Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 2) Emission and Fuel Consumption in Diesel Engines <s> In this paper, the simulation of the multi-objective optimization problem of a diesel engine is performed using the phenomenological model of a diesel engine and the genetic algorithm. The target purpose functions are Specific fuel consumption, NOx, and Soot. The design variable is a shape of injection rate. In this research, we emphasize the following three topics by applying the optimization techniques to an emission problem of a diesel engine. Firstly, the multiple injections control the objectives. Secondly, the multi-objective optimization is very useful in an emission problem. Finally, the phenomenological model has a great advantage for optimization. The developed system is illustrated with the simulation examples. <s> BIB001 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 2) Emission and Fuel Consumption in Diesel Engines <s> Genetic algorithm (GA) and neural network analysis are used to predict the effects of design and operational parameters on engine efficiency and NOx emissions of a natural gas engine. A computer program to calculate the amount of NOx emissions based on a reaction kinetic model is developed. The validity of this program is verified by measurements from a turbocharged, lean-burn, natural gas engine. Using the results from this program, the effects of operational and design parameters of the engine were investigated. Then a wide range of engine parameters are optimised using a simple GA regarding both efficiency and NOx emissions. Because of the large computation requirements especially for NOx level determination, an artificial neural network model based on results of these investigations is used to predict the engine efficiency and NOx emissions. The results show an increase in efficiency as well as the amount of NOx emissions being kept under the constraint value of 250 mg/Nm3 for stationary engines. <s> BIB002 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 2) Emission and Fuel Consumption in Diesel Engines <s> The diesel engine has excellent fuel economy and is widely used especially in commercial vehicles. However, there are ever increasing concerns with regard to environmental problems, drawing attention to air pollution caused by the NOx and Soot exhaust from diesel engines. In this paper, diesel engines that have the small amounts of NOx and SOOT with the high fuel economy are tried to design. To design such diesel engines, one of multi-objective genetic algorithms, SPEA2+[3], is applied. SPEA2+ is an enhancement of SPEA2 and has not only the high searching ability but also the mechanism of maintain the diversity in the design variable space. Through the numerical examples, the effectiveness of SPEA2+ was examined by applying SPEA2+, SPEA2[1], and NSGA-II[2] to the fuel emission scheduling problem. Our results indicated that multi-objective optimization is effective for designing diesel engines. <s> BIB003 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 2) Emission and Fuel Consumption in Diesel Engines <s> An evolutionary algorithm has been developed for the design of diesel engine in order to fulfill present-day and future regulations about pollutant emissions and greenhouse gases. The competitive goals to be achieved in engine optimization are the reduction of emission levels (soot, NOx and HC) and the improvement of specific fuel consumption. They have been taken into account by using a multi-objective approach implemented in a optimization tool called HiPerGEO. The method was applied to the design of a combustion chamber profile and numerical simulations were performed with a modified version of the KIVA3V code to evaluate the fitness values of the solutions. The method allows the optimization with respect to different engine operating conditions, i.e. load and speed values. The required computational time has been reduced by using grid technologies <s> BIB004 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 2) Emission and Fuel Consumption in Diesel Engines <s> Diesel engines are fuel efficient which benefits the reduction of CO2 released to the atmosphere compared with gasoline engines, but still result in negative environmental impact related to their emissions. As new degrees of freedom are created, due to advances in technology, the complicated processes of emission formation are difficult to assess. This paper studies the feasibility of using artificial neural networks (ANNs) in combination with genetic algorithms (GAs) to optimize the diesel engine settings. The objective of the optimization was to find settings that complied with the increasingly stringent emission regulations while also maintaining, or even reducing the fuel consumption. A large database of stationary engine tests, covering a wide range of experimental conditions was used for this analysis. The ANNs were used as a simulation tool, receiving as inputs the engine operating parameters, and producing as outputs the resulting emission levels and fuel consumption. The ANN outputs were then used to evaluate the objective function of the optimization process, which was performed with a GA approach. The combination of ANN and GA for the optimization of two different engine operating conditions was analyzed and important reductions in emissions and fuel consumption were reached, while also keeping the computational times low <s> BIB005 