particle capture in aquatic systems: investigating the ...augustina prita pranoto abstract [3]...
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Particle Capture in Aquatic Systems:
Investigating the Difference between
a Single Collector and an Array
Augustina Prita Pranoto
Supervisor: Dr Marco Ghisalberti
School of Environmental Systems Engineering
Faculty of Engineering, Computing and Mathematics
The University of Western Australia
November 2012
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Augustina Prita Pranoto Abstract
[3]
Abstract For ecological processes such as vegetative filtration, larval settlement, and filter feeding,
there is not sufficient understanding of the mechanisms that control the rate of capture of
suspended particles. In addition, despite the fact that most biological collectors exist in
arrays, experimental and computational research has focused on predicting capture by a
single collector. To address this, experimental runs were conducted to investigate the
variation on dominant capture mechanism on a single collector and an array.
During the experimental component of this study, particle capture efficiencies were
measured for three different array densities: very low, low and medium densities. For each
of the array density, three different array configurations were simulated: square, staggered
and random. Additionally, the trend of particle capture rate with respect to flow velocity,
represented by collector Reynolds number, was also investigated. Wooden dowel rods were
used to simulate the emergent aquatic collectors and pliolite particles were used to model the
various suspended particles that can be found in aquatic systems.
Experimental results suggest that the mechanisms that drive particle capture in an array
differ from those driving capture by a single collector. The capture efficiencies of square and
staggered arrays are roughly three times those of random array, which implies that
configuration is an important factor in particle capture. When compared to the capture
efficiencies of a single collector, a collector in an array was found to have a lower efficiency,
by more than one order of magnitude. This suggests that the dominant capture mechanism
that applies to a single cylinder, direct interaction, does not apply to a collector in an array.
In an array, diffusional deposition caused by random, turbulent motion of vortex shedding is
suggested to be the main capture mechanism. Moreover, this prediction is supported by the
fact that the collectors in an array had an even distribution of deposited particles on the front
and back faces of the collectors; on the other hand, it was found that most of the particles
captured on a single collector were on the front face, which characterises direct interception.
This study has shown that further research is needed to obtain full predictive capability for
particle capture in aquatic systems. The next step to be taken in improving the accuracy of
particle capture rate predictions is to numerically model particle capture with different array
densities, configurations and flow velocities as numerical modelling allows the investigation
of local hydrodynamics, especially how individual stem wakes interact with each other.
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Augustina Prita Pranoto Acknowledgements
[4]
Acknowledgements I would like to thank the following people for their support during the completion of this
project:
Marco Ghisalberti for providing guidance and support, preventing myself from getting lost
and confused with the direction of this project, and helping with the analysis of the results.
John Langan for his continuous help throughout the experimental component of this project,
especially for sealing the leaking flume.
My parents and my brother for their support, especially my dad for proof-reading all the
written assessments of this unit.
Paul Branson for letting me take some of his pliolite particles, Josje van Houwelingen for
helping me with the particles, and Taj Sarker for helping with operating the ADV in the
hydraulics lab.
The people of SESE and hydraulics lab for putting up with my insanity.
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Augustina Prita Pranoto Table of Contents
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Table of Contents Abstract .................................................................................................................................................. 2
Acknowledgements .............................................................................................................................. 4
List of Figures ........................................................................................................................................ 7
List of Tables ......................................................................................................................................... 9
List of Parameters ................................................................................................................................. 9
1. Introduction ................................................................................................................................. 10
2. Background ................................................................................................................................. 11
2.1 Particle capture in aquatic systems .................................................................................. 11
2.1.1 Larval settlement ........................................................................................................ 11
2.1.2 Pollination .................................................................................................................... 12
2.1.3 Filter feeding ............................................................................................................... 12
2.1.4 Vegetative filtration .................................................................................................... 13
2.2 Natural properties of collectors and particles ................................................................ 14
2.2.1 Vegetative species ....................................................................................................... 14
2.2.2 Shoot densities ............................................................................................................ 16
2.2.3 Particle sizes and densities ........................................................................................ 17
2.3 Flow properties ................................................................................................................... 18
2.3.1 Flow velocities ............................................................................................................. 18
2.3.2 Flow around collectors .............................................................................................. 18
2.4 Previous studies of particle capture ................................................................................. 21
2.4.1 Aquatic systems .......................................................................................................... 21
2.4.2 Aerosol ......................................................................................................................... 23
3. Study Aims .................................................................................................................................. 24
Aim 1 ................................................................................................................................................. 24
Aim 2 ................................................................................................................................................. 24
Aim 3 ................................................................................................................................................. 24
4. Methodology ............................................................................................................................... 25
4.1 Open channel flume ........................................................................................................... 25
4.2 Cylinders .............................................................................................................................. 26
4.3 Array densities .................................................................................................................... 27
4.4 Array configuration............................................................................................................ 28
4.5 Particles ................................................................................................................................ 29
4.6 Counting particles .............................................................................................................. 30
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4.7 ADV ...................................................................................................................................... 30
4.8 Flow velocities..................................................................................................................... 31
4.9 Calculating capture efficiencies ........................................................................................ 32
5. Results .......................................................................................................................................... 33
5.1 Capture efficiencies ............................................................................................................ 33
5.1.1 Solid fraction ............................................................................................................... 33
5.1.2 Velocity ........................................................................................................................ 33
5.2 Surface distribution of particles captured ....................................................................... 34
5.3 Test cylinder location ......................................................................................................... 35
5.4 ADV velocity results .......................................................................................................... 37
5.5 Turbulence intensity .......................................................................................................... 39
6. Discussions .................................................................................................................................. 40
6.1 Capture efficiency ............................................................................................................... 40
6.1.1 Solid fraction trend ..................................................................................................... 40
6.1.2 Velocity trend .............................................................................................................. 41
6.2 Surface distribution of particles captured ....................................................................... 45
6.3 Test cylinder location ......................................................................................................... 45
6.4 ADV velocity results .......................................................................................................... 46
6.5 Turbulence intensity .......................................................................................................... 46
6.6 Applications ........................................................................................................................ 47
6.7 Limitations and errors ....................................................................................................... 48
7. Conclusions ................................................................................................................................. 50
8. Recommendations ...................................................................................................................... 51
9. References .................................................................................................................................... 53
Appendix ............................................................................................................................................. 58
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Augustina Prita Pranoto Table of Figures
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List of Figures Figure 1: Pollination of an aquatic vegetation. ............................................................................... 12
Figure 2: An example of filter feeding corals that exist in aquatic systems. .............................. 13
Figure 3: A meadow of Spartina alterniflora. .................................................................................... 15
Figure 4: The vortex development downstream of a collector for ideal two dimensional flow
(Kundu & Cohen 2002). ..................................................................................................................... 19
Figure 5: Particle capture by direct interception occurs when the particles follow the
streamline of the flow, and are then captured when they come within a distance equal to the
particle radius (Palmer et al. 2004). .................................................................................................. 21
Figure 6: Total particle capture in Purich (2006) experiment. ...................................................... 23
Figure 7: Flume set up of the experiments. ..................................................................................... 25
Figure 8: The 3 different array configurations tested throughout the experimental runs. From
top to bottom: square, staggered and random. .............................................................................. 28
Figure 9: A test cylinder in the process of drying. ......................................................................... 30
Figure 10: ADV used to measure flow velocity for each experimental run. .............................. 31
Figure 11: Comparison of capture efficiencies throughout the three tested solid fractions for
each of the array configuration at a flow velocity of roughly 5.7 cm s-1. The capture
efficiencies of square and staggered configurations are approximately 3 times greater than
the random configuration’s at medium and low solid fractions. At very low solid fraction,
the capture efficiencies of the 3 configurations appear to merge. ............................................... 33
Figure 12: Capture efficiency with increasing collector Reynolds number for each of the array
configuration at medium solid fraction. The square and staggered configurations have
capture efficiencies roughly 3 times those of the random configuration. They also have a
slightly decreasing trend while the random configuration’s capture efficiencies are relatively
constant. ............................................................................................................................................... 34
Figure 13: Particle distribution on the collectors' face throughout the tested collector
Reynolds number for the square configuration at medium solid fraction. The deposited
particles were found to be evenly distributed throughout the front and back faces of the test
cylinders. .............................................................................................................................................. 35
Figure 14: Particle capture on each test cylinder in the (a) square, (b) staggered and (c)
random configurations for medium solid fraction. There is no clear trend in capture
efficiency with respect to test cylinder location. ............................................................................ 36
Figure 15: Velocity time series of the flow through random configuration of medium solid
fraction during (a) Run 1, (b) Run 3 and (c) Run 5. The oscillation of the flow velocity is
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Augustina Prita Pranoto Table of Figures
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evident in (a), characterising flow of low turbulence. As flow velocity increases, the standard
deviation also increases; this can be associated with flow of high turbulence. ......................... 38
Figure 16: The change in turbulence intensity with solid fraction for each of the array
configuration. It is shown that turbulence intensity increases with solid fraction for the 3
tested configurations. ......................................................................................................................... 39
Figure 17: Comparison of experimental results for capture efficiency in an array to
calculation result of capture efficiency for a single collector, with respect to solid fraction.
