High order mimetic differences applied to the convection–diffusion equation: a matrix stability analysis

Villamizar, Jorge; Mendoza, Larry; Calderón, Giovanni; Rojas, Otilio; Castillo, Jose E.

doi:10.1007/s13137-023-00232-x

Cited by 2 publications

(1 citation statement)

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“…Recent advancements have been made by Villamizar et al [14], who applied highorder mimetic differences to the convection-diffusion equation, a fundamental equation describing physical phenomena involving the transfer of particles or energy. They utilized the Castillo-Grone (CG) operators, which possess a sextuple of free parameters, to study the stability and precision properties of their numerical scheme.…”

Section: Related Workmentioning

confidence: 99%

Spectral Properties of Mimetic Operators for Robust Fluid–Structure Interaction in the Design of Aircraft Wings

de Curtò,

de Zarzà

2024

Mathematics

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This paper presents a comprehensive study on the spectral properties of mimetic finite-difference operators and their application in the robust fluid–structure interaction (FSI) analysis of aircraft wings under uncertain operating conditions. By delving into the eigenvalue behavior of mimetic Laplacian operators and extending the analysis to stochastic settings, we develop a novel stochastic mimetic framework tailored for addressing uncertainties inherent in the fluid dynamics and structural mechanics of aircraft wings. The framework integrates random matrix theory with mimetic discretization methods, enabling the incorporation of uncertainties in fluid properties, structural parameters, and coupling conditions at the fluid–structure interface. Through spectral and localization analysis of the coupled stochastic mimetic operator, we assess the system’s stability, sensitivity to perturbations, and computational efficiency. Our results highlight the potential of the stochastic mimetic approach for enhancing reliability and robustness in the design of aircraft wings, paving the way for optimization algorithms that integrate uncertainties directly into the design process. Our findings reveal a significant impact of stochastic perturbations on the spectral radius and eigenfunction localization, indicating heightened system sensitivity. The introduction of randomized singular value decomposition (RSVD) within our framework not only enhances computational efficiency but also preserves accuracy in low-rank approximations, which is critical for handling large-scale systems. Moreover, Monte Carlo simulations validate the robustness of our stochastic mimetic framework, showcasing its efficacy in capturing the nuanced dynamics of FSI under uncertainty. This study contributes to the fields of numerical methods and aerospace engineering by offering a rigorous and scalable approach for conducting uncertainty-aware FSI analysis, which is crucial for the development of safer and more efficient aircraft.

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Section: Related Workmentioning

confidence: 99%