To satisfy the performance requirements across multiple speed ranges, a variable-sweep wing (sweep angle range from 25° to 40°) is derived from the BQM-34 “Firebee” drone model. However, predicting aerodynamic characteristics across various flight conditions and sweep angles is a challenging task. Traditional methods like CFD and wind tunnel testing are both time consuming and expensive. In order to efficiently predict the pressure distributions and aerodynamic coefficients, a novel network that combines a Radial Basis Function Network (RBFN) and a Convolutional Auto-Encoder (CAE) is proposed. Two distinct loss function methods, the standard Pressure-Targeted Method (PTM) and the newly developed Comprehensive Evaluation Method (CEM), are employed to optimize the network's predictive performance. These methods are evaluated on datasets with both trained and untrained sweep angles. The results show that while both PTM and CEM accurately predict pressure distributions, the enhanced CEM provides more uniform and reliable predictions. Moreover, the CEM method significantly outperforms PTM in predicting aerodynamic coefficients, reducing errors by over 50%. The proposed RBFN-CAE network with the CEM loss function offers an effective way to predict the aerodynamic characteristics of a variable-sweep wing, improving predictive models in aerodynamic applications.

中文翻译：

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预测可变后掠翼的压力分布和空气动力学系数


为了满足多个速度范围的性能要求，BQM-34 “Firebee” 无人机型号衍生出可变后掠翼（后掠角范围从 25° 到 40°）。然而，预测各种飞行条件和后掠角的空气动力学特性是一项具有挑战性的任务。CFD 和风洞测试等传统方法既耗时又昂贵。为了有效地预测压力分布和空气动力学系数，该文提出一种结合了径向基函数网络 （RBFN） 和卷积自编码器 （CAE） 的新型网络。采用两种不同的损失函数方法，即标准压力目标方法 （PTM） 和新开发的综合评估方法 （CEM），来优化网络的预测性能。这些方法在具有经过训练和未经过训练的扫描角度的数据集上进行评估。结果表明，虽然 PTM 和 CEM 都可以准确预测压力分布，但增强的 CEM 提供了更统一和可靠的预测。此外，CEM 方法在预测空气动力学系数方面明显优于 PTM，将误差减少了 50% 以上。所提出的具有 CEM 损失函数的 RBFN-CAE 网络提供了一种有效的方法来预测可变后掠翼的空气动力学特性，从而改进了空气动力学应用中的预测模型。