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Multi-Objective Optimization of Diesel Engine Emissions and Fuel Economy using Genetic Algorithms and Phenomenological M

Multi-Objective Optimization of Diesel Engine Emissions and Fuel Economy using Genetic Algorithms and Phenomenological Model. Tomoyuki Hiroyasu. Doshisha University Intelligent Systems Design Laboratory. Mitsunori Miki, Jiro Kamiura, Shinya Watanabe Doshisha University. Hiro Hiroyasu

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Multi-Objective Optimization of Diesel Engine Emissions and Fuel Economy using Genetic Algorithms and Phenomenological M

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  1. Multi-Objective Optimization of Diesel Engine Emissions and Fuel Economy using Genetic Algorithms and Phenomenological Model Tomoyuki Hiroyasu Doshisha University Intelligent Systems Design Laboratory Mitsunori Miki, Jiro Kamiura, Shinya Watanabe Doshisha University Hiro Hiroyasu Kinki University

  2. Background • The Diesel engine has a considerable advantage in regards to engine power, fuel economy and durability. • In order to meet the increasingly stringent emission regulations which have been proposed several technical breakthroughs will be required.

  3. Background • Diesel engine designers must efficient and effectively make judgments about design parameters. • Engineer approach to reduce time and save money is to reduce the number of engine tests to determine whether or not engine component designs meet objectives. Design tool which is based on the simulation is essential.

  4. Goal of this work We introduce the design tool which is based on the simulations for determining the parameters of diesel engines. • We would like reduce the amounts of NOx and Soot with the high fuel efficiency. • We will find the parameters of the fuel injection rate, boost pressure, EGR, start of fuel injection (SOI), duration of the fuel injection, and so on.

  5. Goal of this work • In the proposed tool, the optimization technique is used. • The proposed tool is consists of the optimizer and the simulator of the diesel engine combustion. • Optimizer • Multi-Objective Optimization Method • Genetic Algorithms • Neighborhood Cultivation Genetic Algorithm (NCGA) • The tool can suggest design alternatives to users. • Simulator of the diesel engine combustion • Phenomenological Model • HIDECS • This model is suitable for optimization, especially for Genetic Algorithms

  6. What is Optimization? Analyzer Input Data Output data New Searching Point Values of objective function Optimizer Optimization is a problem to find the design variables x that minimize/ maximize the values of objective function f (x) under the constraints g(x)<0.

  7. Optimizer: Genetic Algorithm • simulation of creatures’ heredity and evolution • Multi Point Search • Stochastic Search • Easy to implement to several types of problems • Robust to find global optimum • Suitable for parallel calculation environment Selection Crossover Mutation Evaluation

  8. Evolution of Genetic Algorithms GA can find a global optimum. GA is a multi point search method. Opimum Local optimum

  9. Simulator of the diesel engine combustion:Phenomenological Model, HIDECS • The simulation models of diesel combustion • Thermodynamic model • Phenomenological model • Detailed multi dimensional model • In this study, we use the Phenomenological Model.

  10. Why do we use the Phenomenological Model? • Phenomenological Model • Only a few minutes for • whole calculation • Very simple procedure • for calculation • Various parametric • studies are available • All the equations are derived • by the experiments • Multi-Dimensional Model • Much long time for • calculation • Very complicated • procedure for calculation • Very high skill for the • meshing Hiroshima University, Kinki University Since 1976~ Implementation HIDECS

  11. Fuel Air Fuel Air EGR Characteristics of Super Charge Characteristics of Fuel ( Cetane No. ect. ) Injection Characteristic Design of Inlet Port Injection System Design of Inlet Port Injection System Injection Rate Injection Timing Injection Duration Characteristics Design of Combustion Chamber Design of Combustion Chamber of Air Motion Spray Characteristics Swirl Drop Distribution Squish Spray Tip Penetration Turbulence Spray Angle Vaporizing Characteristics Fuel-Air Mixing Fuel-Air Mixing Characteristics of Ignition Delay Ignition Ignition Flame Propagation Combustion Combustion Partially Pre-mixed Combustion Partially Diffusion Combustion Diffusion of Combustion Products Heat Losses Rate of Heat Release Rate of Heat Release NOx Particulate HC Exhaust Emission Exhaust Emission Block Diagram of Diesel Combustion Block Diagram of Diesel Combustion

  12. Overview of the spray-combustion model • In this model, the spray is divided into many small packages. • No-intermixing among the package is assumed. • Spray tip penetration is defined by the experimental equations. • Mean drop size in each package is defined by the experimental equations.

  13. Air Fuel Mixing Process within Each Package These packages are changing with along to the process of the combustion. These process have to be simulated one by one. In the HIDECS, many equations are utilized to simulate these processes. These equations are derive by the experiments.

  14. Optimization of Diesel Engine Combustion Let’s start optimization!! By the way … • Optimizer • Genetic Algorithm • Simulator of the diesel combustion • Phenomenological Model • HIDECS

  15. In the diesel engine combustion problem … Multi-Objective Optimization Problems • We would like to reduce • the amount of NOx • the amount of Soot • SFC • and so on. • Objective Function = w1 NOX + w2 Soot + w3 SFC • Who knows these weights? • It is known that results are sensitive to the weights. • Therefore, we would like to treat these terms separately.

