The Assessment of an Experimental Simulation of CIPC ...

The Assessment of an Experimental Simulation of CIPC Application

in Potato Stores

FINAL REPORT relating to Objective 1 within Project: REDUCING POSTHARVEST LOSSES AND WASTAGE IN UK POTATO

STORAGE DUE TO SPROUTING DEFRA Project Reference FO/0217

Jenny Holt and Kevin Garry

CU/CoA-2012/02 December 2012

Applied Aerodynamics Group, School of Engineering, Cranfield University,

Bedfordshire, MK43 0AL

cunningtona

Typewritten Text

Annex B1 FO/0217 Objective 1

EXECUTIVE SUMMARY

As part of a wider project to investigate ways of reducing post-harvest losses due to sprouting

(DEFRA Reference FO/0217), the Applied Aerodynamics Group at Cranfield University was tasked

with the development and assessment of a novel experimental sub-scale simulation of the flowfield

within potato stores during the application of CIPC sprout suppressant.

Flow velocity and temperature measurements have been taken of a CIPC application in a research

store at the Potato Council’s Sutton Bridge Crop Storage Research facility (SBCSR). These

measurements have been used in conjunction with full scale video footage and reduced scale smoke

tracer measurements to design a 30% scale model of the research store which behaves in an

aerodynamically similar way. This model made use of small potato varieties in the storage pallets.

The resulting model utilises the release of a tracer gas to replicate the application of CIPC and

concentration measurements, over varying time periods, are used for comparison with full scale

CIPC absorption data. Reasonable agreement has been found between model and scale

concentrations for a measurement period of 2100 seconds after which the simulation as found to

diverge.

The 30% scale model has been used to study the effect of configuration changes including: (i) pallet

horizontal and vertical spacing, (ii) CIPC fog input temperature and (iii) pallet base porosity. A

lowering of fog input temperature was found to be the most effective in terms of tracer

concentration distribution, however it is not possible to say whether the improvement seen for a

limited data-set in a research store would carry through to a full commercial store environment.

A noticeable improvement was also found with an increase in base porosity of individual pallets

particularly in terms of the transport of tracer gas to the bottom of the pallet stacks. The benefit of

this modification is viewed as most likely to be replicated in a commercial store.

In order to remove the need for the use of a real crop during scale testing, a pallet plus potato model

was replicated using materials that gave the same aerodynamic pressure drop across the volume.

These simulated storage pallets were found to compare well to the baseline case when measured

for the same 2100 second application period.

CONTENTS

List of Figures i List of Tables ii Nomenclature iii Introduction iv Aim and Objectives v 1.0 Pressure Drop and Flow Measurements of an Isolated Storage Pallet 1 2.0 Testing and Selection of Material for Scale Modelling 4 3.0 Full Scale Measurement of Fogger Flow Rate into a Research Store 6 4.0 Design and Manufacture of 30% Scale Store 8 5.0 Full Scale Velocity Measurements 11 6.0 Smoke Tracer 30% Scale 17 7.0 Scaling Parameters 19 8.0 30% Scale Velocity Measurements 20 9.0 Tracer Concentration Measurements 26 9.1 Effect of Spacing 37 9.2 Effect of Temperature 44 9.3 Effect of Porosity 51 9.4 Simulation of Complete Storage Pallet 58 10.0 Conclusions 66 11.0 References 67

i

LIST OF FIGURES 1.1 Experimental Rig for Pressure Drop Measurements across a Single Storage Pallet 1 1.2 Data Fit of Pressure Drop Coefficient 2 1.3 Surface Flow Velocity Measurement 3 1.4 Duct and HWA for Surface Flow Measurements 4 2.1 Blower Wind Tunnel Used for Pressure Drop Measurement 4 2.2 Selection of Pressure Drop Characteristics 5 3.1 Orifice Plate 6 3.2 Orifice Plate in CIPC Input Pipe 7 3.3 SCC C4 Petrol Fogger 7 4.1 Dimensions of 30% Scale Store 8 4.2 30% Scale Store 9 4.3 30% Scale Single Storage Pallet 10 4.4 30% Scale Full Store 10 5.1 Pulsed Output Vane Anemometer 12 5.2 Full Scale Velocity Measurement Locations 12 5.3 Normalised Velocity 900mm from Floor 14 5.4 Normalised Velocity 1800mm from Floor 15 5.5 Normalised Velocity 2700mm from Floor 16 6.1-3 Smoke Tracer 30% Scale 17-18 8.0 Measurement Locations 30% Scale Store 20 8.1 Normalised Velocity 300mm from Floor Empty Store 22 8.2 Normalised Velocity 300mm from Floor 23 8.3 Normalised Velocity 600mm from Floor 24 8.4 Normalised Velocity 900mm from Floor 25 9.1 Baseline Configuration 26 9.2 Vertical Spacing 27 9.3 Schematic of Horizontal Spacing Increasing 27 9.4 150mm Horizontal Spacing Increase 28 9.5 Increased Base Porosity for Pallet 28 9.6 Pallet Numbering Schematic for Tracer Measurements 29 9.7-18 Baseline Normalised concentration Pallet 1-12 30-36 9.1.1-12 Normalised concentration Effect of Spacing 38-43 9.2.1-12 Normalised concentration Effect of Temperature 44-50 9.3.1-12 Normalised concentration Effect of Porosity 51-57

9.4.1-12 Normalised concentration Simulated Pressure Drop 59-64 9.4.13 Comparison of Pressure Drop Coefficients 66

ii

LIST OF TABLES 2.1 Pressure Drop Characteristics of Material Samples 5 3.1 Flow Rate Measurement of CIPC Full Scale 8 9.1 Comparison of Baseline Concentration after 2100 Seconds with Full Scale CIPC 37 9.3.1 Comparison of Baseline Pallet with Increased Porosity Pallet 58 9.4.1 Comparison of Baseline with Simulated Pressure Drop Pallet 65

iii

NOMENCLATURE FID – Flame Ionisation Detector CIPC – Chlorpropham DVA – Digital Vane Anemometer LPM – Litres per Minute PPM – Parts per Million HWA – Hot Wire Anemometer q – Dynamic pressure in Pascals P – Static Pressure in Pascals m/s – Meters per second ρ – Density kg/m3 M – Molecular weight g/Mol R – Universal gas constant J/kg K He – Helium

