Journal of Energy and Power Engineering-2012.01

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Volume 6, Number 1, January 2012 (Serial Number 50)

Journal of Energy

and Power Engineering

David Publishing Company

www.davidpublishing.org

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Publication Information:

Journal of Energy and Power Engineering is published monthly in hard copy (ISSN 1934-8975) and online (ISSN1934-8983) by David Publishing Company located at 9460 Telstar Ave Suite 5, EL Monte, CA 91731, USA.

Aims and Scope:Journal of Energy and Power Engineering, a monthly professional academic journal, covers all sorts of researches on Thermal Science, Fluid Mechanics, Energy and Environment, Power System and Automation, Power Electronic,High Voltage and Pulse Power, Sustainable Energy as well as other energy issues.

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Journal of Energy and

Pow er Engineering

Volume 6, Number 1, January 2012 (Serial Number 50)

ContentsClean and Sustainable Energy

1 Simulation of Fast Gas Injection Expansion Phase Experiments under Different Pressures

Donella Pellini, Werner Maschek, Nicola Forgione, Francesco Poli and Francesco Oriolo

12 Assessment of Solar-Coal Hybrid Electricity Power Generating Systems

Moses Tunde Oladiran, Cheddi Kiravu and Ovid Augustus Plumb

20 A Comparison of Different Communication Tools for Distance Learning in Nuclear Education

Glenn Harvel and Wendy Hardmann

34 Aims and First Assessments of the French Hydrogen Pathways Project HyFrance3

Alain Le Duigou, Marie-Marguerite Qumr, Pierre Marion, Philippe Menanteau, Pascal Houel, Laure

Sinegre, Lionel Nadau, Aline Rastetter, Aude Cuni, Philippe Mulard, Loc Antoine and Thierry Alleau

41 Experimental Study of Airflow-Mixture in HVAC Unit by Using PIV

Yu Kamiji, Atsuhiko Terada and Hitoshi Sugiyama

49 H2 Production from Wind Power in a Wind Farm in Spain

Milagros Rey Porto, Mnica Aguado, Raquel Garde, Gabriel Garca and Trinidad Carretero

60 Experimental Studies on Critical Heat Flux in a Uniformly Heated Vertical Tube at Low Pressure

and Flowrates

Husham M. Ahmed

70 Environmental Impacts of Oil and Gas Production in Nigeria

Cecily. O. Nwokocha, Donatus. U. Ugwu, Abiodun. B. Fagbenro and Emmanuel. J. Cookey

76 Impact of the Target Film Thickness on the Properties of the Neutron Tube

Shuang Qiao, Shiwei Jing and Mingrui Zhang

Power and Electronic System

81 A Novel Transient Current-Based Differential Algorithm for Earth Fault Detection in Medium

Voltage Distribution Networks

Mohamed F. Abdel-Fattah and Matti Lehtonen

90 Chaos Control and Modified Projective Synchronization of Chaotic Dissipative Gyroscope Systems

Faezeh Farivar, Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab and Mohammad Ali Nekoui


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101 Investigation of Interline Dynamic Voltage Restorer with Virtual Impedance Injection

Ahmed Elserougi, Ahmed Hossam-Eldin, Ahmed Massoud and Shehab Ahmed

110 Innovative Renewable Energy-Load Management Technology via Controlled Weight Motion

Mohammed A. El-Kady, Mamdooh Saud Thinyyan Al-Saud and Majeed A.S. Alkanhal

117 A Parameter Determination Method of Distribution Voltage Regulators Considering Tap Change

and Voltage Profile

Yuji Hanai, Yasuhiro Hayashi, Junya Matsuki, Yoshiaki Fuwa and Kenjiro Mori

126 The Development of the Generation Expansion Planning System Using Multi-Criteria Decision

Making Rule

Seokman Han, Koohyung Chung and Balho H. Kim

132 Study on the Photovoltaic System with MPT and Supercapacitors

Jan Leuchter, Pavel Bauer and Vladimir Rerucha

140 An Experience of On-site PD Testing for Condition Monitoring of an 11 kV PILC Cable Insulation

System

Xiaosheng Peng, Chengke Zhou and Xiaodi Song

146 Research on IPv6 Transition Evolvement and Security Architecture of Smart Distribution Grid

Data Communication System

Xin Miao and Xi Chen

150 Operational Reliability in Transmission Power Grid

Daniel Morar, Basarab Guzun and Ioan Rodean


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Journal of Energy and Power Engineering 6 (2012) 1-11

Simulation of Fast Gas Injection Expansion Phase

Experiments under Different Pressures

Donella Pellini1, Werner Maschek

1, Nicola Forgione

2, Francesco Poli

2and Francesco Oriolo

2

1. Karlsruhe Institute of Technology, Karlsruhe D-76021, Germany

2. Dipartimento di Ingegneria Meccanica, Nucleare e della Produzione, Universit di Pisa, Pisa 56126, Italy

Received: November 11, 2010 / Accepted: April 28, 2011 / Published: January 31, 2012.

