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Multi-Objective Optimization of Diesel Engine Emissions and Fuel Economy using Genetic Algorithms and Phenomenological M PowerPoint Presentation
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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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slide1

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

background
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.
background1
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.

goal of this work
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.
goal of this work1
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
what is optimization
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.

optimizer genetic algorithm
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

evolution of genetic algorithms
Evolution of Genetic Algorithms

GA can find a global optimum.

GA is a multi point search method.

Opimum

Local optimum

simulator of the diesel engine combustion phenomenological model hidecs
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.
why do we use the phenomenological model
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

slide11

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

slide12

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.
slide13

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.

optimization of diesel engine combustion
Optimization of Diesel Engine Combustion

Let’s start optimization!!

By the way …

  • Optimizer
    • Genetic Algorithm
  • Simulator of the diesel combustion
    • Phenomenological Model
    • HIDECS
in the diesel engine combustion problem
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.
multi objective optimization problems
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]

how to evaluate the solutions
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+

slide18

(x)

f

2

f

(x)

1

Multi Objective GA

1st generation

5thgeneration

10th generation

50th generation

30th generation

slide19

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~
diesel engine combustion problem
Diesel Engine Combustion Problem
  • Design variables
    • Injection Rate
  • Objectives
    • Specific fuel consumption (SFC)
    • NOx
    • Soot
target diesel engine
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.

parameters in gas
Parameters in GAs

Population size 100

Crossover Rate 1.0

Mutation Rate 0.01

Terminal Generation 200

Trial Times 10

calculation resources
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

results
Results

Results are projected on each 2D surface.

results sfc and nox
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.

results smoke
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.

advantages of gas for multi objective optimization problems
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.
design alternatives
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

conclusions
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.
future works
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.
slide33
END
  • Thank you
  • tomo@is.doshisha.ac.jp
  • hiro@hiro.kindai.ac.jp
bit expression of engineering problems
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

slide36

Comparisons of Phenomenological Model and Multidimensional Model (2)

Y: mass concentration of fuel vapor

slide39

Schematic Diagram of the Mass System in the Package

Injection

Ignition

Combustion

Valve Open

Complete Combustion Incomplete Combustion

Combustion

arguments
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.
slide41
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.