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Volume 5
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J. Nucl. Eng., Volume 5, Issue 2 (June 2024) – 5 articles

Cover Story (view full-size image): The lifespan of core graphite under neutron irradiation in a commercial molten salt reactor (MSR) has an important influence on its economy. Flattening the fast neutron flux (≥0.05 MeV) distribution in the core is the main method to extend the graphite irradiation lifespan. In this paper, the effects of the key parameters of MSRs on fast neutron flux distribution, including volume fraction (VF) of fuel salt, pitch of hexagonal fuel assembly, core zoning, and layout of control rod assemblies, were studied. Flattening the fast neutron flux distribution of a commercial MSR core was then carried out by zoning the core into two regions under different VFs. Considering both the fast neutron flux distribution and burnup depth, an optimized core was obtained. View this paper
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11 pages, 2459 KiB  
Technical Note
Immediate Radiological Risk Evaluation after a Hypothetical Radioactive Off-Site Release Event
by Ana Carolina Lodi Lobato, Sérgio Gavazza, Avelino Santos, Rodrigo Carneiro Curzio and Edson R. Andrade
J. Nucl. Eng. 2024, 5(2), 186-196; https://doi.org/10.3390/jne5020013 - 19 Jun 2024
Viewed by 658
Abstract
This study used an analytical computational model to evaluate safe zones in contaminated areas that may result from a hypothetical significant off-site release from a nuclear power plant. The model, considering local atmospheric stability, wind direction, and location, calculates the expected total effective [...] Read more.
This study used an analytical computational model to evaluate safe zones in contaminated areas that may result from a hypothetical significant off-site release from a nuclear power plant. The model, considering local atmospheric stability, wind direction, and location, calculates the expected total effective dose equivalent (TEDE) and potential safety zones. This research, focused on an area near a nuclear facility affected by an accidental release, used SCALE and HotSpot Health Physics codes to simulate the reactor’s core inventory content and off-site release. This study’s findings underscore that the risk of developing solid cancer (testing morbidity) is influenced by both local atmospheric stability and the composition of the potentially affected population (primarily age and sex). These findings, backed by an analytical approach, can significantly influence logistical and operational planning. The utilization of computer simulations can also aid in creating flexible response scenarios to real events. Full article
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Figure 1

Figure 1
<p>General view of the study situation and methodology.</p> Full article ">Figure 2
<p>(<b>a</b>) presents each direction’s maximum expected radiation dose values. (<b>b</b>) shows TEDE for SSW direction (conservative approach).</p> Full article ">Figure 3
<p>(<b>a</b>) shows the simulated plume areas (700, 100, and 50 mSv) for each PG class, and (<b>b</b>) presents the maximum limit distances for each zone and PG class.</p> Full article ">Figure 4
<p>Potentially affected population within the zones of interest (700, 100, and 50 mSv) for all PG classes.</p> Full article ">Figure 5
<p>Estimated relative risk (RR) ratios (male-to-female) for solid cancer considering all locations and selected ages ((<b>a</b>) for 20 and (<b>b</b>) for 70 years old).</p> Full article ">Figure 6
<p>Standard deviation (SD) calculations for RR for PG classes and location for young (20 years old) and elderly (70 years old).</p> Full article ">
18 pages, 7498 KiB  
Article
Core Optimization for Extending the Graphite Irradiation Lifespan in a Small Modular Thorium-Based Molten Salt Reactor
by Xuzhong Kang, Guifeng Zhu, Jianhui Wu, Rui Yan, Yang Zou and Yafen Liu
J. Nucl. Eng. 2024, 5(2), 168-185; https://doi.org/10.3390/jne5020012 - 10 May 2024
Viewed by 830
Abstract
The lifespan of core graphite under neutron irradiation in a commercial molten salt reactor (MSR) has an important influence on its economy. Flattening the fast neutron flux (≥0.05 MeV) distribution in the core is the main method to extend the graphite irradiation lifespan. [...] Read more.