The graph shows that the predicted capture efficiency of a single collector is about 2 orders
of magnitude greater than the capture efficiencies of an array with different solid fractions. 41
Figure 18: Comparison of experimental results for capture efficiency in an array (medium
solid fraction) to the predicted capture efficiency for a single collector, with respect to
collector Reynolds number. The capture efficiencies calculated in this study is roughly 2
orders of magnitude smaller than those of a single collector. ..................................................... 42
Figure 19: Comparison of the experimental results of this study for the random configuration
of medium solid fraction to Purich's (2006) results. It is evident that the capture efficiencies of
the 2 studies differ from one another, implying the need of further studies. ............................ 44
Figure 20: Numerical modelling of particle capture through investigating how stem wake
developed by a collector affects the stem wake of a collector located directly downstream
(Espinosa et al. 2010). ......................................................................................................................... 51
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Augustina Prita Pranoto List of Tables
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List of Tables Table 1: List of parameters used throughout the study .................................................................. 9
Table 2: Tested array densities.......................................................................................................... 27
Table 3: Flow velocities of the experimental runs for medium solid fraction. .......................... 32
Table 4: Flow velocities of the experiments of SF 1.18% (very low) and 2.2% (low). ............... 32
Table 5: Results of experiments with medium solid fraction ....................................................... 58
Table 6: Results of experiments with low solid fraction. .............................................................. 59
Table 7: Results of experiments with very low solid fraction. ..................................................... 59
List of Parameters Table 1: List of parameters used throughout the study
Rec Reynolds number (dimensionless)
u Flow velocity (cm s-1)
dc Collector diameter (cm)
υ Fluid kinematic viscosity (m2 s-1)
Sn Average distance to the nearest neighbouring stems (cm)
ΔS Average spacing (cm)
η Capture efficiency (%)
R Particle ratio (dimensionless)
SF Solid fraction (%)
Nc Number of particles captured (particles)
P0 Initial number concentration (particles m-3)
lc Collector length (cm)
t Capture duration (s)
TI Turbulence intensity (%)
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Augustina Prita Pranoto Introduction
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1. Introduction The capture of suspended particles is one of the important mechanisms affecting several
ecological processes that exist in aquatic systems; those processes include vegetative
filtration, larval settlement (Palmer et al. 2004) and filter feeding (Edler & Georgian 2004).
Vegetative filtration is a very important process which allows the capture of suspended
particles in aquatic systems, improving its ecological health. Moreover, Harvey, Bourget and
Ingram (1995) suggested that the settlement of larvae relies on local hydrodynamics, and
Ayaz and Pedley (1999) stated that filter feeders rely on captured suspended food particles.
This study ties into one of Australia’s research priorities, which is to sustainably manage and
protect its aquatic environments. The overall seagrass population has significantly decreased
over the past three years due to the decrease in light attenuation (Green & Short 2003). It was
suggested that this decrease is caused by eutrophication and sediment resuspension (Newell
& Koch 2004). A more in-depth understanding of particle capture will help manage various
aquatic systems more appropriately and effectively.
A number of studies in recent years have looked at particle capture, some with single
collector and some with an array of collectors; these include experimental studies
undertaken by Palmer et al. (2004) and Purich (2006). In most studies, the particles captured
were quantified as capture efficiency (η), which can be defined as the fraction of particles
removed from the volume of water passing through the projected area of the collector
(Palmer et al. 2004). There are four mechanisms which affect particle capture: direct
interception, inertial impaction, gravitational deposition and diffusional deposition (Shimeta
& Jumars 1991). The dominant mechanism of capture is affected by a number of factors
including whether or not a collector is in an array and how the array density and
configuration vary.
Very few researches focused on capture by collectors in an array even though most biological
collectors exist in an array. To the knowledge of the author, no previous studies looked at the
impact of array configuration on particle capture, even though configuration would have an
impact on the local hydrodynamics of the collectors. Wakes created by neighbouring
structures can affect one another, and collector density and configuration affect the
disturbance created by each collector (White & Nepf 2003). Hence, it is important that
particle capture in an array is investigated to allow a more accurate prediction of its rate in
aquatic systems.
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Augustina Prita Pranoto Background
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2. Background The capture of suspended particles in aquatic systems has been investigated in a number of
studies. Particle capture plays a significant role in various ecological processes in aquatic
environments, including larval settlement, pollination, vegetative filtration and filter feeding.
In the field of environmental engineering, the hydrodynamics that are closely related to the
mechanism of particle capture is of high interest, along with its application to constructed
wetlands and other environmental management practices. A number of previous studies
have looked at particle capture through analysing field data and experimental results, in
some cases the two were compared. Not a lot of research has been done on the particle
capture of a collector in an array, and none looked at the effect of array configuration.
2.1 Particle capture in aquatic systems The capture of suspended particles is an important mechanism in a number of ecological
processes, including, but not limited to larval settlement, pollination, vegetative filtration
and filter feeding.
2.1.1 Larval settlement
There are four phases that make up the colonisation of habitats by marine benthic
invertebrates with planktonic larvae: larval development and dispersal, testing habitat
quality, settlement and metamorphosis (Bourget 1988). Larval settlement can be defined as
larvae’s successful and semipermanent attachment to the substratum (Crimaldi et al. 2002).
Crimaldi et al. (2002) stated that larval recruitment is important for species success,
population density and community structure. Previous studies have looked into the
transport of larvae to the bed, and passive and active larval settlement. Passive larval
settlement is when the phase is entirely controlled by the water currents and active
settlement occurs when a larva evaluates habitat suitability by detecting chemical, biological
and/or physical properties when selecting a microhabitat (Harvey, Bourget & Ingram 1995).
On some occasions, the two types of settlement may occur at the same time. Even though
active processes are involved in microhabitat selection by bivalve larvae, Harvey, Bourget
and Ingram (1995) experimental work and field study showed that passive processes are
sufficient to explain the settlement pattern found in the field. In addition, the simulation of
passive settlement in experimental studies does not exclude active processes associated with
smaller and larger spatial scales, where a close-range discrimination of suitable surfaces and
active rejection of a flow regime occur, respectively. This confirmed the importance of
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understanding how local hydrodynamics affect particle capture for future studies of larval
settlement.
