Transactions of Nanjing University of Aeronautics & Astronautics
Volume 0,Issue 4,2024 Table of Contents
ERD?S Gábor
,
ABAI Kristóf
,
BEREGI Richárd
,
CSEMPESZ János
,
CSERTEG Tamás
,
GODó Gábor
,
HAJóS Mátyás
,
HáY Borbála
,
HORVáTH Dániel
,
HORVáTH Gergely
,
JUNIKI ádám
,
KEMéNY Zsolt
,
KOVáCS András
,
NACSA János
,
PANITI Imre
,
PEDONE Gianfranco
,
TAKáCS Emma
,
TIPARY Bence
,
ZAHORáN László
,
VáNCZA József
2024(4):403-431.
DOI: 10.16356/j.1005-1120.2024.04.001
Abstract:
Research of autonomous manufacturing systems is motivated both by the new technical possibilities of cyber-physical systems and by the practical needs of the industry. Autonomous operation in semi-structured industrial environments can now be supported by advanced sensor technologies， digital twins， artificial intelligence and novel communication techniques. These enable real-time monitoring of production processes， situation recognition and prediction， automated and adaptive （re）planning， teamwork and performance improvement by learning. This paper summarizes the main requirements towards autonomous industrial robotics and suggests a generic workflow for realizing such systems. Application case studies will be presented from recent practice at HUN-REN SZTAKI in a broad range of domains such as assembly， welding， grinding， picking and placing， and machining. The various solutions have in common that they use a generic digital twin concept as their core. After making general recommendations for realizing autonomous robotic solutions in the industry， open issues for future research will be discussed.
Research of autonomous manufacturing systems is motivated both by the new technical possibilities of cyber-physical systems and by the practical needs of the industry. Autonomous operation in semi-structured industrial environments can now be supported by advanced sensor technologies， digital twins， artificial intelligence and novel communication techniques. These enable real-time monitoring of production processes， situation recognition and prediction， automated and adaptive （re）planning， teamwork and performance improvement by learning. This paper summarizes the main requirements towards autonomous industrial robotics and suggests a generic workflow for realizing such systems. Application case studies will be presented from recent practice at HUN-REN SZTAKI in a broad range of domains such as assembly， welding， grinding， picking and placing， and machining. The various solutions have in common that they use a generic digital twin concept as their core. After making general recommendations for realizing autonomous robotic solutions in the industry， open issues for future research will be discussed.
2024(4):432-443.
DOI: 10.16356/j.1005-1120.2024.04.002
Abstract:
Modeling of unsteady aerodynamic loads at high angles of attack using a small amount of experimental or simulation data to construct predictive models for unknown states can greatly improve the efficiency of aircraft unsteady aerodynamic design and flight dynamics analysis. In this paper， aiming at the problems of poor generalization of traditional aerodynamic models and intelligent models， an intelligent aerodynamic modeling method based on gated neural units is proposed. The time memory characteristics of the gated neural unit is fully utilized， thus the nonlinear flow field characterization ability of the learning and training process is enhanced， and the generalization ability of the whole prediction model is improved. The prediction and verification of the model are carried out under the maneuvering flight condition of NACA0015 airfoil. The results show that the model has good adaptability. In the interpolation prediction， the maximum prediction error of the lift and drag coefficients and the moment coefficient does not exceed 10%， which can basically represent the variation characteristics of the entire flow field. In the construction of extrapolation models， the training model based on the strong nonlinear data has good accuracy for weak nonlinear prediction. Furthermore， the error is larger， even exceeding 20%， which indicates that the extrapolation and generalization capabilities need to be further optimized by integrating physical models. Compared with the conventional state space equation model， the proposed method can improve the extrapolation accuracy and efficiency by 78% and 60%， respectively， which demonstrates the applied potential of this method in aerodynamic modeling.