The lifespan of core graphite under neutron irradiation in a commercial molten salt reactor (MSR) has an important influence on its economy. Flattening the fast neutron flux (≥0.05 MeV) distribution in the core is the main method to extend the graphite irradiation lifespan. In this paper, the effects of the key parameters of MSRs on fast neutron flux distribution, including volume fraction (VF) of fuel salt, pitch of hexagonal fuel assembly, core zoning, and layout of control rod assemblies, were studied. The fast neutron flux distribution in a regular hexagon fuel assembly was first analyzed by varying VF and pitch. It was demonstrated that changing VF is more effective in reducing the fast neutron flux in both global and local graphite blocks. Flattening the fast neutron flux distribution of a commercial MSR core was then carried out by zoning the core into two regions under different VFs. Considering both the fast neutron flux distribution and burnup depth, an optimized core was obtained. The fast neutron flux distribution of the optimized core was further flattened by the rational arrangement of control rod channels. The calculation results show that the final optimized core could reduce the maximum fast neutron flux of the graphite blocks by about 30% and result in a more negative temperature reactivity coefficient, while slightly decreasing the burnup and maintaining a fully acceptable core temperature distribution. Full article
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Figure 1
<p>Fuel assembly model.</p> Full article ">Figure 2
<p>Core model of sm-TMSR.</p> Full article ">Figure 3
<p>All configurations of two-region core: (<b>a</b>) Configuration I; (<b>b</b>) Configuration II; (<b>c</b>) Configuration III; (<b>d</b>) Configuration IV; (<b>e</b>) Configuration V; (<b>f</b>) Configuration VI.</p> Full article ">Figure 4
<p>Averaged fast neutron flux and fast neutron flux peak factor in the graphite block varying with the VF of the fuel assembly.</p> Full article ">Figure 5
<p>Fast neutron flux fine distribution in the fuel assembly (the dashed lines are the fluxes located in fuel salt and the solid lines are the fluxes located in graphite block).</p> Full article ">Figure 6
<p>Fission power density distribution of the fuel assembly.</p> Full article ">Figure 7
<p>Averaged fast neutron flux and fast neutron flux peak factor of the graphite block varies with the pitch of the fuel assembly.</p> Full article ">Figure 8
<p>Fast neutron flux fine distribution of the fuel assembly (the dashed lines are the fluxes located in fuel salt and the solid lines are the fluxes located in graphite block).</p> Full article ">Figure 9
<p>Distribution of fission power density in the fuel assembly for pitches of 6 cm, 10 cm, 18 cm, 30 cm, and 40 cm.</p> Full article ">Figure 10
<p>Neutron energy spectrum of the inner core regions.</p> Full article ">Figure 11
<p>Averaged fast neutron flux distributions of the graphite blocks in the <span class="html-italic">X</span>-axis fuel assemblies of the sm-TMSR cores. (<b>a</b>–<b>f</b>) represent the calculation results of the configuration I, II, III, IV, V, and VI cores.</p> Full article ">Figure 12
<p>(<b>a</b>) Averaged fast neutron flux distributions of the graphite blocks and (<b>b</b>) fission power distributions of corresponding fuel assemblies in the IV-1.12%, IV-4.48%, benchmark, IV-40.31%, and IV-62.98% cores.</p> Full article ">Figure 13
<p>Fast neutron flux fine distributions of the <span class="html-italic">X</span>-axis fuel assemblies in the benchmark, IV-4.48%, V-4.48%, VI-2.52%, and VI-40.31% cores (the dashed lines are the fluxes located in fuel salt and the solid lines are the fluxes located in graphite block).</p> Full article ">Figure 14
<p>Fast neutron flux peak factors of each graphite block in the <span class="html-italic">X</span>-axis fuel assemblies of the benchmark, IV-4.48%, V-4.48%, VI-2.52%, and VI-40.31% cores.</p> Full article ">Figure 15
<p>Burnup depth changing with time in the benchmark, IV-4.48%, V-4.48%, VI-2.52%, and VI-40.31% cores.</p> Full article ">Figure 16
