Aerospace Science and Technology最新文献_第9页

Design perspectives of a system identification approach of the NATO AVT-316 multi-swept wing aerodynamics 北约 AVT-316 多掠翼空气动力学系统识别方法的设计视角

IF 5 1区工程技术 Q1 ENGINEERING, AEROSPACE

Aerospace Science and Technology

Pub Date : 2024-09-21 DOI: 10.1016/j.ast.2024.109603

Air supremacy requires enhanced maneuvering capabilities at high angles of attack and even beyond the stall angle. High angle-of-attack (high-α) maneuverability can be achieved using swept wings and tailored leading-edge shapes to replace local and disorganized flow separation with controlled separation-induced leading-edge vortices (LEV). Strong leading-edge vortices delay the stall to higher angles and generate a non-linear lift increment up to the angle of attack where the jet-type flow structure of LEV changes to a reversed-flow bubble (vortex breakdown phenomenon). A multi-swept wing configuration has the potential to delay the vortex breakdown to even higher angles as the vortices formed over the front wing can energize vortices formed over the main wing and lead to vortex merging. This article is focused on understanding the unsteady interactions between multiple leading edge vortices formed over multi-swept wing configurations in the subsonic speed regime. Specifically, this article investigates the aerodynamic characteristics of wings being evaluated under the initiative of the NATO STO AVT-316 Task Group. Highly refined meshes, and the use of hybrid turbulence models such as Detached Eddy Simulations (DES) and Delayed DES are required to accurately resolve the vortical flows over these wings. Simulating all flow conditions of interest is a computationally expensive approach. A system identification method was therefore proposed to rapidly and accurately generate aerodynamic models of these wings. The method uses a novel piece-wise chirp (constant amplitude and increasing frequency) motion as an input signal (training maneuver). The motion computational cost is equivalent to the cost of six static CFD simulations, however it can predict aerodynamic responses of the wings over a wide range of angles of attack. The method has been tested for double and triple delta wings. Some design considerations are provided based on predicted flow features and aerodynamic data. Prediction results with different turbulence models, sting geometries, grid resolutions and an adaptive mesh refinement approach are provided.

空中优势要求在高攻角甚至超过失速角时具有更强的机动能力。高攻角（高α）机动性可通过使用后掠翼和量身定制的前缘形状来实现，从而用可控的分离诱导前缘涡流（LEV）取代局部和无序的气流分离。强前缘涡流可将失速延迟到更高的角度，并在攻角前产生非线性升力增量，此时前缘涡流的喷射型流动结构会转变为反向流动气泡（涡流破裂现象）。由于在前翼上形成的涡流可以激发在主翼上形成的涡流并导致涡流合并，因此多掠翼结构有可能将涡流击穿延迟到更高的角度。本文的重点是了解在亚音速状态下，多后掠角机翼配置上形成的多个前缘涡流之间的非稳定相互作用。具体来说，本文研究了在北约 STO AVT-316 任务组倡议下正在评估的机翼气动特性。为了准确解析这些机翼上的涡流，需要使用高度精细的网格和混合湍流模型（如分离涡模拟 (DES) 和延迟分离涡模拟 (DES)）。模拟所有感兴趣的流动条件是一种计算昂贵的方法。因此，我们提出了一种系统识别方法，用于快速准确地生成这些机翼的气动模型。该方法使用一种新颖的片状啁啾（振幅恒定、频率增加）运动作为输入信号（训练动作）。该运动的计算成本相当于六次静态 CFD 模拟的成本，但它可以预测机翼在大攻角范围内的气动响应。该方法已对双三角翼和三三角翼进行了测试。根据预测的流动特征和气动数据，提供了一些设计考虑因素。还提供了不同湍流模型、尾翼几何形状、网格分辨率和自适应网格细化方法的预测结果。

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引用次数: 0

Graph-accelerated non-intrusive polynomial chaos expansion using partially tensor-structured quadrature rules for uncertainty quantification 利用部分张量结构正交规则的图形加速非侵入式多项式混沌展开，用于不确定性量化

