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An inverse design method for supercritical airfoil based on conditional generative models

Chinese Journal of Aeronautics 2022年第3期35卷 62-74页

作者： Jing WANG Runze LI Cheng HE Haixin CHEN Ran CHENG Chen ZHAI Miao ZHANG Shanghai Aircraft Design and Research Institute Shanghai 200436China School of Aerospace Engineering Tsinghua UniversityBeijing 100084China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

Inverse design has long been an efficient and powerful design tool in the aircraft industry.In this paper,a novel inverse design method for supercritical airfoils is proposed based on generative models in deep learning.A Conditional Variational Auto Encoder(CVAE)and an integrated generative network CVAE-GAN that combines the CVAE with the Wasserstein Generative Adversarial Networks(WGAN),are conducted as generative models.They are used to generate target wall Mach distributions for the inverse design that matches specified features,such as locations of suction peak,shock and aft loading.Qualitative and quantitative results show that both adopted generative models can generate diverse and realistic wall Mach number distributions satisfying the given features.The CVAE-GAN model outperforms the CVAE model and achieves better reconstruction accuracies for all the samples in the dataset.Furthermore,a deep neural network for nonlinear mapping is adopted to obtain the airfoil shape corresponding to the target wall Mach number distribution.The performances of the designed deep neural network are fully demonstrated and a smoothness measurement is proposed to quantify small oscillations in the airfoil surface,proving the authenticity and accuracy of the generated airfoil shapes.

关键词： Conditional Variational AutoEncoder(CVAE) Deep learning Generative Adversarial Networks(GAN) Generative models Inverse design supercritical airfoil

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Pressure distribution feature-oriented sampling for statistical analysis of supercritical airfoil aerodynamics

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Chinese Journal of Aeronautics 2022年第4期35卷 134-147页

作者： Runze LI Yufei ZHANG Haixin CHEN School of Aerospace Engineering Tsinghua UniversityBeijing 100084China

In the field of supercritical wing design, various principles and rules have been summarized through theoretical and experimental analyses. Compared with black-box relationships between geometry parameters and performances, quantitative physical laws about pressure distributions and performances are clearer and more beneficial to designers. With the advancement of computational fluid dynamics and computational intelligence, discovering new rules through statistical analysis on computers has become increasingly attractive and affordable. This paper proposes a novel sampling method for the statistical study on pressure distribution features and performances, so that new physical laws can be revealed. It utilizes an adaptive sampling algorithm, of which the criteria are developed based on Kullback–Leibler divergence and Euclidean distance.In this paper, the proposed method is employed to generate airfoil samples to study the relationships between the supercritical pressure distribution features and the drag divergence Mach number as well as the drag creep characteristic. Compared with conventional sampling methods, the proposed method can efficiently distribute samples in the pressure distribution feature space rather than directly sampling airfoil geometry parameters. The corresponding geometry parameters are searched and found under constraints, so that supercritical airfoil samples that are well distributed in the pressure distribution space are obtained. These samples allow statistical studies to obtain more reliable and universal aerodynamic rules that can be applied to supercritical airfoil designs.

关键词： Adaptive sampling Output space Statistics Pressure distribution features supercritical airfoil

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Robust design of natural laminar flow supercritical airfoil by multi-objective evolution method

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Applied Mathematics and Mechanics(English Edition) 2014年第2期35卷 191-202页

作者：赵轲高正红黄江涛 National Key Laboratory of Aerodynamic Design and Research NorthwesternPolytechnical University China Aerodynamics Research & Development Center

Abstract A transonic, high Reynolds number natural laminar flow airfoil is designed and studied. The γ-θ transition model is combined with the shear stress transport （SST） k-w turbulence model to predict the transition region for a laminar-turbulent boundary layer. The non-uniform free-form deformation （NFFD） method based on the non-uniform rational B-spline （NURBS） basis function is introduced to the airfoil parameterization. The non-dominated sorting genetic algorithm-II （NSGA-II） is used as the search algo- rithm, and the surrogate model based on the Kriging models is introduced to improve the efficiency of the optimization system. The optimization system is set up based on the above technologies, and the robust design about the uncertainty of the Mach number is carried out for NASA0412 airfoil. The optimized airfoil is analyzed and compared with the original airfoil. The results show that natural laminar flow can be achieved on a supercritical airfoil to improve the aerodynamic characteristic of airfoils.