<p>Outlet temperature distribution of the fuel channel along the <span class="html-italic">X</span>-axis in the optimized and benchmark cores.</p> Full article ">Figure 17
<p>Control rod assembly and structure of control rod.</p> Full article ">Figure 18
<p>Schematic diagram of regulating rods and shutdown rods arrangement in the IV-4.48% core.</p> Full article ">Figure 19
<p>Fast neutron flux distributions in IV-4.48% core without (<b>a</b>) and with (<b>b</b>) control rod assemblies.</p> Full article ">Figure 20
<p>Averaged fast neutron flux distribution of the graphite blocks in the IV-4.48% core without and with control rod assemblies.</p> Full article ">
18 pages, 5444 KiB  
Article
The Effects of Irradiation on Structure and Leaching of Pure and Doped Thin-Film Ceria SIMFUEL Models Prepared via Polymer-Templated Deposition
by Alistair F. Holdsworth, Zizhen Feng, Ruth Edge, John P. Waters, Alice M. Halman, David Collison, Kathryn George, Louise S. Natrajan and Melissa A. Denecke
J. Nucl. Eng. 2024, 5(2), 150-167; https://doi.org/10.3390/jne5020011 - 8 May 2024
Viewed by 1305
Abstract
When studying hazardous materials such as spent nuclear fuel (SNF), the minimisation of sample volumes is essential, together with the use of chemically-similar surrogates where possible. For example, the bulk behaviour of urania (UO2) can be mimicked by appropriately-engineered thin films [...] Read more.
When studying hazardous materials such as spent nuclear fuel (SNF), the minimisation of sample volumes is essential, together with the use of chemically-similar surrogates where possible. For example, the bulk behaviour of urania (UO2) can be mimicked by appropriately-engineered thin films of sufficient thickness, and inactive materials such as ceria (CeO2) can be used to study the effects within radioactive systems used to fuel nuclear fission. However, thin film properties are sensitive to the preparative method, many of which require the use of highly toxic precursors and specialised apparatus (e.g., chemical vapour deposition). To address this, we present the development of a flexible, tuneable, scalable method for the preparation of thin-film CeO2 SIMFUEL models with a thickness of ≈5 μm. The effects of γ irradiation (up to 100 kGy) and dopants including trivalent lanthanides (Ln3+) and simulant ε-particles on the structure and long-term leaching of these systems under SNF storage conditions were explored, alongside the context of this within further work. It was found that the sensitivity of CeO2 films to reduction upon irradiation, particularly in the presence of simulant ε-particles, resulted in increased leaching of Ce (as CeIII), while trivalent lanthanides (Nd3+ and Eu3+) had a minimal effect on Ce leaching. Full article
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<p>Schematic of CeO<sub>2</sub> film preparation. Processes conducted at room temperature unless otherwise specified. See <a href="#sec2dot2-jne-05-00011" class="html-sec">Section 2.2</a> and <a href="#sec2dot4-jne-05-00011" class="html-sec">Section 2.4</a> for full details. In summary: Step 1: A 3:1 mixture of 98% H<sub>2</sub>SO<sub>4</sub> and 30% H<sub>2</sub>O<sub>2</sub> is used to hydroxylate the silica surface; Step 2: PDDA/PMAA polymer template solution is drop-cast and evaporated; Step 3: Film precursors are drop-cast and evaporated; Step 4: Film is calcined.</p> Full article ">Figure 2
<p>Variation in CeO<sub>2</sub> film peak formed with calcination time (in h, see figure legend) at 200 °C. Shortest times at the bottom of the graph. No post-processing was applied to these data beyond cropping.</p> Full article ">Figure 3
<p>SEM images of the pure CeO<sub>2</sub> film and elemental map of Ce. The wide zoom images represent a significant fraction of the sample.</p> Full article ">Figure 4
<p>Effect of irradiation on the surface morphology of several doped Ce films. Note the formation of surface cracks.</p> Full article ">Figure 5
