Investigations of the fresh-core cycle-length and the average fuel depletion analysis of the NuScale core

Sadegh-Noedoost, A.; Faghihi, F.; Fakhraei, A.; Amin-Mozafari, M.

doi:10.1016/j.anucene.2019.106995

Cited by 29 publications

(6 citation statements)

References 9 publications

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“…To have a reference model against which to compare the converted core, we created a computational model of a NuScale reactor, using the parameters found in the NuScale specifications [26]. The relevant geometric parameters of a NuScale assembly are presented in Table 1 and Figure 2. The reactor core has a total of 37 fuel assembly in which 12 of these has a mixture of uranium-gadolinium oxide and the remaining 25 fuel assembly are built with pure UO 2 .…”

Section: Methodsmentioning

confidence: 99%

“…The SERPENT code was used to simulate the full core model using the Monte Carlo method. To validate the model, we compared the results with those from Sadegh-Noedoost et al [26], which were obtained using the MCNPX code. Subsequently, simulations of a single NuScale assembly were used as a reference to compare the results obtained with the MOX fuel element.…”

Section: Methodsmentioning

confidence: 99%

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Feasibility to Convert the NuScale SMR from UO2 to a Mixed (U, Th)O2 Core: A Parametric Study of Fuel Element—Seed-Blanket Concept

Stefani¹,

Gonçalves²,

Raitz³

et al. 2023

WJNST

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Delays in the construction of nuclear reactors due to licensing issues have been a problem across the world, affecting projects in Finland, France, and the United States. Small Modular Reactors (SMRs) emerge as a transition between Generations III+ and IV in order to make nuclear energy more competitive with other energy sources, including renewables. In this study, the SMR NuScale, one of the most promising projects today, is investigated for its conversion into a U-233-producing reactor through the Radkowsky seed-blanket fuel element concept, applied in the Shippingport reactor, in a parametric study. Initially, a validation of the reference reactor (NuScale) was carried out with data from technical documents and papers, thus demonstrating the agreement of the computational model carried out with the SERPENT code. Then, a parametric study is carried out to define the area of the seed and blanket region, proportions of enrichment and pitch length. Finally, a comparison is made between the production of U-233, TRU reduction, burn-up extension and neutronic and thermohydraulic safety parameters. This study demonstrates an improvement in the conversion factor and a considerable reduction in the production of TRU, in addition to the production of U-233 with a low proportion of other uranium isotopes that can lead to the beginning of the thorium cycle with already consolidated technologies.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Feasibility to Convert the NuScale SMR from UO2 to a Mixed (U, Th)O2 Core: A Parametric Study of Fuel Element—Seed-Blanket Concept

Stefani¹,

Gonçalves²,

Raitz³

et al. 2023

WJNST

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“…The data of material and geometry used in the Serpent for Monte Carlo calculation is described in Table 1 [13,14].…”

Section: Methodsmentioning

confidence: 99%

Evaluating the Diffusion Approximation Capability on the Integral Pressurized Water Reactor (IPWR) Core Calculation

Ardiansyah

Oktavian²

2021

Atom Indo.

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Diffusion approximation is an important approximation used to model a nuclear reactor core with a quite good accuracy and less computational cost. This approximation has been used widely around the globe for various kinds of nuclear reactors. This diffusion approximation is applied in a two-step method, a method combining a high fidelity transport code and low fidelity diffusion code. Meanwhile, innovations in the nuclear core model continue to make the nuclear reactor core safer, more robust, and smaller. The trend of creating smaller and more modular reactor core is emerging in the last ten years. These innovations will affect the core modeling system. Consequently, for smaller reactors, it is important to evaluate the capability of diffusion approximation if one wants to use a computationally cheaper method to model the reactor core. In this paper, neutron diffusion calculation for 160 MWth integral pressurized water reactor (IPWR) core was conducted using the PARCS nodal diffusion code employing the few-group spatially homogenized cross-sections generated by the Serpent Monte Carlo code. Due to its capability to model any reactor geometry in the high-resolution calculation, the results from Serpent were also used as a reference. Two important parameters are compared between PARCS and Serpent: effective neutron multiplication factor and core power distribution. For the full IPWR core model, a discrepancy of 564 pcm between PARCS and Serpent k eff was observed, while the radial power distribution had a maximum error of 4.71 %. It can be said, to some extent, that the diffusion approximation can be applied to IPWR core analysis. However, further improvement is indeed required if one wants more accurate results with low computational costs.

