The demand for computational tools that are capable to reliably predict the behavior of a nuclear reactor core in a variety of static and dynamic conditions does inevitably require a proper qualification of these tools for the intended purposes. One of the qualification methods is the verification of the code in question. Hereby, the correct implementation of the applied model as well as its flawless implementation in the code are scrutinized. The present work concerns with benchmarking as a substantial part of the verification of the three-dimensional, multigroup neutron kinetics model employed in the transient code POLCA-T. The benchmarking is done by solving some specified and widely used space-time kinetics benchmark problems and comparing the results to those of other, established and well-proven spatial kinetics codes. It is shown that the obtained results are accurate and consistent with corresponding solutions of other codes. In addition, a sensitivity analysis is carried out with the objective to study the sensitivity of the POLCA-T neutronics to variations in different numerical options. It is demonstrated that the model is numerically stable and provide reproducible results for a wide range of various numerical settings. Thus, the model is shown to be rather insensitive to significant variations in input, for example. The other consequence of this analysis is that, depending on the treated transient, the computing costs can be reduced by, for instance, employing larger time-steps during the time-integration process or using a reduced number of iterations. Based on the outcome of this study, one can finally conclude that the POLCA-T neutron kinetics is modeled and implemented correctly and thus, the model is fully capable to perform the assigned tasks.

<p>The broad implementation of three-dimensional full-core modeling, with pin-resolved detail, for computational simulation and analysis of nuclear reactors highlights the importance of accuracy and efficiency in simulation codes for accurate and precise analysis. The primary objective of this dissertation is to develop a high-fidelity code capable of solving time-dependent neutron transport problems with 3D whole-core pin-resolved detail in nuclear reactor cores. Additionally, the dissertation explores the optimization of the code's parallelism to enhance its computational efficiency. To reduce the computational intensity associated with the direct 3D calculation of the neutron transport equation, a high-fidelity neutron transport code called PANDAS-MOC is developed using the 2D/1D approach. The 2D radial solution is obtained using the 2D Method of Characteristics (MOC), the axial 1D solution is determined through the Nodal Expansion Method (NEM), and then two solutions are coupled using transverse leakages to find the 3D solution. The convergence of the iterative scheme is accelerated using the multi-level coarse finite different mesh (ML-CMFD) technique. The code's validation and verification are carried out using the C5G7-TD benchmark exercises.</p> <p><br></p> <p>The significant and innovative aspect of this work involves parallelizing and optimizing the PANDAS-MOC code. Three parallel models are developed and evaluated based on the distributed memory and shared memory architecture: MPI parallel model (PMPI), Segment OpenMP threading hybrid model (SGP), and Whole-code OpenMP threading hybrid model (WCP). When computing the steady state of the C5G7 3D core with the same resources, the obtained speedup relationship between the three models is PMPI \(>\) WCP \(>\) SGP, whereas the WCP model only consumed 60\% of the memory of the PMPI model. Furthermore, the hybrid reduction in the ML-CMFD solver and the parallelism design of the MOC sweep are significant issues that decreased the speedup of WCP. Therefore, this study also addresses further optimizations of these two modules.</p> <p><br></p> <p>Concerning the MOC parallelism, two improvements are discussed: No-atomic schedule and Additional Axial Decomposition (AAD) parallelism. The No-atomic schedule evenly distributed the workload among threads and removes the \textit{omp atomic} clause from the code by predefining the MOC calculation sequence for each launched OpenMP thread while ensuring a thread-safe parallel environment. It can significantly reduce the calculation time and improve parallel efficiency. Furthermore, AAD divides the axial layers and OpenMP threads into multiple groups and restricts each thread to work on the layers designated to the same group. </p> <p>Meanwhile, Flag-Save-Update reduction is designed to increase the computational efficiency of the hybrid MPI/OpenMP reduction operations in the ML-CMFD module. It is accomplished by using the global arrays and status flags and establishing a tree configuration of all threads, and it includes no implicit and explicit barriers. In the case of the C5G7 3D core, the parallel efficiency of the MOC solver is about 0.872 when using 32 threads (=\#MPI \(\times\)\#OpenMP), and the Flag-Save-Update reduction yielded better speedup than the traditional hybrid MPI/OpenMP reduction, and its superiority is more obvious as more OpenMP threads are utilized. As a result, the WCP model outperforms the PMPI model for the overall steady-state calculation.</p> <p><br></p> <p>This research also investigates parallelizable preconditioners to accelerate the convergence of the generalized minimal residual method (GMRES) in the CMFD solver. Preconditioners such as Incomplete LU factorization (ILU), Symmetric Successive Over-relaxation (SOR), and Reduced Symmetric Successive Over-Relaxation (RSOR), are implemented in PANDAS-MOC. Except for RSOR, others are unsuitable for hybrid MPI/OpenMP parallel machines due to their inherent sequential nature and dependency on computation order. Their counterparts using the Red-Black ordering algorithm, namely RB-SOR, RB-RSOR, and RB-ILU, are formatted and examined on benchmark reactors such as TWIGL-2D, C5G7-2D, C5G7-3D, and their corresponding subplane models (TWIGL-2D(5S), C5G7-2D(5S), C5G7-3D(5S)), with relaxed convergence criteria (\(10^{-3}\)). Results show that all preconditioners significantly reduce the required number of iterations to converge the GMRES solutions, and RB-SOR is the best one for most reactors. In the case of C5G7-3D(5S), preconditioners exhibit similar sublinear speedup but demonstrate varying runtimes across all tests for both MG-GMRES and 1G-GMRES. However, the speedup results in 1G-GMRES are more than twice as high as those in MG-GMRES. RB-RSOR has an optimal efficiency of 0.6967 at (4,8), while RB-SOR and RB-ILU have optimal efficiencies of 0.6855 and 0.7275 at (32,1), respectively.</p>

