Pressurized Water Reactor Mixed Oxide/UO2 Transient Benchmark Calculations Using Monte Carlo Serpent 2 Code and Open Nodal Core Simulator ADPRES

Traditional engineering-based core analyses generally rely on a two-stage approach, involving a transport code and a diffusion (e.g. nodal) solver. The accuracy and fidelity of the core solution strongly depend on the generation of few-group constants, which is a topic that has been extensively studied. However, the practice of calculating group constants with Monte Carlo (MC) codes is still relatively new. The main challenge of this approach is the generation of diffusion coefficients. A common approach adopted by many MC codes is the flux separability approximation (FSA). However, diffusion coefficients generated with FSA have trouble matching the heterogenous solution. This is due to a strong coupling between the energetic, spatial, and angular distributions of neutrons. This is a well-documented challenge, with some literature proposing consistent methods to handle energy-angle dependence and others proposing consistent methods to handle energy-space-angle dependence. Recently, the Cumulative Migration Method has been used to generate consistent diffusion coefficients using MC. However, these diffusion coefficients are valid only for infinite single region problems. As such, it is important to revisit the consistent group constant generation method to better handle the energy-space-angle dependence using MC. This paper will lay out the method to generate these consistent group constants using MC and then verify that they can then be used for a two-stage approach using a steady-state nodal expansion method.

Supplementary Materials referenced in the text containing dosimetric comparisons for all test cases were not previously published alongside the paper and are attached here. Single seed in water phantom 13 seeds in water phantom, no plaque, no interseed attenuation (TG-43) 13 seeds in water phantom, no plaque, with interseed attenuation Full plaque in water phantom Full plaque in realistic eye phantom. Calculations for each test case were performed using three Monte Carlo (MC) codes: egs_brachy (based on EGSnrc), MCNP, and TOPAS (based on Geant4). Calculations for test cases 1, 4, and 5 are also performed using a research version of the OncentraBrachy ACE treatment planning system. The figures presented here are summarized in Table VII of the original paper. They demonstrate that overall local agreement ( % Δ D l o c a l $\% \Delta {D}_{local}$ ) between MC codes is approximately 2.5% and local agreement ( % Δ D l o c a l $\% \Delta {D}_{local}$ ) between ACE and MC is approximately 5% (test case 1) to 20% (test cases 4 and 5); global agreement ( % Δ D g l o b a l $\% \Delta {D}_{global}$ ) between MC codes is approximately 2% (4% for test case 5) and global agreement between ACE and MC is approximately 0.4% (test case 1) to 10% (test cases 4 and 5). Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Purpose: The TG‐186 report provides guidance to early adopters of model‐based dose calculation algorithms (MBDCAs) for brachytherapy. A charge of the AAPM‐ESTRO Working Group on MBDCA is to develop well‐defined test case plans, available as references for the software commissioning process to be performed by end‐users. The aim of this work is to develop test case plans for a generic HDR 192Ir source alone and in combination with a vaginal cylinder applicator with 180° tungsten‐alloy shielding in a DICOM‐based water phantom. Methods: A DICOM CT dataset was created with a 30 cm diameter water sphere surrounded by air. The voxel size was 1.33×1.33×1.33 mm3 for evaluating absorbed dose rate (cGy.h−1.U−1). Dose distributions were obtained in the planes x=0, y=0 and z=0. Three geometries were considered with the HDR source (a) at the center of sphere, (b) displaced 12.75 cm laterally, and (c) at the center of the applicator. Monte Carlo (MC) codes (Geant4, Penelope2009, MCNP5, and EGSnrc‐based BrachyDose) and Acuros were used. MC simulations were performed by multiple users to validate consistency of the DICOM reading, calculation set‐up, and dose reporting. Results: For all three geometries, MC results were generally within 2% agreement for r < 14 cm except at the sphere boundary where volume averaging occurred in voxels comprising two materials. There were no trends for r > 14 cm except that dosimetric agreement amongst the codes was limited by computational statistics. Results from Acuros and Penelope2009 were compared using a 2%/1.33 mm λ‐index criteria for r < 7 cm. Voxel pass rates were 100%, 100%, and 94% for geometries (a), (b), and (c), respectively. Conclusions: Dose rate distributions were consistent within statistical uncertainties among MC codes and for different users. Acuros reproduces MC data for the three geometries. These geometries may serve as test case plans for MBDCAs. T. Wareing is employee of Transpire Inc. that developed Acuros.

