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Flame Temperature Measurement in an Internal Combustion Engine

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Martin Vagn HansenMEK-FM-EP-2010-02 March 2010

MSc Thesis

Flame Temperature Measurement in an Internal

Combustion Engine

by

Martin Vagn Hansen, s042283

Danmarks Tekniske Universitet

Department of Mechanical Engineering

Supervisors:

Anders Ivarsson (DTU)

Johan Hult (MAN Diesel)

March 22nd, 2010

Abstract

A non-intrusive thermometry method using full spectral analysis of hot soot radiation is

developed for use in large two stroke diesel engines. The investigation is performed as a

proof of concept, focusing on reliability and accuracy of the method.

By performing quasi one-dimensional measurements of the soot luminescence intensity

and comparing them to ideal values, temperatures and emissivities are calculated. It

is determined that for �ame temperature measurements, the wavelength dependency of

soot emissivity is of great in�uence on the estimated temperatures based on full spectral

analysis.

The capability to isolate the hottest �ame temperature is tested. By calculating the

temperatures closer to the UV range a tendency is found toward increasing temperatures,

but without matching the known �ame temperature. A closer resemblance to the physical

process is attempted by implementing a multiple �ame zone model, but it was not possible

to optimize the numerical procedure and the system is therefore too sensitive toward the

given start values to be reliable.

Preface

This report is my master thesis, with which I complete my Masters degree (MSc) in

mechanical engineering from the Department of Mechanical Engineering at the Technical

University of Denmark (DTU).

This project has been carried out as a collaboration between MAN Diesel and DTU, with

the project period being spent at DTU. All experimental work has been carried out at

the test facilities at DTU.

Throughout this project I have bene�ted from the consultation and assistance of employees

at MAN Diesel and sta� at the Department of Mechanical Engineering at DTU, and for

this I am very thankful.

I would especially like to thank Johan Hult from MAN Diesel and Anders Ivarsson from

DTU for their invaluable assistance, guidance and discussion of results.

Martin Vagn Hansen

s042283

MSc Thesis Contents

Contents

List of Figures iv

List of Tables vi

1 Introduction 1

2 Literature and Theory Review 2

2.1 In-Cylinder Pollutant Formation . . . . . . . . . . . . . . . . . . . . . . . . 2

2.1.1 NOx Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.1.2 Soot Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.2 The Two-Color Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2.1 KL Factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2.2 Choice of Wavelengths . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.3 Multiwavelength Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.4 View Glass Contamination . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3 Experimental Setup 9

3.1 Projection View Through Borescope . . . . . . . . . . . . . . . . . . . . . 10

3.1.1 Optical Fiber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.2 Introduction of Camera Lens . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4 Calibration 15

4.1 Emissivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4.2 Transmissivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4.3 Spectroscope Linearity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Danmarks Tekniske Univeristet i DTU Mechanical Engineering

MSc Thesis Contents

5 Temperature and Emissivity Algorithm 24

6 Alignment of Optical Fiber and Camera 29

6.1 Simultaneous Triggering . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

7 Recalibration and Robustness Test 32

8 Application to Flames 34

8.1 Known Flame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

8.1.1 Varying the Fuel-Air Ratio . . . . . . . . . . . . . . . . . . . . . . . 36

8.1.2 Higher Temperature Flame . . . . . . . . . . . . . . . . . . . . . . . 37

8.1.3 Wavelength Dependency of Soot Emissivity . . . . . . . . . . . . . 38

8.2 Determining the Hottest Flame Temperature . . . . . . . . . . . . . . . . . 39

8.2.1 Multi-zone Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

9 Test of Contaminated Glass 44

10 Result Discussion and Comments 46

11 Systematic Error Sources 48

12 Future Work 49

13 Conclusions 50

References 51

A Matlab Codes I

A.1 Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I

Danmarks Tekniske Univeristet ii DTU Mechanical Engineering

MSc Thesis Contents

A.2 Linearity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV

A.3 Emissivity and Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . V

A.4 Planck Radiation Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . VIII

B Table Values IX

B.1 Tungsten Lamp Current vs. Temperature Curve . . . . . . . . . . . . . . . X

B.2 Emissivity of Tungsten as function of Temperature and Wavelength . . . . XI

C Technical Drawings XII

C.1 Mounting Plate - Design 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . XIII

C.2 Mounting Plate - Design 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . XIV

C.3 50mm Nikkor Lens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XV

C.4 X-Y translator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XVI

C.5 F-mount . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XVII

C.6 Beamsplitter mount . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XVIII

C.7 Camera Mounting Plate . . . . . . . . . . . . . . . . . . . . . . . . . . . . XIX

D Data Files XX

Danmarks Tekniske Univeristet iii DTU Mechanical Engineering

MSc Thesis List of Figures

List of Figures

2.1 Conceptual model of mixing controlled diesel combustion . . . . . . . . . . 2

2.2 2 zone model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.1 Conceptual image of the objective and image through the borescope . . . . 9

3.2 Model showing the image plane and the mounting plate of the �rst design . 10

3.3 The measured objective area and the resulting spectra . . . . . . . . . . . 11

3.4 Categorization of optical �ber properties . . . . . . . . . . . . . . . . . . . 11

3.5 The SLR camera with the optical �ber mounted . . . . . . . . . . . . . . . 12

3.6 The virtual model showing all the components and the image plane . . . . 14

4.1 Tungsten ribbon lamp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

4.2 Emissivity of tungsten . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4.3 Transmissivity of the glass bulb around the tungsten ribbon . . . . . . . . 17

4.4 Correlation between ideal and linear intensities . . . . . . . . . . . . . . . . 18

4.5 Plot showing the ideal counts compared to the measured counts at 693nm . 20

4.6 Comparison of O.O. linearity algorithm and calculated correction factors . 20

4.7 Comparison between linear and non-linear correction . . . . . . . . . . . . 21

4.8 Correction factor as a function of wavelength . . . . . . . . . . . . . . . . . 22

4.9 Plot showing deviation between measured and ideal values as a function of

wavelength . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

5.1 In�uence of start value on resulting minimum . . . . . . . . . . . . . . . . 25

5.2 Plot of the �tted wavelength segments to the measured intensity . . . . . . 27

6.1 Alignment of Optical Fiber and Camera . . . . . . . . . . . . . . . . . . . 30

6.2 Triggering modes setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Danmarks Tekniske Univeristet iv DTU Mechanical Engineering

MSc Thesis List of Figures

7.1 Correlation between the new and old system correction factors . . . . . . . 32

8.1 Setup for �ame measurements . . . . . . . . . . . . . . . . . . . . . . . . . 34

8.2 Sketch of the setup for measuring on �ame and black body . . . . . . . . . 40

8.3 Temperature trend for smaller segments . . . . . . . . . . . . . . . . . . . 41

8.4 Temperature trend for smaller segments centered on intensity slope . . . . 41

8.5 Results from 2 zone �tting function . . . . . . . . . . . . . . . . . . . . . . 42

9.1 Intensity fraction of the sooted view glass . . . . . . . . . . . . . . . . . . . 44

10.1 Bad �t between measured and calculated intensities . . . . . . . . . . . . . 47

Danmarks Tekniske Univeristet v DTU Mechanical Engineering

MSc Thesis List of Tables

List of Tables

4.1 Currents and corresponding temperatures for tungsten lamp . . . . . . . . 17

5.1 Start value matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

5.2 Emissivity and temperature estimations of tungsten lamp at 2000K . . . . 27

7.1 Calculated values using di�erent system correction factors . . . . . . . . . 33

7.2 Calculated values using paired measurements and correction factors . . . . 33

8.1 Measured emissivities and temperatures of the black body . . . . . . . . . 35

8.2 Flame temperatures as a function of heights above burner . . . . . . . . . 35

8.3 Average temperature at di�erent HAB . . . . . . . . . . . . . . . . . . . . 36

8.4 Average temperature for varying φ values . . . . . . . . . . . . . . . . . . . 37

8.5 Average temperature for higher temperature �ame . . . . . . . . . . . . . . 38

8.6 Calculated temperatures using wavelength dependent emissivity . . . . . . 39

Danmarks Tekniske Univeristet vi DTU Mechanical Engineering

MSc Thesis 1 Introduction

1 Introduction

Flame temperature in combustion engines has a very signi�cant e�ect on exhaust emis-

sions, and are therefore an important parameter to monitor when doing parametric engine

studies or applying other emission reduction techniques, like exhaust gas recirculation

(EGR) or water in fuel emulsion (WiFE) in the pursuit of meeting the ever stricter

emission caps being set around the world.

The aim of this project is therefore to develop, test and validate a non-contact �ame

temperature measurement method, which can be used in large two stroke combustion

engines. The main reason for desiring a non-contact measuring method is the ability to

measure the combustion �ame temperature, while maintaining a production type layout

and allow for direct comparison of other design and parameter changes.

The method used in this project consists of performing quasi one dimensional measure-

ments of the spectral intensity of the light emitted by the soot in a �ame, and subsequently

correlating their shape to the ideal spectral energy curves using a least square method

based on Planck's radiation law. For similar methods, generally only two wavelength

bands are used, but this relies on the assumption that the emissivity coe�cient is constant;

while in reality this coe�cient is dependent on wavelength and soot concentration.

The measurements made in this project cover a wide wavelength range and create an

overdetermined system, allowing temperature and emissivity information to be calculated

in various wavelength segments. Then, by determining which wavelength ranges provide

the most accurate information about di�erent areas of the �ame, improvements can be

made to other existing pyrometric methods, e.g. when measuring the hottest part of the

�ame.

A camera is also set up to provide visual information about the measured �ames, and

ascertain whether the measurements are performed on a hot or cold region when measuring

turbulent �ames.

Besides the development of a measurement method, a test is made to determine the

transmissivity of a sooted objective window. This is done to determine whether or not

it is possible to account for or bypass the progressive sooting of the window, which

has a �ltering e�ect on the measured spectral intensities and therefore the estimated

temperature.

Danmarks Tekniske Univeristet 1 DTU Mechanical Engineering

MSc Thesis 2 Literature and Theory Review

2 Literature and Theory Review

2.1 In-Cylinder Pollutant Formation

The major environmental concern with diesel engines is the formation of nitric oxides

(NOx) and particulates (soot). In order to reduce these pollutants, it is necessary to have

a better understanding of the combustion process during which they are formed.

Pollutant formation is highly dependent on temperature, more speci�cally �ame tempera-

ture, which in turn is dependent on how the fuel and air mix, and how this changes during

the work stroke. In a diesel engine the fuel is injected just prior to the expansion stroke,

and thus does not allow enough time for the proper mixture of fuel and air. Therefore

the fuel distribution is non-uniform during the critical parts of the combustion, and this

is considered a major factor in the formation of pollutants.

After injection, and until ignition, fuel and air mix creating a near stoichiometric mixture

which, when burnt, produces very high temperatures and pressures and is called the

premixed burn region. This is then followed by the di�usion burn region, where the rate

of combustion is controlled by the di�usion of oxygen through the �ame front [1] (see

Figure 2.1).

Figure 2.1: Conceptual model of mixing controlled diesel combustion [2]



2.1.1 NOx Formation

Nitrogen oxides (NO and NO2, but jointly referred to as NOx) form in regions of high local

temperature. For diesel combustion this encompasses the initial premixed combustion and

some regions of the sustained di�usion �ame at the outer edges of the fuel jet. For cylinder

combustion, NOx concentrations are governed by thermal equilibrium (Zeldo'vich) [3],

meaning that even though the chemical equilibrium would yield a lower concentration,

the nitric oxide chemistry freezes before reaching this equilibrium. This happens when

the local temperatures drop rapidly due to the contact with the cylinder walls and mixing

with the fresh charge from the inlet valves.

2.1.2 Soot Formation

Soot forms in the fuel-rich zone around individual fuel droplets, where the hydrocarbons

are heated and oxidized by the nearby combustion. Under these conditions, the formation

of soot is thought to take place through the following steps: pyrolysis, nucleation, surface

growth and coagulation, aggregation and oxidation. During pyrolysis the gas phase

hydrocarbons break down or crack to form soot precursors, which then grow into soot

nuclei in the nucleation step. During the surface growth phase, the nuclei grow from 1-2

nm to 10-30 nm and the H/C ratio decreases. Most of the soot will oxidize during the

expansion stroke (over 90% [4]), but that which does not will exit through the exhaust

[1]. It is then that the aggregation phase is meant to occur, joining the larger soot nuclei

into their more known fractal structure, and it is also here that non burnt hydrocarbons,

sulphates and bound water (among others) will condense on the soot, resulting in the

conglomerates called particulates [5].

From Figure 2.1 it is clear to see that the thermal NO production zone and the soot

oxidation zone are almost coincident, creating the so called NOx-Particulate trade o�, in

which high temperatures are needed to oxidize the soot particles but low temperatures are

desired in order to keep NOx emissions low [6]. Because of this it is very useful to determine

the �ame temperature during combustion in order to have a better understanding of the

parameters that in�uence it.

For this, non-intrusive temperature measuring methods such as two color-pyrometry have

been developed.



2.2 The Two-Color Method

The two-color method has successfully been used to determine �ame temperatures, by

applying the fact that soot particles emit a continuous spectra of radiation. The radiation

intensity is split into two wavelength bands using �lters, after which the relation between

the intensity of each of the wavelength bands is used to determine the temperature, hence

the name �two-color method�. A major bene�t of this method is the ability to perform

combustion temperature measurements without signi�cant mechanical modi�cations, thus

not changing the engines operating parameters and allowing engine developers to directly

compare the e�ects of other component or parameter modi�cations, e.g. injection

strategies. Hottel and Broughton used the method on an open �ame in 1932, while

Uyehara applied it to diesel engine prechamber combustion in 1946. In both these cases

the measurements where done on a single point, but it was Matsui et al. who expanded

the method to full view, two-dimensional high speed images in 1982.