Cpd – Coefficient of pressure drop whereteorificepla

potatoes

pdP

PC

g – Acceleration due to gravity m2/s

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INTRODUCTION

The aim of this work is to reduce the amount of the sprout suppressant, CIPC, required to suppress postharvest sprouting in stored potato tubers. We therefore need to improve our understanding of the air circulation within storage units whilst maintaining or indeed improving efficacy of CIPC treatment. This would facilitate modelling to improve the effectiveness of current store systems and the design of future arrangements. The initial approach was to adapt conventional experimental modelling techniques used in building aerodynamics to the layout of a typical potato storage unit. This has involved producing a reduced scale physical model to aid simulation and flow measurement. A number of measurement techniques that are routinely used to assess internal flows in conventional buildings have been adopted to understand the air flow both at full scale and model scale. It is recognised that the method of storing the potato crop in separate pallets will have a significant impact on the air circulation in the store. Following a review of the existing data, it was deemed beneficial to undertake tests on a pallet itself within a controlled flow environment in order to understand how they behave ‘aerodynamically’. This knowledge allowed a test program to be undertaken to identify suitable materials that will behave, at scale, in an aerodynamically similar way to a pallet of potatoes, for a given geometric scale factor. A scale model of a research store at Sutton Bridge has been manufactured at 30% scale and flow measurements, both quantative and qualitative, were undertaken to identify suitable scaling parameters for the input flow. The reduced scale model has been designed such that key changes to the store configuration, namely: (i) CIPC fog input temperature and (ii) pallet stack configuration, could be varied in order to investigate potential alternatives. Prior to the project start the possible use of multiple CIPC fog injection points had been suggested. On discussion and further investigation it was discovered that this had been attempted previously in a commercial store by moving the fogger. It was verbally reported that no improvement had been observed using this method, it was therefore removed from the investigation and replaced with a test on the effect of storage pallet base porosity. The general experimental methodology adopted was as follows: (i) Qualitative data were obtained using smoke tracer injection. This approach is used to establish air recirculation patterns and identify regions within the control volume of high and low local air flow rates. (ii) Quantitative measurements of flow velocity were made using micro-vane anemometers both at model scale and at full scale for validation purposes (iii) The dispersion of airborne particles was evaluated using a suitable tracer gas and Flame Ionisation Detector (FID). This approach involves the introduction of a suitable carrier gas with appropriate buoyancy to simulate temperature differences between the ‘ambient’ air and the gas or vapour of interest. This technique makes it possible to sample the flow at specific locations and evaluate both the magnitude and time history of the species concentration.

v

In order to facilitate these techniques, full scale flow rate and temperature data was obtained from on-site tests in a research store at Sutton Bridge.

AIM

Development of a scale model simulation of the air flow within a R&D potato store and an enhanced understanding of the flow characteristics during CIPC application.

OBJECTIVES

Assessment of flow characteristics of individual potato storage pallets

Assessment of flow characteristics around multiple storage pallets during fog application

Development of 30% scale model simulation of multiple pallet configuration and selection of appropriate flow scaling technique

Assessment of 30% scale flow characteristics and comparison with full scale

Assessment of Tracer concentration measurements and potential validation against full scale CIPC concentration measurements

Tracer concentration measurements of configuration variables including horizontal and vertical spacing, temperature and pallet structure.

1

1.0 PRESSURE DROP AND FLOW MEASUREMENTS OF AN ISOLATED STORAGE PALLET

Figure 1.1 – Experimental Rig for Pressure Drop Measurement Across Single Storage Pallets

From study of previous work [1][2] and by application of wind engineering techniques, it was decided

to produce a scale model of a potato storage pallet that will behave in an aerodynamically similar way

to the full scale pallet in an isentropic flow condition, the pressure drop across the pallet for varying

depths of potatoes (variety: Saxon) was measured.

The storage pallet was subjected to a forced air from a centrifugal blower through a plenum chamber

designed to equalise the pressure across the lower surface. Flow rate into the chamber is recored as a

calibrated pressure drop across an orifice plate. Static pressure measurements were taken with digital

differential pressure transducers at the top of the plenum chamber and at the top of the pallet to

determine the pressure drop.

2

Figure 1.2 – Data Fit of Pressure Drop Coefficient Across Varied Potato Depths

Measurements of pressure drop coefficient, a normalised value defined:

teorificepla

potatoes

pdP

PC

are plotted in Figure 1.2 against normalised potato depth.

A second order polynomial relationship has been determined from the data. The predictable nature

of the pressure drop coefficient allows the matching of a generic isentropic material for scale

modelling. The pressure drop per meter was found to be 9.13 Pa .

The data compared well with that previously found by Irvine et al [2] who report a pressure drop per

metre of 8.62 Pa for lightly soiled Russet Burbank potatoes with an average diameter of 57mm. A

sample of 20 tubers of the Saxon variety tested in this experiment gave an average diameter of

54mm. It should be noted that the oval shapes are dissimilar.

Flow measurements were also taken on the surface of the potatoes using hotwire annemometry

(HWA), to determine the uniformity of the flow through the potatoes at full depth utilising the

methodology developed by Hoffman et. al.[3]. The Dantec 55P11 hot wire probe is held on the

centreline of a shaped duct with a contraction ratio of 1:5 (see Figure 1.4), the duct is

200mmx200mm and designed such that it can be positioned on the surface of the potatoes. The

flow speed recorded using a DANTEC mini Constant Temperature Annemometer CTA, is then

3

considered to be an average of the flow in the 200mm square. The duct is moved in 200mm

increments laterally and longitudinally to achieve full surface coverage.

