Challenge

Week 11: Project 2 - Emission characterization on a CAT3410 engine

YYogessvaran T15 Oct 2022

Introduction

Diesel engine is widely used for non-road purposes, as well as transportation purposes because of its thermal efficiency,

reliability and fuel economy. Diesel engine technology with higher efficiency and lower emission is still being developed.

These developments up to now have led to an increase in the interest in the diesel engines for agriculture and maritime

sectors where fuel economy is an important parameter. The numerical realization of a mathematical model for a physical

system, in the form of a computer program, is shaped by a combination of practical and philosophical decisions which

determine its ultimate form. A mathematical modeling of the internal combustion engine has been constructed and

implemented as a computer program suitable for use on large digital computer system. The model strikes a balance between

three competing factors: (1) the desire for physical realism, (2) the extent of experimental information on the physical

processes occurring in the engine , and (3) the capabilities of today’s generation of computers. Modeling is a very useful

approach for the development of internal combustion engine and optimization of its design parameters. Modeling of an

internal combustion engine will develop as our understanding and knowledge of the physics and chemistry mechanism of the

phenomena and as computers continue to increase their ability to solve complex equations. The models describe the

thermodynamics, fluid flow, heat transfer, combustion and emission formation events of the engines.

Combustion has been a key technology for transportation since the last century. Despite its central role in improving living

standards, it has had significant adverse environmental impacts. Notably, the CO2 emissions it produces have contributed

greatly to global warming. In addition, combustion creates noise and produces pollutants that reduce air quality in urban

areas. Consequently, legislature around the world are introducing increasingly strict regulations requiring the automotive

sector to reduce its emissions. For instance, the European Union has implemented legislation limiting the fleet average CO₂

emissions of vehicles to 130 g/km(depending on vehicle weight). Emissions of 130g CO₂/km correspond to a fuel

consumption of around 5.6 liters per 100 km (5.6l/100 km) of petrol or 4.9 l/100 km of diesel. This limit is expected to be

reduced to 95 g/km in 2021 and then to between 68 and 78 g/km in 2025. Similar regulations have been or will be

implemented in other countries. To meet these requirements, vehicle manufacturers are exploring a range of strategies to

reduce emissions, including geometrical improvements, advanced combustion modes, turbo-charging, and variable valve

timing. However, these techniques increase the complexity of vehicle engines and typically offer only marginal benefits.

The fuel used by IC engines also has major impact on our global environment. Burning of 1 kg of fuel consumes about 15 kg

of air, and significant energy is required to pump it into and out of the engine. In addition, about 3kg of is generated, which

contributes to the world’s annual production of 37 billion tons of , a major greenhouse gas (GHG). Some fear that GHGs can

cause climate change with unpredictable consequences. To address this problem, the international Energy Agency’s roadmap

is to reduce fuel use by 30-50% in new road vehicles worldwide by 2030, and in all vehicle by 2050. Although 2050 appears

distant, the time required to bring new engines to production, together with the years needed for new technology to

permeate the vehicle fleet, means that major effort will be required.

An alternative approach would be to replace emission-generating internal combustion engines with battery electric power

trains, which emit no harmful pollutants directly. This could be a viable solution if the electricity used to power the vehicles

was generated from renewable sources. However, the capacity and working lifespan of currently available batteries are very

limited, and the environmental friendly disposal of used batteries is challenging. Therefore, the complete replacement of

internal combustion engines with battery electric power-trains is not currently a promising general solution to the

environmental problems associated with transportation.

The grand challenge faced by engine and researchers over the next decades will be to devise technological advances that

maximize the engine efficiency, minimize pollutant emissions, and optimize tolerance to a wide variety of fuels in power

generation and transportation systems. Much recent engine research has focused on improving the understanding the

ignition, which is highly dependent on the fuel’s chemical composition, and also on increasing the quality of the air-fuel

mixture for improved combustion efficiency. Future legislation requirements for fuel consumption and emissions have

prompted efforts to develop new engine technologies. This has resulted in extensive research on internal combustion engine

systems because of their potential to reduce fuel consumption and exhaust emissions. Two major factors controlling fuel/air

mixing in ICE are the fuel injection

system and the nozzle geometry. The present injection system needs to provide an improved spray characteristics such as

spray penetration length, fuel atomization, droplet sizes and droplet size distribution to enhance a combustion system

efficiency. Spray characteristics have a huge influence on the combustion system efficiency because the spray directly

controls the dynamics of the combustion process. Spray dynamic are complex multi-scale physical phenomena that are highly

sensitive to the injector nozzle geometry(cavitation), nozzle exit conditions(turbulence), and fuel injection pressure. These

conditions can change the atomization behavior and course of physical processes of the spray after the nozzle exit. The

measurement techniques have some limitations to incorporate all the physical processes. Also, it is very challenging to isolate

all the physical processes. On the other hand, Computational fluid dynamics (CFD) simulations offer an alternative way of

studying these processes, and are becoming an increasingly reliable and effective tools for studying phenomena such as

spray injection and its subsequent development. On the other hand, the simulation techniques are becoming an increasingly

reliable and effective tool for the detailed study of insight phenomena including spray injection and its subsequent processes.

