Abstract
We consider a distributed order diffusion equation with space-dependent conductivity. The distributed order operator is defined via an integral of the usual fractional Caputo derivative multiplied by a weight function \(\omega \), i.e. \(\displaystyle \mathbb {D}_t^\omega u(t)=\int _0^1 \omega (\alpha )\partial _t^\alpha u(t) d\alpha \), where \(\partial _t^\alpha \) is the Caputo derivative of order \(\alpha \) given by \( \displaystyle \partial _t^\alpha u(t)=\frac{1}{\varGamma (1-\alpha )}\int _0^t(t-s)^{-\alpha }u_s(s)ds\).
We establish a new fully discrete finite volume scheme in which the discretization in space is performed using the finite volume method developed in [9] whereas the discretization of the distributed order operator \(\displaystyle \mathbb {D}_t^\omega u\) is given by an approximation of the integral, over the unit interval, using the known Mid Point rule and the approximation of the Caputo derivative \( \displaystyle \partial _t^\alpha u\) is defined by the known L1-formula on the uniform temporal mesh.
We prove rigorously new error estimates in \(L^\infty (L^2)\) and \(L^2(H^1)\)–discrete norms. These error estimates are obtained thanks to a new well developed discrete a priori estimate and also to the fact that the full discretization of the distributed-order fractional derivative leads to multi-term fractional order derivatives but the number of these terms is varying accordingly with the approximation of the integral over (0, 1).
This note is a continuation of our previous work [6] which dealt with the Gradient Discretization method (GDM) for time fractional-diffusion equation in which the fractional order derivative is fixed and it is given in the Caputo sense (without consideration the distributed-order fractional derivative) and conductivity is equal to one.
Supported by MCS team (LAGA Laboratory) of the “Université Sorbonne- Paris Nord”.
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Benkhaldoun, F., Bradji, A. (2023). Convergence Analysis of a Finite Volume Scheme for a Distributed Order Diffusion Equation. In: Georgiev, I., Datcheva, M., Georgiev, K., Nikolov, G. (eds) Numerical Methods and Applications. NMA 2022. Lecture Notes in Computer Science, vol 13858. Springer, Cham. https://doi.org/10.1007/978-3-031-32412-3_6
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