Neuro-fuzzy-based reduced-order modeling for aerodynamic loads computation at high-speed buffet buffeting

在飞机飞行包线的极限处精确确定气动载荷对于减少设计过程中的不确定性和产生关于质量和刚度分布的优化结构是必不可少的。因此，FOR 2895研究组的这个子项目的总体目标是开发一类基于神经模糊的降阶模型（ROM），用于跨音速抖振/抖振的有效载荷计算和分析。将对这些模型的精度、鲁棒性和对马赫数、雷诺数和攻角变化的敏感性进行彻底的研究。因此，降阶模型的结果将与实验和数值参考数据进行比较。ROM的训练过程面临着特殊的挑战，ROM必须适应主要的流动物理效应，以便能够以足够的精度再现非定常气动载荷的频率和振幅相关特性。因此，可以记录高速失速条件下真实运输机配置（XRF-1）的瞬态气动载荷特性。关于ROM，目前的子项目集中于神经网络（“多层感知器网络”、“长短期记忆网络”、“卷积神经网络”），这些网络用于绘制随机激波运动与局部气流分离相结合所产生的非定常气动载荷，从而表现出强非线性相互作用。在第一个项目阶段，有可能取得相对于参考数据非常准确的结果。在第二个项目阶段将继续进行这种建模，并通过扩展来补充，以考虑振动对流动物理机制的影响。这些研究的动机之一是，在ETW风洞模型中测得的压力谱在特征机翼结构本征模态处显示出峰值。在ETW中进行的针对结构振动激励的其他实验应提供有关这一点的进一步数据。因此，叠加振动的抖振情况的影响进行了分析的实验和数值模拟和ROM方法的条件下。此外，为了进行更详细的分析，对结构响应进行单向和双向耦合形式的计算，以代表动态气动弹性系统，从而开发耦合系统的ROM方法。结构动力学分析。

The accurate aerodynamic loads determination at the limits of the aircraft’s flight envelope is essential for reducing uncertainties within the design process and to yield optimized structures with respect to mass and stiffness distributions. The overall goal of this subproject within the research group FOR 2895 is, therefore, to develop a class of neuro-fuzzy-based reduced-order models (ROMs) that can be used for efficient load computation and analysis at transonic buffet/buffeting. These models will be thoroughly investigated concerning their accuracy, robustness and sensitivity against variations of Mach number, Reynolds number and angle of attack. Therefore, the results of the reduced-order models will be evaluated in comparison to experimental and numerical reference data. Particular challenges arise for the training process of the ROMs, which must be adapted to the dominating flow physics effects in order to be able to reproduce the frequency and amplitude related characteristics of the unsteady aerodynamic loads with sufficient accuracy. Hence, the transient aerodynamic load characteristics of a realistic transport aircraft configuration (XRF-1) under high-speed stall conditions can be recorded. With regard to the ROMs, the present subproject focuses on neural networks (“Multi-Layer Perceptron Network”, “Long-Short-Term Memory Network”, “Convolutional Neural Network”), which are used to map unsteady aerodynamic loads as a result of stochastic shock motions combined with local flow separation, thus, representing strongly nonlinear interactions. In the first project phase, it was possible to achieve results of very good accuracy relative to the reference data. This modeling will be continued in the second project phase and supplemented by an extension to consider the influence of vibrations on the flow-physical mechanisms. These investigations are motivated, among others, by the fact that the pressure spectra measured for the wind tunnel model in the ETW showed peaks at characteristic wing structural eigenmodes. Additional experiments in the ETW with targeted excitation of structural vibrations should provide further data on this. The influence of superimposed vibrations on the buffet scenario is thus analyzed by experimental and numerical simulations and ROM methods conditioned by them. In addition, for a further detailed analyses calculations in the form of one-way and two-way couplings for the structural response are carried out in order to represent the dynamic aeroelastic system and, thereby, to develop ROM methods for the coupled system with regard to structural dynamics analyses.