Figure 1.3 – Surface Flow Velocity Measurements

Figure 1.3 shows the results of the hot wire measurements over the surface of the potatoes.

The experiment has shown the flow to be uniform (+/-0.2m/s) over the majority of the surface. The

higher speed region corresponds to the flow input location into the plenum chamber and is thus

attributed to insufficient flow mixing in the plenum rather than a characteristic feature of the

potatoes. The higher speed (0.7-0.8m/s) areas seen in the corners are attributed to the design of the

storage pallet whose slatted design has a sizeable gap some corners . Examination of other storage

pallets has shown this not to be a predictable feature, it is therefore concluded that given the

volumes of pallets seen in commercial stores this features presence or otherwise will be “averaged”

out.

4

Figure 1.4 – Duct and HWA for Surface Flow Measurents Above Potatoes

2.0 TESTING AND SELECTION OF MATERIALS FOR SCALE MODELLING

Figure 2.1 Blower Wind Tunnel used for Measurement of Material Pressure drop

In order to determine the aerodynamic pressure drop experienced by the selected material, a

sample is “sandwiched” in the duct section of a blow down wind tunnel. Static pressure

measurements are taken upstream and downstream of the material using digital pressure

transducers to determine the pressure drop and the dynamic pressure in the duct is measured by a

pitot static tube located in the centre of the duct upstream of the material.

5

Material No. Delta P Pa q Pa Delta P/q

1 18.584 5.674 3.28 2 100.005 29.439 3.40 3 127.996 30.0125 4.26 4 27.275 5.702 4.78 5 32.564 2.142368 15.2 6 152.981 15.136 10.11 7 120.568 5.668 21.27 8 126.816 5.75 22.05 9 363.974 5.914 61.54

10 9.13 0.61 14.91 11 18.584 5.674 3.28

Table 2.1 – Pressure Drop Characteristics of Material Samples

Figure 2.2 – Selection of Pressure Drop Characteristics

Table 2.1 and Figure 2.2 show a selection of the data recorded during the experiments to determine

a suitbale material to act as a scale potato pallet. The extremely low flow rates coupled with the low

porosity led to difficulties determining an exact match. Generally, tests of this nature would use one

porous material such as an open cell foam cut to the scale desired. This is not achievable in this

instance so it was decided that a series of wooden frames would be contructed with a material

exhibiting half the pressure drop of the full scale potato pallet stretched across the top and bottom

faces.

The mateial selected is material number 5, labelled Awning in Figure 2.2, the material is a woven

fabric of warp no.20Nm weft no. 30Nm.

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

0 2 4 6 8 10 12

De

lta

P/q

Medium No.

Mesh 1

Mesh 2

Mesh 3

Grid 1

Awning Net

Grid 2

Honeycomb 1

Honeycomb 2

AutoMesh 1

Potatoes

6

3.0 FULL SCALE MEASUREMENT OF CIPC FOGGER FLOW RATE INTO A RESEARCH STORE

Application of CIPC into commercial stores is currently achieved by suspension of CIPC in a heated

fog. In order to assess the feasibility of temperature variation during fogging, flow rate and effect of

temperature for a commercial fogger was assessed.

Figure 3.1 – Orifice Plate

The output from the fogger is delivered through a 5m long, 100mm diameter reinforced pipe. To

determine the flow rate an orifice plate (see Figure 3.1) was inserted into the pipe at a location 2.5m

from the store (see Figure 3.2). The orifice plate has pressure tappings either side to allow a pressure

drop to be measured across it. CIPC fogger temperature at source was taken from the indicator on

the fog generator, temperature at the orifice plate location was measured with a k-type

thermocouple.

Measurements were conducted for two fogger types: (i)Petrol C4 and (ii) Methanol C2 and for liquid

and solid CIPC with temperature varied between 300 and 450 degrees C at source.

7

Figure 3.2 – Orifice Plate in CIPC Input Pipe

Figure 3.3 – SCC C4 Petrol Fogger

8

Fogger Temperature (°C)

Petrol C4 machine, Pro-Long (L/min)

Petrol C4 machine, Solid CIPC (L/min)

Methanol C2 machine, Pro-Long (L/min)

0 15537 14001

300 23545

350 24794

400 25710 24569 26712

400 25228 26665

450 26195

Table 3.1 – Flow Rate Measurements of CIPC Full Scale

Table 3. 1 shows the results from the flow rate tests at Sutton Bridge, repeat testing showed the

flow rate to be +/- 1000 L/min for a similar case.

In order to facilitate a more flexible testing schedule, flow velocity tests and CIPC application at

Sutton Bridge were done using a Swingfog SN-50. Exit temperature measurments were taken during

measurement phases using k-type thermocouples. Flow rate was determined during initial velociy

testing using a vane anemometer within the store. This anemometer showed significant variation

during some tests. This may be an artefact of the insertion angle of the fogger output nozzle, so to

provide a difinitive output measurements were made at the nozzle exit using a multiport total

presure rake. The velocity distribution was then integrated to determine the output flow rate.

4.0 DESIGN AND MANUFACTURE OF 30% SCALE STORE.

Following the full scale flow rate measurements in a store at Sutton Bridge, a suitable geometric

scale that would facilitate the variation of key parameters whilst also remaining a manageable size

was derived to be 30%. Figure 4.1 shows the chosen model scale dimensions.

9

Figure 4.1 – Dimensions of Model Scale Store

To facilitate the smoke based flow visualisation technique described earlier the model was

manufactured from Perspex sheet supported by a wooden frame with suitable instrument access

panels.

Figure 4.2 – Model 30% Scale Store

The model is manufactured such that smoke, tracer gases or air can be delivered at correctly scaled

flow rates both at the location used in the store at Sutton Bridge and in other locations to assess the

impact. A vent to atmosphere is located on the roof of the model as in the Sutton Bridge store.