In their investigations, Matsui et al. studied the e�ects of unevenness of the distribution

and temperature of soot particles through the line of sight. His investigations indicated

that the soot temperature closely describes the �ame temperature, determining that the

temperature di�erence between gas and soot was negligible after the gas and particles

reached thermal equilibrium, a time which has been shown to be of the order of 10−7

seconds or less [4, 10].

Optical pyrometric measurements are based on Planck's law of radiation, which yields the

spectral energy density of a blackbody emitter, e.g. perfect emitter with an emissivity of

unity at all wavelengths:

Ibb(λ, T ) =C1

λ5[exp

(C2

λT

)− 1

] (2.1)

where C1 and C2 are Planck's radiation constants, λ is the wavelength and T is the

temperature. For a real emitter with spectral emissivity, ε, between 0 and 1, the spectral

energy density is given by:

I(λ, T ) = ε(λ)Ibb(λ, T ) (2.2)

And while single soot particles and thick soot clouds exhibit near blackbody behavior, i.e.

ε ' 1, thinner soot clouds show non-blackbody behavior with ε < 1. The emissivity of a

soot cloud is intricately dependent on the soot concentration and thickness of the cloud



through the line of sight. A commonly used empirical model, which couples these factors

with the wavelength dependency, was proposed by Hottel and Broughton [7]:

ε(λ,KL) = 1− exp

(−KLλα

)(2.3)

where K is the empirical extinction coe�cient of soot, L is the line of sight path-length

through �ame and α is the empirical range constant (usually 1.39 in the visible range).

Combining the product of K and L into a KL factor is commonly used in diesel research

literature, allowing the comparable measure of soot concentrations in various experiments

and con�gurations. This is mainly done due to the limited knowledge of the optical

properties of diesel soot and the practical di�culties in measuring the exact thickness of

the soot cloud [10].

By measuring the spectral energy density at two di�erent wavelengths, and combining

(2.2) and (2.3) for the two chosen wavelengths, it is possible to set up two equations

I(λ1, T ) = ε(λ1, KL)Ibb(λ1, T ) (2.4a)

I(λ2, T ) = ε(λ2, KL)Ibb(λ2, T ) (2.4b)

The set of equations (2.4) can then be solved as a simple problem of two equations with

two unknowns T and KL, once an absolute measure of the spectral intensities I has been

determined, i.e. non-ratiometric method. In many cases though, the emissivity is assumed

to be equivalent at these two wavelengths, simplifying the problem to determining the

ratio between the intensities measured at each wavelength, i.e. ratiometric method. Thus

removing the need to determine the absolute values of the intensities that requires an

absolute calibration of the measuring system, a di�cult task for an engine measurement.

The assumption of equal emissivities is highly dependent on the ratio of KL to λα, which

is clear from equation (2.3) where for KL >> λα, ε approaches 1 for all wavelengths. On

the other hand if KL is low, then the emissivity is greater in the visible spectra than in

the near-IR, leading to a higher intensity ratio and a systematic overestimation of the

temperature.

2.2.1 KL Factor

Since KL is highly dependent on the type of fuel and the size and type of combustion, i.e.

the resulting soot volume fraction. Thus it is necessary to take into consideration these



aspects when determining whether or not using the ratiometric method is applicable. In

[10] the argument is made for high KL values when investigating the combustion process of

large diesel engines running on heavier diesel fuels, and operated at high power levels, i.e.

intense fuel-injection periods. This is especially true when looking at the main combustion

event in which the fuel jet undergoes rich premixed combustion before reaching a di�usion

�ame zone that surrounds the fuel jet periphery.

Furthermore, the nature of a real �ame is heterogeneous leading to complications when

measuring the surface of the soot cloud against a hotter or colder background of soot

emission. Using a 2-zone model a comparison was made, showing that for a cold

background, the system shows the same systematic error as for a homogeneous soot cloud,

while for a hot background a large overestimation of temperature is seen at low soot cloud

temperatures, which is expected due to the large amount of radiation that leaking from

the hotter background [11].

2.2.2 Choice of Wavelengths

The light emitted by the diesel combustion process is mainly comprised of chemilumi-

nescence and soot incandescence, of which soot incandescence is the primary source of

emission (in order of 4-5 orders of magnitude) [12]. Chemiluminescence is mainly present

in the ultraviolet part of the spectrum, while soot incandescence is stronger in the visible

spectrum.

The choice of wavelength bands is of signi�cant importance when trying to determine

the hottest part of the �ame, which in a NOx and particulate emissions sense is the

one of interest as it is here the major formation/oxidation processes take place. This is

because the fraction of the spectral intensity contribution of each temperature zone is

dependent on the wavelength band being measured. To demonstrate this, a simple two

zone model is set up in which one zone is hot (2800K) while the other is cold (1800K)

while both having an emissivity of 0.5. From this model (Figure 2.2) it is shown that at

short wavelengths, i.e. up to 500nm, the fraction of intensity contributed by the hot zone

is almost 1, independent of it's position relative to the cold zone.



(a) Schematic of the 2 zone model

(b) Intensity ratio if T1 is hottest (c) Intensity ratio if T2 is hottest

Figure 2.2: 2 zone model showing the proportion of hot and cold radiation as a function of

wavelength

Based on this it is clear that wavelengths as low as possible are to be chosen for measuring

the hottest �ame temperatures. This is furthermore backed up by the fact that the �ank

of the Planck radiation curve moves toward lower wavelengths at higher temperatures.

Various combinations of wavelength bands as well as di�erent bandwidths have been used

in previous experiments [3, 4, 10], with the governing criteria being the equipment used.

Nevertheless, Matsui's system was setup to measure at three di�erent wavelengths, thus

allowing the comparison between combinations of the three, and they found that the

minimum error was associated with the largest wavelength di�erence.

2.3 Multiwavelength Method

Another approach is the one taken by Iuliis et al., in which advantage of the multi-

wavelength capability of a diode array detector is used. Rather than for temperature

measurements the method is focused on determining soot volume fractions, but the

principal is the same in which the spectral energy density is measured over a wide



wavelength range. This measured spectra can then be �tted to the ideal (eq. (2.2))

by determining the temperature and soot volume fraction by means of a least square

procedure. Through this method a good correlation is made to other experimental

data obtained from other measuring methods (optical pyrometry and rapid insertion

thermocouples) on similar �ames.

Another multiple wavelength method is the multiband method, which in a similar way as

the two color method is used to determine temperatures and emissivities. This method

though, has only been found applied to cold surfaces that do not vary largely from ordinary

room temperature [14, 15, 16].

2.4 View Glass Contamination

Due to the high temperatures and soot concentrations in the combustion chamber,

contamination of the optical access window is expected to occur. In earlier work no

measures where taken to prevent soot build up on the view glass, estimating that the

drop in transmissivity would not have signi�cant impact on the measurements. It was

later shown by Mohammad that the soot deposits on the window could reduce the

transmissivity by up to 50%, a�ecting the determination of the KL factor by up to 50%,

while the temperature measurement only decreased by 8%.


MSc Thesis 3 Experimental Setup

3 Experimental Setup

The experimental rig consists originally of:

• A borescope mounted inside a dummy engine start air valve having a �eld view into

the combustion chamber of 70o through a sapphire window, and a focal length of

5-500 mm.

• A X-Y translator mount, allowing for the mounting and translation of an optical

�ber.

• An optical �ber (various types where used during the experiment).

• A USB2000+ spectroscope from Ocean Optics with an e�ective range 200-800nm.

• A mirror mount including a 50% beam splitter.

Using these, it is the primary objective of the experimental setup to determine the spectral

energy density on a localized part of a �ame, thus allowing temperature measurements

using the whole spectral range of the spectroscope. This meant achieving a projected

image (the image) of the borescope's whole �eld of view (the object) (see Figure 3.1),

after which a small area of the image is measured using an optical �ber and transmitted

to the spectroscope. The borescope used is one of constant focus, meaning that when

focusing on an object at certain distance, the image will stay in focus even if the object

is moved closer or further from the objective window.

Figure 3.1: Conceptual image of the objective and image through the borescope

Once the object area being measured was considered su�ciently small (at least 3mm x

3mm at close range to allow for calibration with a tungsten lamp), a high-speed camera

would be added to the setup, allowing pictures of the measured area to be taken.



3.1 Projection View Through Borescope

Initially it was thought possible to achieve a projected image directly from the endoscope

optics onto the collection plane in which the optical �ber was placed (the image plane),

thus only making it necessary to reduce the diameter and numerical aperture of the �ber

in order to reduce the area being observed on the object, i.e. the �ame. In order to do

this it was thought necessary to get a focused image of the object on the image plane,

and therefore e�ort was put into determining the possible positioning of the image plane.

For this a di�use light source with a sharp black cross was introduced as the object,

making it possible to determine at which distance from the borescope eyepiece it was

possible to achieve a focused image. This was done by adjusting the eyepiece focus and

the distance between the eyepiece and the image plane, until a sharp cross was seen on

the image plane. Once the focused distance from eyepiece to image plane was known, a

mounting plate was designed and built for the X-Y translator (see Appendix C.1).

Figure 3.2: Model showing the image plane and the mounting plate of the �rst design

Using this setup (see Figure 3.2), testing to �nd the size of the objective area being

measured begun. This was done by measuring the signal levels on the spectroscope while

translating the position of the optical �ber. At �rst it was thought possible to achieve

zero signal (no light) by placing the �ber on the black part of the image (the cross), but

it was quickly determined that the �ber (diameter 600µm and NA 0.22) allowed to much

light in, i.e. it sampled too large an area of the image (see Figure 3.3). Therefore di�erent

�ber diameters and apertures where tested.



Figure 3.3: The measured objective area and the resulting spectra

3.1.1 Optical Fiber

Other than the spectral range of an optical �ber, two other parameters can in�uence its

applicability to a particular setup. The diameter of the �ber can determine the way the

light is transported as well as the sampled area (see Figure 3.4(a)).

(a) Fiber types according to diameter (b) Numerical aperture

Figure 3.4: Categorization of optical �ber properties

Secondly the numerical aperture (NA) of the �ber determines the acceptance angle or cone

of the �ber, i.e. the maximum angle at which light can enter the �ber and be transported

through it (see Figure 3.4(b))

After testing two additional �ber types ([200µm and NA 0.35] and [50µm and NA 0.22])

it was determined that, although they reduced the object area being sampled, it was still



too large to satisfy the initial criteria of 3mm x 3mm. Since a 50µm is the smallest �ber

obtainable before getting to single mode �bers, it was deemed necessary to explore other

ways of reducing the measured object area.

3.2 Introduction of Camera Lens

As a �rst alternative to the directly projected image, was the introduction of a camera

lens between the borescope eyepiece and the image plane. This would introduce a second

set of focusing lenses, ideally allowing for a sharper projection and thus making it possible

to reduce the objective area.

As a �rst attempt, an old single lens re�ex (SLR) camera was set up at the point of the

projected image. This made it possible to obtain the correct focus through the camera's

eyepiece, and subsequently open the shutter and see the image projected onto the camera's

�lm plane.

This revealed the main reason why the original approach described in Section 3.1 did not

achieve the expected results. When seeking a projected image there is a big di�erence

between a focused image for ocular perception and a focused image for planar perception,

e.g. a roll of �lm. This meant that the original focused image of the black cross was

actually not focused for planar perception, and therefore the �ber aperture was not

e�ective in reducing the measured objective area.

Figure 3.5: The SLR camera with the optical �ber mounted on the camera's image plane



After learning this fact, the X-Y translator was positioned in such a way that the opening

of the �ber was on the image plane of the camera (see Figure 3.5) This setup immediately

showed reductions in the area measured on the object, achieving object areas of 4mm

x 4mm at 15mm, and 7mm x 7mm at 230mm from the objective lens. From testing it

became clear that the lens had to be mounted at a further distance from the borescope

objective, based on the improvement, i.e. reduction, in the measured area. This is also

in accordance with the minimum focal distance from the camera lens of 60cm, because

even though this distance can be reduced using the optics in the borescope eyepiece, the

combination of the two extreme settings did not produce good enough results.

Once these tests had been done and a rough estimate of the distance from eyepiece to

lens was found, a new mounting plate was designed. Since it was only possible to mount

the camera in one point it was prone to pivoting, and therefore a mount for the lens was

introduced instead of mounting the whole camera. This allowed for a more rigid setup and

centering between the lens and the X-Y translator. The distance between the lens and

the �ber plane was calculated using a technical drawing of the lens (see Appendix C.3)

and measurements on the camera housing.

Using technical drawings (see Appendices C.4, C.5 and C.6) and CAD models of the

parts to be used it was possible to create a virtual model of the desired optical setup

(Figure 3.6). This allowed for alignment corrections and distance estimations for the

design of the mounting plate before producing the actual part (see Appendix C.2).



Figure 3.6: The virtual model showing all the components and the image plane

With the new mounting plate in place, new tests where conducted leading to a measured

object area of 1.5mm x 1.5mm at 15mm, and 6mm x 6mm at 450mm, hereby satisfying

the initial goal of 3mm x 3mm at close range.


MSc Thesis 4 Calibration

4 Calibration

In order to calibrate the experimental setup a blackbody like emitter of known

temperature is needed. The calibration source should be able to achieve temperatures

in a range that matches the expected soot cloud temperatures in the engine. Because

of that fact and its compactness and ease of use, a tungsten ribbon lamp is used as the

calibration source for this experimental setup.

