Identification of passive state-space models of strongly frequency dependent wave radiation forces

被引:6
|
作者
Rogne, Oyvind Y. [1 ,2 ]
Moan, Torgeir [2 ]
Ersdal, Svein [1 ]
机构
[1] Aker Solut, N-1364 Fornebu, Norway
[2] Norwegian Univ Sci & Technol, Ctr Ships & Ocean Struct, N-7491 Trondheim, Norway
关键词
State space models; Wave radiation forces; Time domain simulation; Multibody floating systems; Passivity; RATIONAL APPROXIMATION; DOMAIN; MATRIX;
D O I
10.1016/j.oceaneng.2014.09.032
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
This paper describes a methodology for obtaining passive state space representations of wave radiation forces for floating bodies with zero forward speed oscillating in multiple degrees of freedom. The method is based on fitting rational transfer functions written on pole-residue form to radiation frequency responses calculated using standard boundary element codes. Determination of poles and residues is facilitated by the vector fitting algorithm, originally developed for fitting of frequency responses of electrical networks. Using the poles and residues as parameters in the fitting has advantages over the more common ratio-of-polynomials transfer function formulation when the model order is high, which is typically required when the frequency dependence is strong and wide-banded such as for multibody floating systems. A methodology for perturbing the residues of slightly non-passive systems such that they become passive is also presented. The method is demonstrated successfully for a five-body wave energy converter, an array of circular cylinders and a single cylinder. A discussion and investigation of the truncation of high frequencies is provided for the wave energy converter, and parameter constraints governing the extrapolation of the rational model above the frequencies present in the dataset are suggested. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:114 / 128
页数:15
相关论文
共 50 条
  • [1] Hysteresis Identification Using Nonlinear State-Space Models
    Noel, J. P.
    Esfahani, A. F.
    Kerschen, G.
    Schoukens, J.
    NONLINEAR DYNAMICS, VOL 1, 34TH IMAC, 2016, : 323 - 338
  • [2] Wave digital simulation of passive systems in linear state-space form
    Ochs, Karlheinz
    INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS, 2010, 23 (01) : 42 - 61
  • [3] Parameter identification for nonlinear models from a state-space approach
    Matz, Jules
    Birouche, Abderazik
    Mourllion, Benjamin
    Bouziani, Fethi
    Basset, Michel
    IFAC PAPERSONLINE, 2020, 53 (02): : 13910 - 13915
  • [4] Dynamic state-space models
    Guo, WS
    JOURNAL OF TIME SERIES ANALYSIS, 2003, 24 (02) : 149 - 158
  • [5] Comparison of some initialisation methods for the identification of nonlinear state-space models
    Van Mulders, Anne
    Vanbeylen, Laurent
    2013 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC), 2013, : 807 - 811
  • [6] Nuclear Norm Subspace Identification Of Continuous Time State-Space Models
    Varanasi, Santhosh Kumar
    Jampana, Phanindra
    IFAC PAPERSONLINE, 2018, 51 (01): : 530 - 535
  • [7] Identification of Piecewise Affine State-Space Models via Expectation Maximization
    Rui, Rafael
    Ardeshiri, Tohid
    Bazanella, Alexandre
    2016 IEEE CONFERENCE ON COMPUTER AIDED CONTROL SYSTEM DESIGN (CACSD), 2016, : 1066 - 1071
  • [8] Smoothing algorithms for state-space models
    Briers, Mark
    Doucet, Arnaud
    Maskell, Simon
    ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS, 2010, 62 (01) : 61 - 89
  • [9] Particle filtering, learning, and smoothing for mixed-frequency state-space models
    Leippold, Markus
    Yang, Hanlin
    ECONOMETRICS AND STATISTICS, 2019, 12 : 25 - 41
  • [10] Noise Companion State-Space Passive Macromodeling for RF/mm-Wave Circuit Design
    Ye, Zuochang
    IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2013, 32 (09) : 1435 - 1439