Study of nuclear reactor external vessel passive cooling using computational fluid dynamics

Colombo, Marco; Fairweather, Michael

doi:10.1016/j.nucengdes.2021.111186

Cited by 3 publications

(2 citation statements)

References 62 publications

(92 reference statements)

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“…Passive containment cooling systems are dominated by natural convection and can operate without any power supply. Many passive containment cooling processes have been proposed in recent years, such as reflooding condensation, horizontal or nearly horizontal tube-and-shell heat exchangers, simultaneous condensation inside and pool boiling outside tubes, , and separate heat pipe . Active containment spray systems are dominated by forced convection and can be divided into two types, including steam injected into subcooled water , or subcooled water injected into steam-filled sections .…”

Section: Phase Change For Thermal Management (Tm)mentioning

confidence: 99%

Recent Advances in CFD Simulations of Multiphase Flow Processes with Phase Change

Zhu

Zhang

et al. 2023

Ind. Eng. Chem. Res.

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Phase change is a phenomenon that has been extensively utilized in chemical engineering, energy, electronics, vehicle, and space exploration. From both the science and engineering perspectives, gaining a profound understanding of the intricacies of multiphase flow processes accompanied by heat and mass transfer due to phase change is of fundamental importance. Indeed, the computational fluid dynamics (CFD) has been increasingly applied as an effective tool to give an in-depth and efficient analysis of the phase change processes, thereby enhancing our understanding and capacity to control and optimize the phase change effects in these processes. This paper seeks to provide a comprehensive review of the utilization of CFD methodologies in the simulations of phase change flows across various applications, including temperature management, drying, separation and purification, and others. First, the CFD models at various scales that are developed to elucidate the phase change processes are summarized. Second, the noteworthy advances in the recent CFD simulation work on various phase change processes are presented, respectively. Finally, the challenges and potential prospects to facilitate the application of the phase change flow are discussed. The current review aims to offer insightful guidance for the CFD modeling of phase change processes in numerous engineering application scenarios.

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Section: Phase Change For Thermal Management (Tm)mentioning

confidence: 99%

Recent Advances in CFD Simulations of Multiphase Flow Processes with Phase Change

Zhu

Zhang

et al. 2023

Ind. Eng. Chem. Res.

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“…Leveraging DNNs, we can learn from the high-resolution CFD data to develop coarse mesh model that is able to retain the accuracy of the CFD results. Such an approach is backed by the progresses in highfidelity CFD simulations on complex nuclear reactor components, from fuel assembly to reactor pool [31,32,33,34].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Modeling of Coarse Mesh Turbulence for Reactor Transient Analysis Using Convolutional Recurrent Neural Networks

Liu¹,

Hu²,

Kraus³

et al. 2021

Preprint

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Advanced nuclear reactors often exhibit complex thermal-fluid phenomena during transients. To accurately capture such phenomena, a coarse-mesh three-dimensional (3-D) modeling capability is desired for modern nuclearsystem code. In the coarse-mesh 3-D modeling of advanced-reactor transients that involve flow and heat transfer, accurately predicting the turbulent viscosity is a challenging task that requires an accurate and computationally efficient model to capture the unresolved fine-scale turbulence. In this paper, we propose a data-driven coarse-mesh turbulence model based on local flow features for the transient analysis of thermal mixing and stratification in a sodium-cooled fast reactor. The model has a coarse-mesh setup to ensure computational efficiency, while it is trained by fine-mesh computational fluid dynamics (CFD) data to ensure accuracy. A novel neural network architecture, combining a densely connected convolutional network and a long-short-term-memory network, is developed that can efficiently learn from the spatial-temporal CFD transient simulation results. The neural network model was trained and optimized on a loss-of-flow transient and demonstrated high accuracy in predicting the turbulent viscosity field during the whole transient. The trained model's generalization capability was also investigated on two other transients with different inlet conditions. The study demonstrates the potential of applying the proposed data-driven approach to support the coarse-mesh multi-dimensional modeling of advanced reactors.

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Data-driven modeling of coarse mesh turbulence for reactor transient analysis using convolutional recurrent neural networks

Liu

Kraus

et al. 2022

Nuclear Engineering and Design

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Study of nuclear reactor external vessel passive cooling using computational fluid dynamics

Cited by 3 publications

References 62 publications

Recent Advances in CFD Simulations of Multiphase Flow Processes with Phase Change

Recent Advances in CFD Simulations of Multiphase Flow Processes with Phase Change

Data-Driven Modeling of Coarse Mesh Turbulence for Reactor Transient Analysis Using Convolutional Recurrent Neural Networks

Data-driven modeling of coarse mesh turbulence for reactor transient analysis using convolutional recurrent neural networks

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