2020. serpentTools: A Python Package for Expediting Analysis with Serpent,. Nuclear Science Engineering.

[4] Barthel, F. a. T. H., 2019. Thorium: Geology, Occurrence, Deposits and Resources.

[5] Black, G., Shropshire, D. & Araújo, K., 2021. Small modular reactor (SMR) adoption: Opportunities and challenges for emerging markets. s.l.:Woodhead Publishing Series in Energy.

[6] C.A.Lobo, M. & Stefani, G. L. d., 2024. Thorium as nuclear fuel in Brazil: 1951 to 2023. Nuclear Engineering Design.

[7] Carelli, M. D. e. a., 2010. Economic Features of Integral, Modular, Small-to-Medium Size Reactors. Nuclear Engineering and Design, pp. 3267-3276.

[8] Cunha, C. J. C. M. R. d. et al., 2024. Single heated channel analysis of the AP-Th 1000 concept. Nuclear Engeineering and Design, Volume 420.

[9] Galperin, A. S. M. &. R. A., 1997. Thorium Fuel for Light Water Reactors – Reducing Proliferation Potential of Nuclear Power Fuel Cycle. Science & Global Security, pp. 265-290.

[10] Ghimire, L. a. W. E., 2023. Small Modular Reactors: Opportunities and Challenges as Emerging Nuclear Technologies for Power Production. Journal of Nuclear Engineering and Radiation Science, p. 044501.

[11] Gonçalves, D. M. E., Silva, M. V. d., Cunha, C. J. M. d. & Daniel Artur Pal, a., 2024. Feasibility of Converting NuScale SMRs from UO2 to Mixed (Pu,Th)O2 and (U,Th)O2 Cores: A Parametric Study of the Seed-Blanket Fuel Assembly. Nuclear Engineering and Design, Volume 424.

[12] Hussein, E. M., 2020. Emerging small modular nuclear power reactors: A critical review. Physics Open, p. 100038.

[13] Hussein, E. M., 2020. Emerging small modular nuclear power reactors: A critical review. Physics Open.

[14] IAPWS, 2008. IAPWS Industrial Formulation 1997 for the Thermodynamic Properties of Water and Steam. International Steam Tables: Properties of Water and Steam Based on the Industrial Formulation IAPWS-IF97, pp. 7-150.

[15] Ingersoll, D., 2014. Deliberately small reactors and the second nuclear era. Progress in Nuclear Energy, pp. 128-135.

[16] Ingersoll, M. D. C. &. D. T., 2021. Handbook of Small Modular Nuclear Reactors. s.l.:Woodhead Publishing Series.

[17] International Atomic Energy Agency, 2024. Advanced reactors information system (ARIS).. [Online] Available at: https://aris.iaea.org/sites/power.html

[18] Jain, N. N. U. &. J. J., 2018. A Review of Particle Swarm Optimization. Eng. India Ser. , pp. 407-411.

[19] Jyothi, R. K., Melo, L. G. T. C. d., M.Santos, R. & Yoon, H.-S., 2023. An overview of thorium as a prospective natural resource for future energy. Front. Energy Resources - Nuclear Energy.

[20] Kecek, A., Tucek, K., Holmstrom, S. & Uffelen, P. V., 2016. Development of M5 Cladding Material Correlations in the TRANSURANUS code. JRC Technical Reports, Issue 1, pp. 1-53.

[21] Kennedy, J. &. E. R., 1995. Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, pp. 1942-1948.

[22] Khorasani, S. &. H. S., 2018. Control rod pattern optimization in nuclear reactors using PSO algorithm. Annals of Nuclear Energy, pp. 334-343.

[23] Locatelli, G., Bingham, C. & Mancini, M., 2014. Small modular reactors: A comprehensive overview of their economics and strategic aspects. Progress in Nuclear Energy, pp. 75-85.

[24] Luka Snoj, M. R., 2006. Calculation of Power Density with MCNP in TRIGA reactor. Nuclear Energy for New Europe.

[25] NuScale Power, 2020. Part 2 - Final Safety Analysis Report (Rev.5)- Part 02 -Tier 02 - Chapter 04 - Reactor - Sections 04.01, s.l.: NuScale.

[26] Pambudi, Y. D. S. a. W. W. a. K. B., 2016. Particle Swarm Optimization-Based Direct Inverse Control for Controlling the Power Level of the Indonesian Multipurpose Reactor. Science and Technology of Nuclear Installations, pp. 1-9.

[27] Radkowsky, A. &. G. A., 1998. The Nonproliferative Light Water Thorium Reactor: A New Approach to Light Water Reactor Core Technology. Nuclear Technology, pp. 215-222.

[28] Radkowsky, A. &. G. A., 2000. Thorium Fuel for Light Water Reactors: Reducing Proliferation Potential of Nuclear Power Fuel Cycle. Nuclear Technology, pp. 215-222.

[29] Radkowsky, A., 1985. The Seed-Blanket Core Concept. Nuclear Science and Engineering, pp. 381-387.

[30] Rosner, R. & S, G., 2011. Small Modular Reactors–Key to Future Nuclear Power Generation in the US. Energy policy institute at Chicago.

[31] Silva, M. V. et al., 2024. Optimized modular nuclear reactor project utilizing artificial intelligence: Seed-blanket concept. Nuclear Engineering and Design.

[32] Singh, A. K. &. L. D. K., 2012. A hybrid PSO-GSA algorithm for optimization of control rod patterns in nuclear reactors. Annals of Nuclear Energy, pp. 220-230.

[33] Stefani, G. L. d. et al., 2023. Feasibility to convert the NuScale SMR from UO2 to a mixed (U,Th)O2 core: Parametric study of fuel element - Seed-blanket concept. World Journal of Nuclear Science and Technology.

[34] Todreas, N. E. & Kazimi, M., 2011. Nuclear Systems I -Thermohydraulic fundamentals. s.l.:Taylor & Francis.

[35] Tong, G., Zhang, S., Wang, W. & Yang, G., 2023. A particle swarm optimization routing scheme for wireless sensor networks. Transactions on Pervasive Computing and Interaction.

[36] Valtavirta, E. F. a. Y. B. a. V., 2023. Definition of the neutronics benchmark of the NuScale-like core. Nuclear Engineering and Technology, pp. 3639-3647.

[37] Westinghouse, 2011. AP1000 Design Control Document Rev. 19. , ML11171A500, 2011, s.l.: U.S. Nuclear Regulatory Commission.

[38] Xu, Q. a. W. T. a. W. W., 2021. Nonlinear Dissipative Particle Swarm Algorithm and Its Applications. IEEE Access, pp. 158862-158871.

[39] Xu, Y. J. S. W. G. &. L. Z., 2021. Nonlinear dissipative particle swarm optimization algorithm for nuclear reactor core design. Annals of Nuclear Energy, p. 108124.

[40] Yang, X.-F. X. a. W.-J. Z. a. Z.-L., 2002. Dissipative particle swarm optimization. Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), pp. 1456-1461.