EFFICIENT STOCHASTIC DYNAMIC RESPONSE ANALYSIS OF UNDERWATER VEHICLE VIA DPIM

Chen Hanshu; Chen Guohai; Yang Dixiong; Fu Zhuojia

doi:10.6052/0459-1879-23-606

Volume 56 Issue 6

Jun. 2024

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Chinese Journal of Theoretical and Applied Mechanics > 2024 > 56(6): 1796-1806. > DOI: 10.6052/0459-1879-23-606 CSTR: 32045.14.0459-1879-23-606

Chen Hanshu, Chen Guohai, Yang Dixiong, Fu Zhuojia. Efficient stochastic dynamic response analysis of underwater vehicle via DPIM. Chinese Journal of Theoretical and Applied Mechanics, 2024, 56(6): 1796-1806. DOI: 10.6052/0459-1879-23-606

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EFFICIENT STOCHASTIC DYNAMIC RESPONSE ANALYSIS OF UNDERWATER VEHICLE VIA DPIM

*.
Center for Numerical Simulation Software in Engineering and Sciences, College of Mechanics and Engineering Science, Hohai University, Nanjing 211100, China
†.
State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Department of Engineering Mechanics, Dalian University of Technology, Dalian 116024, Liaoning, China

Received Date: December 15, 2023
Accepted Date: January 21, 2024
Available Online: January 21, 2024
Published Date: January 22, 2024

Graphical Abstract

Abstract

Abstract

The ocean is a treasure trove of resources for humanity. With the continuous exploration of ocean resources, the detection and rational development of these resources has become a hot topic of widespread concern for countries around the world. Consequently, the exploration activities of underwater vehicles have been gradually increasing. However, the ocean environment is characterized by strong stochastic uncertainty, posing significant challenges for the operation and trajectory planning of underwater vehicles. Therefore, it is crucial to accurately predict the stochastic response characteristics of underwater vehicles in random ocean environments. In this paper, a new type of underwater vehicle, equipped with a rudderless paddle, is adopted as the research object. Based on the principle of probability conservation, a probability density integral equation of the underwater vehicle in a random ocean environment is established from a new perspective of stochastic integration. Then, a direct probability integral method (DPIM) based on block parallel computing is proposed in this paper. This method decouples the control equations of the underwater vehicle system from the probability density integral equation, and utilizes block-parallel computations, enabling the efficient stochastic dynamic response analysis of the new type of underwater vehicle under the random sea wave excitation. Furthermore, the computational results of the proposed method are compared with those of the original DPIM and Monte Carlo simulation method to further validate its accuracy and efficiency. The final research results reveal that sea wave significant height and flow velocity are the primary stochastic factors affecting the response of underwater vehicles. An increase in the sea wave's significant height and flow velocity will significantly raise the probability of deviation from the predetermined trajectory of underwater vehicles. In addition, it can be found that the higher sea wave flow velocity may induce the random jumping phenomena, thereby reducing the navigation safety of the underwater vehicle.
- underwater vehicle,
- stochastic response analysis,
- block parallel computing,
- probability density integral equation,
- DPIM

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References (35)