Objective To investigate the impact of metal plate on radiation dose distribution by Monte Carlo (MC) code. Methods The metal plates with 0. 4 thick were placed in water at 5 cm, all the plate irradiated with 6 MV X-ray were simulated by MC code, SSD =100 cm. The percentage depth dose with or without metal implants were compared. Results The surface absorbed doses on incident plane of stainless steel plate and titanium plate were increased by 19. 6％ and 15.7％ respectively as compared water,the dose influence was less than 1.5％ more than 0. 3 cm outside the incidence plane. The doses on the exit surface of stainless steel plate and titanium plate were lowered by 8. 6％ and 8. 2％ when compared with water, the dose impacts of this places where were from the exit surface of stainless steel plate 、titanium plate more than 1.2 cm,0. 9 cm were less than 1.5％. The surface absorbed doses on incident plane of stainless steel plate were increased by 3.9％ respectively as titanium plate, the dose of the exit surface of stainless steel plate and titanium plate was similar. Conclusions MC method is a fast and accurate calculation method. The influence of metal plate on the radiotherapy dose distribution is significant. Under the such condition, the impact of stainless steel plate is much more than that of titanium alloy plate. Key words: Monte Carlo; Radiation dose; Metal implants

BackgroundSeveral research groups have explored the potential of scandium radionuclides for theragnostic applications due to their longer half-lives and equal or similar coordination chemistry between their diagnostic and therapeutic counterparts, as well as lutetium-177 and terbium-161, respectively. Unlike the gallium-68/lutetium-177 pair, which may show different in-vivo uptake patterns, the use of scandium radioisotopes promises consistent behaviour between diagnostic and therapeutic radiopeptides. An advantage of scandium’s longer half-life over gallium-68 is the ability to study radiopeptide uptake over extended periods and its suitability for centralized production and distribution. However, concerns arise from scandium-44’s decay characteristics and scandium-43’s high production costs. This study aimed to evaluate the dosimetric implications of using scandium radioisotopes with somatostatin analogues against gallium-68 for PET imaging of neuroendocrine tumours.MethodsAbsorbed dose per injected activity (AD/IA) from the generated time-integrated activity curve (TIAC) were estimated using the radiopeptides [43/44/44mSc]Sc- and [68Ga]Ga-DOTATATE. The kidneys, liver, spleen, and red bone marrow (RBM) were selected for dose estimation studies. The EGSnrc and MCNP6.1 Monte Carlo (MC) codes were used with female (AF) and male (AM) ICRP phantoms. The results were compared to Olinda/EXM software, and the effective dose concentrations assessed, varying composition between the scandium radioisotopes.ResultsOur findings showed good agreement between the MC codes, with − 3 ± 8% mean difference. Kidneys, liver, and spleen showed differences between the MC codes (min and max) in a range of − 4% to 8%. This was observed for both phantoms for all radiopeptides used in the study. Compared to Olinda/EXM the largest observed difference was for the RBM, of 21% for the AF and 16% for the AM for scandium- and gallium-based radiopeptides. Despite the differences, our findings showed a higher absorbed dose on [43/44Sc]Sc-DOTATATE compared to its 68Ga-based counterpart.ConclusionThis study found that [43/44Sc]Sc-DOTATATE delivers a higher absorbed dose to organs at risk compared to [68Ga]Ga-DOTATATE, assuming equal distribution. This is due to the longer half-life of scandium radioisotopes compared to gallium-68. However, calculated doses are within acceptable ranges, making scandium radioisotopes a feasible replacement for gallium-68 in PET imaging, potentially offering enhanced diagnostic potential with later timepoint imaging.

Optimization treatment of point-wise nuclear data in Monte Carlo criticality and burnup calculations

