Optimizing the placement and content of burnable poison in Gama-Float reactor's fuel assembly
Article Sidebar
Main Article Content
Abstract
Floating Nuclear Power Plant (FNPP) is used to potentially fulfil the energy needs of a remote region. In this context, a new FNPP reactor named Gama-Float was designed to tackle different issues. The first stage of the design process was producing a fuel assembly design without burnable poison. However, the design was inadequate at beginning-of-cycle (BOC) since multiplication factor (k∞) was relatively high and required compensation. Therefore, this research aimed to develop an optimization process using the Method of Characteristics for neutronic calculations in achieving a better design based on a genetic algorithm. The results showed that optimization must satisfy two objectives, namely lowering the multiplication factor and maintaining the fuel cycle length. A subsequent analysis was carried out to select results from the optimization. In this context, the assemblies' initial multiplication factor, fuel cycle length, and power peaking factor (PPF) were analyzed and the final design was selected based on the analysis. The initial multiplication factor and fuel cycle were reduced to 1.0054 and 3644.41 days, while the radial PPF increased to 1.2789 at BOC in an acceptable range. These results highlight the effectiveness of the optimization process in improving neutronic performance and fuel cycle efficiency for the Gama-Float reactor.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Mulyana R. Performance report of the general secretary of energy and mineral resources 2022. Jakarta: Indonesian Ministry of Energy and Mineral Resources; 2022.
Advanced reactor information system (ARIS). Small modular reactor technology catalogue 2024. Vienna: International Atomic Energy Agency; 2024.
Agung A, Sihana, Suryopratomo K. Final report on the development of floating nuclear power plant to fulfill energy requirements in remote areas. Yogyakarta: Universitas Gadjah Mada; 2022.
Torabi M, Lashkari A, Masoudi SF, Bagheri S. Neutronic analysis of control rod effect on safety parameters in Tehran Research Reactor. Nucl Eng Technol. 2018;50:1017–1023.
Nerlander V. Power increase limits to prevent pellet-cladding interaction: Calculation of strain- and fission gas release margins [dissertation]. Uppsala: Uppsala University; 2022.
Human resources training and development. GE BWR/4 advanced technology manual. Chapter 4.4: Pre-conditioning interim operating management guidelines. Washington, DC: United States Nuclear Regulatory Commission; 2004.
Papukchiev A, Liu Y, Schaefer A. Impact of boron dilution accidents on low boron PWR safety. Proceedings of the physics of fuel cycles and advanced nuclear systems: Global developments; 2006 September 10-14; Vancouver, Canada, American Nuclear Society; 2006.
Evans JA, DeHart MD, Weaver KD, Keiser DD. Burnable absorbers in nuclear reactors – A review. Nucl Eng Des. 2022;391:111726.
J A Renier. Development of improved burnable poisons for commercial nuclear power reactors. Technical Report. Oak Ridge, TN (United States): Oak Ridge National Lab (ORNL); 2002 Apr. Report No.: ORNL/TM-2001/238.
van der Merwe L, Hah CJ. Reactivity balance for a soluble boron-free small modular reactor Nucl Eng Technol. 2018;50:648–653.
Mart J, Klein A, Soldatov A. Feasibility study of a soluble boron–free small modular integral pressurized water reactor. Nucl Technol. 2014;188(1):8–19.
Marguet S., editor The technology of pressurized water reactors: From the nautilus to the EPR. Vol. 1. Cham, Switzerland: Springer International Publishing; 2022.
Jo CK, Kim Y, Noh JM. Burnable poison for reactivity management in a very high temperature reactor. Ann Nucl Energy. 2009;36(3):298–304.
Dodd B, Britt T, Lloyd C, Shah M, Goddard B. Novel homogeneous burnable poisons in pressurized water reactor ceramic fuel. Nucl Eng Technol. 2020;52(12):2874–2879.
Xu S, Yu T, Xie J, Yao L, Li Z. Burnable poison selection and neutronics analysis of plate fuel assemblies. Front Energy Res 2021;9:729552.
Bejmer KH, Seveborn O. Enriched Gadolinium as burnable absorber for PWR. Proceedings of the physics of fuel cycles and advanced nuclear systems: Global developments; 2004 April 25-29; La Grange Park, Chicago, USA, American Nuclear Society; 2004. p. 1245-1253.
Jayalal ML, Sai Baba M, SatyaMurty SAV. Application of Genetic Algorithm for optimization of fuel management in nuclear reactors. 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), Ernakulam, India: IEEE; 2016. p. 325–330.
Yilmaz S, Ivanov K, Levine S, Mahgerefteh M. Application of genetic algorithms to optimize burnable poison placement in pressurized water reactors. Ann Nucl Energy. 2006;33(5):446–456.
Jessee MA, Wieselquist WA, Mertyurek U, Kim KS, Evans TM, Hamilton SP, et al. Lattice physics calculations using the embedded self-shielding method in Polaris, Part I: Methods and implementation. Ann Nucl Energy. 2021;150:107830.
Chen Q, Wu H, Cao L. Auto MOC-A 2D neutron transport code for arbitrary geometry based on the method of characteristics and customization of AutoCAD. Nucl Eng Des. 2008;238(10):2828–2833.
Cao L, Wu H, editors. Deterministic numerical methods for unstructured-mesh neutron transport calculation. Woodhead Publishing; 2020.
Isotalo A, Pusa M. Improving the accuracy of the Chebyshev rational approximation method using substeps. Nucl Sci Eng. 2016;183(1):65–77.
Calvin O, Schunert S, Ganapol B. Global error analysis of the Chebyshev rational approximation method. Ann Nucl Energy. 2021;150:107828.
Stewart RH, Palmer TS, DuPont B. A survey of multi-objective optimization methods and their applications for nuclear scientists and engineers. Prog Nucl Energy. 2021;138:103830.
Nayak S. Nature-inspired optimization. In: Nayak S, editor. Fundamentals of optimization techniques with algorithms, Elsevier; 2020. p. 271–296.
Arthur J, Bahran R, Hutchinson J, Pozzi SA. Genetic algorithm for nuclear data evaluation applied to subcritical neutron multiplication inference benchmark experiments. Ann Nucl Energy. 2019;133:853–862.