Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 1) Chamber Optimization: <s> Publisher Summary This chapter explains how computer modeling techniques can be used to solve the real life problem of diesel engine performance improvement. The purpose of is to achieve the lowest emissions level and improved fuel efficiency with respect to European norm for emissions. Emissions level and fuel efficiency can be evaluated using a modified version of KIVA3 code that outputs three values, each of these related to one of the three specific fitness functions to be maximized. The optimization process involves the execution of KIVA3 simulations to calculate fitness values of the chamber geometries taken into consideration during all of the optimization steps. The chapter discusses grid computing technologies to increase the performance of the KIVA-micro-GA, showing how a distributed environment allows reducing the computational time needed by the optimization process, taking advantage of intrinsic parallelism of micro-GA. In fact, their structure allows executing simultaneously KIVA3 simulations over the random meshes and over the geometries that populate the micro-population. The services offered by the system are the micro-GA parameters definition, the submission of the optimization process and the monitoring of the process status. A trusted user can access the implemented services using a grid portal, called DESGrid (Grid for diesel engine simulation). The analysis of the results, achieved by execution of KIVA-micro-GA on three ES40 Compaq nodes, each one equipped by four processors, shows a good reduction in both emissions and fuel consumption. The chapter describes numerical values and related geometries representation obtained after the first steps of the global optimization process execution. <s> BIB001 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 1) Chamber Optimization: <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> BIB002 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 1) Chamber Optimization: <s> Reactivity Controlled Compression Ignition (RCCI) is an engine combustion strategy that that produces low NO{sub x} and PM emissions with high thermal efficiency. Previous RCCI research has been investigated in single-cylinder heavy-duty engines. The current study investigates RCCI operation in a light-duty multi-cylinder engine at 3 operating points. These operating points were chosen to cover a range of conditions seen in the US EPA light-duty FTP test. The operating points were chosen by the Ad Hoc working group to simulate operation in the FTP test. The fueling strategy for the engine experiments consisted of in-cylinder fuel blending using port fuel-injection (PFI) of gasoline and early-cycle, direct-injection (DI) of diesel fuel. At these 3 points, the stock engine configuration is compared to operation with both the original equipment manufacturer (OEM) and custom machined pistons designed for RCCI operation. The pistons were designed with assistance from the KIVA 3V computational fluid dynamics (CFD) code. By using a genetic algorithm optimization, in conjunction with KIVA, the piston bowl profile was optimized for dedicated RCCI operation to reduce unburned fuel emissions and piston bowl surface area. By reducing these parameters, the thermal efficiency of the engine was improved while maintaining low NOx and PMmore » emissions. Results show that with the new piston bowl profile and an optimized injection schedule, RCCI brake thermal efficiency was increased from 37%, with the stock EURO IV configuration, to 40% at the 2,600 rev/min, 6.9 bar BMEP condition, and NOx and PM emissions targets were met without the need for exhaust after-treatment.« less <s> BIB003 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> 1) Chamber Optimization: <s> In the present study, a diesel engine based Combined Heat and Power (CHP) system is optimised using exergoeconomic concept and genetic algorithm. For this purpose, the CHP system is first thermodynamically analysed through energy and exergy. Then cost balances and auxiliary equations are applied to subsystems. Finally an objective function representing fuel cost, cost of exergy loss and destruction and purchase and maintenance cost of the system components is considered for the optimisation study. Furthermore the above procedure is applied for a case study that produces 277 kW of electricity and 282 kW of heat. Also exergetic and exergoeconomic parameters are calculated in optimum case and compared with the base case. The results show that by applying the optimisation approach for our case study, 8.02% reduction in objective function is achieved which is might be considerable in CHP systems optimisation. <s> BIB004
In order to optimize the chamber geometries, with the objective of reducing emission level and fuel efficiency, a micro genetic algorithm is developed in BIB001 . The authors designed a distributed version of the algorithm and speeded it up using grid computing technologies to show how a parallel environment could be used to reduce the computational time. The optimization of combustion chamber of a diesel engine was studied in BIB002 and a hybrid EA was proposed consisting of a genetic algorithm and a PSO. 2) Heat and Power system: The combined heat and power system of a diesel engine was optimized using a genetic algorithm BIB004 . The system was first thermodynamically analyzed through energy and exergy and then an objective function was considered representing the fuel cost, cost of energy loss and distribution, purchase, and maintenance cost of the system. Experimental results provided in this paper showed 8.02% improvement in the objective function when using the genetic algorithm. 3) Piston Bowl Optimization: Reactivity controlled compression ignition (RCCI) is a combustion strategy which offers low NO x and PM emissions and high thermal efficiency. In BIB003 , a genetic algorithm was employed in order to optimize the bowl surface area of a diesel engine. The authors showed that using this method, the RCCI brake efficiency was increased by 3% and NO x and PM emissions were met.