However, due to different scales are involved to address the atomization and nozzle flow, it is challenging the entire

phenomenon (atomization and nozzle flow) in the single CFD frame work.

At present, direct numerical simulations (DNS) is the only computational method capable of resolving all length scales

involved in the atomization processes [5]. Unfortunately, its high computational cost largely restricts its use to academic test

cases. An alternative method with lesser computational costs, the large-eddy simulation (LES) technique, has been widely

used to simulate unsteady multiphase phenomena. LES can accurately capture intrinsically time- and space-dependent

phenomena because it directly resolves large-scale turbulent structures and uses a model to describe sub-grid scale

structures.

Spray modeling

Spray models are very important in engine CFD simulations because they influence the subsequent mixing, ignition,

combustion and emission predictions. Spray models include those describing the flows inside injector nozzles, atomization of

the continuous liquid after the fuel leaves the injector nozzle (primary breakup model), breakup of the formed liquid drops

(secondary breakup model), momentum exchange between the liquid and gas phases (i.e., the drag force that liquid droplets

experience and the deformation of the droplets), collision between liquid drops and its possible outcomes (collision model),

interaction between sprays and walls (e.g., wall-film model), and evaporation of the liquid fuel (evaporation model).

Spray sub-models

Various sub-models were implemented to account for the effects of turbulence , coalescence , evaporation , and droplet

breakup in the fuel spray simulation. A spray process can be represented in the following diagram.

Discrete phase modeling

Fluent provides a model that is especially for spray simulations, or more general suspended particle trajectory simulations.

This is the Discrete Phase Model (DPM) and it is based on the so-called Euler-Lagrange method. In the computational domain

there are two separate phases present, namely, the continuous and the discrete phase (particles). The transport equations

are solved for the continuous phase only and the motion of particles is dealt with particle trajectory calculations. Through an

iterative solution procedure, the mass, momentum and energy interaction between both phases can be realized.

Combustion Modelin Various

combustion models have been developed to simulate a wide variety of combustion phenomena in both spark-ignited (SI) and

compression (CI) engines, including spark and auto ignition, laminar and turbulent combustion, premixed, non-premixed and

stratified-charge combustion, kinetics and mixing-controlled combustion, and flame propagation combustion. For CI engines,

combustion modeling also must resolve two steps: low-temperature chemistry which leads to auto ignition, and the following

high-temperature reactions which contribute most of the heat release.

Since combustion in DI engines is turbulent and many turbulent combustion models with different levels of complexity have

been proposed for engines applications, including models based on RANS and LES turbulence models. However, since

RANSmodels, specifically the k- model and its variants, are the most commonly used turbulence models at present. In gener

al, there are two ways to describe the combustion process in engine cylinders. One is to use a combustion sub-model to

descibe the overall combustion characteristics (i.e., flame propagation, reaction rates, and so on) of a combustion process.

Combustion models such as mixing-controlled combustion models, flamelet models, and probability density function

(PDF)models belong to this category. The other way is to use a detailed reaction mechanism which describes the complex

reaction pathways and reaction rates of the important chemical reactions involved in the combustion process and solve for

the rate of change of each species in the mechanism.

Before describing the models, two variables which are used in these models are introduced. The first is the mixture fraction

(Z) and the second is the reaction progress variable (c). The mixture fraction quantifies the local mass fraction of materials

that originate from the reactant fuel. It is mostly used in the analysis and modeling of non-premixed reacting systems. For

premixed combustion systems, a reaction progress variable is used. The progress variable increases from zero in the

unburned reactants to unity in the burned products. The calculation of the progress variable can be associated with either the

mass fraction of a specific species (combustion products) or temperature.

CONVERGE offers two different methods for modeling the ignition and combustion processes. The first method uses separate

models for ignition and combustion. These models

are based on the Shell ignition model and the Characteristic Time Combustion model, and this method is relatively

computationally inexpensive. The second approach, described in the SAGE detailed chemical kinetics solver section, considers

much of the chemistry taking place in combustion applications. Although the runtime using detailed chemistry can be

significantly longer than with the first approach, the accuracy of the simulation may be greatly enhanced with the inclusion of

detailed chemistry. Nonetheless, there is still often a need for the rapid turnaround time that can be achieved when the

simpler models are used.

