Nuclear Fuel Cycle Simulation Research Articles

The long-lived radionuclides radiotoxicity ranking the nuclear fuel cycle of the Belarusian NPP

Using a methodical approach to the potential biological hazard (radiotoxicity) assessment of radionuclides, computational studies and a comparative analysis of the contributions to the total radiotoxicity of long-lived fission products and actinides accumulated in the nuclear fuel cycle of the Belarusian NPP were carried out. In order to estimate correctly the 237Np isotope mass at the long-term stage of SNF management, the mathematical model of the nuclear fuel cycle of the Belarusian NPP of the code CUB was modified to take into account the transmutation chain of 241Pu, 241Am and 237Np isotopes. The initial concentrations for the main radiation-hazardous radionuclides at the time of fuel unloading from the VVER1200 reactor core are presented in the database “Radiation Characteristics of Spent Nuclear Fuel of the Belarusian NPP” developed by the authors. The database development is carried out in accordance with the recommendations of the RB-093-20 “Radiation and thermophysical characteristics of spent nuclear fuel of pressurized water power reactors and high-power channel reactors”. The main characteristics used in the nuclear fuel cycle simulation in the CUB code, such as the total mass of spent nuclear fuel (2492 tons), the number of spent fuel assemblies (5294) and the average burnup, are taken in accordance with the Spent Fuel Management Strategy of the Belarusian NPP data. It is shown that at the stage up to 100 years, the main contributors to the total radiotoxicity are the isotopes of 238Pu, 239Pu, 240Pu, 243Am, 241Pu, 241Am, 237Np, where contributions of three last of them are 0.07, 49.0 and 0.007%, respectively. But 5000 years later, the contribution of 241Am will be 0.24%, and the isotope 237Np – up to 0.1% of the total actinides radiotoxicity. The results obtained can be used to substantiate for the recovery degree requirements of 237Np, 241Pu, 241Am during the SNF of the Belarusian NPP reprocessing.

Cost-reduced depletion calculation including short half-life nuclides for nuclear fuel cycle simulation

ABSTRACT The Okamura explicit method (OEM) for depletion calculation was developed by modifying the matrix exponential method for dynamic nuclear fuel cycle simulation. The OEM suppressed the divergence of the calculation for short half-life nuclides, even for the longer time step length or the small number of time steps. The computational cost of the OEM was small, equivalent to the Euler method, and it maintained sufficient accuracy for the fuel cycle simulation.

Assessment of the Radiotoxicity of Spent Nuclear Fuel from a Fleet of PWR Reactors

The paper presents the methodology for the estimation of the long-term actinides radiotoxicity and isotopic composition of spent nuclear fuel from a fleet of Pressurized Water Reactors (PWR). The methodology was developed using three independent numerical tools: the Spent Fuel Isotopic Composition database, the Nuclear Fuel Cycle Simulation System and the Monte Carlo Continuous Energy Burnup Code. The validation of spent fuel isotopic compositions obtained in the numerical modeling was performed using the available experimental data. A nuclear power embarking country benchmark was implemented for the verification and testing of the methodology. The obtained radiotoxicity reaches the reference levels at about 1.3 × 105 years, which is common for the PWR spent nuclear fuel. The presented methodology may be incorporated into a more versatile numerical tool for the modeling of hybrid energy systems.

COSI7: THE NEW CEA REFERENCE ELECTRO-NUCLEAR SIMULATION TOOL

Nuclear fuel cycle scenario studies are used as a prospective analysis tool in order to provide stakeholders with decision aid. Nuclear fuel cycle simulation tools model the deployment of reactor fleets, the mass flows in the fuel cycle, and track the nuclear materials. The CEA has been developing for more than 30 years the nuclear fuel cycle tool COSI. The latest version, COSI7, was designed in order to model dynamic systems of ever-increasing complexity with improved user experience. The new developments include functionalities designed to improve the user experience as well as new physical models and post-processing capabilities. COSI7 supersedes COSI6 as the CEA reference nuclear fuel cycle simulation code starting January 2020.

NMB4.0: development of integrated nuclear fuel cycle simulator from the front to back-end

Nuclear Material Balance code version 4.0 (NMB4.0) has been developed through collaborative R&D between TokyoTech&JAEA. Conventional nuclear fuel cycle simulation codes mainly analyze actinides and are specialized for front-end mass balance analysis. However, quantitative back-end simulation has recently become necessary for considering R&D strategies and sustainable nuclear energy utilization. Therefore, NMB4.0 was developed to realize the integrated nuclear fuel cycle simulation from front- to back-end. There are three technical features in NMB4.0: 179 nuclides are tracked, more than any other code, throughout the nuclear fuel cycle; the Okamura explicit method is implemented, which contributes to reducing the numerical cost while maintaining the accuracy of depletion calculations on nuclides with a shorter half-life; and flexibility of back-end simulation is achieved. The main objective of this paper is to show the newly developed functions, made for integrated back-end simulation, and verify NMB4.0 through a benchmark study to show the computational performance.

