atmosphere
Article
Indirect Prediction of Welding Fume Diffusion inside
a Room Using Computational Fluid Dynamics
Sujit Dahal 1,2 , Taehyeung Kim 2, * and Kwangseog Ahn 3
1
2
3
*
Department of Eco-Friendly Offshore Plant FEED Engineering, Changwon National University,
Changwon 641-773, Korea; [email protected]
Department of Environmental Engineering, Changwon National University, Changwon 641-773, Korea
Department of Occupational & Environmental Safety & Health, University of Wisconsin-Whitewater,
809 W. Starin Road, Whitewater, WI 53190, USA; [email protected]
Correspondence: [email protected]; Tel.: +82-3876-7565
Academic Editor: Robert W. Talbot
Received: 13 April 2016; Accepted: 20 May 2016; Published: 25 May 2016
Abstract: Welding is an important and widely used process in the manufacturing and maintenance
of various works involving metals and alloys. While welding has broad applications, the welding
fume generated during the process has impacts on workers’ health, which needs to be addressed.
One of the major steps that can be undertaken to take care of this issue is the use of ventilation, which
requires knowledge of characteristics and dispersion of the welding fume in the workers’ breathing
zone. It is difficult to assess welding fume dispersion from manual measurement due to numerous
welding processes and sufficient data requirement. Numerical prediction of welding fume is dubious
due to several errors. This paper considers the use of numerically predicted CO2 concentrations to
indirectly predict welding fume distribution in workshops. This is based on the assumption that if
the particles are sufficiently small size, they follow the diffusion pattern of gases. Experiments are
carried out in a room with an opening and a welding fume generation system for measurement of
CO2 and fume diffusion. The results show high possibility of predicting welding fume concentration
based on Computational Fluid Dynamics (CFD) simulated CO2 concentration with a correlation
coefficient of 0.74.
Keywords: CFD; health effects of welding fume; indirect prediction; ventilation; welding
fume dispersion
1. Introduction
Welding technology is a necessary process in the construction and maintenance of industries, large
structures, vehicles, ships, offshore structures, and a number of practices involving metal works [1,2].
Nowadays, some of the welding is operated with the help of machines and robots but most is still
carried on manually for precision and quality of work. However, the disadvantage of manual welding
is that the fumes produced during the process have a great impact on workers’ health [1]. These fumes
consist of powders of different solid substances like Cu, Ni, Zn, etc. with sizes ranging from 1 to 7 µm.
Along with the fumes, different gases such as CO2 , CO, NOx , SOx , O3 , etc., and numerous organic
pollutants are also produced during the welding process [3]. The gases are formed and emitted due to
the presence of extremely high temperature, ultraviolet radiation arising from the arc, and reactions of
hot base metal with atmospheric O2 and N2 . However, appearance of CO2 is an exception to those
factors because it is generally used as a shielding gas in many welding processes [4]. Shielding gas
is used in welding to protect the weld pool from oxidization in ambient air and to reduce fume
emission [5]. Some of the examples of welding techniques using shielding gas are gas metal arc
welding, electro-gas welding, laser beam welding, plasma arc cutting, etc. Among these techniques,
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Atmosphere 2016, 7, 74
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gas metal arc welding is the most common and widely employed technique for joining metal and
a vast range of alloys [6], and it uses Ar, He, O2 , and CO2 as major shielding gases. Ar and He, being
inert gases, do not cause major health and environmental risks, and generally produce good-quality
welding, but are approximately ten times more expensive then CO2 . For welding of steel structures,
either 100% CO2 or combination of CO2 and these inert gases can be used. The additional advantage
of using CO2 with other pure gases during welding of metals and alloys is to improve arc stability,
minimize undercut, reduce porosity, and improve the appearance of the weld [2].
This kind of gas shielded welding is frequently performed in enclosed compartments and rooms
such as in shipbuilding industries where the welder has exposure to a high concentration of fumes [7].
Gomes et al. did assessments of airborne ultrafine particles generated from tungsten inert gas,
metal active gas, and friction stir welding, and found that all these processes emitted significant
concentrations of ultrafine particles which were deposited in the lungs of exposed workers [8,9].
Inhalation of these fumes and gases have a great effect on lung function and causes acute as well as
chronic diseases like lung cancer, infection, metal fume fever, etc. Hence, it is very essential to protect
workers from welding fume exposure [10,11]. Reduction of welding fumes from workers zone is
generally achieved by three processes: reduction at the source by changes in process and conditions,
use of ventilation and exhausts, and use of a device for personal protection [1]. After reduction at the
source, there is still a significant amount of fumes released which should be removed by employing
a ventilation method. Welding fume generally consists of ultra-fine and fine particles ranging from
a diameter of less than approximately 0.1 to 3 µm [12], which are difficult to remove by natural
ventilation because these particles are easily influenced by the complex airflow pattern of the room [13].