Abstract: The injection of a high pressure gas into a stagnant liquid pool is the characteristic phenomenon during the expansion phase

of a hypothetical core disruptive accident in liquid metal cooled fast reactors. In order to investigate lots of mechanisms involved in thisphase of the accidents evolution, an experimental campaign called SGI was performed in 1994 in Forschungszentrum Karlsruhe, now

KIT. This campaign consists of nine experiments which have been dedicated to assess the effects of different pressure injection, the

nozzles size and the presence of inner confinement in the formation of the rising bubble. Three of these experiments, which were

focused on the pressure effects, have now been simulated with SIMMER III code and with FLUENT 6.3, a commercial CFD code. Both

codes, despite their different features, have showed a good agreement with the experimental results. In particular, time trend evolutions

of pressures and bubble volumes have been well reproduced by simulation. Furthermore, both codes agree on the shape of the bubble,

even though they have evidenced same discrepancies with the experimental shape.

Key words: Expansion phase, SIMMER III code, FLUENT.

1. Introduction

The sodium cooled fast reactors (SFR) represents

one of the proposed Generation IV systems [1] chosen

from the Generation IV International Forum in order to

enhance the future contribution and benefits of nuclear

energy utilization.

Therefore, the simulation of core disruptive

accidents and the assessment of potential, thermal and

mechanical energy for new design becomes of interest

too. Also new simulation tools have been developed

meanwhile and are applied in this domain.

In case of a severe accident, a mixture of fuel and

steel vapor, which forms an expanding bubble, might

be driven out of the damaged core into the sodium pool

(so-called expansion phase). This causes the sodiums

level raise and, consequently, the cover of gas pressure

Corresponding author: Donella Pellini, Ph.D., researchfield: thermo-hydraulic of liquid metal cooled nuclear reactors.

E-mail: [email protected].

increase.

Experiments dealing with the injection of a high

pressure gas into a stagnant liquid pool, such as the

demonstration fast breeder reactor program (DFBR) in

Japan [2] or the previous campaigns performed by

Tobin and Cagliostro [3], describe characteristic

phenomena taking place during the expansion phase

and are of interest for testing and benchmarking.

In order to investigate further the mechanisms

involved in this phase of the accident evolution, an

experimental campaign called SGI was performedin1994 in former Forschungszentrum Karlsruhe, now

KIT [4].

In addition to the experimental studies, the

expansion phases knowledge needs, also, a lot

numerical analysis focused on studying the bubbles

growth, the interfacial instabilities effects and the

entrainment which plays a crucial role in the expansion

phase [5, 6].

DDAVID PUBLISHING


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Simulation of Fast Gas Injection Expansion Phase Experiments under Different Pressures2

Therefore from the computational viewpoint, a

simple and clear geometry together with the use of

simple materials is helpful in the assessment of codes

which need models to handle the complex phenomena

involved in this phase.

Thus SGI campaign, which fulfils these

requirements, has been chosen to compare the results

obtained from the simulations SIMMER III and

FLUENT codes of three of the nine experiments of

which the campaign is made of.

FLUENT 6.3 [7] is one of the most known

computational commercial fluid-dynamics code. Its

flexible features make it suitable to deal with several

different fluid dynamics problems concerning mixing,turbulence, chemical reactions thus allowing to carry it

out in a lot of conventional industrys fields [8] and in

the nuclear industry, too [9].

SIMMER III is a two-dimensional (2D), three

velocity-fields, multi-component, multiphase, Eulerian

fluid-dynamics code coupled with a space and energy

dependent neutron kinetics model.

It was originally developed to simulate the events

sequence of the Core Disruptive Accidents (CDA) in

Liquid Metal Fast Reactors (LMFR) [10, 11], even

though, over the years, it has been developed into a

flexible tool which can deal with various problems

consistent with its modeling framework such as safety

analysis in advanced fast reactors up to the new

accelerator driven systems [12], steam explosion and

fuel coolant interaction problems [13, 14] and, more

generally, multiphase flow problems [15, 16].

2. Experimental Facility

The SGI (acronym of Schnelle Gas Injection)

campaign has been aimed to investigate on the

mechanism and phenomena involved in the expansion

phase.

In order to achieve this goal, an experimental facility,

which was built by SRI International, California, has

been used in Forschungszentrum Karlsruhe (now KIT)

for a series of experiments in which a certain amount of

gas has been injected into a water pool aiming at

reproducing the typical phenomenology of the

expansion phase in a nuclear reactor. The facility

consists of two vessels connected through a short tube

as it is shown in Fig. 1.

The first vessel is the so-called pressure vessel

which is completely filled with nitrogen at 293.15 K

and with a pressure ranging from 0.3 and 1.1 MPa.

The second vessel is the so-called main vessel and

it consists of an acrylic cylinder with an inner diameter

of 33 cm. Since it simulates the reactor coolant pool, it

has been partially filled with water and partially with

air at 293.15 K at atmospheric pressure.

Furthermore, inner walls have been placed insidethis vessel aimed at reproducing the reactors inner

structures.

Fig. 1 Scheme of the experimental facility for the SGI

campaign [4].