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“…10 Sadegh-Noedoost et al analyzed neutronic and thermal-hydraulic parameters of the NuScale reactor core as well as the fission products using the neutron code MCNP and the RELAP/SCDAP thermal-hydraulic software, with a slight change in fuel enrichment and a change in gadolinium concentration. 11 In one research, neutronic analysis of the NuScale core was done utilizing accident-tolerant fuels with different coating materials. According to the assessed neutron parameters, the results demonstrate that operating the reactor with these materials is safe and feasible.…”

Section: Introductionmentioning

confidence: 99%

Assessment of gadolinium concentration effects on the NuScale reactor parameters and optimizing the fuel composition via machine learning method

Shahmirzaei

Ansarifar

Koraniany

2022

Intl J of Energy Research

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Summary The reactor design includes optimizing parameters such as fuel composition. In this research, the issue of fuel composition optimization in a Small Modular Nuclear Reactor (SMR) is investigated. Indeed, considering the importance of fuel management optimization at the core of a nuclear reactor as a fundamental issue, this study analyzes gadolinium concentration effects on the neutronic and thermal hydraulic parameters in the NuScale reactor. In addition, optimizing the gadolinium concentration in fuel composition has been done via a genetic algorithm (GA) as a Machine Learning method. At first, the core of the NuScale reactor was modeled using neutronic codes (WIMS & CITATION). Then, the core of this reactor was simulated in different concentrations of natural gadolinium with 141 different concentration combinations in fuel assemblies through the mentioned neutronic codes. Furthermore, the neutronic parameters, including excess reactivity, radial and axial power peaking factors (PPFradial, PPFaxial) related to each design, were calculated. Thermal‐hydraulic modeling of the hot channel for each design created was performed using ANSYS FLUENT code, and thermal‐hydraulic parameters including heat transfer coefficient, MDNBR, pressure drop, Vout‐Vin, and Vmax/Vavg, have been obtained. An artificial neural network (ANN) was trained using the obtained data. Finally, optimal gadolinium concentrations in fuels were determined using the ANN by implementing the GA. In the core of the conventional NuScale reactor, there are assemblies with 2%, 6%, and 8% gadolinium concentrations. Via optimization algorithm, in this paper, two sets of optimal gadolinium concentrations have been presented using different appropriate cost functions. First cost function proposed the optimal concentrations as 1.4256%, 4.2606%, and 5.4968%, while, based on the second one, 1.4502%, 4.2552%, and 5.5296% are optimal concentrations. Finally, the reactor core containing the optimal fuel composition has been redesigned and compared with the conventional NuScale reactor core. Most of the neutron and thermal‐hydraulic parameters significantly improved over the NuScale reactor's optimally designed core. This optimization leads to better fuel management and safety in the optimized core than in the core of the NuScale reactor and can also economically increase power plant efficiency.

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Investigations of the fresh-core cycle-length and the average fuel depletion analysis of the NuScale core

Cited by 29 publications

References 9 publications

Feasibility to Convert the NuScale SMR from UO<sub>2</sub> to a Mixed (U, Th)O<sub>2</sub> Core: A Parametric Study of Fuel Element—Seed-Blanket Concept

Feasibility to Convert the NuScale SMR from UO<sub>2</sub> to a Mixed (U, Th)O<sub>2</sub> Core: A Parametric Study of Fuel Element—Seed-Blanket Concept

Evaluating the Diffusion Approximation Capability on the Integral Pressurized Water Reactor (IPWR) Core Calculation

Assessment of gadolinium concentration effects on the NuScale reactor parameters and optimizing the fuel composition via machine learning method

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Investigations of the fresh-core cycle-length and the average fuel depletion analysis of the NuScale core

Cited by 29 publications

References 9 publications

Feasibility to Convert the NuScale SMR from UO&lt;sub&gt;2&lt;/sub&gt; to a Mixed (U, Th)O&lt;sub&gt;2&lt;/sub&gt; Core: A Parametric Study of Fuel Element—Seed-Blanket Concept

Feasibility to Convert the NuScale SMR from UO&lt;sub&gt;2&lt;/sub&gt; to a Mixed (U, Th)O&lt;sub&gt;2&lt;/sub&gt; Core: A Parametric Study of Fuel Element—Seed-Blanket Concept

Evaluating the Diffusion Approximation Capability on the Integral Pressurized Water Reactor (IPWR) Core Calculation

Assessment of gadolinium concentration effects on the NuScale reactor parameters and optimizing the fuel composition via machine learning method

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Feasibility to Convert the NuScale SMR from UO<sub>2</sub> to a Mixed (U, Th)O<sub>2</sub> Core: A Parametric Study of Fuel Element—Seed-Blanket Concept

Feasibility to Convert the NuScale SMR from UO<sub>2</sub> to a Mixed (U, Th)O<sub>2</sub> Core: A Parametric Study of Fuel Element—Seed-Blanket Concept