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Dive into the research topics where Daniil Svyatskiy is active.

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Featured researches published by Daniil Svyatskiy.


Journal of Computational Physics | 2007

Monotone finite volume schemes for diffusion equations on unstructured triangular and shape-regular polygonal meshes

Konstantin Lipnikov; Mikhail J. Shashkov; Daniil Svyatskiy; Yuri V. Vassilevski

We consider a non-linear finite volume (FV) scheme for stationary diffusion equation. We prove that the scheme is monotone, i.e. it preserves positivity of analytical solutions on arbitrary triangular meshes for strongly anisotropic and heterogeneous full tensor coefficients. The scheme is extended to regular star-shaped polygonal meshes and isotropic heterogeneous coefficients.


Journal of Computational Physics | 2009

Interpolation-free monotone finite volume method for diffusion equations on polygonal meshes

Konstantin Lipnikov; Daniil Svyatskiy; Yuri V. Vassilevski

We developed a new monotone finite volume method for diffusion equations. The second-order linear methods, such as the multipoint flux approximation, mixed finite element and mimetic finite difference methods, are not monotone on strongly anisotropic meshes or for diffusion problems with strongly anisotropic coefficients. The finite volume (FV) method with linear two-point flux approximation is monotone but not even first-order accurate in these cases. The developed monotone method is based on a nonlinear two-point flux approximation. It does not require any interpolation scheme and thus differs from other nonlinear finite volume methods based on a two-point flux approximation. The second-order convergence rate is verified with numerical experiments.


Journal of Computational Physics | 2011

Analysis of the monotonicity conditions in the mimetic finite difference method for elliptic problems

Konstantin Lipnikov; Gianmarco Manzini; Daniil Svyatskiy

The maximum principle is one of the most important properties of solutions of partial differential equations. Its numerical analog, the discrete maximum principle (DMP), is one of the most difficult properties to achieve in numerical methods, especially when the computational mesh is distorted to adapt and conform to the physical domain or the problem coefficients are highly heterogeneous and anisotropic. Violation of the DMP may lead to numerical instabilities such as oscillations and to unphysical solutions such as heat flow from a cold material to a hot one. In this work, we investigate sufficient conditions to ensure the monotonicity of the mimetic finite difference (MFD) method on two- and three-dimensional meshes. These conditions result in a set of general inequalities for the elements of the mass matrix of every mesh element. Efficient solutions are devised for meshes consisting of simplexes, parallelograms and parallelepipeds, and orthogonal locally refined elements as those used in the AMR methodology. On simplicial meshes, it turns out that the MFD method coincides with the mixed-hybrid finite element methods based on the low-order Raviart-Thomas vector space. Thus, in this case we recover the well-established conventional angle conditions of such approximations. Instead, in the other cases a suitable design of the MFD method allows us to formulate a monotone discretization for which the existence of a DMP can be theoretically proved. Moreover, on meshes of parallelograms we establish a connection with a similar monotonicity condition proposed for the Multi-Point Flux Approximation (MPFA) methods. Numerical experiments confirm the effectiveness of the considered monotonicity conditions.


Journal of Computational Physics | 2010

A monotone finite volume method for advection-diffusion equations on unstructured polygonal meshes

Konstantin Lipnikov; Daniil Svyatskiy; Yuri V. Vassilevski

We present a new second-order accurate monotone finite volume (FV) method for the steady-state advection-diffusion equation. The method uses a nonlinear approximation for both diffusive and advective fluxes and guarantees solution non-negativity. The interpolation-free approximation of the diffusive flux uses the nonlinear two-point stencil proposed in Lipnikov [23]. Approximation of the advective flux is based on the second-order upwind method with a specially designed minimal nonlinear correction. The second-order convergence rate and monotonicity are verified with numerical experiments.


Journal of Computational Physics | 2008

A multilevel multiscale mimetic (M3) method for two-phase flows in porous media

Konstantin Lipnikov; John David Moulton; Daniil Svyatskiy

We describe a multilevel multiscale mimetic (M^3) method for solving two-phase flow (water and oil) in a heterogeneous reservoir. The governing equations are the elliptic equation for the reservoir pressure and the hyperbolic equation for the water saturation. On each time step, we first solve the pressure equation and then use the computed flux in an explicit upwind finite volume method to update the saturation. To reduce the computational cost, the pressure equation is solved on a much coarser grid than the saturation equation. The coarse-grid pressure discretization captures the influence of multiple scales via the subgrid modeling technique for single-phase flow recently proposed in [Yu. A. Kuznetsov. Mixed finite element method for diffusion equations on polygonal meshes with mixed cells. J. Numer. Math., 14 (4) (2006) 305-315; V. Gvozdev. discretization of the diffusion and Maxwell equations on polyhedral meshes. Technical Report Ph.D. Thesis, University of Houston, 2007; Yu. Kuznetsov. Mixed finite element methods on polyhedral meshes for diffusion equations, in: Computational Modeling with PDEs in Science and Engineering, Springer-Verlag, Berlin, in press]. We extend significantly the applicability of this technique by developing a new robust and efficient method for estimating the flux coarsening parameters. Specifically, with this advance the M^3 method can handle full permeability tensors and general coarsening strategies, which may generate polygonal meshes on the coarse grid. These problem dependent coarsening parameters also play a critical role in the interpolation of the flux, and hence, in the advection of saturation for two-phase flow. Numerical experiments for two-phase flow in highly heterogeneous permeability fields, including layer 68 of the SPE Tenth Comparative Solution Project, demonstrate that the M^3 method retains good accuracy for high coarsening factors in both directions, up to 64 for the considered models. Moreover, we demonstrate that with a simple and efficient temporal updating strategy for the coarsening parameters, we achieve accuracy comparable to the fine-scale solution, but at a fraction of the cost.


