Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> D. Valve Timing <s> The best combination of valve timing events along with three dominant engine operating parameters was observed for optimum performance of an internal combustion spark ignition (ICSI) engine with particle swarm optimisation (PSO) method. A thermodynamics engine simulation programme is considered to evaluate the fitness of each individual. Engine power output and the thermal efficiency are considered for the optimisation independently. Seven engine operating parameters, including all the major valve timing events, are used as inputs with engine knock limit and the maximum valve lift=10 mm as constraints. Five different engine load conditions are considered. All the simulations were carried out at the engine speed of 1500 rev/min. Further, the performance of particle swarm optimisation was compared with the performance of genetic algorithms (GA). <s> BIB001 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> D. Valve Timing <s> The main reason for the efficiency decrease at part load conditions for four-stroke spark-ignition (SI) engines is the flow restriction at the cross-sectional area of the intake system. Traditionally, valve-timing has been designed to optimize operation at high engine-speed and wide open throttle conditions. Several investigations have demonstrated that improvements at part load conditions in engine performance can be accomplished if the valve-timing is variable. Controlling valve-timing can be used to improve the torque and power curve as well as to reduce fuel consumption and emissions. ::: ::: In this paper, a group method of data handling (GMDH) type neural network and evolutionary algorithms (EAs) are firstly used for modelling the effects of intake valve-timing (Vt) and engine speed (N) of a spark-ignition engine on both developed engine torque (T) and fuel consumption (Fc) using some experimentally obtained training and test data. Using such obtained polynomial neural network models, a multi-objective EA (non-dominated sorting genetic algorithm, NSGA-II) with a new diversity preserving mechanism are secondly used for Pareto based optimization of the variable valve-timing engine considering two conflicting objectives such as torque (T) and fuel consumption (Fc). The comparison results demonstrate the superiority of the GMDH type models over feedforward neural network models in terms of the statistical measures in the training data, testing data and the number of hidden neurons. Further, it is shown that some interesting and important relationships, as useful optimal design principles, involved in the performance of the variable valve-timing four-stroke spark-ignition engine can be discovered by the Pareto based multi-objective optimization of the polynomial models. Such important optimal principles would not have been obtained without the use of both the GMDH type neural network modelling and the multi-objective Pareto optimization approach. <s> BIB002 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> D. Valve Timing <s> This paper aims to demonstrate the effectiveness of Multi-Objective Genetic Algorithm Optimization and its robust practical application on the automobile engine valve timing where the variation of performance parameters required for finest tuning to obtain the optimal engine performances. The primary concern is to acquire the clear picture of the implementation of Multi-Objective Genetic Algorithm and the essential of variable valve timing effects on the engine performances in various engine speeds. Majority of the research works in this project were in CAE software environment and method to implement optimization to 1D engine simulation. The paper conducts design optimization of CAMPRO 1.6L (S4PH) engine valve timing at various engine speeds using multi-objective genetic algorithm (MOGA) for the future variable valve timing (VVT) system research and development. This paper involves engine modeling in 1D software simulation environment, GT-Power. GT-Power is one of the CAE tools available in GT-SUITE offers the only true "virtual engine/power train" tool, capable of integrated simulations of the total engine and power train system. The GT-Power model is run simultaneously with mode Frontier to perform multi-objective optimization. Multi-objective Genetic Algorithms (MOGA) are an extension of Genetic Algorithms (GA) that does not require multiple objectives to be aggregated to one value. Instead of static aggregate such as a weighted sum, MOGA dynamically determine an aggregate of multiple objective values of a solution based on its relative quality in the current population, typically as the degree to which the solution dominates others in the current population. ::: ::: By using the MOGA optimization approach in modeFrontier, multi-objective optimization could be done by coupling with several software environment; this does not require any modification of the original model. Thus, it is able to provide professional, reliable and accurate solution. In preprocessing of optimization, modeFrontier Response Surface Method (RSM) is able to model the behavior of engine performances corresponding to the change of design variables. <s> BIB003 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> D. Valve Timing <s> This paper aims to demonstrate the effectiveness of Multi-Objective Genetic Algorithm Optimization and its practical application on the automobile engine valve timing where the variation of performance parameters required for finest tuning to obtain the optimal engine performances. The primary concern is to acquire the clear picture of the implementation of Multi-Objective Genetic Algorithm and the essential of variable valve timing effects on the engine performances in various engine speeds. Majority of the research works in this project were in CAE software environment and method to implement optimization to 1D engine simulation. The paper conducts robust design optimization of CAMPRO 1.6L (S4PH) engine valve timing at various engine speeds using multiobjective genetic algorithm (MOGA) for the future variable valve timing (VVT) system research and development. This paper involves engine modelling in 1D software simulation environment, GT-Power. The GT-Power model is run simultaneously with mode Frontier to perform multiobjective optimization. <s> BIB004
Valve timing is another optimization task that affects the performance of engines. In order to optimize valve timing of an engine, a PSO algorithm was employed in BIB001 , where a thermodynamics simulation of the engine was used to evaluate the fitness of the particles. The algorithm optimized the major valve timing events, with the constraint of knock limit and maximum valve lift of 10 mm, where the objectives are the power output and the thermal efficiency. Traditionally, valve timing has been optimized to improve the torque and power curve, and reduce the fuel consumption and emissions. Variable valve timing is a new way of optimizing the performance. In BIB002 , a neural network system was developed to model the effects of intake valve-timing, engine speed, torque, and fuel consumption of a gasoline engine. Then a MOEA was employed to optimize the variable valve timing. In BIB003 and BIB004 , the optimization of a CAMPRO 1.6L engine valve timing at various engine speeds was performed, where a MOEA was employed with the objective of reducing emission and fuel consumption.