Modeling of unsteady aerodynamic loads at high angles of attack using a small amount of experimental or simulation data to construct predictive models for unknown states can greatly improve the efficiency of aircraft unsteady aerodynamic design and flight dynamics analysis. In this paper， aiming at the problems of poor generalization of traditional aerodynamic models and intelligent models， an intelligent aerodynamic modeling method based on gated neural units is proposed. The time memory characteristics of the gated neural unit is fully utilized， thus the nonlinear flow field characterization ability of the learning and training process is enhanced， and the generalization ability of the whole prediction model is improved. The prediction and verification of the model are carried out under the maneuvering flight condition of NACA0015 airfoil. The results show that the model has good adaptability. In the interpolation prediction， the maximum prediction error of the lift and drag coefficients and the moment coefficient does not exceed 10%， which can basically represent the variation characteristics of the entire flow field. In the construction of extrapolation models， the training model based on the strong nonlinear data has good accuracy for weak nonlinear prediction. Furthermore， the error is larger， even exceeding 20%， which indicates that the extrapolation and generalization capabilities need to be further optimized by integrating physical models. Compared with the conventional state space equation model， the proposed method can improve the extrapolation accuracy and efficiency by 78% and 60%， respectively， which demonstrates the applied potential of this method in aerodynamic modeling.
2024(4):444-457.
DOI: 10.16356/j.1005-1120.2024.04.003
Abstract:
The paper examines the dynamic stall characteristics of a finite wing with an aspect ratio of eight in order to explore the 3D effects on flow topology， aerodynamic characteristics， and pitching damping. Firstly， CFD methods are developed to calculate the aerodynamic characteristics of wings. The URANS equations are solved using a finite volume method， and the two-equation k-ω shear stress transport （SST） turbulence model is employed to account for viscosity effects. Secondly， the CFD methods are used to simulate the aerodynamic characteristics of both a static， rectangular wing and a pitching， tapered wing to verify their effectiveness and accuracy. The numerical results show good agreement with experimental data. Subsequently， the static and dynamic characteristics of the finite wing are computed and discussed. The results reveal significant 3D flow structures during both static and dynamic stalls， including wing tip vortices， arch vortices， Ω-type vortices， and ring vortices. These phenomena lead to differences in the aerodynamic characteristics of the finite wing compared with a 2D airfoil. Specifically， the finite wing has a smaller lift slope during attached-flow stages， higher stall angles， and more gradual stall behavior. Flow separation initially occurs in the middle spanwise section and gradually spreads to both ends. Regarding aerodynamic damping， the inboard sections mainly generate unstable loading. Furthermore， sections experiencing light stall have a higher tendency to produce negative damping compared with sections experiencing deep dynamic stall.
The paper examines the dynamic stall characteristics of a finite wing with an aspect ratio of eight in order to explore the 3D effects on flow topology， aerodynamic characteristics， and pitching damping. Firstly， CFD methods are developed to calculate the aerodynamic characteristics of wings. The URANS equations are solved using a finite volume method， and the two-equation k-ω shear stress transport （SST） turbulence model is employed to account for viscosity effects. Secondly， the CFD methods are used to simulate the aerodynamic characteristics of both a static， rectangular wing and a pitching， tapered wing to verify their effectiveness and accuracy. The numerical results show good agreement with experimental data. Subsequently， the static and dynamic characteristics of the finite wing are computed and discussed. The results reveal significant 3D flow structures during both static and dynamic stalls， including wing tip vortices， arch vortices， Ω-type vortices， and ring vortices. These phenomena lead to differences in the aerodynamic characteristics of the finite wing compared with a 2D airfoil. Specifically， the finite wing has a smaller lift slope during attached-flow stages， higher stall angles， and more gradual stall behavior. Flow separation initially occurs in the middle spanwise section and gradually spreads to both ends. Regarding aerodynamic damping， the inboard sections mainly generate unstable loading. Furthermore， sections experiencing light stall have a higher tendency to produce negative damping compared with sections experiencing deep dynamic stall.
2024(4):458-475.