Monte Carlo (MC) methods are recognized as the gold-standard for dose calculation, however they have not replaced analytical methods up to now due to their lengthy calculation times. GPU-based applications allow MC dose calculations to be performed on time scales comparable to conventional analytical algorithms. This study focuses on validating our GPU-based MC code for proton dose calculation (gPMC) using an experimentally validated multi-purpose MC code (TOPAS) and compare their performance for clinical patient cases. Clinical cases from five treatment sites were selected covering the full range from very homogeneous patient geometries (liver) to patients with high geometrical complexity (air cavities and density heterogeneities in head-and-neck and lung patients) and from short beam range (breast) to large beam range (prostate). Both gPMC and TOPAS were used to calculate 3D dose distributions for all patients. Comparisons were performed based on target coverage indices (mean dose, V95, D98, D50, D02) and gamma index distributions. Dosimetric indices differed less than 2% between TOPAS and gPMC dose distributions for most cases. Gamma index analysis with 1%/1 mm criterion resulted in a passing rate of more than 94% of all patient voxels receiving more than 10% of the mean target dose, for all patients except for prostate cases. Although clinically insignificant, gPMC resulted in systematic underestimation of target dose for prostate cases by 1–2% compared to TOPAS. Correspondingly the gamma index analysis with 1%/1 mm criterion failed for most beams for this site, while for 2%/1 mm criterion passing rates of more than 94.6% of all patient voxels were observed. For the same initial number of simulated particles, calculation time for a single beam for a typical head and neck patient plan decreased from 4 CPU hours per million particles (2.8–2.9 GHz Intel X5600) for TOPAS to 2.4 s per million particles (NVIDIA TESLA C2075) for gPMC. Excellent agreement was demonstrated between our fast GPU-based MC code (gPMC) and a previously extensively validated multi-purpose MC code (TOPAS) for a comprehensive set of clinical patient cases. This shows that MC dose calculations in proton therapy can be performed on time scales comparable to analytical algorithms with accuracy comparable to state-of-the-art CPU-based MC codes.

The Constructive Solid Geometry (CSG) allows modelers to create models by using Boolean operations to combine different types of basic solids. CSG has been adopted by lots of numerical simulation codes as geometric description. In the research of reactor numerical simulation, Monte Carlo (MC) is a common method for neutronics analysis, which needs to calculate the intersections between the random flight paths of neutrons and the geometry. A lot of widely used MC codes (such as Geant4, MCNP) use CSG to describe geometry. The prerequisite to run MC code is to do geometric modeling, which however in many MC codes, is finished by manually writing text files, it is very hard to view the model and it is inefficient to build. In this work, a MC modeling tool (JLAMT) based on UG software is used to build the benchmark model. The tool supplies geometric modeling functions, physical parameter setting functions, and other assistant tools. With a flexible GUI, it helps users to achieve quick modeling.

The nucleation of sulfuric acid-water clusters plays a significant role in the formation of aerosols. Based on a recently developed particle Monte Carlo (MC) Code, we analyze how the growth of sulfuric acid-water clusters is influenced by stochastic fluctuations. We here consider samples of H2SO4-H2O clusters at T = 200 K with a relative humidity of 50%, with particle concentrations between 105 and 107 cm–3 in volumes between 10-6 and 10-2 cm3. We present the temporal evolution of the formation rate and of the size distribution as well as growth rates and the onset time of the nucleation above a given cluster size with and without constant production of new monomers. Clear evidence is revealed by the MC code that fluctuations result in a faster growth rate of the smallest clusters compared to deterministic continuum models that do not contain the stochastic effects. The faster growth of small clusters in turn influences the growth of larger clusters. Depending on the volume size, the onset time for clusters larger than 0.85 nm varies between 1000 s and 20,000 s for n=105 cm–3 and between 10 s and 100 s for n=107 cm–3.

Purpose: Monte Carlo (MC) simulation provides the most accurate results for CT dose calculations. Yet it is a time‐consuming task which often takes long time to run on CPUs. Using the recently developed hardware accelerators such as Graphics Processing Unit (GPU) and the Intel Xeon Phi coprocessor, one can run the MC simulations in massively parallel and reduce the simulation time significantly. In this work we present our effort of developing a new MC suite, Accelerated Radiation‐transport Computations in Heterogeneous EnviRonments (ARCHER), which utilizes NVIDIA GPUs and other accelerators. Methods: We first developed the radiation transport MC codes in C, and then adapted the code to ARCHERGPU and ARCHERMIC which are specific for NVIDIA GPU and Intel MIC architecture, respectively. Currently the code is capable of calculating the dose for CT X‐ray scan and electron radiotherapy. Our code also incorporated a whole CT scanner model and various computational human phantoms. Results: We tested our GPU‐based MC code on NVIDIA Tesla M2090 GPUs and compared its performance with other MC codes running on an Intel Xeon X5650 CPU. Two tests were carried out: 1) For a whole body helical CT scan with RPI‐Adult male obese phantom and 9e8 incoming photons, the ARCHER_GPU code and ARCHER_MIC code are 1200 and 561 times faster, respectively, than MCNPX running on the CPU. 2) For a simple water‐aluminum‐water phantom and 20MeV electron source, the ARCHER_GPU code takes 186 sec to simulate 6e6 electrons, and it is about 1000 times faster than MCNPX running on the CPU. Conclusion: Our preliminary results showed the efficiency of using different hardware accelerators to speed up MC radiation transport simulations for medical dosimetry. On‐going work includes further optimization of the code and implementation of more complex physics models.