Application of sensitivity analysis in DYMOND/Dakota to fuel cycle transition scenarios

The ability to perform sensitivity analysis has been enabled for the nuclear fuel cycle simulator DYMOND through its coupling with the design and analysis toolkit Dakota. To test and demonstrate these new capabilities, a transition scenario and multi-parameter study were devised. The transition scenario represents a partial transition from the US nuclear fleet to a closed fuel cycle with small modular LWRs and fast reactors fueled by reprocessed used nuclear fuel. Four uncertain parameters in this transition were studied – start date of reprocessing, total reprocessing capacity, the nuclear energy demand growth, and the rate at which the fast reactors are deployed – with respect to their impact on four response metrics. The responses – total natural uranium consumed, maximum annual enrichment capacity required, total disposed mass, and total cost of the nuclear fuel cycle – were chosen based on measures known to be of interest in transition scenarios [2] and to be significantly impacted by the varying parameters. Analysis of this study was performed both from the direct sampling and through surrogate models developed in Dakota to calculate the global sensitivity measures Sobol’ indices. This example application of this new capability showed that the most consequential parameter to most metrics was the share of new build capacity that is fast reactors. However, for the cost metric, the scaling factor of the energy demand growth was significant and had synergistic behavior with the fast reactor new build share.

Pu multi-recycling scenarios towards a PWR fleet for a stabilization of spent fuel inventories in France

Nuclear scenario studies are performed to explore the impact of possible evolutions of nuclear fleets. The nuclear fuel cycle simulation tool COSI, developed by CEA, is used to model these dynamic scenarios and to evaluate them with respect to uranium and plutonium management, fuel reprocessing and waste production. In recent years, scenarios have focused on transitions from the current nuclear French fleet to a deployment of SFR. However, the French Multi-annual Energy Planning has recently postponed the deployment of this technology to the second half of the 21st century. Alternative solutions of plutonium management in PWR are investigated to stabilize total inventories of spent nuclear fuels. The MIX concept is based on homogeneous fuel assemblies where fuel rods are composed of plutonium blended with enriched uranium. In this study, a transition from the current French fleet to an EPR™ fleet is simulated. Two power capacities of the future EPR™ fleet are considered. A progressive deployment of fuel multi-recycling in the EPR™ fleet is implemented to enable stabilization of all spent fuels and plutonium inventories. Natural uranium consumption is also minimized thanks to ERU fuel batches in EPR™. Results are compared with plutonium and uranium mono-recycling in a PWR fleet.

FUEL-CYCLE SCENARIO TO REDUCE RADIOACTIVE WASTE FROM LIGHT-WATER REACTOR

It is important to reduce the amount of trans-uranium (TRU) produced from the existing nuclear power plants to realize sustainable nuclear energy since the some TRU nuclides remain for a long time and have high radioactivity and radiotoxicity. One of the promising solutions is to transmute the TRU nuclides to those with lesser radioactivity and radiotoxicity in the existing nuclear reactors. In the current scheme, the TRU nuclides are transmuted in fast reactors and/or accelerator-driven-systems, however, this scenario seems unpromising in Japan: after the Fukushima Daiichi accident, it is required to reduce the production of TRU nuclides from the light-water reactors. In the previous studies, a concept of FORSETI was investigated, and a nuclear-fuel cycle simulation code ATRUNCYS was developed to study the low TRU production scenario. The FORSETI concept consists of two types of fuels: 1) UO2 fuels with high-assay low-enriched-uranium, and 2) MOX fuels with highly fissile concentrated plutonium reprocessed from the FORSETI-UO2 fuels. The current paper focuses on the following two scenarios: a) once-recycled scenario with the current fuel design, and b) once-recycled scenario with the FORSETI concept. The two scenarios were compared by using the ATRUNCYS code where the simulation studies showed that the amount, radioactivity, and radiotoxicity of resulting waste can be decreased in the FORSETI concept: In the case 1), the production of TRU nuclides decreased in the UO2 fuel; In the case 2), the fission rate increased and neutron-capture reactions of 240Pu and 241Pu decreased in the MOX fuels.

