Remarks on the Approximation of the Euler Equations by a Low Order Model

Fluid flows are very often governed by the dynamics of a small number of coherent structures, i.e., fluid features which keep their individuality during the evolution of the flow. The purpose of this paper is to present a way to simulate the Euler equations on the basis of the evolution of such coherent structures. One way to extract from flow simulations some basis functions which can be interpreted as coherent structures is by Proper Orthogonal Decomposition (POD). Then, by means of a Galerkin projection, it is possible to find the system of ODEs which approximates the problem in the finite dimensional space spanned by the basis functions found. Issues concerning the stability and the accuracy of such an approximation are discussed. It is found that a straight-forward Galerkin method is unstable. Some features of discontinuous Galerkin methods are therefore incorporated to achieve stability, which is proved for a linear scalar case. In addition, we propose a way to reduce the cost of the computation and to increase accuracy at the same time. Some one-dimensional computational experiments are presented, including shock tube simulations and rarefaction fans

Optimal Inverse Method for Turbomachinery Design

An adjoint optimization method based on the solution of an inverse problem is proposed. In this formulation, the distributed control is a flow variable on the domain boundary, for example pressure. The adjoint formulation delivers the functional gradient with respect to such flow variable distribution, and a descent method can be used for optimization. The flow constraints are easily imposed in the parametrization of the controls, thus those problems with many strict constraints on the flow solution can be solved very efficiently. Conversely, the geometric constraints are imposed either by additional partial differential equations, or by penalization. Constraining the geometric solution, the classical limitations of the inverse problem design are overcome. Two examples pertaining to internal flows are give

Efficient asymptotic preserving schemes for BGK and ES-BGK models on cartesian grids

This work is devoted to the study of complex flows where hydrodynamic and rarefied regimes coexist. This kind of flows are found in vacuum pumps or hypersonic re-entries of space vehicles where the distance between gas molecules is so large that their microscopic behaviour differ from the average behaviour of the flow and has be taken into account. We then consider two models of the Boltzmann equation viable for such flows: the BGK model dans the ES-BGK model. We first devise a new wall boundary condition ensuring a smooth transition of the solution from the rarefied regime to the hydrodynamic regime. We then describe how this boundary condition (and boundary conditions in general) can be enforced with second order accuracy on an immersed body on Cartesian grids preserving the asymptotic limit towards compressible Euler equations. We exploit the ability of Cartesian grids to massive parallel computations (HPC) to drastically reduce the computational time which is an issue for kinetic models. A new approach considering local velocity grids is then presented showing important gain on the computational time (up to 80$\%$). 3D simulations are also presented showing the efficiency of the methods. Finally, solid particle transport in a rarefied flow is studied. The kinetic model is coupled with a Vlasov-type equation modeling the passive particle transport solved with a method based on remeshing processes. As application, we investigate the realistic test case of the pollution of optical devices carried by satellites due to incompletely burned particles coming from the altitude control thrusters

Iterative Methods for Model Reduction by Domain Decomposition

We propose a method to reduce the computational effort to solve a partial differential equation on a given domain. The main idea is to split the domain of interest in two subdomains, and to use different approximation methods in each of the two subdomains. In particular, in one subdomain we discretize the governing equations by a canonical scheme, whereas in the other one we solve a reduced order model of the original problem. Different approaches to couple the low-order model to the usual discretization are presented. The effectiveness of these approaches is tested on numerical examples pertinent to non-linear model problems including the Laplace equation with non-linear boundary conditions and the compressible Euler equations

A one-shot overlapping Schwarz method for component-based model reduction: application to nonlinear elasticity

We propose a component-based (CB) parametric model order reduction (pMOR) formulation for parameterized nonlinear elliptic partial differential equations (PDEs) based on overlapping subdomains. Our approach reads as a constrained optimization statement that penalizes the jump at the components' interfaces subject to the approximate satisfaction of the PDE in each local subdomain. Furthermore, the approach relies on the decomposition of the local states into a port component -- associated with the solution on interior boundaries -- and a bubble component that vanishes at ports: this decomposition allows the static condensation of the bubble degrees of freedom and ultimately allows to recast the constrained optimization statement into an unconstrained statement, which reads as a nonlinear least-square problem and can be solved using the Gauss-Newton method. We present thorough numerical investigations for a two-dimensional neo-Hookean nonlinear mechanics problem to validate our proposal; we further discuss the well-posedness of the mathematical formulation and the \emph{a priori} error analysis for linear coercive problems

Shape optimization governed by the Euler equations using an adjoint method

A numerical approach for the treatment of optimal shape problems governed by the Euler equations is discussed. Focus is on flows with embedded shocks. A very simple problem is considered: the design of a quasi-one-dimensional Laval nozzle. A cost function and a set of Lagrange multipliers are introduced to achieve the minimum. The nature of the resulting costate equations is discussed. A theoretical difficulty that arises for cases with embedded shocks is pointed out and solved. Finally, some results are given to illustrate the effectiveness of the method

Advection Modes by Optimal Mass Transfer

Classical model reduction techniques approximate the solution of a physical model by a limited number of global modes. These modes are usually determined by variants of principal component analysis. Global modes can lead to reduced models that perform well in terms of stability and accuracy. However, when the physics of the model is mainly characterized by advection, the non-local representation of the solution by global modes essentially reduces to a Fourier expansion. In this paper we describe a method to determine a low-order representation of advection. This method is based on the solution of Monge-Kantorovich mass transfer problems. Examples of application to point vortex scattering, Korteweg-de Vries equation, Von K'arm'an wake and hurricane Dean advection are discussed.Les techniques classiques de réduction de modèle se basent souvent sur une décomposition en modes globaux. Cette représentation n'est pas efficace lorsqu' on essaye de décrire des phénomènes de transport. Dans ce rapport on décrit une méthode, basée sur le transport optimal, pour déterminer une représentation réduite de l'advection. Des exemples sont décrits, concernant le vortex scattering, le sillage de Von Karman, l'équation de Korteweg-de-Vries, le tracking de l'ouragan Dean

Accurate Sharp Interface Scheme for Multimaterials

We present a method to capture the evolution of a contact discontinuity separating two different material. A locally non-conservative scheme allows an accurate and stable simulation while the interface is kept sharp. Numerical illustrations include problems involving fluid and elastic problems

Model order reduction by convex displacement interpolation

We present a nonlinear interpolation technique for parametric fields that exploits optimal transportation of coherent structures of the solution to achieve accurate performance. The approach generalizes the nonlinear interpolation procedure introduced in [Iollo, Taddei, J. Comput. Phys., 2022] to multi-dimensional parameter domains and to datasets of several snapshots. Given a library of high-fidelity simulations, we rely on a scalar testing function and on a point set registration method to identify coherent structures of the solution field in the form of sorted point clouds. Given a new parameter value, we exploit a regression method to predict the new point cloud; then, we resort to a boundary-aware registration technique to define bijective mappings that deform the new point cloud into the point clouds of the neighboring elements of the dataset, while preserving the boundary of the domain; finally, we define the estimate as a weighted combination of modes obtained by composing the neighboring snapshots with the previously-built mappings. We present several numerical examples for compressible and incompressible, viscous and inviscid flows to demonstrate the accuracy of the method. Furthermore, we employ the nonlinear interpolation procedure to augment the dataset of simulations for linear-subspace projection-based model reduction: our data augmentation procedure is designed to reduce offline costs -- which are dominated by snapshot generation -- of model reduction techniques for nonlinear advection-dominated problems