Browsing by Author "Yao, Weigang"

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    A novel empirical model for vertical profiles of downburst horizontal wind speed
    (Wiley, 2024-01-22) Dang, Huixue; Xing, Guohua; Wang, Hailong; Harmanto, Dani; Yao, Weigang
    This study proposes an empirical model for preliminary wind-resist design of downburst flow. Existing empirical models were compared with field data and found to underpredict horizontal wind speed below the height corresponding to the maximum radial velocity, due to the neglect of viscous effects and the evolution of vertical wind profiles along radial direction. To address these deficiencies, semi-empirical piecewise functions including wall shear effects in the local turbulent boundary layer and interpolation functions were proposed to improve the accuracy of existing models. The wind profile based on Coles' theory was found to agree well with field data, with the parabola interpolation function being the most desirable. Using the proposed method, the vertical profile of horizontal wind speed at different local radial locations can be predicted for wind resist design given the inlet wind speed of the downburst flow. Overall, this model improves upon existing empirical models and allows for more accurate wind-resist design.
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    Aerodynamic shape optimization of co-flow jet airfoil using a multi-island genetic algorithm
    (AIP Publishing, 2022-12-07) Jiang, Hao; Xu, Min; Yao, Weigang
    The co-flow jet is a Zero-Net-Mass-Flux (ZNMF) active flow control strategy and presents great potential to improve the aerodynamic efficiency of future fuel-efficient aircraft. The present work is to integrate the co-flow jet technology into aerodynamic shape optimization to further realize the potential of co-flow-jet technology and improve co-flow jet airfoil performance. The optimization results show that the maximum energy efficiency ratio of lift-augmentation and drag-reduction increased by 203.53% (α=0◦) and 10.25% (α=10◦) at the Power-1 condition (power coefficient of 0.3), respectively. A larger curvature is observed near the leading edge of the optimized aerodynamic shape, which leads to the early onset of flow separation and improves energy transfer efficiency from the jet to the free stream. In addition, the higher mid-span of the optimized airfoil is characterized by accelerating the flow in the middle of the airfoil, increasing the strength of the negative pressure zone, thus improving the stall margin and enhancing the co-flow jet circulation.
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    Aircraft parameter estimation using a stacked long short-term memory network and Levenberg-Marquardt method
    (Elsevier, 2023-09-09) Hui, Zhe; Kong, Yinan; Yao, Weigang; Chen, Gang
    To effectively estimate the unknown aerodynamic parameters from the aircraft’s flight data, this paper proposes a novel aerodynamic parameter estimation method incorporating a stacked Long Short-Term Memory (LSTM) network model and the Levenberg-Marquardt (LM) method. The stacked LSTM network model was designed to realize the aircraft dynamics modeling by utilizing a frame of nonlinear functional mapping based entirely on the measured input–output data of the aircraft system without requiring explicit postulation of the dynamics. The LM method combines the already-trained LSTM network model to optimize the unknown aerodynamic parameters. The proposed method is applied by using the real flight data, generated by ATTAS aircraft and a bio-inspired morphing Unmanned Aerial Vehicle (UAV). The investigation reveals that for the two different flight data, the designed stacked LSTM network structure can maintain the efficacy of the network prediction capability only by appropriately adjusting the dropout rates of its hidden layers without changing other network parameters (i.e., the initial weights, initial biases, number of hidden cells, time-steps, learning rate, and number of training iterations). Besides, the proposed method’s effectiveness and potential are demonstrated by comparing the estimated results of the ATTAS aircraft or the bio-inspired morphing UAV with the corresponding reference values or wind-tunnel results.
