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Computational approximation of nonlinear unsteady aerodynamics using an aerodynamic model hierarchy

机译:基于气动模型层次的非线性非定常气动力学计算近似

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摘要

Modeling nonlinear unsteady aerodynamic effects in the simulation of modern fighter aircraft is still a very challenging task. A framework for approximating nonlinear unsteady aerodynamics with a Radial Basis Function neural network is provided. Training data were generated from a hierarchy of aerodynamic models. At the highest level, solutions of the discretized Reynolds-Averaged Navier–Stokes (RANS) equations provide the quantitative and qualitative solution of flow around aircraft, although the results are expensive in terms of computational resources. The Euler simulations are less expensive and provide qualitative data up to moderate angles of attack. The integration of these data is promising for generating accurate aerodynamic models at moderate computational cost. To illustrate the method, an airfoil undergoing pitching and plunging motion is considered. The primary and secondary aerodynamic model data are computed using RANS and Euler equations, respectively. A description for a mapping between the aerodynamic loads and the motion parameters based on the implicit function theorem is described. The mapping is then augmented by adding the secondary data to the input dataset. The selection of training data is then discussed. Once the network is trained, it can compute the unsteady aerodynamic loads from motion descriptions on the order of a few seconds. The framework is examined for different motions, and in all cases, the ROM predictions closely represent the actual aerodynamic responses. It is also demonstrated that the aerodynamic hierarchy aids in the rapid development of a reduced-order model.
机译:在现代战斗机的仿真中,对非线性非稳态空气动力效应进行建模仍然是一项非常艰巨的任务。提供了使用径向基函数神经网络逼近非线性非稳态空气动力学的框架。训练数据是从空气动力学模型的层次结构生成的。在最高级别上,离散化的雷诺平均Navier-Stokes(RANS)方程的解提供了飞机周围流的定量和定性解,尽管结果在计算资源方面很昂贵。欧拉模拟的成本较低,并且可以提供中等角度的定性数据。这些数据的集成有望以适中的计算成本生成精确的空气动力学模型。为了说明该方法,考虑了经过俯仰和俯冲运动的翼型。分别使用RANS和Euler方程计算一级和二级空气动力学模型数据。描述了基于隐函数定理的空气动力学负载与运动参数之间的映射的描述。然后通过将辅助数据添加到输入数据集来增强映射。然后讨论训练数据的选择。一旦对网络进行了训练,它就可以从运动描述中计算出不稳定的空气动力学载荷,大约为几秒钟。检查框架的不同运动,在所有情况下,ROM预测都紧密代表实际的空气动力响应。还证明了空气动力学层次有助于快速发展降阶模型。

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