10

For a full scale store a variety of design and external factors will affect CIPC application. The sealing

of the store, particularly when coupled with external pressure gradients due to prevailing wind

conditions, may have a large effect. This is not replicated at the 30% scale which is contained within

a controlled environment.

The individual sub-scale pallet structure is a geometric replica of the full scale pallet, containing

mixed small varieties of tuber with an average size of 34.4mm based on a sample of 30 tubers (see

Figure 4.3)

Figure 4.3 – 30% Scale Single Storage Pallet

Twelve pallets have been manufactured to allow for stacking combinations to be simulated.

Figure 4.4 -30% Scale Model

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4.1 FOG SIMULATION

The aerodynamic behaviour of a heated fog is very different from a cold fog. In order to ensure the

smoke and/or air to be used at model scale behaves in a comparable fashion, a buoyant gas (Helium)

is mixed with the smoke/air to simulate the effects of temperature. It was observed at Sutton Bridge

that the behaviour of the CIPC fog, after flow input had ceased, was very specific, so initial

experiments have been undertaken to attempt to match the density and buoyancy of the smoke in

the sub-scale model to replicate this behaviour.

The fraction of Helium flow rate is calculated using the Equation of State for the two different

density and gas compositions at constant temperature and pressure:

tracer

Hetracer

Hetracer

tracer

R

R

,

)1(

inlet

tracer

tracerHeHeHe

Hetracer

tracer

tracer

Hetracer

Hetracer

tracer

M

MxMx

M

M

MR

MR

(R = Universal gas constant, M=Molecular weight)

The fraction of Helium flow rate is then

1

inlet

tracerHe

tracerHe

MM

Mx

Flow rates are set and monitored through Platon VA flow meters.

5.0 FULL SCALE VELOCITY MEASUREMENTS

Flow velocity measurements were undertaken in a research store at Sutton Bridge with 12, 0.5

tonne, containers located centrally in the store and filled with tubers (variety: Russet Burbank). The

Swingfog SN-50 was used to supply the CIPC fog. Measurements were taken using 8 micro vane,

pulsed output anemometers. Three sets of readings were taken at each location and data presented

is an average of these three sets.

12

Figure 5.1 – Pulsed Output Vane Anemometer

Measurement locations are shown in figure 5.2

Figure 5.2 – Full Scale Velocity Measurement Locations

Measurements were also taken in the empty store at the same locations shown in Figure 5.2.

Plan View End View

13

At each location measurements were taken with anemometer perpendicular and parallel to the

ground plane in order to acquire both horizontal and vertical components of velocity.

This data is used to determine the appropriate scaling parameters for the 30% scale model flow

input, coupled with flow visualisation studies using smoke tracer. This is discussed in section 7.0.

In the case of both horizontal and vertical velocity component measurements, ten second sample

periods were used at each location in order to obtain an indication of variability. Local velocity data

is normalised using the fogger input velocity as, during the tests, the Swingfog showed a potentially

significant variation in input flow.

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Figure 5.3 – Normalised Velocity 900mm from Floor

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0.5

0 2 4 6 8 10

U/U

i

Time (secs)

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As can been seen in Figures 5.3 to 5.5 the flow velocity is highly unsteady in some locations and close

to zero in others. The flow field is dominated by the horizontal flow component. The magnitude of

the velocity variation at a given location was unexpected and is greater than can be attributed to the

variation in output from the Swingfog fogger.

Further sets of velocity data were taken during a second phase of measurements after re-calibration

of the vane anemometers to eliminate the possibility of hardware issues. However, the results were

consistent with those taken during the first phase.

6.0 SMOKE TRACER 30% SCALE

Initial experiments have been conducted in an empty sub-scale store to establish the required

density and the flow characteristics on exit from the pipe into the store at the same location used

during the full scale measurements at Sutton Bridge.

Figure 6.1 – Smoke Delivery into Scale Model

18



Figures 6.1 to 6.3 show examples of the smoke flow visualisation tests designed to produce

behaviour similar to that seen at full scale. Although highly subjective initial plume behaviour

appears similar to that observed from video of a CIPC application in the research store. Of particular

interest when assessing configurations likely to improve flow transport to the lower pallets is the

lateral transfer through the vertical gaps between individual storage pallets. It should be noted that

the insertion trajectory of the simulated fogger nozzle at model scale had a noticeable impact on the

path of smoke spread. Given the method of application of fog during a typical CIPC fogging

application, it is difficult to see how this may be accounted for between model and semi-commercial

(SBCSR) scale other than by asking for care to be taken by the operator. In a larger, commercial scale

19

the effect of applicator position would be much smaller in comparison with the volume of crop in

the store.

7.0 SCALING PARAMETERS

The scaling parameters usually selected for plume input are:

1. Froude Number: 2U

gBlF

where

B

g = acceleration due to gravity (m2/s)

l = characteristic dimension (m)

U = local flow velocity within store at the inlet location (m/s)

Primarily influences plume buoyancy modelling and requires knowledge of inlet flow temperature

profile relative to store conditions.

2. Inlet velocity ratio: U

U i

Ui = inlet flow velocity (m/s)

U = local flow velocity within store at the inlet location (m/s)

This is a yey factor influencing plume trajectory & displacement, but nothing is known in this case

about the local flow velocity and direction in vicinity of inlet pipe.

3. Inlet flow momentum coefficient: Sq

VmC

j

0

.

Ui = inlet flow velocity (m/s)

.

m = mass flow rate through inlet

S = reference area (m2)

q0= reference dynamic pressure (Pa)

4. Volume flow changes per unit time: storeV

m.

Vstore= volume of store (m3)

20

ρ = density of inflow (kg/m3)

often the primary consideration for HVAC applications where local flow characteristics are not the

primary consideration.