References

[1]	张世童, 张宏伟, 王延辉等. 自主水下航行器导航技术发展现状与分析. 导航定位学报, 2020, 8(2): 1-7 (Zhang Shitong, Zhang Hongwei, Wang Yanhui, et al. Development status and analysis of navigation technology for autonomous underwater vehicles. Journal of Navigation and Positioning, 2020, 8(2): 1-7 (in Chinese) doi: 10.3969/j.issn.2095-4999.2020.02.001 Zhang Shitong, Zhang Hongwei, Wang Yanhui, et al. Development status and analysis of navigation technology for autonomous underwater vehicles. Journal of Navigation and Positioning, 2020, 8(2): 1-7 (in Chinese) doi: 10.3969/j.issn.2095-4999.2020.02.001
[2]	张康宇, 路宽, 程晖等. 基于共振转换器的自主水下航行器动力学建模及减振降噪. 力学学报, 2023, 55(10): 2274-2287 (Zhang Kangyu, Lu Kuan, Cheng Hui, et al. Dynamic modeling and vibration and noise reduction of autonomous underwater vehicles based on resonance changer. Chinese Journal of Theoretical and Applied Mechanics, 2023, 55(10): 2274-2287 (in Chinese) doi: 10.6052/0459-1879-23-217 Zhang Kangyu, Lu Kuan, Cheng Hui, et al. Dynamic modeling and vibration and noise reduction of autonomous underwater vehicles based on resonance changer. Chinese Journal of Theoretical and Applied Mechanics, 2023, 55(10): 2274-2287 (in Chinese) doi: 10.6052/0459-1879-23-217
[3]	MahmoudZadeh S, Powers DMW, Yazdani AM, et al. Efficient AUV path planning in time-variant underwater environment using differential evolution algorithm. Journal of Marine Science and Application, 2018, 17: 585-591 doi: 10.1007/s11804-018-0034-4
[4]	邢炜. 基于前视声呐的AUV避障方法研究. [博士论文]. 哈尔滨: 哈尔滨工程大学, 2019 (Xing Wei. Research on AUV obstacle avoidance method based on forward—looking sonar. [PhD Thesis]. Harbin: Harbin Engineering University, 2019 (in Chinese) Xing Wei. Research on AUV obstacle avoidance method based on forward—looking sonar. [PhD Thesis]. Harbin: Harbin Engineering University, 2019 (in Chinese)
[5]	朱佳莹, 高茂庭. 融合粒子群与改进蚁群算法的 AUV 路径规划算法. 计算机工程与应用, 2021, 57(6): 267-273 (Zhu Jiaying, Gao Maoting. AUV path planning based on particle swarm optimization and improved ant colony optimization. Computer Engineering and Application, 2021, 57(6): 267-273 (in Chinese) doi: 10.3778/j.issn.1002-8331.2008-0243 Zhu Jiaying, Gao Maoting. AUV path planning based on particle swarm optimization and improved ant colony optimization. Computer Engineering and Application, 2021, 57(6): 267-273 (in Chinese) doi: 10.3778/j.issn.1002-8331.2008-0243
[6]	Hadi B, Khosravi A, Sarhadi P. Deep reinforcement learning for adaptive path planning and control of an autonomous underwater vehicle. Applied Ocean Research, 2022, 129: 103326 doi: 10.1016/j.apor.2022.103326
[7]	Wei PF, Song JW, Bi SF, et al. Non-intrusive stochastic analysis with parameterized imprecise probability models: II. Reliability and rare events analysis. Mechanical Systems and Signal Processing, 2019, 126: 227-247
[8]	Xiang HY, Tang P, Zhang Y, et al. Random dynamic analysis of vertical train–bridge systems under small probability by surrogate model and subset simulation with splitting. Railway Engineering Science, 2020, 28: 305-315 doi: 10.1007/s40534-020-00219-6
[9]	Heshmati-Alamdari S, Nikou A, Dimarogonas DV. Robust trajectory tracking control for underactuated autonomous underwater vehicles in uncertain environments. IEEE Transactions on Automation Science and Engineering, 2020, 18(3): 1288-1301
[10]	Yan ZP, Wang M, Xu J. Robust adaptive sliding mode control of underactuated autonomous underwater vehicles with uncertain dynamics. Ocean Engineering, 2019, 173: 802-809 doi: 10.1016/j.oceaneng.2019.01.008