CONVERGE contains the SAGE detailed chemical kinetics solver which models detailed chemical kinetics via a set of

CHEMKIN-formatted input files. To solve the systems of ordinary differential equations (ODEs), SAGE uses the CVODE solver,

which is part of the SUNDIALS (SUite of Nonlinear and DIfferential/ALgebraic equation Solvers) package.

A chemical reaction mechanism is a set of elementary reactions that describe an overall chemical reaction. The combustion of

different fuels can be modeled by changing the mechanism (e.g., there are mechanisms for isooctane, gasoline, n-heptane,

natural gas, etc.). SAGE calculates the reaction rates for each elementary reaction while the CFD solver solves the transport

equations. Given an accurate mechanism, SAGE (in addition to AMR) can be used for modeling many combustion regimes

(ignition, premixed, mixing-controlled). You can use SAGE to model either constant-volume or constant-pressure combustion.

Note that SAGE consistently uses CGS units for all calculations.

Geometry preparation:

Open-W piston Geometry

Omega Piston Geometry

A sector of 60 degree of engine volume is used for geometry. The following piston bowl profiles are imported along with the

geometric details of cylinder to create this engine sector.

Omega piston

Open-W piston

Sector angle

It is calculated as we have assume 6 nozzles inside the injector.

Sector angle = 360∘Nos.ofNozz≤s">360∘Nos.ofNozz≤s

= 60∘">60∘

Note:

Before processing for case-setup, we check the diagnosis to make sure that geometry is free from errors.

Case setup

Open the case setup

Physical parameters

Materials

Simulation parameters

Parcel simulation

In this case we are using the C7H16(n-heptane) as our fuel spray.

Boundary Conditions:

Region Name	Boundary Name	Boundary type	Boundary Motion	Temperature	Motion details	Region initial pressure	Region initial temperature	Region initial species
Cylinder region	Piston	Wall	Moving	553	Piston motion	197000	355	CO2: 0.0014304 H2O: 0.0006296 N2: 0.76795 O2: 0.23029
	Front face		Periodic	-	60 degree rotation about z
	Back face		Periodic	-	Matching boundary
	Back face		Stationary	433
	Cylinder wall		Stationary	433
	Cylinder head		Stationary	523

Mesh

A base mesh was created with element size of 1.4mm and following embedding and AMR settings were applied to refine the

mesh as required.

Sr No.

Embedding name

Embedding type

Embedding on

Embedding mode

Scale→">→

Element size	Embed layers	Radius 1 at co-ordinate 1	Radius 2 at co-ordinate 2	Cylic details
1	Piston embedding	Boundary	Piston	Cyclic	1→">→

0.7mm	1	-	-	From -20 deg to 180 deg(720 deg period)
2	Cyl head embedding	Boundary	Cylinder head	Cyclic	1→">→

0.7mm	1	-	-	From -20 deg to 180 deg(720 deg period)
3	Nozzle	Nozzle	Volume	Cyclic	2→">→

0.35

Radius = 0.001m and 0.003m

Length= 0.01m

From -12 deg to 15 deg(720 deg period)

Base mesh or scale 0 mesh element size is 1.4mm

Velocity AMR

Temperature AMR

Spray modeling:

In the general tab we do the following setting.

Parcel distribution: we choose the option cluster parcels near cone center.

Turbulent dispersion : we choose the O'Rourke model.

Droplet evaporation : We choose the Frossling model.

Evaporation Source: We choose the option source specified species and choose C7H16.

In the spray penetration tab, we choose the 0.97 for the liquid fuel mass fraction.

Bin size for vapor penetrtion = 0.001

Fuel mass fraction for calculating vapor penetration = 0.001

Mass diffusivity constant: We use the Diesel default values.

In the collision/Breakup/Drag, we do the following settings.

Collision model: Choose the NTC-collision model

Collision outcomes: Post collision outcomes

Level of collision mesh : 2

Drop drag model: Dynamic drop drag model

In the wall interaction , we do the following settings.

Spray wall interaction model: Rebound/slide

In the injectors tab, we do the following settings.

Injected species: Diesel2

Rate shape : profile as shown in the below figure.

Models: Kelvin Helmolthz model

Child parcels:

Rayleigh Taylor Model

Discharge coefficients

In the Mass,time and temperature we do the following settings.

