Research on the Fuel Consumption Optimization of Multi-Satellites Formation Establishment

2014 ◽  
Vol 926-930 ◽  
pp. 1589-1592
Author(s):  
Jian Wei Shi ◽  
Jun Liang Liu
Keyword(s):  
Genetic Algorithm ◽  
Fuel Consumption ◽  
Satellite Formation

In view of the multi-satellite formation establishment, the fuel consumption was optimized based on the Niche Genetic Algorithm. Some conclusions were drawn.

Optimization of Turbofan Propulsion Cycle Using a Genetic Algorithm

2010 ◽  
Author(s):  
Adel Ghenaiet
Keyword(s):  
Genetic Algorithm ◽  
Fuel Consumption ◽  
Pressure Ratio ◽  
Engine Performance ◽  
Optimization Procedure ◽  
Specific Fuel Consumption ◽  
High Mass ◽  
Design Parameters ◽  
Inlet Temperature ◽  
Simplifying Assumptions

This paper presents an evolutionary approach as the optimization framework to design for the optimal performance of a high-bypass unmixed turbofan to match with the power requirements of a commercial aircraft. The parametric analysis had the objective to highlight the effects of the principal design parameters on the propulsive performance in terms of specific fuel consumption and specific thrust. The design optimization procedure based on the genetic algorithm PIKAIA coupled to the developed engine performance analyzer (on-design and off-design) aimed at finding the propulsion cycle parameters minimizing the specific fuel consumption, while meeting the required thrusts in cruise and takeoff and the restrictions of temperatures limits, engine size and weight as well as pollutants emissions. This methodology does not use engine components’ maps and operates on simplifying assumptions which are satisfying the conceptual or early design stages. The predefined requirements and design constraints have resulted in an engine with high mass flow rate, bypass ratio and overall pressure ratio and a moderate turbine inlet temperature. In general, the optimized engine is fairly comparable with available engines of equivalent power range.

scholarly journals Dynamic Prediction and Optimization of Energy Efficiency Operational Index (EEOI) for an Operating Ship in Varying Environments

2019 ◽  
Vol 7 (11) ◽  
pp. 402 ◽  
Author(s):  
Chao Sun ◽  
Haiyan Wang ◽  
Chao Liu ◽  
Ye Zhao
Keyword(s):  
Genetic Algorithm ◽  
Energy Efficiency ◽  
Fuel Consumption ◽  
Dynamic Optimization ◽  
Consumption Rate ◽  
Connection Weight ◽  
Fuel Consumption Rate ◽  
Operational Index ◽  
Main Engine ◽  
Ship Speed

The demands for lower Energy Efficiency Operational Index (EEOI) reflect the requirements of international conventions for green shipping. Within this context it is believed that practical solutions for the dynamic optimization of a ship’s main engine and the reduction of EEOI in real conditions are useful in terms of improving sustainable shipping operations. In this paper, we introduce a model for dynamic optimization of the main engine that can improve fuel efficiency and decrease EEOI. The model considers as input environmental factors that influence overall ship dynamics (e.g., wind speed, wind direction, wave height, water flow speed) and engine revolutions. Fuel consumption rate and ship speed are taken as outputs. Consequently, a genetic algorithm is applied to optimize the initial connection weight and threshold of nodes of a neural network (NN) that is used to predict fuel consumption rate and ship speed. Navigation data from the training ship “YUMING” are applied to train the network. The genetic algorithm is used to optimize engine revolution and obtain the lowest EEOI. Results show that the optimization method proposed may assist with the prediction of lower EEOI in different environmental conditions and operational speed.

scholarly journals Optimal Eco-Driving Cycles for Conventional Vehicles Using a Genetic Algorithm

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4362
Author(s):  
Subramaniam Saravana Sankar ◽  
Yiqun Xia ◽  
Julaluk Carmai ◽  
Saiprasit Koetniyom
Keyword(s):  
Genetic Algorithm ◽  
Cost Function ◽  
Fuel Consumption ◽  
Optimization Algorithm ◽  
Internal Combustion Engines ◽  
Optimization Method ◽  
Computational Time ◽  
Weight Factor ◽  
Driving Cycles ◽  
The Cost

The goal of this work is to compute the eco-driving cycles for vehicles equipped with internal combustion engines by using a genetic algorithm (GA) with a focus on reducing energy consumption. The proposed GA-based optimization method uses an optimal control problem (OCP), which is framed considering both fuel consumption and driver comfort in the cost function formulation with the support of a tunable weight factor to enhance the overall performance of the algorithm. The results and functioning of the optimization algorithm are analyzed with several widely used standard driving cycles and a simulated real-world driving cycle. For the selected optimal weight factor, the simulation results show that an average reduction of eight percent in fuel consumption is achieved. The results of parallelization in computing the cost function indicates that the computational time required by the optimization algorithm is reduced based on the hardware used.

Research on Fuel Optimization of the Formation Establishment Based on Double-Impulse

2014 ◽  
Vol 556-562 ◽  
pp. 4453-4456
Author(s):  
Jian Wei Shi ◽  
Yuan Wen Cai ◽  
Wei Qi Xie
Keyword(s):  
Genetic Algorithm ◽  
Fuel Consumption ◽  
Optimization Problem ◽  
Fuel Optimization

In view of fuel consumption problem of the formation establishment, starting from the fly around equation, using double-impulse, the optimization is simulated based on the Niche Genetic Algorithm (NGA). The simulation result shows that the NGA is a good way to solve the fuel consumption optimization problem of formation establishment, and the fuel consumption when the phase determined is greater than the fuel consumption when the phase not determined.

Using Design of Experiment and Genetic Algorithm to Obtain the Optimum Gear Shifting Strategy for a Real Driving Cycles

2012 ◽  
Vol 224 ◽  
pp. 497-503 ◽  
Author(s):  
Aboud Ahmed ◽  
Chang Lu Zhao ◽  
Kai Han ◽  
Fu Jun Zhang ◽  
Feng Wu
Keyword(s):  
Genetic Algorithm ◽  
Fuel Consumption ◽  
Design Of Experiment ◽  
Optimization Method ◽  
Dump Truck ◽  
Post Processing ◽  
Algorithm Optimization ◽  
Driving Cycles ◽  
Shifting Strategy ◽  
Mining Dump Truck

Design of Experiment statistical method and Genetic Algorithms based optimization method are used to obtain the optimum gear shifting strategy that driver can follow to provide best fuel consumption without affecting drivability characteristics of the vehicle according to certain driving cycle. The study is carried on a Mining Dump Truck YT3621 with 9 forward shifts manual transmission. Three loading conditions, no load, 20 ton and 40 ton have been discussed. The truck powertrain is modeled using GT-Drive, and DOE –Post processing tool of the GT-Suit is used for DOE analysis and Genetic Algorithm optimization. Six different real on road driving cycles are used to study the effect of gear shifting strategy on fuel consumption.

scholarly journals A High Fuel Consumption Efficiency Management Scheme for PHEVs Using an Adaptive Genetic Algorithm

Sensors ◽  
2015 ◽  
Vol 15 (1) ◽  
pp. 1245-1251 ◽  
Author(s):  
Wah Lee ◽  
Kim Tsang ◽  
Hao Chi ◽  
Faan Hung ◽  
Chung Wu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Fuel Consumption ◽  
Adaptive Genetic Algorithm ◽  
Management Scheme ◽  
Consumption Efficiency
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