Analyzing the multiphysical coupling between a deformable structural body and the forces imposed on that body from a surrounding fluid can be a challenging and computationally expensive task, especially when the structure, fluid, or both exhibit nonlinear behavior. Consequently, there exists a need for novel reduced-order static aeroelasticity analysis techniques that make efficient use of high-fidelity computational models, especially for preliminary design of next-generation aerostructures with high-aspect ratio lifting surfaces exhibiting large deformations or in situ geometric reconfigurations driven by nonlinear mechanisms. This work presents the compositional static aeroelastic analysis method: an embarrassingly parallelizable data-driven modeling technique that seeks to construct a system-level aeroelastic surrogate model representing the function composition of high-fidelity structural and fluid models in terms of shape parameters characterizing a reduced-order geometric description of the deformed fluid–structure interface. By formulating the static aeroelasticity problem as a fixed point problem, the proposed reduced-order modeling framework removes the need for a reduced-order representation of the traction field acting on the structure, unlike previous data-driven methods that independently train separate fluid and structural surrogate models. Additionally, by replacing the iterative exchange of full-order aeroelastic coupling variables with a statistical exploration of a reduced-order shape parameter space, the minimum computational time for approximating a static aeroelastic response is equivalent to one set of high-fidelity fluid and structural model evaluations. The following work presents the theoretical development of the proposed compositional method and demonstrates its use in two case studies, one of which involves a cantilevered baffle comprised of linear and nonlinear material with large deformations exceeding 35%. Numerical results show close agreement with a conventional partitioned analysis scheme, where tip displacement error is less than 1% in both material cases. It is also demonstrated how traction field information can be reused when considering structural modifications to circumvent the need for additional computationally expensive fluid model evaluations.

中文翻译：

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非线性静态气动弹性的数据驱动建模框架


分析可变形结构体与周围流体施加在该体上的力之间的多物理场耦合可能是一项具有挑战性且计算成本高昂的任务，尤其是当结构、流体或两者表现出非线性行为时。因此，需要新颖的降阶静态气动弹性分析技术，以有效利用高保真计算模型，特别是用于具有大纵横比升力表面的下一代飞机结构的初步设计，表现出大变形或由非线性机构驱动的原位几何重新配置。这项工作提出了成分静态气动弹性分析方法：一种令人尴尬的可并行数据驱动建模技术，旨在构建一个系统级气动弹性代理模型，该模型表示高保真结构和流体模型的函数组成，根据形状参数表征变形流体-结构界面的降阶几何描述。通过将静态气动弹性问题表述为不动点问题，所提出的降阶建模框架消除了对作用在结构上的牵引场的降阶表示的需要，这与以前独立训练单独的流体和结构代理模型的数据驱动方法不同。此外，通过将全阶气动弹性耦合变量的迭代交换替换为降阶形状参数空间的统计探索，近似静态气动弹性响应的最小计算时间相当于一组高保真流体和结构模型评估。 以下工作介绍了所提出的组合方法的理论发展，并在两个案例研究中演示了其使用，其中一个涉及由线性和非线性材料组成的悬臂挡板，其大变形超过 35%。数值结果表明，与传统的分区分析方案非常吻合，在两种材料情况下，尖端位移误差都小于 1%。此外，还演示了在考虑结构修改时如何重用牵引场信息，以避免对额外的计算成本高昂的流体模型评估的需求。