The Vortex Particle Method(VPM)is a meshless Lagrangian vortex method.Its low numerical dissipation is exceptionally suitable for wake simulation.Nevertheless,the inadequate numerical stability of VPM prevents its wid...The Vortex Particle Method(VPM)is a meshless Lagrangian vortex method.Its low numerical dissipation is exceptionally suitable for wake simulation.Nevertheless,the inadequate numerical stability of VPM prevents its widespread application in high Reynolds number flow and shear turbulence.To better simulate these flows,this paper proposes the stability-enhanced VPM based on a Reformulated VPM(RVPM)constrained by conservation of angular momentum,integrating a relaxation scheme to suppress the divergence of the vorticity field,and further coupling the Sub-Grid Scale(SGS)model to account for the turbulence dissipation caused by vortex advection and vortex stretching.The validity of the RVPM is confirmed by simulating an isolated vortex ring's evolution.The results also demonstrate that the relaxation scheme of vorticity enhances the numerical stability of the VPM by mitigating the divergence of the vorticity field.The leapfrogging vortex rings simulation demonstrates that the RVPM with the present SGS model can more precisely feature the leapfrog and fusion of vortex rings and has improved numerical stability in high Reynolds number flows.The round turbulent jet simulation confirms that the stability-enhanced VPM can stably simulate shear turbulence and accurately resolve fluctuating components and Reynolds stresses in the turbulence.展开更多
Humans’initial desire for flight stems from the imitation of flying creatures in nature.The excellent flight performance of flying animals will inevitably become a source of inspiration for researchers.Bio-inspired f...Humans’initial desire for flight stems from the imitation of flying creatures in nature.The excellent flight performance of flying animals will inevitably become a source of inspiration for researchers.Bio-inspired flight systems have become one of the most exciting disruptive aviation technologies.This review is focused on the recent progresses in bio-inspired flight systems and bionic aerodynamics.First,the development path of Biomimetic Air Vehicles(BAVs)for bio-inspired flight systems and the latest mimetic progress are summarized.The advances of the flight principles of several natural creatures are then introduced,from the perspective of bionic aerodynamics.Finally,several new challenges of bionic aerodynamics are proposed for the autonomy and intelligent development trend of the bio-inspired smart aircraft.This review will provide an important insight in designing new biomimetic air vehicles.展开更多
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)netwo...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.展开更多
基金co-supported by the National Natural Science Foundation of China(No.12402272)the Natural Science Basic Research Program of Shaanxi Province,China(No.2024JC-YBQN-0024)the Fundamental Research Funds for the Central Universities,China(No.D5000240030)。
文摘The Vortex Particle Method(VPM)is a meshless Lagrangian vortex method.Its low numerical dissipation is exceptionally suitable for wake simulation.Nevertheless,the inadequate numerical stability of VPM prevents its widespread application in high Reynolds number flow and shear turbulence.To better simulate these flows,this paper proposes the stability-enhanced VPM based on a Reformulated VPM(RVPM)constrained by conservation of angular momentum,integrating a relaxation scheme to suppress the divergence of the vorticity field,and further coupling the Sub-Grid Scale(SGS)model to account for the turbulence dissipation caused by vortex advection and vortex stretching.The validity of the RVPM is confirmed by simulating an isolated vortex ring's evolution.The results also demonstrate that the relaxation scheme of vorticity enhances the numerical stability of the VPM by mitigating the divergence of the vorticity field.The leapfrogging vortex rings simulation demonstrates that the RVPM with the present SGS model can more precisely feature the leapfrog and fusion of vortex rings and has improved numerical stability in high Reynolds number flows.The round turbulent jet simulation confirms that the stability-enhanced VPM can stably simulate shear turbulence and accurately resolve fluctuating components and Reynolds stresses in the turbulence.
基金This work was supported by the National Natural Science Foundation of China(Nos.11872293,11672225 and 11602199)China Postdoctoral Science Foundation(No.2017M623184)+1 种基金the National Key Laboratory of Science and Technology on Aerodynamic Design and Research of China(No.6142201190408)the Key Laboratory of Aerodynamics Noise Control of China(Nos.1801ANCL20180103,1901ANCL20190108),Australian Research Council(Nos.DP200101500 and DE160101098),and the Program of Introducing Talents of Discipline to Universities of China(known as the‘‘111”Program,No.B18040).
文摘Humans’initial desire for flight stems from the imitation of flying creatures in nature.The excellent flight performance of flying animals will inevitably become a source of inspiration for researchers.Bio-inspired flight systems have become one of the most exciting disruptive aviation technologies.This review is focused on the recent progresses in bio-inspired flight systems and bionic aerodynamics.First,the development path of Biomimetic Air Vehicles(BAVs)for bio-inspired flight systems and the latest mimetic progress are summarized.The advances of the flight principles of several natural creatures are then introduced,from the perspective of bionic aerodynamics.Finally,several new challenges of bionic aerodynamics are proposed for the autonomy and intelligent development trend of the bio-inspired smart aircraft.This review will provide an important insight in designing new biomimetic air vehicles.
基金co-supported by the National Natural Science Foundation of China(No.52192633)the Natural Science Foundation of Shaanxi Province,China(No.2022JC-03)the Fundamental Research Funds for the Central Universities,China(No.XJSJ23164)。
文摘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.
