Computational Details

This section provides details of the computational approach used for the simulation of the present flow problem. Firstly, the main numerical features of the code MIGALE are given. Then, information about the computational grid is provided. Finally, the statistical quantities and their computation are explained.

Computational approach

The CFD code MIGALE can solve both compressible and incompressible flow problems and implements different flow models ranging from the Euler to the RANS equations. The solver uses the Discontinuous Galerkin (DG) method for the spatial discretization of the governing equations, here the compressible Navier-Stokes equations. The discontinuous nature of the numerical solution requires introducing a special treatment of the inviscid interface flux and of the viscous flux. For the former it is common practice to use suitably defined numerical flux functions, which ensure conservation and account for wave propagation. To this purpose, in the compressible case, the exact Riemann solver of Gottlieb et al. (1988) is mainly used. For the latter, the 2nd-scheme of Bassi and Rebay (BR2) is employed, see Bassi et al. (1997). The devised method uses hierarchical and orthonormal polynomial basis functions defined in the physical (mesh) space and relies for the time integration on, primarily, high-order Rosenbrock linearly-implicit schemes, see Bassi et al. (2015). The Jacobian matrix of the spatial discretization is computed analytically and linear systems are solved using preconditioned GMRES methods from the PETSc library (Portable Extensible Toolkit for Scientific Computations). For production runs time integration is coupled with time-step adaptation strategies able to improve the robustness and the efficiency in terms of time-to-solution of simulations, see Noventa et al. (2020).

For the present computation the fourth-order DG spatial discretization is combined with the fifth-order eight stages Rosenbrock-type scheme of Di Marzo (1993).

Spatial and temporal resolution, grids

The computational grid is made of roughly 54 million elements with quadratic edges, resulting in more than 1 billion Degrees of Freedom (DoF) per equation. Fig. 1 illustrates the mesh above the bottom boundary from the inlet (left) to the outlet (right). It is clearly visible the mesh coarsening applied downstream the wing profile to to reduce the solution gradients, thus mitigating spurious perturbations possibly originating at the outlet boundary. This coarsening is applied in two steps: (i) in both span and stream direction above the solid wall boundary condition for $10<x/T<17$ and (ii) only in stream direction above the symmetry boundary condition $x/T>17$ . Fig. 2 shows the detail of what aforementioned. Moreover, it allows to appreciate an additional coarsening in span direction towards the lateral boundaries $\pm 2.5<z/T<\pm 4$ . With this choice we want to alleviate the computational cost of the simulation by limiting the highly refined mesh to the region in proximity of the airfoil profile. The mesh is then extruded in the bottom boundary normal direction with a growing factor equal to $1.15$ , see Fig. 3.

Figure 1: Wing-body junction. Computational grid above the bottom boundary (top view)

Figure 2: Wing-body junction. Detail of the computational grid above the bottom boundary (top view)

Figure 3: Wing-body junction. Detail of the computational grid near the junction

For the temporal integration, a fifth order linearly implicit Rosenbrock scheme is applied with an adaptive time stepping technique. The resulting average time step is roughly $\Delta t\approx 1/285\,CTU$ , where $CTU$ is the convective time unit defined with respect to the reference velocity and the step height. In Tab. 1 are reported the wall space and time resolution at the checkpoint ( $x_{ckp}/H=-3.5$ ). Here, in order to account for the high-order nature of the numerical schemes employed, the wall resolution is obtained as

$\Delta x^{+}={{\dfrac {1}{\sqrt[{3}]{N_{DoF}}}}{\dfrac {\Delta x}{\delta _{\nu }}}},\qquad \Delta t^{+}={{\dfrac {1}{q}}{\dfrac {\Delta t\,u_{\tau }}{\delta _{\nu }}}}$ ,

where $N_{DoF}$ is the number of Degrees of Freedom per equation and $q$ the time integration order (here $N_{DoF}=20$ and $q=5$ ).

The flow has been simulated for $40\,CTU$ with a DG polynomial approximation of degree 1. After that, the polynomial degree has been increased to 3 and the simulation has been continued for $50\,CTU$ before gathering statistics for additional $75\,CTU$ .

${\Delta x^{+}}$	${\Delta y_{1}^{+}}$	${\Delta z^{+}}$	${\Delta t^{+}}$
${17.8}$	${1.30}$	${18.6}$	${0.10}$

Table 1: Near-wall space and time resolution at the checkpoint (

x_{ckp}/H=-3.5

)

Computation of statistical quantities

Within code MIGALE turbulence statistics are collected on the run (see Appendix B of the list of desirable quantities (PDF)) in a set of points within each mesh element. The points location is the same as that of a nodal multi-dimensional Lagrangian basis obtained by the tensor product of a one-dimensional Lagrange basis built on a set of evenly spaced points at edges. The number of points on each edge is set to $k+1$ to be able to properly represent a $k$ -degree polynomial function. This choice results in $(k+1)^{3}$ points for each element.

The statistics collected during the production run are those of the list of baseline quantities for compressible codes reported in Tab. 4 of the list of desirable quantities (PDF). Starting from this list, the set of statistics reported in Tab.1 of the same document is computed in a single postprocessing step exploiting simple algebraic manipulations and spatial derivations. In particular, spatial derivatives are computed analytically within each mesh element starting from a least square approximation of degree $k+1$ over the set of points.

References

Gottlieb, J. J. and Groth, C. P. T.: Assessment of Riemann solvers for unsteady one-dimensional inviscid flows of perfect gases. Journal of Computational Physics, Vol. 78 (1988)
Bassi, F., Rebay, S., Mariotti, G., Pedinotti, S. and Savini, M.: A high-order accurate discontinuous finite element method for inviscid and viscous turbomachinery flows. in: R. Decuypere, G. Dibelius (Eds.), Proceedings of the 2nd European Conference on Turbomachinery Fluid Dynamics and Thermodynamics, Technologisch Instituut, Antwerpen, Belgium, (1997)
Bassi, F., Botti, L., Colombo, A., Ghidoni, A. and Massa, F.: Linearly implicit Rosenbrock-type Runge-Kutta schemes applied to the Discontinuous Galerkin solution of compressible and incompressible unsteady flows. Computers & Fluids (2015)
https://petsc.org/release/ PETSc Web page
G. Noventa, G., Massa, F., Rebay, S., Bassi, F. and Ghidoni, A.: Robustness and efficiency of an implicit time-adaptive discontinuous Galerkin solver for unsteady flows. Computers & Fluids (2020)
Di Marzo, G.: RODAS5(4) - Méthodes de Rosenbrock d'ordre 5(4) adaptées aux problèmes différentiels-algébriques. MSc Mathematics Thesis, Faculty of Science, University of Geneva, (1993)

Contributed by: Alessandro Colombo (UNIBG), Francesco Carlo Massa (UNIBG), Michael Leschziner (ICL/ERCOFTAC), Jean-Baptiste Chapelier (ONERA) — University of Bergamo (UNIBG), ICL (Imperial College London), ONERA

Front Page

Description

Computational Details

Quantification of Resolution

Statistical Data

Instantaneous Data

Storage Format