Based on the known parameters, full scale flow measurements and smoke flow visualisation, (1)

Froude Number and (4)Volume changes per unit time were chosen as likely key scaling parameters

in this case.

8.0 30% SCALE VELOCITY MEASUREMENTS

Measurements of local flow velocity were then taken in the sub-scale store at Cranfield both for an

empty store and for a store with the same pallet configuration used at SBCSR, at the locations shown

in Figure 8.0 using Kimo MiniAir miniature digital vane anemometers. As with the full scale

measurements, three sets of measurements were taken at each location and data presented are an

average of these three sets. Temperature/buoyancy effects are simulated by mixing Helium with air

as previously discussed.

Figure 8.0 – Measurement Locations 30% Scale Store

Plan View End View

21

Figures 8.1 to 8.4 show the comparison between full scale measurments and 30% scale

measurements where input flow rate has been scaled by both Froude Number or Volume changes

per unit time.

22

Figure 8.1 – Normalised Velocity 300mm from Floor for an Empty Store

-0.1

0.0

0.1

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0 5 10

U/U

i

Time (secs)

inflow

-0.1

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i

Time (secs)

Full Scale

Volume

FroudeNo.

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inflow

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i

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Volume

FroudeNo.

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Figure 8.3– Normalised Velocity 600mm from Floor

inflow

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inflow

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i

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i

Time (secs)

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Volume

Froude No.

26

The unsteady nature of the flow field is replicated at 30% scale making an exact match challenging,

particularly given that, in areas where model scale velocities differ from full scale, there is no over- or

under-prediction across all cases. It was decided that scaling based on volume changes per second is

generally seen to offer better correlation with full scale data.

9.0 TRACER CONCENTRATION MEASUREMENTS

Once appropriate scaling parameters had been determined, concentration measurements were

undertaken using FID and injection of tracer gas, using two Signal Instruments 3000MO detectors. Unless

otherwise stated, simulated temperature corresponds to the measurements taken for the Swingfog SN-

50 during tests at Sutton Bridge, 272 °C. (It should be noted that this is significantly below that stated by

the manufacturer. In view of this the thermocouples were tested against a known temperature source

but no error was found. The disparity between the measured and expected temperature of the output

cannot therefore be explained.)

FID measurements were taken for 7 configurations of the store:

1. – Baseline as per velocity tests, see Figure 9.1

Figure 9.1 – Baseline Configuration

2. – Baseline with aerodynamically similar pallets no potatoes

3. – Simulated temperature 50 degrees above baseline, 322 °C

4. – Simulated temperature 50 degrees below baseline, 222 °C

5. – Simulated temperature 100 degrees below baseline, 172 °C

6. – Vertical spacing between pallets doubled (25mm to 50mm), see Figure 9.2

27

Figure 9.2 – Vertical Spacing Doubled from Baseline

7. – Horizontal spacing between pallets 75mm (0.25m full scale), see Figure 9.3

Figure 9.3 – Schematic of Horizontal Spacing Increase

28

8. - Horizontal spacing between pallets 150mm (0.5m full scale), see Figure 9.4

Figure 9.4 – 150mm Horizontal Spacing Increase

9. – Base of storage pallets replaced with wire mesh to increase porosity, see Figure 9.5

Figure 9.5 – Increased Base Porosity Pallet

Concentration measurements are taken at the centre of each pallet.

Due to the unsteady nature of the flow determined from the velocity tests, each configuration has

been measured three times and the data presented is an average of the three runs. Measurements

are taken over a period of two hours. The limitation of two FID’s meant that only two pallets could

be measured at a time. This limitation is potentially important given that ambient conditions cannot

be maintained constant over a period of 36 hours (the time taken to complete one configuration).

29

Figure 9.6 – Pallet Numbering Schematic for Tracer Measurements

Figure 9.6 shows the pallet stacking arrangement for the tracer measurements. Initially the baseline case,

which is a direct 30% scale simulation of the Sutton Bridge Research store, was set up. Data has been

provided for a CIPC application, with 157ml of CIPC applied over a 2 minute 22 second with the Swingfog

SN-50. It should be noted that according to the manufacturer’s specification a typical CIPC application is

28ml per tonne (delivering 14g CIPC per tonne, SproutNip and Prolong labels). For the six tonnes in the

'full scale' store that is 168ml. With the 1mm nozzle on the SN-50 Swingfog this delivers approximately

20.5litres per hour or 341.6ml per minute. So application time should have been 30 seconds. Due to the

relative timing of the tests the slower application rate was unknown at the time of the tracer

measurement. Therefore all tracer measurements are for an input time of 30 seconds, the results have

been normalised by the possible maximum concentration based on source input therefore the main

impact of this mismatch is the increased length of time the flow inside the store was agitated and so

mixed by the input flow during application.

Data is presented for the baseline case in Figures 9.7 to 9.18 by pallet, see figure 9.6 for numbering

schematic. It is anticipated that the buoyant gas based tracer simulation will deviate from the true case

with time as in reality the CIPC suspension is deposited as the fog disperses whereas in the simulated case

Pallet 7-9

7 top

9 bottom

Pallet 1-3

1 top

3 bottom

Pallet 10-12

10 top

12 bottom

Pallet 4-6

4 top

6 bottom

30

the gas vents from the container. The tracer data has been integrated with time and normalised by

source concentration also integrated with to determine the total exposure over a series of time intervals

up to two hours. The full scale data is presented normalised by source, it is plotted on the same axis for

comparison but is an absolute value and not a function of time.