IF 5 1区工程技术 Q1 ENGINEERING, AEROSPACE

Aerospace Science and Technology

Pub Date : 2024-09-20 DOI: 10.1016/j.ast.2024.109607

Recently, the graph-accelerated non-intrusive polynomial chaos (NIPC) method has been proposed for solving uncertainty quantification (UQ) problems. This method leverages the full-grid integration-based NIPC method to address UQ problems while employing the computational graph transformation approach, AMTC, to accelerate the tensor-grid evaluations. This method exhibits remarkable efficacy on a broad range of low-dimensional (three dimensions or less) UQ problems featuring multidisciplinary models. However, it often does not scale well with problem dimensions due to the exponential increase in the number of quadrature points when using the full-grid quadrature rule. To expand the applicability of this method to a broader range of UQ problems, this paper introduces a new framework for generating a tailored, partially tensor-structured quadrature rule to use with the graph-accelerated NIPC method. This quadrature rule, generated through the designed quadrature approach, possesses a tensor structure that is tailored for the computational model. The selection of the tensor structure is guided by an analysis of the computational graph, ensuring that the quadrature rule effectively capitalizes on the sparsity within the computational graph when paired with the AMTC method. This method has been tested on one 4D and one 6D UQ problem, both originating from aircraft design scenarios and featuring multidisciplinary models. Numerical results show that, when using with graph-accelerated NIPC method, our approach generates a partially tensor-structured quadrature rule that outperforms the full-grid Gauss quadrature and the designed quadrature methods (more than 40% reduction in computational costs) in both of the test problems.

最近，有人提出了图加速非侵入式多项式混沌（NIPC）方法，用于解决不确定性量化（UQ）问题。该方法利用基于全网格整合的 NIPC 方法来解决 UQ 问题，同时采用计算图转换方法 AMTC 来加速张量网格评估。该方法在以多学科模型为特征的各种低维（三维或更小）UQ 问题上表现出显著的功效。然而，由于使用全网格正交规则时正交点数量呈指数增长，该方法通常不能很好地扩展问题维度。为了将这种方法的适用范围扩大到更广泛的 UQ 问题，本文介绍了一种新的框架，用于生成定制的、部分张量结构的正交规则，与图形加速 NIPC 方法一起使用。通过设计的正交方法生成的正交规则具有针对计算模型量身定制的张量结构。张量结构的选择以计算图分析为指导，确保正交规则在与 AMTC 方法配对时能有效利用计算图中的稀疏性。该方法已在一个 4D 和一个 6D UQ 问题上进行了测试，这两个问题都源自飞机设计方案，并具有多学科模型的特点。数值结果表明，当使用图加速 NIPC 方法时，我们的方法生成的部分张量结构正交规则在两个测试问题中都优于全网格高斯正交和设计正交方法（计算成本减少 40% 以上）。

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引用次数: 0

Rapid identification and early warning of axial compressor stall based on multiscale CNN-SVM-FC model 基于多尺度 CNN-SVM-FC 模型的轴流压缩机失速快速识别与预警

IF 5 1区工程技术 Q1 ENGINEERING, AEROSPACE

Aerospace Science and Technology

Pub Date : 2024-09-20 DOI: 10.1016/j.ast.2024.109604

Early prewarning of compressor stall and surge is crucial to avoid aircraft engine instability, yet it is challenging due to the complex and unstable flow field characterized by multiple modes and multiscale features. To enhance the multi-scale feature representation capability of Convolutional Neural Network-Support Vector Machine (CNN-SVM) algorithm, a novel classifier modelling method combined multiscale windows with CNN-SVM is introduced for stall prewarning in this paper, named Multiscale CNN-SVM-FC. Multiscale detection windows are utilized to adaptively identify various pressure features during the stall process. Additionally, to reduce the false alarm rate, a fuzzy control algorithm is integrated with the temporal accumulation of prediction results from the multi-branch network for joint analysis. A series of test data from a five-stage axial compressor at different operating speeds is used to verify this method. The results indicate that the proposed Multiscale CNN-SVM-FC method enhances the accuracy of classification and reduces the false alarm rate compared to the standard CNN-SVM model, achieving over 99% accuracy in identifying unstable states under various speeds. Compared to three traditional stall prewarning methods, the Multiscale CNN-SVM-FC model provides an average warning signal 164 milliseconds ahead of stall, and reduces the uncertainty associated with threshold selection, which typically relies on engineering experience.