<p dir="ltr">The research focuses on improving core simulation procedures in Boiling Water Reactors (BWRs) by leveraging machine learning techniques. Aimed at better fuel planning and enhanced safety, a machine learning model has been developed to predict errors in existing low-fidelity, diffusion-based core simulators. The machine learning models have demonstrated the capability to accurately and efficiently predict errors in core eigenvalue and power distribution in BWR Operations. This results in a significant improvement over conventional simulation methods in nuclear reactors without increasing computational complexity.</p>

Submitted by Mery Piedad Zamudio Igami (mery@ipen.br) on 2017-03-10T16:45:35Z No. of bitstreams: 0 / Made available in DSpace on 2017-03-10T16:45:35Z (GMT). No. of bitstreams: 0 / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / O presente estudo foi realizado para verificar a possibilidade de redução do núcleo do reator IEA-R1 do IPEN/CNEN-SP. Cálculos neutrônicos foram desenvolvidos para um conjunto de novas configurações para que, a posteriori, a análise termo-hidráulica e de segurança pudessem ser realizadas. As novas configurações analisadas são menores por diversos motivos, como obter uma melhor utilização do combustível, melhor distribuição dos fluxos de nêutrons, dentre outros. Para que se possa atingir tais configurações, a densidade de Urânio no combustível deve ser aumentada. Neste estudo, combustíveis de U3Si2-Al com 4,8gU/cm3 foram testados e novos núcleos para o reator IEA-R1 foram propostos e discutidos. A análise neutrônica não impõe restrições aos núcleos estudados. A análise termohidráulica mostrou que as margens de segurança e os perfis de temperatura ao longo das placas combustíveis não excedem os limites de projeto. Os coeficientes de temperatura obtidos para os novos núcleos, no caso isotérmico, são todos negativos, conforme desejado. A queima mostrou que núcleos supercompactos não apresentam excesso de reatividade suficiente para o funcionamento dos mesmo, ao se utilizar combustíveis com 4,8gU/cm3. Um APR (Acidente de Perda de Refrigerante) foi simulado para os núcleos remanescentes. A ruptura da fronteira do primário se mostrou o acidente mais crítico, devido ao curto tempo para o esvaziamento completo da piscina do reator. As temperaturas atingidas após o descobrimento foram calculadas e não excedem aquelas cujos valores propiciam empolamento nas placas combustíveis (475 °! a 550 °!), uma vez que se obedeça os tempos de esvaziamento seguro da piscina para as novas configurações. / Tese (Doutorado em Tecnologia Nuclear ) / IPEN/D / Instituto de Pesquisas Energéticas e Nucleares - IPEN-CNEN/SP / FAPESP: 11/17090-7

PWR, fuel assembly, benchmark, burnup, lattice, SCALE, Polaris, validation, reactivity