Nonlinear identification via connected neural networks for unsteady aerodynamic analysis
Nonlinear identification via connected neural networks for unsteady aerodynamic analysis
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DOI：
10.1016/j.ast.2018.03.034
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发表时间：
2018-06
影响因子：
5.6
通讯作者：
M. Winter;C. Breitsamter
中科院分区：
文献类型：
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作者：
M. Winter;C. Breitsamter
本文提出了一种基于循环局部线性神经模糊模型（NFM）与多层感知器（MLP）神经网络串联的非线性系统辨识策略。具有输出反馈的NFM最初用于多步提前预测，而MLP神经网络则用于对NFM的时间序列响应进行非线性准静态校正。尽管该方法一般适用于任何非线性辨识任务，但该方法作为一种降阶建模（ROM）技术，用于降低非定常气动仿真的计算量。为了验证该方法对非定常气动建模的保真性，对NLR 7301翼型在跨声速工况下进行了研究，并特别考虑了运动诱导的气动力。因此，通过强制运动同时激发俯仰自由度和俯仰自由度，获得用于模型校准的训练数据，同时使用计算流体动力学（CFD）求解器计算各自的气动响应。给出了序列非线性辨识过程，并对所得模型进行了推广。此外，还引入了一种基于蒙特卡罗的训练方法来估计统计误差，该方法在空气动力学降阶建模中是一种新颖的方法。结果表明，与全阶解相比，该方法能较准确地再现系统的基本线性和非线性特性。此外，通过与已建立的ROM方法进行比较，结果表明，连接神经网络方法可以提高仿真和泛化性能。
In the present work, a nonlinear system identification strategy is proposed which is based on the series connection of a recurrent local linear neuro-fuzzy model (NFM) and a multilayer perceptron (MLP) neural network. The NFM with output feedback is initially used for multi-step ahead predictions, whereas the MLP neural network is a posteriori employed to perform a nonlinear quasi-static correction of the NFM's time-series response. The novel identification approach is utilized exemplarily as a reduced-order modeling (ROM) technique to lower the computational effort of unsteady aerodynamic simulations, although the approach is generally applicable to any nonlinear identification task. In order to demonstrate the method's fidelity for unsteady aerodynamic modeling, the NLR 7301 airfoil is investigated at transonic flow conditions, while the motion-induced aerodynamic forces are considered in particular. Therefore, the pitch and plunge degrees of freedom are simultaneously excited via forced motions to obtain the training data for model calibration, while the respective aerodynamic response is computed using a computational fluid dynamics (CFD) solver. The sequential nonlinear identification process as well as the generalization of the resulting model is presented. Besides, a Monte-Carlo-based training procedure, which is novel in the context of aerodynamic reduced-order modeling, is introduced to estimate statistical errors. It is shown that the essential linear and nonlinear system characteristics are accurately reproduced by the new approach compared to the full-order solution. Moreover, by examining the results in comparison to established ROM methods it is indicated that the connected neural network approach leads to an enhanced simulation and generalization performance.