The lamp used is a Phillips lamp type W1 GGV12i, which is supplied by MAN Diesel, and

comes with a calibration curve (current vs. temperature) from 1961 (see Appendix B.1).

As shown in Figure 4.1(a), the lamp features a tungsten strip roughly 9mm x 3mm, and

the point of calibration on the ribbon should be as close as possible to the point indicated

by the metal �lament (Figure 4.1(b)).

(a) Tungsten ribbon lamp (b) Magni�ed view of the tungsten ribbon

Figure 4.1: The tungsten ribbon lamp used to calibrate the experimental setup

The tungsten lamp is powered by a current regulated power supply capable of reaching

30A, but the recommendations state that the lamp should not be used at currents over

13.6A for prolonged periods, and should be limited to 15.25A for short period usage.

When performing measurements, the lamp was left to stabilize for a short period of time.

Since the tungsten lamp is not a perfect blackbody, considerations had to be made to

take into account the emissivity of tungsten and also the transmissivity of the window

material (assumed to be fused silica).



4.1 Emissivity

The tungsten ribbon is not a perfect blackbody, which means it has an emissivity < 1.

The emissivity of tungsten varies as a function of temperature and wavelength, so these

factors have to be taken into account when calibrating the setup. From table values found

in [18] (table can be found in Appendix B.2) a series of spline curves are found to �t the

emissivity of tungsten at any wavelength, for a particular temperature (Figure 4.2)

Figure 4.2: Emissivity of tungsten as a function of temperature and wavelength. The solid lines

represent the calculated spline curves.

Using the found spline curves it is possible to attribute each wavelength a particular

emissivity at a given temperature, ε(λ, T ), and then use these in the calibration process.

4.2 Transmissivity

The transmissivity of the glass is given by τ(λ) = 1 − 2(n(λ)−1n(λ)+1

), where n is the index of

refraction (wavelength dependent) of fused silica and the values of which are found in the

MellesGriot optical materials catalogue. From the tabulated values, a polynomial is �tted

and this is then used to obtain the transmissivity at any given wavelength (Figure 4.2).



Figure 4.3: Transmissivity of the glass bulb around the tungsten ribbon

The emissivity and transmissivity are then be multiplied to the ideal blackbody radiation

curve in order to determine the e�ective radiation from the tungsten lamp:

I(λ, T )eff = ε(λ, T ) · τ(λ) · Ibb(λ, T ) (4.1)

To calibrate the setup, the tungsten lamp is placed close to the objective window, in

such a way as to ensure that the objective area being measured by the optical �ber was

contained entirely in the tungsten ribbon

A series of spectral energy density measurements are then made for di�erent current

settings (see Table 4.1) and the spectra are stored for analysis. In order to obtain

usable measurements, di�erent integration times are used, i.e. the time over which

the spectroscope measures the incoming signal. This integration time is later used to

normalize the di�erent spectra in order to reach a common comparison level.

Current [A] 6.85 7.85 9.1 10.5 12 13.6 15.25

Temperature [K] 1600 1800 2000 2200 2400 2600 2800

Int. time [ms] 750 500 150 50 20 9 5

Table 4.1: Currents and corresponding temperatures for tungsten lamp

As it is intended for the setup to measure combustion temperatures inside a large

two stroke engine, it is chosen that the calibration be done with the highest possible



temperature spectra, i.e. 2800 K, in order to calibrate at an equivalent temperature range

as that expected during engine combustion. The calibration consists on determining a

correction factor to be applied to all measurements made through the apparatus, thus

accounting for any losses or distortions through the system. This correction factor is

obtained by dividing the e�ective spectral energy density from the tungsten lamp at

15.25A (equivalent to 2800K) with the measured spectra from the spectroscope thus

obtaining a factor vector:

F =Ieff

Imeasured(4.2)

The �rst attempts at calibrating the system resulted in good correlation between the

measured and ideal values for wavelength values between 485nm and 715nm, and

while values above 715nm showed a distinct deviation from the ideal values, which

increased at lower temperatures (see Figures 4.4(c) and 4.4(d)), the values below 485nm

showed a characteristic �sagging� away from the ideal values, independent of temperature

(Figure 4.4(a) and 4.4(b)).

(a) Sagging at 1800K (b) Sagging at 2600K

(c) Deviation at 1800K (d) Deviation at 2600K

Figure 4.4: Graphs of the correlation between the ideal (blue) and the measured (red) intensities

using the correction factor



At �rst it was thought that the sagging was caused by a lesser signal to noise ratio in that

wavelength range, but that would mean that the characteristic would increase for lower

signal levels, i.e. lower temperatures, which it did not do signi�cantly. Attempts to a�ect

the sagging e�ect where done in a short parameter study, but none changed the size of

the sag, - only its vertical position. Finally it was thought necessary to test the linearity

correction done by the spectroscope software, which squatted very low count values and

thus could be suspected of a�ecting the measurements in the near-UV spectrum where

count values are small.

4.3 Spectroscope Linearity

Based on the spectroscope's literature (Ocean Optics), the measured spectra are linear to

∼ 92% if left uncorrected, while they are linear to > 99.8% if the spectra are corrected

using their linearization polynomial. In order to verify the linearization polynomial

the same procedure is used, in which the linearity is captured as a plot of normalized

counts/sec versus counts for a constant light source at di�erent integration intervals.

To do so, a series of spectra at various integration types are measured of the tungsten

ribbon lamp at a constant current, i.e. a constant light source. Then an algorithm is

developed which, for a given wavelength, creates an ideal count scale by normalizing

the maximum count measured at that wavelength and creates an ideal value for all the

measured integration times. These values are then �tted to a straight line onto which the

measured values can be corrected to (Figure 4.5).

A correction factor can then be calculated by dividing the ideal values with the measured

values and subsequently inverting the result in order to allow comparison with the Ocean

Optics correction, which is divided onto the measured count values.

After repeating this correction curve process for all wavelengths, a concatenation of values

from all wavelengths is created in order to �t a global linearity polynomial applicable to

the entire spectra. Trying to �t the new correction factors to a 7th degree polynomial, like

the one used by Ocean Optics, results in a very poor �t and attempts with higher degree

polynomials does not improve the �t.



Figure 4.5: Plot showing the ideal counts compared to the measured counts at 693nm

A comparison between the correction factor supplied by the Ocean Optics polynomial,

and those factors calculated in the linearity check can be seen in Figure 4.6. Because of

the poor �ts achieved and the fact that the correction factor is close to 1 for count values

above 500 it is decided that for the purpose of this experiment it seems more prudent not

to linearise the measured data.

(a) (b)

Figure 4.6: Comparison of O.O. linearity algorithm (in green) and calculated correction factors

(in blue). (a) shows the O.O. linearization polynomial compared to the correction factors

calculated from the measured data. (b) is a detailed view of the 0-10000 counts area.



Repeating the calibration process, with the non-linearised data, shows a much closer

correlation between the measured and ideal spectral energy densities, suggesting that the

decision to use the non-linearised data is justi�ed in this particular case. In Figure 4.7 it

can be seen that the application of the non-linear system correction factor eliminates the

sagging for at the lower wavelengths and furthermore improves the correlation at higher

wavelengths.

(a) Sagging at 1800K (b) Sagging at 2600K

(c) Deviation at 1800K (d) Deviation at 2600K

Figure 4.7: Comparison between the correlation of the measured intensities using the linear (red

line) and non-linear (black line) correction factor

Based on the tendencies found when applying the correction factor to the spectral

measurements made on the tungsten lamp, measurements below 400nm and above

approximately 750nm are considered unreliable due to the fact that outside these limits

the corrected measured spectra deviate strongly from the ideal Planck radiation. This can

also be seen in the shape of the correction factor as a function of wavelength (Figure 4.8)

where the measured spectra outside the mentioned limits need large corrections to equate

to the ideal when compared to the spectra within the limits, and are therefore considered



less reliable.

Figure 4.8: Correction factor as a function of wavelength showing the limits

It is important to point out that in order for the system correction factor to be applicable,

the spectra to which it is applied need to be normalized to the same integration time as

that of the one used to determine the calibration factor, which in this case is 5ms.

In Figure 4.9 the percentile deviation between the measured and ideal spectral energy

densities are shown, where the non-linear deviations are showed as solid lines and the

linear deviations are dotted. It can be seen that for lower temperatures, the deviation

between the corrected measured values and the ideal increases, thus showing the necessity

to calibrate the system in the same range as the temperatures expected to be measured.

One can also see that the deviations increase signi�cantly for very low and very high

wavelengths.



Figure 4.9: Plot showing deviation between measured and ideal values as a function of wavelength

The Matlab codes used to determine the system calibration factor and to check the

linearity of the spectroscope can be found in appendices A.1 and A.2.


MSc Thesis 5 Temperature and Emissivity Algorithm

5 Temperature and Emissivity Algorithm

Once the correction factor is calculated, an algorithm is created to calculate the

temperature and emissivity corresponding to any measured spectral energy density. This

is done by using a least square curve �tting method and applying it to wavelength segments

of the spectra.

The least square method �nds the x values that best �t the equation

minx

12‖F (x, xdata)− ydata‖22 =

12

m∑i=1

(F (x, xdatai)− ydatai)2 (5.1)

for which, in this case, x is a vector consisting of emissivity and temperature, i.e.

x = [ε, T ], xdata corresponds to a wavelength segment of m values, e.g. [450nm, 475nm],

while ydata is the measured and corrected spectral energy density for the same wavelength

segment. The function F is a Planck radiation equation where the emissivity and

temperature are unknown variables

F (x, λ) = x1 ·C1

λ5[exp

(C2

λx2

)− 1

] (5.2)

The minimization function can be restricted by preset upper and lower bounds, thus

con�ning any output to be within realistic results. These results can later be evaluated as

to whether or not they result in good approximations to the desired function. In this case

the emissivity is restricted within ε =]0, 1[ and the temperature to T =]500K, 4000K[, as

the values expected to be found should lie within these parameters.

In order to start the minimization a start value for emissivity and temperature need to

be provided. This start value can have a large in�uence on the resulting output because

of the �well seeking� characteristic of the minimization function (see Figure 5.1), in which

a start value 1 or 2 will result in a local minimum A, while start values 3 or 4 will result

in the global minimum B, which is the desired result.



Figure 5.1: In�uence of start value on resulting minimum

To account for this a start value matrix (in this case 11 x 11) is created (Table 5.1),

the values of which are run through as start values for the minimization function, and

thus yielding 121 possible solution combinations to the curve �tting problem for each

wavelength segment.

(ε = 0, T = 4000) (ε = 0.1, T = 4000) · · · (ε = 0.9, T = 4000) (ε = 1, T = 4000)

(ε = 0, T = 3650). . . . .

.(ε = 1, T = 3650)

......

(ε = 0, T = 850) . .. . . . (ε = 1, T = 850)

(ε = 0, T = 500) (ε = 0.1, T = 500) · · · (ε = 0.9, T = 500) (ε = 1, T = 500)

Table 5.1: Start value matrix

Each of these solution combinations has to subsequently be analyzed with respect to how

good a �t results between the measured spectra and the ideal Planck radiation when

using the found x values for that start value combination. To do so, the average residual

between the measured values and the function values is divided by the mean spectral



energy density of the wavelength segment being analysed

criteria = 100 ·

√√√√ m∑i=1

(F (x, xdatai)− ydatai)2∣∣∣∣∣(

m∑i=1

Imeasi

)/m

∣∣∣∣∣(5.3)

This results in a percentage deviation, which can be used as a criterion to determine

how accurately the calculated emissivity and temperature combination �ts the measured

spectra. This criterion is calculated for each of the start value combinations in Table 5.1

yielding a new 11 x 11 matrix with a deviation criterion for each start value combination.

When analyzing the deviation criteria matrix it is necessary to consider the deviation

itself as well as the homogeneity of the criteria. It is clear that for a high deviation

criteria, the resulting emissivity and temperature values for that start value combination

cannot be considered to be accurate. Single cases of low deviation cannot be considered

accurate either, and therefore a loop is inserted in order to determine the lowest deviation

criteria of a given wavelength segment and secondly determine how many other start

value combinations reach the same deviation criteria (to within 0.5%). Using the deviation

criteria and the homogeneity criteria it is then possible to determine if a given combination

of emissivity and temperature can be considered accurate.

Another control of the �tting algorithm is applying it to wavelength segments of various

sizes and compare the results. As well as giving an idea as to how robust the algorithm is,

this step also optimizes the calculation time used when running the algorithm by �nding

a good compromise between small segments which can give more accurate results, but

are more susceptible to noise and time consuming, and larger segments which are the

opposite but have the bene�t of using the entire slope/gradient of the measured spectra

when determining the temperature.

To test the temperature determination algorithm, it is applied to the measured spectra

of the tungsten ribbon lamp, a source of known emissivity and temperature at a given

current. Using this as a test allows for a proper use of the criteria to determine if the

temperature and emissivity estimations are good, since the actual values can be extracted

from table values. Figure 5.2 shows how well the calculated values �t the measured

spectra for each wavelength segment, and the calculated values are shown in Table 5.2.

From these results it is clear that values determined by the algorithm correlate quite well



with the expected values (ε ≈ 0.4 and T ≈ 2000K), and that by analyzing the deviation

criteria (table) and the �t (graph) it is possible to determine if the calculated values can

be trusted. In this particular case all deviation are small except for the �rst segment.

The higher deviation criteria is expected to be caused by the low signal to noise ratio at

the beginning of the segment.

