Sensitivity analysis of the 1-D SFR thermal stratification model via discrete adjoint sensitivity method

Lu, Cihang; Wu, Zeyun

doi:10.1016/j.nucengdes.2020.110920

Cited by 5 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A typical example is the mixing and thermal stratification phenomena in large pools or enclosures. Such phenomena can be observed in various reactor systems, including the cold-and hot-pool mixing in pooltype sodium-cooled fast reactors [2], reactor cavity cooling system behavior in high-temperature gas-cooled reactors [3], passive containment cooling in advanced light-water reactors [4], and thermal stratification in boiling-water reactor suppression pools [5].…”

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

View full text Add to dashboard Cite

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.

show abstract