<p>(<b>Left</b>) Time resolved emission spectrum (TRES) of the 5% Eu-doped CeO<sub>2</sub> film as prepared and prior to irradiation following 397 nm excitation with a microsecond pulsed xenon flashlamp using a 0.2 ms delay time and a 455 nm longpass filter. The 3D spectrum was constructed of lifetime decays recorded every 2 nm across the emission region of Eu<sup>3+</sup> (500–750 nm) and showed the typical intra configurational f-f electronic transitions of Eu<sup>3+</sup> ions arising from the <sup>5</sup>D<sub>0</sub> → <sup>7</sup>F<sub>J</sub> (J = 1, 2, 3, and 4) transitions. (<b>Right</b>) Spectrally sliced emission spectrum from the TRES at given time points of the kinetic traces showing the decrease in the <span class="html-italic">pseudo</span>-steady state spectra emission intensity with time.</p> Full article ">
22 pages, 69446 KiB  
Article
Numerical Investigation of Butterfly Valve Performance in Variable Valve Sizes, Positions and Flow Regimes
by Anutam Bairagi, Mingfu He and Minghui Chen
J. Nucl. Eng. 2024, 5(2), 128-149; https://doi.org/10.3390/jne5020010 - 24 Apr 2024
Cited by 1 | Viewed by 1208
Abstract
Reliability and efficiency of valves are necessary for precise control and sufficient heat-flow to heat application plants for the integrated energy systems of nuclear power plants (NPPs). Strategic Management Analysis Requirement and Technology (SMART) valves’ ability to control flow and assess environmental parameters [...] Read more.
Reliability and efficiency of valves are necessary for precise control and sufficient heat-flow to heat application plants for the integrated energy systems of nuclear power plants (NPPs). Strategic Management Analysis Requirement and Technology (SMART) valves’ ability to control flow and assess environmental parameters stands out for these requirements. Their ability to sustain the downstream flow rate, prevent reverse flow, and maintain pressure in the heat transport loop is much more efficient with the integration of sensors and intelligent algorithms. For assessing valve performance and monitoring, mechanical design and operating conditions are two important parameters. In this study, the butterfly valves of three different sizes are simulated with water and steam using STAR-CCM+ in various flow regimes and positions to analyze performance parameters to strategize an automated control system for efficiently balancing the heat–transport network. Also, flow behavior is studied using velocity and pressure fields for valve–body geometry optimization. It can be observed, through performance parameters, that the valves are suitable for operation between 30° and 90° positions with significantly low loss coefficients and high flow coefficients, and the performance parameters follow a certain pattern in both water and steam flow in each scenario. Full article
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Figure 1
<p>Cross-section of the butterfly valve.</p> Full article ">Figure 2
<p>At Re = 150: (<b>a</b>) our model; (<b>b</b>) simulation model by Y. Mu et al. [<a href="#B19-jne-05-00010" class="html-bibr">19</a>].</p> Full article ">Figure 3
<p>At Re = 15 k: (<b>a</b>) our model; (<b>b</b>) simulation model by Mu et al. [<a href="#B19-jne-05-00010" class="html-bibr">19</a>].</p> Full article ">Figure 4
<p>Grid independence verification.</p> Full article ">Figure 5
<p>Meshing and refinement on the (<b>a</b>) outside and (<b>b</b>) inside.</p> Full article ">Figure 6
<p>Stagnation regions.</p> Full article ">Figure 7
<p>Bernoulli effect region.</p> Full article ">Figure 8
<p>Vortex formations.</p> Full article ">Figure 9
<p>Flow separation and unification.</p> Full article ">Figure 10
<p>Performance parameter plots for water flow: (<b>a</b>,<b>b</b>) DN65—<span class="html-italic">K</span>, (<b>c</b>) DN80—<span class="html-italic">K<sub>v</sub></span>, (<b>d</b>) DN80—<span class="html-italic">K</span>, (<b>e</b>) DN100—<span class="html-italic">K<sub>v</sub></span>, (<b>f</b>) DN100—<span class="html-italic">K</span>.</p> Full article ">Figure 10 Cont.
