Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 3) Fuel Economy in Diesel Engines: <s> Computational optimization of a high-speed diesel engine, combined with diesel engine size-scaling, is presented. A multi-objective genetic algorithm was employed to simultaneously optimize fuel consumption and engine-out emissions of the down-scaled version of a previously optimized baseline engine. By separating the design parameters into hardware parameters (e.g., the piston bowl geometry) and controllable parameters (e.g., injection pressure and timings), multiple operating conditions were optimized simultaneously. A new variable was introduced to evaluate the convergence of the optimization, defined as the ratio of the number of Pareto designs and the number of valid designs in each generation. Particular interest was placed on the effect of injection pressure on the optimization of the engine and whether the previously optimized baseline engine design holds for different engine sizes. For 32 generations, totaling 1024 designs, no better design than the initial optimum, which was generated for the ba... <s> BIB001 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 3) Fuel Economy in Diesel Engines: <s> The fuel efficiency and emission reductions are the two consistent drivers for diesel engine development. Although many control devices mounted in diesel engine and in addition to the development of individual emission control methods, optimization techniques are required to utilize these methods in finding optimal engine operating conditions efficiently. This research was focused on the minimization of nitrogen oxides (NOx) emission and soot emission, as well as improving brake specific fuel consumption (BSFC) and power in a single cylinder diesel engine, which can be modeled as a multi-objective optimization problem. Many approaches have been applied to multi-objective optimization problems. In recent years, particle swarm optimization (PSO) method has been successfully applied to different areas since its advent. It has been demonstrated that PSO is an effective method for optimization problem. <s> BIB002 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 3) Fuel Economy in Diesel Engines: <s> In the present study, optimization of engine operating conditions was performed to reduce fuel consumption for stoichiometric diesel combustion. Microgenetic algorithms were used, and variations of injection strategy, spray included angle, and initial conditions (such as temperature and pressure) were evaluated. The KIVA code, coupled with the Chemkin chemistry solver, was used for three-dimensional CFD modeling, and the ignition and combustion processes were modeled using a reduced n-heptane mechanism. The KIVA code in use was verified against pressure histories and emissions for stoichiometric diesel combustion obtained experimentally. ::: ::: Optimized engine operating conditions for minimum fuel consumption were found at the 43rd generation. The optimized case includes 50–50% split injection, reduced spray included for reduction of unburned HC in crevices, and lower oxygen concentration to retard combustion phasing. This optimization yielded an 11.8% improvement in fuel consumption, along with simultaneous reductions of soot, NOx, CO, and HC. <s> BIB003
In order to optimize the fuel consumption of a diesel engine, under constraint of NO x emission, a genetic algorithm was used in  . In this paper, first using Kriging model, an emulator of the diesel engine was developed and then the genetic algorithm was applied to optimize the parameters. A MOEA was employed in BIB001 , where the fuel consumption and engine emissions of a diesel engine were simultaneously optimized. In BIB002 , a multiobjective optimization algorithm was adopted to improve the BSFC, and simultaneously minimize NO x and soot emissions of a diesel engine. In order to reduce the fuel consumption for stoichiometric diesel combustion in a diesel engine, a micro-genetic algorithm was employed in BIB003 , where the optimization parameters were the injection strategy, spray included angle and initial conditions like temperature and pressure. This optimization yielded 11.8% improvement in fuel consumption with simultaneous deduction of soot, NO x , CO, and HC.