Preserving large-scale features in simulations of elastic turbulence
Sumithra R. Yerasi, Jason R. Picardo, Anupam Gupta, Dario Vincenzi

TL;DR
This paper investigates numerical methods for simulating elastic turbulence, highlighting how certain tensor decompositions and transformations affect the accuracy of large-scale flow features, and emphasizes the importance of mathematical bounds for validation.
Contribution
It compares tensor decomposition techniques and introduces a determinant-based bound to identify accurate simulations, emphasizing the role of the logarithmic Cholesky transformation.
Findings
Cholesky-log decomposition preserves large-scale flow patterns.
Symmetric square root decomposition distorts flow structures.
Artificial diffusion significantly alters the flow dynamics.
Abstract
Simulations of elastic turbulence, the chaotic flow of highly elastic and inertialess polymer solutions, are plagued by numerical difficulties: The chaotically advected polymer conformation tensor develops extremely large gradients and can loose its positive definiteness, which triggers numerical instabilities. While efforts to tackle these issues have produced a plethora of specialized techniques -- tensor decompositions, artificial diffusion, and shock-capturing advection schemes -- we still lack an unambiguous route to accurate and efficient simulations. In this work, we show that even when a simulation is numerically stable, maintaining positive-definiteness and displaying the expected chaotic fluctuations, it can still suffer from errors significant enough to distort the large-scale dynamics and flow-structures. Focusing on two-dimensional simulations of the Oldroyd-B and FENE-P…
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Taxonomy
TopicsComputational Physics and Python Applications · Tensor decomposition and applications · Lattice Boltzmann Simulation Studies
