Preprint
Review

This version is not peer-reviewed.

Numerical Methods for Incompressible Viscous Flows: A Comparative Review of Boundary Element, Domain Discretization, and Meshfree Approaches

Submitted:

07 April 2026

Posted:

08 April 2026

You are already at the latest version

Abstract
The numerical simulation of incompressible viscous flows remains a central pillar of modern computational fluid dynamics (CFD). Over the past decades, a wide spectrum of numerical methodologies has been developed, reflecting fundamentally different mathematical formulations and discretization philosophies. Among these, domain-based approaches—such as finite difference, finite element, finite volume, and meshfree methods—have emerged as versatile and general-purpose frameworks, while boundary element methods provide efficient alternatives for problems governed by linear physics, particularly in unbounded domains. This review presents a comprehensive examination of the historical development, mathematical foundations, and computational characteristics of these approaches for Newtonian incompressible flows. Emphasis is placed on the conceptual distinctions between boundary-integral and domain-based formulations, their applicability to internal and external flow regimes, and their compatibility with turbulence modeling strategies, including Reynolds-averaged Navier–Stokes (RANS), large-eddy simulation (LES), and direct numerical simulation (DNS). The intention is to provide a unified perspective that clarifies the strengths and limitations of the principal CFD methodologies and offers guidance on their suitability for different classes of flow problems.
Keywords: 
;  ;  ;  ;  
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Prerpints.org logo

Preprints.org is a free preprint server supported by MDPI in Basel, Switzerland.

Subscribe

Disclaimer

Terms of Use

Privacy Policy

Privacy Settings

© 2026 MDPI (Basel, Switzerland) unless otherwise stated