Browsing by Author "Yang, Daqing"
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Item Open Access Identification of the key design inputs for the FEM-based preliminary sizing and mass estimation of a civil aircraft wing box structure(Elsevier, 2021-12-14) You, Chao; Yasaee, Mehdi; He, Shun; Yang, Daqing; Xu, Yigeng; Dayyani, Iman; Ghasemnejad, Hessam; Guo, Shijun; Webb, Phil; Jennings, James; Federico, GiovanniFEM-based preliminary structural sizing has been successfully carried out for a typical single-aisle wing box structure using MSC Nastran, by considering various load cases representing typical aircraft manoeuvres, engine loads, landing and ground handling conditions. The strength, buckling and fatigue criteria have been applied as the design constraints for sizing. The resultant total mass and the structural (static and modal) behaviour of the sized wing box model have been verified against a validated high-fidelity wing box model. A sensitivity analysis has been performed to evaluate the influence of the number of design fields and the selected design inputs (i.e. load cases and design constraints) on the accuracy of sizing and mass estimation of the wing box. This sensitivity analysis has also been extended to the static and modal behaviour of the wing box structure obtained from sizing. It provides an insight into the significance of considering the buckling and fatigue constraints, aircraft rolling loads, engine loads and landing loads in sizing, in addition to the commonly-applied 2.5 g aircraft pull-up loads under the strength constraint. The findings of this study highlight the trade-off between the sizing efficiency and accuracy of a civil aircraft wing for modelling purposes.Item Open Access Nonlinear aeroelastic behavior of an airfoil with free-play in transonic flow(Elsevier, 2019-12-16) He, Shun; Guo, Shijun; Li, Wenhao; Yang, Daqing; Gu, Yingsong; Yang, ZhichunAn investigation has been made into the nonlinear aeroelastic behavior of an airfoil system with free-play nonlinear stiffness in transonic flow. Computational Fluid Dynamics (CFD) and Reduced Order Model (ROM) based on Euler and Navier-Stokes equations are implemented to calculate unsteady aerodynamic forces. Results show that the nonlinear aeroelastic system experiences various bifurcations with increasing Mach number. Regular subcritical bifurcations are observed in low Mach number region. Subsequently, complex Limit Cycle Oscillations (LCOs) and even non-periodic motions appear at specific airspeed regions. When the Mach number is increased above the freeze Mach number, regular subcritical bifurcations occur again. Comparisons with inviscid solutions are used to identify and elaborate the effect of viscosity with the help of aeroelastic analysis techniques, including root locus, Single Degree of Freedom (SDOF) flutter and aerodynamic influence coefficient (AIC). For low Mach numbers in the transonic regime, the viscosity has little effect on the linear flutter characteristic because of limited influence on AIC, but a remarkable impact on the nonlinear dynamic behavior due to the sensitivity of the nonlinear structure. As the Mach number increases, the viscosity becomes significantly important due to the existence of shock-boundary layer interaction. It affects the unstable mechanism of linear flutter, impacts the aerodynamic center and hence the snap-through phenomenon, influences the AIC and consequently the nonlinear aeroelastic response. When the Mach number is increased further, the shock wave dominates the air flow and the viscosity is of minor importance.Item Open Access Rolling active control for an aircraft of seamless aeroelastic wing(Inderscience Publishers, 2009-12-31T00:00:00Z) Wang, Zhengjie; Yang, Daqing; Guo, Shijun J.This paper presents an investigation into the controllability for an aircraft of seamless aeroelastic wing. The research is aimed at the design of control laws for the aircraft rolling by actively operating a pair of an unconventional hingeless flexible leading and trailing control surfaces at different flight speed. The main challenge is how to achieve a specified rolling rate for the aircraft when the control effectiveness drops down and even crosses over the rolling reversal point within the flight envelope. This phenomenon is mainly due to the aeroelastic effect of the large sweptback and highly flexible wing design for weight saving. The investigation shows that control laws varying with the flight speed can be designed to achieve the rolling control target.Item Open Access Structure health monitoring of a composite wing based on flight load and strain data using deep learning method(Elsevier, 2022-01-29) Lin, Minxiao; Guo, Shijun; He, Shun; Li, Wenhao; Yang, DaqingAn investigation was made into a method for Structural Health Monitoring (SHM) of a composite wing using Convolutional Neural Network (CNN) model. In this method, various aerodynamic loads of an aircraft during flight and corresponding strain data were used for CNN model training. The proposed method was demonstrated by numerical simulation using vortex lattice method for aerodynamic loads of an A350-type aircraft in over a thousand flight conditions and a Finite Element (FE) model as a digital twin of the full-scale composite wing. To represent the measurement of 324 sensors mounted in the 18 skin-rib joints of the inboard wing, strain data from the 18x18 elements of the FE model in the sensor locations were calculated corresponding to the flight loadings. The strain data from the original structure FE model were employed to train a CNN model that was classified as healthy samples. Damaged elements were then introduced in random locations to produce data samples corresponding to the same set of flight loads for the CNN model training. In the subsequent damage detection process using the trained CNN model, confusion matrix, uncertainty and sensitivity analysis were evaluated. The study results show that robust damage detection results can be obtained with 99% accuracy without noise and 97% accuracy with 2% Gaussian noise. In the damage localization process, threshold value was set at 1.5, 2 or 2.5, and 83% overall accuracy was achieved using the CNN model when the threshold value was 1.5. The study demonstrated that the proposed method is efficient, accurate and robust.