2.1.2 Pollination Aquatic vegetation pollination, pictured in Figure 1, is an important process in marine
environments as it determines the long-term stability and maintenance of local populations
(Ackerman 2002). The pollination of seagrass and other aquatic vegetation relies heavily on
the particle capture mechanism associated with the flow, which in turn affects the encounter
rate of pollen with the individual vegetative elements (Palmer et al. 2004). Pollination of
seagrass is a passive, abiotic process, meaning that the capture of pollens relies solely on the
local hydrodynamics around the vegetation (Ackerman 2002). This is dissimilar to the
reproductive process of many algae as algae propagules are motile.
Figure 1: Pollination of an aquatic vegetation.
Water currents play a significant role in the process of submarine pollination, also known as
hydrophily (Ackerman 2000). Not only is the capture rate affected by water currents, it was
suggested that the evolution of pollen shape in hydrophily can be associated with the local
hydrodynamics in the substratum (Ackerman 2000).
2.1.3 Filter feeding Filter feeders include all species that only capture food particles that flow over or through
their filtering systems (Jeschke, Kopp & Tollrian 2004), an example is pictured in Figure 2.
Jeschke, Kopp and Tollrian (2004) include suspension feeders (protozoans, rotifers, sponges),
trap-builders (hydromedusae), and sediment filter feeders (sea cucumbers) as filter feeders.
Suspension feeders are the most common out of the three and they usually operate at low
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flow velocities (Jeschke, Kopp & Tollrian 2004). Filter feeders have a few important roles in
the ecosystem, including repairing water quality, maintaining the reliability and stability of
the ecosystem, contributing to habitat heterogeneity and accelerating the transport of
chemical elements (Ostroumov 2005). As part of those roles, they remove various particles
of a broad range of sizes from the water (Ostroumov 2005). The general properties of filter
feeders are high retention efficiency, possession of a low-energy pump system in active filter
feeders and opportunistic feeding properties (Coma et al. 2001).
Figure 2: An example of filter feeding corals that exist in aquatic systems.
Filter feeders can be grouped into two types based on their use of water currents to collect
food particles. Active filter feeders produce these currents while passive filter feeders use
already existing local hydrodynamics (Jeschke, Kopp & Tollrian 2004). Whiles and Dodds
(2002) found that the number of individual filter feeders increase with organic seston
(bioseston) concentrations. Therefore, this suggests that water velocity, water depth and
quality of substrate can affect filter feeder diversity and abundance, as those factors influence
bioseston concentrations (Whiles & Dodds 2002).
2.1.4 Vegetative filtration Vegetative filtration involves the removal of suspended particles from the water through the
assistance of vegetative elements. It has long been known that the presence of vegetation
increases particle removal by increasing residence time and reducing resuspension (Palmer
et al. 2004). Based on a model study by (Gacia, Granata & Duarte 1999), vegetated beds are 15
times greater at removing suspended particles than barren beds.
In coastal marshes, sediment transport, deposition and resuspension are important in
maintaining ecological stability by regulating their surface elevation compared to sea level
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(Christiansen, Wiberg & Milligan 2000). The dynamics of particulate matter is also significant
in freshwater wetlands as it contributes to the health of tree islands, which act as biodiversity
hotspots (Bazante et al. 2006). In riparian wetlands, changes in sediment fluxes and
deposition rates of particles modify the primary productivity, nutrient cycling and species
composition, which suggests that the fate of particles is also important in riparian wetlands.
The water quality in the wetland will also be improved by removal of suspended particles as
they adsorb nutrients and pollutants such as heavy metals (Huang et al. 2008).
Mechanisms such as dispersion and advection play a role in the distribution of particles.
Dispersion in this case includes bed-induced shear, mechanical dispersion, turbulent
diffusion and exchange between mobile and immobile water zones (Nepf 2004). The
distribution of particles due to spatial and temporal variations in the flow that are not
accounted for by advection, is affected by dispersion.
Beds with vegetative community of high density is usually associated with muddification,
which is when the concentration of fine particles and organic matter in the sediment is
increased relative to neighbouring barren beds (Nepf 2012). On the other hand, patchier beds
are usually associated with sandification, where a decrease in fine particle concentration and
organic matter occur (Van Katwijk et al. 2010). This is usually associated with the fact that
higher levels of turbulence can be found in sparse patches compared to a densely vegetated
area (Nepf 2012).
2.2 Natural properties of collectors and particles Research on a number of properties of aquatic systems was necessary for the development of
this study. For the purpose of the experimental component of this study, stationary collectors
will be used; most of the stationary collectors in natural systems are vegetation. In this
section, a background study on the natural properties of collectors and particles is included.
2.2.1 Vegetative species There is a diverse range of vegetation types that exist in various aquatic systems. These
vegetation play an important role in the ecological balance of aquatic systems, which can be
group into seagrasses, tidal marshes and wetlands (Purich 2006).
Seagrasses represent a very small percentage of angiosperm flora (
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Duarte 2000). In most cases, only one seagrass species exist in any one meadow, especially in
the temperate zone (Hemminga & Duarte 2000). Seagrass meadows play an important role in
developing highly productive ecosystems (Duarte 2002); the mechanism through which they
capture pollens and suspended particles (vegetative filtration) is important for the ecological
balance of the ecosystems.
Along with the seagrasses, tidal marshes promote some of the most productive
environments on Earth, despite the fact that the significance of marsh production on adjacent
estuarine and coastal ecosystems has been debated for decades (Kneib 1997). The hydraulic
properties that exist in tidal marshes are also important in the groundwater discharge into
estuaries; it was found that groundwater has a longer residence time and is more thoroughly
mixed with surface water and pore water in tidal marsh soils (Harvey & Odum 1999). Some
of the most common species of tidal marsh belong to the genus Spartina (cordgrass). These
species promote the removal of suspended particles from the water; these additional
mechanisms that increase removal of suspended particles include particle retention to the
vegetative elements and assistance in sedimentation or particles. For example, Spartina
alterniflora (Figure 3), a Spartina species native to the east coast of the USA, was found to be
responsible of 50% of the removal of suspended particles in tidal marshes (Stumpf 1983). In
addition, a study by Leonard, Hine and Luther (1995) suggested that another species of tidal
marsh, Juncus roemerianus, account for 10% of the removal of suspended particles in tidal
marshes. Those findings of the two studies emphasise the key role held by tidal marshes in
vegetative filtration in aquatic systems.
Figure 3: A meadow of Spartina alterniflora.
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This third vegetation type is wetlands; wetlands are transitional system between aquatic and
terrestrial systems (Purich 2006). Wetlands are very important because they control the
contaminants, nutrients and sediments transport between the two systems. They play a
considerable role in the global carbon dynamics through their large soil carbon pools, high
methane emissions, and potential for carbon sequestration in peat formation, sediment
deposition and plant biomass (Bridgham et al. 2006). Moreover, wetlands provide habitat,
improve water quality and reduce coastal erosion; all of these services are affected by the
hydrodynamics within the vegetated areas (Nepf 2012). The types of vegetation that can be
found in these systems are usually emergent (Oldham & Sturman 2001), including
mangroves and rushes (Hirano, Madden & Welch 2003).
2.2.2 Shoot densities The shoot densities of aquatic vegetation are usually different for every species, depending
on factors such as climatic conditions, and soil and flow properties. In turn, the density of
aquatic vegetation influences faunal recruitment, survival and density (Boström, Jackson &
Simenstad 2006) and their ability to stabilise sediments (Worcester 1995). Several typical
shoot densities are presented below to allow an accurate representation of aquatic vegetation
densities in the experimental design.