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 2) Emission and Fuel Consumption in Diesel Engines <s> A hybrid evolutionary algorithm, consisting of a genetic algorithm (GA) and particle swarm optimization (PSO), is proposed. Generally, GAs maintain diverse solutions of good quality in multi-objective problems, while PSO shows fast convergence to the optimum solution. By coupling these algorithms, GA will compensate for the low diversity of PSO, while PSO will compensate for the high computational costs of GA. The hybrid algorithm was validated using standard test functions. The results showed that the hybrid algorithm has better performance than either a pure GA or pure PSO. The method was applied to an engineering design problem—the geometry of diesel engine combustion chamber reducing exhaust emissions such as NOx, soot and CO was optimized. The results demonstrated the usefulness of the present method to this engineering design problem. To identify the relation between exhaust emissions and combustion chamber geometry, data mining was performed with a self-organising map (SOM). The results indicate th... <s> BIB006 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 2) Emission and Fuel Consumption in Diesel Engines <s> Genetic Algorithms (GAs) generally maintain diverse solutions of good quality in multi-objective problems, while Particle Swarm Optimization (PSO) shows rapid convergence to the optimum solution. Previous studies indicated that search abilities can be improved by simply coupling these two algorithms; GA compensates for the low diversity of PSO, while PSO compensates for the high computational costs of GA. In this study, the configurations of the two methods when used in a fully coupled hybrid algorithm were investigated to achieve an improvement in diversity and convergence simultaneously for application to real-world engineering design problems. The new hybrid algorithm was validated using standard test function problems, and it was demonstrated that the new hybrid algorithm showed better performance than the simply coupled hybrid algorithm, as well as both pure GA and pure PSO. Especially, the new hybrid algorithm shows robust search ability regardless of initial population selection. This feature is very important in real-world engineering design problems, which do not allow multiple optimization runs to be implemented due to heavy computational costs. The new method was applied to optimization of a diesel engine combustion chamber to reduce exhaust emissions, such as NOx and soot. The results demonstrated the applicability of the present method to real-world design problems. In addition, important geometry design variables controlling the emission performance were investigated to obtain useful knowledge about low emission diesel engine design. <s> BIB007 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 2) Emission and Fuel Consumption in Diesel Engines <s> This study implemented a particle swarm optimization (PSO) algorithm to accelerate the engine development process in order to achieve low emissions. The PSO algorithm is a stochastic, population-based evolutionary optimization algorithm. In this study, PSO was integrated with diesel engine experiments to reduce emissions while maintaining high fuel efficiency. A merit function was defined to help reduce multiple emissions simultaneously. Engine operations using both single- and double-injection strategies were optimized. The present PSO algorithm was found to be very effective in finding the favorable operating conditions for low emissions. The optimization usually took 40–70 experimental runs to find the most favorable operating conditions under the constraints specified in the present testing. High EGR levels, small pilot amount, and late main injection were suggested by the PSO. Multiple emissions were reduced simultaneously without a compromise in the brake specific fuel consumption. In a favorable ca... <s> BIB008 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 2) Emission and Fuel Consumption in Diesel Engines <s> In this study, single and also multi-objective (MO) genetic algorithms (GAs) were used for optimisation of performance and emissions of a diesel engine. Population space and initial population of both GAs were obtained by Artificial Neural Network (ANN). Specific fuel consumption (Sfc), NOx, power (P), torque (Tq) and air-flow rate (Afr) were reduced to %7.7, %8.51, %30, %4 and %7.4 respectively whereas HC increased at the rate of %10.5 by traditional single objective GA. HC, CO2, P and Sfc were reduced to %17.6, %30.05, %31.8 and %14.5 respectively whereas NOx increased at the rate of %13 by using multi-objective GA with Nondominated Sorting Genetic Algorithm II (NSGA II). %14.5 fuel reduction against %31 power reduction have never been obtained in the previous studies. This shows the effective usage of MOGA with NSGA II in optimisation of fuel diesel engine performance parameters. <s> BIB009 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 2) Emission and Fuel Consumption in Diesel Engines <s> In this paper, a mathematical model of the SCR catalytic converter is replaced with the neural network model to accelerate the optimization process. The Euro steady state calibration test data set is used to simulate the inlet properties of the SCR catalytic converter. For each chosen condition, a separate neural network is developed. In order to generate sufficient data to form a neural network for each condition, the original mathematical model was run several times at the temperature and inlet NOx concentration of each condition with a range of different ammonia concentrations. Subsequently, using MATLAB ® software, the neural network model is trained and tested for each condition. Ammonia dosing optimization is performed using multi objective genetic algorithm module of MATLAB <s> BIB010
