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Keyword: marine engineering

paper

Extreme nonlinear ship response estimations by active learning reliability method and dimensionality reduction for ocean wave

Tomoki Takami*, Masaru Kitahara, Jørgen Juncher Jensen, Sadaoki Matsui

An efficient extreme ship response prediction approach in a given short-term sea state is devised in the paper. The present approach employs an active learning reliability method, named as the active learning Kriging + Markov Chain Monte Carlo (AK-MCMC), to predict the exceedance probability of extreme ship response. Apart from that, the Karhunen-Loève (KL) expansion of stochastic ocean wave is adopted to reduce the number of stochastic variables and to expedite the AK-MCMC computations. Weakly and strongly nonlinear vertical bending moments (VBMs) in a container ship, where the former only accounts for the nonlinearities in the hydrostatic and Froude-Krylov forces, while the latter also accounts for the nonlinearities in the radiation and diffraction forces together with slamming and hydroelastic effects, are studied to demonstrate the efficiency and accuracy of the present approach. The nonlinear strip theory is used for time domain VBM computations. Validation and comparison against the crude Monte Carlo Simulation (MCS) and the First Order Reliability Method (FORM) are made. The present approach demonstrates superior efficiency and accuracy compared to FORM. Moreover, methods for estimating the Mean-out-crossing rate of VBM based on reliability indices derived from the present approach are proposed and are validated against long-time numerical simulations.

Marine Structures / 2024
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paper

Effect of wave–current interaction on gap resonance between side-by-side barges

Yunfeng Ding, Jens Honoré Walther, Yanlin Shao

We investigate piston-mode fluid resonance within the narrow gap formed by two identical fixed barges in a side-by-side configuration, utilizing a two-dimensional fully nonlinear numerical wave tank. The focus is on examining the effects of uniform and shear currents. Under ‘wave+uniform-current’ conditions, a certain current speed is identified, beyond which the gap resonance reduces dramatically and monotonically with the current speed. This reduction is attributed to a stronger increase in damping compared to wave excitation, qualitatively explained by a linearized massless damping lid model. Furthermore, we study the effects of waves propagating on shear currents, maintaining an identical ambient current speed at the gap depth. Complementary to previous studies on this topic, our study reveals that the velocity profile of the studied shear current has an insignificant effect on the resonant gap amplitudes. The ambient current velocity at the gap depth is a more important key parameter to consider when assessing wave-induced gap responses, leading to a non-negligible increase in the resonant gap response. Consequently, disregarding the influence of currents in engineering practices is not a conservative approach.

Applied Ocean Research / 2024
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paper

Monitoring hydrodynamic vessel performance by incremental machine learning using in-service data

Malte Mittendorf, Ulrik Dam Nielsen, Ditte Gundermann

An adaptive machine learning framework is established for an implicit determination of the performance degradation of a ship due to marine growth, i.e., biofouling. The framework is applied in a case study considering telemetry data of a cruise ship operating predominantly in the Caribbean Sea. The dataset encompasses seven years including three dry-docking intervals and several in-water cleaning events. The COVID-19 period receives special focus due to the drastic change in the operational profile. A main outcome of the study is a comparison of the derived performance estimate to the corresponding results of the industry standard ISO 19030. Additional aspects of the present study include the use of special regularization techniques for incremental machine learning and the increase of transparency through the implementation of prediction intervals indicating model uncertainty. Overall, it is found that the developed machine learning framework shows good agreement with the industry standard underlining its plausibility.

Ship Technology Research / 2024
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book

Predicting Underwater Radiated Noise from Ship Propellers

Joseph Praful Tomy

Underwater radiated noise (URN) from ship propellers has attracted increasing interest in recent years due to its adverse environmental effects on marine life and their communication channels. The environmental concern to reduce shipping noise and the industrial requirements for faster computational tools are driving factors that promote research in the specialized domain of hydroacoustics. This thesis deals with the development of such a computationally efficient numerical tool, which can be used in the prediction of underwater radiated noise in the early design phase of propellers.