Zostera marina, a common species of seagrass, was found to have an average shoot density of
250 shoots m-2 based on a study conducted by Boström, Jackson and Simenstad (2006). The
same study also found that the shoot density of Ruppia maritima may exceed 1000 shoots m-2.
In a study conducted by Worcester (1995) found a shoot density ranging between 133 and
330 shoots m-2, at a different study area to Boström, Jackson and Simenstad (2006).
For tidal marshes, it was found that Spartina anglica has a shoot density range of 362 to 1396
shoots m-2 (Peralta et al. 2008). Dai and Wiegert (1996) recorded shoot densities of
approximately 90 to 120 shoots m-2 for tall Spartina alterniflora (stem height around 10 cm),
without the effect of nitrogen fertiliser. A field study conducted by Neumeier (2005)
recorded a shoot density range of 1398 to 2218 shoots m-2 for salt marshes of Spartina
maritima. Another field study of tidal marshes undertaken by Leonard, Hine and Luther
(1995) found in their study area a shoot density of greater than 250 shoots m-2 for Juncus
roemerianus, which is considered of high density for the species.
There are fewer studies with regards to shoot densities in wetlands, compared to the other
two aquatic vegetation types (Purich 2006). A study looking at the impact of increased water
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levels on wetland vegetation recorded a mean shoot density of 200 shoots m-2 for emergent
species (Van der Valk, Squires & Welling 1994). Another field study on Scirpus olyeni
recorded shoot densities ranging from approximately 300 to 1000 shoots m-2, based on un-
chambered control plots (Rasse, Peresta & Drake 2005). Rea and Ganf (1994) investigated the
response of wetlands to different water regimes and they recorded a shoot density range of
100 to 1600 shoots m-2 for Baumea arthrophylla.
2.2.3 Particle sizes and densities Suspended particles in aquatic systems may consist of marine larvae, sediment, pollutants
and pollen, amongst other materials. Those are the particles that may eventually experience
retention by collectors such as aquatic vegetation and filter feeders. One of the aspects that
affect the significant mechanism of the capture process is the physical properties of the
particles, including their size, density and shape.
Sediments that are suspended in aquatic systems may comprise sands, silts or clays. These
sediments can be classified based on their sizes under the British Standard system; sand
particles have diameters of 60-2000 µm, silt particles are within 2 – 60 µm and clay particles
are less than 2 µm in diameter. The typical sediment density of an aquatic system is 2650 kg
m-3 (Whitlow 2001).
Marine larvae and pollen are some of the biological particles that can be found suspended in
aquatic systems. There are two types of pollens, abiotic and biotic. It was suggested that
abiotic pollens are smaller in diameter compared to biotic pollens; abiotic pollen has a
diameter range of 20 – 60 µm while biotic pollens’ diameter is around 200 µm (Ackerman
2000). Therefore, the settling velocity of abiotic pollens is less than that of biotic pollens. It
was noted by Ackerman (2000) that pollen shape evolution is influenced by the water flow
through the seagrass meadow. The shapes of the pollen may differ for every species; for
example, the shape of Zostera marina pollen is filamentous. These pollens have a diameter of
roughly 7.5 µm and length of 3 – 5 mm. Based on experimental studies, it was suggested that
1000-10000 Zostera marina pollen are required to pollinate a single flower (Ackerman 2002).
Typically, the density of Zostera marina pollen is 1070 kg m-3.
Larvae size at hatching varies in accordance with egg size, which differs with species
(Panagiotaki & Geffen 2006). It was found that the size range of a herring larvae (Clupea
harengus L.) is roughly 8.3 – 9.3 mm (Panagiotaki & Geffen 2006). In contrast, the sizes of
bivalve larvae, including mussels and scallops, are usually within 125 – 300 µm (Harvey,
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Bourget & Ingram 1995). Larvae density in aquatic systems is typically around 1400 kg m-3
(Harvey, Bourget & Ingram 1995).
2.3 Flow properties 2.3.1 Flow velocities
The flow velocities that can be found in natural aquatic systems generally range from
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hand, turbulent flow has high Reynolds numbers which indicates that inertial forces are
dominant. It is characterised by random eddies, vortices and other flow fluctuations. Low
Reynolds numbers and high Reynolds numbers associated with the flow around cylindrical
collectors can be grouped into Re > 1 respectively (Purich 2006). Laminar flow
around a cylindrical collector is illustrated in situation (A) in Figure 4, while the
hydrodynamics of turbulent flow is pictured in situation (B). As evident in the figure, the
level of turbulence increases as flow velocity, and thus Reynolds number, increases. Vorticity
is produced due to the no slip boundary condition that is present between the collector and
the fluid; vorticity becomes more confined behind the collector due to advection (Purich
2006). In ideal two dimensional flows, two small standing eddies are generated directly
downstream of the collector, as shown in situation (B) in Figure 4 for Reynolds numbers
greater than 4. As Reynolds number increases to 40, the length of the two small eddies
increases until the wake becomes partially turbulent in the transition region. This occurs
when Reynolds number is greater than 40, when the wake becomes unstable. The wake then
develops into the von Karman vortex street (Kundu 1990) which is the two staggered rows of
vortices downstream of the collector where the vortices rotate in opposite directions.
Figure 4: The vortex development downstream of a collector for ideal two
dimensional flow (Kundu & Cohen 2002).
It is known that emergent canopies affect the mean and turbulent flow over the entire water
column. On barren beds, the eddies that are dissipated by the canopy scale with water depth.
However, the length scale of the turbulence is independent of the water depth in a channel
with rigid vegetation (Tanino & Nepf 2008); it is influenced by the stem diameter, d, or the
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average distance to the nearest neighbouring stems, Sn. In the case of array configuration, the
average spacing and the average distance to the nearest neighbouring stem are equal to one
another, ΔS = Sn, but in a random array, the average nearest-neighbouring distance is smaller
than the average spacing (Nepf 2012).
A study conducted by White and Nepf (2003) investigated the interactions between
collectors at high packing density. When a high array density is present, wakes developed
downstream of two neighbouring collectors can affect on another or even coalesce. It was
suggested that independent wakes only exist for packing densities less than 0.05 for a square
array (White & Nepf 2003), which is consistent to the density at which wake interference
begins to reduce drag in a square array (Nepf 1999). As previously discussed, the average
spacing in a random array is greater than in a square array, consequently this packing
density is slightly higher for a random array. Packing densities can reach 0.4 in some
freshwater wetlands while most coastal and bank vegetation is within the packing density
limit of 0.05, therefore it is important that models of collector interactions also investigate
higher packing densities (White & Nepf 2003).
Harvey, Bourget and Ingram (1995) and Palmer et al. (2004) suggested that there are four
main mechanisms associated with the capture of suspended particles in aquatic systems:
direct interception, inertial impaction, gravitational deposition and diffusional deposition.
Direct interception occurs when a particle is captured while travelling on a streamline. In this
case, it is assumed that a particle is captured when it approaches a collector within a distance
equal to its radius, as shown in Figure 5. Inertial impaction describes capture due to
deviation of a particle from a streamline caused by the particle’s inertia. The particle’s inertia
can be defined by Stokes number, Stk, which is a function of collector Reynolds number,
particle ratio and specific gravity. Particle ratio is the ratio of particle diameter on collector
diameter. Diffusional deposition is linked to capture of particles via any random process
such as Brownian motion or turbulence. Lastly, gravitational deposition occurs when
particles settle out of the water due to the gravitational force.
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Figure 5: Particle capture by direct interception occurs when the particles follow the
streamline of the flow, and are then captured when they come within a distance
equal to the particle radius (Palmer et al. 2004).
