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首页> 外文会议>AIAA modeling and simulation technologies conference;AIAA SciTech forum >Experimental Design and Statistical Modeling Methodology for Wind Tunnel Aerodynamics of an Agile Missile to Improve the Simulation Accuracy and Performance
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Experimental Design and Statistical Modeling Methodology for Wind Tunnel Aerodynamics of an Agile Missile to Improve the Simulation Accuracy and Performance

机译：敏捷导弹风洞空气动力学的实验设计和统计建模方法，以提高仿真精度和性能

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摘要

Wind tunnel testing is an essential procedure to investigate the aerodynamics forces and moments. In this paper, a methodology is presented to perform such test in an efficient way. Experimental design process is carried out before the testing in order to cover the flight regime as much as possible with the least possible number of tests. After the determination of the test matrix and conducting the wind-on tests, the modeling of the data is the next step. On that matter, two nonlinear modeling techniques are presented and compared with each other. First technique is the well-known Artificial Neural Networks (ANN) and the second one is relatively lesser known but a powerful modeling algorithm Multivariate Adaptive Regression Splines (MARS). After the data is modeled using both approaches, their statistical metrics are compared and the models are integrated into a high fidelity 6D0F equations of motion model. Since all this effort is to improve the simulation accuracy and performance, 6D0F simulations are performed using three different flight scenarios that cover the flight regime of the missile. Afterwards, obtained trajectory and flight parameters are compared. Finally, various flight conditions are produced and the models are evaluated in batch mode to see their performances in terms of computational speed.
机译：风洞测试是研究空气动力和力矩的必不可少的程序。在本文中，提出了一种以有效方式执行此类测试的方法。为了在尽可能少的测试次数的情况下尽可能覆盖飞行状态，在测试之前进行了实验设计过程。确定测试矩阵并进行预测试后，下一步就是数据建模。关于这一点，提出了两种非线性建模技术并将其相互比较。第一种技术是众所周知的人工神经网络（ANN），而第二种技术则鲜为人知，但功能强大的建模算法是多元自适应回归样条（MARS）。使用这两种方法对数据进行建模后，将比较它们的统计指​​标，并将模型集成到运动模型的高保真度6D0F方程中。由于所有这些努力都是为了提高仿真的准确性和性能，因此使用涵盖导弹飞行状态的三种不同的飞行场景执行6D0F仿真。然后，比较获得的轨迹和飞行参数。最后，产生各种飞行条件，并以批处理模式评估模型，以查看其在计算速度方面的性能。

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