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In this paper, a data-driven dynamic efficiency model is developed for efficiency evaluation and comparison of ship electric powertrain with various system configurations and load-sharing methods. Based on the proposed method, the entire powertrain efficiency is assessed from the fuel consumption to the propulsion unit and the rest of the onboard load. The efficiency model is repeated for the conventional diesel-electric and the hybrid power system with batteries. In the latter case, the efficiency of the battery system is also included in the model. Then, the analysis is extended for different power system architectures such as AC- and DC onboard power systems. As a case study, system efficiency in a cruise ship is investigated using a real operational profile. A comprehensive analysis is performed to demonstrate the loss distribution in each subsystem of a hybrid AC- and DC power system. For a fair comparison between AC and DC, the battery charge level is equalized based on fuel compensation. The case study shows that hybridizing the ship power system increases system efficiency and enhances operational flexibility for the studied use case vessel. Further, the DC hybrid power system can improve the efficiency of the whole ship powertrain thanks to the variable speed operation of engines.

The hybridization of power systems offers the low-emission and energy-efficient marine vessels. However, it increases the complexity of the power system. Design, analysis, control, and optimization of such a sophisticated system require reliable and efficient modeling tools to cover a wide range of applications. In this work, a typical DC hybrid power system model is developed using a bond graph modeling approach. Necessary component models of varying degrees of fidelity are developed and integrated to build a system model with reasonable accuracy. The developed system model, along with the rule-based energy management system, is used to simulate the entire system and to investigate the load-sharing strategies as well as the system stability in various operating scenarios. Moreover, the simulation results are validated with experimental results conducted on a full-scale laboratory setup of the DC hybrid power system and with a ship load profile. The results show that the system model is capable of capturing the fundamental dynamics of the real system. The hybrid power system model is further used to analyze the bus voltage deviation from its nominal value. The computational efficiency presented by the system is fairly good as it can simulate faster than real-time. The developed system model can be used to build up a comprehensive simulation platform for different system analysis and control designs. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

A common approach for finding the direction of ocean waves is to use the phase-time-path-differences (PTPDs) between a static array of various types of sensors mounted on either the sea surface or seabed. However, some practical drawbacks of such arrays are that they tend to be expensive, difficult to install, and fixed in location. We show that the PTPD approach can be generalized to a portable shipboard array of spatially distributed sensors rendering it more practical. In this respect, we derive a nonlinear PTPD model for shipboard sensor arrays and prove that the wave direction and wave number can be resolved from a minimum of three noncollinear sensors using observability analysis. Moreover, based on our PTPD model, we propose an unscented Kalman filter algorithm for online estimation of the wave direction and wave number, which offers a convenient framework for adding multiple measurements and incorporating uncertainties. Our experimental results from model basin testing with a model ship equipped with several inertial measurement units (IMUs) confirm that the wave direction and wave number can be estimated from the wave-induced motions of a surface vessel with a minimum of three noncollinear IMUs. In this study, we consider parameter estimation for regular waves and assume a dynamically positioned surface vessel with small roll and pitch angles.

Control barrier functions (CBFs) have recently emerged as a means to ensure safety of controlled dynamical systems. CBFs are suitable for obstacle avoidance, where the CBF is often constructed from the distance and relative velocity between the vehicle and the obstacle. For vehicles required to maintain non-zero forward speed, ordinary (non-hybrid) CBFs cannot ensure safety due to vanishing control authority when the vehicle is oriented directly towards the obstacle. In this paper, synergistic CBFs are proposed, which is an intuitive extension of CBFs using ideas from synergistic Lyapunov functions. A synergistic CBF for obstacle avoidance for nonholonomic vehicles is constructed by shifting the orientations with vanishing control authority. This induces a penalty for traversing the obstacle in the counterclockwise or clockwise direction, where a logic variable is used to determine the preferred direction. The performance of the CBF is illustrated by a case study.

Hybrid electric vehicles powered by fuel cells and batteries have attracted significant attention as they have the potential to eliminate emissions from the transport sector. However, fuel cells and batteries have several operational challenges, which require a power and energy management system (PEMS) to achieve optimal performance. Most of the existing PEMS methods are based on either predefined rules or prediction that are not adaptive to real-time driving conditions and may give solutions that are far from the actual optimal solution for a new drive cycle. Therefore, in this paper, an intelligent PEMS using reinforcement learning is presented, that can autonomously learn the optimal policy in real time through interaction with the onboard hybrid power system. This PEMS is implemented and tested on the simulation model of the onboard hybrid power system. The propulsion load is represented by the new European drive cycle. The results indicate that the PEMS algorithm is able to improve the lifetime of batteries and efficiency of the power system through minimizing the variation of the state of charge of battery. © 2019. This is the authors' manuscript to the article.

