MULTI-OBJECTIVE DIFFERENTIAL EVOLUTION AND NSGA-II OPTIMIZATION ALGORITHMS COMPARISON WHEN APPLIED TO A SINGLE-CYLINDER INTERNAL COMBUSTION ENGINE

Author(s):  
Stephan Hennings Och ◽  
Luís Mauro Moura ◽  
Germano Menzel
Author(s):  
Michael R. Buchman ◽  
W. Brett Johnson ◽  
Amos G. Winter

Turbocharging can provide a cost effective means for increasing the power output and fuel economy of an internal combustion engine. A turbocharger added to an internal combustion engine consists of a coupled turbine and compressor. Currently, turbocharging is common in multi-cylinder engines, but it is not commonly used on single-cylinder engines due to the phase mismatch between the exhaust stroke (when the turbocharger is powered) and the intake stroke (when the engine intakes the compressed air). The proposed method adds an air capacitor, an additional volume in series with the intake manifold, between the turbocharger compressor and the engine intake, to buffer the output from the turbocharger compressor and deliver pressurized air during the intake stroke. This research builds on previous work where it was shown experimentally that a power gain of 29% was achievable and that analytically a power gain of 40–60% was possible using a turbocharger and air capacitor system. The goal of this study is to further analyze the commercial viability of this technology by analyzing the effect of air capacitor turbocharging on emissions, fuel economy, and power density. An experiment was built and conducted that looked at how air capacitor sizing affected emissions, fuel economy, and the equivalence ratio. The experimental data was then used to calibrate a computational model built in Ricardo Wave. Finally this model was used to evaluate strategies to further improve the performance of a single cylinder diesel turbocharged engine with an air capacitor.


2014 ◽  
Vol 945-949 ◽  
pp. 473-477
Author(s):  
You Jian Wang ◽  
Guang Zhang

The design of engine valve spring generally belongs to multi-objective optimum design. The traditional trying means and the graphical methods are difficult to solve the multi-objective optimization problem, and the traditional multi-objective algorithms have certain defects. The elitist non-dominated sorting genetic algorithm (NSGA-II) is an excellent multi-objective algorithm, which is widely used to solve problems of multi-objective optimization. This method can improve the design quality and efficiency, and it has much more engineering practical value.


Sign in / Sign up

Export Citation Format

Share Document