The most widely-used ventilation method for control of these contaminants is local exhaust ventilation
because it is more effective compared to general ventilation and dilution ventilation [14]. The design of
local exhaust ventilation system to efficiently capture most of the fugitive fumes requires knowledge of
concentration pattern of the dust near the contaminant source and transport of fumes from their source
of origin to exhaust. Transport and concentration of fume is affected by factors like exhaust openings,
ambient air currents, momentum of contaminant source, mobility of worker, etc. [15]. It is a difficult task
to manually investigate transport pattern and concentration variation through experiments because
sufficient numbers of measurements for more than 80 different welding and associated processes are
difficult to achieve [16].
Instead of carrying out experiments, numerical methods can be used to predict the concentration of
the fume particles, depending on provided conditions and related factors. Based on numerical methods,
research has been done for prediction of particle concentration using Computational Fluid Dynamics
(CFD) but the simulated results frequently have some divergence from the experimental results.
The errors are due to limitations on the predictive methods, which do not take some factors such as
particle size distribution and particle rebounding process into consideration [17]. Unlike modeling of
particulate matter, CFD has been used in many previous studies with good performance to predict
CO2 concentration profile and dispersion in a single room using natural as well as mechanical
ventilation [18,19]. Lambert et al. compared the measured and CFD simulated values of CO2 decay in
a controlled environment and found that the percent error between computational and experimental
data was as low as 5.2% and concluded CFD as an effective tool to study ventilation behaviors [20].
The purpose of this study is to simulate the diffusion of CO2 and find some analogy to support
utility of indirect prediction of welding fume concentration. This conjecture is based on the theory
that if the particles are in range of sub micrometer level, their motion properties follow the diffusion
rules of gases i.e., Brownian diffusion for movement in the air [21,22]. The initial part of the study is
conducting measurement of welding fume particle size to find out if the majority of particles belong
to sub micrometer range, which is followed by further experiments. The present work is directed
towards the comparison of welding fume distribution measured inside the room and CO2 distribution
simulated using CFD. If a satisfactory correlation between these two components is achieved, it
provides an opportunity to formulate a basis for further studies related to this aspect.
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2. Methodology
2.
2. Methodology
Methodology
2.1. Experimental
Experimental Setup
Setup
2.1.
The physical
physical model
model is
is aaa room
room (L
(Lˆ×× W
Wˆ
H == 2900
2900 mm
mm ˆ
2800
mm ˆ
2200
mm)
with
single
The
room
(L
W
×× H
×× 2800
×× 2200
2800 mm
2200 mm)
mm) with
with aa single
opening
(W
×
H
=
600
mm
×
800
mm)
as
shown
in
Figure
1.
The
welding
apparatus
is
set
up
at
the
opening (W
800
mm)
as as
shown
in in
Figure
1. The
welding
apparatus
is set
(W ׈HH==600
600mm
mm× ˆ
800
mm)
shown
Figure
1. The
welding
apparatus
is up
set at
upthe
at
corner
of the
the
room,
andand
it consists
consists
of aaofHIC
HIC
500c
welder,
welding
table,
base
metal
(L ××(LB
Bˆ×× BH
Hˆ== H
400
corner
of
room,
and
it
of
500c
welder,
welding
table,
base
metal
(L
400
the
corner
of the
room,
it consists
a HIC
500c
welder,
welding
table,
base
metal
=
mm
×
400
mm
×
18
mm),
KS
D0062
welding
torch,
and
CO
2
gas
cylinder.
The
welding
system
is
mm
× 400
mmmm
× 18ˆmm),
KS KS
D0062
welding
torch,
and
2 gas
400 mm
ˆ 400
18 mm),
D0062
welding
torch,
andCO
CO
cylinder.The
Thewelding
welding system
system is
2 gascylinder.
automated, and
and the
the welding
welding conditions
conditions are
are set
set as
as follows:
follows: (1)
(1) arc
arc voltage
voltage =
= 38V;
38V; (2)
(2) welding
welding speed
speed =
=
automated,
0.6 m/min;
m/min;
(3)
wire
diameter
1.2
mm;
(4)
wire
speed
7070
mm/s;
(5)(5)
welding
current
350
A; and
and
(6)
0.6
70
mm/s;
(5)
welding
current
== 350
A;
(6)
m/min;(3)
(3)wire
wirediameter
diameter===1.2
1.2mm;
mm;(4)
(4)wire
wirespeed
speed== =
mm/s;
welding
current
= 350
A; and
shield
gas
flow
=
30
L/min.
The
configuration
of
the
welding
process
is
shown
in
Figure
1.
Figure
shield
gasgas
flow
= 30
L/min.