  16. Multi-Objective Optimization Problems • In multi-objective optimization problems, there are not only one objective but also several objectives. Objective function Min f1(X)=SFC f2(X)=NOx Design variables Feasible region Profile of fuel injection rate better NOx [g/kW hour] Pareto optimal solutions =[x1,x2,...,x12] ・Pareto Optimum Solutions better SFC [g/kW hour]

  17. How to evaluate the solutions? 3 f 2 1 1 Pareto optimal solutions f 1 Pareto-optimal Set The set of non-inferior individuals in each generation. Ranking number of dominant individuals Rank ＝ １＋

  18. (x) f 2 f (x) 1 Multi Objective GA 1st generation 5thgeneration 10th generation 50th generation 30th generation

  19. Genetic Algorithm for Multi-Objective Optimization • VEGASchaffer (1985) • MOGA Fonseca (1993) • DRMOGA Hiroyasu, Miki, Watanabe (2000) • SPEA2 E. Zitzler, M. Laumanns (2001) • NPGA2 Erickson, Mayer, Horn (2001) • NSGA-II Deb, Goel (2001) • NCGA ～Neighborhood Cultivation GA～

  20. System Overview

  21. Diesel Engine Combustion Problem • Design variables • Injection Rate • Objectives • Specific fuel consumption (SFC) • NOx • Soot

  22. Target Diesel Engine Bore 102 mm Stroke 105 mm Compression Ratio 17 Engine Speed 1800 rpm Swirl Ratio 1.0 Nozzle Hole Diameter 0.2 mm Nozzle Hole Number 4 hline Injected Fuel Volume 40.0 mg/st Injection Timing -5 ATDCdeg. Injection Duration 18 deg.

  23. Parameters in GAs Population size 100 Crossover Rate 1.0 Mutation Rate 0.01 Terminal Generation 200 Trial Times 10

  24. Calculation Resources • We used the PC Cluster systems. • There are 32 CPUs. • There are 31 slaves and one master. • There are 20100 simulations of the HIDECS. • The total execution time is 11425 [s](3.17 H). (If you use one CPU, it takes about one and a half day.) • The average execution time of one trial of the HIDECS is 11.86[s]. • The parallel efficiency is more than 95%. CPU Pentium III(1GHz)*32 Memory 512/CPU OS Linux 2.4.4 Network FastEthernet TCP/IP Communication Library LAM

  25. Results Results are projected on each 2D surface.

  26. Results

  27. Results, SFC and NOx In the solutions who has the smallest value of NOx, the fuel is injected at two steps. This double step injection is known as “Pirot Injection”. It can reduce the NOx emission, because the medium in-cylinder pressure can be obtained to prevent the NOx formation. In the solution who has the smallest value of SFC, the most fuel is injected at the beginning. The early injection causes the better fuel-air-mixing at the early stage of the combustion process that results high maximum in-cylinder pressure and high engine output.

  28. Results, Smoke In the solution who has the smallest value of smoke, the fuel is mostly injected in the middle of the injection period. This may be caused by the reduction of the incomplete fuel combustion in the combustion stroke. The small amount of fuel injected in the early stage evaporates and combusts. This operation may help the rest of fuel combust completely in a better environment.

  29. Advantages of GAs for multi-objective optimization problems • The GAs can find the Pareto optimum solutions with one trial. • The formulation of the problem is very easy. (It needs not weight parameters) • The designers can derive the several types of the solutions. It is very useful in the upper stage of the designing. • Even in the bottom stage, when the constraints are formulated as the objective functions, the relation ship between the objective function and the constraints are made clarified.

  30. Design Alternatives SFC Best SFC:183.7 Nox:1.743 SMOKE:0.2605 Candidate 3 SFC:196.1 Nox:0.7846 SMOKE:0.2224 NOx Best SFC:299.6 Nox:0.4309 SMOKE:0.1539

  31. Conclusions • In this study, multi-objective optimization problem is focused. We can derive several solutions at one trial. • In this study, using the Phenomenological model and genetic algorithm, the amount of the NOx, Soot, and SFC are minimized by changing the shape of the fuel injection rate. • By the proposed system, the Pareto optimum solutions are successfully derived. This information of the Pareto solutions are very useful for the designers. • Since the calculation cost of the phenomenological model (HIDECS) is very small, it is very suitable for the optimization.

  32. Future Works • The more factors will be target as design variables such as, boost pressure, EGR, start of fuel injection (SOI), duration of the fuel injection, and so on. • The alternate expression of the injection pressure shape is considered. • In this study, the characteristics of the derived solutions are not discussed. In the future work, these characteristics are examined precisely.

  33. END • Thank you • tomo@is.doshisha.ac.jp • hiro@hiro.kindai.ac.jp

  34. Bit expression of engineering problems 0 1 1 0 1 1 0 0 1 1 0 1 Target problems z y 0 x Chromosome Decoding Phenotype Encoding Genotype Individual

  35. Comparisons of Phenomenological Model and Multidimensional Model (1)

  36. Comparisons of Phenomenological Model and Multidimensional Model (2) Y: mass concentration of fuel vapor

  37. Comparisons of Phenomenological Model and Multidimensional Model (3)

  38. Comparisons of Phenomenological Model and Multidimensional Model (4)

  39. Schematic Diagram of the Mass System in the Package Injection Ignition Combustion Valve Open Complete Combustion Incomplete Combustion Combustion

  40. Arguments • Phenomenological Model is not precise enough to apply to optimization. • Phenomenological model is using the equations that are derived directly from the experiments. Therefore, the results of this model are very fit to the experiments. • Even the multi-dimensional model has a lot of assumptions. That means that there is a possibility there are some errors.

  41. Most important thing is that • If we can call the small number of the simulations, we can not find the global optimum point. The restriction is often happens from the simulation that needs the high calculation cost.

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