Wavelength segment [nm] Emissivity [-] Temperature [K] Criteria [%]

400-461 0.345 2020.4 13.68

461-522 0.433 1988.9 0.86

522-581 0.487 1973.9 0.27

581-639 0.376 2015.5 0.19

639-696 0.408 2000.5 0.19

696-751 0.262 2091.7 0.60

Table 5.2: Emissivity and temperature estimations from measured spectra of tungsten lamp at

2000K

Figure 5.2: Plot of the �tted wavelength segments (di�erent colors) to the measured intensity

Finally it must be said that, though the algorithm calculates both the emissivity and

temperature, the emissivity is quite an uncertain term compared to the temperature.

This is due to its high dependency on various factors like optical thickness (for �ame

measurements) and wavelength. And while the temperature is an intricate part of

determining the shape of the spectral energy density curve, i.e. determines the gradient



of the Planck curve, and can be estimated fairly accurately through other methods, the

emissivity is merely a scaling factor to the measured intensity and is very di�cult to

determine accurately.

The Matlab codes implementing the emissivity and temperature algorithm can be found

in appendices A.3 and A.4.


MSc Thesis 6 Alignment of Optical Fiber and Camera

6 Alignment of Optical Fiber and Camera

Once a good calibration of the system is made and the emissivity and temperature

algorithm is working properly it is important to see which point on the �ame is being

measured. To do so a beam splitter is used to divide the signal in such a way that 50%

of the light is let through to the optical �ber, while the other 50% is diverted into a high

speed CCD camera.

After adjusting the camera in such a way as to point directly onto the beam splitter,

the latter is adjusted so that an image is captured of the light source at the end of the

borescope. After doing so and attempting to focus the image, it was deemed necessary

to increase the distance between the camera and the beam splitter in order to achieve a

properly focused image. This was impossible with the mounting holes predetermined for

the camera, and therefore an extension mounting plate had to be manufactured in order

to achieve the necessary distance (Appendix C.7).

Once a focused image was achieved, the next step was to align the camera and optical

�ber in such a way that the line of sight of the optical �ber was approximately the same

as the line of sight of the camera. This is necessary in order to be able to determine which

part of the �ame is being measured, independently of the distance from the objective

window. If the two are not aligned, then the optical �ber will be measuring the spectral

energy density of a di�erent pixel region of the camera depending on the distance from

the objective window.

To align the two devices, a LED light shining through a 2.5mm diameter hole in a plate is

used. The plate is placed at a distance of approx. 450mm from the objective window and

in such a way as to register the maximum possible signal on the spectrometer. Next the

position of the centre pixel of the light dot is marked on the image recorded by the camera

(Figure 6.1(a)). Finally the light source is moved as close to the view glass as possible, and

while maintaining the maximum possible signal on the spectrometer (without adjusting

it's position), the beam splitter position is adjusted so that the centre of the light dot

coincides as closely as possible with pixel marking done when the light source was at a

distance.

This results in a coinciding line of sight for both devices as shown in Figure 6.1. The

spectra shown under the images are the spectra measured at the time of alignment. The

reason why the spectra are di�erent in intensity is due to the cone-like behaviour of the



objective area measured, so while at close range the LED �lls the entire measured area, at

a distance it only covers a certain percentage of the measured area and there is therefore

a di�erence in the measured intensities.

(a) Marking of the centre pixel of light source (b) Approx. matching of centers when at close

range

Figure 6.1: Alignment of line of sight between the optical �ber and camera

6.1 Simultaneous Triggering

In order to correlate the measured spectra with the captured images it is necessary

to be able to match the two in time, i.e. synchronize their measurements. For this a

simultaneous triggering method is needed, and even though the camera and spectrometer

have external triggering options, they must be setup to match each other.

(a) External hardware triggering of spectroscope (b) Random reset triggering of high speed camera

Figure 6.2: Schematic showing how the chosen triggering options are executed by the hardware



For the spectrometer, the triggering option is set to �External Hardware�, in which case

the spectrometer idles until it receives an external triggering signal. When the triggering

signal is received, a spectrum is measured for the integration time set in the software.

Once the integration time has elapsed, the spectrometer returns to its idle state. Things

to take into consideration when using this type of triggering are that one cannot average

a series of measurements, nor should one engage the �Electric Dark Correction� option as

this a�ects the measurements when using external triggering. After each spectrum has

been measured they have to be saved manually.

The high-speed camera's external triggering is set in the �triggering mode� option, and

this should be set to �random reset�. When this option is chosen, one is prompted to select

a number of frames to be recorded. By considering the chosen frame rate on the camera,

a number of frames equal to the integration time on the spectrometer should be chosen,

e.g. for a frame rate of 1000 fps one has to choose 50 frames to match an integration time

of 50ms. When all settings are ready the record button is pressed, after which the camera

is ready to receive en external trigger. The camera will record until its 2048 frames have

passed or it is stopped manually, but it will only be the predetermined number of frames

that have been recorded. As with the spectrometer, the frame sequence has to be saved

manually.

When both sets of data have been saved (the spectra and the image series) it is then

possible to start a new measuring sequence.


MSc Thesis 7 Recalibration and Robustness Test

7 Recalibration and Robustness Test

Adjustments to the orientation of the beam splitter and the optical �ber were done in

order to perform the alignment between the camera and optical �ber, so for good measure

a new calibration is performed. From this new calibration, a new system correction

factor is calculated and this correction factor is then compared to the one obtained earlier

(Section 4). This is done in order to test the whole systems robustness against positioning

adjustments.

Figure 7.1: Correlation between the new and old system correction factors

From Figure 7.1 it is clear to see that there are some major di�erences between the

correction factors. The di�erences below 400nm are expected to be caused by signal noise

as well as those above 750nm, and are not deemed important. It is therefore the ratios

between 400nm and 750nm that are considered important, especially in the 400nm to

500nm region because of the big di�erence between the correction factors.

While di�erences are expected due to di�erent adjustments made to the setup (translation

of the optical �ber and adjustment of the beam splitter), it is not the magnitude of the

deviations that is troubling, but the shape. The steep gradient encountered points to a

signi�cant factorial di�erence that might have an e�ect on the gradient of the corrected

spectral intensity, and therefore the temperature determination.

To determine if these di�erences have an e�ect on the resulting temperature measure-

ments, and how big this e�ect might be, each of the correction factors is applied to a

measured spectra obtained from the tungsten lamp at 2000K. The resulting emissivities


MSc Thesis 7 Recalibration and Robustness Test

and temperatures are then compared and shown in Table 7.1

Wavelength

segment [nm]

Emissivity [-] Temperature [K]

Corr2 Corr1 Di�erence [%] Corr2 Corr1 Di�erence [%]

400-461 0.302 0.094 -68.80 2046.6 2245.6 9.72

461-522 0.382 0.096 -74.68 2013.8 2231.8 10.83

522-581 0.472 0.404 -14.49 1984.7 2002.3 0.89

581-639 0.390 0.582 49.24 2015.2 1943.1 -3.58

639-696 0.442 0.942 112.85 1992.8 1865 -6.41

696-751 0.291 0.742 154.91 2078.2 1909.2 -8.13

Table 7.1: Comparison between calculated emissivities and temperatures using di�erent system

correction factors on the same measured spectra

From these values it is clear that the largest deviations are present on the emissivity, while

the deviations in the temperature measurements are comparably very small.

Based on these results it can be expected that adjustments to the position of the �ber

and/or the orientation of the beam splitter have an in�uence on the system calibration,

and thus the calculated values, but this in�uence is much higher on the emissivity than on

the temperature. As a consequence it is considered prudent to perform a new calibration

of the system after adjustments have been made to the beam splitter position as well as

translations of the optical �ber. This way it is possible to use the appropriate correction

factor on a given measured spectra which reduces the comparable deviation between

measurements signi�cantly (Table 7.2).

Wavelength

segment [nm]

Emissivity [-] Temperature [K]

Set1 Set2 Di�erence [%] Set1 Set2 Di�erence [%]

400-461 0.345 0.302 -12.43 2020.4 2046.6 1.30

461-522 0.433 0.382 -11.78 1988.9 2013.8 1.25

522-581 0.487 0.472 -3.10 1973.9 1984.7 0.55

581-639 0.376 0.390 3.80 2015.5 2015.2 -0.01

639-696 0.408 0.442 8.40 2000.5 1992.8 -0.38

696-751 0.262 0.291 10.81 2091.7 2078.2 -0.65

Table 7.2: Comparison of calculated values using paired measurements and correction factors


MSc Thesis 8 Application to Flames

8 Application to Flames

To test and validate the setup, an experiment with a previously studied �ame [19] is set

up. In this experiment, the borescope is placed as to have line of sight through a chamber

containing a �at burner onto a black body of know temperature (Figure 8.1). Without

translating the position of the �ber, the rig is oriented so the black body �lled the entire

measured objective area (maximum spectral signal), and the free line of sight through the

chamber was con�rmed through the camera view (Figure 8.1(c)).

(a) (b)

(c)

Figure 8.1: Experimental setup for �ame measurements. (a) Shows the borescope to the left,

burner chamber in the middle and the black body right. (b) Close up showing the optical access

channels through the chamber. (c) View of the optical access through the burner chamber with

the tube edge (red), the black body (green) and edge of the �ame lifted from the burner (yellow)



To validate the setup and control the calibration, a control measurement of the black

body at 1373K is made, yielding the results shown in Table 8.1, of which the �rst segment

should be disregarded due to the bad �tting criteria.

Wavelength segment [nm] Emissivity [-] Temperature [K] Criteria [%]

494-525 0.369 1435.1 2.69

525-556 0.782 1380.8 1.40

556-587 0.996 1363 1.09

587-618 0.873 1373 1.09

618-648 0.995 1363.2 1.24

648-678 0.994 1363.7 1.21

678-707 0.685 1397.2 1.67

Table 8.1: Measured emissivities and temperatures of the black body

From these results it can be said with reasonable certainty that the setup is well centered

on the black body. The transmittance through the windows on the optical access tubes is

also tested and it yields a transmission loss of ≈ 80%, which correlates well with each of

the windows having a loss of ≈ 90% as expected. Contrary to expectation, including this

parameter into the �tting function does not in�uence the obtained results in a signi�cant

way, and is therefore neglected.

Flame measurements can then made at di�erent heights above the burner (HAB), on the

�ame alone as well as with the black body as a background. These can subsequently be

compared to similar measurements made by Ivarsson.

8.1 Known Flame

Measurements where done on a helium stabilized, fuel rich (φ = 2.15) ethene-air �ame

described in [19], for which the temperature has previously been measured (Table 8.2)

HAB [mm] 20 30 40 50 75 100 125

Temperature [K] 1734 1657 1613 1564 1433 1341 1281

Table 8.2: Flame temperatures as a function of heights above burner [19]



The developed algorithm is then applied to the obtained spectra, yielding average

temperatures ≈ 200K above those in Table 8.2.

Nr. of segments 20mm 30mm 40mm 50mm 75mm 100mm 125mm

1 segm. [K] 1915 1838 1772 1684 1513 1349 1229

�t criteria [%] 4.91 6.18 8.87 9.44 16.76 22.21 32.27

4 segm. [K] 1918 1837 1768 1685 1517 1350 1228

�t criteria [%] 5.97 7.81 11.86 13.16 27.82 40.65 67.19

16 segm. [K] 1711 1656 1610 1561 1432 1372 1308

�t criteria [%] 5.84 7.52 11.46 12.75 27.50 41.53 71.57

Table 8.3: Average temperature calculated at di�erent HAB, for di�erent segment sizes of the

same wavelength interval, 500nm-638nm

It is found that for higher �ame heights the �tting criteria deteriorates. This is considered

to be due to the decreasing optical thickness of the �ame combined with the slight waving

of the �ame top that greatly reduces the measured intensity from the �ame.

Through trial and error it is found that by increasing the number of segments in the

wavelength interval, i.e. reducing the size of the wavelength segments being �tted, it

is possible to reach average temperatures within 5% of the values in Table 8.2 (see

Table 8.3), but the standard deviation between the temperatures calculated for each

wavelength segment is ≈ 160K so it is decided to investigate other probable reasons for

the temperature overestimation.

8.1.1 Varying the Fuel-Air Ratio

To determine if other parameters, such as the optical thickness or light emission from

other combustions products, in�uence the temperature measurements, a fuel-air ratio (φ)

study is made. In this experiment φ is varied in such a way that the �ame goes from a

very faint light emission near stoichiometric conditions to a very bright and sooty �ame at

rich conditions. The measurements are done without changing the position of the burner

with relation to the optical access, so the height of the �ame above the burner changes

with the increasing gas �ow needed to achieve higher values of φ.

By decreasing φ to the point where no soot is visible on the �ame, it is possible to measure

a spectral intensity and determine if the system sensitivity is capable of measuring any



light emissions from other species such as H2O, which is known to have a broad spectrum

of emission at low wavelengths (550-620nm) [20]. If this where the case, the increased

intensity in this region caused by the sum of the emittance from the soot and H2O might

cause the overestimation of temperature due to the change in the intensity gradient, which

is used by the �tting algorithm to determine the temperature.

In the opposite case, by richening the mixture the �ame soot volume fraction of the �ame

is increased thus increasing the intensity of light emitted by the soot. This would in turn

increase the fraction of light emitted by the soot when compared to any other emission

source and therefore reduce the in�uence of this �noise� on the measured spectra. The

averaged calculated temperatures for di�erent segment sizes are shown in Table 8.4. Here

it can be seen that for �ames with a high enough soot concentration (above φ = 2) the

tendency is the same as for the temperatures in Table 8.3, in which the temperature

decreases by increasing the number of �tted segments. The measured �ames with a φ

value below 2 are disregarded due to the very high �tting criteria (< 120% deviation),

which is caused by the very low or nonexistent light intensity from the soot within the

e�ective range of the system, making the signal to noise ratio very poor. In the opposite

case, for increasing fuel-air ratios the �tting criteria improves, pointing to an improvement

in the system accuracy for increased soot density.