[11]	周春晓, 汪锐琼, 聂肇坤等. 基于最大熵方法的水下航行体结构动力响应概率建模. 力学学报, 2018, 50(1): 114-123 (Zhou Chunxiao, Wang Ruiqiong, Nie Zhaokun, et al. Probabilistic modelling of dynamic response of underwater vehicle structure via maximum entropy method. Chinese Journal of Theoretical and Applied Mechanics, 2018, 50(1): 114-123 (in Chinese) doi: 10.6052/0459-1879-17-022 Zhou Chunxiao, Wang Ruiqiong, Nie Zhaokun, et al. Probabilistic modelling of dynamic response of underwater vehicle structure via maximum entropy method. Chinese Journal of Theoretical and Applied Mechanics, 2018, 50(1): 114-123 (in Chinese) doi: 10.6052/0459-1879-17-022
[12]	Guo SS. Nonstationary solutions of nonlinear dynamical systems excited by Gaussian white noise. Nonlinear Dynamics, 2018, 92: 613-626 doi: 10.1007/s11071-018-4078-4
[13]	王文杰, 封建湖. 一维非线性系统FPK方程的TVD Runge-Kutta WENO型差分解. 动力学与控制学报, 2018, 16(3): 206-210 (Wang Wenjie, Feng Jianhu. Solution of TVD runge-kutta and WENO scheme to the FPK equations of one-dimension nonlinear system. Journal of Dynamic and Control, 2018, 16(3): 206-210 (in Chinese) Wang Wenjie, Feng Jianhu. Solution of TVD runge-kutta and WENO scheme to the FPK equations of one-dimension nonlinear system. Journal of Dynamic and Control, 2018, 16(3): 206-210 (in Chinese)
[14]	Denisov SI, Bystrik YS. Exact stationary solutions of the Kolmogorov–Feller equation in a bounded domain. Communications in Nonlinear Science and Numerical Simulation, 2019, 74: 248-259 doi: 10.1016/j.cnsns.2019.03.023
[15]	Zheng ZB, Dai HZ. A new fractional equivalent linearization method for nonlinear stochastic dynamic analysis, Nonlinear Dynamics, 2018, 91: 1075-1084
[16]	Naess A, Iourtchenko D, Batsevych O. Reliability of systems with randomly varying parameters by the path integration method. Probabilistic Engineering Mechanics, 2011, 26(1): 5-9 doi: 10.1016/j.probengmech.2010.05.005
[17]	Di Matteo A. Path Integral approach via Laplace’s method of integration for nonstationary response of nonlinear systems. Meccanica, 2019, 54(9): 1351-1363 doi: 10.1007/s11012-019-00991-8
[18]	Chen LC, Hu F, Zhu WQ. Stochastic dynamics and fractional optimal control of quasi integrable Hamiltonian systems with fractional derivative damping. Fractional Calculus and Applied Analysis, 2013, 16(1): 189-225 doi: 10.2478/s13540-013-0013-z
[19]	孙诣博, 魏莎, 丁虎等. 基于路径积分法的输液管道随机动态响应分析. 力学学报, 2023, 55(6): 1371-1381 (Sun Yibo, Wei Sha, Ding Hu, et al. Stochastic dynamic response analysis of pipe conveying fluid based on the path integral method. Chinese Journal of Theoretical and Applied Mechanics, 2023, 55(6): 1371-1381 (in Chinese) doi: 10.6052/0459-1879-23-032 Sun Yibo, Wei Sha, Ding Hu, et al. Stochastic dynamic response analysis of pipe conveying fluid based on the path integral method. Chinese Journal of Theoretical and Applied Mechanics, 2023, 55(6): 1371-1381 (in Chinese) doi: 10.6052/0459-1879-23-032
[20]	Landau D, Binder K. A Guide to Monte Carlo Simulations in Statistical Physics. Cambridge: Cambridge University Press, 2021
[21]	Meng Z, Zhang D, Li G, et al. An importance learning method for non-probabilistic reliability analysis and optimization. Structural and Multidisciplinary Optimization, 2019, 59(4): 1255-1271 doi: 10.1007/s00158-018-2128-7
[22]	Chen GH, Yang DX. Direct probability integral method for stochastic response analysis of static and dynamic structural systems. Computer Methods in Applied Mechanics and Engineering, 2019, 357: 112612 doi: 10.1016/j.cma.2019.112612
