Key Takeaways & Executive Findings
- •• A MATLAB/Simulink simulation platform integrating multi-environmental resistance, propeller efficiency, and EEOI calculation modules was developed for polar ice-class merchant vessels. • Route segmentation considering wind, wave, and ice conditions enables precise forecasting of main engine power, fuel consumption, and EEOI for each segment. • Speed design optimization based on the simulation model reduced the whole-route EEOI by 3.114% and fuel consumption by 9.17 tons. • The study extends energy efficiency optimization from conventional waters to polar ice areas, addressing a critical gap in green shipping research.
Abstract
In order to accurately forecast the main engine fuel consumption and reduce the Energy Efficiency Operational Indicator (EEOI) of merchant ships in polar ice areas, the energy transfer relationship between ship-machine-propeller is studied by analyzing the complex force situation during ship navigation and building a MATLAB/Simulink simulation platform based on multi-environmental resistance, propeller efficiency, main engine power, fuel consumption, fuel consumption rate and EEOI calculation module. Considering the environmental factors of wind, wave and ice, the route is divided into sections, the calculation of main engine power, main engine fuel consumption and EEOI for each section is completed, and the speed design is optimized based on the simulation model for each section. Under the requirements of the voyage plan, the optimization results show that the energy efficiency operation index of the whole route is reduced by 3.114% and the fuel consumption is reduced by 9.17 t.
1. Introduction
The International Maritime Organization (IMO) officially implemented the Ship Energy Efficiency Operational Indicator (EEOI) in 2011, which has promoted the development of green shipping. From the perspective of ship operation, the main engine speed is directly related to the ship's speed, which is closely linked to energy efficiency indicators such as resistance during navigation and fuel consumption. Therefore, finding the optimal speed for a given route is an important way to reduce the EEOI and achieve energy savings and emissions reduction.
A considerable amount of analysis and research has already been conducted on the energy efficiency of ships operating in conventional waterways. Liu [1] established an interference model of navigational environment, and derived the relationship between EEOI and sea state conditions, characteristic wave height, loading rate, cargo capacity, main engine speed, etc. Fan [2] utilized the ship-engine-propeller relationship to establish the main engine energy consumption model based on Simulink, and compared the main engine energy efficiency with that of the ship's actual voyage through the real ship algorithm. Ni [3] analyzed the EEOI and made a sensitivity analysis of the ship EEOI with the sailing speed, berthing time, full load rate factor, ship type factor, etc. Huo [4] studied the energy saving of the ship during sailing by optimizing the ship's section and subsection speeds in order to determine the lowest total fuel consumption. Most of the researches have focused on the energy efficiency of ships operating in conventional waters, but not much work has been done on the energy efficiency of ships navigating in ice. Finland-Sweden proposed a formula in the ice-class specification guidelines to calculate the minimum power for ice-class ships [5], with this formula additionally defining a minimum speed for Finnish-Swedish ice-class ships in specific channel ice conditions. By comparing the empirical formula method for forecasting the minimum main engine power of ice-class vessels in the Finnish-Swedish ice-class specification guidelines, Ni et al [6] proposed the discrete element method to calculate the ice resistance and establish the resistance-velocity curve, through which the main engine power forecasting method for ice-class vessels was established. Hou et al [7] applied uncertainty analysis and optimization theory to solve the minimum speed optimization model, using the main engine speed of ice-class ships as the design variable, validating the EEOI as an effective reference index for energy efficiency optimization in ice-class ships.
Loading authentic research manuscript (Pages 1–5)...
LU Yu, LI Chen-ran, ZHU Xiang-hang, LI Shi-an, GU Zhu-hao, LIU She-wen (2025). Energy Efficiency Operating Indicator Forecasting and Speed Design Optimization for Polar Ice Class Merchant Vessels. SinoTechIntel Verified Research. https://doi.org/10.3969/j.issn.1007-7294.2025.06.005
Research & Educational Purpose Only:The translations, structured abstracts, analytical annotations, and data reports provided by SinoTechIntel are intended exclusively for academic research, internal corporate R&D, and educational benchmarking. They do not constitute formal engineering, chemical safety, legal, or professional advice.
Copyright & Intellectual Property Notice: Original copyright of the underlying source articles and experimental data remains with the respective authors, institutions, and original publishing journals. SinoTechIntel claims intellectual property only over its proprietary translations, analytical syntheses, and AEO structured enhancements in accordance with international fair use and academic citation principles.
Frequently Asked Questions
What is the main objective of the study?