: Using the phenomenological model of a diesel engine and a multiobjective optimization problem, the specific fuel consumption, NO x , and soot of the engine was optimized in BIB001 . The aim was to design the shape of injection rate. They performed simulations and showed the capability of the genetic algorithm in finding good results. They then extended their optimization algorithm in BIB003 , where a MOEA called SPEA2+ was used for optimizing the emission and fuel economy of the engine. A genetic algorithm was employed in  to reduce the emission and fuel consumption of a diesel engine. The engine control parameters were boost pressure, exhaust gas recirculation, start of injection, and injection rate shape. In order to optimize the control map of hydrocarbon addition to diesel exhaust gas for HC type selective catalytic reduction (SCR) DENO x , a numerical model and a new optimization technique were adopted in  . The numerical model presented in this paper predicted the performance of HCDeNO x with diesel fuel as a supplemental reductant and an evolutionary programming algorithm optimizes the control map of HC. Through experiments, they showed that the NO x conversion with the optimized control map was found to be 21% greater than that of the conventional control. In order to reduce the emission levels of a diesel engine, a MOEA is employed in BIB004 , to optimize the combustion chamber profile of the engine. The authors performed the numerical simulations with a modified version of the KIVA3V code to evaluate the fitness values of the solutions. In BIB006 and BIB007 , in order to reduce the exhaust emission of a diesel engine, the geometry of a diesel engine combustion chamber was optimized by a combination of genetic algorithms and PSO algorithms. A large database of stationary engine tests covering a wide range of experimental conditions of a diesel engine were used in BIB005 to model the engine. An artificial neural network was used, where the engine operating parameters were fed to the network as inputs and the outputs were the resulting emission levels and fuel consumption. The neural network was then used for fitness evaluation purpose and a genetic algorithm was used to optimize the emissions and fuel consumption of the engine. In  , optimizing the performance of a diesel engine was investigated with the objective of optimizing NO x emission, soot, CO, HC, ISFC, and peak PRR. A genetic algorithm was used by adjusting six parameters: the boost pressure, EGR rate, fraction of premixed fuel and start of late injection timing. In order to reduce emissions while maintaining the performance in both single and double injection strategies, PSO was used in BIB008 . They showed that the NO x and PM emissions were reduced when their proposed method was used. In BIB009 , single and MOEAs were used for optimization of performance and emissions of a diesel engine. In diesel engines, there is a trade-off between the fuel economy and NO x values. Developing the diesel engines which can adapt themselves with the environment could be a step forward. Therefore, there is a need to Pareto solutions that can express the trade-off between the fuel economy and NO x emissions. To cover a variety of driving conditions, the dominated solutions should had a wide diversity not only in the objective space, but also in the design variable space. To perform this, a MOEA called SPEA2+ was used in BIB003 to design a diesel engine. The results showed that the solutions have a diversity not only in the objective space but also in the design variable space. They showed that an engine could be designed to adapt their parameters to the changing in driving environment. The NO x emission reduction of a diesel engine was addressed in BIB010 , where the Euro steady state calibration test data set was used to make a neural model of the SCR catalytic converter for each chosen condition. In order to generate sufficient data for each condition, a mathematical model of the SCR converter was run. Then these neural models, in conjunction of a MOEAs were used with the objective of reducing NO x and limiting the outlet ammonia concentration of the SCR catalytic converter. Other examples of the application of meta-heuristic algorithms in reducing diesel engine emissions includes the use of PSO in  . 3) Emission in Natural Gas Engines: In order to reduce the NO x emission of a natural gas engine, genetic algorithms, and neural networks were employed in BIB002 . In this paper, to calculate the amount of NO x emissions of a natural gas engine, a neural network model was developed, the validity of which was then verified by measurements from a turbocharged, lean-burn, and natural gas engine. The results of the model were then used to study the effects of the operational and design parameters of the engine. Using a genetic algorithm, the parameters of the engine were optimized to reduce the NO x emissions. Their experimental results suggested that their proposed method could reduce the NO x emissions down to 250 mg/Nm 3 for stationary engines.