scholarly journals SHIP ROUTING AND SPEED OPTIMIZATION WITH HETEROGENEOUS FUEL CONSUMPTION PROFILES

2018 ◽  
Author(s):  
GABRIEL ANDRE HOMSI
Keyword(s):  
Fuel Consumption ◽  
Ship Routing ◽  
Speed Optimization

scholarly journals Coupled Engine-Propeller Selection Procedure to Minimize Fuel Consumption at a Specified Speed

2021 ◽  
Vol 9 (1) ◽  
pp. 59
Author(s):  
Mina Tadros ◽  
Roberto Vettor ◽  
Manuel Ventura ◽  
Carlos Guedes Soares
Keyword(s):  
Fuel Consumption ◽  
Selection Procedure ◽  
Optimization Procedure ◽  
Operating Point ◽  
Power Prediction ◽  
Ship Routing ◽  
Marine Propellers ◽  
Operational Profile ◽  
Calm Water ◽  
Engine Operating Point

This study presents a practical optimization procedure that couples the NavCad power prediction tool and a nonlinear optimizer integrated into the Matlab environment. This developed model aims at selecting a propeller at the engine operating point with minimum fuel consumption for different ship speeds in calm water condition. The procedure takes into account both the efficiency of the propeller and the specific fuel consumption of the engine. It is focused on reducing fuel consumption for the expected operational profile of the ship, contributing to energy efficiency in a complementary way as ship routing does. This model assists the ship and propeller designers in selecting the main parameters of the geometry, the operating point of a fixed-pitch propeller from Wageningen B-series and to define the gearbox ratio by minimizing the fuel consumption of a container ship, rather than only maximizing the propeller efficiency. Optimized results of the performance of several marine propellers with different number of blades working at different cruising speeds are also presented for comparison, while verifying the strength, cavitation and noise issues for each simulated case.

scholarly journals Vessel Performance Analysis and Fuel Management

2015 ◽  
Author(s):  
Lawrence Mak ◽  
Dong Cheol Seo ◽  
Andrew Kuczora ◽  
Michael Sullivan
Keyword(s):  
Fuel Consumption ◽  
Performance Indicators ◽  
Performance Monitoring ◽  
Considerable Increase ◽  
Current Paper ◽  
Speed Optimization ◽  
Analysis System ◽  
Distance Travel ◽  
Operational Data ◽  
Insight Into

A prototype Vessel Performance Monitoring and Analysis System (VPMAS) was deployed on a ferry to acquire needed performance data, to help improve vessel performance and reduce fuel consumption. A paper published in 2014 described preliminary data trends observed, key performance indicators computed, data products explored and exploratory tools developed for crews to gain insight into their vessel operation. The current paper describes further analysis of the operational data for speed optimization in calm sea states and the preliminary development of trim optimization software. It was found that trip durations around 7 hours (13.3 knots) use the least amount of fuel. The least amount of fuel is used when the excess distance travelled is minimized and the voyage time is optimized. There is a lot of leeway in terms of voyage time and excess distance travel by the ship before there is a heavy penalty on fuel consumption. Considering only a mean draft of 6 m and an average speed of 14 knots in the current paper, the optimal trim condition for the ferry is around −0.6 m (bow down), which reduces the resistance by 15% compared to the even keel condition. Positive trim causes the considerable increase of the total resistance consistently.

scholarly journals Research on Speed Optimization Strategy of Hybrid Electric Vehicle Queue Based on Particle Swarm Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Shaohua Wang ◽  
Chengquan Yu ◽  
Dehua Shi ◽  
Xiaoqiang Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Particle Swarm ◽  
Superior Performance ◽  
Optimization Strategy ◽  
Swarm Optimization ◽  
Speed Optimization ◽  
Hybrid Electric