压气机失速和喘振的早期预警对于避免飞机发动机失稳至关重要，但由于流场复杂且不稳定，具有多种模式和多尺度特征，因此具有挑战性。为了增强卷积神经网络-支持向量机（CNN-SVM）算法的多尺度特征表示能力，本文引入了一种将多尺度窗口与 CNN-SVM 相结合的新型分类器建模方法，用于失速预警，命名为多尺度 CNN-SVM-FC。多尺度检测窗口用于自适应地识别失速过程中的各种压力特征。此外，为了降低误报率，还将模糊控制算法与多分支网络预测结果的时间累积相结合，进行联合分析。一系列不同运行速度下的五级轴流压缩机测试数据被用来验证这种方法。结果表明，与标准 CNN-SVM 模型相比，所提出的多尺度 CNN-SVM-FC 方法提高了分类的准确性，降低了误报率，在识别不同转速下的不稳定状态方面达到了 99% 以上的准确率。与三种传统的失速预警方法相比，多尺度 CNN-SVM-FC 模型能在失速前平均提前 164 毫秒发出预警信号，并减少了与阈值选择相关的不确定性（阈值选择通常依赖于工程经验）。

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引用次数: 0

Adaptive neural network based quadrotor UAV formation control under external disturbances 外部干扰下基于自适应神经网络的四旋翼无人机编队控制

IF 5 1区工程技术 Q1 ENGINEERING, AEROSPACE

Aerospace Science and Technology

Pub Date : 2024-09-20 DOI: 10.1016/j.ast.2024.109608

The formation control of a team comprised of multiple quadrotor Unmanned Aerial Vehicles (UAVs) may severely be affected by the unknown external disturbances. The external disturbances are caused by wind forces to create aero-dynamical disturbances. This article addresses the robust formation control problem of multiple UAVs system despite the effect of external disturbances that allow sustaining a stable network connection among the UAVs and maintaining different formations assigned to them. First, a Radial Basis Function Neural Network (RBFNN) based model is developed to reciprocate the external disturbances along the positional and the attitude subsystems. Then incorporating the estimated disturbance values a distributed adaptive formation controller is devised using the Lyapunov theory. It consists of a positional and an attitude controller associated with the translational and the rotational movements of the UAVs. The stability is validated by satisfying the criteria of the Lyapunov stability function. The UAVs are connected through variable adjacency matrix based directed network topology and the network connectivity is established through the properties of the Laplacian Matrix. The robustness of the designed controller is justified via rigorous simulation studies for different sets of desired formations such as triangular, squared, tetrahedron, octahedron and cube shaped. The reference trajectories are considered as spiral, straight line and circular shaped. The time varying external disturbances are considered of sinusoidal waveform of different magnitudes. The simulation results signifies that the proposed RBFNN based formation controller reciprocate different sinusoidal waveforms to achieve the desired formations successfully. Extensive comparative studies demonstrate the efficacy of the proposed adaptive formation controller over the existing controllers presented in the literature for different shapes of trajectories and desired formations.

由多个四旋翼无人飞行器（UAV）组成的团队的编队控制可能会受到未知外部干扰的严重影响。外部干扰是由风力造成的空气动力干扰。本文探讨了多无人机系统在外部干扰影响下的鲁棒编队控制问题，使无人机之间保持稳定的网络连接，并维持分配给它们的不同编队。首先，开发了一个基于径向基函数神经网络（RBFNN）的模型，以对位置和姿态子系统的外部干扰进行倒推。然后，结合估计的干扰值，利用 Lyapunov 理论设计出分布式自适应编队控制器。它由与无人飞行器平移和旋转运动相关的位置控制器和姿态控制器组成。通过满足 Lyapunov 稳定函数的标准来验证稳定性。无人飞行器通过基于可变邻接矩阵的有向网络拓扑结构进行连接，并通过拉普拉斯矩阵的特性建立网络连接。通过对三角形、正方形、四面体、八面体和立方体等不同理想形状的严格模拟研究，证明了所设计控制器的鲁棒性。参考轨迹被视为螺旋形、直线形和圆形。外部时变干扰为不同幅度的正弦波。仿真结果表明，所提出的基于 RBFNN 的编队控制器可以往复处理不同的正弦波形，从而成功实现所需的编队。广泛的比较研究表明，针对不同形状的轨迹和所需队形，所提出的自适应队形控制器比文献中现有的控制器更有效。

