arXiv subjects

Valentin Nkana Ngan

Publications and source records attributed to Valentin Nkana Ngan.

4 recordsLinked to original sources

arXiv2026

An efficient hyper reduced-order model for segregated solvers for geometrical parametrization problems

We propose an efficient hyper-reduced order model (HROM) designed for segregated finite-volume solvers in geometrically parametrized problems. The method follows a discretize-then-project strategy: the full-order operators are first assembled using finite volume or finite element discretizations and then projected onto low-dimensional spaces using a small set of spatial sampling points, selected through hyper-reduction techniques such as DEIM. This approach removes the dependence of the online computational cost on the full mesh size. The method is assessed on three benchmark problems: a linear transport equation, a nonlinear Burgers equation, and the incompressible Navier--Stokes equations. The results show that the hyper-reduced models closely match full-order solutions while achieving substantial reductions in computational time. Since only a sparse subset of mesh cells is evaluated during the online phase, the method is naturally parallelizable and scalable to very large meshes. These findings demonstrate that hyper-reduction can be effectively combined with segregated solvers and geometric parametrization to enable fast and accurate CFD simulations.

math.NA↗

arXiv2025

A reduced-order model for segregated fluid-structure interaction solvers based on an ALE approach

This article presents a Galerkin projection-based reduced-order modelling (ROM) approach for segregated fluid-structure interaction (FSI) problems, formulated within an Arbitrary Lagrangian Eulerian (ALE) framework at low Reynolds numbers using the Finite Volume Method (FVM). The ROM is constructed using Proper Orthogonal Decomposition (POD) and incorporates a data-driven technique that combines classical Galerkin projection with radial basis function (RBF) networks. The results demonstrate the numerical stability and accuracy of the proposed method relative to the high-fidelity model. The ROM successfully captures transient flow fields and, importantly, the forces acting on the moving structure without exhibiting unphysical growth or divergence over time. This is further supported by the bounded evolution of error metrics and physical observables, which remain consistent with the full-order simulations throughout the prediction horizon. The method's effectiveness is validated through a benchmark vortex-induced vibration (VIV) case involving a circular cylinder at Reynolds number Re=200. The hybrid ROM approach yields an accurate and efficient tool for solving FSI problems involving mesh motion.

math.NA↗

arXiv2024

A hybrid reduced-order model for segregated fluid-structure interaction solvers in an ALE approach at high Reynolds number

This study introduces a first step for constructing a hybrid reduced-order models (ROMs) for segregated fluid-structure interaction in an Arbitrary Lagrangian-Eulerian (ALE) approach at a high Reynolds number using the Finite Volume Method (FVM). The ROM is driven by proper orthogonal decomposition (POD) with hybrid techniques that combines the classical Galerkin projection and two data-driven methods (radial basis networks , and neural networks/ long short term memory). Results demonstrate the ROM ability to accurately capture the physics of fluid-structure interaction phenomena. This approach is validated through a case study focusing on flow-induced vibration (FIV) of a pitch-plunge airfoil at a high Reynolds number 10000000.

physics.flu-dyn↗

arXiv2024

A segregated reduced order model of a pressure-based solver for turbulent compressible flows

This article provides a reduced-order modelling framework for turbulent compressible flows discretized by the use of finite volume approaches. The basic idea behind this work is the construction of a reduced-order model capable of providing closely accurate solutions with respect to the high fidelity flow fields. Full-order solutions are often obtained through the use of segregated solvers (solution variables are solved one after another), employing slightly modified conservation laws so that they can be decoupled and then solved one at a time. Classical reduction architectures, on the contrary, rely on the Galerkin projection of a complete Navier-Stokes system to be projected all at once, causing a mild discrepancy with the high order solutions. This article relies on segregated reduced-order algorithms for the resolution of turbulent and compressible flows in the context of physical and geometrical parameters. At the full-order level turbulence is modeled using an eddy viscosity approach. Since there is a variety of different turbulence models for the approximation of this supplementary viscosity, one of the aims of this work is to provide a reduced-order model which is independent on this selection. This goal is reached by the application of hybrid methods where Navier-Stokes equations are projected in a standard way while the viscosity field is approximated by the use of data-driven interpolation methods or by the evaluation of a properly trained neural network. By exploiting the aforementioned expedients it is possible to predict accurate solutions with respect to the full-order problems characterized by high Reynolds numbers and elevated Mach numbers.

physics.flu-dyn↗