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Sail-assisted technology can reduce greenhouse-gas emissions by saving the energy consumption of ships with wind energy utilization. The distribution characteristics of marine wind resources are critical to the energy-saving effect of sail-assisted ships. However, due to the lack of effective energy-saving evaluation methods for improving the utilization rate of wind energy, a high potential for wind energy utilization still exists. A novel energy-saving evaluation method based on the wind energy resource analysis of typical ship routes is proposed in this paper. First, a three-degree-of-freedom motion model for sail-assisted ships considering the wing sail forces is constructed. Then, a wind resource acquisition and analysis method based on spatial–temporal interpolation is proposed. On this basis, the wind field probability matrix and wing sail force matrix are established. Ultimately, an energy-saving evaluation method for sail-assisted ships on typical routes is proposed by combining the sailing condition of ships. The case study results show that the energy-saving effect of a wing sail-assisted oil tanker that sailed on the China-to-Middle East route was more than 5.37% in 2021 and could reach 9.54% in a single voyage. It is of great significance to realize the popularization and application of sail-assisted technology, thus improving the greenization of the shipping industry. [ABSTRACT FROM AUTHOR]

When the wind direction changes, rotating the sail to keep it at the optimal angle of attack can effectively utilize offshore wind resources to improve the ship's energy efficiency. The hydraulic system usually drives the slewing of the sail onboard. The functioning, as well as the safety of hydraulic system will be directly affected in case of leakage failure occurs. Therefore, the leakage fault diagnosis is essential to improve the sail-assisted effect as well as the reliability of the sail slewing system. In this paper, a novel wavelet packet transform (WPT)–fuzzy pattern recognition (FPR) based leakage fault diagnosis method is proposed. In order to analyze the different leakage fault features of the hydraulic system, a simulation model is established, and its effectiveness is verified by the hydraulic testbed. Then, the sensitive feature of flow and pressure signal for different leakage faults is extracted by a WPT-based method. On this basis, an FPR-based leakage fault diagnosis method is proposed. The diagnosis results show that the proposed method has an accuracy of 94% for nine leakage fault modes. This work contributes to realizing the greenization of the shipping industry by improving the utilization rate of offshore wind resources. [ABSTRACT FROM AUTHOR]

Ship Fuel Consumption Prediction (SFCP) is the foundation of ship energy efficiency assessment and optimization. However, existing research neglects to examine the model extrapolation performance, leading to significant degradation in predictive accuracy when models face dataset shift. To address this, a novel dual-stage grey-box stacking (DSGBS) model is proposed. First, based on the traditional grey-box model (GBM), a light grey-box model (LGBM) is proposed to enhance the extrapolation ability by incorporating more prior knowledge. Then, an improved stacking framework is used to fuse multiple GBMs to build the DSGBS model. Finally, a physics-based white-box model (WBM) is established, along with black-box model (BBM), traditional GBM, and LGBM based on nine machine learning algorithms. The extrapolation performance of these models is compared using data from three independent voyages. Results show that DSGBS model has a significant advantage in extrapolation performance, reducing its RMSE by about 63.51 %, 10.91 %, and 52.52 %, respectively, compared to the best model in BBMs, the best model among GBMs and LGBMs, and WBM. Therefore, the DSGBS model mitigates prediction accuracy loss from dataset shift, significantly improve the extrapolation performance, and support the practical application of ship energy efficiency management, with great significance for reducing operation cost and emission. • A DSGBS model with high extrapolation performance is proposed for ship fuel consumption prediction. • Reveals that BBM's predicted performance significantly degrades in extrapolation. • An LGBM is proposed that relies only on the physical model to generate input features. • First full revelation of the differences in extrapolation performance across models. • DSGBS model improves extrapolation performance by 63.51 %, 52.52 % and 10.91 % compared to BBM, WBM and GBM. [ABSTRACT FROM AUTHOR]