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    Analysis of the post-flutter aerothermoelastic characteristics of hypersonic skin panels using a CFD-based approach
    (Elsevier, 2021-08-30) Quan, Enqian; Xu, Min; Yao, Weigang; Cheng, Xiang
    The present work aims to investigate the post-flutter aerothermoelastic behaviours of the hypersonic skin panels by using the integrated aerothermoelastic analysis framework developed in this paper. The aerodynamic loading and heating are computed simultaneously by solving Reynolds-averaged Navier-Stokes equations (RANS). The structural and thermal finite element models of a hypersonic skin panel are built and solved numerically to model the structural dynamics and thermal conduction. An implicit predictor-corrector scheme is employed to address the fluid-thermal-structural interactions. The aerothermoelastic characteristics of a two-dimensional hypersonic panel obtained using both one-way and two-way coupling strategies are systematically compared and discussed. The results show that: 1) The air viscosity delays the onset of flutter significantly, albeit aggravates thermal effect on the flutter instability; 2) The buckled panel can be similarly predicted by both the one-way and two-way coupling strategies. In contrast, the two-way coupling captures shockwave/boundary layer interactions leading to high local temperature; 3) The modal transition is predicted when structural displacement feeds back into the aerothermoelastic analysis. 4) The variation of temperature gradient along the panel thickness is analogous to the time-domain displacement response as revealed by two-way coupling strategy; 5) One-way coupling predicts lower maximum Von Mises stress as compared with the two-way coupling counterpart under the conditions employed in the present study.
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    Data-Driven Nonintrusive Model-Order Reduction for Aerodynamic Design Optimization
    (American Institute of Aeronautics and Astronautics, 2024-05-13) Moni, Abhijith; Yao, Weigang; Malekmohamadi, Hossein
    Fast and accurate evaluation of aerodynamic characteristics is essential for aerodynamic design optimization because aircraft programs require many years of design and optimization. Therefore, it is imperative to develop sufficiently fast, robust, and accurate computational tools for industry routine analysis. This paper presents a nonintrusive machine-learning method for building reduced-order models (ROMs) using an autoencoder neural network architecture. An optimization framework was developed to identify the optimal solution by exploring the low-dimensional subspace generated by the trained autoencoder. To demonstrate the convergence, stability, and reliability of the ROM, a subsonic inverse design problem and a transonic drag minimization problem of the airfoil were studied and validated using two different parameterization strategies. The robustness and accuracy demonstrated by the method suggest that it is valuable in parametric studies, such as aerodynamic design and optimization, and requires only a small fraction of the cost of full-order modeling.
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    Data-Driven Reduced Order Modelling for Aerodynamic Shape Optimisation
    (American Institute of Aeronautics and Astronautics, 2023-01-19) Moni, Abhijith; Yao, Weigang; Malekmohamadi, Hossein
    Despite the increase in computational capabilities, the computational simulation-based approach for aerodynamic shape optimization remains formidable for industrial routine applications. To make this approach acceptable in design practice, data-driven methodologies for building reduced-order models (ROMs) have been proposed. Instead of simplifying the model computed using computational fluid dynamics (CFD), the proposed ROM methodology aims to directly reduce the computational complexity of the model non-intrusively. In this article, we propose a machine learning method for building reduced-order models using multi-variate neural networks and demonstrate how it can maintain accuracy for making predictions in the highly non-linear transonic regime. The pyOptSparse framework is used to provide an interface to the ROM-based optimization process. To demonstrate the convergence, stability, and reliability of the ROM, the ADODG (AIAA Aerodynamic Design Optimization Discussion Group) NACA 0012 first benchmark case of inviscid transonic optimization is extensively studied using the SU2 software package and used for the purpose of validating the proposed ROM methodology.
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    Data-driven reduced-order modeling for nonlinear aerodynamics using an autoencoder neural network
    (AIP Publishing, 2024-01-03) Moni, Abhijith; Yao, Weigang; Malekmohamadi, Hossein
    The design of commercial air transportation vehicles heavily relies on understanding and modeling fluid flows, which pose computational challenges due to their complexity and high degrees of freedom. To overcome these challenges, we propose a novel approach based on machine learning (ML) to construct reduced-order models (ROMs) using an autoencoder neural network coupled with a discrete empirical interpolation method (DEIM). This methodology combines the interpolation of nonlinear functions identified based on selected interpolation points using DEIM with an ML-based clustering algorithm that provides accurate predictions by spanning a low-dimensional subspace at a significantly lower computational cost. In this study, we demonstrate the effectiveness of our approach by the calculation of transonic flows over the National Advisory Committee of Aeronautics 0012 airfoil and the National Aeronautics and Space Administration Common Research Model wing. All the results confirm that the ROM captures high-dimensional parameter variations efficiently and accurately in transonic regimes, in which the nonlinearities are induced by shock waves, demonstrating the feasibility of the ROM for nonlinear aerodynamics problems with varying flow conditions.