TheThe
configuration
of of
thethe
welding
(6)
shield
flow
= 30
L/min.
configuration
weldingprocess
processisisshown
shownin
inFigure
Figure 1.
1. Figure
Figure 22
displays
five
points,
each
at
the
height
of
600
mm
(lower
part)
and
1600
mm
(upper
part),
to
imitate
displays five points, each at the height of 600 mm (lower part) and 1600 mm (upper part), to imitate
the stature
stature of
of aa welder
welder working
working in
in sitting
sitting and
and standing
standing positions,
positions, respectively.
respectively. The
The placement
placement of
of the
the
the
measurement
points
is
shown
in
Figure
3
where
four
points
are
in
the
corners
and
one
point
is
in
the
measurement points is shown in Figure 3 where four points are in the corners and one point is in the
center of
of the
the room.
room. These
These points
points
are
selected
to
measure
size
and
number
of
welding
fume
particles,
center
points are
are selected
selected to
to measure
measure size
size and
and number
number of
of welding
welding fume
fume particles,
particles,
welding
fume
concentration
and
CO
2
concentration.
An
exhaust
fan
(Model:
MJVF-40,
MJ
AIRTECH
welding fume concentration and CO22 concentration. An exhaust fan (Model: MJVF-40,
MJVF-40, MJ AIRTECH
CO., Ltd.
Ltd. Seoul,
Seoul, Korea)
Korea) with
with maximum
maximum flow
flow rate
rate of
of 40
40 m
m33/min
/min
isisused
used
to
ventilate
the
room.
CO.,
/minis
usedto
toventilate
ventilatethe
theroom.
room.
Figure 1.
1. Schematic
Schematic diagram
diagram of
of welding
welding apparatus
apparatus setup.
setup.
Figure
Figure 2.
2. Schematic
Schematic diagram
diagram of
of experimental
experimental setup
setup and
and measurement
measurement points.
Figure
points.
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Figure
measurement points.
points.
Figure 3. Top view of the model with measurement
2.2.Field
FieldMeasurement
Measurement
2.2.
Experiments are
are carried
carried out
out to
to measure the number and
Experiments
and size
size of
of welding
weldingfume,
fume,concentration
concentrationofof
welding
fume
and
concentration
of
CO
2
inside
the
room.
Initially,
the
number
and
welding fume and concentration of CO2 inside the room. Initially, the number andsize
sizeof
ofwelding
welding
fumeparticles
particles is
is measured
measured using MET ONE
fume
ONE 237B
237B Portable
Portable Air
Air Particle
ParticleCounter
Counter(Hach
(HachCompany,
Company,
Loveland,CO,
CO,
USA).
is welded
using
an automated
welding apparatus
with
Loveland,
USA).
TheThe
basebase
metalmetal
is welded
using an
automated
welding apparatus
with continuous
continuous
generation
of
fumes,
and
all
the
measurements
are
done
10
min
after
the
commencement
generation of fumes, and all the measurements are done 10 min after the commencement of the
of the apparatus.
The particle
is operated
for at
5 min
at arate
flow
of 2 per
Liters
per minute
apparatus.
The particle
countercounter
is operated
for 5 min
a flow
of rate
2 Liters
minute
and sixand
size
six
size
channels,
i.e.,
0.3,
0.5,
0.7,
1,
2,
and
5
μm
are
used
to
measure
the
number
of
particles
of
each
channels, i.e., 0.3, 0.5, 0.7, 1, 2, and 5 µm are used to measure the number of particles of each size per
size
per
L of airBefore
sample.
Beforethe
running
theinstrument
welding instrument
and subsequent
measurements,
10
L of
air10
sample.
running
welding
and subsequent
measurements,
the particle
the
particle
counter
is
used
to
measure
initial
particle
count
in
each
size
range
inside
the
room.
This
counter is used to measure initial particle count in each size range inside the room. This background
background
measurement
of
particle
count
is
deducted
from
the
total
count
of
welding
fume
measurement of particle count is deducted from the total count of welding fume particles in each
particles
in and
eachthe
sizeresults
range,are
andreported.
the resultsCO
are is
reported.