Establishing an accurate fuel consumption prediction model is essential to optimize energy efficiency in wing-diesel hybrid ships. This study proposes an improved Blending ensemble learning method for predicting fuel consumption. Initially, the original data is cleaned using artificial experience and Interquartile Range (IQR) method. A high-dimensional feature selection method, variance analysis- Pearson correlation-mutual information-permutation importance (VA-PC-MI-PI), is then proposed to identify 16 key features from the initial 111 features. Furthermore, 9 algorithms are employed for prediction analysis under different temporal resolution data conditions. Based on the results, optimal time step and algorithm models are determined to construct the Blending ensemble model (BEM). Subsequently, the feature data of "sailing speed" and "bow draft", which account for the highest permutation importance, are imported into the metadata set to alleviate insufficient BEM data utilization. Additionally, Extra Trees (ET) model is chosen as the meta-learner for adaptation and Bayesian method is employed for global optimization. The resulting improved Blending ensemble model (IBEM) exhibits optimal prediction accuracy and generalization ability with an RMSE of 66.04 kg/h, representing a reduction of 16.99% and 13.86% compared to the Support Vector Machine (SVM) model and BEM, respectively. Additionally, the IBEM exhibits significantly smaller residuals and absolute residuals, demonstrating superior predictive stability and consistency. This study provides an important reference and basis for energy efficiency prediction, control, and optimization of wing-diesel hybrid ships. [Display omitted] • Highly accurate prediction of fuel consumption for wing-diesel hybrid ships. • Proposed a high-dimensional feature selection method based on the combined application of VA-PC-MI-PI. • Explored the influence of temporal resolution on each algorithmic model. • Analyzed the permutation importance of each feature in different algorithmic models. • Improved the Blending ensemble learning method based on enhanced metadata set, optimized meta learner and Bayesian global optimization. [ABSTRACT FROM AUTHOR]

The utilization of wind-assisted propulsion technology could reduce ship fuel consumption and emission of pollutants. In the study, the effect of steady heel angles on the aerodynamic characteristics of wing sails was investigated. Full-scale three-dimensional simulations with the ship's superstructure were conducted at heel angles of 0°–15° in all apparent wind directions. The correctness of the numerical computations is verified by comparing them with the wind tunnel experimental data. Overall, the heel is detrimental to the capture of wind energy by the wing sails. The aerodynamic forces of the sail system decrease with increasing heel angles. Specifically, the average thrust performance is attenuated by 4.7%, 13.5% and 20.5%, respectively, at heel angles of 5°, 10°, and 15° compared to the upright condition. Due to changes in the airflow distribution around the sails, the aerodynamic force of sails in heeled to windward and heeled to leeward decreases by the reduced area of the low-pressure zone on the suction side and the high-pressure zone on the pressure side, respectively. Finally, a prediction scheme for assessing the effect of rolling motion on sail performance is proposed by steady heel results. • The effect of the heel on the aerodynamic performance of wing sails is determined. • The aerodynamic forces of the sail system decrease with increasing heel angles. • Thrust performance reduction of the sail system up to 20.5% at a 15° heel angle. • Pressure distribution variations on both sides lead to sail performance reduction. [ABSTRACT FROM AUTHOR]

Accurate fuel consumption prediction is essential for optimizing the operation of wing-diesel hybrid vessels and improving energy efficiency. This paper proposes a grey box model (GBM) for wing-diesel hybrid vessel fuel consumption prediction based on feature construction. Both parallel and series grey box modelling methods, as well as six machine learning (ML) algorithms are adopted to establish twelve combinations of prediction models. Then, a feature construction method based on the aerodynamic performance of the wing and the energy relationship of the hybrid system is proposed. Three types of wing features, namely wing thrust, wing thrust power, and wing fuel consumption savings are constructed and introduced into each combination respectively. Finally, based on noon report data of a wing-diesel hybrid vessel, the combinations are trained and validated. The best combination is obtained by considering the root mean square error (RMSE), which is parallel modeling method, random forest (RF) algorithm, and wing fuel consumption savings feature. Its RMSE decreased by 41.7 % compared to the white box model (WBM). Therefore, the GBM proposed in this paper can predict the daily fuel consumption of wing-diesel hybrid vessels with high accuracy, facilitating operational optimization and contributing to the greenization and decarbonization of the shipping industry. • A GBM for predicting fuel consumption of wing-diesel hybrid ships is proposed. • Method proposed for constructing wing features to improve GBM prediction performance. • The data integration method is adopted to improve the quality of meteorological data. • The GBM prediction error (RMSE) is reduced by 41.7 % compared to the original WBM. • The GBM can predict the fuel consumption of hybrid system under various conditions. [ABSTRACT FROM AUTHOR]