Matteo Franciolini

Adaptive design of experiments for efficient and accurate estimation of aerodynamic loads

Purpose The purpose of this paper is to document an efficient and accurate approach to generate aerodynamic tables using computational fluid dynamics. This is demonstrated in the context of a concept transport aircraft model. Design/methodology/approach Two designs of experiment algorithms in combination with surrogate modelling are investigated. An adaptive algorithm is compared to an industry-standard algorithm used as a benchmark. Numerical experiments are obtained solving the Reynolds-averaged Navier–Stokes equations on a large computational grid. Findings This study demonstrates that a surrogate model built upon an adaptive design of experiments strategy achieves a higher prediction capability than that built upon a traditional strategy. This is quantified in terms of the sum of the squared error between the surrogate model predictions and the computational fluid dynamics results. The error metric is reduced by about one order of magnitude compared to the traditional approach. Practical implications This work lays the ground to obtain more realistic aerodynamic predictions earlier in the aircraft design process at manageable costs, improving the design solution and reducing risks. This may be equally applied in the analysis of other complex and non-linear engineering phenomena. Originality/value This work explores the potential benefits of an adaptive design of experiment algorithm within a prototype working environment, whereby the maximum number of experiments is limited and a large parameter space is investigated.

Hybrid OPENMP/MPI parallelization of a high-order Discontinuous Galerkin CFD/CAA solver

This paper describes the implementation of an hybrid OpenMP/MPI parallelization strategy in a Discontinuous Galerkin solver used for DNS and LES or CAA computations, to fruitfully exploit the modern massively parallel HPC facilities. It is usually believed that the sheared memory view of OpenMP can easily increase the parallel efficiency of codes dealing with multi-core clusters. The idea consists of running calculations on those machines restricting as much as possible the use of the MPI library to the communications between nodes and exploiting the shared memory paradigm within a node. However, in practice, the achievement of a real parallel performance gain is not straightforward. Moreover, as far as DG solvers are concerned, almost nothing is reported in the current literature about the hybrid MPI/OpenMP implementation. In this work a colouring algorithm has been employed for OpenMP. The resulting hybrid strategy performs quite satisfactory, since generally it is more efficient of the pure MPI implementation. However, the performances are heavily dependent on hardware platforms, as well as on computational details such as the polynomial order of space discretization or the number of computational elements. Several scalability tests have been performed, resulting in the conclusion that the best performance can be achieved only with a proper choice of the number of MPI partition and OpenMP threads to be used within a single node. The reliability of the method was here assessed by solving the Taylor Green vortex problem at Reynolds numbers equal to 800 and 1600 and the Linear Euler acustic scattering from a rigid sphere.

OpenMP Parallelization Strategies for a Discontinuous Galerkin Solver

This paper aims to report on the open multi-processing (OpenMP) parallel implementation of a fully unstructured high-order discontinuous Galerkin (DG) solver for computational fluid dynamics and computational aeroacoustics applications. Even if the use of OpenMP paradigm is confined to shared memory systems, it has some advantages over the use of the message passing interface (MPI) library, and getting the best of this approach potentially improves the parallel efficiency of codes running on clusters of multi-core nodes. While with MPI the use of a domain decomposition algorithm is almost unavoidable, the OpenMP shared memory context offers several opportunities. Three strategies, here optimised for a DG solver, are presented and compared: the first refers to a customization of a colouring approach, the second mimics an MPI implementation in the OpenMP context, while the third method is somehow half way between the previous two. The numerical tests performed on both inviscid and viscous test cases indicate that, thanks to the compactness of the DG discretization, all the code versions perform quite satisfactory. In particular, the domain decomposition algorithm reaches the highest level of parallel efficiency at low computational loads while the colouring approach excels at larger computational loads and it can be easily implemented within an existing MPI code. Moreover, colouring is very well suited to deal with hardware accelerators, an opportunity given by the OpenMP 4.0 standard. Finally, the performance gain observed in using a hybrid MPI/OpenMP version of the DG code on high performance computing facilities is demonstrated.

A Matrix-Free Incompressible DG Algorithm for the Simulation of Turbulent Flows

The paper deals with an efficient implementation of a discontinuous Galerkin solver for the simulation of incompressible turbulent flows. The artificial compressibility flux approach is employed for space discretization, while a Rosenbrock-type Runge–Kutta scheme coupled with a matrix-free linear solver is used for the implicit time integration. The code has been here applied to solve the turbulent Rayleigh–Benard convection at different Rayleigh numbers. The results prove the reliability of the proposed solution strategy and its suitability for the Implicit Large Eddy Simulation of turbulent flows.