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Simulation of Fast Gas Injection Expansion Phase Experiments under Different Pressures 3

As it was previously mentioned, the two vessels

have been connected through a short tube, which was

closed by two sliding doors and a thin metal foil above

the doors.

The doors were opened by exploding

hydrogen-oxygen gas which drove two pistons towards

the doors, pushing them sideways in opposite

directions. Their role was to open the gas flow cross

section within a short time with the minimum

disturbance of the gas flow.

The campaign has investigated the effects of

different pressure, nozzle diameter and the presence of

inner confinement walls on the formation of the rising

bubble. Therefore, nine tests have been performed

depending on these parameters, as reported in Table 1.

Three tests, respectively 91, 93 and 95, have been

considered in this work.

3. Numerical Simulation

In order to perform a comparison between two

different kinds of codes the tests 91, 93, 95 have been

chosen. In fact, these tests have been arranged with the

same geometrical features, that is the presence of an

inner vessel wall with a diameter of 23 cm and the

nozzle diameter equal to 9 cm.

The only parameter changed has been the nitrogen

injection pressure which has been respectively set

equal to 1.1 MPa for test 91, 0.6 MPa for test 93 and 0.3

MPa for test 95 (see Table 1).

3.1 SIMMER III Features and Geometrical Domain

As mentioned previously, SIMMER III is a

two-dimensional (2D), three velocity-fields,multi-component, multiphase, Eulerian fluid-dynamics

code coupled with a space and energy dependent

neutron kinetics model.

SIMMER III handles by default LMFR core

materials (fuel, coolant, control and fission gas) in solid,

liquid and vapor phases.

The materials mass distribution is modeled through

27 density components and the energy distributions are

modeled by only 16 energy components, because some

Table 1 Experimental conditions of the nine tests performed.

Testnumber

Presenceof innerstructures

Innerstructurediameter (cm)

Nozzlediameter(cm)

Initialpressure(MPa)

21 yes 23 6 1.1

29 no 33 9 1.130 no 33 6 1.1

32 no 33 9 0.6

33 yes 9 9 1.1

37 yes 9 9 0.3

91 yes 23 9 1.1

93 yes 23 9 0.6

95 yes 23 9 0.3

density components can be assigned to the same energy

components (e.g. a mixture of vapor components can

be defined with the same energy component).

The materials density and energy components areobtained through the calculation of mass, momentum

and energy conservation equations using three

velocity-fields (two liquids and one vapor).

The fluid-dynamics solution algorithm is based on a

time factorization method (Four Step Algorithm)

which determines intra-cell interfacial area source

terms, heat and mass transfer and momentum exchange

functions separately from inter-cell fluid convection.

A higher-order spatial differencing scheme is the

standard scheme to reduce numerical diffusion and, in

particular, it is necessary in treating multiphase

phenomena.

Concerning the simulations, a cylindrical domain has

been set up for representing the test section (see Fig. 2). It

has been subdivided into 31 radial and 68 axial cells

and inside it, the main vessel takes up the radial cells

from 1 to 31 and the axial cells from 32 to 68.

The cover gas region is included in the axial cells

from 56 to 68 and in the cells from 1 to 31 radially,according to the volume provided.

The pressure vessel, the connection tube and the

inner walls inside the main vessel have been shaped

throughno calculation regions.

3.2 CFD Code and Computational Domain

FLUENT features make this code suitable for

carrying out a CFD simulation of the experiments

chosen and for the comparison with SIMMER III code.


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Fig. 2 SIMMER III geometrical domain.

The simple geometry of the experimental facility

allows to set up a two dimensional axial symmetric

computational domain (see Fig. 3), which has been

divided into about 10800 cells.

The time step chosen for the simulations is equal

to10-6

s.

The time step and the mesh have been settled onafter a preliminary activity aimed to verify the

solutions independence from both parameters.

Among the several turbulence models available in

FLUENT, the standard - model has been chosen

aimed at describing the turbulent motion of the two

fluids.

Furthermore, a standard wall functions approach has

been used for the near wall region modeling. A first

order upwind scheme interpolates the convective terms

in the momentum equation while the pressure-velocity

coupling is numerically taken into account through the

SIMPLE algorithm.

In order to simulate the phenomena involved in the

SGI experiments, the Volume of Fluid (VOF) model

has been selected. This model, as largely suggested

[17], is the most suitable to deal with problems

involving the formation and the motion of a large gas

bubble in a liquid. In fact, the VOF model determines

Fig. 3 Computational domain for FLUENT simulation.

the interface motion between two immiscible phases

with different density and viscosity through a phase

indicator f defined as volume fraction function.

Therefore, f = 1 and f = 0 represent the two extremes,

respectively, for the liquid and the gas phase while the

range included between these values is representative

for a two-phase cell with a part of interface.

The continuity equations solution for each phase

accomplishes the interface tracking. Obviously, the

assumptions of absence of phase change, the

incompressibility of the liquid phase, homogeneity and

immiscibility of the two fluids with a well defined

interface has to be made. According to this, for the SGI

simulations gas has been considered the primary phase

and water the secondary phase.