CO2atis the
measured
at the ten measurement
size
range,
measured
ten measurement
points using
2
points
using
GrayWolf
portable
DirectSense
IAQ
meter
(GrayWolf
Sensing
Solutions,
Shelton,
GrayWolf portable DirectSense IAQ meter (GrayWolf Sensing Solutions, Shelton, CT, USA) with CT,
CO2
USA) with accuracy
CO2 measuring
accuracy
of ±3%
rdg which
or ±50 displays
ppm, which
thevalues
measured
values
in
measuring
of ˘3%
rdg or ˘50
ppm,
the displays
measured
in ppm
in the
ppm in the monitor. Real-time monitoring of CO2 is conducted 10 minutes after kickoff of the
monitor. Real-time monitoring of CO2 is conducted 10 minutes after kickoff of the welding apparatus
welding apparatus at all 10 measurement points. Gravimetric method is used to measure the weight
at all 10 measurement points. Gravimetric method is used to measure the weight of welding fume
of welding fume particles at all measurement points, and the measured weight is converted into
particles at all measurement points, and the measured weight is converted into concentration at each
concentration at each point. The welding fume concentration measuring system is operated at a flow
point. The welding fume concentration measuring system is operated at a flow of 2 L/min for an hour
of 2 L/min for an hour and consists of a Personal Air Sampler (Model 224-PCXR4, SKC Inc., Eighty
and consists of a Personal Air Sampler (Model 224-PCXR4, SKC Inc., Eighty Four, PA, USA), Polyvinyl
Four, PA, USA), Polyvinyl Chloride filter paper (pore size = 5 μm, diameter = 37 mm: SKC Inc.,
Chloride filter paper (pore size = 5 µm, diameter = 37 mm: SKC Inc., Eighty Four, PA, USA), and
Eighty Four, PA, USA), and a precision analytical balance (Sartorius R 160 D, Elk Grove, IL, USA).
a precision analytical balance (Sartorius R 160 D, Elk Grove, IL, USA). Three separate measurements
Three separate measurements are conducted for both welding fume concentration and CO2
are conducted for both welding fume concentration and CO2 concentration, and the average of these
concentration, and the average of these measurements is used for comparison with simulated
measurements is used for comparison with simulated concentration CO2 .
concentration CO2.
2.3. CFD Simulation
2.3. CFD Simulation
Commercially available CFD software ANSYS Airpak 3.0.16 (Fluent Inc., Lebanon, NH, USA)
Commercially available CFD software ANSYS Airpak 3.0.16 (Fluent Inc., Lebanon, NH, USA) is
is used in this study to predict the CO2 concentration distribution in the room. Airpak is one of
used in this study to predict the CO2 concentration distribution in the room. Airpak is one of the
the most acceptable softwares to simulate indoor air quality which incorporates the powerful solver
most acceptable softwares to simulate indoor air quality which incorporates the powerful solver
FLUENT [23]. Airpak uses FLUENT to solve turbulent flow equations based on finite volume method.
FLUENT [23]. Airpak uses FLUENT to solve turbulent flow equations based on finite volume
Equations of conservation of energy, mass and momentum of incompressible air are solved by FLUENT
method. Equations of conservation of energy, mass and momentum of incompressible air are solved
to find the solution of the model. The two-equation K-ε turbulence model is used to solve turbulent
by FLUENT to find the solution of the model. The two-equation K-ε turbulence model is used to
flow conditions. There is a presence of mixed convection in the room due to the occurrence of both
solve turbulent flow conditions. There is a presence of mixed convection in the room due to the
natural and forced convection. Natural convection is caused by buoyancy forces due to density
occurrence of both natural and forced convection. Natural convection is caused by buoyancy forces
differences in different parts of the room. The density difference is mainly caused by high temperature
due to density differences in different parts of the room. The˝density difference is mainly
caused by
3 /min, CO flow
gas
supplied
into
the
building
(i.e.,
CO
temperature
=
600
C,
CO
flow
=
0.03
m
2
2
high temperature gas supplied into the building (i.e., CO2 temperature = 600 °C, CO2 flow =2 0.03
diameter
= 0.2
cm, and CO2 0.2
velocity
= 0.33
at welding
Density
gradient
not only
m3/min, CO
2 flow diameter =
cm, and
CO2 m/s
velocity
= 0.33 m/spoint).
at welding
point).
Densityisgradient
caused by temperature gradient, but other factors are also associated with it. But for this study,
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is not only caused by temperature gradient, but other factors are also associated with it. But for this
is not only caused by temperature gradient, but other factors are also associated with it. But for this
density
to temperature
differences differences
from heat sources
is only
taken into
consideration
study, gradient
density due
gradient
due to temperature
from heat
sources
is only
taken intoas
study, density gradient due to temperature differences from heat sources is only taken into
the
effects
caused
by
natural
convection
are
far
lower
than
the
effects
caused
by
forced
convection.
consideration
as
the
effects
caused
by
natural
convection
are
far
lower
than
the
effects
caused
consideration as the effects caused by natural convection are far lower than the effects caused
byby
3
forced
convection.
Forced
convection
occurs
due
to aai.e.,
ventilation
system,
i.e.,an
anexhaust
exhaust
with
Forced
convection
occurs
due
to a ventilation
an exhaust
fan with
airflow
of fan
20fan
m
/min.
forced
convection.
