This paper investigates the influence of a crown wall on wave overtopping on rubble mound breakwaters. Existing data is used to modify the EurOtop overtopping formula updated by Eldrup et al. (2022) to cover the influence of the crown wall. The effect of raising the wall above the armor crest (elevated wall) or lowering the wall below the armor crest (lowered wall) is investigated. A crown wall at the armor crest level is considered as the reference case. By increasing the elevation of either the armor crest or the crown wall, overtopping is reduced and by lowering either of them, overtopping increases. The influence of the crown wall height, elevated or lowered compared to the armor crest, is not considered accurately in the present design guidelines and thus corrections are suggested. For an elevated wall, a modified crest width has been defined, to better describe the presence of the armor crest in front of the wall. For the lowered wall the effective freeboard might be taken as the average of the wall and armor freeboards. The improvement compared to existing methods is significant, especially for breakwaters with a large elevated wall. The proposed modifications to the EurOtop Manual increase the range of applicability with respect to the wall configuration.
Performance data from ships is subject to distributional shifts, sometimes referred to as concept drift. In this study, synthetic monitoring data is simulated for the KVLCC2, considering publicly available reference data and a semi-empirical simulation framework. Neural networks are trained to predict the required shaft power and to overcome the deterioration in model accuracy due to concept drift, three methods of incremental learning are applied and compared: (1) Layer freezing, (2) regularization, and (3) elastic weight consolidation. Furthermore, an implicit methodology for quantifying the changing hull and propeller performance is presented. In addition, a generic feature engineering framework is used for eliminating insignificant features. In two investigations, sudden and incremental concept drift scenarios are examined, and the effect of different uncertainty categories on model performance is studied in parallel based on three different datasets. As a main finding, it is confirmed that data quality is of great importance for accurate machine learning-driven performance monitoring — even in simulated environments. Furthermore, the study shows that freezing layers during incremental learning proves to be most robust and accurate, but it will be part of future work to examine this on actual sensor data.
A practical estimation methodology of the mean added resistance in irregular waves is shown, and the present paper provides statistical analyses of estimates for ships in actual conditions. The study merges telemetry data of more than 200 in-service container vessels with ocean re-analysis data from ERA5. Theoretical estimates relying on spectral calculations of added resistance are made for both long- and short-crested waves and are based on a combination of a parametric expression for the wave spectrum and a semi-empirical formula for the added resistance transfer function. The theoretical estimates are compared to predictions from an indirect calculation of added resistance relying on shaft power measurements and empirical estimates of the remaining resistance components. Overall, the comparison reveals a bias in bow oblique waves and higher sea states of the spectral estimates as well as the large variance of the empirically derived predictions — particularly in beam-to-following waves. One of the study’s main findings, confirming previous studies but based on a much larger dataset than in earlier similar studies, is that added resistance assessment based on in-service data is complex due to significant associated uncertainties.
A practical estimation methodology of the mean added resistance in irregular waves is shown, and the present paper provides statistical analyses of estimates for ships in actual conditions. The study merges telemetry data of more than 200 in-service container vessels with ocean re-analysis data from ERA5. Theoretical estimates relying on spectral calculations of added resistance are made for both long- and short-crested waves and are based on a combination of a parametric expression for the wave spectrum and a semi-empirical formula for the added resistance transfer function. The theoretical estimates are compared to predictions from an indirect calculation of added resistance relying on shaft power measurements and empirical estimates of the remaining resistance components. Overall, the comparison reveals a bias in bow oblique waves and higher sea states of the spectral estimates as well as the large variance of the empirically derived predictions — particularly in beam-to-following waves. One of the study’s main findings, confirming previous studies but based on a much larger dataset than in earlier similar studies, is that added resistance assessment based on in-service data is complex due to significant associated uncertainties.
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.
A crucial component for unmanned underwater vehicles (UUVs), including remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs), are the thrusters, which, in addition, are sensitive to damage during operations in harsh environments. This paper presents a study on the impact of incipient faults on the performance of thruster propellers used in offshore operations. The study evaluates the reduction in propeller performance due to wear and tear under realistic working conditions. The study employs a combination of experimental data analysis and signal processing techniques, including fast Fourier transforms and harmonics analysis, to identify faults and assess their severity. The results show that worn propellers can be identified through 5th-order harmonics and rotational velocity changes. The paper concludes with a proposal for future research using a model-based approach to enhance fault detection capabilities further.
This scientific study aims to compare the significance of onboard positioning of two different classes of wind propulsion systems for retrofit installations to maximize fuel and emissions savings. The study focuses on comparing the performance a low lift-to-drag ratio wind propulsion system, the Rotor Sail, and a high lift-to-drag ratio one, the DynaRig, installed at different places on a real 84000 DWT bulk carrier ship to identify the most efficient placement of these two distinct systems to achieve maximum fuel efficiency. The investigation involves a comprehensive analysis of available deck spaces, and performance prediction program modeling is employed to estimate potential fuel savings for a typical route followed by the vessel. The results show that placing the WPS far forward, close to the hydrodynamic centre of lateral resistance, results in overall higher savings. Both WPS classes see a penalty when placed far from the hydrodynamic centre of lateral resistance, reducing their overall savings potential. However, Rotor Sails are more adversely affected due to their enhanced side force generation per unit thrust. Consequently, the placement of Rotor Sails becomes crucial, especially under upwind conditions, while DynaRigs prove more versatile for installations in the aft. This research provides valuable insights into enhancing the ship's energy efficiency and reducing its environmental impact in the maritime industry.
The number of marine scrubbers installed in industry has been on the rise over the past decade and is expected to continue in the coming years. Therefore, it is essential to ensure that the design of the scrubbers enables as an efficient operation as possible. In this study, an optimization of the exhaust cover inside an in-line scrubber was carried out. The optimization was done by combining a computational fluid dynamics model working on a simplified geometry with the method of feasible directions in order to reduce the pressure loss caused by the exhaust cover. The design is constrained in both height and width of the points making up the exhaust cover to ensure proper drainage of water and to avoid invalid designs. It was found that the optimized design reduced the pressure loss by 42% compared to the initial design. Furthermore, the scalability of the original design was investigated with the same height constraint enforced on the design variables. The result of the scalability analysis showed that the radius of the exhaust cover for the optimal designs scales linearly with the diameter of the scrubber, while the pressure loss was found to increase quadratically as the diameter of the scrubber increases.
Scrubbers have gained importance in the maritime sector following recent tightening of the emission legislation regarding sulphur. In this work, a model framework based on an Eulerian-Eulerian multiphase model for a packed bed marine scrubber has been developed. The framework account for both dispersed droplets and a packed bed, where sub-models for interfacial forces and heat- and mass transfer are applied for the respective regions. Additionally, a chemistry model and boundary conditions for the nozzles injecting seawater into the scrubber are also implemented. The model framework is calibrated using data from an ocean-going vessel, where the model predictions were within 3% of the measured pressure loss while the discrepancy in the gas and liquid temperatures were between 0.5% and 3.5%. The sulphur concentration predicted by the model varies between − 24% and 25%. However, the concentrations were within 5 ppm of the measured values for all but a single data set.
Floating Power Plant (FPP) develops a hybrid floating wind and wave energy device. Pitching Wave Energy Converters (WECs) interact with the supporting structure, amplifying the motion of the WECs within the design wave frequency range. In this work we focus on the effect of the chamber geometry – without the WEC – in amplifying the waves inside the chamber. The simulations are carried out using two-phase Navier-Stokes simulations. We investigate the wave propagation and the interaction between waves and the fixed support structure. The simulations are compared to experimental tests performed in the wave basin at Aalborg University.