Hostname: page-component-669899f699-b58lm Total loading time: 0 Render date: 2025-04-24T18:18:51.676Z Has data issue: false hasContentIssue false
  • English
  • Français

Modeling and analysis of nonlinear dynamics of axisymmetric vector nozzle based on deep neural network

Published online by Cambridge University Press:  11 November 2024

X. Wang Open the ORCID record for X. Wang [Opens in a new window] ,
H. Hu Open the ORCID record for H. Hu [Opens in a new window] ,
Z. Chen ,
H. Wang ,
L. Ye  and
G. Ye
X. Wang*
Affiliation:
School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China Key Laboratory of Vibration and Control of Aero-Propulsion System, Ministry of Education, Northeastern University, Shenyang, PR China AECC Shenyang Engine Research Institute, Shenyang, China
H. Hu
Affiliation:
School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China
Z. Chen
Affiliation:
School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China
H. Wang
Affiliation:
School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China
L. Ye
Affiliation:
AECC Shenyang Engine Research Institute, Shenyang, China
G. Ye
Affiliation:
AECC Shenyang Engine Research Institute, Shenyang, China
*
Corresponding author: X. Wang; Email: wangxy@me.neu.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

The axisymmetric nozzle mechanism is the core part for thrust vectoring of aero engine, which contains complex rigid-flexible coupled multibody system with joints clearance and significantly reduces the efficiency in modeling and calculation, therefore the kinematics and dynamics analysis of axisymmetric vectoring nozzle mechanism based on deep neural network is proposed. The deep neural network model of the axisymmetric vector nozzle is established according to the limited training data from the physical dynamic model and then used to predict the kinematics and dynamics response of the axisymmetric vector nozzle. This study analyses the effects of joint clearance on the kinematics and dynamics of the axisymmetric vector nozzle mechanism by a data-driven model. It is found that the angular acceleration of the expanding blade and the driving force are mostly affected by joint clearance followed by the angle, angular velocity and position of the expanding blade. Larger joint clearance results in more pronounced fluctuations of the dynamic response of the mechanism, which is due to the greater relative velocity and contact force between the bushing and the pin. Since axisymmetric vector nozzles are highly complex nonlinear systems, traditional numerical methods of dynamics are extremely time-consuming. Our work indicates that the data-driven approach greatly reduces the computational cost while maintaining accuracy, and can be used for rapid evaluation and iterative computation of complex multibody dynamics of engine nozzle mechanism.

Keywords

deep learningaxisymmetric vector nozzledeep neural networkmultibody dynamicsjoint clearance
Type
Research Article
Information
The Aeronautical Journal , Volume 129 , Issue 1335 , May 2025 , pp. 1167 - 1181
Check for updates
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Royal Aeronautical Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

Footnotes

*

These two authors contributed equally to this work.

