000145353 001__ 145353
000145353 005__ 20241024135330.0
000145353 0247_ $$2doi$$a10.3390/math12081217
000145353 0248_ $$2sideral$$a140118
000145353 037__ $$aART-2024-140118
000145353 041__ $$aeng
000145353 100__ $$ade Curtò, J.
000145353 245__ $$aSpectral Properties of Mimetic Operators for Robust Fluid–Structure Interaction in the Design of Aircraft Wings
000145353 260__ $$c2024
000145353 5060_ $$aAccess copy available to the general public$$fUnrestricted
000145353 5203_ $$aThis 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.
000145353 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000145353 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000145353 700__ $$0(orcid)0000-0002-5844-7871$$ade Zarzà, I.$$uUniversidad de Zaragoza
000145353 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000145353 773__ $$g12, 8 (2024), 1217$$pMathematics (Basel)$$tMathematics$$x2227-7390
000145353 8564_ $$s3096773$$uhttps://zaguan.unizar.es/record/145353/files/texto_completo.pdf$$yVersión publicada
000145353 8564_ $$s2535092$$uhttps://zaguan.unizar.es/record/145353/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000145353 909CO $$ooai:zaguan.unizar.es:145353$$particulos$$pdriver
000145353 951__ $$a2024-10-24-12:11:04
000145353 980__ $$aARTICLE