Combustion engine optimization: a multiobjective approach

An analytical methodology to efficiently evaluate design alternatives in the conversion of a Common Rail Diesel engine to either CNG dedicated or dual fuel engine has been presented in a previous investigation. The simulation of the dual fuel combustion was performed with a modified version of the KIVA3V code including a modified version of the Shell model and a modified Characteristic Time Combustion model. In the present investigation, this methodology has been validated at two levels. The capability of the simulation code in predicting the emissions trends when changing pilot specification, like injected amount, injection pressure and start of injection, and engine configuration parameters, like compression ratio and axial position of the diesel injector has been verified. The second validation was related to the capability of the proposed computer-aided procedure in finding optimal solutions in a reduced computational time. Therefore, a multi-objective genetic algorithm was run for 100 generations with a population of 50 individuals including the same geometric and control variables taken into account in the first validation. The optimization was aimed at minimizing HC and NO x emissions. Three Pareto solutions selected from the results of the optimization were tested experimentally as a final validation. The potential improvement of the combustion process that can be obtained by optimizing the shape of the bowl is finally addressed in the paper.

Abstract: It is feared that the increasing population of vehicles in the world and the depletion of fossil-based fuel reserves could render transportation and other activities that rely on fossil fuels unsustainable in the long term. Concerns over environmental pollution issues, the high cost of fossil-based fuels and the increasing demand for fossil fuels has led to the search for environmentally friendly, cheaper and efficient fuels. In the search for these alternatives, liquefied petroleum gas (LPG) has been identified as one of the viable alternatives that could be used in place of gasoline in spark-ignition engines. The objective of the study was to present the modeling and multi-objective optimization of brake mean effective pressure and hydrocarbon emissions for a spark-ignition engine retrofitted to run on LPG. The use of a one-dimensional (1D) GT-Power™ model, together with Group Method of Data Handling (GMDH) neural networks, has been presented. The multi-objective optimization was implemented in MATLAB® using the non-dominating sorting genetic algorithm (NSGA-II). The modeling process generally achieved low mean squared errors (0.0000032 in the case of the hydrocarbon emissions model) for the models developed and was attributed to the collection of a larger training sample data using the 1D engine model. The multi-objective optimization and subsequent decisions for optimal performance have also been presented.

COMPSYSTECH'10, 2010

In this study, single and also multi-objective (MO) genetic algorithms (GAs) were used for optimisation of performance and emissions of a diesel engine. Population space and initial population of both GAs were obtained by Artificial Neural Network (ANN). Specific fuel consumption (Sfc), NOx, power (P), torque (Tq) and air-flow rate (Afr) were reduced to %7.7, %8.51, %30, %4 and %7.4 respectively whereas HC increased at the rate of %10.5 by traditional single objective GA. HC, CO2, P and Sfc were reduced to %17.6, %30.05, %31.8 and %14.5 respectively whereas NOx increased at the rate of %13 by using multiobjective GA with Nondominated Sorting Genetic Algorithm II (NSGA II). %14.5 fuel reduction against %31 power reduction have never been obtained in the previous studies. This shows the effective usage of MOGA with NSGA II in optimisation of fuel diesel engine performance parameters.

One of the factors limiting the utilization of piston internal combustion engines for aircraft propulsion is the performance decrease increasing the altitude of operation. This is due to the negative effect of air density reduction increasing the altitude on cylinder filling. A solution to this problem is represented by the engine supercharging. Unfortunately, in two stroke engines, the cylinder filling efficiency is antithetical to the cylinder scavenging efficiency. With the aim of guaranteeing an optimal balance between engine performance and specific consumption, an engine breathing system optimization is needed. In this work, the results obtained running a multi-objective optimization procedure aiming at performance increase and fuel consumption reduction of an aircraft two stroke supercharged diesel engine at various altitudes are analyzed. During the optimization procedure, several geometric parameters of the intake and exhaust systems as well as geometric and operating engine parameters have been varied. Then, a multi-objective optimization algorithm based on genetic algorithms has been run to obtain the configurations optimizing the engine performance at Sea Level (take-off conditions) and fuel consumption at 10680 m (cruise conditions).

Journal of Zhejiang University SCIENCE A, 2013

In this paper, a multi-objective particle swarm optimization (MOPSO) algorithm and a nondominated sorting genetic algorithm II (NSGA-II) are used to optimize the operating parameters of a 1.6 L, spark ignition (SI) gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxides (NO x), which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks (ANNs) were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms: NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NO x , respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NO x , respectively.

2002

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.