Figure 9.7– Baseline Normalised Concentration Pallet 1

0.000

0.002

0.004

0.006

0.008

0.010

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0.014

0.016

0.018

0.020

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0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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Exposure Time (secs)

Baseline

Full Scale

31



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

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0.026

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0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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Baseline

Full Scale

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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Baseline

Full Scale

32



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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Baseline

Full Scale

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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Baseline

Full Scale

33



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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Baseline

Full Scale

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000

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Baseline

Full Scale

34



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000

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Baseline

Full Scale

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000

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Baseline

Full Scale

35



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 2000 4000 6000 8000

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Baseline

Full Scale

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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Baseline

Full Scale

36


From figures 9.7 to 9.18 it can be seen that the cross over point of the two lines, taken as an indicator of

where the simulations begin to diverge, is not consistent.

However, viewing data from the full scale CIPC concentrations it can be seen that concentrations for

pallets 7-12 follow an unusual pattern in comparison to palletes 1-6. Pallets 7-12 show higher

concentrations on the middle pallets than on the upper pallet and similar concentration level in the top

and bottom pallets. The 30% scale simulation has not replicated this result. The mechanism for transport

of fog around the pallet is assumed to have two primary phases, the initial injection and accompanying

momentum of the plume and then the deposition due to bouyancy over time once the injection phase

has finished. The mismatch between full and model scale on the injection side of the store suggests a

mismatch during the injection phase. As previously noted, smoke tracer tests have highlighted the

importance of injection angle to the initial path of the fog which cannot be replicated. Plume shape

modelling between the full and scale plume can also not be guaranteed and its significance is not known.

Discussion with project team members suggest that the pattern of concentration seen for pallets 1-6 is in

general more typical for commercial scale stores where the deposition is primarily governed by the

vertical transport through the pallets as the fog cools and settles. With this in mind, pallets 1-6 have been

elected as more typical of concentration spread. The average time at which the siulation appears to

diverge from the full scale case is averaged at 2100 seconds. Table 9.1 shows the comparison between

concentration as a function of source for full scale and 30% scale by pallet after this time period.

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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Baseline

Full Scale

37

Pallet Number Full Scale

conc. measured/conc. source

30% Scale

conc. measured/conc. source

1 0.017 0.017

2 0.011 0.011

3 0.007 0.006

4 0.016 0.016

5 0.013 0.012

6 0.007 0.006

7 0.014 0.019

8 0.013 0.013

9 0.010 0.008

10 0.011 0.017

11 0.012 0.012

12 0.01 0.006

Table 9.1– Comparison of Baseline Normalised Concentration after 2100 Seconds with full Scale CIPC Normalised Concentration

Table 9.1 shows a reasonable trend of agreement between full scale and model scale for relative values

between upper, middle and lower pallets for pallets 1-6 as previously discussed. For pallets 7-12

agreement in the middle pallets (8 and 11) is very good but large discrepancies are seen between the

upper and lower pallets.

The agreement in trend is seen as sufficient to allow assesment of the effect of configuration changes at

this scale.

9.1 EFFECT OF SPACING

Horizontal and vertical spacing between individual containers has been varied, figures 9.2 and 9.3. Figures

9.1.1 to 9.1.12 present data for the baseline case, 75m horizontal spacing, 150mm horizontal spacing

(0.25m and 0.5m full scale respectively) and 50mm spacing vertical (baseline spacing is 25mm).

38

Figure 9.1.1–Normalised Concentration – Effect of Spacing Pallet 1


0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

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0.026

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0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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Baseline

75mm Spacing

150mm Spacing

50mm Height Spacing

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

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0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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Baseline

75mm Spacing

150mm Spacing

50mm Height Spacing

39



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

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0 1000 2000 3000 4000 5000 6000 7000 8000

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Baseline

75mm Spacing

150mm Spacing

50mm Height Spacing

0.000

0.002

0.004

0.006

0.008

0.010

0.012

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0.018

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Baseline

75mm Spacing

150mm Spacing

50mm Height Spacing

40



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

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0.026

0.028

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0 1000 2000 3000 4000 5000 6000 7000 8000

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Baseline

75mm Spacing

150mm Spacing

50mm Height Spacing

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

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0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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Baseline

75mm Spacing

150mm Spacing

50mm Height Spacing

41



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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Baseline

75mm Spacing

150mm Spacing

50mm Height Spacing

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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Baseline

75mm Spacing

150mm Spacing

50mm Height Spacing

42



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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Baseline

75mm Spacing

150mm Spacing

50mm Height Spacing

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

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Baseline

75mm Spacing

150mm Spacing

50mm Height Spacing

43



The results show no overall performance improvement. For 150mm spacing (0.5m full scale) there is a

general increase in transport of the tracer to the rear so improving concentration levels in pallets 4-6 and

10-12. Concentration levels in pallets 6 and 12 the lowest pallets are noticeable improved by this

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

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0 1000 2000 3000 4000 5000 6000 7000 8000

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Baseline

75mm Spacing

150mm Spacing

50mm Height Spacing

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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Baseline

75mm Spacing

150mm Spacing

50mm Height Spacing

44

increased transport rearwards. This is coupled with a general lowering of concentraion levels in the front

pallets 1-3 and 7-9.

Increasing the vertical spacing between the pallets showed limited improvement, mainly in the highest

pallets.

9.2 EFFECT OF TEMPERATURE

Simulated input temperature of the gas has been varied by +50, -50 and -100 °C from the baseline case,

i.e. 322, 222 and 172 °C whilst maintaining the same flow rate.

Figures 9.2.1 to 9.2.12 present the baseline case and varied temperture cases.

