netsse.analys.sawb ================== .. py:module:: netsse.analys.sawb .. autoapi-nested-parse:: Python implementation of various algorithms for sea state estimation using the **wave-buoy analogy** (WBA), i.e. using the wave-induced responses of ships considered as sailing wave buoys (SAWB). .. dropdown:: Copyright (C) 2023-2026 Technical University of Denmark, R.E.G. Mounet :color: primary :icon: law *This code is part of the NetSSE software.* NetSSE is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. NetSSE is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/. To credit the author, users are encouraged to use below reference: .. code-block:: text Mounet, R. E. G., & Nielsen, U. D. NetSSE: An open-source Python package for network-based sea state estimation from ships, buoys, and other observation platforms (version 2.2). Technical University of Denmark, GitLab. February 2026. https://doi.org/10.11583/DTU.26379811. *Last updated on 20-02-2026 by R.E.G. Mounet* Functions --------- .. autoapisummary:: netsse.analys.sawb.genspecres netsse.analys.sawb.specres Module Contents --------------- .. py:function:: genspecres(Rxy, Hxy, freq, beta, opt=None) Computes an estimate of the point wave spectrum using a generalised version of the spectral-residual method from Brodtkorb et al. (2017) and Nielsen et al. (2018). The cross-spectra from any number of responses are used as input. Using the motion transfer functions of the vessel, the wave spectrum is estimated through iteration. .. warning:: Long-crested wave conditions are assumed in this sea state estimation method. The present version of the algorithm does not allow to distinguish waves coming from port and starboard sides. :param Rxy: The response cross-spectra. .. note:: The phase of the complex-valued response spectra is not important in this implementation, and the amplitude (absolute value) alone can be provided without this affecting the estimation results. :type Rxy: array_like of shape (Nresp,Nfreq) :param Hxy: The RAO products for the ship, provided in the same order of response pairs as for the response cross-spectra `Rxy`. .. note:: The phase of the complex-valued transfer functions is not important in this implementation, and the amplitude alone can be provided without this affecting the estimation results. :type Hxy: array_like of shape (Nresp,Nbeta,Nfreq) :param freq: The frequencies of the response spectra and the transfer functions (should match). :type freq: array_like of shape (Nfreq,) :param beta: The discretized heading angles [deg] at which the transfer functions are known. .. tip:: For a port-starboard symmetric ship, directions from 0 deg to 180 deg only can be considered to lower the computational cost. :type beta: array_like of shape (Nbeta,) :param opt: Optional parameters controlling the SSE calculation. Available options are: - 'maxiter' : int, or array_like of shape (`Nresp`,) Maximum number of iterations (default: 50). - 'tolCoef' : float, or array_like of shape (`Nresp`,) Tolerance coefficient (default: 0.1). - 'gainFact' : float, or array_like of shape (`Nresp`,) Gain factor, as a fraction of the maximum gain value (default: 0.5). - 'weights' : float, or array_like of shape (`Nresp`,) Weights given to each response in the calculation of the final spectrum estimate (default: equal weight for each response, i.e. wij = 1/`Nresp`). .. note:: `array_like` entries of the dictionary can be input for a response-specific option. In such case, the array elements must be provided in the same order as for the response spectra. :type opt: dict, optional :returns: * **S_wave** (*array_like of shape (Nfreq,)*) -- The estimated 1-D wave spectrum. * **beta_est** (*float*) -- The estimated relative wave heading [degrees]. * **num_it** (*array_like of shape (Nresp,)*) -- The average number of iterations used per heading angle, for the individual response pairs. .. rubric:: References 1. Brodtkorb, A. H., Nielsen, U. D., & Sørensen, A. J. (2018). Online wave estimation using vessel motion measurements. IFAC-PapersOnLine, 51(29), 244–249. https://doi.org/10.1016/j.ifacol.2018.09.510 2. Brodtkorb, A. H., Nielsen, U. D., & Sørensen, A. J. (2018). Sea state estimation using vessel response in dynamic positioning. Applied