An Application of Multi-Criteria Genetic Algorithms to the Optimization of a Common-Rail Injector
01 Jan 2002-pp 251-258
TL;DR: In this article, an optimization method is proposed to optimize the injection profile and the time response of a high pressure common rail injection system by adjusting the geometric and dynamic characteristics of an electronically controlled injector.
Abstract: The aim of the present investigation is to optimize the injection profile and the time response of a high pressure common rail injection system by adjusting the geometric and dynamic characteristics of an electronically controlled injector. The optimization method is based on the use of genetic algorithms which include the operators of crossover, mutation and elitist reproduction. As evaluation function for the GA a 1D simulation code of the injection systems, developed and extensively tested by the authors, has been used. The 1D model is based on the concentrated volume method and includes the effect of friction on the dynamics of the movable parts. Cavitation is also taken into account by the code as well as the effect on pressure wave propagation of the air and vapor mass fraction in the fuel. Conservation equations are integrated by using the characteristic method. The electromagnetic force generated by the solenoid on the head of the injector is simulated with an empirical function obtained by fitting experimental data. The optimized injection profile was defined by evaluating the predicted performance of a small bore DI diesel engine in terms of Brake Mean Effective Pressure (BMEP), soot and NOx emissions using a modified version of the KIVA-3V code. For the definition of the best injection profile injected quantity and start of injection were kept constants and only single-injection strategies were considered. The geometric and dynamic parameters (i.e. spring stiffness) of a commercial five holes VCO injector were used as baseline case. An optimized configuration of the selected parameters has been found. The optimized combinations of the investigated parameters are compared with the original values of the commercial injector as well as their predicted performance on the engine application.Copyright © 2002 by ASME
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TL;DR: In this article, the authors used GA to optimize the parameters of the combustion chamber, including the bowl volume and the squish-to-bowl volume ratio, to obtain the best compromise of the selected fitness functions.
Abstract: The optimization procedure adopted in the present investigation is based on Genetic Algorithms (GA) and allows different fitness functions to be simultaneously maximized. The parameters to be optimized are related to the geometric features of the combustion chamber, which ranges of variation are very wide. For all the investigated configurations, bowl volume and squish-to-bowl volume ratio were kept constant so that the compression ratio was the same for all investigated chambers. This condition assures that changes in the emissions were caused by geometric variations only. The spray injection angle was also considered as a variable parameter. The optimization was simultaneously performed for different engine operating conditions, i.e. load and speed, and the corresponding fitness values were weighted according to their occurrence in the European Driving Test. The evaluation phase of the genetic algorithm was performed by simulating the behavior of each chamber with a modified version of the KIVA3V code. The parameters for the sprays and the combustion models were adjusted according to the experimental data of a commercial chamber geometry taken as baseline case. Three fitness functions were defined according to engine emission levels (soot, NOx and HC) and a penalty function was used to account for engine performance. The goal of the optimization process was to select a chamber giving the best compromise of the selected fitness functions. Furthermore, chambers optimizing each single fitness function were also analyzed. The influence of the geometric characteristics on emissions has also been investigated in the paper.
109 citations
Cites methods or result from "An Application of Multi-Criteria Ge..."
...This does not confirm the results found by Carroll [13] in the case of single objective optimization, and the test performed by the authors in a previous investigation [15] where the comparison has been carried out in the case of only twoobjective functions....
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...In the present investigation, the optimization process was performed with a multi-objective genetic algorithm already tested by the authors and applied to engine optimization problems [14-16]....
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TL;DR: In this article, a microgenetic algorithm (mGA) code was applied to optimize experimentally an HSDI single-cylinder diesel engine equipped with a common rail fuel-injection system in order to reduce NOx, soot, and bsfc simultaneously.
Abstract: A microgenetic algorithm (mGA) code was applied to optimize experimentally an HSDI single-cylinder diesel engine equipped with a common rail fuel-injection system in order to reduce NOx, soot, and bsfc simultaneously. Four control factors were used, namely, start-of-injection (SOI) timing, intake boost pressure level, cooled exhaust gas recirculation (EGR) rate, and fuel-injection pressure. The search space was designed to be within the experimental capabilities of the engine and control system. The engine testing was done at 1550 r/min, and 25 per cent load. The optimum results showed significant improvements for the NOx and soot emissions. Through analysis of the combustion characteristics, the mechanisms of emission reduction were revealed. The optimum featured a long ignition delay due to retarded SOI timing, and low combustion temperatures as a result of high EGR rates. The resulting long time for mixing and low temperatures helps suppress soot formation. To explore further the effect of mixing on emissions in the low-temperature combustion regime, factors that enhance turbulent mixing rates, including the use of high injection pressures and post injections were examined. The results show that optimal post injections are useful further to reduce emissions when they feature a short injection pulse with an optimal dwell time between injections. (A)
44 citations
TL;DR: In this article, the effect of combustion chamber shape and injection strategy on diesel engine emissions and to find an optimized configuration to reduce NOx, soot and HC without significantly decreasing engine performance was achieved by using a multistep optimization process based on genetic algorithms.