Optimization under uncertainty for robust fuel cycle analyses

SummaryOptimization of nuclear fuel cycles is essential for experts and policy makers for studying and analyzing the future of the nuclear energy. In the case of advanced electronuclear transition scenarios, multiple parameters with a complex dependence have to be fine‐tuned in order to achieve a set of predefined objectives. However, in the presence of uncertainties the solutions obtained in this way may not be stable since small perturbations could break the delicate balance between different parts of the scenario. In this work, the optimization of an uncertainty European‐based sustainable transition scenario has been studied. This scenario, which has been analyzed with the TR_EVOL nuclear fuel cycle simulator system, is aimed at reducing the transuranic inventory masses while keeping the fuel cycle costs. To that end, an extension of the DEMO evolutionary multiobjective algorithm has been implemented within TR_EVOL for allowing the inclusion of constraints and uncertainties with a methodology that can be used by any fuel cycle simulator. Results show the importance of coupling optimization and uncertainty analyses due to the suboptimal and unstable solutions that can be obtained if not considered jointly. In addition, the uncertainties shrink the decision space. It was found that in their presence the transuranic mass can be reduced and stabilized by a factor ranging between 65% and 71% with an increase of the cost of 16% and 18.5% after 300 years of operation by using advanced systems when compared with an open fuel cycle strategy.

Demand-Driven Deployment Capabilities in Cyclus, a Fuel Cycle Simulator

The present U.S. nuclear fuel cycle faces challenges that hinder the expansion of nuclear energy technology. The U.S. Department of Energy identified four nuclear fuel cycle options that make nuclear energy technology more desirable. Successfully analyzing the transitions from the current fuel cycle to these promising fuel cycles requires a nuclear fuel cycle simulator that can predictively and automatically deploy fuel cycle facilities to meet user-defined power demand. This work introduces and demonstrates the demand-driven deployment capabilities in Cyclus, an open-source nuclear fuel cycle simulator framework. User-controlled capabilities such as time-series forecasting algorithms, supply buffers, and facility preferences were introduced to give users tools to minimize power undersupply in a transition scenario simulation. The demand-driven deployment capabilities are referred to as d3ploy. We demonstrate the capability of d3ploy to predict future commodities’ supply and demand, and automatically deploy fuel cycle facilities to meet the predicted demand in four transition scenarios. Using d3ploy to set up transition scenarios saves the user simulation setup time compared to previous efforts that required a user to manually calculate and use trial and error to set up the deployment scheme for the supporting fuel cycle facilities.

Sparse Polynomial Chaos expansion for advanced nuclear fuel cycle sensitivity analysis

Uncertainty and sensitivity analyses are required in fuel cycle analyses for studying the viability of electronuclear scenarios by means of their response to variations in the input parameters. This implies that nuclear fuel cycle simulators have to be extended for dealing with this kind of problems in order to become reliable tools. However, given their complexity, the excessive computational effort these analyses demand constraint the ability of the codes to solve the problem without making any approximation. In this work, we propose to use a methodology based on a sparse Polynomial Chaos expansion that, given the low computational demand, makes it possible to be used for sensitivity analyses in any fuel cycle code. This has been applied to a European collaborative advanced scenario where the results have been compared with previous studies with the aim of showing the potential that this technique has when applied to fuel cycle studies.

Deep learning approach to nuclear fuel transmutation in a fuel cycle simulator

We trained a neural network model to predict Pressurized Water Reactor (PWR) Used Nuclear Fuel (UNF) composition given initial enrichment and burnup. This quick, flexible, medium-fidelity method to estimate depleted PWR fuel assembly compositions is used to model scenarios in which the PWR fuel burnup and enrichment vary over time. The Used Nuclear Fuel Storage, Transportation & Disposal Analysis Resource and Data System (UNF-ST&DARDS) Unified Database (UDB) provided a ground truth on which the model trained. We validated the model by comparing the U.S. UNF inventory profile predicted by the model with the UDB UNF inventory profile. The neural network yields less than 1% error for UNF inventory decay heat and activity and less than 2% error for major isotopic inventory. The neural network model takes 0.27 s for 100 predictions, compared to 118 s for 100 Oak Ridge Isotope GENeration (ORIGEN) calculations.We also implemented this model into Cyclus, an agent-based Nuclear Fuel Cycle (NFC) simulator, to perform rapid, medium-fidelity PWR depletion calculations. This model also allows discharge of batches with assemblies of varying burnup.Since the original private data cannot be retrieved from the model, this trained model can provide open-source depletion capabilities to NFC simulators. We show that training an artificial neural network with a dataset from a complex fuel depletion model can provide rapid, medium-fidelity depletion capabilities to large-scale fuel cycle simulations.