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引用次数: 0

Geometric extended state observer on TSE(3) with fast finite-time stability: Theory and validation on a multi-rotor vehicle 具有快速有限时间稳定性的 TSE(3) 几何扩展状态观测器：理论与多旋翼飞行器验证

IF 5 1区工程技术 Q1 ENGINEERING, AEROSPACE

Aerospace Science and Technology

Pub Date : 2024-09-20 DOI: 10.1016/j.ast.2024.109596

This article presents an extended state observer for a vehicle modeled as a rigid body in three-dimensional translational and rotational motions. The extended state observer is applicable to a multi-rotor aerial vehicle with a fixed plane of rotors, modeled as an under-actuated system on the state-space

T SE (3)

, the tangent bundle of the six-dimensional Lie group

SE (3)

. This state-space representation globally represents rigid body motions without singularities. The extended state observer is designed to estimate the resultant external disturbance force and disturbance torque acting on the vehicle. It guarantees stable convergence of disturbance estimation errors in finite time when the disturbances are constant, and finite time convergence to a bounded neighborhood of zero errors for time-varying disturbances. This extended state observer design is based on a Hölder-continuous fast finite time stable differentiator that is similar to the super-twisting algorithm, to obtain fast convergence. Numerical simulations are conducted to validate the proposed extended state observer. The proposed extended state observer is compared with other existing research to show its advantages. A set of experimental results implementing disturbance rejection control using feedback of disturbance estimates from this extended state observer is also presented.

本文提出了一种用于三维平移和旋转运动中的刚体飞行器的扩展状态观测器。扩展状态观测器适用于具有固定旋翼平面的多旋翼飞行器，该飞行器在六维李群 SE(3) 的切线束 TSE(3) 状态空间上被建模为欠动系统。这种状态空间表示法全局地表示了无奇异点的刚体运动。扩展状态观测器旨在估算作用在车辆上的外部干扰力和干扰力矩。当扰动恒定时，它能保证扰动估计误差在有限时间内稳定收敛；当扰动时变时，它能保证扰动估计误差在有限时间内收敛到零误差的有界邻域。这种扩展状态观测器的设计是基于一个赫尔德连续快速有限时间稳定微分器，该微分器类似于超扭曲算法，以获得快速收敛。为了验证所提出的扩展状态观测器，我们进行了数值模拟。将所提出的扩展状态观测器与其他现有研究进行了比较，以显示其优势。此外，还介绍了利用该扩展状态观测器的扰动估计反馈实现扰动抑制控制的一组实验结果。

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引用次数: 0

A gradient-enhanced univariate dimension reduction method for uncertainty propagation 用于不确定性传播的梯度增强型单变量降维方法

IF 5 1区工程技术 Q1 ENGINEERING, AEROSPACE

Aerospace Science and Technology

Pub Date : 2024-09-20 DOI: 10.1016/j.ast.2024.109602

The univariate dimension reduction (UDR) method stands as a way to estimate the statistical moments of the output that is effective in a large class of uncertainty quantification (UQ) problems. UDR's fundamental strategy is to approximate the original function using univariate functions so that the UQ cost only scales linearly with the dimension of the problem. Nonetheless, UDR's effectiveness can diminish when uncertain inputs have high variance, particularly when assessing the output's second and higher-order statistical moments. This paper proposes a new method, gradient-enhanced univariate dimension reduction (GUDR), that enhances the accuracy of UDR by incorporating univariate gradient function terms into the UDR approximation function. Theoretical results indicate that the GUDR approximation is expected to be one order more accurate than UDR in approximating the original function, and it is expected to generate more accurate results in computing the output's second and higher-order statistical moments. Our proposed method uses a computational graph transformation strategy to efficiently evaluate the GUDR approximation function on tensor-grid quadrature inputs, and uses the tensor-grid input-output data to compute the statistical moments of the output. With an efficient automatic differentiation method to compute the gradients, our method preserves UDR's linear scaling of computation time with problem dimension. Numerical results show that the GUDR is more accurate than UDR in estimating the standard deviation of the output and has a performance comparable to the method of moments using a third-order Taylor series expansion.

