Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

On the Development of an Onboard Sea State Estimator Based on Numerical Vessel Motion Data
Carl Reiner, Ole Detlefsen, Moustafa Abdel-Maksoud
Institute for Fluid Dynamics and Ship Theory, Hamburg University of Technology.
Hamburg, Germany

ABSTRACTapproaches: RAO-based optimization approaches for the inverted wavestructure interaction problem, linear Kalman filter approaches and nonlinear Baye networks. Some of the existing methods are alreadydeveloped to the point where they are classified by their authors as
operational tools for the acquisition of sea state data (Nielsen and Dietz,2020).
However, some drawbacks are linked to these methods. First, due to themodel based approach, some a priori knowledge is required regarding

Within the Joint Research Project RetroLadung, weight-optimized cell
guides with integrated motion sensors and data-driven decision support
systems (DSS) are developed. A basic functionality of the DSS is theestimation of sea states based on measurement data from acceleration
sensors. A convolutional neural network (CNN) is created for this task
and trained with simulation data. This paper presents the early
development stage of the network during which unimodal, short-crested
seas described with JONSWAP spectra are considered. Thereby, theforward speed is excluded. Model tank tests are conducted by theInstitute for Fluid Dynamics and Ship Theory (FDS) and results are used
to validate the predictions of the sea state estimation (SSE).

KEY WORDS: SSE; DSS; CNN; sea-state; short-crested; parameterfree; data-driven

NOMENCLATURE
AI – artificial intelligence, ANN – artificial neural network, CNN –convolutional neural network, CSD – cross-spectral density, LSTM –long short-term memory, PSD – power spectral density, RNN – recurrent
neural network, SSE – sea state estimation
Figure 1 Spatial evaluation range ofthe hindcast wave data
the model response behavior in arbitrary sea states. This knowledge isusually expressed by transfer functions (equivalently response amplitudeoperators (RAO)) which linearly describe how the vessel of concern
reacts to harmonic excitations of arbitrary frequencies. It is widelyaccepted, that these frequency-dependent transfer functions depend on
operational parameters such as the loading condition and the travel speed
but also on external influences such as water depth, current and wave
height (e.g. Han et al. (2021)). This leads to the paradox that sea state
estimations become unreliable specifically when their significance is at
its greatest (Nielsen et al. (2021)), e.g. in severe weather conditions or incoastal waters with currents and shallows. For this reason, research has
recently focused on the possibility of SSE using model-free, data-driven
methods. A number of approaches to this have been publishedrecently:(Tu et al. (2018), Cheng et al. (2019), Mak and Düz (2019),
Cheng et al. (2020), Liu et al. (2020), Kawai et al. (2021), Zhang et al.
(2021)). Various kinds of artificial neural networks (ANN) are used forship motion based sea state estimation. Among the ANN methods,
convolutional neural networks and recurrent neural networks (RNN), or
long short-term memory (LSTM) networks, are used in particular. TheseINTRODUCTION
Using ship motion data to determine the excitatory sea state is a topic
that has been discussed for nearly fifty years. According to Pascoal et al.
(2017), first attempts for sea state estimation (SSE) using ship motion
data were made during the 108th Research Committee of the Shipbuilding Research Association of Japan in 1971 (Takekuma andTakahashi, 1973). After a quiescent period in the eighties and nineties,
Pascoal et al. (2017) observed a renewed and intensified interest for thetopic during the early twenty-first century when for the first time authors
started to investigate on mathematical details, optimal sensor positions,vessel speed and algorithm performance. During this period the authors
first addressed the 3-1 mapping problem that arises with following waves
when converting the encountered wave spectrum of a travelling vessel toan absolute wave spectrum due to the ambiguous Doppler shift (e.g. Isekiand Ohtsu (2000)). It seems to the authors of this paper, that a new andongoing wave of interest for SSE using ship motions started around2015. Today a variety of approaches exist. They can be divided intoclassic methods based on physical models and data-driven artificial
intelligence methods. The first group includes the following types of2144