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Technical Paper

Diesel Engine Combustion Chamber Geometry Optimization Using Genetic Algorithms and Multi-Dimensional Spray and Combustion Modeling

2001-03-05
2001-01-0547
The recently developed KIVA-GA computer code was used in the current study to optimize the combustion chamber geometry of a heavy -duty diesel truck engine and a high-speed direct-injection (HSDI) small-bore diesel engine. KIVA-GA performs engine simulations within the framework of a genetic algorithm (GA) global optimization code. Design fitness was determined using a modified version of the KIVA-3V code, which calculates the spray, combustion, and emissions formation processes. The measure of design fitness includes NOx, unburned HC, and soot emissions, as well as fuel consumption. The simultaneous minimization of these factors was the ultimate goal. The KIVA-GA methodology was used to optimize the engine performance using nine input variables simultaneously. Three chamber geometry related variables were used along with six other variables, which were thought to have significant interaction with the chamber geometry.
Technical Paper

Heavy-Duty Diesel Combustion Optimization Using Multi-Objective Genetic Algorithm and Multi-Dimensional Modeling

2009-04-20
2009-01-0716
A multi-objective genetic algorithm methodology was applied to a heavy-duty diesel engine at three different operating conditions of interest. Separate optimizations were performed over various fuel injection nozzle parameters, piston bowl geometries and swirl ratios (SR). Different beginning of injection (BOI) timings were considered in all optimizations. The objective of the optimizations was to find the best possible fuel economy, NOx, and soot emissions tradeoffs. The input parameter ranges were determined using design of experiment methodology. A non-dominated sorting genetic algorithm II (NSGA II) was used for the optimization. For the optimization of piston bowl geometry, an automated grid generator was used for efficient mesh generation with variable geometry parameters. The KIVA3V release 2 code with improved ERC sub-models was used. The characteristic time combustion (CTC) model was employed to improve computational efficiency.
Technical Paper

Simultaneous Reduction of Engine Emissions and Fuel Consumption Using Genetic Algorithms and Multi-Dimensional Spray and Combustion Modeling

2000-06-19
2000-01-1890
A computational optimization study is performed for a heavy-duty direct-injection diesel engine using the recently developed KIVA-GA computer code. KIVA-GA performs full cycle engine simulations within the framework of a Genetic Algorithm (GA) global optimization code. Design fitness is determined using a one-dimensional gas -dynamics code for calculation of the gas exchange process, and a three-dimensional CFD code based on KIVA-3V for spray, combustion and emissions formation. The performance of the present Genetic Algorithm is demonstrated using a test problem with a multi-modal analytic function in which the optimum is known a priori. The KIVA-GA methodology is next used to simultaneously investigate the effects of six engine input parameters on emissions and performance for a high speed, medium load operating point for which baseline experimental validation data is available.
Technical Paper

Application of Micro-Genetic Algorithms for the Optimization of Injection Strategies in a Heavy-Duty Diesel Engine

2005-04-11
2005-01-0219
In this paper, optimized single and double injection schemes were found using multi-dimensional engine simulation software (KIVA-3V) and a micro-genetic algorithm for a heavy duty diesel engine. The engine operating condition considered was at 1737 rev/min and 57 % load. The engine simulation code was validated using an engine equipped with a hydraulic-electronically controlled unit injector (HEUI) system. Five important parameters were used for the optimization - boost pressure, EGR rate, start-of-injection timing, fraction of fuel in the first pulse and dwell angle between first and second pulses. The optimum results for the single injection scheme showed significant improvements for the soot and NOx emissions. The start of injection timing was found to be very early, which suggests HCCI-like combustion. Optimized soot and NOx emissions were reduced to 0.005 g/kW-hr and 1.33 g/kW-hr, respectively, for the single injection scheme.
Technical Paper

An Experimental Study on Emissions Optimization Using Micro-Genetic Algorithms in a HSDI Diesel Engine

2003-03-03
2003-01-0347
Current automotive diesel engine research is motivated by the need to meet more-and-more strict emission regulations. The major target for future HSDI combustion research and development is to find the most effective ways of reducing the soot particulate and NOx emissions to the levels required by future emission regulations. Recently, a variety of statistical optimization tools have been proposed to optimize engine-operating conditions for emissions reduction. In this study, a micro-genetic algorithm technique, which locates a global optimum via the law of “the survival of the fittest”, was applied to a high-speed, direct-injection, single-cylinder (HSDI) diesel engine. The engine operating condition considered single-injection operation using a common-rail fuel injection system was at 1757 rev/min and 45% load.
Technical Paper

Reduction of Emissions and Fuel Consumption in a 2-Stroke Direct Injection Engine with Multidimensional Modeling and an Evolutionary Search Technique

2003-03-03
2003-01-0544
An optimization study combining multidimensional CFD modeling and a global, evolutionary search technique known as the Genetic Algorithm has been carried out. The subject of this study was a 2-stroke, spark-ignited, direct-injection, single-cylinder research engine (SCRE). The goal of the study was to optimize the part load operating parameters of the engine in order to achieve the lowest possible emissions, improved fuel economy, and reduced wall heat transfer. Parameters subject to permutation in this study were the start-of-injection (SOI) timing, injection duration, spark timing, fuel injection angle, dwell between injections, and the percentage of fuel mass in the first injection pulse. The study was comprised of three cases. All simulations were for a part load, intermediate-speed condition representing a transition operating regime between stratified charge and homogeneous charge operation.
Journal Article

Assessment of Optimization Methodologies to Study the Effects of Bowl Geometry, Spray Targeting and Swirl Ratio for a Heavy-Duty Diesel Engine Operated at High-Load

2008-04-14
2008-01-0949
In the present paper optimization tools are used to recommend low-emission engine combustion chamber designs, spray targeting and swirl ratio levels for a heavy-duty diesel engine operated at high-load. The study identifies aspects of the combustion and pollution formation that are affected by mixing processes, and offers guidance for better matching of the piston geometry with the spray plume geometry for enhanced mixing. By coupling a GA (genetic algorithm) with the KIVA-CFD code, and also by utilizing an automated grid generation technique, multi-objective optimizations with goals of low emissions and fuel economy were achieved. Three different multi-objective genetic algorithms including a Micro-Genetic Algorithm (μGA), a Nondominated Sorting Genetic Algorithm II (NSGA II) and an Adaptive Range Multi-Objective Genetic Algorithm (ARMOGA) were compared for conducting the optimization under the same conditions.
Journal Article

Optimization of a HSDI Diesel Engine for Passenger Cars Using a Multi-Objective Genetic Algorithm and Multi-Dimensional Modeling

2009-04-20
2009-01-0715
A multi-objective genetic algorithm coupled with the KIVA3V release 2 code was used to optimize the piston bowl geometry, spray targeting, and swirl ratio levels of a high speed direct injected (HSDI) diesel engine for passenger cars. Three modes, which represent full-, mid-, and low-loads, were optimized separately. A non-dominated sorting genetic algorithm II (NSGA II) was used for the optimization. High throughput computing was conducted using the CONDOR software. An automated grid generator was used for efficient mesh generation with variable geometry parameters, including open and reentrant bowl designs. A series of new spray models featuring reduced mesh dependency were also integrated into the code. A characteristic-time combustion (CTC) model was used for the initial optimization for time savings. Model validation was performed by comparison with experiments for the baseline engine at full-, mid-, and low-load operating conditions.
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