DOI: 10.16356/j.1005-1120.2024.04.004
Abstract:
A hybrid identification model based on multilayer artificial neural networks （ANNs） and particle swarm optimization （PSO） algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials. For the direct model， the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing， emitting， and non-scattering 2D axisymmetric gray medium in the background of laser flash method. For the identification part， firstly， the temperature field and the incident radiation field in different positions are chosen as observables. Then， a traditional identification model based on PSO algorithm is established. Finally， multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process. The results show that compared with the traditional identification model， the time cost of the hybrid identification model is reduced by about 1 000 times. Besides， the hybrid identification model remains a high level of accuracy even with measurement errors.
A hybrid identification model based on multilayer artificial neural networks （ANNs） and particle swarm optimization （PSO） algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials. For the direct model， the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing， emitting， and non-scattering 2D axisymmetric gray medium in the background of laser flash method. For the identification part， firstly， the temperature field and the incident radiation field in different positions are chosen as observables. Then， a traditional identification model based on PSO algorithm is established. Finally， multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process. The results show that compared with the traditional identification model， the time cost of the hybrid identification model is reduced by about 1 000 times. Besides， the hybrid identification model remains a high level of accuracy even with measurement errors.
2024(4):476-487.
DOI: 10.16356/j.1005-1120.2024.04.005
Abstract:
Blades are one of the important components on aircraft engines. If they break due to vibration failure， the normal operation of the entire engine will be offected. Therefore， it is necessary to measure their natural frequency before installing them on the engine to avoid resonance. At present， most blade vibration testing systems require manual operation by operators， which has high requirements for operators and the testing process is also very cumbersome. Therefore， the testing efficiency is low and cannot meet the needs of efficient testing. To solve the current problems of low testing efficiency and high operational requirements， a high-precision and high-efficiency automatic test system is designed. The testing accuracy of this system can reach ±1%， and the testing efficiency is improved by 37% compared to manual testing. Firstly， the influence of compression force and vibration exciter position on natural frequency test is analyzed by amplitude-frequency curve， so as to calibrate servo cylinder and four-dimensional motion platform. Secondly， the sine wave signal is used as the excitation to sweep the blade linearly， and the natural frequency is determined by the amplitude peak in the frequency domain. Finally， the accuracy experiment and efficiency experiment are carried out on the developed test system ，whose results verify its high efficiency and high precision.
Blades are one of the important components on aircraft engines. If they break due to vibration failure， the normal operation of the entire engine will be offected. Therefore， it is necessary to measure their natural frequency before installing them on the engine to avoid resonance. At present， most blade vibration testing systems require manual operation by operators， which has high requirements for operators and the testing process is also very cumbersome. Therefore， the testing efficiency is low and cannot meet the needs of efficient testing. To solve the current problems of low testing efficiency and high operational requirements， a high-precision and high-efficiency automatic test system is designed. The testing accuracy of this system can reach ±1%， and the testing efficiency is improved by 37% compared to manual testing. Firstly， the influence of compression force and vibration exciter position on natural frequency test is analyzed by amplitude-frequency curve， so as to calibrate servo cylinder and four-dimensional motion platform. Secondly， the sine wave signal is used as the excitation to sweep the blade linearly， and the natural frequency is determined by the amplitude peak in the frequency domain. Finally， the accuracy experiment and efficiency experiment are carried out on the developed test system ，whose results verify its high efficiency and high precision.
2024(4):488-501.
DOI: 10.16356/j.1005-1120.2024.04.006
Abstract:
Improving structures involves comparing old and new designs on a key parameter. Calculating the percent change in performance is a method to assess. This paper proposes a cost-effective analogy by generating replicas of additive manufactured aluminum alloy （AlSi10Mg） body-centered cubic lattice （BCC） based turbine blade （T106C） with the same in poly-lactic acid （PLA） material and their comparison in the context of percent change for natural frequencies. Initially， a cavity is created inside the turbine blade （hollow blade）. Natural frequencies are obtained experimentally and numerically by incorporating BCC at 50% and 80% of the cavity length into the hollow blade for both materials. The cost of manufacturing the metal blades is 90% more than that of the PLA blades. The two material blade designs show a similar percentage variation， as the first-order mode enhancs more than 5% and the second-order mode more than 4%. To observe the behavior in another material， both blades are analyzed numerically with a nickel-based U-500 material， and the same result is achieved， describing that percent change between designs can be verified using the PLA material.
