A point cloud deep neural network metamodel method for aerodynamic prediction

Aiming to reduce the high expense of 3-Dimensional (3D) aerodynamics numerical simulations and overcome the limitations of the traditional parametric learning methods, a point cloud deep learning non-parametric metamodel method is proposed in this paper. The 3D geometric data, corresponding to the object boundaries, are chosen as point clouds and a deep learning neural network metamodel fed by the point clouds is further established based on the PointNet architecture. This network can learn an end-to-end mapping between spatial positions of the object surface and CFD numerical quantities. With the proposed aerodynamic metamodel approach, the point clouds are constructed by collecting the coordinates of grid vertices on the object surface in a CFD domain, which can maintain the boundary smoothness and allow the network to detect small changes between geometries. Moreover, the point clouds are easily accessible from 3D sensors. The point cloud deep learning neural network, which employs re-sampling technique, the spatial transformer network and the fully connected layer, is developed to predict the aerodynamic characteristics of 3D geometry. The effectiveness of the proposed metamodel method is further verified by aerodynamic prediction and robust shape optimization of the ONERA M6 wing. The results show that the proposed method can achieve more satisfactory agreement with the experimental measurements compared to the parametric-learning-based deep neural network.     

Deployment of SMP Miura-ori sheet and its application: Aerodynamic drag and RCS reduction

Origami, such as Miura-ori, is the art of folding two-dimensional materials into complex, elaborate, and multifunctional three-dimensional objects. In this paper, SMP MO sheet are prepared, and the accuracy of deployable process is verified by experiments. The folding and deployable process of SMP MO sheet is divided into 4 stages, and each stage is described in detail. The stiffness of smart deployable stage is characterized by an exponential decline at the beginning and a gradual decrease to 0, and this is similar to the theoretical shear equivalent modulus in the Y direction. The effects of various parameters on strain and stress are also explored. The purpose of studying these mechanical characteristics is to provide driving force reference in application; In terms of application, the flow field and electromagnetic characteristics of MO sheet in different directions are studied. The aerodynamic drag and RCS reduction of MO unit cell and graded MO sheet during the deployable process are evaluated. When the dihedral fold angle is about 45°, the RCS reduction and drag reduction characteristics of MO sheet are relatively optimal, which is most beneficial to morphing aircraft.     

超参数自适应的MOEA/D-DE算法在翼型气动隐身优化中的应用

MOEA/D-DE 算法易于实现，被广泛应用于处理多目标优化问题，但其超参数CR 和F 对算法性能影响较大。基于MOEA/D-DE 算法框架、利用Sobol 全局灵敏性分析方法对差分进化算子中的交叉控制参数CR进行改进，使用莱维飞行策略控制比例因子F，使算法中的超参数拥有自适应能力，得到超参数自适应的MOEA/D-DE 算法——MOEA/D-DEAH 算法；对MOEA/D-DEAH 算法、不同超参数设置的MOEA/D-DE算法和NSGAII 算法进行函数测试和翼型气动隐身优化算例对比。结果表明：MOEA/D-DEAH 算法性能良好，具有较强的鲁棒性，气动隐身优化效果也比其他算法更好。     

带落角落速约束的导弹虚拟期望落角末制导律

为解决导弹末制导阶段同时考虑落角和落速约束时带来的过载需求大、落速散布广的问题,提出一种基于虚拟期望落角的末制导律。首先,提出虚拟期望落角的概念,设计过渡函数降低末制导初期过载需求；然后,分析过渡函数各参数对落角、落速影响,设计预测-校正算法计算期望参数；为了提高预测效率与精度,使用深度神经网络离线训练弹道数据集。实际飞行中,基于扩展卡尔曼滤波在线辨识气动参数摄动,提高算法的适应性。蒙特卡洛仿真结果表明,所提出的算法能够降低末制导初期过载需求。在满足落角约束与位置精度的前提下,落速控制精度在±15 m/s以内。     

Study of aerodynamic and inertial forces of a experimental and numerical methods

The force-generation mechanism of a dovelike flapping-wing micro air vehicle was studied by numerical simulation and experiment. To obtain the real deformation pattern of the flapping wing, the digital image correlation technology was used to measure the dynamic deformation of the wing. The dynamic deformation data were subsequently interpolated and embedded into the CFD solver to account for the aeroelastic effects. The dynamic deformation data were further used to calculate the inertial forces...     

