An optimisation framework for the development of explicit discrete forward and inverse filters. (15th April 2023)

• Record Type: Journal Article
• Authors: Nikolaou, Z.; Vervisch, L.; Domingo, P.
• Is Part Of: Computers & fluids. Volume 255(2023)
• Publication Date: 2023-04-15
• Subjects: Discrete filters -- Deconvolution -- Large-Eddy-simulation -- Optimisation -- van Cittert; Fluid dynamics -- Data processing -- Periodicals; 532.050285
• DOI: 10.1016/j.compfluid.2023.105840
• Languages: English
• ISSNs: 0045-7930

Abstract

Abstract: Discrete filters are used in numerous digital signal processing applications and numerical simulations, for anti-aliasing, de-noising, and post-processing. Our specific interest is for application in large-eddy simulations. In this work, we investigate analytically the reconstruction properties of different filters, and how their discrete approximations using different rules affect the convergence and accuracy of the reconstructed signal. Following this analysis, a constrained and adaptive optimisation framework is proposed for the automated calculation of explicit forward but also direct-inverse discrete filter coefficients for a given filter transfer function. The optimised forward filters are shown to perform well with stable reconstruction using classic van Cittert iterations. The optimised direct-inverse filters eliminate the need to apply any iterations thereby substantially reducing the computational cost required for reconstruction which is one of the main challenges associated with deconvolution-based modelling in large-eddy simulations. Highlights: Filtering/reconstruction properties of common filters are investigated analytically. Necessary reconstruction stability conditions for discrete filters are obtained. A novel optimisation approach is proposed for obtaining discrete filter coefficients. Optimised inverse filters alleviate the need to apply van Cittert iterations.