单变量降维（UDR）方法是一种估算输出统计矩的方法，在一大类不确定性量化（UQ）问题中非常有效。UDR 的基本策略是使用单变量函数逼近原始函数，从而使 UQ 成本与问题维度成线性关系。然而，当不确定输入具有高方差时，UDR 的有效性就会降低，尤其是在评估输出的二阶和高阶统计矩时。本文提出了一种新方法--梯度增强单变量降维（GUDR），通过在 UDR 近似函数中加入单变量梯度函数项来提高 UDR 的精度。理论结果表明，在逼近原始函数方面，GUDR 近似值有望比 UDR 精确一个数量级，而且在计算输出的二阶和高阶统计矩时有望产生更精确的结果。我们提出的方法采用计算图转换策略，在张量网格正交输入上高效评估 GUDR 近似函数，并利用张量网格输入输出数据计算输出的统计矩。利用高效的自动微分方法计算梯度，我们的方法保留了 UDR 计算时间随问题维度线性缩放的特点。数值结果表明，GUDR 在估计输出标准差方面比 UDR 更准确，其性能可与使用三阶泰勒级数展开的矩方法相媲美。

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引用次数: 0

A variable fidelity approach for predicting aerodynamic wall quantities of hypersonic vehicles using the ConvNeXt encoder-decoder framework 利用 ConvNeXt 编码器-解码器框架预测高超音速飞行器气动壁面数量的可变保真度方法

IF 5 1区工程技术 Q1 ENGINEERING, AEROSPACE

Aerospace Science and Technology

Pub Date : 2024-09-19 DOI: 10.1016/j.ast.2024.109605

Computational fluid dynamics (CFD) simulations for obtaining three-dimensional hypersonic vehicle aerodynamic characteristics are resource-intensive. Deep learning offers a promising alternative for predicting aerodynamic wall quantities but typically requires a large dataset, which conflicts with the intention to reduce CFD costs. To address this, we propose a novel prediction model leveraging variable fidelity data to alleviate computational demands. The model utilizes an encoder-decoder architecture with ConvNeXt blocks as operators, processing variable fidelity data as inputs and outputs. We also developed a U-Net with residual blocks (ResUNet) for performance comparison. Transformation and topology patching techniques were applied to tackle the challenges posed by large grid volumes and complex topologies in three-dimensional vehicles. Results demonstrate that the ConvNeXt Encoder-Decoder predicts peak regions more accurately than ResUNet and aligns closely with CFD in low-value areas. The ConvNeXt Encoder-Decoder maintains maximum heat flux errors below 1 % and viscous drag errors below 3.81 % across varying angles of attack. It exhibits superior fitting performance with minimal deviation from CFD compared to ResUNet. Prediction accuracy decreases under multiple inflow changes compared to the angle of attack variations alone. Heat flux predictions show high consistency with CFD, with relative errors below 6.38 %, whereas friction predictions exhibit higher errors with 10.28 % maximum error and 0.28 % minimum error. The model accurately predicts friction variation trends in peak regions at the blunt edge's central plane but performs poorly in low-value areas. In summary, the ConvNeXt Encoder-Decoder accurately predicts the wall quantities under varying multiple inflow conditions.

用于获取三维高超音速飞行器气动特性的计算流体动力学（CFD）模拟需要大量资源。深度学习为预测气动壁面量提供了一种有前途的替代方法，但通常需要大量数据集，这与降低 CFD 成本的初衷相冲突。为了解决这个问题，我们提出了一种利用可变保真度数据的新型预测模型，以减轻计算需求。该模型采用以 ConvNeXt 块为运算器的编码器-解码器架构，将可变保真度数据作为输入和输出进行处理。我们还开发了一个带有残差块的 U-Net（ResUNet），用于性能比较。我们采用了变换和拓扑修补技术，以应对三维车辆中的大网格体积和复杂拓扑所带来的挑战。结果表明，ConvNeXt 编码器-解码器对峰值区域的预测比 ResUNet 更准确，在低值区域与 CFD 非常接近。ConvNeXt 编码器-解码器能在不同的攻角范围内将最大热通量误差保持在 1 % 以下，将粘性阻力误差保持在 3.81 % 以下。与 ResUNet 相比，它具有出色的拟合性能，与 CFD 的偏差最小。与单独的攻角变化相比，预测精度在多流入量变化时有所降低。热通量预测与 CFD 高度一致，相对误差低于 6.38%，而摩擦力预测误差较大，最大误差为 10.28%，最小误差为 0.28%。该模型准确预测了钝边中心平面峰值区域的摩擦变化趋势，但在低值区域的表现较差。总之，ConvNeXt 编码器-解码器能准确预测不同多流入条件下的壁面量。