<p>Performance parameter plots for water flow: (<b>a</b>,<b>b</b>) DN65—<span class="html-italic">K</span>, (<b>c</b>) DN80—<span class="html-italic">K<sub>v</sub></span>, (<b>d</b>) DN80—<span class="html-italic">K</span>, (<b>e</b>) DN100—<span class="html-italic">K<sub>v</sub></span>, (<b>f</b>) DN100—<span class="html-italic">K</span>.</p> Full article ">Figure 11
<p>Performance parameter plots for steam flow: (<b>a</b>) DN65—<span class="html-italic">K<sub>v</sub></span>, (<b>b</b>) DN65—<span class="html-italic">K</span>, (<b>c</b>) DN80—<span class="html-italic">K<sub>v</sub></span>, (<b>d</b>) DN80—<span class="html-italic">K</span>, (<b>e</b>) DN100—<span class="html-italic">K<sub>v</sub></span>, (<b>f</b>) DN100—<span class="html-italic">K</span>.</p> Full article ">Figure 11 Cont.
<p>Performance parameter plots for steam flow: (<b>a</b>) DN65—<span class="html-italic">K<sub>v</sub></span>, (<b>b</b>) DN65—<span class="html-italic">K</span>, (<b>c</b>) DN80—<span class="html-italic">K<sub>v</sub></span>, (<b>d</b>) DN80—<span class="html-italic">K</span>, (<b>e</b>) DN100—<span class="html-italic">K<sub>v</sub></span>, (<b>f</b>) DN100—<span class="html-italic">K</span>.</p> Full article ">
14 pages, 4827 KiB  
Article
Neutron Yield Predictions with Artificial Neural Networks: A Predictive Modeling Approach
by Benedikt Schmitz and Stefan Scheuren
J. Nucl. Eng. 2024, 5(2), 114-127; https://doi.org/10.3390/jne5020009 - 31 Mar 2024
Viewed by 943
Abstract
The development of compact neutron sources for applications is extensive and features many approaches. For ion-based approaches, several projects with different parameters exist. This article focuses on ion-based neutron production below the spallation barrier for proton and deuteron beams with arbitrary energy distributions [...] Read more.
The development of compact neutron sources for applications is extensive and features many approaches. For ion-based approaches, several projects with different parameters exist. This article focuses on ion-based neutron production below the spallation barrier for proton and deuteron beams with arbitrary energy distributions with kinetic energies from 3 MeV to 97 MeV. This model makes it possible to compare different ion-based neutron source concepts against each other quickly. This contribution derives a predictive model using Monte Carlo simulations (an order of 50,000 simulations) and deep neural networks. It is the first time a model of this kind has been developed. With this model, lengthy Monte Carlo simulations, which individually take a long time to complete, can be circumvented. A prediction of neutron spectra then takes some milliseconds, which enables fast optimization and comparison. The models’ shortcomings for low-energy neutrons (<0.1 MeV) and the cut-off prediction uncertainty (±3 MeV) are addressed, and mitigation strategies are proposed. Full article
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Figure 1

Figure 1
<p>Different scenarios for the projected availability for neutron beamtime in Europe. Its differences are caused by the assumption of different remaining runtimes from existing machines and different commissions of new instruments, especially at the ESS. Enhanced includes faster commissioning of the ESS instruments and longer runtime, while Degraded assumes faster decommissioning and delays in the ESS commissioning. The detailed explanation for each scenario is given in the ESFRI’s report [<a href="#B4-jne-05-00009" class="html-bibr">4</a>] (pp. 66–77).</p> Full article ">Figure 2