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    Enhancing the Goman–Khrabrov dynamic stall model through flow delay analysis
    (AIP Publishing, 2025-01-08) Zheng, Boda; Yao, Weigang; Xu, Min
    The complete dynamic stall process encompasses a series of complex developmental stages, such as flow separation, leading edge vortex shedding, and reattachment. Unlike static stall, dynamic stall exhibits hysteresis, rendering phenomenological models as complex nonlinear state-space systems, often accompanied by numerous empirical parameters, which complicates practical applications. To address this issue, the Goman-Khrabrov (G-K) dynamic stall model simplifies the state space and retains only two empirical parameters related to time delays. Our study finds that different developmental stages of dynamic stall exhibit various time delay scales. The G-K dynamic stall model, which utilizes a first-order time-invariant inertia system, forcibly unifies the time scales across different stages. Consequently, this leads to intractable non-physical modeling errors. This paper introduces the latest revised G-K model that employs a time-varying state space system. This model not only maintains a concise form but also eliminates the non-physical modeling errors previously mentioned. In response to the challenge of identifying empirical parameters, this paper presents a parameter identification method for both the original and revised G-K models utilizing a Physics-Informed Neural Network (PINN). The revised model was validated through dynamic stall load prediction cases for mild, moderate and deep dynamic stall on various airfoils, achieving a maximum accuracy improvement of up to 74.5%. The revised G-K model is capable of addressing a broader range and more complex practical applications.
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    Experimental design for a novel co-flow jet airfoil
    (Springer, 2023-12-01) Jiang, Hao; Yao, Weigang; Xu, Min
    The Co-flow Jet (CFJ) technology holds significant promise for enhancing aero-dynamic efficiency and furthering decarbonization in the evolving landscape of air transportation. The aim of this study is to empirically validate an optimized CFJ airfoil through low-speed wind tunnel experiments. The CFJ airfoil is structured in a tri-sectional design, consisting of one experimental segment and two stationary segments. A support rod penetrates the airfoil, fulfilling dual roles: it not only maintains the structural integrity of the overall model but also enables the direct measurement of aerodynamic forces on the test section of the CFJ air-foil within a two-dimensional wind tunnel. In parallel, the stationary segments are designed to effectively minimize the interference from the lateral tunnel walls. The experimental results are compared with numerical simulations, specifically focusing on aerodynamic parameters and flow field distribution. The findings reveal that the experimental framework employed is highly effective in characterizing the aerodynamic behavior of the CFJ airfoil, showing strong agreement with the simulation data.
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    Lock-in mechanism of flow over a low-Reynolds-number airfoil with morphing surface
    (Elsevier, 2019-12-19) Kang, W.; Xu, M.; Yao, Weigang; Zhang, J.
    To understand the frequency lock-in mechanism of flow separation control of an airfoil at low Reynolds number, a systematic analysis is performed by extracting the Lagrangian Coherent Structures (LCSs) from the unsteady flow. The actuation is considered via periodic morphing surface, and the dynamical behaviors between morphing surface and unsteady flow are studied from the viewpoint of fluid transport. Attention is drawn to fluid transport and lift improvement when the actuation frequency is locked onto the vortex shedding frequency. The results show that the fluid particle near the actuator is accelerated by the actuation and interacts with the slow fluid particle in boundary layer on the airfoil surface. The so-called stirring jet mechanism is observed, whereby a cusp structure is formed like a jet acting on the flow, which enhances the fluid transport from main stream into separation zone by reducing dead air zone effectively. The results also show that the actuation frequency is found to be the key factor for lift enhancement and determines the cusp structures and the vortex strength on the upper surface of the airfoil.
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    Manifold Learning for Aerodynamic Shape Design Optimization
    (MDPI, 2025-03-19) Zheng, Boda; Moni, Abhijith; Yao, Weigang; Xu Min
    The significant computational cost incurred due to the iterative nature of Computational Fluid Dynamics (CFD) in traditional aerodynamic shape design frameworks poses a major challenge, especially in the context of modern integrated design requirements and increasingly complex design conditions. To address the demands of modern design, we developed an efficient aerodynamic shape design framework based on our previous work involving the locally linear embedding plus constrained optimization genetic algorithm (LLE+COGA) high-fidelity reduced-order model (ROM). An active manifold (AM) auto-en/decoder was employed to address the dimensionality curse arising from an excessively large design space. The fast mesh deformation method was utilized for high-precision, rapid mesh deformation, significantly reducing the computational cost associated with transferring geometric deformations to CFD fine mesh. This work addressed the transonic optimization problem of the undeflected Common Research Model (uCRM) three-dimensional wing (with an aspect ratio of 9), involving 241 design variables. The results demonstrate that the optimized design achieved a significant reduction in the drag coefficient by 38.9% and 54.5% compared to the baseline in Case 1 and Case 2, respectively. Additionally, the total optimization time was shortened by 62.6% and 57.7% in the two cases. Moreover, the optimization outcomes aligned well with those obtained from the FOM-based framework, further validating the effectiveness and practical applicability of the proposed approach.
