4.5 Article

Multifidelity Aeroelastic Model for Corrugated Morphing Structures

期刊

JOURNAL OF AIRCRAFT
卷 60, 期 1, 页码 120-129

出版社

AMER INST AERONAUTICS ASTRONAUTICS
DOI: 10.2514/1.C036692

关键词

Aeroelastic Analysis; Aerodynamic Characteristics; Vortex Lattice Method; Finite Element Analysis; Structural Simulation; Rectangular Wing; Aerodynamic Performance; Camber; Reynolds Averaged Navier Stokes; Structural Analysis

向作者/读者索取更多资源

This paper proposes an efficient aeroelastic model for morphing structures with corrugated panels, combining different fidelities of aerodynamic models. The influence of model fidelity on simulation results is studied through three different morphing cases simulated with two fidelities of aeroelastic models. Based on the comparison study, a new efficient aeroelastic model is proposed, which speeds up the calculations while maintaining the computational accuracy.
In this paper, an efficient aeroelastic model for morphing structures with corrugated panels is proposed, combining different fidelities of aerodynamic models. A finite element method with an actuation model is used to analyze the structural deformation of morphing structures. A vortex lattice method and Cartesian-grid-based computational fluid dynamics are individually coupled with the structural model to build the medium- and high-fidelity aeroelastic models. Three different morphing cases are simulated with the two fidelities of aeroelastic models to understand the influence of the model fidelity on the simulation results. Based on the comparison study, a new efficient aeroelastic model is proposed where the medium-fidelity aeroelastic model speeds up the calculations in the aeroelastic loop and the high-fidelity aeroelastic model computes accurate aerodynamic characteristics and necessary actuation loads. Computational time of the proposed method is half of the high-fidelity model while maintaining the computational accuracy.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据