为宁静太阳3D辐射流体动力学模拟开发亚网格湍流输运的机器学习模型
Developing Machine Learning Models of Subgrid Turbulent Transport for Quiet Sun 3D Radiative Hydrodynamic Simulations
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中文总结 AI 辅助
研究针对宁静太阳3D辐射流体动力学模拟中的亚网格湍流输运问题，开发3D卷积神经网络，集成速度矢量和标量特征预测雷诺应力张量分量，与其他模型比较，结果表明该模型能更准确重构应力张量分量，展现深度学习在该领域的潜力。
中文摘要 AI 辅助
太阳等离子体动力学的数值模拟受计算网格分辨率影响，常需估算与小尺度流湍流相关的亚网格过程。本文研究在宁静太阳实际流体动力学模拟中，用深度学习技术作为亚网格湍流输运的替代模型。描述了3D卷积神经网络（CNN）的开发，利用不同激活函数和架构设计捕捉3D速度场空间依赖性，聚焦雷诺应力张量分量预测。将3DCNN模型与其他模型比较，结果显示其能更准确重构雷诺应力张量分量，对数数据变换可提升模型性能。结果证明深度学习估算太阳对流层上部和低层大气雷诺应力张量分量的潜力，是传统湍流模型的可行替代方案。
英文摘要
Numerical modeling of solar plasma dynamics is affected by the resolution of the computational grid. This often requires the estimation of subgrid processes related to the small-scale flow turbulence, as these processes play a critical role in momentum transport and energy dissipation. In this work, we investigate the use of deep learning techniques as surrogate models for subgrid turbulent transport in realistic hydrodynamic simulations of the quiet Sun. We describe the development of a 3D Convolutional Neural Network (CNN) to capture spatial dependencies in 3D velocity fields, leveraging different activation functions, as well as different architectural designs. We specifically focus on the prediction of Reynolds stress tensor components. The resultant model integrates velocity vector components and scalar features, such as plasma density, to enhance prediction accuracy. We compare the 3DCNN model to other types of models, such as a Multilayer Perceptron (MLP) and physics-based Gradient and Smagorinsky models, and show that the final model design reconstructs the Reynolds stress tensor components more accurately. Specifically, a 3DCNN model achieves an average improvement of ~31% on diagonal components and ~8% on the off-diagonal components of the stress tensor. Additionally, we show that applying a logarithmic data transformation of the target stress tensor components, to handle heavily skewed data, improves model performance. Results demonstrate the potential of deep learning, particularly CNNs, to approximate Reynolds stress tensor components for the upper solar convection zone and lower atmosphere, making them a viable candidate for modeling subgrid processes and a promising alternative to traditional turbulence models.
