kh2d-solver: A Python Library for Idealized Two-Dimensional
  Incompressible Kelvin-Helmholtz Instability
We present an open-source Python library for simulating two-dimensional
incompressible Kelvin-Helmholtz instabilities in stratified shear flows. The
solver employs a fractional-step projection method with spectral Poisson
solution via Fast Sine Transform, achieving second-order spatial accuracy.
Implementation leverages NumPy, SciPy, and Numba JIT compilation for efficient
computation. Four canonical test cases explore Reynolds numbers 1000--5000 and
Richardson numbers 0.1--0.3: classical shear layer, double shear configuration,
rotating flow, and forced turbulence. Statistical analysis using Shannon
entropy and complexity indices reveals that double shear layers achieve
2.8times higher mixing rates than forced turbulence despite lower Reynolds
numbers. The solver runs efficiently on standard desktop hardware, with
384times192 grid simulations completing in approximately 31 minutes. Results
demonstrate that mixing efficiency depends on instability generation pathways
rather than intensity measures alone, challenging Richardson number-based
parameterizations and suggesting refinements for subgrid-scale representation
in climate models.
68d510fcf9b028b804d3c259
Sandy H. S. Herho
claimed_verified
2025-09-25T10:41:33.050Z
68d51061ab9204b0c8a0ceb2
https://cdn-avatars.huggingface.co/v1/production/uploads/68d51061ab9204b0c8a0ceb2/CVmg6wo4yBqIZgGZUYSX8.jpeg
Sandy Hardian Susanto Herho
user
sandyherho
68d510fcf9b028b804d3c25a
Nurjanna J. Trilaksono
68d510fcf9b028b804d3c25b
Faiz R. Fajary
68d510fcf9b028b804d3c25c
Gandhi Napitupulu
68d510fcf9b028b804d3c25d
Iwan P. Anwar
68d510fcf9b028b804d3c25e
Faruq Khadami
68d510fcf9b028b804d3c25f
Dasapta E. Irawan
kh2d-solver: A Python Library for Idealized Two-Dimensional
  Incompressible Kelvin-Helmholtz Instability
We present an open-source Python library for simulating two-dimensional
incompressible Kelvin-Helmholtz instabilities in stratified shear flows. The
solver employs a fractional-step projection method with spectral Poisson
solution via Fast Sine Transform, achieving second-order spatial accuracy.
Implementation leverages NumPy, SciPy, and Numba JIT compilation for efficient
computation. Four canonical test cases explore Reynolds numbers 1000--5000 and
Richardson numbers 0.1--0.3: classical shear layer, double shear configuration,
rotating flow, and forced turbulence. Statistical analysis using Shannon
entropy and complexity indices reveals that double shear layers achieve
2.8times higher mixing rates than forced turbulence despite lower Reynolds
numbers. The solver runs efficiently on standard desktop hardware, with
384times192 grid simulations completing in approximately 31 minutes. Results
demonstrate that mixing efficiency depends on instability generation pathways
rather than intensity measures alone, challenging Richardson number-based
parameterizations and suggesting refinements for subgrid-scale representation
in climate models.