2.4 Previous studies of particle capture 2.4.1 Aquatic systems
Field studies
Several studies have looked into the field measurements of particle capture in various
aquatic environments. One of them was undertaken by Harvey, Bourget and Ingram (1995);
in the study, passive sedimentation of marine bivalve larvae on filamentous epibenthic
structures was investigated. The field measurements were taken from Baie de Jacques-
Cartier and Baie des Chaleurs, Canada on the 3 most abundant bivalve species: Mytilus
edulis, Cerastoderma pinnulatum and Hiatella arctica. The field measurements of this study,
combined with some experimental results, suggest that the processes involved in the
settlement stage of marine larvae can be represented by the passive processes. Another study
by Edler and Georgian (2004) on net-spinning caddisflies, an important group of filter
feeders in many aquatic ecosystems, found that the capture of sievable particles were more
efficient than smaller particles. In other words, the study postulated that the rate of particle
capture by a filter feeder is considerably affected by the particle type, ranging in shape and
size.
Numerical modelling: Single collector
In a study by Espinosa et al. (2010), the capture of suspended particles was modelled
through the simulation of a 2-D flow of particle-laden fluid around cylindrical collectors;
OpenFOAM was used to solve the fluid and particle dynamics for a large range of Reynolds
number and diameter ratio. It was predicted that capture efficiency increases with Reynolds
number and the results appeared to match single collector capture efficiency results of the
experimental study (Palmer et al. 2004).
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Experimental: Single collector
There has been a number of studies on particle capture on a single collector. However,
Palmer et al. (2004) will be the main study used for comparison with the results of this study.
In their study, an assumption made by Shimeta and Jumars (1991) that direct interception is
the only significant mechanism in particle capture was applied. As part of their experimental
component, a single cylindrical collector was used with roughness, collector diameter and
flow rate as their variable parameters. Particle capture on smooth and rough collectors was
simulated and three different collector diameters were modelled, 0.635, 1.27 and 2.54 cm,
while the collector Reynolds number ranges from 130 to 240. It was suggested that the
capture efficiency has a strong dependence on particle ratio, R. In addition, the collector
Reynolds number, Rec, also influences capture efficiency, although not as strongly as particle
ratio. For smooth surfaces, the experimental results show that the capture efficiencies range
from 0.032 to 0.48% through the 3 different collector diameter. For rough surfaces, 3
experimental runs were conducted, resulting in capture efficiencies of 0.27, 0.27 and 0.42%
for a collector diameter of 1.27 cm. Through these experimental results, Palmer et al. (2004)
generated the following empirical relationship:
( )
Palmer et al. (2004) found that the predicted capture efficiencies are consistent with
published values of capture efficiencies by cylindrical branches (Harvey, Bourget & Ingram
1995), suggesting that the empirical relationship can also be applied to more complex
structures. In addition, the results indicate that the overall capture efficiency increases with
roughness.
Experimental: Array of collectors
The rate of capture of suspended particles on a collector that exist in an array has not been
extensively studied in the past. As previously mentioned, most natural collectors in aquatic
systems exist in an array, not as a single collector therefore it is important that collectors are
modelled in array to allow a more realistic simulation of natural particle capture processes.
One of the previous studies that investigated particle capture in an array of collectors was
conducted by Purich (2006). In this study, different array densities were simulated to
investigate the rate of capture of suspended particles with different collector Reynolds
numbers (flow velocities). There were 3 different array densities, low, medium and high,
which corresponds to 500, 1000 and 2000 collectors on the pegboard. The location of each
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collector was determined pseudo-randomly for a bed length of approximately 3 m and 5 test
collectors were used per experimental run.
Purich (2006) found that total particle capture was highest for array with medium density
and the lowest for high density, as shown in Figure 6. The total capture at the highest
collector Reynolds number for medium density is roughly six times greater than that of high
density array. The experimental results also showed that there was no variation in particle
capture with different test cylinder positions.
Figure 6: Total particle capture in Purich (2006) experiment.
2.4.2 Aerosol There has been a considerable amount of work in the field of particle capture for aerosols.
Particle capture is an important mechanism with regards to aerosols for a number of natural
processes including vegetative interception of dust, salt and trace elements, and pollination
(Smith 1977). The use of trees, for example, is an important removal mechanism of particles
from the air. In general, particle capture by trees is mainly influenced by the tree species,
planting design and location to the source of particulate pollution (Freer-Smith, El-Khatib &
Taylor 2004). Beckett, Freer‐Smith and Taylor (2001) field study results showed that trees
with finer, more complex structure of foliage are associated with higher capture efficiencies.
The capture efficiencies of trees for wind speeds of 3 to 9 m s-1 roughly ranges from 0.01
to1.2% for the leaves or needles and
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3. Study Aims There are 3 specific aims in this study, with the main purpose of improving the general
understanding of particle capture in aquatic systems.
Aim 1 Investigate the difference in particle capture between a single collector and an array.
Particle capture in aquatic systems have been modelled as a single collector in previous
studies, however very limited research has been conducted where experiments or
computational modelling investigated an array of collectors. Modelling particle capture as an
array of collectors will create a more realistic prediction of particle capture in aquatic systems
as most, if not all, of the natural collectors exist in an array.
Aim 2 Determine the importance of array configuration.
The configuration of the array has not been extensively investigated in past studies of
particle capture in aquatic systems. Array configuration significantly affects the local
hydrodynamics that exist through the array of collectors. This will in turn influence the rate
of capture of suspended particles and possibly the dominating capture mechanism.
Aim 3 Investigate the distribution of particles captured on the collector.
The distribution of particles captured is significantly affected by the capture mechanism that
is dominant in the flow, which is in turn influenced by characteristics such as the Reynolds
number. Therefore, investigating the distribution of particles captured on the collector can
help confirm the dominating mechanism of particle capture in that particular array.
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4. Methodology For the purpose of this study, laboratory experiments were conducted in order to investigate
capture of suspended particles in a modelled aquatic system. The simulation utilises an open
channel flume with an array of collectors simulated by wooden dowel rods where a number
of test cylinders was used per experimental run. A known mass of particle was then added
to the water before the experiments were conducted for a period of time. To measure the
flow velocity of each experimental run, a MicroADV was installed near the outlet of the
flume. The captured particles were then manually counted before analysed using Microsoft
Excel through comparing capture efficiencies.
4.1 Open channel flume In this study, the experiments were conducted in an open channel flume with length 400 cm,
width 25 cm and height 24 cm. The flume was filled with approximately 132 L of water; the
recirculation of water was achieved using a pool pump (Davey Power Ace CR 300, model
number PACR300-0) with a valve attached downstream of it, connected to an inlet to and an
outlet from the flume with PVC pipes. The pump was used to achieve the desired flow
velocities, in accordance with the chosen velocities for each run.
Figure 7: Flume set up of the experiments.
Directly downstream of the inlet to the flume, a sheet of foam is placed as a flow stabiliser, as
the water flow from the inlet would be quite turbulent; the foam is approximately 4.9 cm
long, 24.4 cm wide and 23.4 cm high relative to the flume. The foam density had to be low to
allow water and particles to continuously flow through, preventing a change in water level
and minimising the number of particles stuck to the foam. The water level was maintained at
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12 cm for each of the experimental runs. During each of the experimental runs, the foam was
shaken once every 3 minutes to resuspend the particles that may have deposited.
After the foam, a plastic honeycomb with cell size 1.3 cm and length 55.5 cm was placed as a
flow straightener, distributing the flow evenly throughout the width and height of the flume,
allowing an even particle capture probability throughout the array of collectors and the
length of each collector.
