Preeti Sar | Searcharxiv

arXiv2026

A machine learning framework for developing quasilinear saturation rules of turbulent transport from linear gyrokinetic data

A new neural network model for a quasilinear saturation rule has been developed to map linear gyrokinetic data to nonlinear saturated potential magnitudes to predict the total energy and particle fluxes. The training dataset is taken from the high resolution simulation database generated from nonlinear gyrokinetic turbulence simulations with the CGYRO code for developing the SAT3 model. This new model, named SAT3-NN, overall is able to capture the 1D saturated potential magnitudes of the dataset more accurately than SAT3, as depicted by lower percentage errors in the peak locations and peak values of the 1D saturated potentials. The resulting fluxes also had smaller deviations from the nonlinear CGYRO data as compared to previous saturation models such as SAT0 - SAT2. Consistent with SAT3, SAT3-NN is able to recreate the anti-gyroBohm scaling of fluxes seen for the TEM-dominated cases considered.

physics.plasm-ph↗

arXiv2025

Micro-macro kinetic flux-vector splitting schemes for the multidimensional Boltzmann-ES-BGK equation

The kinetic Boltzmann equation models gas dynamics over a wide range of spatial and temporal scales. Simplified versions of the full Boltzmann collision operator, such as the classical Bhatnagar-Gross-Krook (BGK) and the closely related Ellipsoidal-Statistical-BGK (ES-BGK) operators, can dramatically reduce the computational cost of solving kinetic equations numerically. Classical BGK yields incorrect transport coefficients (relative to the full Boltzmann collision operator) at low Knudsen numbers, whereas ES-BGK captures them correctly. In this work, we develop a finite-volume method based on a micro-macro decomposition of the distribution function, which requires a smaller velocity mesh than direct kinetic methods for low and intermediate Knudsen numbers. The macro portion of the model is a fluid model with a moment closure derived from the heat-flux tensor calculated from the micro portion. The micro portion is obtained by applying to the original kinetic equation a projector into the orthogonal complement of the null space of the collision operator -- this projector depends on the macro portion. In particular, we extend the technique of Bennoune, Lemou, and Mieussens [{\it Uniformly stable schemes for the Boltzmann equation preserving the compressible Navier-Stokes asymptotics, J. Comput. Phys. (2008)}] to two-space dimensions, the ES-BGK collision operator, and problems with reflecting wall boundary conditions. The collision operator in the micro and macro equations is handled via L-stable implicit time discretizations, while the transport terms are computed via kinetic flux vector splitting (for the macro equations) and upwind differencing (for the micro equation). The resulting scheme is applied to various test cases in 1D and 2D. The 2D version of the code is parallelized using MPI, and we present weak- and strong-scaling studies with varying numbers of processors.

math.NA↗