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    Modal Phase Study on Lift Enhancement of a Locally Flexible Membrane Airfoil Using Dynamic Mode Decomposition
    (MDPI, 2025-04-06) Kang, Wei; Hu, Shilin; Chen, Bingzhou; Yao, Weigang
    The dynamic mode decomposition serves as a useful tool for the coherent structure extraction of the complex flow fields with characteristic frequency identification, but the phase information of the flow modes is paid less attention to. In this study, phase information around the locally flexible membrane airfoil is quantitatively studied using dynamic mode decomposition (DMD) to unveil the physical mechanism of the lift improvement of the membrane airfoil. The flow over the airfoil at a low Reynolds number (Re = 5500) is computed parametrically across a range of angles of attack (AOA = 4°–14°) and membrane lengths (LM = 0.55c–0.70c) using a verified fluid–structure coupling framework. The lift enhancement is analyzed by the dynamic coherent patterns of the membrane airfoil flow fields, which are quantified by the DMD modal phase propagation. A downstream propagation pressure speed (DPP) on the upper surface is defined to quantify the propagation speed of the lagged maximal pressure in the flow separation zone. It is found that a faster DPP speed can induce more vortices. The correlation coefficient between the DPP speed and lift enhancement is above 0.85 at most cases, indicating the significant contribution of vortex evolution to aerodynamic performance. The DPP speed greatly impacts the retention time of dominant vortices on the upper surface, resulting in the lift enhancement.
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    Nonintrusive Aerodynamic Shape Optimisation with a POD-DEIM Based Trust Region Method
    (MDPI, 2023-05-17) Marques, Simão; Kob, Lucas; Robinson, Trevor T.; Yao, Weigang
    This work presents a strategy to build reduced-order models suitable for aerodynamic shape optimisation, resulting in a multifidelity optimisation framework. A reduced-order model (ROM) based on a discrete empirical interpolation (DEIM) method is employed in lieu of computational fluid dynamics (CFD) solvers for fast, nonlinear, aerodynamic modelling. The DEIM builds a set of interpolation points that allows it to reconstruct the flow fields from sets of basis obtained by proper orthogonal decomposition of a matrix of snapshots. The aerodynamic reduced-order model is completed by introducing a nonlinear mapping function between surface deformation and the DEIM interpolation points. The optimisation problem is managed by a trust region algorithm linking the multiple-fidelity solvers, with each subproblem solved using a gradient-based algorithm. The design space is initially restricted; as the optimisation trajectory evolves, new samples enrich the ROM. The proposed methodology is evaluated using a series of transonic viscous test cases based on wing configurations. Results show that for cases with a moderate number of design variables, the approach proposed is competitive with state-of-the-art gradient-based methods; in addition, the use of trust region methodology mitigates the likelihood of the optimiser converging to, shallower, local minima.
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    Nonlinear Manifold Learning and Model Reduction for Transonic Flows
    (American Institute of Aeronautics and Astronautics, 2023-09-12) Zheng, Boda; Yao, Weigang; Xu, Min
    It is aspirational to construct a nonlinear reduced-order model (ROM) with the ability to predict computational fluid dynamics (CFD) solutions accurately and efficiently. One major challenge is that the nonlinearity cannot be captured adequately by interpolation algorithm in low-dimensional space. To preserve the nonlinearity of CFD solutions for transonic flows, a new ROM is presented by integrating manifold learning into a constrained optimization, whereby a neighborhood preserving mapping is constructed by locally linear embedding (LLE) algorithm. Reconstruction errors are minimized in LLE by solving a least square problem subject to weight constraints. A loss function is proposed in the constrained optimization to preserve the geometric properties between high-dimensional space and low-dimensional manifolds. The proposed ROM is validated to predict nonlinear transonic flows over RAE 2822 airfoil and undeflected NASA Common Research Model with aspect ratio 9, in which nonlinearities are induced by shock waves. All results confirm that the ROM replicates CFD solutions accurately at fraction of the cost of CFD calculation or the full-order modeling.