[23]	Li XL, Chen GH, Wang Y, et al. A unified approach for time-invariant and time-variant reliability-based design optimization with multiple most probable points. Mechanical Systems and Signal Processing, 2022, 177: 109176 doi: 10.1016/j.ymssp.2022.109176
[24]	赵昊广, 齐向东, 郑佳奇等. 新型自主水下航行器的运动控制研究与应用. 舰船科学技术, 2017, 39(12): 48-52 (Zhao Haoguang, Qi Xiangdong, Zheng Jiaqi, et al. The motion control and application of a new type autonomous underwater vehicle. Ship Science and Technology, 2017, 39(12): 48-52 (in Chinese) doi: 10.3404/j.issn.1672-7649.2017.12.011 Zhao Haoguang, Qi Xiangdong, Zheng Jiaqi, et al. The motion control and application of a new type autonomous underwater vehicle. Ship Science and Technology, 2017, 39(12): 48-52 (in Chinese) doi: 10.3404/j.issn.1672-7649.2017.12.011
[25]	Borlaug ILG, Pettersen KY, Gravdahl JT. Tracking control of an articulated intervention autonomous underwater vehicle in 6DOF using generalized super-twisting: Theory and experiments. IEEE Transactions on Control Systems Technology, 2020, 29(1): 353-369
[26]	Zhang YD, Liu XF, Luo MZ, et al. MPC-based 3-D trajectory tracking for an autonomous underwater vehicle with constraints in complex ocean environments. Ocean Engineering, 2019, 189: 106309 doi: 10.1016/j.oceaneng.2019.106309
[27]	王静, 唐军武, 何宜军等. X-波段雷达近海海浪频谱反演的神经网络模型. 海洋学报, 2013, 35(2): 43-51 (Wang Jing, Tang Junwu, He Yijun, et a1. Retrieval of near shore wave frequency spectrum with X-band radar based on neural network. Acta Oceanologica Sinica, 2013, 35(2): 43-51 (in Chinese) Wang Jing, Tang Junwu, He Yijun, et a1. Retrieval of near shore wave frequency spectrum with X-band radar based on neural network. Acta Oceanologica Sinica, 2013, 35(2): 43-51 (in Chinese)
[28]	Bitner-Gregersen EM, Waseda T, Parunov J, et al. Uncertainties in long-term wave modelling. Marine Structures, 2022, 84: 103217 doi: 10.1016/j.marstruc.2022.103217
[29]	曹永辉, 石秀华. 水下航行器轨迹跟踪控制与仿真. 计算机仿真, 2006, 23(7): 19-21 (Cao Yonghui, Shi Xiuhua. Trajectory tracking control and simulation of AUV. Computer Simulation, 2006, 23(7): 19-21 (in Chinese) doi: 10.3969/j.issn.1006-9348.2006.07.006 Cao Yonghui, Shi Xiuhua. Trajectory tracking control and simulation of AUV. Computer Simulation, 2006, 23(7): 19-21 (in Chinese) doi: 10.3969/j.issn.1006-9348.2006.07.006
[30]	Li J, Chen JB. Stochastic Dynamics of Structures. New York: John Wiley & Sons, 2009
[31]	Chen JB, Yang JY, Li J. A GF-discrepancy for point selection in stochastic seismic response analysis of structures with uncertain parameters. Structural Safety, 2016, 59: 20-31 doi: 10.1016/j.strusafe.2015.11.001
[32]	Chen GH, Yang DX. A unified analysis framework of static and dynamic structural reliabilities based on direct probability integral method. Mechanical Systems and Signal Processing, 2021, 158: 107783
[33]	Tao TZ, Zhao GZ, Yu Y, et al. A fully adaptive method for structural stochastic response analysis based on direct probability integral method. Computer Methods in Applied Mechanics and Engineering, 2022, 396: 115066
[34]	Botev ZI, Kroese JFGP. Kernel density estimation via diffusion. The Annals of Statistics, 2010, 38(5): 2916-2957
[35]	Zhang YX, Liu JP, Yu JP. Adaptive asymptotic tracking control for autonomous underwater vehicles with non-vanishing uncertainties and input saturation. Ocean Engineering, 2023, 276: 114280 doi: 10.1016/j.oceaneng.2023.114280