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引用次数: 0

Gust response alleviation of the aircraft wing aeroelastic systems in supersonic flows using nonlinear energy sink 利用非线性能量汇减轻飞机机翼气动弹性系统在超音速气流中的阵风响应

IF 5 1区工程技术 Q1 ENGINEERING, AEROSPACE

Aerospace Science and Technology

Pub Date : 2024-09-19 DOI: 10.1016/j.ast.2024.109601

In order to reduce the gust response level of the aircraft wing aeroelastic systems in supersonic flows, the nonlinear energy sink (NES) is introduced into the aeroelastic system, and the effect of the nonlinear energy sink on the aeroelastic gust response is studied. The gust response dynamic model without and with NES is established by Lagrange equation, considering the cubic nonlinear characteristics of both plunge and pitch stiffness. The piston theory model as well as the '1-cos' model are employed to describe the unsteady aerodynamic load and the gust velocity, respectively. Besides, the comparison study of the gust response amplitude and energy characteristics of the wing aeroelastic systems with and without NES are analyzed. For systems with NES, the amplitude of gust dynamic response decreases rapidly, while vibration can be transmitted to the NES mechanism and causing its vibration. From an energy perspective, about 72.15 % of the original vibration energy has been transferred into NES, and further consumed through its damping. The influence of NES parameters, such as frequency ratio, mass ratio, damping ratio, and installation position on gust response, is discussed. Numerical simulations demonstrate that: as the frequency ratio, damping ratio and mass ratio increase, the dynamic response amplitude of the aeroelastic system decays faster, and the energy transferred from the wing system also increases. In addition, an optimal relative distance for the installation of NES can be found with best gust alleviation effect. The influence of nonlinear energy sink on the targeted transfer of vibration energy in aeroelastic systems are analyzed under different working conditions. The NES shows great gust alleviation effect and good robustness under different flight speeds, gust intensities, and gust lengths.

为了降低飞机机翼气动弹性系统在超音速气流中的阵风响应水平，在气动弹性系统中引入了非线性能量汇（NES），并研究了非线性能量汇对气动弹性阵风响应的影响。考虑到俯冲和俯仰刚度的立方非线性特性，通过拉格朗日方程建立了无 NES 和有 NES 的阵风响应动力学模型。活塞理论模型和 "1-cos "模型分别用于描述非稳定气动载荷和阵风速度。此外，还分析了有无 NES 的机翼气动弹性系统的阵风响应振幅和能量特征对比研究。对于有 NES 的系统，阵风动态响应振幅迅速减小，同时振动会传递到 NES 机构并引起其振动。从能量角度来看，约有 72.15 % 的原始振动能量被传递到 NES，并通过其阻尼作用进一步消耗。本文讨论了 NES 参数（如频率比、质量比、阻尼比和安装位置）对阵风响应的影响。数值模拟表明：随着频率比、阻尼比和质量比的增加，气动弹性系统的动态响应振幅衰减得更快，从机翼系统传递的能量也随之增加。此外，还可以找到安装 NES 的最佳相对距离，从而获得最佳的阵风缓解效果。分析了不同工况下非线性能量汇对气动弹性系统振动能量定向传递的影响。在不同的飞行速度、阵风强度和阵风长度下，非线性能量沉降器都表现出了很好的阵风缓解效果和鲁棒性。

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引用次数: 0

Multi-constrained predictive optimal control for spacecraft attitude stabilization and tracking with performance guarantees 用于航天器姿态稳定和跟踪的具有性能保证的多约束预测优化控制

