Xin Q. Diesel Engine System Design

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488 Diesel engine system design

turbulence, chemical reaction, boundary layer, and heat transfer. It has

demonstrated strong potential and practicality to be used as a design tool

to predict diesel emissions and optimize combustion chamber design and

air–fuel–combustion system matching. The challenges with the KIVA are

the complexity of model tuning and the long computing time required.

There are numerous publications on the KIVA simulations. Those conducted

by Han et al. (1996) and Bianchi et al. (2002) are good examples for a diesel

engine system engineer to get familiar with this area. The KIVA modeling

work is reviewed by Reitz and Rutland (1995) and Reitz and Sun (2010).

The CFD simulation with the KIVA code and the commercial CFD codes

(such as STAR-CD, VECTIS, FIRE, and FLUENT) on the characteristics

of the diesel engine in-cylinder ow, turbulence, and spray is reviewed by

Basha and Raja Gopal (2009).

7.4.6 Heuristic macro-parameter-dependent approach

Heuristics are the doctrine of obtaining new insights and universally valid

design and calculation methods with the law of similarity or partially empirical

simplication. It is a counterbalance to the theoretical KIVA method. The

heuristics lead to more detailed insights if detailed models are established.

The ‘macro’ means the global apparent engine performance parameters

and the zero-dimensional in-cylinder bulk parameters. Diesel emissions are

extremely complex to model in micro detail due to the various mechanisms

involved in the ignition delay, the premixed and diffusion ame combustion

processes. For example, the creation of soot is determined by the local

temperature and the oxygen concentration during the events of the radical

chain reaction in the core of the fuel jet, the adsorption of polycyclic aromatic

hydrocarbons, the processes of polymerization, cyclization, coagulation and

agglomeration in the soot formation and reoxidation phases. The idea of using

a macro-parameter-dependent approach to build emissions models is to try

to bypass the difculties encountered by the traditional phenomenological

model. The approach tries to link the emissions to more fundamental engine

performance parameters, which are either the apparent macro cycle-average

parameters such as the air–fuel ratio, or the macro instantaneous crank-angle

based parameters such as the in-cylinder bulk temperature, in a relatively

simplied combined theoretical form. It still conforms to the basic principles

of emission chemistry. Its theoretical foundation is far beyond the empirical

emissions data processing. It is indeed a practical semi-empirical approach,

especially suitable for the needs of engine system design and analysis. The

advantage of such a model is that it may eliminate many of the disadvantages

in the empirical approach and the traditional phenomenological approach.

Most importantly, it is the only feasible emission prediction model that

can be used in future advanced intelligent real-time emissions-model-based

Diesel-Xin-07.indd 488 5/5/11 11:51:32 AM



Decide time steps and

space zones

Local maximum temperature

in the ﬁrst burning zone

Local maximum temperature

in the i

burning zone

Calculate in-cylinder

species

and N

concentra-

tions in burning and

burned zones

and N

concentra-

tions in burning and

burned zones

Distribute species into

space zones

Soot formation and

oxidation in burning

and burned zones

Calculate homogeneous

air–fuel ratio for soot

Soot

Final total No

Final total soot

Soot formation and

oxidation in burning

and burned zones

… … +

Exhaust valve opening

Burn rate

Start of combustion

Crank angle (degree)

–10 0 10 20 30 40 50 60 70 80 90 100 110 120 130

Time step 1

Time step 2

Time step 3

Time step 4

Final time step

Piston displacement

U: unburned space zone

B: burning space zone

D: burned space zone

Key requirements for emissions models

used in diesel engine system design:

(1) Computationally fast

(2) High-ﬁdelity crank-angle-resolution

(3) Based on combustion chemistry/physics

(4) Real-time capable for engine controls

(5) Easy-to-tune with minimum number of

tuning coefﬁcients.

Key gradients in the heuristic macro-

parameter-dependent emissions

models:

(1) Virtual space zones of burning, burned

and unburned

(2) Multiple time zones (steps)

(3) Effects of local maximum temperature,

heat transfer and mass transfer

between the space zones

(4) Based on fundamental research result

of soot formation

(5) Decouple soot formation and oxidation

to clarify the process of soot dynamics

(6) Analytical algorithms for fast close-form

algebraic solutions in the emissions

model.

D D

7.12 Summary of

model structure

of heuristic

macro-parameter-

dependent

emissions model.

Diesel-Xin-07.indd 489 5/5/11 11:51:32 AM

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490 Diesel engine system design

electronic engine controls to handle both the steady-state and the transient

control needs. The technique of building such a model can also be applied

to build the macro emissions models for aftertreatment devices, where the

technical challenge is actually less because the variation of the gas properties

within an engine cycle is not as complex as the in-cylinder combustion

processes. This type of macro model is not intended to predict the heat

release rate or evaluate the combustion chamber design. Heat release rate is

used as input in the model. The predictability of the model is limited to the

given hardware of combustion and fuel systems. If the effects of different

combustion hardware design need to be simulated, the KIVA simulation

must be used. The real-time heuristic macro models of NO

and soot were

proposed and detailed by Zheng and Xin (2009). Figure 7.12 illustrates the

model theory and the model structure.

The best emissions model is not necessarily the most complicated theoretical

model; rather it should be a simple, practical, and most efcient model for

engineering design applications.

Transient emissions modeling is another great challenge in engine system

design. Information in this area can be found in Hamburg and Throop (1984),

Jiang and Gerpen (1992), Bazari (1994), Bi and Han (1995), Ramamurthy

et al. (1998), Cui et al. (2001), Shirota et al. (2001), Ericson et al. (2005),

Andersson et al. (2006a, 2006b), Zhang et al. (2007), Brahma et al. (2009),

and Hirsch and Re (2009).

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491Combustion, emissions, and calibration for system design

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492 Diesel engine system design

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493Combustion, emissions, and calibration for system design

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