Quantification of the differences introduced by nuclear fuel cycle simulators in advanced scenario studies

Uncertainty propagation and quantification, when applied to the field of nuclear fuel cycle scenario studies, usually only considers a set of easily quantifiable input parameters, leaving out the effects of the modelling approaches. In order to extend the validity of these analyses, two different codes, ANICCA and TR_EVOL (developed respectively by SCK·CEN and CIEMAT), have been benchmarked through a study of an advanced and realistic nuclear fuel cycle scenario with the aim of assessing the impact of the use of different tools in the fuel cycle scenario uncertainty quantification. Additionally, a classical uncertainty propagation analysis was done following the total Monte Carlo and sensitivity approaches in order to compare the system uncertainties with the dissimilarities due the simulators. Results shows that the impact of the fuel cycle simulators cannot be neglected for certain observables, and that their effects become relevant as the scenarios extends over time due their cumulative effect.

A game-theoretic approach to nuclear fuel cycle transition analysis under uncertainty

We present a novel methodology for optimizing nuclear fuel cycle transitions that incorporates a game-theoretic approach and captures interactions among multiple decision makers. The methodology is demonstrated using a two-person sequential game with uncertainty, where the two players represent a policy maker and an electric utility company, though the method generalizes to any number and type of individual decision making entities. Coupled with a sophisticated nuclear fuel cycle simulator, rich transition scenarios may be analyzed to identify robust transition strategies. These strategies explicitly treat uncertainties using a stochastic programming approach, devising optimal near-term hedging strategies that simultaneously consider all possible states of the world, maintaining flexibility to allow for intelligent recourse decisions once uncertainties are resolved. In the demonstration game, reactor technology and fuel cycle scheme adopted by the electric utility are shown to depend on both the policy maker’s decisions and the distributions over uncertain technological and economic outcomes.

Synergistic spent nuclear fuel dynamics within the European Union

The French 2012–2015 Commission Nationale d’Evaluation Reports emphasize preparation for a transition from Light Water Reactors (LWRs) to Sodium-Cooled Fast Reactors (SFRs). We used the Cyclus nuclear fuel cycle simulator to explore the feasibility of enabling a French transition to an SFR fleet by using Used Nuclear Fuel (UNF) from other European Union (EU) nations. A Cyclus simulation captured nuclear power deployment in the EU from 1970 to 2160. In this simulation, France begins its planned transition to SFRs as existing LWRs are decommissioned. These SFRs are fueled with UNF accumulated by other EU nations and reprocessed in France. The impact of reactor lifetime extensions and SFR breeding ratios on time-to-transition were investigated with additional simulations. These simulations demonstrate that France can avoid deployment of additional LWRs by accepting UNF from other EU nations, that lifetime extensions delay time-to-transition, and improved breeding ratios are not particularly impactful.

Standardized verification of the Cyclus fuel cycle simulator

Many nuclear fuel cycle simulators can analyze transitions from once-through to advanced nuclear fuel cycles. Verification studies compare various fuel cycle analysis tools to test agreement and identify sources of difference. A recent verification study, Feng et al. (2016) established transition scenario test case specifications and accordingly evaluated national laboratory nuclear fuel cycle simulators, DYMOND, VISION, ORION, and MARKAL. This work verifies the performance of Cyclus, the agent-based, open-source fuel cycle simulator, using the test case specifications in Feng et. al. In this work, Cyclus demonstrates agreement with the results from the previous verification study. Minor differences reflect intentional, detailed material tracking in the Cycamore reactor module. These results extend the example results in Feng et al. to further enable future verification of additional nuclear fuel cycle simulation tools.