Abstract: The present investigation aims at defining a new methodology to analyse the effect of combustion chamber shape and injection strategy on diesel engine emissions and to find an optimized configuration to reduce NOx, soot and HC without significantly decreasing engine performance. This was achieved by using a multistep optimization process based on genetic algorithms. The first step was related to the optimization of combustion chamber shape for a fixed injection strategy. The optimization was firstly performed with respect to a single-pulse injection strategy and a six-hole injector design. Then the process was repeated for an innovative injection strategy characterized by the injection of a relevant quantity of fuel far from top dead centre (large early injection) and a seven-hole injector. The combustion chamber shape was defined according to five geometric parameters, whose ranges of variation were very wide. For all the investigated con- figurations, the bowl volume and squish-bowl volume ratio were kept constant so that the compression ratio was the same for all the investigated chambers. The spray injection angle was also considered as a variable parameter. The optimization was simultaneously performed for different engine operating conditions, i.e. load and speed values, weighted according to their occurrence in the European Driving Test. From the results of this step, an optimized chamber configuration was selected and, at the same time, the effect of each geometric characteristic on NOx and soot emissions was underlined. In the second step, the combustion chamber shape was kept constant and the optimization process was aimed at identifying an optimized injection strategy for a fixed engine operating mode. Injection pressure, pilot and main advances and pilot energizing time were considered as input for the second step of the optimization process. The numerical tools used in the investigation include a modified version of the KIVA-3V code and a multi-objective genetic algorithm. The code capability to simulate engine performance was assessed by comparing numerical results with experimental data available for the baseline engine configuration. Three fitness functions were defined according to engine emission levels (soot, NOx and HC) and a penalty function was used to account for engine performance and fuel consumption. (A)
23 citations
11 Apr 2005
21 citations
01 Jan 2002
TL;DR: In this paper, a multi-objective genetic algorithm combined with an experimental investigation carried out on a test bench, by using a DI Diesel engine, was proposed to determine the competitive fitness functions.
Abstract: The optimization method proposed in the present study consists of a multi-objective genetic algorithm combined with an experimental investigation carried out on a test bench, by using a DI Diesel engine. The genetic algorithm selects the injection parameters for each operating condition whereas the output measured by the experimental apparatus determines the fitness in the optimization process. The genetic algorithm creates a random population, which evolves combining the genetic code of the most capable individuals of the previous generation. Each individual of the population is represented by a set of parameters codified with a binary string. The evolution is performed using the operators of crossover, mutation and elitist reproduction. This genetic algorithm allows competitive fitness functions to be optimized with a single optimization process. For the determination of the overall fitness function the concept of Pareto optimality has been implemented. In this work, the input variables used for the optimization method are injection parameters like start of pilot and main injection, injection pressure and duration. The engine used is a FIAT 1929 cc DI diesel engine, in which the traditional injection system has been replaced by a common rail high pressure injection system.The competitive fitness functions were determined based on the measured values of fuel consumption, emissions levels (i.e. NOx, soot, CO, CO2, HC); combustion noise and overall engine noise, for each operating conditions. The optimization was performed for different engine speed and torque conditions typical of the EC driving cycles.
16 citations
Cites methods from "An Application of Multi-Criteria Ge..."
...A test performed by the authors [10] put in evidence that microGAs allow a very good definition of the Pareto front in the case of multi-objective optimization for a given number of evaluations....
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References
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01 Jan 1996
TL;DR: The effects of elitism, single point and uniform crossover, creep mutation, different random number seeds, population size, niching and the number of children per pair of parents on the performance of the GA for this problem were studied.
Abstract: This paper presents results from the first known application of the genetic algorithm (GA) technique for optimizing the performance of a laser system (chemical, solid-state, or gaseous). The effects of elitism, single point and uniform crossover, creep mutation, different random number seeds, population size, niching and the number of children per pair of parents on the performance of the GA for this problem were studied. Micro-GAs (μGA) were also tested. The best overall performer was the uniform crossover μGA with a population size of 5. The uniform crossover μGA was also able to find the global maximum of an order-3 deceptive function which the other tested GAs failed to optimize.
192 citations
"An Application of Multi-Criteria Ge..." refers result in this paper
...This is consistent with the results of Carroll [7]....
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05 Mar 2001
148 citations
"An Application of Multi-Criteria Ge..." refers methods in this paper
...Multidimensional modeling as evaluation method for genetic algorithms optimization has been widely applied by Senecal and his coworkers[4-6]....
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89 citations
"An Application of Multi-Criteria Ge..." refers methods in this paper
...Multidimensional modeling as evaluation method for genetic algorithms optimization has been widely applied by Senecal and his coworkers[4-6]....
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40 citations
"An Application of Multi-Criteria Ge..." refers background in this paper
...A detailed description of this code can be found in [9],[10] and [11]....
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AVL1
TL;DR: In this paper, the authors describe the concept selection, design and performance of a fuel injection equipment (FIE) which provides high flexibility in shaping the injection rate, and demonstrate that the boot injection can be performed with fully opened needle during the whole injection period and no throttling device limits the fuel flow in front of the nozzle to reduce the injection rates.
Abstract: The paper describes the concept selection, design and performance of a fuel injection equipment (FIE) which provides high flexibility in shaping the injection rate. With this injection system standard and boot shaped injection rates as well as pilot injections and post injections can be achieved throughout the hole speed and load range. Special emphasis was drawn to realize boot rate shaping by pressure modulation rather than by throttling the fuel flow (i.e.: the system is operated with fully opened needle during the whole injection period and no throttling device limits the fuel flow in front of the nozzle to reduce the injection rate). Initial engine tests on a single cylinder research engine with 2 liter displacement were carried out at one operating point (1,000 rpm, 200 mm{sup 3}/str = 75% of full load fueling). Boot and pilot (split) injection rate shaping strategies are compared to a standard injection without rate shaping. At constant smoke and BSFC the boot injection shows a considerable improvement potential in NOx emissions of up to {minus}14%, or NOx and BSFC can be reduced simultaneously by {minus}9% and {minus}7%, respectively. The results with pilot injection are less promising than the results with boot injection. Furthermore, theymore » are sensitive to pilot timing and to injection pressure as well as fueling during pilot injection.« less
36 citations
"An Application of Multi-Criteria Ge..." refers background in this paper
...[2] reproduced this ideal shape of the rate of injection by using pressure modulated injectors....
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