arXiv subjects

Manthan Verma

Publications and source records attributed to Manthan Verma.

3 recordsLinked to original sources

arXiv2026

Kolmogorov Scaling for Total Energy and Cross Helicity in Magnetohydrodynamic Turbulence

The problem of scaling in isotropic magnetohydrodynamic (MHD) turbulence has remained unresolved, with competing predictions of $k^{-5/3}$ (Kolmogorov) and $k^{-3/2}$ (Iroshnikov-Kraichnan) scalings. In this paper, we address this long-standing controversy using high-resolution numerical simulations on $8192^2$ and $1536^3$ grids. We show that the total energy and cross helicity spectra are closer to $k^{-5/3}$ than $k^{-3/2}$. The fluxes and structure functions of the total energy and cross helicity also demonstrate robust support for Kolmogorov scaling. The magnetic energy shows $k^{-5/3}$ spectrum, but the kinetic energy exhibits $k^{-3/2}$ spectrum; the latter spectrum is due to the energy transfers from the magnetic field to the velocity field.

physics.plasm-ph↗

arXiv2025

Numerical Demonstration of Kolmogorov Scaling in Magnetohydrodynamic Turbulence

The two leading models of isotropic magnetohydrodynamic (MHD) turbulence have competing predictions: $k^{-5/3}$ (Kolmogorov) and $k^{-3/2}$ (Iroshnikov-Kraichnan) scalings. This paper identifies the valid MHD turbulence model using high-resolution numerical and diagnostics-structure functions, intermittency exponents, and energy spectra and fluxes of imbalance MHD. The energy spectra of our forced MHD simulations on $8192^2$, $4096^2$, $1024^3$, and $512^3$ support Kolmogorov's k^{-5/3} spectrum over Iroshnikov-Kraichnan's k^{-3/2} spectrum, but the difference in the spectral exponents is small. However, the numerically computed third-order structure functions and intermittency exponents support Kolmogorov scaling in both two and three dimensions. Also, the energy fluxes of the imbalance MHD follow the predictions of Kolmogorov scaling. These results would help in better modelling of solar wind, solar corona, and dynamos.

physics.plasm-ph↗

arXiv2022

Scalable Multi-node Fast Fourier Transform on GPUs

In this paper, we present the details of our multi-node GPU-FFT library, as well its scaling on Selene HPC system. Our library employs slab decomposition for data division and MPI for communication among GPUs. We performed GPU-FFT on $1024^3$, $2048^3$, and $4096^3$ grids using a maximum of 512 A100 GPUs. We observed good scaling for $4096^3$ grid with 64 to 512 GPUs. We report that the timings of multicore FFT of $1536^3$ grid with 196608 cores of Cray XC40 is comparable to that of GPU-FFT of $2048^3$ grid with 128 GPUs. The efficiency of GPU-FFT is due to the fast computation capabilities of A100 card and efficient communication via NVlink.

physics.comp-ph↗