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We present a novel methodology to generate curved high-order meshes featuring optimal mesh quality and geometric accuracy. The proposed technique combines a distortion measure and a geometric Full-size image (<1 K)-disparity measure into a single objective function. While the element distortion term takes into account the mesh quality, the Full-size image (<1 K)-disparity term takes into account the geometric error introduced by the mesh approximation to the target geometry. The proposed technique has several advantages. First, we are not restricted to interpolative meshes and therefore, the resulting mesh approximates the target domain in a non-interpolative way, further increasing the geometric accuracy. Second, we are able to generate a series of meshes that converge to the actual geometry with expected rate while obtaining high-quality elements. Third, we show that the proposed technique is robust enough to handle real-case geometries that contain gaps between adjacent entities. ; This research was partially supported by the Spanish Ministerio de Economía y Competitividad under grand contract CTM2014-55014-C3-3-R, and by the Government of Catalonia under grand contract 2014-SGR-1471. The work of the last author was supported by the European Commission through the Marie Sklodowska-Curie Actions (HiPerMeGaFlows project). ; Peer Reviewed ; Postprint (published version)

We present a novel methodology to generate curved high-order meshes featuring optimal mesh quality and geometric accuracy. The proposed technique combines a distortion measure and a geometric Full-size image (<1 K)-disparity measure into a single objective function. While the element distortion term takes into account the mesh quality, the Full-size image (<1 K)-disparity term takes into account the geometric error introduced by the mesh approximation to the target geometry. The proposed technique has several advantages. First, we are not restricted to interpolative meshes and therefore, the resulting mesh approximates the target domain in a non-interpolative way, further increasing the geometric accuracy. Second, we are able to generate a series of meshes that converge to the actual geometry with expected rate while obtaining high-quality elements. Third, we show that the proposed technique is robust enough to handle real-case geometries that contain gaps between adjacent entities. ; This research was partially supported by the Spanish Ministerio de Economía y Competitividad under grand contract CTM2014-55014-C3-3-R, and by the Government of Catalonia under grand contract 2014-SGR-1471. The work of the last author was supported by the European Commission through the Marie Sklodowska-Curie Actions (HiPerMeGaFlows project). ; Peer Reviewed ; Postprint (published version)

We present a stable high-order hybridizable discontinuous Galerkin (HDG) formulation coupled with high-order diagonal implicit Runge–Kuta (DIRK) schemes to simulate slightly compressible one-phase flow through porous media. The HDG stability depends on the selection of a single parameter and its definition is crucial to ensure the stability and to achieve the high-order properties of the method. Thus, we extend the work of Nguyen et al. in J Comput Phys 228, 8841–8855, 2009 to deduce an analytical expression for the stabilization parameter using the material parameters of the problem and the Engquist-Osher monotone flux scheme. The formulation is high-order accurate for the pressure, the flux and the velocity with the same convergence rate of P+1, being P the polynomial degree of the approximation. This is important because high-order methods have the potential to reduce the computational cost while obtaining more accurate solutions with less dissipation and dispersion errors than low order methods. The formulation can use unstructured meshes to capture the heterogeneous properties of the reservoir. In addition, it is conservative at the element level, which is important when solving PDE's in conservative form. Moreover, a hybridization procedure can be applied to reduce the size of the global linear system. To keep these advantages, we use DIRK schemes to perform the time integration. DIRK schemes are high-order accurate and have a low memory footprint. We show numerical evidence of the optimal convergence rates obtained with the proposed formulation. Finally, we present several examples to illustrate the capabilities of the formulation. ; This work has been supported by FEDER and the Spanish Government, Ministerio de Ciencia Innovación y Universidades grant project contract PGC2018-097257-B-C33 and Ministerio de Economía y Competitividad under grant BES-2015-072833. ; Peer Reviewed ; Postprint (author's final draft)

We present a memory-efficient high-order hybridizable discontinuous Galerkin (HDG) formulation coupled with high-order fully implicit Runge-Kutta schemes for immiscible and incompressible two-phase flow through porous media. To obtain the same high-order accuracy in space and time, we propose using high-order temporal schemes that allow using large time steps. Therefore, we require unconditionally stable temporal schemes for any combination of element size, polynomial degree and time step. Specifically, we use the Radau IIA and Gauss-Legendre schemes, which are unconditionally stable, achieve high-order accuracy with few stages, and do not suffer order reduction in this problem. To reduce the memory footprint of coupling these spatial and temporal high-order schemes, we rewrite the non-linear system. In this way, we achieve a better sparsity pattern of the Jacobian matrix and less coupling between stages. Furthermore, we propose a fix-point iterative method to further reduce the memory consumption. The saturation solution may present sharp fronts. Thus, the high-order approximation may contain spurious oscillations. To reduce them, we introduce artificial viscosity. We detect the elements with high-oscillations using a computationally efficient shock sensor obtained from the saturation solution and the post-processed saturation of HDG. Finally, we present several examples to assess the capabilities of our formulation. ; This work has been supported by FEDER and the Spanish Government, Ministerio de Economía y Competitividad grant project contract CTM2014-55014-C3-3-R, Ministerio de Ciencia Innovación y Universidades grant project contract PGC2018-097257-B-C33 and the grant BES-2015-072833. ; Peer Reviewed ; Postprint (author's final draft)

We present a new disparity functional to measure and improve the geometric accuracy of a curved high-order mesh that approximates a target geometry model. We have devised the disparity to account for compound models, be independent of the entity parameterization, and allow trimmed entities. The disparity depends on the physical mesh and the auxiliary parametric meshes. Since it is two times differentiable on all these variables, we can minimize it with a second-order method. Its minimization with the parametric meshes as design variables measures the geometric accuracy of a given mesh. Furthermore, the minimization with both the physical and parametric meshes as design variables improves the geometric accuracy of an initial mesh. We have numerical evidence that the obtained meshes converge to the target geometry (unitary normal) algebraically, in terms of the element size, with order 2p (2p-1, respectively), where p is the polynomial degree of the mesh. Although we obtain meshes with non-interpolative boundary nodes, we propose a post-process to enforce, if required by the application, meshes with interpolative boundary nodes and featuring the same order of geometric accuracy. In conclusion, we can obtain super-convergent orders, at least for sufficiently smooth parametric curve (surface) entities, for meshes of polynomial degrees up to 4 (3, respectively). In perspective, this super-convergence might enable using a lower polynomial degree to approximate the geometry than to approximate the solution without hampering the required geometric accuracy for high-order analysis. ; This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme under grant agreement No 715546. This work has also received funding from the Generalitat de Catalunya under grant number 2017 SGR 1731. This work has been supported by FEDER and the Spanish Government, Ministerio de Ciencia, Innovación y Universidades grant project contract PGC2018-097257-B-C33. The work of Xevi Roca has been partially supported by the Spanish Ministerio de Economía y Competitividad under the personal grant agreement RYC-2015-01633. ; Peer Reviewed ; Postprint (published version)