Identifying gusts and turbulence events is of primary importance for designing future gust load alleviation systems, calculating airframe load, and analysing incidents. Due to the impossibility of their direct measurement, indirect methods are used and ad hoc experiments are necessary to validate the methodology. This paper employs Convolutional Neural Network and Long Short Term Memory (CNN-LSTM) as well as CNN models for in-flight gust identification. Two aeroelastic models, with different levels of fidelity, representative of a civil and commercial aircraft, are used to generate gust responses to train and test the Deep Learning (DL) models. The results highlight the capability of both LSTM-CNN and CNN models in reconstructing gusts across the entire flight envelope of a civil commercial aircraft. The CNN model demonstrated its ability to identify gusts and turbulence when they occur concurrently, similar to real-world scenarios, in a significantly shorter amount of time. Furthermore, its application to wind tunnel gust response measurements, where the inflow has previously been characterised, demonstrated the effectiveness of the proposed methodology for experimental measurements.

中文翻译：

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使用深度学习模型进行实验和数值阵风识别

识别阵风和湍流事件对于设计未来阵风载荷减轻系统、计算机身载荷和分析事件至关重要。由于无法直接测量，因此需要使用间接方法，并且需要进行临时实验来验证该方法。本文采用卷积神经网络和长短期记忆 (CNN-LSTM) 以及 CNN 模型进行飞行中阵风识别。代表民用和商用飞机的两个具有不同保真度水平的气动弹性模型用于生成阵风响应以训练和测试深度学习 (DL) 模型。结果突显了 LSTM-CNN 和 CNN 模型在重建民用商用飞机整个飞行包线内的阵风方面的能力。 CNN 模型展示了其在极短的时间内识别同时发生的阵风和湍流的能力，类似于现实世界的场景。此外，其在风洞阵风响应测量中的应用（先前已表征了流入量）证明了所提出的实验测量方法的有效性。