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

在飞行器的飞行包线范围内准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至关重要的。因此，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(ROMs)，该模型可用于跨音速抖振/抖振的有效负载计算和分析。将深入研究这些模型的准确性、稳健性和对马赫数、雷诺数和攻角变化的敏感性。因此，降阶模型的结果将通过与实验和数值参考数据进行比较来评估。对ROMS的训练过程提出了特殊的挑战，它必须适应主要的流动物理效应，以便能够足够准确地再现非定常气动载荷的频率和幅度相关特性。因此，可以记录实际运输机构型(XRF-1)在高速失速条件下的瞬时气动载荷特性。关于ROMS，本子项目侧重于神经网络(“多层感知器网络”、“长期-短期记忆网络”、“卷积神经网络”)，这些网络用于绘制由于随机激波运动和局部流动分离而产生的非定常气动载荷，从而表现出强烈的非线性相互作用。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。这一模拟将在第二项目阶段继续进行，并补充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中为风洞模型测量的压力谱在特征机翼结构特征模式处显示峰值。ETW中有针对性地激发结构振动的其他实验应该会提供有关这方面的进一步数据。因此，通过实验和数值模拟以及受其约束的ROM法，分析了叠加振动对抖振情景的影响。此外，为了进行进一步的详细分析，还进行了单向和双向耦合形式的结构响应计算，以表示气动弹性动力系统，从而发展了耦合系统的结构动力学分析的ROM法。

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.