Fuel, 2021

In this study, experiments and numerical simulations were used to improve the fuel efficiency of compression ignition engine using a gasoline-diesel blended fuel and an optimization technology. The blended fuel is directly injected into the cylinder with various blending ratios. Combustion and emission characteristics were investigated to explore the effects of gasoline ratio on fuel blend. The present study showed that the advantages of gasoline-diesel blended fuel, high thermal efficiency and low emission, were maximized using the numerical optimization method. The ignition delay and maximum pressure rise rate increased with the proportion of gasoline. As the gasoline fraction increased, the combustion duration and the indicated mean effective pressure decreased. The homogeneity of the fuel-air mixture was improved due to longer ignition delay. Soot emission was significantly reduced up to 90% compared to that of conventional diesel. The nitrogen oxides emissions of the blended fuel increased slightly when the start of injection was retarded toward top dead center. For the numerical study, KIVA-CHEMKIN multi-dimensional CFD code was used to model the combustion and emission characteristics of gasoline-diesel blended fuel. The micro genetic algorithm coupled with the KIVA-CHEMKIN code were used to optimize the combustion chamber shape and operating conditions to improve the combustion performance of the blended fuel engine. The optimized chamber geometry enhanced the fuel efficiency, for a level of nitrogen oxides similar to that of conventional diesel over a variety of operation ranges.

This study is aimed at investigating the optimum combination of various engine operating parameters for obtaining highest brake thermal efficiency and lowest emissions of smoke from a single cylinder diesel engine fuelled with Honge oil- ethanol blend. During this work, the experiments are designed using a tool known as design of experiments based on Taguchi and grey relational approaches. Engine operating parameters, namely, injector opening pressure, fuel injection timing and compression ratio are varied at three levels and the responses like brake thermal efficiency, brake specific energy consumption, emissions of oxides of nitrogen and smoke opacity are investigated. A combination of injection pressure of 220 bar, injection timing of 27° before top dead centre and a compression ratio of 18 resulted in highest brake thermal efficiency and lowest smoke opacity. Results of confirmation tests indicated good agreement with predicted quantities.

Materials Today: Proceedings, 2018

Fuel economy and exhaust emission of automobiles have become the causes of concern these days owing to depletion of the nonrenewable sources of energy at a global scale and due to global warming. This paper involves the multi-objective optimization of process parameters of a VCR Petrol Engine to obtain minimum level of Hydrocarbon (HC) and Carbon-monoxide (CO) emission in the exhaust and Brake specific fuel consumption (BSFC). The control parameters were selected as compression ratio, spark advance, load, air-fuel ratio (in terms of number of turns of air screw) and fuel knocking tendency (in terms of octane rating of fuel). The experiments were carried out using Taguchi's orthogonal array L 27 for the said five control parametersat three operating levels each. Using Grey Relational Analysis (GRA) the multi-objective optimization was performed to determine the optimal solution. Analysis of Variance (ANOVA) was carried out to determine the significant and contributing control parameter.In the study the interaction of factors was also considered. The optimum level of control parameters are found to be at 10:1 for compression ratio, 20 0 for spark Advance, 1.2 kW for load, 1 turn for air screw and fuel havingless knocking tendency. It was seen from the ranking of the process parameters that load is the most dominant factor on the output response followed by compression ratio and spark advance. From the ANOVA it was experienced that in case of HC emission compression ratio is the most significant factor followed by spark advance. For BSFC, load was the most significant factor followed by compression ratio, whereas in case of CO emission, number of turns of air screw was the only significant factor.

Energy Conversion and Management, 2016

This study is concerned with the application of two major kinds of optimization algorithms on the baseline diesel engine in the class of evolutionary and non-evolutionary algorithms. The multi-objective genetic algorithm and non-linear programming by quadratic Lagrangian (NLPQL) method have completely different functions in optimizing and finding the global optimal design. The design variables are injection angle, half spray cone angle, inner distance of the bowl wall, and the bowl radius, while the objectives include NOx emission, spray droplet diameter, indicated mean effective pressure (IMEP), and indicated specific fuel consumption (ISFC). The restrictions were set on the objectives to distinguish between feasible designs and infeasible designs to sort those cases that cannot fulfill the demands of diesel engine designers and emission control measures. It is found that a design with deeper bowl and more encircled shape (higher swirl motion) is more suitable for NO x emission control, whereas designs with a bigger bowl radius, and closer inner wall distance of the bowl (Di) may lead to higher engine efficiency indices. Moreover, it was revealed that the NLPQL could rapidly search for the best design at Run ID 41 compared to genetic algorithm, which is able to find the global optima at last runs (ID 84). Both techniques introduce almost the same geometrical shape of the combustion chamber with a negligible contrast in the injection system.