In this paper we present a method to treat interface jump conditions for constant coefficients Poisson problems that allows the use of standard "black box" solvers, without compromising accuracy. The basic idea of the new approach is similar to the Ghost Fluid Method (GFM). The GFM relies on corrections applied on nodes located across the interface for discretization stencils that straddle the interface. If the corrections are solution-independent, they can be moved to the right-hand-side (RHS) of the equations, producing a problem with the same linear system as if there were no jumps, only with a different RHS. However, achieving high accuracy is very hard (if not impossible) with the "standard" approaches used to compute the GFM correction terms.In this paper we generalize the GFM correction terms to a correction function, defined on a band around the interface. This function is then shown to be characterized as the solution to a PDE, with appropriate boundary conditions. This PDE can, in principle, be solved to any desired order of accuracy. As an example, we apply this new method to devise a 4 th order accurate scheme for the constant coefficients Poisson equation with discontinuities in 2D. This scheme is based on (i) the standard 9-point stencil discretization of the Poisson equation, (ii) a representation of the correction function in terms of bicubics, and (iii) a solution of the correction function PDE by a least squares minimization. Several applications of the method are presented to illustrate its robustness dealing with a variety of interface geometries, its capability to capture sharp discontinuities, and its high convergence rate.
In many complex aerospace applications, it is common to use numerical tools to evaluate the unsteady aerodynamic behavior. The present work presents an alternate formulation for the state-space representation of aeroelastic systems based on digital control theory that is shown to be more effective and accurate for the coupling of numerical solutions with such systems. The application of the z transform allows for direct frequency domain representations of the aerodynamic solutions without the need for approximating models, as generally occurs in other state-space formulations. This fact also makes this new methodology a more straightforward procedure for aeroelastic analyses. A typical section model of a NACA 0012 airfoil at transonic flow conditions is used as a test case to assess the correctness and accuracy of the proposed formulation. The present results are compared with data obtained from continuous-time state-space formulations and through the direct integration of the aerodynamic and structural dynamic equations.
This paper presents Transform & Learn, a physics-informed surrogate modeling approach that unites the perspectives of model reduction and machine learning. The proposed method uses insight from the physics of the problem-in the form of partial differential equation (PDE) models-to derive a state transformation in which the system admits a quadratic representation. Snapshot data from a high-fidelity model simulation are transformed to the new state representation and subsequently are projected onto a low-dimensional basis. The quadratic reduced model is then learned via a least-squares-based operator inference procedure. The state transformation thus plays two key roles in the proposed method: it allows the task of nonlinear model reduction to be reformulated as a structured model learning problem, and it parametrizes the machine learning problem in a way that recovers efficient, generalizable models. The proposed method is demonstrated on two PDE examples. First, we transform the Euler equations in conservative variables to the specific volume state representation, yielding low-dimensional Transform & Learn models that achieve a 0.05% relative state error when compared to a high-fidelity simulation in the conservative variables. Second, we consider a model of the Continuously Variable Resonance Combustor, a single element liquid-fueled rocket engine experiment. We show that the specific volume representation of this model also has quadratic structure and that the learned quadratic reduced models can accurately predict the growing oscillations of an unstable combustor.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.