Improving structures involves comparing old and new designs on a key parameter. Calculating the percent change in performance is a method to assess. This paper proposes a cost-effective analogy by generating replicas of additive manufactured aluminum alloy （AlSi10Mg） body-centered cubic lattice （BCC） based turbine blade （T106C） with the same in poly-lactic acid （PLA） material and their comparison in the context of percent change for natural frequencies. Initially， a cavity is created inside the turbine blade （hollow blade）. Natural frequencies are obtained experimentally and numerically by incorporating BCC at 50% and 80% of the cavity length into the hollow blade for both materials. The cost of manufacturing the metal blades is 90% more than that of the PLA blades. The two material blade designs show a similar percentage variation， as the first-order mode enhancs more than 5% and the second-order mode more than 4%. To observe the behavior in another material， both blades are analyzed numerically with a nickel-based U-500 material， and the same result is achieved， describing that percent change between designs can be verified using the PLA material.
7 A Computational Framework for Parachute Inflation Based on Immersed Boundary/Finite Element Approach
2024(4):502-514.
DOI: 10.16356/j.1005-1120.2024.04.007
Abstract:
A computational framework for parachute inflation is developed based on the immersed boundary/finite element approach within the open-source IBAMR library. The fluid motion is solved by Peskin’s diffuse-interface immersed boundary （IB） method， which is attractive for simulating moving-boundary flows with large deformations. The adaptive mesh refinement technique is employed to reduce the computational cost while retain the desired resolution. The dynamic response of the parachute is solved with the finite element approach. The canopy and cables of the parachute system are modeled with the hyperelastic material. A tether force is introduced to impose rigidity constraints for the parachute system. The accuracy and reliability of the present framework is validated by simulating inflation of a constrained square plate. Application of the present framework on several canonical cases further demonstrates its versatility for simulation of parachute inflation.
A computational framework for parachute inflation is developed based on the immersed boundary/finite element approach within the open-source IBAMR library. The fluid motion is solved by Peskin’s diffuse-interface immersed boundary （IB） method， which is attractive for simulating moving-boundary flows with large deformations. The adaptive mesh refinement technique is employed to reduce the computational cost while retain the desired resolution. The dynamic response of the parachute is solved with the finite element approach. The canopy and cables of the parachute system are modeled with the hyperelastic material. A tether force is introduced to impose rigidity constraints for the parachute system. The accuracy and reliability of the present framework is validated by simulating inflation of a constrained square plate. Application of the present framework on several canonical cases further demonstrates its versatility for simulation of parachute inflation.
2024(4):515-525.
DOI: 10.16356/j.1005-1120.2024.04.008
Abstract:
This paper presents a method to study the free vibration of a plate with circular holes. The circular hole is regarded as a virtual small plate in which the mass density and Young’s modulus are zero. Therefore， the free vibration problem of the circular hole plate can be transformed into the free vibration problem of the equivalent rectangular plate with non-uniform thickness. The model is derived from the spectral geometry method （SGM）， and the displacement of the plate with circular holes is expanded by the modified Fourier series. Virtual springs are added to the boundary of the plate to simulate the boundary conditions of simply supported and fixed supports. The accuracy of this method is verified by comparison with the finite element calculation results. The relationship between modal numerical solutions of plates with circular holes and boundary conditions and geometry of the plate is studied.
This paper presents a method to study the free vibration of a plate with circular holes. The circular hole is regarded as a virtual small plate in which the mass density and Young’s modulus are zero. Therefore， the free vibration problem of the circular hole plate can be transformed into the free vibration problem of the equivalent rectangular plate with non-uniform thickness. The model is derived from the spectral geometry method （SGM）， and the displacement of the plate with circular holes is expanded by the modified Fourier series. Virtual springs are added to the boundary of the plate to simulate the boundary conditions of simply supported and fixed supports. The accuracy of this method is verified by comparison with the finite element calculation results. The relationship between modal numerical solutions of plates with circular holes and boundary conditions and geometry of the plate is studied.