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    Nonlinear reduced order modeling for nonlinear response simulation of panels via Large Eddy Simulation
    (Elsevier, 2021-06-20) Xu, W.; Xu, M.; An, X.; Yao, Weigang
    Supersonic vehicles are subject not only to aerodynamic heating, but also to different acoustics, one of which is aeroacoustic induced by pressure fluctuation of high speed flow. The state-of-art structure sonic fatigue analysis is conducted by using uniformly distributed random White Gaussian Noise (WGN). However, uniformly distributed excitation is usually not consistent with the actual situation, and the validity of the method needs further investigation. In the present study, a nonlinear reduced-order model (NLROM) is presented to compute nonlinear response of isotropic and composite plates. The NLROM is based on finite element (FE) model and is constructed by means of Galerkin projection of the full order system onto a small subspace. The input of the NLROM is aerodynamic and aeroacoustic loads, which are computed by Large Eddy Simulation (LES) and interpolated from aerodynamic grid to structure node by using Radial Basis Function (RBF). The nonlinear response of the isotropic and composite plates is computed by NLROM and compared with WGN. The results show that the NLROM offers nearly an order of magnitude speed up as compared with direct FE simulation and predicts shorter sonic fatigue life than WGN.
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    Reduced Order Modelling for Multi-disciplinary Design Optimisation
    (2023-10) Moni, Abhijith; Yao, Weigang; Malekmohamadi, Hossein
    Aircraft design and optimization in the industry require multiple design rounds usually a trade-off between various objectives and constraints. When multi-disciplinary design optimization (MDO) is of particular interest, the problem becomes more complex because it requires the interaction of different disciplines such as aerodynamics and structural analysis to attain the objectives. Although the field of numerical simulation for solving partial differential equations (PDEs) has significantly developed, enabling the solution to complex dynamics, especially large-scale industrial applications, is still computationally expensive. To meet this interactive requirement in aircraft design optimization, computational methodologies that are fast, reliable, and accurate for routine industry analysis are essential. This work proposes a non-intrusive, data-driven approach for constructing reduced-order models (ROMs) with machine learning (ML) techniques capable of solving PDE-constrained MDO problems. This proposed methodology intends to embed the high-dimensional nonlinear data onto low-dimensional subspace with ML algorithms based on the method of snapshots collected from high-fidelity simulations to make the process of repeatedly solving large-scale MDO problems feasible. However, the training of these reduced-order models is challenging when there are many design parameters to consider, such as in MDO problems. In order to address the challenge associated with training the ROMs, this work will present the algorithms for training the ROM with a piecewise-global reduced-order basis with a confined area of design space. In this article, initial findings pertaining to the prediction of flowfield, coefficients of pressure, as well as forces and moments, using the proposed methodology are presented. These preliminary results are a fundamental step toward the application of this methodology in solving MDO cases. This research brings data-driven multidisciplinary design optimization one step closer to being a practical tool for developing reliable and energy-efficient aircraft configurations, which require routine analyses early in the design cycle.
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    A reduced-order model for gradient-based aerodynamic shape optimisation
    (Elsevier, 2020-08-11) Yao, Weigang; Simão, Marques; Trevor, Robinson; Cecil, Armstrong; Liang, Sun
    This work presents a reduced order model for gradient based aerodynamic shape optimization. The solution of the fluid Euler equations is converted to reduced Newton iterations by using the Least Squares Petrov-Galerkin projection. The reduced order basis is extracted by Proper Orthogonal Decomposition from snapshots based on the fluid state. The formulation distinguishes itself by obtaining the snapshots for all design parameters by solving a linear system of equations. Similarly, the reduced gradient formulation is derived by projecting the full-order model state onto the subspace spanned by the reduced basis. Auto-differentiation is used to evaluate the reduced Jacobian without forming the full fluid Jacobian explicitly during the reduced Newton iterations. Throughout the optimisation trajectory, the residual of the reduced Newton iterations is used as an indicator to update the snapshots and enrich the reduced order basis. The resulting multi-fidelity optimisation problem is managed by a trust-region algorithm. The ROM is demonstrated for a subsonic inverse design problem and for an aerofoil drag minimization problem in the transonic regime. The results suggest that the proposed algorithm is capable of aerodynamic shape optimization while reducing the number of full-order model queries and time to solution with respect to an adjoint gradient based optimisation framework.