SIAM Journal on Scientific Computing | 2013

Anderson Acceleration for Nonlinear Finite Volume Scheme for Advection-Diffusion Problems

Konstantin Lipnikov; Daniil Svyatskiy; Yuri V. Vassilevski

We consider the solution of systems of nonlinear algebraic equations that appear in a positivity preserving finite volume scheme for steady-state advection-diffusion equations. We propose and analyze numerically an efficient strategy for accelerating the Picard method when it is applied to these systems. The strategy is based on the Anderson acceleration and the adaptive inexact solution of linear systems. We demonstrate its numerical robustness for three black-box preconditioners.


Mathematical Models and Methods in Applied Sciences | 2014

M-Adaptation in the mimetic finite difference method

Vitaliy Gyrya; Konstantin Lipnikov; Gianmarco Manzini; Daniil Svyatskiy

The mimetic finite difference method produces a family of schemes with equivalent properties such as the stencil size, stability region, and convergence order. Each member of this family is defined by a set of parameters which can be chosen locally for every mesh element. The number of parameters depends on the geometry of a particular mesh element. M-Adaptation is a new adaptation methodology that identifies a member of this family with additional (superior) properties compared to the other schemes in the family. We analyze the enforcement of the discrete maximum principles for the diffusion equation in the primal and dual forms, the reduction of numerical dispersion and anisotropy for the acoustic wave equation, and the optimization of the performance of multi-grid solvers.


Archive | 2011

Monotonicity Conditions in the Mimetic Finite Difference Method

Konstantin Lipnikov; Gianmarco Manzini; Daniil Svyatskiy

The maximum principle is a major property of solutions of partial differential equations. In this work, we analyze a few constructive algorithms that allow one to embed this property into a mimetic finite difference (MFD) method. The algorithms search in the parametric family of MFD methods for a member that guarantees the discrete maximum principle (DMP). A set of sufficient conditions for the DMP is derived for a few types of meshes. For general meshes, a numerical optimization procedure is proposed and studied numerically.


Journal of Computational Physics | 2017

An electrostatic Particle-In-Cell code on multi-block structured meshes

Collin S. Meierbachtol; Daniil Svyatskiy; Gian Luca Delzanno; Louis James Vernon; J. David Moulton

Abstract We present an electrostatic Particle-In-Cell (PIC) code on multi-block, locally structured, curvilinear meshes called Curvilinear PIC (CPIC). Multi-block meshes are essential to capture complex geometries accurately and with good mesh quality, something that would not be possible with single-block structured meshes that are often used in PIC and for which CPIC was initially developed. Despite the structured nature of the individual blocks, multi-block meshes resemble unstructured meshes in a global sense and introduce several new challenges, such as the presence of discontinuities in the mesh properties and coordinate orientation changes across adjacent blocks, and polyjunction points where an arbitrary number of blocks meet. In CPIC, these challenges have been met by an approach that features: (1) a curvilinear formulation of the PIC method: each mesh block is mapped from the physical space, where the mesh is curvilinear and arbitrarily distorted, to the logical space, where the mesh is uniform and Cartesian on the unit cube; (2) a mimetic discretization of Poissons equation suitable for multi-block meshes; and (3) a hybrid (logical-space position/physical-space velocity), asynchronous particle mover that mitigates the performance degradation created by the necessity to track particles as they move across blocks. The numerical accuracy of CPIC was verified using two standard plasma–material interaction tests, which demonstrate good agreement with the corresponding analytic solutions. Compared to PIC codes on unstructured meshes, which have also been used for their flexibility in handling complex geometries but whose performance suffers from issues associated with data locality and indirect data access patterns, PIC codes on multi-block structured meshes may offer the best compromise for capturing complex geometries while also maintaining solution accuracy and computational efficiency.


international conference on conceptual structures | 2010

A multiscale multilevel mimetic (M3) method for well-driven flows in porous media☆

Konstantin Lipnikov; J. David Moulton; Daniil Svyatskiy

Abstract The multiscale multilevel mimetic (M 3 ) method was designed in [13] for the accurate modeling of two-phase flows in highly heterogeneous porous media on general polygonal meshes. In this article, it is extended to well-driven flows in porous media. We demonstrate its ability to treat accurately non-orthogonal locally-refined meshes and tensorial material properties.

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Konstantin Lipnikov

Los Alamos National Laboratory

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J. David Moulton

Los Alamos National Laboratory

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Gianmarco Manzini

Los Alamos National Laboratory

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Yuri V. Vassilevski

Moscow Institute of Physics and Technology

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John David Moulton

Los Alamos National Laboratory

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Mikhail J. Shashkov

Los Alamos National Laboratory

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Adam L. Atchley

Los Alamos National Laboratory

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Cathy J. Wilson

Los Alamos National Laboratory

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David Moulton

Los Alamos National Laboratory

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Ethan T. Coon

Los Alamos National Laboratory

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