Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> H. Shape Optimization <s> Intake port shape affects the quality of an engine significantly. In this paper, we present a method for improving an existing geometry with evolutionary algorithm type optimization. Characteristic parameters of different port shapes are calculated with a self-developed CFD program. However only small deformations allowed on original design, significant improvement achieved. Proposed robust parallel evolutionary algorithm seems to be a suitable for other optimization problems on heterogeneous non-reliable cluster of workstations. <s> BIB001 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> H. Shape Optimization <s> In previous study [1] the Non-dominated Sorting Genetic Algorithm II (NSGA II) [2] performed better than other popular Multi-Objective Genetic Algorithms (MOGA) in engine optimization that sought optimal combinations of the piston bowl geometry, spray targeting, and swirl ratio. NSGA II is further studied in this paper using different niching strategies that are applied to the objective-space and design-space, which diversify the optimal objectives and design parameters accordingly. Convergence and diversity metrics are defined to assess the performance of NSGA II using different niching strategies. It was found that use of the design niching achieved more diversified results with respect to design parameters, as expected. Regression was then conducted on the design datasets that were obtained from the optimizations with two niching strategies. Four regression methods, including K-nearest neighbors (KN), Kriging (KR), Neural Networks (NN), and Radial Basis Functions (RBF), were compared. The results showed that the dataset obtained from optimization with objective niching provided a more fitted learning space for the regression methods. The KN, KR, outperformed the other two methods with respect to the prediction accuracy. Furthermore, a log transformation to the objective-space improved the prediction accuracy for the KN, KR, and NN methods but not the RBF method. The results indicate that it is appropriate to use a regression tool to partly replace the actual CFD evaluation tool in engine optimization designs using the genetic algorithm. This hybrid mode saves computational resources (processors) without losing optimal accuracy. A Design of Experiment (DoE) method (the Optimal Latin Hypercube method) was also used to generate a dataset for the regression processes. However, the predicted results were much less reliable than results that were learned using the dynamically increasing datasets from the NSGA II generations. Applying the dynamical learning strategy during the optimization processes allows computationally expensive CFD evaluations to be partly replaced by evaluations using the regression techniques. The present study demonstrates the feasibility of applying the hybrid mode to engine optimization problems, and the conclusions can also extend to other optimization studies (numerical or experimental) that feature time-consuming evaluations and have highly non-linear objective-spaces.Copyright © 2009 by ASME <s> BIB002 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> H. Shape Optimization <s> A passenger car high-speed direct-injection diesel engine operating at low-load conditions in the modulated kinetic combustion mode was optimized using a multidimensional computational fluid dynamics code and a multi-objective genetic algorithm. Spray targeting, piston bowl geometry, and swirl ratio were optimized. Since the combustion is mainly kinetics controlled, detailed chemistry was considered through a recently developed adaptive multi-grid chemistry (AMC) model. The numerical results from the AMC model, including the pressure and pollutant emissions, were first validated on the baseline engine for a parametric sweep by comparison with the results from the standard KIVA-CHEMKIN model. The AMC model was found to give consistent results with the KIVA-CHEMKIN model, with a computational cost that is less than half that of the KIVA-CHEMKIN model. Optimal designs from the optimization were also validated using the full KIVA-CHEMKIN model and were found to reduce the fuel consumption and/or pollutant emissions. Start-of-injection timing was found to be the primary parameter influencing the fuel consumption and soot emissions for the engine operating in the low-load condition. Later injection benefits the fuel consumption and soot reduction. However, further retardation of the injection timing leads to reduced combustion efficiency and even misfire, and results in higher unburned hydrocarbon emissions. Different piston bowl shapes have different responses to bulk flow motions and the resulting geometry-generated turbulence will affect soot formation and oxidation. <s> BIB003 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> H. Shape Optimization <s> Abstract This paper describes the optimization of combustion chamber geometry and engine operating conditions for stoichiometric diesel combustion, targeting lower gross indicated specific fuel consumption. The KIVA code, coupled with a micro genetic algorithm population of nine for each generation was used. The optimization variables were composed of ten variables related to the combustion chamber geometry and engine operating conditions. In addition, an auto mesh generator was developed for generating various kinds of combustion chambers, such as open-crater, re-entrant, deep, and shallow types. In addition, the computational models were validated against the experimental results for a stoichiometric process in terms of the combustion pressure history and emissions. Through the preset optimization, a 35% improvement in the gross indicated that specific fuel consumption was achieved. In addition, the optimization results showed that the optimum engine operating conditions employed a premixed charge compression ignition combustion regime with early injection and a narrow spray included angle. Furthermore, a higher boost pressure was used to prevent fuel film formation. <s> BIB004 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> H. Shape Optimization <s> Abstract This paper describes the optimization of compression ignition engines fueled with dimethyl ether (DME), an alternative to diesel fuel. In order to calculate in-cylinder flow and combustion processes, a KIVA code, coupled with the Chemkin chemistry solver, was used. The optimization processes were performed based on a micro-genetic algorithm with a population number of five for each generation. The optimization processes were performed for one hundred generations. In addition, the computational mesh generated an auto-mesh generator, which was able to produce a computational mesh based on the given parameters, such as cup depth and Beizer curve definitions. The optimized design for the DME engine was also compared to that of conventional diesel engines for combustion and emission characteristics. The results showed that significant merit value improvements were achieved for both DME and diesel engines with the presented optimization. For the optimized design of DME engines, the combustion and emission characteristics were significantly different from those of diesel engines because of differences in fuel properties. For conventional diesel engine cases, a 136% improvement in merit value was achieved during the optimization process from the baseline to the optimized design. <s> BIB005
A number of papers have used meta-heuristic algorithms to optimize the geometric shape of some parts of engines. 1) Piston Bowl Geometry Optimization: Different versions of genetic algorithms were compared in  , to optimize the piston bowl geometry, spray targeting, and swirl ratio. The experimental studies presented in this paper suggested that NSGA II  performed better than the other algorithms. This was then improved in BIB002 , where NSGA II was studied using different niching strategies applied to the objective and design spaces. This mechanism diversifies the optimal objectives and design parameters. The piston bowl geometry, spray targeting, and swirl ratio of a diesel engine were optimized in BIB003 , using a MOEA. An adaptive multigrid chemistry model is developed and the numerical results from the model were used in the optimization process. The objectives in the optimization process were reducing the fuel consumption and pollutant emissions. 2) Combustion Chamber Geometry Optimization: The combustion chamber geometry and engine operating conditions for a stoichiometric diesel combustion was optimized in BIB004 , where the aim of the optimization was to reduce the specific fuel consumption. The optimization algorithm used was a micro genetic algorithm, where the combustion chamber was represented by ten variables. Experimental results suggested a 35% improvement in the specific fuel consumption. The same method was also applied to optimize the chamber geometry of an engine fueled with dimethyl ether with the objective of improving the merit value BIB005 . A 136% improvement in merit value was reported to be achieved during the optimization process. 3) Intake Ports Optimization: The intake ports in a diesel engine affects the performance of an engine. A parallel evolutionary optimization algorithm was used in BIB001 to optimize the intake ports geometry of a diesel engine.