Nr. of segments φ 1.6 φ 1.7 φ 1.8 φ 1.9 φ 2 φ 2.1 φ 2.3 φ 2.4

1 segm. [K] 936 520 515 1825 1974 1945 1904 1875

�t criteria [%] 2318.06 246.84 387.49 122.31 6.02 2.04 1.13 1.06

4 segm. [K] 928 528 541 1382 1840 1932 1916 1894

�t criteria [%] 1407.99 258.02 561.53 197.27 6.77 2.15 1.22 0.99

16 segm. [K] 928 536 629 1250 1606 1747 1788 1772

�t criteria [%] 2062.45 328.02 551.73 776.46 6.68 2.20 1.26 1.05

Table 8.4: Average temperature calculated for varying φ values, for di�erent segment sizes of the

same wavelength interval, 500nm-638nm

8.1.2 Higher Temperature Flame

By changing the reactants of the �ame to ethene-oxygen a higher �ame temperature can

be reached thus increasing the intensity of the light emitted by the soot. The �ame



measured in this test has φ = 2.36 and an adiabatic �ame temperature of 2800K. Here

the �ame temperatures are calculated at two di�erent heights above the burner yielding

the temperatures in Table 8.5, in which the tendency is the same as in the two previous

cases (lower temperature for smaller segments).

Nr. of segments 15mm 20mm

1 segm. [K] 1929 1869

�t criteria [%] 1.055 1.163

4 segm. [K] 1934 1876

�t criteria [%] 0.820 0.933

16 segm. [K] 1827 1780

�t criteria [%] 0.843 0.952

Table 8.5: Average temperature calculated for a higher temperature �ame, for di�erent segment

sizes of the same wavelength interval, 500nm-638nm

Noticeable in this case is the low �ame temperatures compared to the calculated adiabatic

�ame temperature. This is presumably caused by the lack of �ame lift above the burner,

resulting in substantial heat losses through the watercooled burner plate and the fact that

the highest temperature should be found at a lower point in the �ame.

8.1.3 Wavelength Dependency of Soot Emissivity

From the investigations made it is clear that by �tting smaller wavelength segment to

the ideal curve, a better approximation is found to the expected temperature. This

is attributed to the fact that soot is not a grey body and therefore its emissivity is a

function of wavelength, and therefore by attempting to �t the spectra emitted by the

soot to a grey body Planck curve an error will occur. The governing parameter when

attempting to �t one large wavelength segment is the curvature/slope of the measured

intensity, and if the emissivity varies through the segment this will alter the slope of the

intensity with respect to a grey body curve thus a�ecting the temperature calculation.

For longer segments this alteration is more pronounced, and therefore by choosing smaller

segments the variation in emissivity is smaller along the segment, thus reducing its e�ect

and making the temperature calculations more dependent on the numerical value of the

intensity.



Based on the empirical model proposed by Hottel and Broughton (eq. (2.3)), this

wavelength dependency is added to the Planck function of the temperature calculating

algorithm, so that eq. (5.2) becomes:

F (x, λ) =

(1− exp

(−x1λα

))· C1

λ5[exp

(C2

λx2

)− 1

] (8.1)

in which x1 is now the KL factor that is assumed to be constant at a given HAB, and α

is set to 1.39 as described in the literature.

Fitting the measured spectra with the new algorithm and using only one wavelength

segment yields the values in Table 8.6, which are less than 1% from the values presented

in [19]. It is possible to improve on the �tting criteria by making the wavelength interval

smaller, but this does not change the temperature signi�cantly.

HAB [mm] 20 30 40 50 75 100 125

Temperature [K] 1724.5 1669.3 1616.4 1551.7 1437.1 1337.0 1281.6

Emissivity [-] 0.024 0.030 0.032 0.040 0.047 0.061 0.052

Criteria [%] 9.784 12.353 18.373 19.164 33.293 43.143 60.468

Table 8.6: Calculated temperatures using wavelength dependent emissivity of soot and �tted to

the whole wavelength interval 400nm-700nm

The calculated emissivities show a tendency to increase, which is expected due to the

increase in soot density with decreasing temperature.

8.2 Determining the Hottest Flame Temperature

Measurements through the �ame and onto the black body (Figure 8.2) are used to

determine if it is possible to determine the highest temperature through the line of sight

of the system. By measuring onto the black body, the measured spectra is a summation

of the intensity from the �ame and the black body and it can then be investigated if the

setup can determine the highest temperature, which in this case is the �ame.



Figure 8.2: Sketch of the setup for measuring on �ame and black body

Since hotter temperatures translate the slope of the spectra further toward the UV region

it is expected that by �tting smaller wavelength segments it will become possible to

distinguish the highest temperature in the lower wavelengths, as the �ame will become a

stronger, or even sole contributor to the joint intensity. By comparing the temperatures

calculated in section 8.1.3 to the temperatures calculated for small wavelength segments

(Figure 8.3) it can be seen that there is an upward going trend for the temperature when

moving toward smaller wavelengths.



Figure 8.3: Temperature trend for smaller segments in the 400nm-700nm interval

The wavelength segments that yield temperatures above the temperatures measured on

the �ame alone might be caused by the very low intensity in this wavelength region.

Therefore a �t of similar segment sizes is made of the wavelength interval in which the

intensity slope begins (Figure 8.4).

Figure 8.4: Temperature trend for smaller segments centered on intensity slope, in the 500nm-

638nm interval

By doing this it is it is clear that even though there is an increase in the calculated

temperature at lower wavelengths, it does not reach the same temperature as when



measuring on the �ame alone. This would indicate that in order to determine the hottest

temperature based on a line of sight measurement it is necessary to incorporate a model

with multiple zones. This is a rough way to account for the variation in temperature and

emissivity through the �ame, as well as the emittance from another source, e.g. the black

body.

8.2.1 Multi-zone Model

By inserting an additional zone into the �tting function it is possible to account for

two sources of intensity and their interaction with each other. The �tting function then

becomes

IT = ε1 · I1 + (1− ε1) · ε2 · I2 (8.2)

where ε1 and I1 correspond to the �ame (the zone closest to the detector) and ε2 and I2

are from the black body. The additional term, (1− ε1), accounts for the absorption of the

black body intensity through the �ame.

Using this �tting function it was possible to separate the two sources of radiation, but

the optimizing algorithm is found to be extremely dependent on the given start values.

(a) HAB 20mm (b) HAB 50mm

Figure 8.5: Results from the 2 zone �tting function showing good correlation for HAB 20mm,

but an overestimation of the �ame temperature at HAB 50mm

Figure 8.5 shows the results obtained from the two zone �tting function in which the start

values are set close to the known values at 20mm HAB, leading to an overestimation of

the temperature at 50mm HAB. In the opposite case, if the start values where set closer



to the known values at 50mm, it would result in an underestimation of the temperature

at 20mm. This shows that the calculated values are dependent on the given start values.

This points to the �tting function having multiple optimal minima, and before the end

of this project it was not possible to optimize the numerical process in order to solve this

problem.


MSc Thesis 9 Test of Contaminated Glass

9 Test of Contaminated Glass

The measurement of the transmissive properties of the sooted view glass gives the

possibility to determine its �ltering characteristics. The �ltering characteristics can

determine if the view glass acts as grey �lter, i.e. reduces the transmissivity through it by

an equal amount at all wavelengths, or if there are wavelength bands that are not a�ected

by the contamination, and which pass through with approximately the same intensity.

To determine the �ltering characteristics of the sooted access window, a contaminated

specimen is placed between the tungsten lamp used for calibration and the borescope

objective window. Intensity spectra are measured, after which the glass is removed

and unobstructed measurements are made. These measurements are then compared to

determine which e�ects the soot layer has on the intensity transmittance.

Figure 9.1: Intensity fraction passing through the sooted view glass as a function of wavelength

From Figure 9.1 it is clear that the fraction of the spectral intensity passing through

the sooted window is very low (about 5-15%) for wavelengths between 450 and 800nm.

The decrease in signal is not constant along the wavelength range, but it is however

approximately linear which suggests that the contaminated window acts almost as a grey

�lter. The segments which have an inherited low signal, i.e. very low and very high

wavelengths, do not serve as any indication due to the fact that even without the �lter

they do not measure signi�cant intensities.

This decrease in transmissivity is considered to arise gradually as the layer of soot


MSc Thesis 9 Test of Contaminated Glass

and other contaminants on the window increases, and it can therefore be assumed to

be an approximately linear decrease in transmissivity as a function of engine runtime.

Combining the signal attenuation as a function of wavelength and the decrease in

transmissivity as a function of engine run time, it is considered possible to incorporate

this as a correction parameter into optical measurements done on the engine.


MSc Thesis 10 Result Discussion and Comments

10 Result Discussion and Comments

Based on the measurements performed on �ames and other emitters it has been found that

for this method it is important to adjust the �tting Planck function to suit the measured

object. Since this project is aimed at �ame temperature measurements, the wavelength

dependency of the soot emissivity is a key factor in successful measurements.

The validity of the obtained results has to be determined through a combined assessment

of the �tting criteria and the relative �t of the wavelength segments to the measured

spectra. Only when both are good, i.e. low percentage deviation and a good �t to the

measured values, one can safely assume that the calculated temperature and emissivity

values are a realistic estimate, though one has to take into account the in�uence of signal

noise on the �tting criteria.

Based on a combined qualitative analysis of the measured spectra, the system correction

factor and the �tted values it is estimated that this setup has an e�ective range between

400 and 750nm, with a dependency on the temperature and emissivity of the measured

object. After various tests it is found that the highest accuracy/correlation with the

expected results is obtained on the wavelength sections that contain the upward going

slope of the measured spectra.

In order for the algorithm to be able to approximate the temperature of a given wavelength

segment, it is necessary that the measured/corrected spectra exhibit similar tendencies

as the ideal Planck curve to which they are being �tted to, i.e. curvature gradient. If

the gradient of the system corrected spectra is too di�erent from the gradient of the ideal

curve at any temperature, the estimated values will be useless (Figure 10.1).


MSc Thesis 10 Result Discussion and Comments

Figure 10.1: Bad �t of the last three wavelength segments due to dissimilar gradient tendencies

When performing intensity measurements it is important to adjust the integration time

in such a way as to take advantage of the spectrometers whole dynamic range, i.e. the

maximum of the measured spectra should be as close to the spectrometers saturation

point as possible. This way the highest amount of information is gathered about the

measured object in a particular spectrum. One also has to take into consideration that

the background noise has to be scaled accordingly, i.e. it has to be the same integration

time, in order for it to be a proportionate amount of background noise that is subtracted

from the intensity spectra.

The e�ective temperature range of the system is dependent on the spectral intensity of

the measured �ame. For high emissivities, the temperature can be as low as 1100-1200K,

while low emissivities need signi�cantly higher temperatures.


MSc Thesis 11 Systematic Error Sources

11 Systematic Error Sources

Based on the results obtained during this project a discussion of potential systematic

errors can be presented.

Due to the wavelength dependency of the emissivity of tungsten, and the empirical nature

of the values used to determine it, it is expected that this parameter has a small in�uence

on the system correction factor, along with the transmissivity of the tungsten lamp. As

these parameters are scaling factors, they in turn should only a�ect the calculated values

as a scaling factor, i.e. the calculated emissivity of the �ame, and thus not in�uence the

temperature estimations signi�cantly.

As the measured spectra is determined as a mean of the light emitted from the measured

objective area, and as this area increases as the object is further away from the view glass,

it is expected that turbulent �ames measured at a distance will contribute with both hot

and cold soot areas. This will result in averaged measurements of �ames at the edge of

the intended scope of the setup (the bore diameter of the test engine).


MSc Thesis 12 Future Work

12 Future Work

A direct continuation of this project would involve the insertion of the setup into MAN

Diesel's test engine, and the measurement of the spectral energy densities from the internal

combustion. In order to move on to this step, a more comprehensive alignment of the

camera and optical �ber should be achieved. By achieving a complete alignment it would

be possible to map the translation of the optical �ber as a function of the pixel map

on the camera's CCD chip, thus introducing the possibility to determine and adjust the

positioning of the �ber (and hence the measured objective area) based on the images

captured by the camera.

Whether a better alignment is achieved or not, it should be considered necessary

to investigate the in�uence of the beam splitter orientation. Even though a short

investigation was performed in this study, it only encompassed two beam splitter positions,

so an expansion of this investigation would be bene�cial when considering system

alignment capabilities.

In order to reduce the in�uence of the averaging of emitted light by a �ame, it would

be necessary to reduce the objective �eld of view even further. By measuring a smaller

�ame area compared to the one achieved in this project, the e�ect of �ame turbulence

on the temperature measurements might be reduced, by allowing the capture of spectra

form only the hot or cold part of the �ame at any given time.

Improvements of the physical model and/or the numerical process used in the multiple

zone approach should be made. This should result in unique solutions, which are less

dependent on the given start values and are therefore more reliable.

Work can also be done on the characterization of the sooting of the view glass. By

obtaining sooted windows extracted from the engine at di�erent running intervals, it

would allow for a closer determination of the time dependence between the soot build

up and the subsequent reduction in light transmission. This might then be implemented

as a correction factor, allowing for comparable temperature measurements throughout a

continuous engine run or test cycle.


MSc Thesis 13 Conclusions

13 Conclusions

A �ame temperature measurement method using full spectral analysis of the radiating

soot has been developed for application in large two stroke diesel engines.

