Efficient implementation of adaptive order reconstructions

TL;DR

This paper introduces a new CWENOZ reconstruction method that efficiently combines polynomials of different degrees, maintaining accuracy and reducing computational cost by avoiding multiple nonlinear weight calculations.

Contribution

The paper proposes a novel CWENOZ approach with infinitesimal linear weights for low-degree polynomials, improving efficiency while preserving accuracy in high-order reconstructions.

Findings

Achieves similar accuracy and oscillation control as previous methods.

Reduces computational time by up to 20%.

Provides general guidelines for CWENOZ parameter selection.

Abstract

Including polynomials with small degree and stencil when designing very high order reconstructions is surely beneficial for their non oscillatory properties, but may bring loss of accuracy on smooth data unless special care is exerted. In this paper we address this issue with a new Central WENOZ (CWENOZ) approach, in which the reconstruction polynomial is computed from a single set of non linear weights, but the linear weights of the polynomials with very low degree (compared to the final desired accuracy) are infinitesimal with respect to the grid size. After proving general results that guide the choice of the CWENOZ parameters, we study a concrete example of a reconstruction that blends polynomials of degree six, four and two, mimicking already published Adaptive Order WENO reconstructions [Balsara, Garain and Shu, J.Comput.Phys., 2016], [Arbogast, Huang and Zhao, SIAM J.Numer.Anal., 2018]. The novel reconstruction yields similar accuracy and oscillations with respect to the previous ones, but saves up to 20% computational time since it does not rely on a hierarchic approach and thus does not compute multiple sets of nonlinear weights in each cell.