Forced
convection
occurs system,
due to
ventilation
system,
i.e.,
with
3/min. A fine mesh of hexa-unstructured geometry is used to discretize the domain. A
of
20
m
Aairflow
fine
mesh
of
hexa-unstructured
geometry
is
used
to
discretize
the
domain.
A
grid
independency
3
airflow of 20 m /min. A fine mesh of hexa-unstructured geometry is used to discretize the domain. A
grid
test
isisperformed
one
and
two
finer
meshes.
The
finemesh
mesh
finallyof
test
is independency
performed
using
one
coarse
andusing
two finer
meshes.
The
fine
mesh
finally
generated
consists
grid
independency
test
performed
using
one coarse
coarse
and
two
finer
meshes.
The
fine
finally
generated
consists
of
1.1
million
cells.
The
3-D
model
developed
in
Airpak
with
the
same
geometries
1.1 million
cells.
The of
3-D
in Airpak
with the in
same
geometries
and features
as the
generated
consists
1.1model
milliondeveloped
cells. The 3-D
model developed
Airpak
with the same
geometries
and
features
setup
4.
and
features
thephysical
physical
setup4.isisshown
shown in
in Figure
Figure 4.
physical
setupas
isasthe
shown
in Figure
Figure 4. Model and mesh generation in Airpak.
Figure
mesh generation
generation in
inAirpak.
Airpak.
Figure 4.
4. Model
Model and
and mesh
3. Results and Discussion
3.3.Results
Results and
and Discussion
Discussion
3.1. Welding Fume Particle Size Distribution
3.1.
3.1.Welding
WeldingFume
FumeParticle
ParticleSize
SizeDistribution
Distribution
Figure 5 shows the variation in welding fume particle size in the room. The respective particle
Figure
55shows
the
in
fume
particle size
size in
in the
the room.
room.The
Therespective
respectiveparticle
particle
Figure
shows
thevariation
variation
in welding
welding
fume
count
at the
upper (1600
mm height)
and lower
(600 particle
mm height) sections
of the experimental
room is
count
at
the
upper
(1600
mm
height)
and
lower
(600
mm
height)
sections
of
the
experimental
roomisis
count
at the
mmcount
height)
and lower
mmS1height)
of the
experimental
room
shown
in upper
Table (1600
S1. The
presented
in(600
Table
is thesections
result of
deducting
background
shown
in
Table
S1.
The
count
presented
in
Table
S1
is
the
result
of
deducting
background
measurement
shown
in Tablefrom
S1.the
The
count
presented
in Table
S1 isFrom
the Figure
result 5,
ofit deducting
background
measurement
total
measurement
in each
size range.
is evident that
most of
from
total measurement
in
each
sizeand
range.
From
it isFigure
evident
most
ofthat
particles’
measurement
from
measurement
inabout
each
sizeFigure
range.
From
it is less
evident
thethe
particles’
sizes the
are total
less than
0.5 μm
99.6%
of the 5,
particles
have5,
a that
size
than
1the
μmmost
(subof
sizes
are
less
than
0.5
µm
and
about
99.6%
of
the
particles
have
a
size
less
than
1
µm
(sub
micrometer),
which
thethan
size that
actsand
as gases
Therefore,
reveals
a possibility
themicrometer),
particles’ sizes
areisless
0.5 μm
aboutduring
99.6% diffusion.
of the particles
have this
a size
less than
1 μm (sub
which
the size
that isacts
gases
during
Therefore,
this
reveals
athis
possibility
thatiswelding
fume
particles
and
2 used
as shielding
gas emitted
at
the same
point
source
inwelding
this
micrometer),
which
theas
size
thatCO
acts
as diffusion.
gases
during
diffusion.
Therefore,
reveals
athat
possibility
study
mightand
have
similar
spread
theasemitted
room.
To
explain
this
theory,
a in
comparison
is
made
fume
particles
CO
shielding
gas
at the
point
this
study
might
have
that
welding
fume
particles
and
COover
2 used
shielding
gassame
emitted
at source
the same
point
source
in this
2 used as
between
theover
concentrations
of
welding
fume and
CO
2 at thethis
same
points.
similar
spread
the
room.spread
Toboth
explain
this
aTocomparison
is made
between
the concentrations
study
might
have
similar
over
the theory,
room.
explain
theory,
a comparison
is made
ofbetween
both welding
fume and CO
at thewelding
same points.
the concentrations
of2 both
fume and CO2 at the same points.
Figure 5. Size distribution of welding fume particles (%).
Figure5.
5. Size
Size distribution
distribution of
Figure
of welding
welding fume
fumeparticles
particles(%).
(%).
Atmosphere 2016, 7, 74
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Atmosphere 2016, 7, 74
Atmosphere 2016,
7, 74
3.2. Measured
and Simulated
Concentration of Welding Fume and CO2
6 of 10
6 of 10
6 of 10
Atmosphere 2016, 7, 74
3.2.