The next part of the flume is the array; three sheets of pegboard were used to hold the array
of wooden dowel rods; these sheets have a cumulative length of approximately 3 metres and
they took up the entire width of the flume. Wooden dowel rods were used to simulate the
benthic structure that will develop the hydrodynamic properties within the flow. Three
different array densities and array configurations were tested during the experiments, as
listed in Table 1. For the highest array density, the experiments were conducted at five
different flow velocities, while the other array densities were only tested on the median flow
velocity due to time constraint.
Approximately 30 cm from the outlet, the array of collectors are installed in accordance with
the array configuration. A Sontek Micro ADV was installed downstream of the collectors in
order to obtain an accurate measurement of flow velocity experienced by the collectors. The
dowel rods continue for the remainder of the pegboard, followed by another sheet of foam;
the foam was used to minimise the turbulent effect of the outlet drawing water out of the
flume. A photograph of this set up is shown in Figure 7.
4.2 Cylinders Wooden dowel rods of length 16 cm and diameter 0.63 cm were used to model the natural
collectors found in aquatic systems. The diameter of the collectors falls within the range of
stem diameters that can be found in salt marshes, which is 0.2-1.2 cm (Tanino & Nepf 2008).
The pegboards have a hole diameter of 0.65 cm, which allowed the rods to be installed easily.
The wooden dowel rods eventually became saturated and a tight fit was achieved despite the
0.02 cm diameter difference between the rods and the pegboard holes; this is why for the
initial run, the dowel rods had to be installed one day prior to the run to allow time for a
tight fit to be achieved. As the water level was maintained at 12 cm at all times, the particles
were measured 3 cm from the bottom and 5 cm from the water level, giving a capture length
of 4 cm; this was conducted to avoid the effect of boundary layer flows on the rate of particle
capture. The test cylinders used in the square configuration runs were also divided into two
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equal parts vertically, to allow separate measurements of the particles captured on the front
and back face of the cylinders.
Six collectors were used for the highest array density while twelve were used for the lower
array densities. Before being installed into the pegboard, the test cylinders had to be covered
by black multipurpose grease (Penrite Molygrease EP 3%), which modelled as the sticky
periphyton layer that can be found on most aquatic vegetation. A washer was used to obtain
an even, smooth surface on the test cylinders by sliding each of the cylinders carefully
through the washer.
The test cylinders were left in the flume for 15 minutes for each run, allowing enough
particles to be deposited on the grease; they were inserted in accordance with the tested
configuration of the run. The cylinders were removed in the same order as they were
inserted into the pegboard, allowing roughly the same period of time for data collection on
each test cylinder. The cylinders are then allowed to dry to minimise the reflectivity of the
grease surface, allowing an easier counting process; during this drying process, the cylinders
were stored upright on a spare pegboard to enable easy identification of test cylinder flume
location. The grease covered area that is not within the sampled length was then carefully
cleared, before the deposited particles were counted.
4.3 Array densities Table 2: Tested array densities.
Array density Number of rods
in pegboard
Density (shoot
m-2)
∆S (cm) SF (%)
Very low density 268 358 5.1 1.18
Low density 468 625 3.8 2.2
Medium density 1025 1369 2.5 4.95
Three array densities were tested for the experiments; these densities and their
corresponding solid fractions are listed in Table 2. Initially, only medium density was going
to be tested, however after further discussions, it was decided that low and very low
densities should be investigated to determine the general trend of particle capture. To allow
comparison with past and future studies, the array density is expressed as solid fraction (SF),
which can be calculated through Equation (3).
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(3)
where ∆S is average spacing and d is the collector diameter. The medium, low and very low
densities correspond to solid fractions of 0.0495, 0.022 and 0.0118 respectively, as shown in
Table 2. The array densities were chosen in accordance with the natural stem density of
aquatic vegetation, as discussed in Chapter 2.2.2.
4.4 Array configuration
Figure 8: The 3 different array configurations tested throughout the experimental
runs. From top to bottom: square, staggered and random.
Different array configurations have a different effect on the disturbance created by each
collector (White & Nepf 2003). Consequently, three different array configurations were
investigated in how they affect capture efficiency for the experimental work of this study.
They were chosen based on previous related studies on the hydrodynamics around emergent
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vegetation as listed in Tanino and Nepf (2008), including a study by Koch and Ladd (1997).
The three configurations were square, staggered and random; three photographs of the three
different configurations are shown in Figure 8. To determine the exact number of rods in the
array, the square configuration was set up first for each array density, as the square
configuration could only be set up a certain way following the pattern of the holes on the
pegboard. To obtain the random configuration, an online true random generator
(Random.org) was used to generate the hole numbers where the dowel rods would be
inserted. Random.org was developed and is operated by Mads Haahr of the School of
Computer Science and Statistics at Trinity College, Dublin; it generates true random
numbers based on atmospheric noise.
4.5 Particles To model the suspended particles, pliolite particles (Vinyltoluene-acrylate copolymer,
supplied by the Goodyear Tyre & Rubber Company) were used in the laboratory
experiments. The particles are white and thus visible when captured on the test cylinders,
which were covered with black grease. The particle size used in the experiments were based
on the size range chosen by Purich (2006), which was 212-250 µm. This range corresponds to
the size of medium sand and some marine larvae. The particle size range was achieved
through sieving the particles through a 212µm and a 300 µm standard size sieve, using a
mechanical device. The retained particles were then manually sieved through a sieve with
smaller diameter of hole size 250 µm, giving the desired size range. The particles have a
specific gravity of 1.03, which is very close to the specific gravity of water (1.00). However,
Purich (2006) found that the particles were sinking to the bottom of the flume instead of
being suspended in the water during her trial run. Therefore approximately 3 kg of salt was
added into the water to increase the specific gravity of the water to 1.025, preventing the
particles from sinking. Moreover, to minimise the number of sunken particles, the water was
mixed by hand immediately before and gently three times during the experiment.
During the trial experimental runs, the chosen size range proved to be visible to the naked
eyes during manual counting and the rate of sedimentation was minimal. Hence this
confirms the appropriate use of the chosen size range.
The mass of pliolite particles used during the experiment was 14.0094 g; the particles were
added in at the beginning before any of the experimental runs. Before they were added into
the water, the particles were mixed with two drops of dishwashing detergent to prevent
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clumping. As the particles were constantly recirculated, it was assumed that the number of
particles removed during all of the experimental runs were negligible.
To calculate the number concentration of the added particles necessary for analytical
calculations, the steps taken by Purich (2006) was implemented and resulted in a number
concentration of 1.77 × 107 particles m-3.
4.6 Counting particles The captured particles on each test cylinder were counted manually. After one particular
experiment had been run, the test cylinders were left out to dry overnight (Figure 9) before
the captured particles were counted. For the experiments with SF 4.95% with square
configuration, the test cylinders used had two lines separating the front face and the back
face to investigate the distribution of the captured particles; the lines also prevented counting
the same particle twice. Manually counting the particles minimises the likelihood of counting
a white dot that is not a particle by making sure that it is not caused by reflections.
Figure 9: A test cylinder in the process of drying.
4.7 ADV To measure the flow velocity a Sontek MicroADV (Acoustic Doppler Velocimeter) was
installed immediately downstream of the test cylinders. A sound pulse is transmitted by the
ADV probe, pictured in Figure 10, which is then reflected by a particle in the water; the pulse
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is transmitted at a set frequency. The ADV probe measures velocity in x-, y- and z-directions.
For the experiments conducted, the parameter of interest is the velocity.
Figure 10: ADV used to measure flow velocity for each experimental run.
The ADV was set to a frequency of 25 Hz and a salinity of 26 gL-1. Prior to the first
experiment of the day, the temperature of the water was measured; the value was then
entered into the ADV settings which allowed a temperature measurement accurate to the
nearest degree. For most of the experiments, the temperature was set as 15OC.