IF 5 1区工程技术 Q1 ENGINEERING, AEROSPACE

Aerospace Science and Technology

Pub Date : 2024-09-19 DOI: 10.1016/j.ast.2024.109599

This paper investigates a novel predictive control approach for spacecraft attitude stabilization and tracking problems with consideration of performance constraints, angular velocity limitation and control saturation. First, the prescribed performance control (PPC) structure and barrier Lyapunov function (BLF) are utilized to design the cost function of optimal control problem. Subsequently, a multi-constrained predictive optimal controller for spacecraft attitude stabilization and tracking with performance guarantees is devised via exploiting an explicit receding horizon optimization control strategy under actuator saturation. Compared with the existing methods, the major merit of the proposed one lies in the semi-analytic solving scheme of optimal control problems with high computing efficiency and simultaneous handling of multiple constraints without increasing computational burden. Finally, two illustrative examples are employed to validate the effectiveness of the proposed control method.

本文研究了一种用于航天器姿态稳定和跟踪问题的新型预测控制方法，其中考虑了性能约束、角速度限制和控制饱和。首先，利用规定性能控制（PPC）结构和障碍李亚普诺夫函数（BLF）来设计最优控制问题的代价函数。随后，通过利用显式后退视界优化控制策略，设计了一种用于航天器姿态稳定和跟踪的多约束预测最优控制器，并保证了其性能。与现有方法相比，所提方法的主要优点在于最优控制问题的半解析求解方案具有较高的计算效率，并能在不增加计算负担的情况下同时处理多个约束条件。最后，通过两个示例验证了所提控制方法的有效性。

引用次数: 0

General framework for unsteady aerodynamic prediction of airfoils based on deep transfer learning 基于深度迁移学习的机翼非稳态气动预测总体框架

IF 5 1区工程技术 Q1 ENGINEERING, AEROSPACE

Aerospace Science and Technology

Pub Date : 2024-09-19 DOI: 10.1016/j.ast.2024.109606

Analyzing the unsteady aerodynamic performance of airfoils under dynamic stall using computational fluid dynamics (CFD) is computationally intensive. Although deep learning models can quickly predict aerodynamic parameters, their generalization capability on a small-scale dataset is often poor. This paper presents a novel deep transfer learning (TL) framework that combines model-based and instance-based transfer methods, termed synergistic instance-model TL. This framework facilitates rapid predictions of unsteady aerodynamic performance for various airfoils and pitch oscillations from the small-scale dataset. The framework integrates the accelerated training speed of model-based methods with the dynamic dataset expansion benefits of instance-based approaches. Initially, a pre-trained Wasserstein-deep convolutional generative adversarial network (W-DCGAN) is developed, combining a convolutional neural network with a generative adversarial network to predict aerodynamic hysteresis loops for the SC1095 airfoil in the source domain. The framework then fine-tunes the pre-trained model and incorporates weighted source domain dataset into the small-scale target domain dataset, producing the transferred model W-DCGAN-TL. This approach significantly reduces prediction inaccuracies compared to model-based and non-TL methods when applied to the small-scale dataset. The framework's flexibility allows the use of pre-trained models and datasets from related aerodynamic problems to address issues with insufficient data. Consequently, it is expected to reduce the dependency on extensive datasets, enhance design efficiency, and minimize resource requirements.

使用计算流体动力学（CFD）分析动态失速下机翼的非稳定气动性能需要大量计算。虽然深度学习模型可以快速预测气动参数，但其在小规模数据集上的泛化能力往往较差。本文提出了一种新颖的深度迁移学习（TL）框架，它结合了基于模型和基于实例的迁移方法，被称为协同实例-模型 TL。该框架有助于从小规模数据集快速预测各种机翼和俯仰振荡的非稳定气动性能。该框架集成了基于模型方法的加速训练速度和基于实例方法的动态数据集扩展优势。首先，开发了一个预先训练好的瓦瑟斯坦深度卷积生成对抗网络（W-DCGAN），将卷积神经网络与生成对抗网络相结合，预测 SC1095 机翼在源域中的气动滞后环。然后，该框架对预训练模型进行微调，并将加权源域数据集纳入小尺度目标域数据集，从而生成传输模型 W-DCGAN-TL。在应用于小规模数据集时，与基于模型的方法和非 TL 方法相比，这种方法大大降低了预测误差。该框架的灵活性允许使用预训练模型和相关空气动力学问题的数据集来解决数据不足的问题。因此，该框架有望减少对大量数据集的依赖，提高设计效率，并最大限度地减少资源需求。

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