Figure 9.2.1–Normalised Concentration – Effect of Temperature Pallet 1

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

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0 1000 2000 3000 4000 5000 6000 7000 8000

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Baseline - 272 deg C

222 deg C

172 deg C

322 deg C

45



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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222 deg C

172 deg C

322 deg C

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

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0 1000 2000 3000 4000 5000 6000 7000 8000

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222 deg C

172 deg C

322 deg C

46



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

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0.026

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0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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222 deg C

172 deg C

322 deg C

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

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0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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222 deg C

172 deg C

322 deg C

47



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

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0.026

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0 1000 2000 3000 4000 5000 6000 7000 8000

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222 deg C

172 deg C

322 deg C

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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222 deg C

172 deg C

322 deg C

48



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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/ ∫c

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222 deg C

172 deg C

322 deg C

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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222 deg C

172 deg C

322 deg C

49



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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222 deg C

172 deg C

322 deg C

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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222 deg C

172 deg C

322 deg C

50


An improvement in exposure is seen in all cases for the 172 degree condition (100 degrees C reduction

from baseline) and in the majority of cases for 222 degrees C condition. The less bouyant gas has less

upward transport momentum and so a greater proportion remains in the lower portions of the simulated

store and is moved through the storage pallets during application. In comparison the hotter and

therefore more bouyant gas reduces the exposure of the middle and lower pallets significantly.

Table 3.1 suggests that a 100 degree reduction in temperature does not significantly effect the fogger

flow rate, although further study would be necessary to determine whether the energy is sufficient to

fully volatise the solvent in which the CIPC is suspended.

Further study is also necessary to determine whether the beneficial effect seen in a limited stacking

arrangement is replicated with a greater number of pallets where horizonal transport of CIPC would be

severaly restricted as momentum from the initial injection was lost.

In the research store the volume of potatos and storage pallets as a fraction of total store volume is

generally low in comparison to a commercial store. In particular, the headroom above the potatos is

greater in the research store. The potential effect of this volume variation is uncertain, as the volume of

CIPC fog injected per store volume is not a constant due to the large variation in store design. If the

volume of fog is high compared to the unoccupied volume in the store, then the lower head room may

mean that a greater portion of the fog must be forced laterally through the pallet stacks which is similar

to the low temperature case where the less bouyant plume cannot rise. However, if the volume of the fog

is still low in comparison to the space availaible it would mean that the fog would take the least

constricted path and settle on the periphery of the stacks rather than travel laterally through the deep

stacks. This would tend to negate any improvement of the lower temperature fog.

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

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222 deg C

172 deg C

322 deg C

51

The lower injection temperature may benefit from being coupled with the 150mm spacing as the increase

transport of the tracer through to the rear pallets for a less bouyant gas may provide an improvement in

deposition when a greater number of containers are stacked together in a commercial store.

9.3 POROUS PALLET BASE

The effect of an increase in porosity of the base of the individual storage pallets in order to aid transport

through the stacks, is investigated. The slatted pallet base is replaced by a chicken wire one with an

extremely high level of porosity. As the CIPC fog is applied at a high temperature an increase in base

porosity may aid the vertical transport of CIPC throught to the lowest pallets in the stack.

Figures 9.3.1 to 9.3.12 present data comparing integrated base line data over a series of time periods

with data from the porous base case.

Figure 9.3.1–Normalised Concentration – Effect of Porosity Pallet 1

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000

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Baseline

Porous Base

52



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000

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Baseline

Porous Base

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000

∫co

nc.

me

asu

red

/∫co

nc.

so

urc

e


Baseline

Porous Base

53



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

∫co

nc.

me

asu

red

/∫co

nc.

sou

rce


Baseline

Porous Base

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 2000 4000 6000 8000

∫co

nc.

me

asu

red

/∫co

nc.

so

urc

e


Baseline

Porous Base

54



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

∫co

nc.

me

asu

red

/∫co

nc.

so

urc

e


Baseline

Porous Base

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000

∫co

nc.

me

asu

red

/∫co

nc.

so

urc

e


Baseline

Porous Base

55



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000

∫co

nc.

me

asu

red

/∫co

nc.

so

urc

e


Baseline

Porous Base

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000

∫co

nc.

me

asu

red

/∫co

nc.

so

urc

e


Baseline

Porous Base

56



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

∫co

nc.

me

asu

red

/∫co

nc.

so

urc

e


Baseline

Porous Base

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

∫co

nc.

me

asu

red

/∫co

nc.

so

urc

e


Baseline

Porous Base

57


For all cases, except pallet 8, the more porous base provided either the same or better performance than

the baseline. Pallets closer to the source at the top of stacks generally saw no or small improvements

whereas the base of the stacks had a large increase in concentration in all cases.

The reduction observed in pallet 8 may possibly be explained by the relatively high values seen in the

baseline case when compared with the other pallets at the centre of stacks.

Table 9.3.1 presents a comparison of data between the baseline and porous base cases for a time period

of 2100 seconds.

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

∫co

nc.

me

asu

red

/∫co

nc.

so

urc

e


Baseline

Porous Base

58

Pallet Number Baseline Porous Base % Difference

1 0.017 0.017 0

2 0.011 0.012 8.7

3 0.006 0.007 15.4

4 0.016 0.018 11.8

5 0.012 0.012 0

6 0.006 0.007 15.4

7 0.019 0.019 0

8 0.013 0.012 -8

9 0.008 0.007 13.3

10 0.017 0.019 11.1

11 0.012 0.013 8

12 0.006 0.007 15.3

Table 9.3.1 – Comparison of Baseline Pallet with Increased Porosity Pallet

It is anticipated that an improvement seen here should be replicated in a commercial store as the

downward transport mechanism is predominant.

This configuration relies on an increased downward porosity, this would be affected by the size and

packing density of the tubers contained within the storage pallet and so the magnitude of change will be

greatly influenced by tuber shape and packing density.

9.4 SIMULATION OF COMPLETE STORAGE PALLET.

This configuration is a simulation of a storage pallet utilising the pressure drop measurements reported in

section 2.0. The potatos are removed and the selected fabric stretched across the base and top frame to

create a unit representing the same pressure drop properties as a field scale potato pallet.

Whilst aerodynamically similar, it should be empasised that this pallet does not simulate the volume

occupied by the potatoes.