Different maximum velocity ranges were used to obtain the most accurate data for the
different flow velocities that were tested. For the lowest flow velocity, a range of 10 cms-1 was
used; the second lowest flow velocity had a range of 30 cms-1 while for the three highest flow
velocities, the maximum range was set to 100 cms-1. The lowest maximum ranges that result
in high SNR were chosen to minimise the effect of noise in velocity measurements.
4.8 Flow velocities The experiments were conducted on a range of flow velocities that could be found in natural
systems as discussed in Section 2.3.1. The collector Reynolds number (Rec) for each
experimental run was calculated to enable easy comparison with past and future
experiments, through Equation (1). As the collector diameter and kinematic viscosity were
kept constant, the only variable that changes for every run is the flow velocity.
For medium solid fraction, the experiments were conducted with 5 different flow velocities,
listed in Table 3 along with the corresponding collector Reynolds numbers.
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Table 3: Flow velocities of the experimental runs for medium solid fraction.
Experiments Square Staggered Random
u (cms-1) Rec u (cms-1) Rec u (cms-1) Rec
Run 1 2.81 177 2.77 175 2.64 166
Run 2 4.27 269 4.56 287 4.8 302
Run 3 5.41 341 5.9 372 5.7 359
Run 4 8.67 546 8.12 512 8.04 507
Run 5 9.5 599 9.98 629 9.28 585
For the two lower solid fractions, the experiments were only conducted at the median
velocity due to the time constraint. Table 4 lists the exact flow velocities and the
corresponding collector Reynolds numbers of those solid fractions, which are comparable to
Run 3 of medium solid fraction.
Table 4: Flow velocities of the experiments of SF 1.18% (very low) and 2.2% (low).
SF Square Staggered Random
u (cms-1) Rec u (cms-1) Rec u (cms-1) Rec
0.0118 5.86 369 5.36 338 5.44 343
0.022 5.48 345 6.11 385 6.07 382
4.9 Calculating capture efficiencies The calculated capture efficiency for each experiment was obtained through the following
equation:
(4)
As shown in Equation (4), capture efficiency is directly
proportional to the number of particles captured (Nc), and inversely proportional to the
initial number concentration in the flume (P0), the flow velocity (u), the collector diameter
(dc), the collector length (lc) and the capture duration (t).
For the purpose of this study, loss of particles from the initial number concentration was
considered negligible as the amplitude reading obtained by the ADV was unable to be
calibrated into particle concentration. Therefore, it was assumed that the initial number
concentration in the flume remains constant.
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5. Results
5.1 Capture efficiencies
5.1.1 Solid fraction The graph of capture efficiencies of the 3 different configurations for the 3 tested solid
fractions is shown in Figure 11. All raw particle capture data is attached as Appendix. The
graph shows that in general, the square configuration has the highest capture efficiency and
the random configuration has the lowest. The square configuration is associated with the
highest capture efficiency, except for the lowest solid fraction, which shows that staggered
configuration will result in the highest capture efficiency. The capture efficiency for square
configuration decreased from 0.0189 to 0.0077% at very low solid fraction. As the solid
fraction decreases, the capture efficiency of the 3 configurations merged towards lower
capture efficiency. The capture efficiency in a random configuration stayed relatively
constant over the solid fraction; it ranges from 0.00481% at medium solid fraction to
0.00691% at low solid fraction.
Figure 11: Comparison of capture efficiencies throughout the three tested solid
fractions for each of the array configuration at a flow velocity of roughly 5.7 cm s -1.
The capture efficiencies of square and staggered configurations are approximately 3
times greater than the random configuration’s at medium and low solid fractions. At
very low solid fraction, the capture efficiencies of the 3 configurations appear to
merge.
5.1.2 Velocity The trend of capture efficiency with velocity at medium solid fraction for the 3 configuration
is illustrated in Figure 12. Capture efficiencies of both the square and staggered
configurations (regular configuration) is relatively similar, compared to the random
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
0.02
0 1 2 3 4 5 6
η (
%)
SF (%)
Square
Staggered
Random
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configuration (non-regular configuration), which is roughly one third of the other two. The
configuration that has the highest capture efficiency for most of the tested collector Reynolds
number is the square configuration. The capture efficiencies of the square and staggered
configurations have a slightly decreasing trend, while the capture efficiency of the random
configuration stays relatively constant. For the two regular configurations, the capture
efficiency ranges between 0.0107 to 0.0222% and the random configuration had capture
efficiencies that fall between 0.00208 and 0.00510%.
Figure 12: Capture efficiency with increasing collector Reynolds number for each of
the array configuration at medium solid fraction. The square and staggered
configurations have capture efficiencies roughly 3 times those of the random
configuration. They also have a slightly decreasing trend while the random
configuration’s capture efficiencies are relatively constant.
5.2 Surface distribution of particles captured The distributions of particles captured on the surface of the test collectors for the square
configuration with medium solid fraction are shown in Figure 13. The distribution was
averaged from the 6 test cylinders that were tested during the experimental runs with
medium solid fraction. It is evident that throughout the 5 tested collector Reynolds numbers,
an even distribution is present throughout the front and back faces of all the test cylinders.
The percentage of particles captured on the back face of the test cylinders ranged between
50.99 and 64.15%.
0
0.005
0.01
0.015
0.02
0.025
0 100 200 300 400 500 600 700
η (
%)
Rec
Square
Staggered
Random
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Figure 13: Particle distribution on the collectors' face throughout the tested collector
Reynolds number for the square configuration at medium solid fraction. The
deposited particles were found to be evenly distributed throughout the front and
back faces of the test cylinders.
5.3 Test cylinder location The capture efficiency of each test cylinder in the square, staggered and random
configurations, of medium solid fraction, is shown in Figure 14(a), (b) and (c) respectively.
Raw particle capture data for each of the rod is attached as Appendix.
The capture efficiency of individual test cylinders in the square configuration has a greater
variation from each other. However, throughout the 3 different configuration, there is no one
test cylinder that has a significant trend; for example, there is no one test cylinder that has
the highest capture efficiency for all the tested collector Reynolds number.
0%
50%
100%
177 269 341 546 599
Par
ticl
es C
aptu
red
Rec
Front Face
Back Face
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Figure 14: Particle capture on each test cylinder in the (a) square, (b) staggered and (c)
random configurations for medium solid fraction. There is no clear trend in capture
efficiency with respect to test cylinder location.
0
0.1
0.2
0.3
0.4
0.5
0 100 200 300 400 500 600 700
η (
%)
Rec
00.020.040.060.08
0.10.120.140.160.18
0 100 200 300 400 500 600 700
η (
%)
Rec
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 100 200 300 400 500 600 700
η (
%)
Rec
(a)Square
(a) Staggered
(c) Random
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5.4 ADV velocity results The velocity was measured using the ADV for all of the experimental runs. A 60-second
snippet of the velocity measurement in the x-direction for the random configuration of the
medium solid fraction is plotted in a graph and shown in Figure 15(a), (b) and (c) for Runs 1,
2 and 3 respectively. The graph shows the first 60 seconds of the velocity measurement and it
demonstrates how velocity varies over time. The trend that is present during the first 60
seconds of the measurement continues throughout the entire capture duration, which is 15
minutes. It can be observed from Figure 15that the velocity during Run 1 oscillates relatively
regularly. This oscillation becomes more irregular as collector Reynolds number (flow
velocity) increases, which is evident in Figure 15(b) (Run 3) and even more irregular for the
highest tested collector Reynolds number, represented by Run 5 which is shown in Figure
15(c). It was found that the velocity trend that is present for random configuration was
similar to that present in square and staggered configurations. Similarly, the velocity trend
that was present in Run 3 in the medium solid fraction was also found in the low and very
low solid fractions.