[1]	Zhou Yongfeng, Li Jie. ANALYTICAL SOLUTIONS OF THE GENERALIZED PROBABILITY DENSITY EVOLUTION EQUATION WITH MULTIDIMENSIONAL RANDOM VARIABLES: THE CASE OF EULER-BERNOULLI BEAM[J]. Chinese Journal of Theoretical and Applied Mechanics, 2024, 56(9): 2659-2668. DOI: 10.6052/0459-1879-24-001
[2]	Xu Yazhou, Tian Rui. A PSEUDO-SPECTRAL METHOD FOR THE GENERALIZED DENSITY EVOLUTION EQUATION[J]. Chinese Journal of Theoretical and Applied Mechanics, 2024, 56(8): 2415-2422. DOI: 10.6052/0459-1879-23-633
[3]	Sun Yibo, Wei Sha, Ding Hu, Chen Liqun. STOCHASTIC DYNAMIC RESPONSE ANALYSIS OF PIPE CONVEYING FLUID BASED ON THE PATH INTEGRAL METHOD[J]. Chinese Journal of Theoretical and Applied Mechanics, 2023, 55(6): 1371-1381. DOI: 10.6052/0459-1879-23-032
[4]	Chen Jianbing, Lü Mengze. A NEW METHOD FOR THE PROBABILITY DENSITY OF MAXIMUM ABSOLUTE VALUE OF A MARKOV PROCESS[J]. Chinese Journal of Theoretical and Applied Mechanics, 2019, 51(5): 1437-1447. DOI: 10.6052/0459-1879-19-104
[5]	Shi Sheng, Du Dongsheng, Wang Shuguang, Li Weiwei. NON-UNIFORM TIME STEP TVD SCHEME FOR PROBABILITY DENSITY EVOLUTION FUNCTION WITH IMPROVEMENT OF INITIAL CONDITION[J]. Chinese Journal of Theoretical and Applied Mechanics, 2019, 51(4): 1223-1234. DOI: 10.6052/0459-1879-18-446
[6]	Jiang Zhongming, Li Jie. ANALYTICAL SOLUTIONS OF THE GENERALIZED PROBABILITY DENSITY EVOLUTION EQUATION OF THREE CLASSES STOCHASTIC SYSTEMS[J]. Chinese Journal of Theoretical and Applied Mechanics, 2016, 48(2): 413-421. DOI: 10.6052/0459-1879-15-221
[7]	Chen Jianbing, Zhang Shenghan. PROBABILITY DENSITY EVOLUTION ANALYSIS OF NONLINEAR RESPONSE OF STRUCTURES WITH NON-UNIFORM RANDOM PARAMETERS[J]. Chinese Journal of Theoretical and Applied Mechanics, 2014, 46(1): 136-144. DOI: 10.6052/0459-1879-13-174
[8]	Strategy of selecting points via number theoretical method in probability density evolution analysis of stochastic response of structures[J]. Chinese Journal of Theoretical and Applied Mechanics, 2006, 38(1): 127-133. DOI: 10.6052/0459-1879-2006-1-2005-054
[9]	THE PROBABILITY DENSITY FUNCTIONS OF THE STATIONARY RESPONSE OF CERTAIN CLASSES OF NONLINEAR SYSTEMS TO STOCHASTIC EXCITATION[J]. Chinese Journal of Theoretical and Applied Mechanics, 1991, 23(1): 92-102. DOI: 10.6052/0459-1879-1991-1-1995-813
[10]	样条积分方程法分析弹塑性板弯曲[J]. Chinese Journal of Theoretical and Applied Mechanics, 1990, 22(2): 241-245. DOI: 10.6052/0459-1879-1990-2-1995-940

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