The numerical model is developed with two major objectives – versatility in assessing the relative contributions from the major propeller-noise generating mechanisms, and rapidity in prediction of overall noise behaviour. It uses the Farassat-1A solid-FWH formulation of the Ffowcs-Williams- Hawkings equation by defining equivalent acoustic sources on the propeller blade, sheet cavity and tip vortex cavity surfaces. In particular, the application of the solid-FWH formulation to the tip vortex cavity model is the major novelty in this thesis.

The hydrodynamic flow solution is obtained from a potential flow based solver ESPPRO, which includes analytical models of sheet cavitation and tip vortex cavitation. The hydroacoustic numerical model developed within this thesis, DoLPHiN, is a Python-based code that is primarily designed to accept input from ESPPRO; but during the research, the code has also been adapted to read input from the commercial, finite-volume-based Navier-Stokes solver, STAR-CCM+.

The numerical model implementations are verified through analytical case studies for simple geometrical shapes, such as a pulsating sphere and an oscillating cylindrical cavity. The verification study is further extended for propeller geometries by identifying approximate reference solutions in simplified operating conditions. The numerical tool is validated for industrial application through comparison of its noise prediction with model-scale and full-scale noise measurements. Specific characteristics of the propeller noise spectrum are identified in order to evaluate its noise prediction capabilities. The uncertainty factors involved when validating with experimental measurements are also explored in detail. Furthermore, a design study is presented, which shows potential use of the numerical tool in practical propeller design and optimization applications.

Technical University of Denmark / 2024
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paper

Energy extraction potential from wave-induced ship motions using linear generators

Ulrik D. Nielsen*, Harry B. Bingham, Rasmus Bjørk

This paper presents an assessment of the energy harvesting potential from wave-induced motions when producing electricity by linear generators installed on ships. The study estimates an upper maximum energy extraction potential by not considering the electro-mechanical coupling; neither is mechanical and electrical dissipation considered. The analysis of the harvested energy is made using simulated data in a case study investigating three different ships (by size). Specifically, the case study reveals that, in moderate to mildly severe sea states, the power harvested from the environment using linear generators may reach values around 1–2 kW/tons of seismic mass. Thus, it is unrealistic to imagine ship designs where linear generators are thought to provide a ship's necessary propulsion power but, on the other hand, they may serve to supplement the main engine for auxiliary power generation.

Sustainable Energy Technologies and Assessments / 2024
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paper

Uncertainty-associated directional wave spectrum estimation from wave-induced ship responses using Machine Learning methods

Ulrik D. Nielsen, Kazuma Iwase, Raphaël E.G. Mounet, Gaute Storhaug

This paper presents an assessment of three methods used for sea state estimation via the wave buoy analogy, where measured ship responses are processed. The three methods all rely on Machine Learning exclusively but they have different output; Method 1 provides bulk parameters, Method 2 yields a point wave spectrum and the wave direction, while Method 3 gives the directional wave spectrum in non-parametric form. The assessment is made using full-scale data from an in-service container ship in cross-Atlantic service. Training and testing of the methods are made using data from a wave radar, and the three methods perform well. An uncertainty measure, equivalently, a trust level indicator, based on the variation between the post-processed outputs of the methods is proposed, and this facilitates determination of estimates with small errors; without knowing the ground truth.

Ocean Engineering / 2024
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paper

Onboard identification of stability parameters including nonlinear roll damping via phase-resolved wave estimation using measured ship responses

Tomoki Takami, Ulrik Dam Nielsen, Jørgen Juncher Jensen, Atsuo Maki, Sadaoki Matsui, Yusuke Komoriyama

Accurate estimation of the roll damping of a ship is important for reliable prediction of roll motions. In particular, characterization and prediction of parametric roll incidence and other events associated with large roll angles require detailed knowledge about the damping terms. In the present paper, an approach to identify the stability parameters, i.e. linear and nonlinear roll damping coefficients in conjunction with the natural roll frequency, based on onboard response measurements is proposed. The method starts by estimating the encountered wave profile using wave-induced response measurements other than roll, e.g., heave, pitch, and sway motions. The estimated wave profile is then fed into a physic-based nonlinear roll estimator, and then the stability parameters that best reproduce the measured roll motion are identified by optimization. In turn, in-situ identification can be achieved while simultaneously collecting the response measurements. A numerical investigation using synthetic response measurements is made first, then follows an experimental investigation using a scaled model ship. Good results have been obtained in both long-crested and short-crested irregular waves.