4. Comparison among Calculations and

Experimental Results

4.1 Test 91

4.1.1 Pressure

One of the parameter chosen for the comparison

among codes and experimental results is the pressure

because of the availability of direct experimental


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measurements. As it has been mentioned previously,

the test 91 has been performed injecting nitrogen at 1.1

MPa and 293.15 K in the main vessel containing water.

As can be seen in Fig. 4, which shows the

experimental and calculated pressure in the cover gas

region, at about 0.0074 s a sharp peak of about 12 MPa

arises due to the injection of the gas. In fact, the gas

bubble begins to grow inside the water, raising the

water level and at the same time compressing the cover

gas.

The maximum pressure is reached when the bubble

pushes the water up to the top of the main vessel

moment and reaches the maximum dimension before it

breaks up. Therefore, the pressure decreases rapidly.The experimental curve shows a very small second

peak of about 2 MPa at about 0.08 s. This indicates the

experimental pool surface is domed but the pressure

wave following the impact at the top corner of the

facility is reduced on its way to the top center.

Both codes are in good agreement with the

experimental results, even though SIMMER simulation

shows a very small delay in timing with respect to

FLUENT, which results overlaps the experimental ones.

Concerning the second small peak, SIMMER

estimates a value of 2.3 MPa at about 0.0085 s while

FLUENT presents timing in coincidence with

experimental data but a value of 2.8 MPa. In any case,

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0.0E+00 3.0E-03 6.0E-03 9.0E-03 1.2E-02 1.5E-02 1.8E-02

Time [s]

Pressure[MPa]

Experimental

SIMMER III

FLUENT

Fig. 4 Pressure transient in cover gas region for Test 91.

the second peak is more defined from the simulations

with respect to experiment. This is probably due to a

slight overestimation of the pressure wave from both

codes. Experimentally, the pressure stabilizes at about

0.7 MPa after 0.009 s. SIMMER III results are in

agreement with this trend, even though the calculated

value is slightly lower than the experimental results

(about 0.6 MPa). FLUENTs results show some

oscillations around a value of 0.7 MPa.

It must be note that after 0.015 s FLUENT evaluates

a pressure increase up to 1.6 MPa, perhaps due to an

overestimation of bubble generation.

The pressure trend in the pressure vessel is shown in

Fig. 5. When the sliding doors open the nitrogen beginsto flow into the water pool, causing the expanding

bubble to rise and, at the same time, decreasing the

pressure in this vessel.

The pressure reaches its minimum of 0.52 MPa at

about 0.01 s due to the break-up of the expanding

bubble in the water region which decrease the pressure

in the main vessel, allowing the lower vessel pressure

to rise.

Experimental data are in very good agreement with

both codes, even though SIMMER reproduces almost

exactly experimental values and trends. FLUENT

shows more oscillations during the first 0.005 s and a

small underestimation of the minimum pressure value.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

0.0E+00 3.0E-03 6.0E-03 9.0E-03 1.2E-02 1.5E-02 1.8E-02

Time [s]

Pressure[

MPa]

Experimental

SIMMER III

FLUENT

Fig. 5 Pressure transient in pressure vessel for Test 91.


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4.1.2 Gas Bubble Volume

Since the bubble growth plays a crucial role in the

experiment, the evaluation of the gas bubble volume

was chosen for the comparison.

The experimental data are available up to 0.00538 s

that is the instant when considerable cavitation at the

upper edge of the inner cylinder walls sets in.

During this time there is a very good agreement

between experimental and calculated results, as

highlighted in Fig. 6.

In particular, the two codes evaluate in the same way

the bubble volume, therefore, an overlapping of the

results obtained from simulations can be noted during

this time range.Despite this agreement, as can be seen in Fig. 7

where the sequence of the bubble shapes development

is shown, both codes exhibit some discrepancies in the

prediction of the bubbles shape with respect to the

contour plot of the bubble surface obtained in the

experiment through high speed photographs. Since the

earlier moments of the bubbles growth (0.00277 s) the

experimental bubbles shape is domed while, at the

same time frame, the bubbles shape obtained from

both codes presents a concavity which becomes more

marked as the time increases, up to 0.00538 s.

4.1.3 Gas Velocity

The last parameter chosen for this work has been the

gas exit velocity from the nozzle.

The experimental data have been estimated from the

displaced water volume and therefore they are not

obtained from a direct measurement.

Furthermore, they are available just for a very short

time ranged from 0.0029 to 0.0046 s. Therefore, this

comparison must be focused on the FLUENT and

SIMMER results.

The calculated velocity data has been taken from the

bottom cell closed to the axis, both for FLUENT and

SIMMER.

As can be noted in Fig. 8, even though the two codes

exhibit the same trend, FLUENT shows more

oscillation during the first 0.0085 s with respect to

SIMMER and the experimental results.

0.0E+00

5.0E-04

1.0E-03

1.5E-03

2.0E-03

2.5E-03

3.0E-03

3.5E-03

4.0E-03

2.0E-03 3.0E-03 4.0E-03 5.0E-03 6.0E-03Time [s]

Volume[m3]

Experimental

SIMMER III

FLUENT

Fig. 6 Gas bubble volume comparison for Test 91.