59

Figure 9.4.1–Normalised Concentration – Simulated Pressure Drop Pallet 1


0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

∫co

nc.

me

asu

red

/∫co

nc.

so

urc

e


Baseline

Simulated Box

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

∫co

nc.

me

asu

red

/∫co

nc.

so

urc

e


Baseline

Simulated Box

60



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

∫co

nc.

me

asu

red

/∫co

nc.

so

urc

e


Baseline

Simulated Box

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

∫co

nc.

me

asu

red

/∫co

nc.

sou

rce


Baseline

Simulated Box

61



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

∫co

nc.

me

asu

red

/∫co

nc.

so

urc

e


Baseline

Simulated Box

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

∫co

nc.

me

asu

red

/∫co

nc.

so

urc

e


Baseline

Simulated Box

62



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000

∫co

nc.

me

asu

red

/∫co

nc.

so

urc

e


Baseline

Simulated Box

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000

∫co

nc.

me

asu

red

/∫co

nc.

so

urc

e


Baseline

Simulated Box

63



0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000

∫co

nc.

me

asu

red

/∫co

nc.

so

urc

e


Baseline

Simulated Box

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

∫co

nc.

me

asu

red

/∫co

nc.

so

urc

e


Baseline

Simulated Box

64



Table 9.4.1 gives a comparison between baseline and simulation cases for a 2100 second time period.

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

∫co

nc.

me

asu

red

/∫co

nc.

so

urc

e


Baseline

Simulated Box

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

0.030

0 1000 2000 3000 4000 5000 6000 7000 8000

∫co

nc.

me

asu

red

/∫co

nc.

so

urc

e


Baseline

Simulated Box

65

Pallet Number Baseline Simulation

1 0.017 0.017

2 0.011 0.011

3 0.005 0.006

4 0.0016 0.017

5 0.010 0.010

6 0.005 0.006

7 0.019 0.019

8 0.012 0.012

9 0.006 0.007

10 0.018 0.018

11 0.012 0.012

12 0.006 0.007

Table 9.4.1–Normalised Concentration Comparison of Baseline with Simulated Pressure Drop Pallet

The lower pallets show higher concentrations in all cases in comparison with the baseline. This is probably

due to a greater dissipation rate of the gas from the pallets above with time when compared to pallets

with tubers in. Some variance was expected as the potatoes used in the baseline case are not a directly

measured scale of the Saxon potato lot used for the pressure test and, as previously stated, packing

density will affect concentration distribution.

Pressure drop tests using the same setup as reported in Section 1.0 were carried out on a smaller more

rounded variety that would pack more densely to confirm the expected result. Figure 9.4.13 shows the

comparison in pressure drop coefficient with normalised height for both Saxon and Estima varieties.

66

Figure 9.4.13 – Comparison of Pressure Drop Coefficient

10.0 CONCLUSIONS

Full scale measurements show the flow field within the reearch store at SBCSR to be highly unsteady, far

more so than expected.

The Swingfog fogger used in the research store has a high exhaust speed variation. It is unknown how this

compares with commercial foggers. This unsteady output creates difficulties in achieving a successful

scale model

The primary flow direction in the store is parallel to the ground plane, however, the vertical component is

not insignificant.

Scaling parameters have proven difficult to match. Flow was initially assumed to be driven by the plume

effects and so Froude Number was thought to be the most suitable. Results suggest that there may be

two distinct stages governing behaviour with convective diffusion being a significant driver. After the

tests, volume changes per unit time were found to give a better overall match, however, concentration

measurements in the region of the plume could not be matched. Smoke tracer tests highlighted a large

sensitivity to the fog injection angle which may explain some of the disparity. The result is significant for

this particular simulation with used twelve storage pallets but may be “damped” out in a commercial

store.

Normalised baseline concentration measurements gave the best agreement for normalised exposure

after approimately 2100 seconds, after this time the simulation was seen to diverge as expected. On the

opposite side to the inlet the trend of decreasing concentration with height in the stack agreed well with

the CIPC concentraion measurements taken from an actual application.

The correlation of trends between full and model scale was seen as sufficient to proceed with

assessment of the effect of store/pallet parameter changes. The effect of pallet spacing showed no

0

0.2

0.4

0.6

0.8

1

1.2

0 0.001 0.002 0.003 0.004 0.005 0.006

De

pth

of

Po

tato

es/

Max

de

pth

Pressure Drop Coefficient

Saxon

Estima

67

consitently better performer, however, increasing pallet spacing to 150mm (0.5m at full scale) improved

transport of tracer material to the rear of the pallet which may show more benefit with greater packing

densities.

Variation of fogger input temperaure showed an improvement in concentration for all cases when

simulated input temperature was reduced by 100 degrees C from baseline. Flow rate measurements

acquired from a commercial fogger suggest that such a temperature reduction is feasible. The benefit in a

commercial store may be reduced as greater packing density will slow lateral transport. The

compbination of reduced temperature and 0.5m spacing may be of benefit.

The introduction of more porous pallet base had a positive effect on the levels of tracer detected in the

lower pallets, with a 13-15% increase in level from baseline. This method may be most successful in a

commercial store as vertical transport is considered to be the primary method by which pallets in the

centre of a store are dosed. The level of benefit will depend greatly on crop and crop packing density.

Simulation of an individual pallet with materials representing the pressure drop showed considerable

promise and may represent a useful R&D tool for studying overall trends in behaviour of parameter

changes, without the need for crop.

11.0 REFERENCES

[1] Rastovski, A. et al. Storage of Potatoes: Post Harvest Behaviour, Store Design, Storage Practice, Handling. Second Edition. Pudoc, Wageningen. 1987 [2] Irvine.D, Jayas.D, Mazza.G Resistance to Air Flow Through Clean and Soiled Potatoes. American Society of Agricultural Engineers 0001-2351 / 93 / 3605-1405 [3] Hoffman.T, Maly.P, Furll.C, Schiebe.K. Harvest and Storage of Potatoes in Pallets. Poljoprivredna Tehnika Godina XXXII Broj 2, December 2007. Strane: 55 – 60

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