During the development of the method it has been found that the wavelength dependency

of soot emissivity has a signi�cant in�uence on the calculated temperatures, changing

the slope of the measured intensity when compared to that of a gray body of similar

temperature, and therefore leading to a temperature overestimation. This temperature

overestimation is partially removed by calculating the temperature of small wavelength

segments, in which case the emissivity variation along the segment is less signi�cant thus

yielding better results. Introducing a semi empirical model of soot emissivity to the �tting

function improves results signi�cantly, and allows for good temperature determination

using the whole e�ective wavelength range of the system, therefore taking better advantage

the temperature determining characteristic of the Planck curve, i.e. the slope.

By performing measurements of the �ame against a colder but much stronger emitter

an investigation into the capability to measure the highest �ame temperature is made,

and two approaches are attempted. First it is attempted to isolate the hottest emitter

by measuring closer to the UV wavelength region, but even though there is a tendency

to measure hotter temperatures at lower wavelengths it is not possible to separate it

completely from the colder background. After this a multiple �ame zone model is

implemented to the algorithm, but it was not possible to optimize the numerical process

and the method is therefore still susceptible to the given start values to be of good use.

Based on the results found during this project it is expected that a two color method

needs to either incorporate the wavelength dependency of soot emissivity, or use narrow

wavelength bands, hereby minimizing the in�uence of the wavelength dependency. In

order to determine the highest temperature of the �ame a multiple �ame zone model

needs to be implemented, requiring a number of �colors� equal to the number of variables

in the model. This step does however require a better understanding of the optimization

used by the �tting function.


MSc Thesis References

References

[1] J.B. Heywood. Internal Combustion Engine Fundamentals. McGraw-Hill, 1998.

[2] J.E. Dec. A conceptual model of di diesel combustion based on laser-sheet imaging.

SAE Technical Paper series, (970873), 1997.

[3] M.P.B. Musculus, S. Singh, and R.D. Reitz. Gradient e�ects on two-color soot optical

pyrometry in a heavy-duty di diesel engine. Combustion and Flame, 153(1-2):216 �

227, 2008.

[4] Y. Matsui, T. Kamimoto, and S. Matsuoka. A study on the time and space resolved

measurement of �ame temperature and soot concentration in a di diesel engine by

two-color method. SAE Technical Paper series, (790491), 1979.

[5] J.P.A. Neeft, M. Makkee, and J.A. Moulijn. Diesel particulate emission control. Fuel

processing technology, 47:1 � 69, 1996.

[6] S. C. Sorenson. Engine Principles and Vehicles. not yet published, 2009.

[7] H.C. Hottel and F.P. Broughton. Determination of true temperature and total

radiation from luminous gas �ames. Industrial and engineering chemistry, 4(2):166

� 175, 1932.

[8] Y. Matsui, T. Kamimoto, and S. Matsuoka. Formation and oxidation o� soot

particulates in a di diesel engine - an experimental study via the two-color method.

SAE Transactions, 91:1923 � 1935, 1982.

[9] Y. Matsui, T. Kamimoto, and S. Matsuoka. A study on the application of the two-

color method to the measurement of the �ame temperature and soot contentrations

in diesel engines. SAE Technical Paper series, (800970), 1980.

[10] J. Vattulainen, V. Nummela, R. Herneberg, and J. Kytölä. A system for quantitative

imaging diagnostics and its application to pyrometric in-cylinder �ame-temperature

measurements in large diesel engines. Measurement Science Technology, 11:103 � 119,

2000.

[11] MAN Diesel SE. Internal communication with MAN Diesel. Internal, 2009.


MSc Thesis References

[12] S. Singh, R.D. Reitz, and M.P.B. Musculus. 2-color thermometry experiments and

high-speed imaging of multi-mode diesel engine combustion. SAE Technical Paper

series, (2005-01-3842), 2005.

[13] S. De Iuliis, M. Barbini, S. Benecchi, F. Cignoli, and G. Zizak. Determination of the

soot volume fraction in an ethylene di�usion �ame by multiwavelength analysis of

soot radiation. Combustion and Flame, 115(1-2):253 � 261, 1998.

[14] Sharon Sade and Abraham Katzir. Spectral emissivity and temperature

measurements of selective bodies using multiband �ber-optic radiometry. Journal

of Applied Physics, 96(6):3507�3513, 2004.

[15] V. Scharf, N. Naftali, O. Eyal, S.G. Lipson, and A. Katzir. Theoretical evaluation of

a four-band �ber-optic radiometer. APPLIED OPTICS, 40(1):104�111, 2001.

[16] V. Scharf and A. Katzir. Four-band �ber-optic radiometry for determining the true

temperature of gray bodies. APPLIED PHYSICS LETTERS, 77(19):2955�2957,

2000.

[17] I.S. Mohammad. Simultaneous Pyrometer Measurements Along Three Directions in

an Open Chamber Diesel. PhD thesis, University of Wisconsin - Madison, 1990.

[18] L. Ornstein. Tables of the emissivity of tungsten as function of wavelength and

temperature. Physica, 3(6), 1936.

[19] A. Ivarsson. Modeling of heat release and emissions from droplet combustion of multi

component fuels in compression ignition engines. PhD thesis, DTU, 2009.

[20] A.G. Gaydon. The Spectroscopy of Flames. Chapman and Hall, 2nd edition, 1974.


MSc Thesis A Matlab Codes

A Matlab Codes

In this appendix are printouts of the Matlab code used in the various parts of this report.

A.1 Calibration

1 clc

2 clear a l l

3 close a l l

4 %% Load data and constants

5 load temp %load temperatures corresponding to tungsten emissivities

6 load lambda %load wavelengths corresponding to tungsten emissivities

7 load emis %load tungsten emissivities

8 load spec t ra3 %loads calibration spectra

9 load back4 %loads background spectra

10 load i n t 4 %loads integration time vector

11 C1=299792458^2∗6.62606896e−34; %Planck 's first constant [W*m^2]

12 C2=299792458∗6.62606896 e−34; %Planck 's second constant [J*m]

13 k=1.380650424e−23; %Boltzmann 's constant [J/K]

14 %% Treat the loaded spectra

15 h=f s p e c i a l ( ' gauss ian ' , [ 4 0 1 ] , 1 0 ) ; %filtering options

16 back4_f1 ( : , 1 )= back4 ( : , 1 ) ;

17 back4_f1 ( : , 2 )= im f i l t e r ( back4 ( : , 2 ) , h ) ; %filter the measured data to a smoother curve

18 back4_f ( : , 1 )= back4_f1 ( 2 1 : length ( back4 ( : , 1 ) ) −20 , 1 ) ; %removes first and last 20 values

19 %of the vector

20 back4_f ( : , 2 )= back4_f1 ( 2 1 : length ( back4 ( : , 2 ) ) −20 , 2 ) ; %removes first and last 20 values

21 %of the vector

22 back4_f ( : , 2 )= back4_f ( : ,2)−mean( back4_f ( 1 : 1 0 0 , 2 ) ) ; %subtracts the mean background noise

23

24 spectra3_f ( : , 1 )= spec t ra3 ( 2 1 : length ( spec t ra3 ( : , 1 ) ) −20 , 1 ) ; %removes first and last 20

25 %values of the vector

26 for x=2: length ( spec t ra3 ( 1 , : ) )

27 spect ra321 ( : , x)=spec t ra32 ( 2 1 : length ( spec t ra32 ( : , 1 ) ) −20 , x ) ; %removes first and last

28 %20 values of the vector

29 spec t ra31 ( : , x)=spect ra321 ( : , x)−back ( : , 2 ) ; %subtracts the mean background noise

30 %from each spectrum

31 spec t ra3 ( : , x)=spec t ra31 ( : , x)−mean( spec t ra31 (1 : 100 , x ) ) ; %zeroes the spectra

32 end

33 l=spectra3_f ( : , 1 ) ; %wavelength range [nm]

34 l_v=l .∗1 e−9; %converts wavelength range into [m]

35 tau = −1.797e−19∗ l .^6 + 8.717 e−16∗ l .^5 − 1 .764 e−12∗ l .^4 + 1.916 e−09∗ l .^3 − . . .

36 1 .192 e−06∗ l .^2 + 4.111 e−04∗ l + 8 .681 e−01; %transmission through quartz as function

37 %of wavelenght , wavelength input in [nm]

38 for j =1: length ( temp ( 1 , : ) )

39 emi ( : , j )=spline ( lambda ( : , 1 ) , emis ( : , j ) , l ) ; %Generate an interpolation of tungsten

40 %emissivity at various temperatures

41 end

Danmarks Tekniske Univeristet I DTU Mechanical Engineering


42 %% Linearize using OceanOpticts polynomial

43 c=[−5.18367e−32 ,1.37019 e−26 ,−1.53261e−21 ,9.39307 e −17 , . . .

44 −3.40145e−12 ,7.26206 e−8 ,−8.43799e−4 ,5.09778 e0 ] ; %coefficients from the O.O.

45 %linarization polynomium

46 spectra3_l = [ ] ;

47 spectra3_l ( : , 1 )= spectra3_f ( : , 1 ) ;

48 for j =2: length ( spectra3_f ( 1 , : ) )

49 spectra3_l ( : , j )=spectra3_f ( : , j ) . / polyval ( c , spectra3_f ( : , j ) ) ; %linearized spectra

50 %using O.O. linarization polynomium

51 end

52 %% spectral radiance corrected for emissivity and transmission

53 for j =1: length ( temp (1 , : ) ) −1

54 i r ( : , j )=tau .∗ emi ( : , j ) . ∗ ( ( 2 ∗C1 ) . / ( l_v .^5 .∗ (exp(C2 . / ( l_v .∗ k∗temp (1 , j ) ) ) −1 ) ) ) ;

55 %Planck 's radiation law [W/(m^3*sr)], input in SI units

56 end

57 factor_n=i r ( : , 7 ) . / spectra3_f ( : , 8 ) ;

58 f a c to r_ l=i r ( : , 7 ) . / spectra3_l ( : , 8 ) ;

59 %% Plots

60 figure ( ) %T=2800 comparison

61 plot ( l , i r ( : , 7 ) , '−b ' , l , factor_n .∗ spectra3_f ( : , 8 ) , '−k ' , l , f a c to r_ l .∗ spectra3_l ( : , 8 ) , '−r ' , . . .

62 ' LineWidth ' , 2 )

63 legend ( ' I d e a l Planck ' , 'Measured i n t e n s i t y − non l inea r ' , 'Measured i n t e n s i t y − l i n e a r ' )

64 xlabel ( 'Wavelength [nm] ' , ' f o n t s i z e ' , 13)

65 ylabel ( ' I n t e n s i t y [W/(m^3∗ s r ) ] ' , ' f o n t s i z e ' ,13)66


68 plot ( l , i r ( : , 6 ) , '−b ' , l , factor_n .∗ spectra3_f ( : , 7 ) . ∗ i n t 4 ( length ( i n t4 ) ) . / in t4 ( 6 ) , '−k ' , . . .69 l , f a c to r_ l .∗ spectra3_l ( : , 7 ) . ∗ i n t 4 ( length ( i n t4 ) ) . / in t4 ( 6 ) , '−r ' , ' LineWidth ' , 2 )

















Danmarks Tekniske Univeristet II DTU Mechanical Engineering














109 %% Percentile deviation

110 p_2600n=im f i l t e r ( ( ( ( i r ( : ,6)− factor_n .∗ spectra3_f ( : , 7 ) ∗ i n t 4 ( length ( i n t4 ) ) . / in t4 ( 6 ) ) . / . . .

111 i r ( : , 6 ) ) ∗ 1 0 0 ) , h ) ;112 p_2400n=im f i l t e r ( ( ( i r ( : ,5)− factor_n .∗ spectra3_f ( : , 6 ) ∗ i n t 4 ( length ( i n t4 ) ) . / in t4 ( 5 ) ) . / . . .

113 i r ( : , 5 ) ) ∗10 0 , h ) ;114 p_2200n=im f i l t e r ( ( ( i r ( : ,4)− factor_n .∗ spectra3_f ( : , 5 ) ∗ i n t 4 ( length ( i n t4 ) ) . / in t4 ( 4 ) ) . / . . .




121 i r ( : , 1 ) ) ∗10 0 , h ) ;122 p_2600l=im f i l t e r ( ( ( ( i r ( : ,6)− f a c to r_ l .∗ spectra3_l ( : , 7 ) ∗ i n t 4 ( length ( i n t4 ) ) . / in t4 ( 6 ) ) . / . . .

123 i r ( : , 6 ) ) ∗ 1 0 0 ) , h ) ;124 p_2400l=im f i l t e r ( ( ( i r ( : ,5)− f a c to r_ l .∗ spectra3_l ( : , 6 ) ∗ i n t 4 ( length ( i n t4 ) ) . / in t4 ( 5 ) ) . / . . .

125 i r ( : , 5 ) ) ∗10 0 , h ) ;126 p_2200l=im f i l t e r ( ( ( i r ( : ,4)− f a c to r_ l .∗ spectra3_l ( : , 5 ) ∗ i n t 4 ( length ( i n t4 ) ) . / in t4 ( 4 ) ) . / . . .




133 i r ( : , 1 ) ) ∗10 0 , h ) ;134

135

136 figure ( )

137 plot ( l , p_2600n , l , p_2400n , l , p_2200n , l , p_2000n , l , p_1800n , l , p_1600n , ' LineWidth ' , 2 )

138 hold on

139 plot ( l , p_2600l , ' : ' , l , p_2400l , ' : ' , l , p_2200l , ' : ' , l , p_2000l , ' : ' , l , p_1800l , ' : ' , l , p_1600l , . . .