Fig. 7 Experimental and numerical bubbles shape

evaluation for Test 91.

-3.0E+02

-2.0E+02

-1.0E+02

0.0E+00

1.0E+02

2.0E+02

3.0E+02

0.0E+00 3.0E-03 6.0E-03 9.0E-03 1.2E-02 1.5E-02 1.8E-02Time [s]

GasExitVelocity[m

/s]

Experimental

SIMMER III

FLUENT

Fig. 8 Gas exit velocity for Test 91.


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SIMMER tends to overestimate slightly the

maximum velocity, although the timing of both codes

agrees completely.

From 0.0086 s to 0.011 s the two codes show same

trends and values, but afterwards FLUENT, the

minimum velocity value is smaller than SIMMER III

value. This might explain the overestimation of the

minimum pressure value observed in Fig. 5.

4.2 Test 95

4.2.1 Pressure

Fig. 9 shows the experimental and calculated

pressure in the cover gas region considering an

injection gas pressure of 0.3 MPa. This test has to beconsidered as the lower extreme of the three tests

chosen for this comparison because of its lowest initial

gas injection pressure (0.3 MPa).

Due to the lower injection pressure, the bubble

expands slower with respect to test 91, thus

compressing the cover gas region in a weaker manner.

Therefore, the experimental pressure peak reaches the

maximum value of about 0.5 MPa in about 0.0175 s

and the pressure raise exhibits a mild peak. The

pressure decrease is milder than in test 91, too.

Concerning the simulations, Fig. 9 highlights a quite

good agreement between the codes and the

experimental data, although some differences can be

noted.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.0E+00 5.0E-03 1.0E-02 1.5E-02 2.0E-02 2.5E-02 3.0E-02

Time [s]

Pressure[MPa]

Experimental

SIMMER III

FLUENT


Both codes highlight a little delay (about 0.001 s) in

reaching the maximum. FLUENT results show a slight

underestimation of the pressure curve, with a

maximum peak of about 0.45 MPa.

SIMMER pressure curve for the first 0.0165 s can be

placed between the experimental and the FLUENT

curve.

Afterwards, SIMMER evaluates a first peak which is

sharper in the experiment and then it overestimates the

main peak, reaching a value of 0.546 MPa.

Furthermore, the pressure decrease presents a

steeper slope with respect of the other two curves and a

lower peak at 0.022 s.

From 0.025 s on both codes show some oscillations.The pressure curve of the pressure vessel, shown in

Fig. 10, is similar to the trend shown in Fig. 5, because

of the same phenomenology.

As can be expected, the minimum of 0.18 MPa is

reached at about 0.0185 s, therefore later than in test 91.

Experimental data are in better agreement with both

codes than in the cover gas region. In particular,

SIMMER curve matches the experimental results up to

the minimum and then shows a little underestimation of

the pressure values. FLUENT curve presents lots of

oscillations during the whole transient and a little

underestimation of the minimum.


As can be seen in Fig. 11, the evaluation of the

bubble volume is in good agreement between FLUENT

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.0E+00 5.0E-03 1.0E-02 1.5E-02 2.0E-02 2.5E-02 3.0E-02

Time [s]

Pressure[MPa]

Experimental

SIMMER III

FLUENT



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0.0E+00

5.0E-04

1.0E-03

1.5E-03

2.0E-03

2.5E-03

3.0E-03

3.5E-03

0.0E+00 2.0E-03 4.0E-03 6.0E-03 8.0E-03 1.0E-02 1.2E-02

Time [s]

Volum

e

[m

3]

Experimental

SIMMER III

FLUENT

Fig. 11 Gas bubble volume comparison for Test 95.

and SIMMER, even though the experimental curves

slope is slightly steeper than those obtained from the

simulations.

Observing Fig. 12, which shows the bubbles shape

growth at 0.0048 s (early stage) and at 0.0117 s

(advanced stage), it is possible to note that from the

early stage on, the bubble growth numerically

evaluated shows a very pronounced concavity similarlyto what has been observed in Fig. 7 for test 91.

Therefore, the bubble volume is underestimated in

both codes. However, it must be pointed out that the

experimental expanding bubble exhibits a dimple in the

dome, instead of homogeneous dome as shown in Fig.7

for test 91. The reasons for these differences in the

bubble shape have not been clarified yet.

4.2.3 Gas Velocity

The evaluation of the gas exit velocity from the

nozzle is shown in Fig. 13.

Likewise to test 91, the availability of experimental

data just for a very short time (from 0.0061 s to 0.01 s)

allows focusing on the comparison between the two

codes.

The experimental data shows a linear trend, which is

better approached from SIMMER simulation, although

the evaluation of the velocity is slightly

underestimated.

Fig. 12 Experimental and numerical bubbles shape

evaluation for Test 95.

-150

-100

-50

0

50

100

0.0E+00 5.0E-03 1.0E-02 1.5E-02 2.0E-02 2.5E-02 3.0E-02

Time [s]

GasExitVelocity[m

/s]

Experimental

SIMMER III

FLUENT


FLUENTs curve shows oscillation that are not

evaluated from SIMMER even though SIMMER curve

seemed to represent a sort of averaged curve for

FLUENT.

