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An innovative approach for prediction of aerodynamic coefficients in shock tunnel testing with soft computing techniques

机译：具有软计算技术的冲击隧道试验中空气动力学系数预测的一种创新方法

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A blunt bi-conic aluminum model, integrated with a three component accelerometer force balance system, is tested in the IITB-Shock Tunnel at various angle of inclinations with an intention to measure aerodynamic coefficients. Initially, Genetic Algorithm (GA) is employed to deduce the orthogonal inputs and their responses from distributed point loads applied on the test model during calibration experiments. Time histories of these loads and their responses are then used to train the architecture of Adaptive Neuro Fuzzy Inference System, ANFIS. This ANFIS architecture is further employed for force prediction from the acquired acceleration responses during shock tunnel experiments. Testing of the experimental acceleration responses of 0 degrees and 10 degrees angle of attack experiments showed encouraging agreement for recovered aerodynamic coefficients with the accelerometer balance theory based predictions. These results clearly showed the necessity to consider multiple point loads and their acceleration responses for training the soft computing algorithm or calibration of the force balance. Further, use of only one point force and its responses, for training ANFIS, is not only seen to have discrepancy in prediction but also is noted to be non-unique due to choice of the loading point. Moreover, it is recommended to choose the loading point near the center of pressure for the tested experimental conditions to incur lower discrepancy in prediction using single point loading data for ANFIS training. (C) 2018 Elsevier Ltd. All rights reserved.
机译：与三个组件加速度计力平衡系统集成的钝双圆锥铝型模型，以各种倾斜角度在IITB震动隧道中进行测试，其目的是测量空气动力学系数。最初，采用遗传算法（GA）在校准实验期间从测试模型上应用的分布点负荷推导出正​​交输入及其响应。然后使用这些负载的时间历史及其响应来训练Adaptive Neuro模糊推理系统，ANFIS的架构。该ANFIS架构进一步用于从休克隧道实验期间从获取的加速响应中的力预测。测试0度和10度的攻击角度的实验加速度响应显示出令人鼓舞的空气动力学系数与加速度计平衡理论的基于预测的恢复空气动力学系数的协议。这些结果清楚地表明需要考虑多点负载及其加速响应，以训练软计算算法或力平衡的校准。此外，仅使用一个点力及其响应，用于训练ANFIS，不仅在预测中具有差异，而且由于选择装载点，也指出是非独特的。此外，建议选择在测试的压力中心附近的装载点，以便使用用于ANFIS训练的单点加载数据在预测中产生更低的差异。 （c）2018年elestvier有限公司保留所有权利。

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