Traffic lights intersections are common in cities and have an impact on the energy consumption of vehicles, so it is significant to optimize the velocities of vehicles in urban road conditions. The novel speed optimization strategy for hybrid electric vehicle (HEV) queue that helps reduce fuel consumption and improve traffic efficiency is presented in this paper, where real-world traffic signal information is used to construct the research scenario. The initial values of the target velocities are obtained based on the signal phase and timing (SPAT). Then the particle swarm optimization (PSO) algorithm is used to solve the nonlinear constrained problem and obtain the optimal target velocities based on vehicle to vehicle communication (V2V) and vehicle to infrastructure communication (V2I). The lower controller, which applies rule based control strategy, is designed to split the power of the engine and two electric motors in a power split HEV, which is quite promising because of its advantages in fuel economy. Simulation results demonstrate the superior performance of the proposed strategy in reducing fuel consumption of the HEV queue and improving traffic smoothness.

Research on Fixed Route Speed Optimization Based on Deep Neural Network and Genetic Algorithm

2020 ◽  
Author(s):  
Ziming Wang ◽  
Shunhuai Chen ◽  
Liang Luo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fuel Consumption ◽  
Bp Neural Network ◽  
Optimization Model ◽  
Power Model ◽  
Multi Objective ◽  
Speed Optimization ◽  
Main Engine ◽  
Engine Power

Abstract In the downturn of the shipping industry, optimizing the speed of ships sailing on fixed routes has important practical significance for reducing operating costs. Based on the ship-engine-propeller matching relationship, this paper uses BP neural network to build main engine power model, and correction factors are introduced into the main engine power model to reflect the influence of wind and wave. The Kalman filter algorithm is used to filter the data collected by a river-sea direct ship during the voyage from Zhoushan to Zhangjiagang. The filtered data and the meteorological data obtained from the European Medium-Range Weather Forecast Center are used as the data set of the BP neural network to predict the main engine power. Based on the main engine power model, a multi-objective optimization model of ship speed under the influence of actual wind and waves was established to solve the conflicting goals of reducing sailing time and reducing main engine fuel consumption. This multi-objective model is solved by a non-dominated fast sorting multi-objective genetic algorithm to obtain the Pareto optimal solution set, thereby obtaining the optimal speed optimization scheme. Compared with the original navigation scheme, the navigation time is reduced by 8.83%, and the fuel consumption of the main engine is reduced by 12.95%. The results show that the optimization model can effectively reduce the fuel consumption and control the sailing time, which verifies the effectiveness of the algorithm.

scholarly journals Tramp Ship Routing and Scheduling with Speed Optimization Considering Carbon Emissions

2019 ◽  
Vol 11 (22) ◽  
pp. 6367 ◽  
Author(s):  
Houming Fan ◽  
Jiaqi Yu ◽  
Xinzhe Liu
Keyword(s):  
Carbon Emissions ◽  
Simulated Annealing Algorithm ◽  
Neighborhood Search ◽  
Solution Quality ◽  
Ship Routing ◽  
Routing And Scheduling ◽  
Genetic Simulated Annealing Algorithm ◽  
Speed Optimization ◽  
Ship Scheduling ◽  
The Impact

The International Maritime Organization (IMO) proposed to reduce the total CO2 emissions of the maritime sector by 50% by 2050, and strive to gradually achieve the zero-carbon target. Therefore, shipping companies need to consider environmental impacts while pursuing benefits. In view of the tramp ship scheduling with speed optimization problem, considering carbon emissions, the configuration of owner ships and charter ships, and the impact of sailing speed on ship scheduling with the target of minimizing the total costs of shipping companies, multi-type tramp ship scheduling and speed optimization considering carbon emissions is established. A genetic simulated annealing algorithm based on a variable neighborhood search is proposed to solve the problem. Firstly, the ship type is matched with the cargo. Then the route is generated according to the time constraint, and finally, the neighborhood search strategy is adopted to improve the solution quality. The effectiveness of the proposed model and algorithm is verified by an example, which also confirms that ship scheduling and sailing speed joint optimization can reduce costs and carbon emissions. Research results can not only deepen the study of the theory of tramp scheduling but also to effectively solve the tramp shipping schedule considering carbon emissions problems faced by companies to provide theoretical guidance.

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