Similarly to what observed in Fig. 8 for test 91

FLUENT underestimates the minimum velocity and

the evaluation of the last part of the curve (from 0.022 s

on) with respect to SIMMER, although both codes

agree on the reaching of a relative maximum of 30 m/s

at about 0.026 s and the following velocity decrease.


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4.3 Test 93

4.3.1 Pressure

Test 93 has been performed injecting nitrogen at 0.6

MPa, therefore, it might be considered as a middle casebetween test 91 and test 95. In Fig. 14 cover gas

pressure curves are shown.

As can be seen the experimental pressure trend looks

like more similar to the test 91 than to the text 95,

exhibiting a sharp peak. The peak reaches its maximum

of about 4.76 MPa in about 0.01 s. This value is very

close to the value found for the test 93.

Even the pressure decrease shows similarities with

test 91, although a series of three lower peaks can be

noted during the phase of pressure decrease which lasts

up to 0.016 s.

The peaks width therefore can be placed between

the two widths of test 91 and test 95. Afterwards, the

pressure stabilizes at about 0.35 MPa.

Concerning the simulations, Fig. 14 highlights a

substantial agreement between the codes and the

experimental data, although some differences can be

noted.

Both codes exhibit a delay which starts for both at0.0075 s thus the maximum is reached after about

0.005 s from SIMMER and 0.004 s from FLUENT

with respect to the experimental results.

The peaks value is underestimated, too. While

SIMMER evaluates a peak of 4.3 MPa, FLUENT

evaluates the maximum at 3.3 MPa.

Furthermore, during the pressure decrease FLUENT

curve shows three defined peaks that might be connected

to the three close peaks observed experimentally.

Afterwards, both codes are in good agreement with

experimental results.

Fig. 15, which shows the pressure in the lower vessel,

highlights the good agreement between the codes and

the experimental results, especially up to 0.012 s.

In the following time, similarly to the simulations of

the two previous cases, FLUENT and SIMMER

minimum pressure value is smaller than the

experimental one.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

0.0E+00 3.0E-03 6.0E-03 9.0E-03 1.2E-02 1.5E-02 1.8E-02Time [s]

Pressure[MPa

]

Experimental

SIMMER III

FLUENT


0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.0E+00 3.0E-03 6.0E-03 9.0E-03 1.2E-02 1.5E-02 1.8E-02

Time [s]

Pressure[MPa]

Experimental

SIMMER III

FLUENT



As can be seen in Fig. 16, the curves obtained from

FLUENT and SIMMER match each other in the time

range considered. Nevertheless, the codes evaluated a

milder slope than that obtained from experiment.

Comparing these results with Fig. 6 and Fig. 11, it is

possible to note that the evaluation of the bubble

volume behaves in an intermediate manner. In fact, in

test 91, the numerical curves almost match the

experimental one, while in test 95 it is possible to

observe a certain deviation from the experimental

values.

The deviation of test 93 is smaller than in test 95 and,

obviously larger than in test 91.


15/160


0.0E+00

5.0E-04

1.0E-03

1.5E-03

2.0E-03

2.5E-03

3.0E-03

0.0E+00 2.0E-03 4.0E-03 6.0E-03 8.0E-03

Time [s]

Volume[m3]

Experimental

SIMMER III

FLUENT

Fig. 16 Gas bubble volume comparison for Test 93 .

4.3.3 Gas Velocity

The evaluation of the gas exit velocity from the

nozzle is shown in Fig. 17.

Likewise to the other two tests, the experimental

data are available just for a time ranging from 0.004 s

to 0.006 s. In this time range SIMMER shows a good

agreement with the experimental data.

In analogy to what has been observed for test 91 and

test 95, FLUENT exhibits oscillations during the first

0.011 s, underestimating the minimum gas exit velocityvalue.

5. Conclusions

The injection of a high pressure gas into a stagnant

liquid pool, which characterizes phenomena of the

expansion phase of a hypothetical core disruptive

accident in liquid metal cooled fast reactors, has been

investigated through the experimental campaign SGI

performed in 1994 in Forschungszentrum Karlsruhe,

now KIT.

Three tests of the campaign have been simulated

with two different codes, SIMMER III and FLUENT,

and the results have been compared with the

experimental data.

This activity has turned out to be helpful in assessing

the possible differences in the results owing to the use

of a multi-field, multi-phase, multi-component

accident code like SIMMER III and a CFD code like

-250

-200

-150

-100

-50

0

50

100

150

200

0.0E+00 3.0E-03 6.0E-03 9.0E-03 1.2E-02 1.5E-02 1.8E-02

Time [s]

GasExitVelocity

[m

/s]

Experimental

SIMMER III

FLUENT


FLUENT, which of course have different features and

different levels of details.

Moreover, this activity has provided a further

qualification of the SIMMER III code, in simulating

Core Disruptive Accidents (CDA) phenomena

involved in sodium fast reactors.

