particle capture in aquatic systems: investigating the ...augustina prita pranoto abstract [3]...

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.

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.

Augustina Prita Pranoto Table of Contents

[5]

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

Augustina Prita Pranoto Table of Contents

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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

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 (%)

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.

Augustina Prita Pranoto Background

[11]

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


[13]

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


[14]

(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 (


[15]

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


[17]

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


[19]

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


[20]

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.


[21]

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).


[22]

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


[23]

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

Augustina Prita Pranoto Study Aims

[24]

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.

Augustina Prita Pranoto Methodology

[25]

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


[26]

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


[27]

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).


[28]

(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


[29]

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


[30]

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


[31]

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.


[32]

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.

Augustina Prita Pranoto Results

[33]

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


[34]

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


[35]

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


[36]

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


[37]

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.


[38]

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


[39]

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

Augustina Prita Pranoto Discussions

[40]

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.


[41]

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


[42]

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


[43]

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.

particle capture in aquatic systems: investigating the ...augustina prita pranoto abstract [3]...

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