The main objective is to accurately forecast main engine fuel consumption and reduce the Energy Efficiency Operational Indicator (EEOI) of merchant ships in polar ice areas through speed design optimization.
How was the simulation platform developed?
A MATLAB/Simulink simulation platform was built, integrating modules for multi-environmental resistance, propeller efficiency, main engine power, fuel consumption, fuel consumption rate, and EEOI calculation.
What environmental factors were considered?
The study considered wind, wave, and ice conditions, dividing the route into sections to account for varying environmental impacts.
What were the key results of the optimization?
The optimization reduced the whole-route EEOI by 3.114% and fuel consumption by 9.17 tons, demonstrating significant energy savings.
Why is this study important for polar shipping?
It addresses a gap in energy efficiency research for ice-class ships, providing a method to optimize speed and reduce emissions in challenging polar environments.
Related Technical Papers & Translations
A Novel Approach for Enhanced Brain Tumor Segmentation Using Multimodal MRI and Deep Learning
Brain tumor segmentation from multimodal MRI is crucial for diagnosis and treatment planning. In this study, we propose a novel deep learning framework that integrates structural and functional imaging modalities to improve segmentation accuracy. Our method employs a multi-scale attention mechanism and a hybrid loss function to handle class imbalance and boundary ambiguity. Evaluated on the BraTS benchmark, our approach achieves state-of-the-art performance, with Dice scores of 0.91, 0.87, and 0.84 for whole tumor, core, and enhancing tumor, respectively. Furthermore, we demonstrate the generalizability of our model across different scanners and protocols. Our findings suggest that the proposed method can significantly aid clinical decision-making and surgical planning.
Investigation of coupled acoustic and electrical responses and early warning approaches during re-loading of damaged coal
Initial damage from engineering disturbances in deep coal mining degrades mechanical properties and heightens dynamic-hazard risks, challenging conventional monitoring. This study probes the coupled acoustic-electrical responses of initially damaged coal under reloading and develops a multi-parameter, multi-level dynamic integrated early-warning model. Using a true-triaxial Split Hopkinson Pressure Bar (SHPB) system, we prepared specimens with graded damage by varying static deviatoric stresses and dynamic impacts. Uniaxial compression reloading was conducted with synchronous acoustic emission (AE) and resistivity monitoring. Joint time-domain responses of force, acoustics, and electricity delineated distinct loading stages. Time-frequency features were extracted via Fourier and wavelet transforms; crack architecture was quantified by 3D AE localization and fractal-dimension analysis. Initial damage markedly reduced load-bearing capacity. Resistivity decreased sharply with increasing deviatoric stress, while cumulative AE counts increased strongly. The AE spectrum evolved from bimodal to broadband with low- and high-frequency enhancement. The resistivity spectrum showed progressive bandwidth broadening, energy amplification, and high-frequency advancement. The AE spatial fractal dimension rose significantly during compaction. An integrated warning system combining multiscale entropy fusion, Temporal Convolutional Network (TCN)-Transformer forecasting, recurrence-network analysis, and a Bayesian framework yielded a 28.4 s lead time, offering a theoretical basis and technical pathway for intelligent prevention of dynamic hazards.
Influence of aggregate particle size on fracture behavior and energy evolution of cemented rockfill in the post-peak stage
Cemented rockfill (CRF) combines structural support with sustainable reuse of coal-derived solid waste. This study integrates digital image correlation, acoustic emission monitoring, and finite–discrete element simulations to investigate mechanical behavior, fracture development, and energy evolution of CRF containing 54% aggregate content with three grain-size distributions (5–10, 10–20, and 20–30 mm). Results indicate finer aggregates raise compressive strength and elastic modulus, and increase post-peak softening and residual stiffness. Fracture patterns transition from dominantly unidirectional failure in coarse specimens to pronounced X-shaped conjugate shear in fine specimens, with cracks initiating at boundaries and propagating inward. The proportion of failed joints at comparable strains decreases markedly with finer gradation, reflecting a more homogeneous crack network that enhances post-peak load retention and produces frequent minor stress fluctuations. Energy analyses reveal a coarse > medium > fine ordering in cumulative dissipation; however, finer aggregates delay rapid kinetic and dissipative energy release, promoting slower energy redistribution and improved load resistance. These findings quantify how aggregate gradation controls deformational mechanisms, crack topology, and energy partitioning, and provide design guidance for optimizing aggregate size and cementitious composition to enhance ductility, energy absorption, and structural reliability of CRF in underground engineering.