The main results concerning the activity performed

can be summarized as in the following:

the comparison among the experimental pressurecurves and the numerical results obtained from the twocodes has shown a very good agreement, both for the

main and the pressure vessel;

the evaluation of the bubble volume is matchingbetween the codes and it is in good agreement with

experimental data. An underestimation of the bubble

volume can be observed at lower initial gas injection

pressure;

the analysis of the bubbles shape has revealedsome discrepancies between the experimental results

and the numerical simulation performed with both

codes. In fact, SIMMER and FLUENT have exhibited

a concavity in the center of the bubble which are not

observed in the experiments;

the comparison of the gas exit velocity performedwith the two codes has shown a general agreement

between them, although an oscillatory trend has been

noticed in the simulation of the three test with

FLUENT.


16/160


The simulation activity has revealed the comparable

capability of SIMMER III with respect to FLUENT

code in reproducing the experimental findings, getting

similar results with reduced computational resources

and time.

References

[1] World Nuclear Association, Generation IV Reactors [online],http://www.world-nuclear.org/info/default.aspx?id=530&

terms=Generation%20IV (updated Dec. 2010).

[2] T. Nakamura, H. Kaguchi, I. Ikarimoto, Y. Kamishima, K.Koyama, S. Kubo, et al., Evaluation method for structural

integrity assessment in core disruptive accident of fast

reactor, Nucl. Eng. Des. 227 (2004) 97-123.

[3] R.J. Tobin, D.J. Cagliostro, Energetics of simulatedHCDA bubble expansions: Some potential attenuation

mechanisms, Nucl. Eng Des. 58 (1980) 85-95.

[4] L. Meyer, D. Wilhelm, Investigation of the Fluid Dynamicsof a Gas Jet Expansion in a Liquid Pool, Technical report

KfK5307, Forschungszentrum Karlsruhe, 1994.

[5] M. Epstein, H.F. Fauske, S. Kubo, T. Nakamura, K.Koyama, Liquid entrainment by an expanding core

disruptive accident bubbleA Kevin/Helmotz

phenomenon, Nucl. Eng. Des. 210 (2001) 53-77.

[6] M.J. Tan, J.M. Delhaye, An experimental study of liquidentrainment by expanding gas, J. Fluids Engineering 109

(1987) 436-441.

[7] FLUENT 6.3 Users Guide, Fluent Inc., Cavendish Court,Lebanon, Sept., 2006.

[8] W.J. Rider, D.B. Kothe, Reconstructing volume tracking,Journal of Computational Physics 141 (2) (1998) 112-152.

[9] P. Di Marco, N. Forgione, M. Tarantino, Analysis of thetransient rise of an initially spherical gas bubble in a

stagnant liquid, in: Proceedings of XXIV National

Congress UIT on Heat Transfer, Napoli, Italy, 2006.

[10] M. Sharabi, W. Ambrosini, N. Forgione, H. Shuisheng,

SCWR rod bundle thermal analysis by a CFD code, in:

Proceedings of 16th International Conference on Nuclear

Engineering, Orlando, FL, USA, 2008, pp. 1-7.

[11] S. Kondo, Y. Tobita, K. Morita, N. Shirakawa, SIMMERIII: A Computer Program for LMFR Core Disruptive

Accident Analysis, Technical report JNC TN 9400

2003-071, Research Document, O-arai Engineering

Center, Japan Nuclear Cycle Development Institute, 2003.

[12] Y. Tobita, Sa. Kondo, H. Yamano, S. Fujita, K. Morita, W.Maschek, et al., The development of SIMMER-III, an

advanced computer program for LMFR safety analysis, in:

Proceeding of the IAEA/NEA Technical Meeting on Use

of Computational Fluid Dynamics (CFD) Codes for Safety

Analysis Reactors Systems Including Containment, Pisa,

Italy, Nov. 11-14, 2002.

[13] K. Morita, A. Rineiski, E. Rineiski, E. Kiefhaber, W.Maschek, M. Flad, et al., Mechanistic SIMMER III

analyses of severe transient in accelerator driver systems

(ADS), in: Proceedings of the 9th International

Conference on Nuclear Engineering (ICONE 9), Nice

Acropolis, France, Apr. 8-12, 2001.

[14] K. Morita, S. Kondo, Y. Tobita, D.J. Brear, SIMMER IIIapplications to fuel-coolant interactions, Nucl. Eng. Des.

189 (1999) 337-357.

[15] S. Wang, M. Flad, W. Maschek, P. Agostini, D. Pellini, G.Bandini, et al., Evaluation of a steam generator tube

rupture accident in an accelerator driven system with lead

cooling, Prog. Nucl. Energy 50 (2008) 363-369.

[16] D. Wilhelm, P. Coste, Experience with the multiphasecode SIMMER and its models for interfacial area

convection, in: Proceedings of International Meeting on

Trends in Numerical and Physical Modelling for Industrial

Multiphase Flows, Cargese, France, Sept. 27-29, 2000.