Mechanical Systems and Signal Processing / 2024
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paper

Solving for hydroelastic ship response using a high-order finite difference method on overlapping grids at zero speed

Baoshun Zhou, Mostafa Amini-Afshar, Harry B. Bingham, Yanlin Shao, Šime Malenica, Matilde H. Andersen

This work extends an existing seakeeping tool (OceanWave3D-seakeeping) to allow for the efficient and accurate evaluation of the hydroelastic response of large flexible ships sailing in waves. OceanWave3D-seakeeping solves the linearized potential flow problem using high-order finite differences on overlapping curvilinear body-fitted grids. Generalized modes are introduced to capture the flexural responses at both zero and non-zero forward speed, but we focus on the zero speed case here. The implementation of the hydroelastic solution is validated against experimental measurements and reference numerical solutions for three test cases. The ship girder is approximated by an Euler–Bernoulli beam, so only elastic bending deformation is considered and sheer effects are neglected. Some controversy has long existed in the literature about the correct form of the linearized hydrostatic stiffness terms for flexible modes, with Newman (1994) and Malenica and Bigot (2020) arriving at different forms. We provide here a complete derivation of both forms (including the gravitational terms) and demonstrate the equivalence of the buoyancy terms for pure elastic motions.

Marine Structures / 2024
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paper

Prediction of Ship Main Particulars for Harbor Tugboats Using a Bayesian Network Model and Non-Linear Regression

Omer Karacay, Caglar Karatug, Tayfun Uyanik, Yasin Arslanoğlu, Abderezak Lashab*

Determining the key characteristics of a ship during the concept and preliminary design phases is a critical and intricate process. In this study, we propose an alternative to traditional empirical methods by introducing a model to estimate the main particulars of diesel-powered Z-Drive harbor tugboats. This prediction is performed to determine the main particulars of tugboats: length, beam, draft, and power concerning the required service speed and bollard pull values, employing Bayesian network and non-linear regression methods. We utilized a dataset comprising 476 samples from 68 distinct diesel-powered Z-Drive harbor tugboat series to construct this model. The case study results demonstrate that the established model accurately predicts the main parameters of a tugboat with the obtained average of mean absolute percentage error values; 6.574% for the Bayesian network and 5.795%, 9.955% for non-linear regression methods. This model, therefore, proves to be a practical and valuable tool for ship designers in determining the main particulars of ships during the concept design stage by reducing revision return possibilities in further stages of ship design.

Applied Sciences (Switzerland) / 2024
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paper

Retrofitting Technologies for Eco-Friendly Ship Structures: A Risk Analysis Perspective

Athanasios Kolios*

This paper presents a detailed risk assessment framework tailored for retrofitting ship structures towards eco-friendliness. Addressing a critical gap in current research, it proposes a comprehensive strategy integrating technical, environmental, economic, and regulatory considerations. The framework, grounded in the Failure Mode, Effects, and Criticality Analysis (FMECA) approach, adeptly combines quantitative and qualitative methodologies to assess the feasibility and impact of retrofitting technologies. A case study on ferry electrification, highlighting options like fully electric and hybrid propulsion systems, illustrates the application of this framework. Fully Electric Systems pose challenges such as ensuring ample battery capacity and establishing the requisite charging infrastructure, despite offering significant emission reductions. Hybrid systems present a flexible alternative, balancing electric operation with conventional fuel to reduce emissions without compromising range. This study emphasizes a holistic risk mitigation strategy, aligning advanced technological applications with environmental and economic viability within a strict regulatory context. It advocates for specific risk control measures that refine retrofitting practices, guiding the maritime industry towards a more sustainable future within an evolving technological and regulatory landscape.

Journal of Marine Science and Engineering / 2024
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