An efficient eigenvalue bounding method: CFL condition revisited

TL;DR

This paper introduces a new, efficient eigenvalue bounding method for CFL condition estimation in CFD, enhancing portability and allowing larger time-steps with minimal computational overhead.

Contribution

A novel inexpensive eigenvalue bounding method that relies on sparse matrix-vector products, improving code portability and enabling larger time-steps in CFD simulations.

Findings

The method is effective across various mesh types.

It allows significantly larger time-steps compared to traditional CFL approaches.

Implementation is straightforward and enhances cross-platform portability.

Abstract

Direct and large-eddy simulations of turbulence are often solved using explicit temporal schemes. However, this imposes very small time-steps because the eigenvalues of the (linearized) dynamical system, re-scaled by the time-step, must lie inside the stability region. In practice, fast and accurate estimations of the spectral radii of both the discrete convective and diffusive terms are therefore needed. This is virtually always done using the so-called CFL condition. On the other hand, the large heterogeneity and complexity of modern supercomputing systems are nowadays hindering the efficient cross-platform portability of CFD codes. In this regard, our leitmotiv reads: relying on a minimal set of (algebraic) kernels is crucial for code portability and maintenance! In this context, this work focuses on the computation of eigenbounds for the above-mentioned convective and diffusive matrices which are needed to determine the time-step \`a la CFL. To do so, a new inexpensive method, that does not require to re-construct these time-dependent matrices, is proposed and tested. It just relies on a sparse-matrix vector product where only vectors change on time. Hence, both implementation in existing codes and cross-platform portability are straightforward. The effectiveness and robustness of the method are demonstrated for different test cases on both structured Cartesian and unstructured meshes. Finally, the method is combined with a self-adaptive temporal scheme, leading to significantly larger time-steps compared with other more conventional CFL-based approaches.