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0
5
10
15
20
0 10 20 30 40 50 60
Vel
oci
ty (
cm s
-1)
Time (s)
0
5
10
15
20
0 10 20 30 40 50 60
Vel
oci
ty (
cm s
-1)
Time (s)
-5
0
5
10
15
20
0 10 20 30 40 50 60
Vel
oci
ty (
cm s
-1)
Time (s)
Figure 15: Velocity time series of the flow through random configuration of medium
solid fraction during (a) Run 1, (b) Run 3 and (c) Run 5. The oscillation of the flow
velocity is evident in (a), characterising flow of low turbulence. As flow velocity
increases, the standard deviation also increases; this can be associated with flow of
high turbulence.
(c) Run 1
(b) Run 3
(a) Run 5
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5.5 Turbulence intensity Turbulence Intensity, TI, is one way of describing the extent of fluctuation that is present in
the flow; it is expressed as a percentage. Generally, a turbulence intensity of 1% or lower is
described as low and a turbulence intensity that is greater than 10% is considered high.
Turbulence Intensity is calculated via dividing the root-mean-square (standard deviation) of
the measured velocities by the average velocity:
̅ (5)
The turbulence intensity calculated for the 3 configurations throughout the 3 tested solid
fractions is plotted in Figure 16. The general trend that is evident from the graph is that
turbulence intensity increases as solid fraction increases; in other words, as the collector
array density increases the intensity of the turbulence of the flow that is present through the
array also increases. For medium solid fraction, the highest turbulence intensity is associated
with square configuration, while the staggered and random configurations have roughly the
same intensity. For low solid fraction, the 3 configurations have very similar values for
turbulence intensity, as shown in Figure 16. For very low solid fraction, the lowest intensity
occurred in the flow through the staggered configuration while the random configuration
had the highest turbulence intensity value.
Figure 16: The change in turbulence intensity with solid fraction for each of the array
configuration. It is shown that turbulence intensity increases with solid fraction for
the 3 tested configurations.
0
5
10
15
20
25
30
35
40
45
0 1 2 3 4 5 6
TI
(%)
SF (%)
Square
Staggered
Random
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6. Discussions
6.1 Capture efficiency
6.1.1 Solid fraction trend The capture efficiency of the 3 array configurations is included in Figure 11 and it shows that
for the staggered and square configurations, the capture efficiencies stayed relatively
constant for medium and low solid fractions. As solid fraction decreases to very low, the
capture efficiencies appear to merge to a lower value, towards the capture efficiency of the
random configuration. This may be caused by the fact that as solid fraction decreases, the
spacing between collectors increases, causing less interaction between stem wakes behind
each collector and allowing the stem wakes to act as individual stem wakes and decreases
the extent of turbulence. Therefore, it can be suggested that in higher solid fractions, where
the array density is higher, the configuration does have a significant effect in determining
rate of particle capture while in lower solid fractions, the array configuration matters less.
An error analysis was undertaken for the experimental results of the capture efficiencies in
the different configurations. This was conducted by calculating the 95% confidence intervals
and plotting them on a graph, as shown in Figure 17. The calculation shows that the random
and square configurations have the smallest and largest margins of error respectively. The
margins of error of the two regular configurations are found to overlap for medium and low
solid fractions. In the case of the very low solid fraction, the margins of error for all 3 of the
array configurations overlap.
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Figure 17: Comparison of experimental results for capture efficiency in an array to
calculation result of capture efficiency for a single collector, with respect to solid
fraction. The graph shows that the predicted capture efficiency of a single collector is
about 2 orders of magnitude greater than the capture efficiencies of an array with
different solid fractions.
In Figure 17, capture efficiency results from the experimental runs of this study are
compared to the calculated value of capture efficiency of a single collector for this particular
flow velocity, shown as the purple line. The capture efficiency for a flow velocity of
approximately 5.7 cm s-1 was calculated through Equation (2). To allow a clear comparison,
capture efficiency is plotted on a log axis. It is shown in Figure 17 that the single collector
capture efficiency is significantly higher than those of the array of collectors; this finding
disagrees with the expectation that the capture efficiencies of the 3 tested array
configurations will eventually merge to the capture efficiency of a single collector. This
expectation was made because as solid fraction decreases, each collector acts more as an
individual collector with individual stem wakes, causing a decrease in stem wake interaction
between collectors. Due to the unexpected difference, it is suggested that further studies
repeat the experiments conducted in this study to confirm the results.
6.1.2 Velocity trend Similarly with the solid fraction trend, the single collector capture efficiencies were also
calculated for the 5 collector Reynolds number tested during the medium solid fraction
experimental runs. As indicated by Equation (2), capture efficiency for a single collector is
proportional to collector Reynolds number to the power of 0.708. It is important to note that
in Equation (4), which is used to calculate the capture efficiency for a collector in an array,
0.0001
0.001
0.01
0.1
1
0 1 2 3 4 5 6
η (
%)
SF (%)
Square
Staggered
Random
Single collector
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capture efficiency is inversely proportional to flow velocity hence it is also inversely
proportional to collector Reynolds number.
Similar to the capture efficiency values in Figure 17, an error assessment was also conducted
for the capture efficiency values of the medium solid fraction experimental runs, throughout
the 5 tested flow velocities. In Figure 18, these margins of error are plotted with the capture
efficiency in a log axis; these were obtained from the calculation of the 95% confidence
interval. The calculated margins of error of the 2 regular configurations are larger than that
of the random configuration and they are shown to overlap one another.
In general, the graph in Figure 18 shows that the magnitude and trend of capture efficiency
for a single collector and a collector in an array is different. It can be postulated that this
variation is significantly accounted for by the differing capture mechanism that dominates
the particle capture in the two different conditions. The vortex shedding that occurs in an
array of collectors is extremely crucial in determining the flow conditions in the array, which
in turn affect the rate of particle capture.
Figure 18: Comparison of experimental results for capture efficiency in an array
(medium solid fraction) to the predicted capture efficiency for a single collector, with
respect to collector Reynolds number. The capture efficiencies calculated in this
study is roughly 2 orders of magnitude smaller than those of a single collector.
It is evident that the calculated capture efficiency of a single collector increases with collector
Reynolds number, as indicated in Figure 18. This is the opposite of the general trend found
0.001
0.01
0.1
1
10
0 100 200 300 400 500 600 700
η (
%)
Rec
Square
Staggered
Random
Single collector
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in the square and staggered configurations, which is slightly decreasing, for capture
efficiency. The decrease in capture efficiency of a collector in the 2 regular array
configurations may be caused by the change in dominating particle capture mechanism with
increasing Reynolds number. It is suggested that the dominating capture mechanism for
higher collector Reynolds number is the diffusional deposition, due to the turbulence created
by the high flow velocity (Purich 2006), in the form of vortex shedding. On the other hand, at
lower collector Reynolds number, direct interception may also affect particle capture rate.
When compared to the trend of capture efficiency for a single collector, the random array
configuration is the most similar. This would have been thought to be caused by the fact that
the test cylinders of the random configuration experimental run were placed apart from each
other, minimising the interaction between the stem wakes created by each test cylinder.
However, it is shown in Figure 18 that in terms of magnitude, the capture efficiency of the
random configuration is two orders of magnitude lower than that of the single collector. It
was expected that as collector Reynolds number decreases, the array configuration matters
less and that the capture efficiency for the three configurations will be similar to that of the
single collector. This is because as collector Reynolds number decreases, flow velocity
decreases and thus the flow becomes less turbulent, allowing direct interception come into
play.