Nozzle_0 configuration

Combustion Modeling

Emissions modeling

Results:

Pressure

Behaviour of the pressure plot is correct for both pistons. In the above figure we can easily see that the open-W piston has

the highest pressure than the omega piston.

Mean temperature

In the above graph, we can easily observe that the mean temperature is higher in omega piston as compared to the open-W

case. This higher mean temperature produces the faster burning of fuel. This higher temperature is going to rise the NOx and

soot formation.

In the contours images, we can easily observe the temp variations in the omega and open-W piston.

Max temperature

Max temperature in both cases (Omega and open-W) are almost equal.

Heat release rate

The heat release rate can be shown in the above figure. We can easily observe that Omega piston has a higher heat release

rate than the open-W piston. The faster burning in the case of omega piston is the reason of higher heat release rate.

Integrated Heat release rate

In the above plot we can easily see that omega piston has the higher integrated_HR, this is happening due to high

temperature in omega piston.

Spray parcels

The above plot shows the no of parcel in the chamber which are in the form of droplets. In open-W case, curve has a lower

peak because when the spray enter into the chamber it travels for some distance and then it forms a film on the piston

surface. The fuel film eventually burns out by evaporation but it reduces the number of parcels in drop phase. But in Omega

case, the parcels gets enough time to evaporate and thus they burn out faster and we can see less drop parcels after 10

degree crank angle.

Spray penetration

Spray penetration length was defined as the distance from the nozzle tip to the farthest spray region from the nozzle tip. In

the above plot we can easily see that the open-W piston have long penetration than the Omega piston. The reason behnid

the increase of penetration length in Open-W piston is that due to low temperature droplets are not disintegrated faster abd

that's why do not evaporate faster. While in Omega piston case, due to instant increase in temperature droplets are

evaporated faster.

Unburned hydrocarbon

In the above hydrocarbon plot we can easily see that the open-W piston has a high hydrocarbon than the omega piston. The

reason behnid of low hydrocarbon in omega piston is that due tp high temperature fuel evaporated fastely and there is not

adequate amount of fuel is remaining. But in the case of open-W piston, we can observe in the above plots thers is not any

kind of increase in temperature is occuring, that's why fuel is not completely burn and there is adequate amount of fuel is

remaing which produces hydrocarbons.

In the CO plot, we can easily see that the omega piston has the highest peak then of open-w piston. But in this case we are

not considering the peaks, we can see that the area under curve in open-W case has more than the omega piston. More area

occupy means that there is too much amount of carbon monoxide.

Combustion occuring in omega piston case is fastly

Due to high temp in omega piston case, NOx are produces and due to low temperature in open-W piston NOx are not

produces. NOx always produces at high temperature because at high temperature all the fuel are burn, only the amount of

air is remaining that's why NOx are produces.

Combustion Efficiency

Open W bowl piston

ηc=IHRm×LCVfuel">ηc=IHRm×LCVfuel

Where IHR is the integrated heat release rate and we obtained from the IHR graph.

LCV is the lower calorific value

IHR = 7206.21

LCV for diesel = 45.5 MJ/kg

m = 1.621e-4 kg

97.7040%

Omega bowl piston

eta_c = frac{IHR}{m times LCV_(fuel)

Where IHR is the integrated heat release rate and we obtained from the IHR graph.

LCV is the lower calorific value

IHR = 7279.44

LCV for diesel = 45.5 MJ/kg

m =1.621e-4 kg

98.6969%

Power and torque calculation

The result of engine performance is calculated from tool of Converge CFD. It gave the following output

Parameter	Open W	Omega
Gross work(Nm)	3028.53	3409.28
Duration used to calculate gross work (deg)	270.156	270.171
IMEP(Pa)	1.24076e+6	1.39575e+6
CA 10 (deg)	0.70348	1.00833
CA 50 (deg)	17.8347	12.5019
CA 90 (deg)	53.0275	27.5283

Power calculation for Omega piston using engine calculator

Work = 3409

Duration = 270.171

Power =work/sec

RPM = 1600

RPS = 26.667

1 rotation = 360 degree

DPS = 26.667*360 = 9600

Time(sec) = 270.171/9600 = 0.0281428

Power = 3409/0.0281428 = 121132.2256 KJ, 121.1322256 KW

Torque calculation

Power = 2*pi*N*T

Torque(T) = Power/(2*Pi*N)

Where N- rev/sec

T-Torque (N.m)

Power – KW

N = 26.667 rev/s

Power = 121132.2256 KJ, 121.1322256 KW

Torque = 723.312369

Power calculation for Open-W piston using engine calculator

Work = 3028