Measured and Simulated Concentration of Welding Fume and CO2
The
results ofand
measured
welding
measured
CO2 , and simulated CO2 at
3.2. Measured
Simulated concentration
Concentration of of
Welding
Fumefume,
and CO
2
The results
measured
concentration
of welding
fume,
measured
CO2, and simulated CO2 at
3.2. Measured
andofSimulated
Concentration
of Welding
Fume
and
2
ten measurement
points
inside the
room are shown
in Figures
6–8CO
respectively.
Although the concentration
The results ofpoints
measured
concentration
of welding
fume,
measured
CO2, and simulated
COthe
2 at
ten measurement
inside
the room are
shown in
Figures
6–8, respectively.
Although
units ten
for welding
fume
and CO
arethe
different,are
thewelding
similarity
of measured
pattern
among
three
graphs CO
is certainly
The results
of measured
of
fume,
CO2, and
simulated
2 at
2 concentration
measurement
inside
shown
in Figures
6–8, respectively.
the
concentration
unitspoints
for welding
fumeroom
and CO2 are
different,
the similarity
of pattern Although
among three
ten
measurement
the
room
are
shown
in regression
Figuressimilarity
6–8,analysis
respectively.
Although
the that
evident.
Further
reasoning
ofinside
these
results
done
using
to find
correlation
concentration
unitspoints
for welding
fume
andisCO
2ofare
different,
pattern
among
three
graphs
is certainly
evident.
Further
reasoning
these
resultsthe
is done usingofregression
analysis
to
concentration
for welding
fume
and CO2of
arethese
different,
pattern among
three
existsfind
between
theunits
species.
graphs
is certainly
Further
reasoning
resultsthe
is similarity
done usingofregression
analysis
to
correlation
thatevident.
exists between
the
species.
graphs
is certainly
Furtherthe
reasoning
find correlation
thatevident.
exists between
species. of these results is done using regression analysis to
find correlation that exists between the species.
Figure 6. Concentration of measured welding fume at ten measurement points.
Figure 6. Concentration of measured welding fume at ten measurement points.
Figure 6. Concentration of measured welding fume at ten measurement points.
Figure 6. Concentration of measured welding fume at ten measurement points.
Figure 7. Concentration of measured CO2 at ten measurement points.
Figure 7. Concentration of measured CO2 at ten measurement points.
Figure
7. Concentration
CO22atatten
tenmeasurement
measurement
points.
Figure
7. Concentrationofofmeasured
measured CO
points.
Figure 8. Concentration of simulated CO2 at ten measurement points.
Figure 8. Concentration of simulated CO2 at ten measurement points.
Figure 8. Concentration of simulated CO2 at ten measurement points.
Figure 8. Concentration of simulated CO2 at ten measurement points.
Atmosphere 2016, 7, 74
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Atmosphere 2016, 7, 74
7 of 10
3.2.1.
Measured
Welding
Concentrations
3.2.1.
Measured
WeldingFume
Fumevs.
vs.Measured
MeasuredCO
CO22 Concentrations
3.2.1.
Measured
Welding
Fume vs. Measured
CO20.96
Concentrations
The
R2R(coefficient
ofofdetermination)
measuredCO
CO2 2and
andmeasured
measured
welding
2 (coefficient
The
determination) value
value of
of 0.96 between
between measured
welding
2 (coefficient
fume
concentrations
as
shown
in
the
regression
plot
as
shown
in
Figure
9
explains
that
the
concentration
The
R
of
determination)
value
of
0.96
between
measured
CO
2
and
measured
welding
fume concentrations as shown in the regression plot as shown in Figure 9 explains that the concentration
of fume
welding
fume
particles
and
in
room
atmosphere
similar.
other
words,
this
concentrations
as shown
in
the
regression
plot
as shown
in Figure
9very
explains
that
the
concentration
of welding
fume
particles
andCO
CO
spread
in the
the
atmosphere
isisvery
similar.
InIn
other
words,
this
2 2spread
of
welding
fume
particles
CO
2 spread
in the of
room
atmosphere
is room.
very
similar.
Incorrelation
other words,
this
shows
that
both
the
species
have
similar
patterns
distribution
inthe
the
room.
the
is higher,
shows
that
both
the
speciesand
have
similar
patterns
distribution
in
AsAs
the
correlation
is higher,
that
bothprediction
the
speciesishave
similar
patterns
of distribution
inprediction
the room.
As
the correlation is higher,
theshows
error
during
which
contributes
betterprediction
accuracy.
the
error
during
prediction
islower,
lower,
which
contributes
totobetter
accuracy.
the error during prediction is lower, which contributes to better prediction accuracy.