The presence of sea ice is the predominant risk for ship operations in the Arctic, and monitoring of ice condition around a vessel is crucial during all times of operation. This paper presents a system for online onboard assessment of ice condition. It is demonstrated that ice-induced accelerations in the bow section of the hull follow a bivariate t-distribution and parameters of the distribution have a one-to-one relation to ice condition. This paper suggests a methodology to monitor the ice condition in real time through estimation of parameters that characterize the distribution of hull accelerations. It is shown how a Kullback–Leibler divergence measure can classify ice condition among a set of pretrained conditions. An absolute measure of ice load is suggested as an alternative for situations when pretraining data are not available. The alternative algorithm quantifies the condition through the entropy of measured accelerations. This paper presents a computationally easy methodology and tests against data collected during Arctic transit of an icebreaker. Furthermore, the classification results are compared with the results from two standard methods from machine learning, decision tree and a support vector machine approaches. The results show that the statistical methods provide robust assessment of the prevailing ice conditions, independent of visual and weather conditions. Also, the comparison shows that the statistical classification methods, designed by process knowledge, provide steadier and more reliable results. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Floating structures oscillate in waves, where these wave-induced motions may be critical for various marine operations. An important consideration is thereby given to the sea states at the planning and operating stages for an offshore project. The most important information extracted from a sea state is the directional wave spectrum, indicating wave direction, significant wave height, and wave spectrum peak period. Among several available methods of measuring and estimating the directional wave spectrum, the wave buoy analogy technique based on vessel motion responses is an in situ and almost real-time solution without extra costs of devices. If the forms of the wave spectra are not predefined in the estimation, the method is called a nonparametric approach. Its most remarkable advantage is the flexible form, but the smoothness should be regulated. After the discrete Fourier transform has been applied to the measured vessel motions, smoothing is necessary. However, this process results in disturbed vessel cross-spectra and a lowpass characteristic of the windowing function. This paper presents a nonparametric approach for directional wave spectrum estimation based on vessel motion responses. It introduces novel smoothness constraints using Bezier surface and includes a more robust estimate using L1 optimization. Both techniques are applied to the wave buoy analogy for the first time. Numerical simulations are conducted to verify the proposed algorithm. /© 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license(http://creativecommons.org/licenses/by/4.0/)

In this paper, we are comparing fuel consumption on a case study hybrid marine power plant with diesel gensets and batteries. Optimization methods are used to find an optimal operating point for the gensets, under different power demands, with regards to fuel consumption andNOx emissions. Three different power system example configurations for an offshore construction vessel are explored in this study, and the simulations are carried out to compare the resulting fuel consumption and NOx emissions for these power system configurations. Copyright © 2018 by ASME

This paper introduces the concept of distributed motion sensing for stationkeeping vessels in ice infested waters. During the SKT 2017 project, conducted in February and March 2017 in the Bay of Bothnia, five inertial measurement units were installed on the vessel Magne Viking. Four of the sensor units were installed at different positions inside the hull of the vessel, which enabled the system to locally measure ice-induced vibrations in the hull of the vessel. The fifth sensor unit was installed at a central position of the vessel and served as reference sensor for measuring the acting global load on the vessel. Under stationkeeping the global load measured on the ship should be close to zero, because the environmental load is equal to the force from the stationkeeping system. However the remaining four motion sensor units in the hull also measured locally induced vibrations. The study shows that this sensor configuration allows for the detection of changes in the acting load against the vessel. This is demonstrated with motion data obtained during the stationkeeping trials on the vessel Magne Viking. Copyright © 2018 by ASME

This paper proposes several schemes for optimal online configuration and load sharing for a shipboard power system of a typical offshore vessel, having a number of varying capacity gensets. Different methods are presented for optimal online scheduling and minimization of specific fuel oil consumption using mixed integer linear programming. The simulations for optimization of specific fuel oil consumption for three different scheduling methods demonstrate their properties. Copyright © 2018 by ASME

Surveys were conducted in the Norwegian Sea to evaluate the existing human factors standards and their implementations on two different offshore supply vessels designs. The results are reported in two parts. This paper (Part I) covers the theoretical background and reports all the technical aspects of the research: noise, motion and slamming that includes measurements, analyses and evaluations. Noise level and motion were measured by means of sound level meter and accelerometer respectively. Data were collected in two periods: summer and winter. Results of measurements were compared with the existing criteria and discrepancies are identified. It can be concluded that some of the existing human factors criteria are inadequate and less relevant to the actual experience on board and they are ineffective to induce comfort on both supply vessels studied. Improvements and revisions are strongly recommended with respect to noise, motion and slamming criteria. An extended methodology to predict MII is also advised. The other paper (Part II) reports the human factors subjective evaluation performed by the seafarers where multivariate analyses were performed to reveal a human factors model in ship design and operations. This is the pre-peer reviewed version of an article, which has been published in final form at [https://www.ingentaconnect.com/contentone/asne/nej/2016/00000128/00000004/art00019]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.

In this paper a numerical model is used to investigate the level ice performance of a double-acting intervention vessel, and the results are compared with a limited set of experimental data. The icebreaking capability and maneuverability in level ice are analyzed by evaluating the behavior of the vessel when it is running both ahead and astern. The simulated icebreaking patterns, h–v curves, and turning circles in different modes of operation are discussed and compared partly with the corresponding ice model tests. The simulation results can supplement the experimental data by providing more information about the vessel's maneuverability in level ice and identifying the physical foundation for the exhibited performance of the vessel. The paper also presents the implementation of a random crack size model for more realistic icebreaking behavior, giving more consistent evaluation of the ship's performance in various ice conditions. © 2014. This is the authors’ accepted and refereed manuscript to the article. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/