Ocean Research, 70, 76–86. https://doi.org/10.1016/j.apor.2017.09.005 3. Nielsen, U. D., Brodtkorb, A. H., & Sørensen, A. J. (2018). A brute-force spectral approach for wave estimation using measured vessel motions. Marine Structures, 60, 101–121. https://doi.org/10.1016/j.marstruc.2018.03.011 4. Brodtkorb, A. H., & Nielsen, U. D. (2023). Automatic sea state estimation with online trust measure based on ship response measurements. Control Engineering Practice, 130. https://doi.org/10.1016/j.conengprac.2022.105375 .. rubric:: Example >>> S_wave, beta_est, num_it = genspecres(Rxy, Hxy, freq, beta, opt=None) .. py:function:: specres(Rxy, TRF, freq, beta, opt=None) Computes an estimate of the sea state using the spectral-residual method from Brodtkorb et al. (2017) and Nielsen et al. (2018). The heave, roll, and pitch response cross-spectra are used as input. Using the motion transfer functions of the vessel, the wave spectrum is estimated through iteration. .. warning:: Long-crested wave conditions are assumed in this sea state estimation method. .. note:: This Python code is based on a MATLAB/Simulink implementation by Astrid H. Brodtkorb. :param Rxy: The complex-valued response spectra, given as in: ``Rxy = [heaveheave, rollroll, pitchpitch, heaveroll, heavepitch, rollpitch]``. :type Rxy: array_like of shape (Nfreq,6) :param TRF: The transfer functions of the ship, in heave, roll, and pitch, concatenated along the second axis as in: ``TRF = [heave_TF,roll_TF,pitch_TF]`` .. note:: The phase of the complex transfer functions is not important in this implementation, and the amplitude alone can be provided without this affecting the estimation results. :type TRF: array_like of shape (Nfreq,3*Nbeta) :param freq: The frequencies of the response spectra and the transfer functions (should match). :type freq: array_like of shape (Nfreq,) :param beta: The discretized heading angles [deg] at which the transfer functions are known. .. tip:: For a port-starboard symmetric ship, directions from 0 deg to 180 deg only can be considered to lower the computational cost. :type beta: array_like of shape (Nbeta,) :param opt: Optional parameters controlling the SSE calculation. Available options are: - 'maxiter' : int, or array_like of shape (6,) Maximum number of iterations (default: 50). - 'tolCoef' : float, or array_like of shape (6,) Tolerance coefficient (default: 0.1). - 'gainFact' : float, or array_like of shape (6,) Gain factor, as a fraction of the maximum gain value (default: 0.5). - 'weights' : float, or array_like of shape (6,) Weights given to each response in the calculation of the final spectrum estimate (default: equal weight for each response, i.e. wij = 1/6). .. note:: `array_like` entries of the dictionary can be input for a response-specific option. In such case, the array elements must be provided in the same order as for the response spectra. :type opt: dict, optional :returns: * **S_wave** (*array_like of shape (Nfreq,)*) -- The estimated 1-D wave spectrum. * **beta_est** (*float*) -- The estimated relative wave heading [degrees]. * **num_it** (*array_like of shape (6,)*) -- The average number of iterations used per heading angle, for the individual response pairs organised as: ``num_it = [it3, it4, it5, it34, it35, it45]``. .. rubric:: References 1. Brodtkorb, A. H., Nielsen, U. D., & Sørensen, A. J. (2018). Online wave estimation using vessel motion measurements. IFAC-PapersOnLine, 51(29), 244–249. https://doi.org/10.1016/j.ifacol.2018.09.510 2. Brodtkorb, A. H., Nielsen, U. D., & Sørensen, A. J. (2018). Sea state estimation using vessel response in dynamic positioning. Applied Ocean Research, 70, 76–86. https://doi.org/10.1016/j.apor.2017.09.005 3. Nielsen, U. D., Brodtkorb, A. H., & Sørensen, A. J. (2018). A brute-force spectral approach for wave estimation using measured vessel motions. Marine Structures, 60, 101–121. https://doi.org/10.1016/j.marstruc.2018.03.011 4. Brodtkorb, A. H., & Nielsen, U. D. (2023). Automatic sea state estimation with online trust measure based on ship response measurements. Control Engineering Practice, 130. https://doi.org/10.1016/j.conengprac.2022.105375 .. rubric:: Example >>> S_wave, beta_est, num_it = specres(Rxy, TRF, freq, beta, opt=None)