140 ' : ' , ' LineWidth ' , 2 )

141 legend ( ' 2600ôK ' , ' 2400ôK ' , ' 2200ôK ' , ' 2000ôK ' , ' 1800ôK ' , ' 1600ôK ' , ' 2600ôK ' , ' 2400ôK ' . . .

Danmarks Tekniske Univeristet III DTU Mechanical Engineering


142 , ' 2200ôK ' , ' 2000ôK ' , ' 1800ôK ' , ' 1600ôK ' )


144 ylabel ( ' P e r c e n t i l e dev i a t i on [%] ' , ' f o n t s i z e ' , 13)

145 axis ( [ 4 00 800 −5 5 ] )

A.2 Linearity

1 clc

2 clear a l l

3 close a l l

4 %% Load data and treat measured spectra

5 load i n t

6 load n l i n e a r

7

8 h=f s p e c i a l ( ' gauss ian ' , [ 4 0 1 ] , 1 0 ) ; %filtering options

9

10 n l inea r_f ( : , 1 )= n l i n e a r ( 2 1 : length ( n l i n e a r ( : , 1 ) ) −20 , 1 ) ; %removes first and last 20 values

11 %of the vector

12 for x=2: length ( n l i n e a r ( 1 , : ) )

13 n l inear_f1 ( : , x)= im f i l t e r ( n l i n e a r ( : , x ) , h ) ; %filter the data to a smoother curve

14 n l inea r_f ( : , x)=n l inear_f1 ( 2 1 : length ( n l i n e a r ( : , 1 ) ) −20 , x ) ; %removes first and last 20

15 %values of the vector

16 n l inea r_f ( : , x)=n l inea r_f ( : , x)−mean( n l i n e a r ( 1 : 132 , x ) ) ; %subtracts the mean background

17 %noise from each spectrum

18 end

19

20 %% Linearize using OceanOpticts polynomial

21 c=[−5.18367e−32 ,1.37019 e−26 ,−1.53261e−21 ,9.39307 e −17 , . . .

22 −3.40145e−12 ,7.26206 e−8 ,−8.43799e−4 ,5.09778 e0 ] ; %coefficients from the O.O.

23 %linarization polynomial

24 l i n e a r = [ ] ;

25 l i n e a r ( : , 1 )= n l inea r_f ( : , 1 ) ;

26 for j =2: length ( n l inea r_f ( 1 , : ) )

27 l i n e a r ( : , j )=n l inea r_f ( : , j ) . / polyval ( c , n l i nea r_f ( : , j ) ) ; %linearized using O.O.

28 %linarization polynomial

29 end

30 %% Create own polynomial

31 j =1;

32 for k=302:68:1662

33 p_line = [ 1 , 0 ] ; %coefficients for straight line

34 for m=1: length ( i n t )

35 counts_idea l (m, j )=n l inea r_f (k , length ( n l inea r_f ( 1 , : ) ) ) ∗ i n t (m)/ i n t ( length ( i n t ) ) ;

36 %Creates an ideal count scale

37 end

38 l i n e v a l u e s ( : , j )=polyval ( p_line , counts_idea l ( : , j ) ) ; %Values of ideal counts on

39 %straight line

40 corr_curve ( : , j )=1./( l i n e v a l u e s ( : , j ) . / n l inea r_f (k , 2 : length ( n l inea r_f ( 1 , : ) ) ) ' ) ;

41 %Values to correct the measured values to the ideal line

42 [ p_corr ( : , j ) , S ,mu]=polyf it ( n l inea r_f (k , 2 : length ( n l inea r_f ( 1 , : ) ) ) ' , corr_curve ( : , j ) , 2 0 ) ;

Danmarks Tekniske Univeristet IV DTU Mechanical Engineering


43 %coefficients to fit a polynomium to the correction values

44 muv( : , j )=mu; %std.dev. and mean of the coefficients in p_corr

45 long ( j ∗ length ( i n t )−43: j ∗ length ( i n t ) ,1)= n l inea r_f (k , 2 : length ( n l inea r_f ( 1 , : ) ) ) ' ;

46 %concatenates all count values into one vector

47 long ( j ∗ length ( i n t )−43: j ∗ length ( i n t ) ,2)= corr_curve ( : , j ) ' ;

48 %concatenates all correction coefficients into one vector

49 j=j +1;

50 end

51

52 long=sort rows ( long ) ; %sorts all values in the vector wrt. the count number

53 n=1;

54 for x=1:22: length ( long ( : ,1)) −21 %loop to piecewise average the values of counts

55 %and corr.coeff.

56 long_avg (n ,1)=mean( long (x : x+21 ,1)) ;

57 long_avg (n ,2)=mean( long (x : x+21 ,2)) ;

58 n=n+1;

59 end

60 p_all=polyf it ( long_avg ( : , 1 ) , long_avg ( : , 2 ) , 9 ) ; %returns the coefficients of the new

61 %linearization polynomium

62 figure ( ) %plot of the correction factors and the O.O. polynomial

63 plot ( long_avg ( : , 1 ) , long_avg ( : , 2 ) , ' x ' )

64 hold on

65 plot ( long_avg ( : , 1 ) , polyval ( c , long_avg ( : , 1 ) ) , ' g ' )

66 plot ( long_avg ( : , 1 ) , polyval ( p_all , long_avg ( : , 1 ) ) , ' r ' )

A.3 Emissivity and Temperature

1 clc

2 clear a l l

3 close a l l

4 %% Load data and constants

5 load sky l %load measured spectra

6 load f ac torn2no %load correction factor

7 load back %load background measurements

8 in t4 =6000; %set integration time for the measured data

9

10 back_f ( : , 1 )= back ( 2 1 : length ( back ( : , 1 ) ) −20 , 1 ) ; %removes first and last 20 values of

11 %the vector

12 back_f ( : , 2 )= back ( 2 1 : length ( back ( : , 1 ) ) −20 , 2 ) ; %removes first and last 20 values of

13 %the vector

14 skyl_f ( : , 1 )= sky l ( 2 1 : length ( sky l ( : , 1 ) ) −20 , 1 ) ; %removes first and last 20 values of

15 %the vector

16 for x=2: length ( sky l ( 1 , : ) )

17 skyl_f2 ( : , x)=sky l ( 2 1 : length ( sky l ( : , 1 ) ) −20 , x ) ; %removes first and last 20 values

18 %of the vector

19 skyl_f1 ( : , x)=skyl_f2 ( : , x)−back_f ( : , 2 ) ; %subtracts the mean background noise from

20 %each spectrum

21 skyl_f ( : , x)=skyl_f1 ( : , x)−mean( skyl_f1 (1 : 100 , x ) ) ; %subtracts the mean background

22 %noise from each spectrum

Danmarks Tekniske Univeristet V DTU Mechanical Engineering


23 end

24

25 l=skyl_f ( : , 1 ) ; %wavelength range [nm]

26 l_v=l .∗1 e−9; %converts wavelength range into [m]

27 %% Piecewise curvefitting

28 P=[550 1306 ] ; %index of start and end of wavelength segment and segment size

29 for m=1:11 %create start value matrix

30 for n=0:10

31 EMI(m, n+1)=−eps−n∗ . 95 e3 ;32 TEM(n+1,m)=4−n ∗0 . 3 5 ;33 end

34 end

35 lb=[−1e5 ,0 .5+eps ] ; %lower boundaries

36 ub=[−eps ,4−eps ] ; %upper boundaries

37 %setting options for the curve fitting function

38 opt ions = opt imset ( ' Display ' , ' f i n a l ' , ' LargeSca le ' , ' on ' , . . .

39 'TolX ' ,1 e−6, 'TolFun ' ,1 e−15, ' Der ivat iveCheck ' , ' o f f ' , . . .

40 ' D iagnos t i c s ' , ' o f f ' , ' FunValCheck ' , ' o f f ' , . . .

41 ' Jacobian ' , ' o f f ' , ' JacobMult ' , [ ] , . . . % JacobMult set to [] by default

42 ' JacobPattern ' , ' spa r s e ( ones ( Jrows , J c o l s ) ) ' , . . .

43 'MaxFunEvals ' , 4 0 0 0 0 , . . .

44 ' DiffMaxChange ' ,5 e−1, ' DiffMinChange ' ,1 e − 8 , . . .

45 ' PrecondBandWidth ' ,0 , ' TypicalX ' , ' ones ( numberOfVariables , 1 ) ' , . . .

46 'MaxPCGIter ' , 'max(1 , f l o o r ( numberOfVariables /2) ) ' , . . .

47 'TolPCG ' , 0 . 001 , ' MaxIter ' , 4 0 0 0 , . . .

48 ' LineSearchType ' , ' quadcubic ' , ' LevenbergMarquardt ' , ' on ' , . . .

49 'OutputFcn ' , [ ] , ' PlotFcns ' , [ ] ) ;

50 for k=2: length ( skyl_f ( 1 , : ) ) %set the measured spectra to calculate on

51 ir_m ( : , k−1) = ( skyl_f ( : , k ) . ∗ factor_n2_no ∗5/ in t4 )/1 e11 ; %determine the measured

52 %intensity and "normalize"

53 for j =1: length (P)−1 %do calculations for each segment

54 o=1;

55 crit_min ( j )=100;

56 c r i t_t ( j , k−1)=0;

57 for m=1:11

58 for n=1:11

59 x0=[EMI(n ,m) , TEM(n ,m) ] ; %start guesses for emissivity and temperature

60 %, with temperature in kilo K

61 [ x , resnorm ] = l s q c u r v e f i t (@planck_tk , x0 , l (P( j ) :P( j +1 ) , 1 ) , . . .

62 ir_m(P( j ) :P( j +1) ,k−1) , lb , ub , opt ions ) ; %

63 X1(n ,m, j , k−1)=x ( 1 ) ;64 X2(n ,m, j , k−1)=x ( 2 ) ;65 c r i t (n ,m, j )=100∗( sqrt ( resnorm /( (P( j+1)−P( j ) − 1 ) ) ) / . . . %fitting criteria

66 (abs (sum( ir_m(P( j ) :P( j +1) ,k−1))/(P( j+1)−P( j ) ) ) ) ) ;67 i f c r i t (n ,m, j )<crit_min ( j )−crit_min ( j )∗0 .05 %min. fittign criteria

68 crit_min ( j )= c r i t (n ,m, j ) ;

69 c r i t_t ( j , k−1)=1;

70 e l s e i f c r i t (n ,m, j )<=crit_min ( j )+crit_min ( j )∗0 .005 & . . .

71 c r i t (n ,m, j )>=crit_min ( j )−crit_min ( j )∗0 .00572 c r i t_t ( j , k−1)=cr i t_t ( j , k−1)+1;

Danmarks Tekniske Univeristet VI DTU Mechanical Engineering


73 end

74 X_i(o , 1 : 2 , j , k−1)=x ;75 X_i(o , 3 , j , k−1)= c r i t (n ,m, j ) ;

76 o=o+1;

77 end

78 end

79 end

80 end

81

82 for k=2length ( skyl_f ( 1 , : ) ) %create matrix with all values

83 for j =1: length (X_i ( 1 , 1 , : , k−1))

84 i f X_i(1 , 1 , j , k−1)~=085 ind=find (X_i ( : , 1 , j , k−1)==0);

86 i f ind~=0

87 X_m( j , 1 : 2 : 5 , k−1)=mean(X_i ( 1 : ind −1 , : , j , k−1)) ;

88 X_m( j , 2 : 2 : 6 , k−1)=std (X_i ( 1 : ind −1 , : , j , k−1)) ;

89 X_m( j , 7 , k−1)=min(X_i ( 1 : ind −1 ,3 , j , k−1)) ;

90 else

91 X_m( j , 1 : 2 : 5 , k−1)=mean(X_i ( : , : , j , k−1)) ;

92 X_m( j , 2 : 2 : 6 , k−1)=std (X_i ( : , : , j , k−1)) ;

93 X_m( j , 7 , k−1)=min(X_i ( : , 3 , j , k−1)) ;

94 end

95 end

96 X_m( j , 8 , k−1)=( c r i t_t ( j , k−1)/(n∗m))∗100 ;97 end

98 end

99

100 for k=2: length ( skyl_f ( 1 , : ) ) %plot the measured and fitted values

101 figure (k−1)102 plot ( skyl_f ( : , 1 ) , ir_m ( : , k−1) , 'b ' , ' Linewidth ' , 2 )

103 hold on

104 for j =1: length (P)−1105 plot ( l (P( j ) :P( j +1) ,1) , planck_tk (X_m( j , 1 : 2 : 3 , k−1) , l (P( j ) :P( j + 1 ) , 1 ) ) , . . .

106 ' LineWidth ' ,2 , ' Color ' , [ rand , rand , rand ] )

107 end


109 ylabel ( ' I n t e n s i t y ' , ' f o n t s i z e ' , 14)

110 legend ( 'Measured I n t e n s i t y ' , ' F i t t ed I n t e n s i t y ' )

111 % axis ([237.8 876.6 -.002 0.2])

112 end

Danmarks Tekniske Univeristet VII DTU Mechanical Engineering


A.4 Planck Radiation Equation

1 function F = planck_tk (x , l )

2 C1=299792458^2∗6.62606896e−34; %Planck 's first constant [W*m^2]

3 C2=299792458∗6.62606896 e−34; %Planck 's second constant [J*m]

4 k=1.380650424e−23; %Boltzmann 's constant [J/K]

5 l_v=l ∗1e−9; %convert wavelength from [nm] to [m]

6

7 e1=1−exp( x ( 1 ) . / ( l . ^ 1 . 3 9 ) ) ; %wavelength dependent soot emissivity

8 I1=(2∗C1 ) . / ( l_v .^5 .∗ (exp(C2 . / ( l_v .∗ k∗x (2)∗1000)) −1)) ; %ideal planck radiation

9

10 F = ( e1 .∗ I1 )/1 e11 ; %"normalized" ideal soot intensity

Danmarks Tekniske Univeristet VIII DTU Mechanical Engineering

MSc Thesis B Table Values

B Table Values

In this appendix are table values of some of the data used in this project.