<p>Flowchart of the modeling approach and the later usage of the resulting Surrogate model. The dashed lines indicate data flow, the solid lines indicate the input of the parameters from <a href="#jne-05-00009-t001" class="html-table">Table 1</a>, and the dashed and dash-dotted lines indicate the surrogate output. The dotted line indicates the spectrum model, and the dash-dotted line indicates the cut-off model. <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>X</mi> <mo>|</mo> <mi>Y</mi> <mo>)</mo> </mrow> </semantics></math> are the real Neutron spectra, where <span class="html-italic">X</span> is the energy value and <span class="html-italic">Y</span> (<math display="inline"><semantics> <mrow> <mi>Y</mi> <mo>(</mo> <mi>X</mi> <mo>)</mo> </mrow> </semantics></math>) the count rate at the corresponding <span class="html-italic">X</span>. The hat indicates normalized quantities. The concrete definitions are given in Equations (<a href="#FD1-jne-05-00009" class="html-disp-formula">1</a>) and (<a href="#FD2-jne-05-00009" class="html-disp-formula">2</a>). The predicted quantities are indexed accordingly. Both are needed to extract a physically meaningful spectrum. We would like to reference the supplied scripts and examples for more details. Refer to the data availability statement for further details.</p> Full article ">Figure 3
<p>Simulation description plots for the Monte Carlo simulation geometry. (<b>a</b>) CAD of the simulation setup. Created using PHITS Angel tool. (<b>b</b>) Crosssection in the z-y-plane. Created using PHITS Angel tool. (<b>c</b>) Simulation sketch, true to scale, with ion source (red). The converter is centered at the origin.</p> Full article ">Figure 4
<p>Model verification against the simulation data for proton-induced reactions. Solid lines indicate the model prediction (M), while the dashed lines are the resulting Monte Carlo spectra (D).</p> Full article ">Figure 5
<p>Model verification against the simulation data for deuteron-induced reactions. Solid lines indicate the model prediction (M), while the dashed lines are the resulting Monte Carlo spectra (D).</p> Full article ">Figure 6
<p>Experimental data compared to the model (solid lines with shaded uncertainty). (<b>a</b>) Data taken by Kamada et al. in 2011 with <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mi>p</mi> </msub> <mo>=</mo> <mn>11</mn> <mo> </mo> <mrow> <mi mathvariant="normal">M</mi> <mi mathvariant="normal">e</mi> <mspace width="-0.21251pt"/> <mi mathvariant="normal">V</mi> </mrow> </mrow> </semantics></math> [<a href="#B18-jne-05-00009" class="html-bibr">18</a>]. (<b>b</b>,<b>c</b>) contain data taken by Osipenko et al. in 2013 with <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mi>p</mi> </msub> <mo>=</mo> <mn>62</mn> <mo> </mo> <mrow> <mi mathvariant="normal">M</mi> <mi mathvariant="normal">e</mi> <mspace width="-0.21251pt"/> <mi mathvariant="normal">V</mi> </mrow> </mrow> </semantics></math> [<a href="#B19-jne-05-00009" class="html-bibr">19</a>]. The scattering angle is listed in the legend, the model prediction is the same color as the data, and the data have been multiplied by a factor as indicated by the legend’s prefix to increase the readability of the plot.</p> Full article ">Figure 7
<p>Our model compared to the data extracted from ([<a href="#B10-jne-05-00009" class="html-bibr">10</a>], Figure 6). Our model gives the dashed lines with the corresponding uncertainty bands.</p> Full article ">Figure 8
<p>Model output for the conventional accelerators in p+Be configuration.</p> Full article ">Figure 9
<p>Model and simulation output for a TNSA proton beam.</p> Full article ">
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