2024(4):526-540.
DOI: 10.16356/j.1005-1120.2024.04.009
Abstract:
Under single-satellite observation， the parameter estimation of the boost phase of high-precision space non-cooperative targets requires prior information. To improve the accuracy without prior information， we propose a parameter estimation model of the boost phase based on trajectory plane parametric cutting. The use of the plane passing through the geo-center and the cutting sequence line of sight （LOS） generates the trajectory-cutting plane. With the coefficient of the trajectory cutting plane directly used as the parameter to be estimated， a motion parameter estimation model in space non-cooperative targets is established， and the Gauss-Newton iteration method is used to solve the flight parameters. The experimental results show that the estimation algorithm proposed in this paper weakly relies on prior information and has higher estimation accuracy， providing a practical new idea and method for the parameter estimation of space non-cooperative targets under single-satellite warning.
Under single-satellite observation， the parameter estimation of the boost phase of high-precision space non-cooperative targets requires prior information. To improve the accuracy without prior information， we propose a parameter estimation model of the boost phase based on trajectory plane parametric cutting. The use of the plane passing through the geo-center and the cutting sequence line of sight （LOS） generates the trajectory-cutting plane. With the coefficient of the trajectory cutting plane directly used as the parameter to be estimated， a motion parameter estimation model in space non-cooperative targets is established， and the Gauss-Newton iteration method is used to solve the flight parameters. The experimental results show that the estimation algorithm proposed in this paper weakly relies on prior information and has higher estimation accuracy， providing a practical new idea and method for the parameter estimation of space non-cooperative targets under single-satellite warning.
2024(4):541-554.
DOI: 10.16356/j.1005-1120.2024.04.010
Abstract:
The detection of foreign object intrusion is crucial for ensuring the safety of railway operations. To address challenges such as low efficiency， suboptimal detection accuracy， and slow detection speed inherent in conventional comprehensive video monitoring systems for railways， a railway foreign object intrusion recognition and detection system is conceived and implemented using edge computing and deep learning technologies. In a bid to raise detection accuracy， the convolutional block attention module （CBAM）， including spatial and channel attention modules， is seamlessly integrated into the YOLOv5 model， giving rise to the CBAM-YOLOv5 model. Furthermore， the distance intersection-over-union_non-maximum suppression （DIoU_NMS） algorithm is employed in lieu of the weighted non-maximum suppression algorithm， resulting in improved detection performance for intrusive targets. To accelerate detection speed， the model undergoes pruning based on the batch normalization （BN） layer， and TensorRT inference acceleration techniques are employed， culminating in the successful deployment of the algorithm on edge devices. The CBAM-YOLOv5 model exhibits a notable 2.1% enhancement in detection accuracy when evaluated on a self-constructed railway dataset， achieving 95.0% for mean average precision （mAP）. Furthermore， the inference speed on edge devices attains a commendable 15 frame/s.
The detection of foreign object intrusion is crucial for ensuring the safety of railway operations. To address challenges such as low efficiency， suboptimal detection accuracy， and slow detection speed inherent in conventional comprehensive video monitoring systems for railways， a railway foreign object intrusion recognition and detection system is conceived and implemented using edge computing and deep learning technologies. In a bid to raise detection accuracy， the convolutional block attention module （CBAM）， including spatial and channel attention modules， is seamlessly integrated into the YOLOv5 model， giving rise to the CBAM-YOLOv5 model. Furthermore， the distance intersection-over-union_non-maximum suppression （DIoU_NMS） algorithm is employed in lieu of the weighted non-maximum suppression algorithm， resulting in improved detection performance for intrusive targets. To accelerate detection speed， the model undergoes pruning based on the batch normalization （BN） layer， and TensorRT inference acceleration techniques are employed， culminating in the successful deployment of the algorithm on edge devices. The CBAM-YOLOv5 model exhibits a notable 2.1% enhancement in detection accuracy when evaluated on a self-constructed railway dataset， achieving 95.0% for mean average precision （mAP）. Furthermore， the inference speed on edge devices attains a commendable 15 frame/s.
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