关键词： non-uniform free-form deformation （NFFD） method transition model,natural laminar flow （NFL） airfoil supercritical airfoil non-dominated sorting geneticalgorithm II （NSGA-II） robust design surrogate model

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Low-Speed Aerodynamic Characteristics of supercritical airfoil with Small High-Lift Devices from Flow Pattern Measurements

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Journal of Flow Control, Measurement & Visualization 2020年第4期8卷 159-172页

作者： Thai Duong Nguyen Masashi Kashitani Masato Taguchi Aerospace Department National Defense Academy of Japan Yokosuka Japan

In this paper, the lift coefficients of SC-0414 airfoil are estimated by applying modified Yamana’s method to the flow visualization results, which are obtained by utilizing the smoke tunnel. The application of the modified Yamana’s method is evaluated with two calculation methods. Additionally, the lift estimation, wake measurements, and numerical simulations are performed to clarify the low-speed aerodynamic characteristics of the SC airfoil with flaps. The angle of attack was varied from −5° to 8°. The flow velocity was 12 m/s and the Reynolds number was 1.6 × 105. As a result, the estimated lift coefficients show a good agreement with the results from reference data and numerical simulations. In clean condition, the lift coefficients calculated from the two methods show quantitative agreement, and no significant difference could be confirmed. However, the slope of the lifts calculated from ys is higher and closer to the reference data than those obtained from sc, where ys denotes the height where the distance from the streamline to the reference line is the largest, and sc denotes the displacement of the center of pressure from the origin of the coordinate, respectively. In the case of flaps, the GFs have an observable effect on the aerodynamic performance of the SC-0414 airfoil. When the height of the flap was increased, the lift and drag coefficients increased. The installation of a GF with a height equal to 1% of the chord length of the airfoil significantly improved the low-speed aerodynamic performance of SC airfoils.

关键词： supercritical airfoil Gurney Flap Potential Flow Flow Visualization

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Oscillation quenching and physical explanation on freeplay-based aeroelastic airfoil in transonic viscous flow

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Chinese Journal of Aeronautics 2023年第10期36卷 124-136页

作者： Yayun SHI Shun HE Gaowei CUI Gang CHEN Yan LIU State Key Laboratory for Strength and Vibration of Mechanical Structures School of Aerospace EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Aeronautics Northwestern Polytechnical UniversityXi’an 710072China Beijing Institute of Structure and Environment Engineering Beijing 100076China Shenyang Aircraft Design Institute Shenyang 110036China

Limit Cycle Oscillation(LCO)quenching of a supercritical airfoil(NLR 7301)considering freeplay is investigated in transonic viscous flow.Computational Fluid Dynamics(CFD)based on Navier-Stokes equations is implemented to calculate transonic aerodynamic forces.A loosely coupled scheme with steady CFD and an efficient graphic method are developed to obtain the aerodynamic preload.LCO quenching phenomenon is observed from the nonlinear dynamic aeroelastic response obtained by using time marching approach.As the airspeed increases,LCO appears then quenches,forming the first LCO branch.Following the quenching region,LCO occurs again and sustains until the divergence of the response,forming the second LCO branch.The quenching of LCOs was addressed physically based on the aerodynamic preload and the linear flutter characteristic.An“island”of stable region is observed in the flutter boundary,i.***.the flutter speed versus the mean Angle of Attack(AoA).The LCO quenches when the aerodynamic preload crosses this stable region with the increasing of airspeed.The LCO quenching of this model in transonic flow is essentially induced by destabilizing effect from aerodynamic preload,since the flutter speed is sensitive to AoA due to aerodynamic nonlinearity.

关键词： Freeplay Nonlinear aeroelasticity supercritical airfoil Transonic flutter Unsteady flow

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A generative deep learning framework for airfoil flow field prediction with sparse data

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Chinese Journal of Aeronautics 2022年第1期35卷 470-484页

作者： Haizhou WU Xuejun LIU Wei AN Hongqiang LYU MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjing 211106China Key Laboratory of Aerodynamic Noise Control Mianyang 621000China State Key Laboratory of Aerodynamics Mianyang 621000China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Deep learning has been probed for the airfoil performance prediction in recent years.Compared with the expensive CFD simulations and wind tunnel experiments,deep learning models can be leveraged to somewhat mitigate such expenses with proper means.Nevertheless,effective training of the data-driven models in deep learning severely hinges on the data in diversity and quantity.In this paper,we present a novel data augmented Generative Adversarial Network(GAN),daGAN,for rapid and accurate flow filed prediction,allowing the adaption to the task with sparse data.The presented approach consists of two modules,pre-training module and fine-tuning module.The pre-training module utilizes a conditional GAN(cGAN)to preliminarily estimate the distribution of the training data.In the fine-tuning module,we propose a novel adversarial architecture with two generators one of which fulfils a promising data augmentation operation,so that the complement data is adequately incorporated to boost the generalization of the model.We use numerical simulation data to verify the generalization of daGAN on airfoils and flow conditions with sparse training data.The results show that daGAN is a promising tool for rapid and accurate evaluation of detailed flow field without the requirement for big training data.

关键词： CFD Flow field Generative adversarial networks(GANs) Sparse data supercritical airfoil

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