Analysis of Nuclear Renewable Hybrid Energy Systems Modeling and Nuclear Fuel Cycle Simulators

Growing concerns over the impact of fossil fuels on climate change have driven efforts to find sources of energy with low emissions. In response, fluctuating renewable energy sources, such as solar and wind power, are growing to meet more of the electricity demand. However, maintaining reliable energy accessibility to the grid requires a stable, nonfluctuating source of power. Nuclear power plants (NPPs) provide nearly emissions-free, reliable energy to the grid (refer to “IPCC Fifth Assessment Report,” Intergovernmental Panel on Climate Change; https://www.ipcc.ch/report/ar5/). To best reduce reliance on fossil fuels while ensuring reliable energy generation and profitability, nuclear renewable hybrid energy systems (NRHESs) focus on tightly coupling renewable generation with a NPP by colocating the generation sources in an industrial park. The industrial park consists of at least the NPP, the renewable energy source, and some form of industrial process that consumes the energy not used by the grid. In this paper, we analyze the computational modeling approaches currently being pursued for NRHESs. We further investigate similarities between nuclear fuel cycle simulators (NFCSs) and NRHESs to determine how NRHES development can benefit from the development of NFCSs. This paper begins by reviewing past research on NRHESs to determine the necessary functionality of modeling software. After determining the necessary software capabilities for an NRHES model, we discuss the characteristics of a NFCS. The characteristics found common to both systems include desirability of a flexible modular design; open source; ability to be coupled to external pieces of software, including economic modeling, optimization methods, and sensitivity analysis; and results that are usable to technical and nontechnical people alike.

Transmutation considerations of LWR and RBMK spent nuclear fuel by the fusion–fission hybrid system

The performance of the fusion–fission hybrid system based on the molten salt (flibe) blanket, driven by a plasma based fusion device, was analyzed by comparing transmutation scenarios of actinides extracted from the LWR (Sweden) and RBMK (Lithuania) spent nuclear fuel in the scope of the EURATOM project BRILLIANT. The IAEA nuclear fuel cycle simulation system (NFCSS) has been applied for the estimation of the approximate amount of heavy metals of the spent nuclear fuel in Sweden reactors and the SCALE 6 code package has been used for the determination of the RBMK-1500 spent nuclear fuel composition. The total amount of trans-uranium elements has been estimated in both countries by 2015. Major parameters of the hybrid system performance (e.g., kscr, keff, Φn(E), equilibrium conditions, etc.) have been investigated for LWR and RBMK trans-uranium transmutation cases. Detailed burn-up calculations with continuous feeding to replenish the incinerated trans-uranium material and partial treatment of fission products were done using the Monteburns (MCNP + ORIGEN) code system. About 1.1 tons of spent fuel trans-uranium elements could be burned annually with an output of the 3 GWth fission power, but the equilibrium stage is reached differently depending on the initial trans-uranium composition. The radiotoxicity of the remaining LWR and RBMK transmuted waste after the hybrid system operation time has been estimated.

Analysis of uranium oxide fuel transmutation in VVER-1000 reactor using VISTA and WIMS-D4 codes

In this study, the front end and back end of Uranium Oxide (UOX) fuel cycle were first calculated using nuclear fuel cycle simulation system (VISTA) code. The front end calculated values are 19595.3 ton/year for ore-U, 196 ton/year for natural-U, 196 ton/year for UF6, 111.4 metric ton separative work unit (MTSWU) for enrichment requirements, 172.211 ton/year depleted-U and 23.8 ton/year for Fresh Fuel (FF) requirements. The back end calculated values were 23.8 ton/year for Spent Fuel (SF), 0.024 ton/year for Actinide Inventory (AI) and 0.981 ton/year for Fission Product (FP) isotopes. After that the transmutation of UOX fuel results calculated by VISTA benchmarked with WIMS-D4 code. The calculations carried out in 30,000, 35,000, 40,000 and 45,000 MWd/tHM discharge burnup stages. The comparison of results showed that variance was less than 15%.

Rapid methods for radionuclide contaminant transport in nuclear fuel cycle simulation

Nuclear fuel cycle and nuclear waste disposal decisions are technologically coupled. However, current nuclear fuel cycle simulators lack dynamic repository performance analysis due to the computational burden of high-fidelity hydrolgic contaminant transport models. The Cyder disposal environment and repository module was developed to fill this gap. It implements medium-fidelity hydrologic radionuclide transport models to support assessment appropriate for fuel cycle simulation in the Cyclus fuel cycle simulator.Rapid modeling of hundreds of discrete waste packages in a geologic environment is enabled within this module by a suite of four closed form models for advective, dispersive, coupled, and idealized contaminant transport: a Degradation Rate model, a Mixed Cell model, a Lumped Parameter model, and a 1-D Permeable Porous Medium model. A summary of the Cyder module, its timestepping algorithm, and the mathematical models implemented within it are presented. Additionally, parametric demonstrations simulations performed with Cyder are presented and shown to demonstrate functional agreement with parametric simulations conducted in a standalone hydrologic transport model, the Clay Generic Disposal System Model developed by the Used Fuel Disposition Campaign Department of Energy Office of Nuclear Energy.