References

Burcham, F. Jr, Ray, R., Conners, T., & Walsh, K. (1998, July). Propulsion flight research at NASA Dryden from 1967 to 1997. In 34th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit (p. 3712).Google Scholar
Wallace, H., & Bowers, D. (1982). Advanced nozzle integration for air combat fighter application. In 18th Joint Propulsion Conference (p. 1135).CrossRefGoogle Scholar
Koziel, S., Çalık, N., Mahouti, P., & Belen, M.A. (2021). Accurate modeling of antenna structures by means of domain confinement and pyramidal deep neural networks. IEEE Transactions on Antennas and Propagation, 70(3), 2174–2188.Google Scholar
Nguyen, L., & Gilert, W. (1990, May). Impact of emerging technologies on future combat aircraft agility. In Orbital Debris Conference: Technical Issues andFuture Directions (p. 1304).CrossRefGoogle Scholar
Rajesh, A.M., Doddamani, S., K,M.K., & Bharath, K.N. (2020). Dry sliding wear simulation of hybrid aluminum metal matrix composites. Advanced Composites and Hybrid Materials, 3, 120-126.CrossRefGoogle Scholar
Kirkwood, C., Economou, T., Pugeault, N., & Odbert, H. (2022). Bayesian deep learning for spatial interpolation in the presence of auxiliary information. Mathematical Geosciences, 54(3), 507–531.Google Scholar
Flamm, J., Deere, K., Mason, M., Berrier, B., & Johnson, S. (2007, July). Experimental study of an axisymmetric dual throat fluidic thrust vectoring nozzle for supersonic aircraft application. In 43rd AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit (p. 5084).CrossRefGoogle Scholar
Counts, B. (2016). It’s Time to Cut Our Losses on the F-35. International Policy Digest, 3(8).Google Scholar
Miao, H., Li, B., Liu, J., He, A., & Zhu, S.. (2019). Effects of revolute clearance joint on the dynamic behavior of a planar space arm system. Proceedings of the Institution of Mechanical Engineers, 233(5), 1629–1644.Google Scholar
Xiulong, C., Yonghao, J., Yu, D., & Qing, W.. (2018). Dynamics behavior analysis of parallel mechanism with joint clearance and flexible links. Shock and Vibration, 2018(PT.2), 1–17.Google Scholar
Li, Y., Wu, J., Tedrake, R., Tenenbaum, J.B., & Torralba, A. (2018). Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids. arxiv preprint arxiv:1810.01566.Google Scholar
Go, M.S., Han, S., Lim, J.H., & Kim, J.G. (2024). An efficient fixed-time increment-based data-driven simulation for general multibody dynamics using deep neural networks. Engineering with Computers, 40(1), 323–341.Google Scholar
Yu-Tong, L., & Yu-**n, W. (2019, August). Singularities in Axisymmetric Vectoring Exhaust Nozzle and a Feasible Singularity-Free Approach. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 59261, p. V006T09A015). American Society of Mechanical Engineers.Google Scholar
Ansari, H., Tupy, M., Datar, M., & Negrut, D. (2010). Construction and use of surrogate models for the dynamic analysis of multibody systems. SAE International Journal of Passenger Cars-Mechanical Systems, 3(2010-01-0032), 8–20.CrossRefGoogle Scholar
Geman, A., Geman, D., Graffigne, C., & Dong, P. (1984). IEEE Transactions on Pattern Analysis and Machine Intelligence.Google Scholar
Kraft, S., Causse, J., & Martinez, Aurélie. (2019). Black-box modelling of nonlinear railway vehicle dynamics for track geometry assessment using neural networks. Informa UK Limited(9).Google Scholar
Jian, W., Xiaochuan, M.A., Jiayin, C., Jingmang, X.U., & Ping, W. (2017). Study of matching performance of chn60n rail with different wheel treads in high-speed railway. Journal of the China Railway Society.Google Scholar
Kurvinen, E., Suninen, I., Orzechowski, G., Choi, J.H., Kim, J.G., & Mikkola, A. (2021). Accelerating design processes using data-driven models. In Real-time Simulation for Sustainable Production (pp. 65–76). Routledge.CrossRefGoogle Scholar
Byravan, A., & Fox, D.. (2017). Se3-nets: learning rigid body motion using deep neural networks. IEEE.Google Scholar
Choi, H.S., An, J., Han, S., Kim, J.G., Jung, J.Y., & Choi, J., et al. (2021). Data-driven simulation for general-purpose multibody dynamics using deep neural networks. Springer Netherlands(4).CrossRefGoogle Scholar
Choi, H.S., An, J., Kim, J.G., Jung, J.Y., & Choi, J.H.. (2019). Data-driven simulation for general purpose multibody dynamics using deep neural networks.CrossRefGoogle Scholar
