<p> ECE/CS/ME 539: Introduction to Neural Network and Fuzzy</p><p>Systems</p><p>Final Project Proposal, Fall 2005 Title: Engine Operating Parameter Optimization using Genetic Algorithm</p><p>With more-and-more stringent emission standards, future diesel engine technologies will need to incorporate advanced combustion strategies with optimized engine operating parameters for achieving low emissions while maintaining fuel economy and power density. Genetic algorithms (GA) are being used by engine researchers to optimize engine design and operating parameters to achieve these goals. Genetic algorithms are global search techniques based on the mechanics of natural selection which combine a “survival of the fittest” approach with some randomization and/or mutation. Unlike local search techniques, which are typically highly dependent on the starting point and have constraints such as solution continuity and differentiability, as a global search method, GA place few constraints on the solution domain and are thus much more robust for ill-behaved solution spaces. In addition, GA tends to converge to a global optimum for multi-modal functions with many local extremum. These features make GA suitable for the optimization of engine design and operating parameter.</p><p>In this study, a micro-Genetic Algorithm (μGA) will be coupled with a 3D engine Computational Fluid Dynamic (CFD) code KIVA to optimize six engine operating parameters. A merit function is defined based on three engine performance parameters. Several penalty functions are also defined based on the physical constraints of the engine. The results will be used as inputs for the development of a multi-layer perceptron (MLP) configuration. The MLP network will be used to predict the engine performance based on the engine operating parameters.</p><p>Reference</p><p>1. Carroll, D. L., “Chemical Laser Modeling with Genetic Algorithms,” AIAA Journal, 34, 338, 1996. 2. Krishnakumar, K., “Micro-Genetic Algorithms for Stationary and Non-Stationary Function Optimization,” SPIE 1196, Intelligent Control and Adaptive Systems, 1989. 3. Senecal, P.K., and Reitz, R.D., “Simultaneous Reduction of Engine Emissions and Fuel Consumption Using Genetic Algorithms and Multi-Dimensional Spray and Combustion Modeling,” SAE 2000-01-1890, 2000. 4. Senecal, P.K., “Numerical Optimization Using the Gen4 Micro-Genetic Algorithm Code”, ERC Document, 2000</p>