Figure 9. Correlation of measured welding fume and measured CO2.
Figure
9. Correlation of measured welding fume and measured CO2 .
Figure 9. Correlation of measured welding fume and measured CO2.
3.2.2. Measured CO2 vs. Simulated CO2 Concentrations
3.2.2. Measured CO2 vs. Simulated CO2 Concentrations
3.2.2. Measured CO2 vs. Simulated CO2 Concentrations
Figure 10 is the regression plot of measured and simulated CO2 with a R2 value
of 0.78. This is a
2 value of 0.78. This is
Figure
1010isisthe
plot
of
andsimulated
simulated
CO
with
2 of
Figure
theregression
regression
plot
of measured
measured
CO
2 with
aCO
Ra22 R
value
of 0.78. This
is a
relatively high
coefficient
value,
which
supportsand
that the prediction
concentration
through
a relatively
high
coefficient
value,
which
supports
that
the
prediction
of
CO
concentration
through
2
relatively
coefficient
value,
which supports
the welding
prediction
of CO2 concentration
through
computer high
simulation
is finely
accurate.
Hence, inthat
an arc
workshop,
which uses CO
2 as a
computer
isis finely
accurate.
Hence,ininananarc
arcwelding
welding
workshop,
computer
simulation
finely
accurate.
workshop,
uses
CO
2 CO
as 2a as
shieldingsimulation
gas,
it is possible
to predict
theHence,
concentration
distribution
of
CO
2 withwhich
thewhich
help
ofuses
computer
a shielding
gas,it it
is possible
to predict
the concentration
distribution
of CO
with
the help of
shielding
gas,
is possible
to predict
the concentration
distribution
of CO2 with
the 2help
of computer
simulation.
computer
simulation.
simulation.
Figure 10. Correlation of simulated CO2 and measured CO2.
Figure 10. Correlation of simulated CO2 and measured CO2.
Figure 10. Correlation of simulated CO2 and measured CO2 .
3.3.3. Measured Welding Fume vs. Simulated CO2 Concentrations
3.3.3. Measured Welding Fume vs. Simulated CO2 Concentrations
The comparison
ofFume
measured
welding fume
and simulated CO2 concentrations portrays a R2
3.2.3. Measured
Welding
vs. Simulated
CO2 Concentrations
comparison
simulated
COthat
2 concentrations
portrays 2a Rof2
valueThe
of 0.74
as shownofinmeasured
Figure 11.welding
In other fume
wordsand
it can
be implied
change in concentration
The
comparison
of
measured
welding
fume
and
simulated
CO
concentrations
portrays
a R value
2 that change in concentration
value
of 0.74
as shown
in Figure
11.predicted
In other words
it can
be implied
of
measured
welding
fume
could be
through
simulated
CO
2 with accuracy of around 74%.
of measured
0.74 as shown
in
Figure
11.
In
other
words
it
can
be
implied
that
change
in
concentration
of
welding
fume
could
be
predicted
through
simulated
CO
2
with
accuracy
of
around
74%.
This is also a relatively high value of coefficient of determination, and it supports that there is a
measured
welding
fume could
be predicted
through
simulated COand
accuracythat
of around
2 with
This is also
a relatively
high value
of coefficient
of determination,
it supports
there is 74%.
a
Atmosphere 2016, 7, 74
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Atmosphere 2016, 7, 74
8 of 10
This is also a relatively high value of coefficient of determination, and it supports that there is
a possibility
possibility of
of achieving
achievingaameaningful
meaningfulprediction
predictionofofwelding
weldingfume
fumeconcentration
concentration
indirectly
through
indirectly
through
CO
CO
simulatedresults.
results.
2 2simulated
Figure 11. Correlation of simulated CO2 and measured welding fume.
Figure 11. Correlation of simulated CO2 and measured welding fume.
4. Conclusions
4. Conclusions
This paper presents a possibility of an alternative method for prediction of welding fume
This paper inside
presents
a possibility
an forced
alternative
methodi.e.,
for by
prediction
weldingCO
fume
concentration
a closed
room of
with
ventilation,
use of of
simulated
2
concentration
inside
a
closed
room
with
forced
ventilation,
i.e.,
by
use
of
simulated
CO
concentration.
2
concentration. The research was conducted by measuring CO2 and welding fume concentration,
and
The
research was
conducted
measuring
CO2 and
welding
fume
concentration,
andevaluated
comparingusing
it with
comparing
it with
predictedbyCO
2 concentration.
The
extent of
their
relationship was
predicted
CO
concentration.
The
extent
of
their
relationship
was
evaluated
using
regression
analysis.
