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NASA NTRS1983

Flare loop radiative hydrodynamics. IV - Dynamic evolution of unstable semiempirical loop models

The evolution of the unstable solar atmosphere into the nonlinear phase, in response to various perturbations, is followed. The initial dynamic evolution of the atmosphere follows the predictions of linear stability analysis. In the nonlinear phase, rapid changes are confined to the transition region; these changes are manifested as a propagation of the transition region through the plasma, i.e., chromospheric evaporation or condensation. Global evolution therefore proceeds on the coronal conductive time scale. The rate of propagation of the transition region is determined by the imbalance between the energy supplied by thermal conduction from the corona and radiative cooling within the transition region itself. Flow velocities in the lower corona during evaporation or condensation are, in the cases studied, of order 3 km/s. The observed dynamic evolution is consistent with the existence of relatively long-lived coronal loops whose brightnesses vary on the evaporative time scale.

An, C.-H.↗
NASA NTRSDate not supplied

Towards Surrogate Modeling of Subgrid Turbulent Transport for 3D Radiative Hydrodynamic Simulations of the Quiet Sun

In this work, we investigate the use of deep learn-ing techniques as surrogate models, to enhance the estimationof effects of subgrid turbulent transport for 3D radiatuve hy-drodynamic simulations of the quiet Sun. We develop two dis-tinct 3D Convolutional Neural Networks (3DCNNs) to capturespatio-temporal dependencies in 3D velocity fields, leveragingdifferent activation functions and architectural designs. Thesemodels integrate both averaged velocity vector components andscalar features such as plasma density to enhance predictionaccuracy. Additionally, a Multilayer Perceptron (MLP) modelis employed to approximate complex nonlinear relationships,offering a comparison in performance between convolutionaland fully connected architectures. Logarithmic transformationis applied to the targets to handle heavily skewed data, im-proving model performance. All models are compared againsta physics-based Gradient Model. Results show that the 3DCNNmodels excel at approximating Reynolds stress tensors, makingthem a candidate for assisting in producing reduced resolutionsimulations, and thereby reducing computational overheadwhile maintaining higher accuracy than the baseline. Thesefindings demonstrate the potential of deep learning, particu-larly CNNs, to advance scalable and accurate simulations ofsolar dynamics, offering a promising alternative to traditionalturbulence models.

Heliophysics↗
NASA NTRSDate not supplied

Surrogate Modeling of Subgrid Turbulent Transport Based on 3D Radiative Hydrodynamic Simulations of the Quiet Sun

Turbulent Transport: Plays a critical role in astrophysical plasmas, such as the solar interior, spanning multiple scales and challenging traditional modeling approaches. Objective: Develop machine learning (ML) models—MLP and CNN—to predict subgrid Reynolds stress tensors from StellarBox 3D simulations of the solar atmosphere. Benchmarking: Compare ML-driven models against physics-based Gradient and Smagorinsky approaches.

SMD↗