Danmarks Tekniske Univeristet IX DTU Mechanical Engineering

P h y s i c a I I I , n o 6 J u n i 1 9 3 6

TABLES OF THE EMISSIVITY OF TUNGSTEN AS A FUNCTION OF WAVELENGTH FROM 0,23- 2.0 IN THE REGION OF TEMPERATURE 1600°-3000°K.

C o m m u n i c a t i o n b y L . S . O r n s t e i n f r o m t h e P h y s i c a l I n s t i t u t e o f t h e

U n i v e r s i t y o f U t r e c h t

In his dissertation (Reflectivity and emissivity of Tungsten (with a new method to determine the total reflectivity of any surface in a simple and accurate way) 1934), Dr. H. C. H a m a k e r has determined the emissivity of tungsten as a function of wavelength and temperature for the region of 0.23-1.00 ~ and 1000°-3000°K.

T l i n ~ 1 6 0 0 1 8 0 0 2 0 0 0 2 2 0 0 2 4 0 0 2 6 0 0 2 8 0 0 3 0 0 0 ° K

w

0 . 2 3 0 . 2 4 0 . 2 5 0 . 2 7 5 0 . 3 0 O . 3 2 5 0 • 3 5 0 • 3 7 5 0 . 4 0 0 . 4 2 5 0 . 4 5 0 . 5 0 0 . 5 5 0 . 6 0

0 . 6 5 0 . 7 0 0 . 7 5 0 . 8 0

0 . 9 0 I . O0 1 . 1 0

1 . 2 0 1 . 3 0 l . 4 0 1 . 5 0 1 . 6 0 1 . 7 0 1 . 8 0 l . 9 0 2 . 0 0

O. 4 0 6 • 4 3 2 • 4 6 2 . 4 8 5 . 4 8 8 • 4 7 6 . 4 6 9 • 4 7 6 . 4 7 9 • 4 7 3 . 4 7 0 . 4 5 9 • 4 5 3 . 4 4 7 . 4 4 0 , 4 3 6 • 4 3 0 . 4 1 8 • 3 9 8 . 3 7 5 • 3 4 5 . 3 1 7 . 2 9 5 • 2 7 8 • 2 6 4 • 2 5 2

• 2 4 2

. 2 3 3

. 2 2 5

. 2 1 7

O. 4 0 3 • 4 2 9 • 4 5 9 • 4 8 2 . 4 8 6 • 4 7 4 • 4 6 8 . 4 7 5 • 4 7 6 . 4 7 0 • 4 6 6 . 4 5 6 . 4 5 1 . 4 4 5 . 4 3 8 . 4 3 4 . 4 2 6 . 4 0 9 • 3 8 7 • 3 6 3 • 3 3 4 • 3 0 8 • 2 8 8 . 2 7 3 . 2 6 1 . 2 5 1 . 2 4 3 . 2 3 6 . 2 3 0 . 2 2 3

0 . 4 0 O . 4 2 7 • 4 5 6 • 4 7 8 • 4 8 3 • 4 7 2 • 4 6 7 • 4 7 3 • 4 7 4 • 4 6 7 • 4 6 3 . 4 5 3 . 4 4 8 • 4 4 3 . 4 3 6 . 4 3 1 • 4 2 2

. 4 0 1 • 3 7 6 . 3 5 1 . 3 2 2 • 2 9 8 • 280

• 268

. 2 5 8

. 2 5 1 • 2 4 5 • 2 4 0 • 2 3 4 • 2 2 8

0 , 3 9 8 • 4 2 4 • 4 5 3 . 4 7 5 . 4 8 1 • 4 7 0 . 4 6 6 . 4 7 2 . 4 7 1 • 4 6 3 • 4 5 9 • 4 5 0 . 4 4 6 . 4 4 1 • 4 3 4 • 4 2 9 . 4 1 8 . 3 9 2 • 3 6 5 • 3 3 9 . 3 1 1 . 2 8 9 . 2 7 3 • 2 6 3 • 2 5 6 . 2 5 0 • 246

• 243

.239

• 234

0 . 3 9 5 . 4 2 1 • 4 5 0 • 4 7 2 . 4 7 8 • 4 6 9 . 4 6 5 . 4 7 1 • 4 6 8 . 4 6 0 . 4 5 6 • 4 4 7 • 4 4 3 • 4 3 8 • 4 3 2 . 4 2 7 . 4 1 4 • 3 8 3 . 3 5 4 . 3 2 7 • 299

• 279

• 266

. 2 5 8 . 2 5 3 . 2 4 9 . 2 4 7 . 2 4 6 . 2 4 3 • 2 3 9

0 . 3 9 2 . 4 1 8 • 4 4 8 • 4 6 9 • 4 7 5 . 4 6 7 . 4 6 4 . 4 7 0 • 4 6 6 • 4 5 7 • 4 5 2 . 4 4 4 . 4 4 1 • 4 3 6 • 4 3 0 . 4 2 5 . 4 1 0 • 3 7 5 • 3 4 2 . 3 1 5 . 2 8 8 . 2 7 0 . 2 5 8

. 2 5 3

• 2 5 0 . 2 4 9

• 2 4 8 . 2 4 9 . 2 4 8 • 2 4 5

0 . 3 9 0 . 4 1 6 • 4 4 5 • 4 6 6 • 4 7 3 . 4 6 5 • 4 6 3 • 4 6 9 • 4 6 3 . 4 5 3 . 4 4 9 . 4 4 1 . 4 3 9 • 4 3 4 • 4 2 8

] . 4 2 3 • 4 0 5

I . 366 . 3 3 1 • 3 0 2 • 2 7 6

• 260

I .251 . 2 4 7

i . 2 4 7 . 2 4 8 .250

• 2 5 3 • 2 5 2

I . 2 5 1

O. 3 8 7 . 4 1 3 • 4 4 2 • 4 6 3 . 4 7 0 . 4 6 3 . 4 6 2 • 4 6 8 • 4 6 0 • 4 5 0 . 4 4 5 . 4 3 8 • 4 3 6 • 4 3 2 • 4 2 6 • 4 2 0 . 4 0 1 . 3 5 7 • 3 2 0 . 2 9 0

. 2 6 5

. 2 5 1 • 2 4 4 • 2 4 2

• 2 4 4 . 2 4 7 . 2 5 1 . 2 5 6

. 2 5 7

. 2 5 6

- - 561 P h y s i c a I I I 3 6

MSc Thesis C Technical Drawings

C Technical Drawings

In this appendix are technical drawing of the designed mounting plates as well as the

other parts used in the setup.

Danmarks Tekniske Univeristet XII DTU Mechanical Engineering

Institut for Mekanik, Energi & Konstruktion

2800 Kgs.Lyngby Sekt. for Konstruktion og Produktudvikling

A

A

B

B

5080

M4

M444

14

4

8

0

4

25

M4

12

15

0,75

21

0,75

45°

13

Ændringer

BemærkningerStk-vægtMaterialeDB-navnAntalBeskrivelse / dimensionPos.

11:1Skala

Tegn.nr: Rev.nr:Format: A4 Tegn.titel:

Draw.(DB): MOUNTING_BRACKET Vægt:Matr:MOUNTING_BRACKETDB-navn:

Dato: 11-Nov-09

Sek. Fluid Mek.

Martin HansenNavn:

A-ASECTION

B-BSECTION



655

225

10

M4

15

44

M4

4

12,6

160

4

25,2

0

4

43,254 0

2,5 12,8

11,5

25

M4

0,75

12,86,4

Ændringer


Flui Mekanik Sek.1:1Skala


Draw.(DB): LENS_MOUNT_PLATE71356996185,246Vægt:Matr:LENS_MOUNT_PLATEDB-navn:

Dato: 11-Jan-10

27213993

Martin VagnNavn:

Alle huller er målsat efter dette hul

1.18"(30mm)

1.40"(35.6mm)

2.36"(60mm)

2.36"(60mm)

1.18"(30mm)

1.40"(35.6mm)

0.24" ( 6mm) THRUFOR USE WITH ER SERIES RODS4 PLACES

ADAPTER RINGNIKON F-MOUNT

RELEASE BUTTON

2.80"(71.1mm)

2.80"(71.1mm)

1.40"(35.6mm)

0.25"(6.4mm)

0.25" (6.4mm) DEEPM4-0.7 MOUNTING HOLE

METRIC ID MARK

1.40"(35.6mm)

0.25"(6.4mm)

0.50"(12.7mm)

0.74"(18.7mm)

0.81"(20.6mm)

0.25" (6.4mm) DEEP#8-32 MOUNTING HOLE

LOCKING PIN

8 PLACES

ER RODLOCKING SET SCREW

SM2 ( 2.035"-40) SERIES INTERNAL THREAD0.43" (10.9mm) DEEP

SM2RR RETAINING RING INCLUDED1.73" ( 44.0mm) CLEAR APERTURE

SPANNER WRENCH SLOTSFOR USE WITH SPW604 & SPW801

D

C

B

AA

B

C

D

12345678

8 7 6 5 4 3 2 1

THE INFORMATION CONTAINED IN THISDRAWING IS THE SOLE PROPERTY OFTHORLABS, INC. ANY REPRODUCTIONIN PART OR AS A WHOLEWITHOUT THE WRITTEN PERMISSION OFTHORLABS, INC. IS PROHIBITED.

PROPRIETARY AND CONFIDENTIAL

DRAWNENG APPR.MFG APPR.

DATENAME

SIZEB

DWG. NO.

REV

SCALE: 1:1 SHEET 1 OF 1

08/28/08BG03/21/08TD

TD 03/21/08

PART NO.

TITLE:

MATERIAL:A

LCP0418003-E01

F-MOUNT 60mm CAGE PLATE

THORLABS, INC. PO BOX 366NEWTON NJ

N/A

3.0"76.2mm

3.0"76.2mm

ø50.8mm2.0"

8.0mm

DEPTH OFOPTIC SEAT

.315"

1.5"38.1mm

38.1mm1.5"

ø45.9mm1.807"

SPECIFICATIONS SUBJECT TO CHANGE WITHOUT NOTICEDIMENSION ARE FOR REFERENCE ONLY

REV58855

TITLE

®

DWG NO

2" DIAMETER KINEMATIC MOUNT WITH 3-SCREW ADJUSTMENT

Edmund Optics000

3X M6x0.25

2X COUNTERBOREFOR 8-32 OR M4

SCREW

.244"6.20mm

.374"9.5mm

.5"12.7mm

45.6mm1.797"

6.35mm.25"

ROLL

PITCH

SET SCREW

YAW



AA

6

130

360

6,5

25

19

6,5

92

80

6 M6

M (US fra top)6 (US fra bund)6

227

M6

80

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Draw.(DB): HSCAM_PLATE0,000Vægt:Matr:HSCAM_PLATEDB-navn:

Dato: 16-Feb-10

27213993

Martin HansenNavn:

A-ASECTION

MSc Thesis D Data Files

D Data Files

This appendix includes the spectral measurements done during this project (CD), along

with a description of the included folders.

An example of a �le name is:

7,5︸︷︷︸1

( 1︸︷︷︸2

_ 50︸︷︷︸3

_ yny︸︷︷︸4

).txt

for which

1. The type of experiment measured. This can be amperage of the tungsten lamp,

height above �ame, BB temperature etc.

2. The integration time in milliseconds.

3. The number of spectra averaged in the measurement.

4. Indicates Dark noise correction ON (y), Linearity correction OFF (n) and Stray

light correction ON (y).

Each folder contains spectra for a speci�c test:

• BB_glass: Experiment to determine the transmissivity of the pyrex glass at the

optical access of the �at �ame burner.

• Contaminated: Experiment with measurements with and without the sooted view

glass between the objective and the tungsten lamp.

• Flame1: Measurements at di�erent �ame heights on an ethylene-air �ame with and

without BB background, and in this folder:

� Skyl: measurements with purging tubes.

� Noskyl: measurements without purging tubes.

� Skyl2: measurements at HAB 15mm and di�erent integration times.

• Flame2: Measurements with varying fuel-air ratios with and without BB back-

ground.

• Flame3: Measurements at di�erent heights for an ethylene-oxygen �ame with and

without BB background.

• Kalibrering1: �rst system calibration measurement on tungsten lamp, before camera

alignment.

Danmarks Tekniske Univeristet XX DTU Mechanical Engineering

MSc Thesis D Data Files

• Kalibrering2: second system calibration measurement on tungsten lamp, after

camera alignment.

• Kalibrering3: Corroboration on the second calibration measurement.

• Linaritet1: data for linearity analysis.

• Linaritet2: data for second linearity analysis.

The CD also includes an electronic version of this report as well as sample Matlab codes.

Danmarks Tekniske Univeristet XXI DTU Mechanical Engineering

DTU Mechanical Engineering

Section of Fluid Mechanics

Technical University of Denmark

Nils Koppels Allé, Bld. 403

DK- 2800 Kgs. Lyngby

Denmark

Phone (+45) 45 25 43 00

Fax (+45) 45 88 43 25

www.mek.dtu.dk

Documents

Flame Temperature Measurement in an Internal Combustion …etd.dtu.dk/thesis/261950/M_Vagn_Hansen.pdf · Flame Temperature Measurement in an Internal ... which can be used in large