Search arXiv⌕ Search

arXiv · 2610.19748

Finite Volume Methods

Abstract

This chapter focuses on finite volume methods. The finite volume method is a discretization method that is well suited for the numerical simulation of various types (for instance, elliptic, parabolic, or hyperbolic) of conservation laws; it has been extensively used in several engineering fields, such as fluid mechanics, heat and mass transfer, or petroleum engineering. Some of the important features of the finite volume method are similar to those of the finite element method: it may be used on arbitrary geometries, using structured or unstructured meshes, and it leads to robust schemes. The finite volume method is locally conservative because it is based on a ``balance'' approach: a local balance is written on each discretization cell that is often called ``control volume;'' by the divergence formula, an integral formulation of the fluxes over the boundary of the control volume is then obtained. The fluxes on the boundary are discretized with respect to the discrete unknowns.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Robert Eymard, Thierry Gallou{ë}t, Rapha{è}le Herbin. 2026-10-14. Finite Volume Methods. https://arxiv.org/abs/2610.19748

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Optimal Polynomial Tractability Exponents for the Inverse Star Discrepancy

The inverse of the star discrepancy $n^\ast(d, \varepsilon)$ satisfies \[ d \varepsilon^{-1} \lesssim n^{\ast}(d,\varepsilon)\lesssim d\varepsilon^{-2} \] for all $d \in \mathbb{N}$ and $0 < \varepsilon < \varepsilon_0$. The upper bound was established by Heinrich, Novak, Wasilkowski and Woźniakowski (2001), while the lower bound is due to Hinrichs (2004). Steinerberger (2023) subsequently gave an elementary proof of the latter result. These bounds imply that the inverse of the star discrepancy depends linearly on the dimension, but the exact exponent of $\varepsilon^{-1}$ had remained open. In this paper we prove a lower bound which shows that the exponent $2$ of $\varepsilon^{-1}$ in the upper bound cannot be improved. More precisely, for every $0<α<1$ and fixed $0 0$ and $\varepsilon_{α,A}>0$ such that, for every $0<\varepsilon<\varepsilon_{α,A}$ and every integer $d$ satisfying \[ A\varepsilon^{-α}\le d\le B\varepsilon^{-α}, \] one has \[ n^\ast(d,\varepsilon) \ge c_{α,B}\,d\,\varepsilon^{-(2-α)}. \] Along these polynomial strips the right-hand side is of order $\varepsilon^{-2}$. Consequently, every uniform polynomial upper estimate $n^{\ast}(d,\varepsilon)\le C d^q\varepsilon^{-p}$ must satisfy $p\ge2$. Together with the lower bound of Hinrichs (2004), which forces $q\ge1$, this proves that the exponents $p=2$ and $q=1$ in the Heinrich--Novak--Wasilkowski--Woźniakowski upper bound are individually optimal. In particular, the optimal exponent $p^\ast = 2$.

math.NA↗

The Normal Procrustes Problem: A Riemannian Optimization Approach

For given $m \times n$ data matrices $X, Y$, we investigate the Normal Procrustes Problem---the least squares optimization problem that aims to minimize $\|AX-Y\|_F^2$, where $A$ is constrained to be a normal $m \times m$ matrix. As far as the author of this article is aware, no other method that attempts to solve the Normal Procrustes Problem exists in the literature; we thus propose what is, to our knowledge, the first such method. We, furthermore, adapt our approach to address the Real Normal Procrustes Problem, where $A$ must be real. In our treatment of these problems, we first reduce our complex and real objective functions to be purely optimizable over the Riemannian manifolds of the unitary and real orthogonal matrices, respectively. This reduction enables us to apply techniques in Riemannian manifold optimization to approximate solutions to both. The Closest Normal Matrix and Real Closest Normal Matrix Problems are both special cases of their respective Procrustes Problems (by setting $m = n$ and $X = I_m$) and have been previously studied in the literature. Our approach thus recovers a novel Riemannian optimization method for approximating solutions to both these problems. We further numerically test the performance of our method across all such problems (including against previously developed algorithms on the Closest Normal Matrix Problems) and obtain competitive residuals and favorable scaling in wall-clock time.

math.NA↗

Galerkin finite element method for time fractional diffusion-wave equations with constant time delay

This paper studies numerical methods for the time fractional diffusion-wave equations with the constant delay. Using a symmetric fractional-order reduction technique and introducing the auxiliary variable $w$, we reformulate the original problem as an equivalent coupled system and for the first time carry out a rigorous regularity analysis of $w$. Based on the symmetric graded time mesh, the coupled problem is discretised by the $L1$ scheme in time and a standard finite element method in space, and the stability result of the developed fully discrete finite element scheme is established by means of a discrete fractional Grönwall inequality. Furthermore, we derive a new comparison principle and reconstruct a new barrier function on each interval $((k-1)τ,kτ]$, $1\le k\le K$ according to the properties of the symmetric graded mesh. Based on the new barrier function, we obtain new relevant properties, which lead to a novel and crucial stability property for the discrete fractional operator. Combining the obtained local truncation error in time gives a local error estimate for the fully discrete finite element scheme, which shows that our convergence result provides a unified framework that seamlessly encompasses both pointwise-in-time error estimates on uniform meshes and global-in-time error estimates on symmetrical graded meshes. Numerical experiments confirm the theoretical results.

math.NA↗