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    Revisit of the variable stiffness method for aeroelastic computations with/without thermal effects based on computational fluid dynamics
    (Elsevier, 2021-07-01) Quan, Enqian; Xu, Min; Yao, Weigang; Yan, Xunliang
    In the present study, a variable stiffness method (VSM) is revisited for aeroelastic computations with/without thermal effects. Unlike the traditional CFD/CSM coupling method (TM), which predicts aeroelastic responses by varying freestream conditions (e.g. freestream density, velocity), the freestream conditions in VSM can be fixed. The VSM is first verified theoretically and adopted to predict nonlinear aeroelastic responses. For aeroelastic computations, the method is applied to predict flutter onset of Isogai wing section and limit cycle oscillation (LCO) of Goland wing at transonic conditions. The structural free-play nonlinearity is included in the Isogai wing section aeroelastic system to further demonstrate the method. The aeroelastic computation with thermal effects is considered as the flutter onset prediction of a simply supported panel in supersonic flow. It is shown that the VSM method can replicate the nonlinear aeroelastic responses predicted by its traditional CFD/CSM coupling method counterpart under the same flow similarity parameters (e.g. Mach number, Reynolds number), whereby the freestream conditions need to be adjusted. Limitations of the VSM are also pointed out and discussed. To further assess the VSM, an ARMA model is constructed under the framework of the VSM and demonstrated by flutter onset prediction of the Isogai wing section at transonic regime.
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    Utilizing Global-Local Neural Networks for the Analysis of Non-Linear Aerodynamics
    (Elsevier, 2024-07-02) Moni, Abhijith; Yao, Weigang; Malekmohamadi, Hossein
    In addressing the computational challenges pervasive in engineering where time and cost limitations are key concerns, particularly within the Computational Fluid Dynamics (CFD) domain, Reduced Order Models (ROMs) have emerged as instrumental tools. Focused on reducing computational complexity without intrusively modifying the computational model, this study centres on the strategic application of aerodynamic ROMs, which provide efficient computation of distributed quantities and aerodynamic forces. This work presents ROMs for non-linear aerodynamic applications, integrating principal component analysis (PCA) with Global Local Neural Networks (GLNN). The effectiveness of the proposed methodology is demonstrated by leveraging dependency on the parameter space created with non-linear high-fidelity CFD data, incorporating viscous simulation for a comprehensive approach. Results are first presented for a two-dimensional airfoil case and then for a three-dimensional test case featuring a transonic wing-body-tail transport aircraft configuration (NASA Common Research Model). In transonic flows, the proposed ROMs demonstrate the ability to accurately capture both the location and strength of shocks, as well as forces and moments for unseen prediction points. This highlights the efficiency of the proposed method in navigating complex aerodynamic scenarios, achieving comparable accuracy to full-order modelling but at orders of magnitude less computational time, for unseen parameters outside the ROM training set within the parameter space.
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    Wing Design Optimization and Stall Analysis with Co-flow Jet Active Control
    (AIP Publishing, 2024-04-25) Jiang, Hao; Yao, Weigang; Zheng, Boda; Xu, Min
    Coupled with Co-flow Jet (CFJ) technology, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was utilized for the multi-objective combination optimization of an Optimized Co-flow Jet (OCFJ) wing, based on National Advisory Committee for Aeronautics (NACA) 6421. A high-precision numerical simulation using the Delayed Detached Eddy Simulation (DDES) model was performed on the optimized wing to investigate the three-dimensional flow separation characteristics after static stall. The stall improvement was investigated by adjusting the momentum coefficient of the injection. The results show that the optimized wing exhibits significant improvements in aerodynamic performance and corrected aerodynamic efficiency. At an angle of attack of 10◦, the average lift increased by 16.25% and the drag decreased by 27.23% compared to the CFJ6421 wing, while effectively addressing the problem of low modified aerodynamic efficiency of the CFJ wing at lower angles of attack. By utilizing higher momentum and improving the boundary layer control capability, flow separation is effectively suppressed, thus achieving the goal of stall recovery of the CFJ wing.