Bernal, E., J.Orús, J.M.Rodríguez-Fortún, Nadal, I., & Putz, T.. (2011). Use of co-simulation and model order reduction techniques in automotive industry: Application to an electric park brake (EPB). EAEC European automotive congress. Area of Research, Development & Technological Services, Instituto Tecnológico de Aragón (ITA), 7-8 María de Luna, 50018 Zaragoza, Spain;Area of Research, Development & Technological Services, Instituto Tecnológico de Aragón (ITA), 7-8 María de Luna, 50018.Google Scholar
Ting, J.A., Mistry, M., Peters, J., Schaal, S., & Nakanishi, J.. (2006). A Bayesian Approach to Nonlinear Parameter Identification for Rigid Body Dynamics. Robotics: Science and Systems II, August 16-19, 2006. University of Pennsylvania, Philadelphia, Pennsylvania, USA. DBLP.Google Scholar
Kim, J.H., & Joo, C.B.. (2014). Numerical construction of balanced state manifold for single-support legged mechanism in sagittal plane. Multibody System Dynamics, 31(3), 257–281.CrossRefGoogle Scholar
Yunguang, Y., Shi, D., Krause, P., & Hecht, M.. (2019). A data-driven method for estimating wheel flat length. Vehicle System Dynamics.Google Scholar
Ye, Y.G., Shi, D.C., Poveda-Reyes, S., & Hecht, M. (2020). Erratum to: Quantification of the influence of rolling stock failures on track deterioration. Journal of Zhejiang University-SCIENCE A, 21, 938–938.CrossRefGoogle Scholar
Newmark, N.M.. (1959). A method of computation for structural dynamics. Proc Asce, 85(1), 67–94.Google Scholar
Negrut, D., Rampalli, R., Ottarsson, G., & Sajdak, A.. (2007). On an implementation of the hilber-hughes-taylor method in the context of index 3 differential-algebraic equations of multibody dynamics (detc2005-85096). Journal of Computational & Nonlinear Dynamics, 2(1), 73–85.Google Scholar
Li, Y., Wang, C., & Huang, W.. (2019). Rigid-flexible-thermal analysis of planar composite solar array with clearance joint considering torsional spring, latch mechanism and attitude controller. Nonlinear Dynamics.Google Scholar
Zhang, H., Wu, Q., Zeng, H., Meng, L., Luo, Z., & Han, Q.. (2024). Analysis of the dynamic of vector nozzle adjustment mechanism considering the effect of joint clearance. Journal of Vibration Engineering & Technologies, 12(4), 6137–6154.CrossRefGoogle Scholar
Changpeng, C.A.I., Yerong, P.E.N.G., Zheng, Q., & Zhang, H. (2022). Vector deflection stability control of aero-engine based on linear active disturbance rejection. Chinese Journal of Aeronautics, 35(8), 221–235.Google Scholar
Du, Q., Li, J., **e, L., Fu, X., & Wang, Y. (2022, September). Research on Reliability Analysis of Axial-Symmetric Vectoring Exhaust Nozzle Based on Parametric Modeling. In Journal of Physics: Conference Series (Vol. 2338, No. 1, p. 012061). IOP Publishing.CrossRefGoogle Scholar
Zheng, Q., Zhao, P., Zhang, D., & Wang, H.. (2021). Mr-dcae: manifold regularization-based deep convolutional autoencoder for unauthorized broadcasting identification. International Journal of Intelligent Systems.CrossRefGoogle Scholar
Zheng, Q., Zhao, P., Wang, H., Elhanashi, A., & Saponara, S. (2022). Fine-grained modulation classification using multi-scale radio transformer with dual-channel representation. IEEE Communications Letters, 26(6), 1298–1302.Google Scholar
Zheng, Q., Tian, X., Yu, Z., Wang, H., Elhanashi, A., & Saponara, S. (2023). DL-PR: Generalized automatic modulation classification method based on deep learning with priori regularization. Engineering Applications of Artificial Intelligence, 122, 106082.Google Scholar
Zheng, Q., Saponara, S., Tian, X., Yu, Z., Elhanashi, A., & Yu, R. (2024). A real-time constellation image classification method of wireless communication signals based on the lightweight network MobileViT. Cognitive Neurodynamics, 18(2), 659–671.Google ScholarPubMed
Zheng, Q., Tian, X., Yu, Z., Ding, Y., Elhanashi, A., Saponara, S., & Kpalma, K. (2023). MobileRaT: A Lightweight Radio Transformer Method for Automatic Modulation Classification in Drone Communication Systems. Drones, 7(10), 596.Google Scholar
He, X., Zhao, K., & Chu, X. (2021). AutoML: A survey of the state-of-the-art. Knowledge-based systems, 212, 106622.Google Scholar
Deng, R., Kong, F., & Kim, H.D. (2014). Numerical simulation of fluidic thrust vectoring in an axisymmetric supersonic nozzle. Journal of Mechanical Science and Technology, 28, 4979–4987.CrossRefGoogle Scholar
Hedrih, K.R.S.. (2019). Vibro-impact dynamics of two rolling heavy thin disks along rotate curvilinear line and energy analysis. Nonlinear Dynamics, 98(4), 2551–2579.Google Scholar