Patient dose estimation in cardiac computed tomography with a 320 detector row scanner based on Monte Carlo simulation

A simplified Fusion-Fission Hybrid System (FFHS) deterministic calculation model has been developed in order to study the coupling between a fusion machine and a subcritical fission system. Monte-Carlo (MC) codes are very flexible and they have to be considered as reference calculation tools for complex systems like FFHS. On the other hand, deterministic codes can provide, in shorter calculation time respect to MC codes, parametric and sensitivity analysis, also by exploiting General Perturbation Theory (GPT) methodologies, which can usefully support the global FFHS theoretical analysis. This work describes the comparison between the results obtained for a simplified FFHS model by using a MC code (MCNP6.1, MC N-Particle) and a deterministic code (ERANOS). In particular, in this preliminary study, integral parameters like k eff , thermal power, neutron fluxes and some key reaction rate profiles, will be compared. If the comparison between the two mentioned codes will give encouraging results further, more refined, studies will be performed in order to consider deterministic codes as a powerful workhorse for the FFHS analysis, including time dependent analysis devoted to transient and reactivity monitoring issues.

Evaluation of neutronics parameters during RSG-GAS commissioning by using Monte Carlo code

Purpose: For microdosimetric calculation event-by-event Monte Carlo (MC) simulation is considered the most accurate, but it is very time-consuming. In this work we present an event-by-event MC simulation of low energy electron and proton for accelerated microdosimetric MC simulations on a graphic processing unit (GPU). Methods: The MC simulation of particle was implemented in C and executed on a multi-core CPU, and a commercially available general purpose GPU using the compute unified device architecture (CUDA). Additionally, a hybrid implementation scheme was realized by employing OpenMP and CUDA so that both GPU and multi-core CPU were utilized simultaneously. The two implementation schemes have been tested and compared with the sequential single threaded MC simulation on the CPU. A calculation time comparison was established on the speed-up for a set of benchmarking cases of electron and proton. Results: Dosimetric results were obtained with both the parallel and serial MC codes. A GPU over CPU speed-up of 67.2 and 19.2 times was achieved for 300 eV and 2 keV electron tracks respectively. For proton tracks, the GPU-based code was approximately 5 times faster than the CPU-single-thread code. By incorporating a multi-core CPU and running the MC code simultaneously on the GPU and CPU, an increase of 2%-7% and 20% in the speedup was noticed for electrons and protons tracks respectively. A good dosimetric agreement between the CPU- and GPU-based MC methods was observed for both electrons and proton. Conclusions: A GPU-based MC method for microdosimetric calculations of low energy electron and proton has been presented. The results indicate the capability of our GPU-based implementation for accelerated MC simulations of both electron and proton without loss of accuracy. Lastly, the potential of a hybrid approach by utilizing simultaneously a GPU and a multi-core CPU for further acceleration of MC microdosimetric calculations has been demonstrated.

A new homogenization method of control rod assemblies has been developed to accurately calculate control rod worth in fast reactors. In this method, the homogenized cross sections are defined so as to preserve the reaction rate ratio between a control rod region and a surrounding fuel region in a supercell model before and after homogenization. This new method has an advantage that the cross section of adjacent fuel assemblies is not necessary to change. From supercell calculation, this method produced k ∞ values which agreed with the reference heterogeneous calculation within 0.2%Δk. The flux weighting method underestimated k ∞ up to 1.4%Δk and the reaction rate preservation method did not produced a converged cross section. In the verification test of core calculation in RZ geometry, we confirmed that the reaction rate ratio preservation method is more accurate than the flux weighting method for control rod worth calculations. It was found from core calculations that the control rod worth obtained by the flux weighting method is overestimated compared to that by the reaction rate ratio preservation method by about 3%. This overestimation is remarkable for a safety rod with 90wt% enriched 10B than for a shim rod with 40wt% enriched 10B.