径向非均匀进气喷湿对跨声速压气机气动性能影响研究

针对湿压缩技术的应用，为了深入认识径向非均匀进气喷湿对压气机气动性能的影响，以某跨声速压气机级为研究对象，采用CFD技术结合欧拉-拉格朗日法对径向非均匀进气喷湿条件下压气机气液两相流场进行数值模拟，对比研究了叶顶、叶中和叶根局部喷湿三种不同径向位置喷湿条件对压气机气动性能及失稳边界的影响规律，并通过喷湿前后流动径向匹配规律及流动结构的变化解释了影响机理。结果表明：叶顶喷湿相比叶中和叶跟喷湿是更好的喷湿方案，叶顶喷湿时压气机压比和效率升高程度更大，且稳定裕度降低程度更低；当液滴粒径小于50μm，喷射速度大于20m/s时，离心力对液滴径向迁移的影响可以忽略不计，液滴在经过压气机级流道过程中几乎不发生径向迁移，蒸发冷却效应只发生于喷湿的局部叶高范围；由于径向非均匀进气喷湿时压气机内部质量流量会向喷湿的局部叶高范围聚集，导致喷湿的主要影响——即轴向速度的降低和轮缘功的升高在喷湿的局部范围程度较弱，而在没有喷湿的局部叶高范围程度较强。     

叶顶间隙偏差对叶片气动性能影响的不确定性研究

在压气机叶片加工过程中，实际加工得到的叶片外形与设计叶型不可避免会存在一些偏差。为研究叶片叶顶间隙尺寸波动对性能的影响，以Rotor37为研究对象，采用三维定常数值模拟方法，基于非嵌入式混沌多项式中二维正态分布变量产生方法，分别对叶片前缘叶顶间隙和尾缘叶顶间隙进行尺寸波动干扰，研究了叶顶间隙不确定性对气动性能和流场的不确定性影响，评估了公差带内叶顶间隙偏差与气动性能变化的相关性，并探究了叶顶间隙变化对叶片稳定裕度的影响机理。研究发现，叶顶间隙偏差对等背压条件下叶片质量流量、等熵效率和总压比的平均水平几乎不会造成影响；叶顶间隙偏差所造成的气动性能分布标准差随工况点向不稳定边界的逼近而逐渐增大。同时，叶顶间隙不确定性会导致样本稳定裕度均值有所降低，不确定性分析中稳定裕度均值相对原型叶片下降了2.42%。流场结构方面，叶顶间隙偏差主要影响80%叶高以上部分流场；叶顶间隙偏差对叶顶区域泄漏流产生的影响，导致了叶片稳定裕度的变化。     

后桨桨距角对某共轴对转螺旋桨性能影响规律研究

共轴对转螺旋桨的桨距角对前后排桨的桨间气动干扰有重要影响,能够改变螺旋桨的气动性能。为了研究后桨桨距角对共轴对转螺旋桨的气动干扰影响,改善螺旋桨的气动性能,在来流马赫数0.453 的情况下,通过调节后桨桨距角的方式对6×6 构型的共轴对转螺旋桨进行数值计算,数值计算中使用非定常雷诺平均纳维—斯托克斯（URANS）方程结合SST 湍流模型的方法,并采用T-Rex 高质量网格生成技术研究桨距角对共轴对转螺旋桨桨间气动干扰的变化规律。结果表明：后桨在前排桨产生的预旋气流作用下,能够吸收一部分前桨的切向滑流能量,且气动效率高于前桨,前后桨的气动参数在一个旋转周期内出现12 次周期性波动;共轴对转桨的前后桨转速相同时,前桨桨距角不变,减小后桨桨距角,前后桨的气动效率都会增加,后桨效率提升明显。     

Transfer learning: A new aerodynamic force identification network based on adaptive EMD and soft thresholding in hypersonic wind tunnel

The aerodynamic test in the pulse combustion wind tunnel is very important for the design, evaluation and optimization of aerodynamic characteristics of the hypersonic aircraft. The test accuracy even affects the success or failure of hypersonic aircraft development. In the aerodynamic test of pulse combustion wind tunnel, the aerodynamic signal is disturbed by the inertial force signal, which seriously affects the test accuracy of aerodynamic force. Aiming at the above problems, this paper innovatively proposes an aerodynamic intelligent identification method, that is the transfer learning network based on adaptive Empirical Modal Decomposition (EMD) and Soft Thresholding (TLN-AE&ST). Compared with the existing aerodynamic intelligent identification model based on deep learning technology, this study introduces the transfer learning idea into the aerodynamic intelligent identification model for the first time. The TLN-AE&ST effectively alleviates the problem of scarcity of training samples for intelligent models due to the high cost of wind tunnel tests, and provides a new idea for further implementation of deep learning technology in the field of wind tunnel aerodynamic testing. And this study designed residual attention block with soft threshold and dense block with adaptive EMD in TLN-AE&ST model. Residual attention block with soft threshold module can more effectively suppress the influence of instrument noise signal on model training effect. Dense block with adaptive EMD makes the deep learning model no longer a black box to a certain extent, and has certain physical significance. Finally, a series of wind tunnel tests were carried out in the Φ = 2.4 m pulse combustion wind tunnel of China Aerodynamic Research and Development Center to verify the effectiveness of TLN-AE&ST.     

Adaptive data fusion framework for modeling of non-uniform aerodynamic data

Multi-fidelity Data Fusion(MDF) frameworks have emerged as a prominent approach to producing economical but accurate surrogate models for aerodynamic data modeling by integrating data with different fidelity levels. However, most existing MDF frameworks assume a uniform data structure between sampling data sources; thus, producing an accurate solution at the required level, for cases of non-uniform data structures is challenging. To address this challenge, an Adaptive Multi-fidelity Data Fusion(...