2
2
regression analysis. The squared correlation coefficient (R ) of the experimental CO2 and welding
2 ) of the experimental CO and welding fume concentration is
The
squared
correlation
coefficient
2
fume
concentration
is very
high (R2(R
= 0.96),
which means the behaviors
of welding fume and the CO2
very
high
(R2 = 0.96),
means
the behaviors
ofthe
welding
fume and
the CO2 used
asrelationship
shielding gas
used
as shielding
gaswhich
are very
similarly
spread in
atmosphere.
Comparison
of the
are
very similarly
spread
in the
atmosphere.
Comparison
of the
relationshipshows
between
predicted
CO2
between
predicted
CO2 with
measured
CO2 and
welding fume
concentration
R2 values
of 0.77
2 values of 0.77 and 0.74 respectively,
with
CO2 and
welding
fume
concentration
shows
andmeasured
0.74 respectively,
which
are also
relatively
high values
andRback
a meaningful prediction. Based
on theare
results,
it can be established
numerical
simulations
are sufficiently
to indirectly
which
also relatively
high valuesthat
andthe
back
a meaningful
prediction.
Based onuseful
the results,
it can be
calculate appropriate
welding simulations
fume emissions
in the case that
proper
measurement
technique
and
established
that the numerical
are sufficiently
useful
to indirectly
calculate
appropriate
instrument
is
unavailable
for
direct
measurement.
It
is
possible
to
explain
this
compatibility.
The
welding fume emissions in the case that proper measurement technique and instrument is unavailable
diffusion
of
fume
particles
and
other
gases
is
comparable
as
measurement
of
particle
size
showed
for direct measurement. It is possible to explain this compatibility. The diffusion of fume particles
thatother
moregases
than is
99.5%
of fume as
particle
size is below
1 μm and
they that
tendmore
to follow
diffusion
and
comparable
measurement
of particle
size that
showed
thanthe
99.5%
of fume
pattern
of
gases.
As
this
study
was
not
aimed
in
developing
a
model
for
prediction
of
welding
fume,
particle size is below 1 µm and that they tend to follow the diffusion pattern of gases. As this study
butnot
to aimed
determine
the possibility
of indirect
prediction
based fume,
on diffusion
characteristics,
future
was
in developing
a model
for prediction
of welding
but to determine
the possibility
research
should
be
directed
towards
unearthing
the
relationship
of
these
two
species
by
changing
of indirect prediction based on diffusion characteristics, future research should be directed towards
model parameters
and verifying
it byspecies
usingbyadequate
of measurements.
Further
unearthing
the relationship
of these two
changingnumbers
model parameters
and verifying
it by
experiment and simulation can also be applied with complex building setup to illustrate the
using adequate numbers of measurements. Further experiment and simulation can also be applied
significance of this method in more realistic conditions.
with complex building setup to illustrate the significance of this method in more realistic conditions.
Supplementary Material: The following is available online at http://www.mdpi.com/2073-4433/7/6/74/s1.
Supplementary
Materials:
The following
is fume
available
online at http://www.mdpi.com/2073-4433/7/6/74/s1.
Table S1: Number
and diameter
of welding
particles.
Table S1: Number and diameter of welding fume particles.
Acknowledgments: This research was financially supported by Changwon National University, South Korea in
Acknowledgments: This research was financially supported by Changwon National University, South Korea in
2014–2015.The
Theauthors
research
waswish
partially
supported
by Brain
Korea 21 Plus,
and Ventech
Corp., South
Korea. The
2014–2015.
also
to thank
the editor
and anonymous
reviewers
for valuable
suggestions.
authors also wish to thank the editor and anonymous reviewers for valuable suggestions.
Author Contributions: Sujit Dahal was involved in literature review experimental works, conduction of CFD
simulation,
analysis of the
results,
and
writing
thein
manuscript.
Kwangseog
Ahn contributed
to the concept
and
Author Contributions:
Sujit
Dahal
was
involved
literature review
experimental
works, conduction
of CFD
methodology
of the research
and assisted
in reviewing
and editing
the manuscript.
Taehyeung
Kimconcept
designed
simulation, analysis
of the results,
and writing
the manuscript.
Kwangseog
Ahn contributed
to the
andthe
concept
and methodology
of the
research,
theand
experimental
and analysis
of results
assisted
methodology
of the research
and
assistedsupervised
in reviewing
editing theworks
manuscript.
Taehyeung
Kimand
designed
in reviewing and editing the manuscript. All authors have read and approved the final manuscript.
the concept and methodology of the research, supervised the experimental works and analysis of results and
Conflicts
Interest: The
no conflict
interest.
assisted of
in reviewing
andauthors
editing declare
the manuscript.
Allof
authors
have read and approved the final manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
Atmosphere 2016, 7, 74
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article distributed under the terms and conditions of the Creative Commons Attribution
(CC-BY) license (http://creativecommons.org/licenses/by/4.0/).