[17] K. Morita, T. Matsumoto, K. Fukuda, Y. Tobita, H.Yamano, I. Sato, Experimental verification of the fast

reactor safety analysis code SIMMER-III for transient

bubble behavior with condensation, Nucl. Eng. Des. 238

(2008) 49-56.


17/160


Assessment of Solar-Coal Hybrid Electricity Power

Generating Systems

Moses Tunde Oladiran1, Cheddi Kiravu

1and Ovid Augustus Plumb

2

1. Faculty of Engineering and Technology, University of Botswana, Gaborone, P/Bag 0061, Botswana

2. Department of Mechanical Engineering, University of Wyoming, Laramie, WY 82071, USA

Received: February 04, 2011 / Accepted: June 09, 2011 / Published: January 31, 2012.

Abstract: Botswana currently depends on electricity generated from coal-based power plant or electricity supplied from the border in

South Africa. The country has good reserves of coal and the solar radiation is sufficiently high to make solar thermal attractive for

generating electricity. The paper presents two conceptual coal-fired power station designs in which a solar sub-system augments heat

to the feed heaters or to the boiler. The thermal and economic analyses showed enhanced system performance which indicates that

solar power could be embedded into existing fossil fuel plants or new power stations. Integrating solar energy with existing or new

fossil fuel based power plants could reduce the cost of stand-alone solar thermal power stations, reduce CO2 emissions and produce

experience necessary to operate a full scale solar thermal electricity generation facility.

Key words: Hybrid systems, solar, coal, economics, performance.

Nomenclature

A Annuitized construction costs, $

CC Total construction costs, $

dr Discount rate

h Specific enthalpy, kJ/kg

Total system mass flow rate, kg/hrQ Heat input, kJ/kg

Qs Total solar input, kJ/hr

Qmc Maximum heat load for open feedwater heater, kJ/hr

Qsc Solar input to closed feedwater heater, kJ/hr

W Pump of turbine work, kJ/kg

yc Fraction of total mass flow extracted for closed feedwater

heater, dimensionless

Plant thermal efficiency

yo Fraction of total mass flow extracted for open feedwater

heater, dimensionless

1. Introduction

Electricity requirements in Botswana are supplied

through local generation at Morupule coal-based power

station and imports from the Southern Africa Power

Corresponding author: Moses Tunde Oladiran, Ph.D.,associate professor, research fields: applied energy, energymanagement, engineering education. E-mail:[email protected].

Pool, mainly from Eskom of the Republic of South

Africa (RSA). Botswana has abundant reserves of coal

estimated at 212 billion tones in various fields though

only one field is currently operational. With these huge

coal resources it is likely that Botswana would depend

on coal fired power stations for future electricity needs.

Indeed several such power stations are at various stages

of development to make the country self sufficient in

electricity and possibly become a power exporter to

neighbouring states. As CO2 generated per capita will

grow steadily, environmental issues would increasingly

be of concern.

The country is a signatory to the United NationsFramework Convention on Climate Change (UNFCCC)

and the Kyoto Protocol to control greenhouse gas

emissions. Some of the options under consideration to

limit CO2 emission from power stations include use of

advanced power systems such as integrated gasification

combined cycle (IGCC) systems and carbon

sequestration technologies to recapture and store carbon

dioxide (i.e. carbon capture and storage). To generate

DDAVID PUBLISHING


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Assessment of Solar-Coal Hybrid Electricity Power Generating Systems 13

electricity on a long term basis there are aggressive

plans to introduce renewable technologies especially

solar thermal and biomass systems. Botswana has one

of the highest solar radiation regimes in the world

because the country receives over 3200 hours of

sunshine per annum and the average daily insolation on

a horizontal surface is approximately 21 MJ/m2

[1].

From an economic, strategic and environmental point

of view it is appropriate to promote the use of solar

energy in Botswana. However, solar thermal electricity

involves huge capital costs.

Current economic assessments place the cost of

generating electricity using solar thermal power,

including storage, at 0.16 $/kWh compared to hardcoal at 0.05 $/kWh [2]. This has both discouraged

investment in solar thermal power and negatively

impacted innovation that results from operating

experience with new facilities. Solar-coal hybrid

thermal power plants have been proposed more

recently as an economically viable alternative to

stand-alone solar thermal power plants [3].

The advantage of the hybrid facility is that, in many

locations, transmission capacity already exists

whereas for a stand-alone solar facility transmission

capability may not be available in locations where the

solar radiation is sufficient to warrant investment in

solar thermal generating plants. In addition, the

solar-coal hybrid does not require storage which is

estimated to be approximately 15% of the total cost of

construction for a solar thermal facility utilizing

parabolic trough technology [2]. Furthermore, the

hybrid technology appears to offer retrofit potential

for existing coal fired power plants located whereannual solar radiation is sufficiently high. Finally, it

may be possible to generate additional power without

adding turbine capacity. Therefore, it is plausible to

use hybrids of solar-coal as a transition stage to fully

renewable energy production [3-5].

The current paper examined a generic coal fired

facility located where the solar insolation is

approximated by that at Morupule, the location of

Bostwanas single coal-fired power plant. Two possible

hybrid configurations are considered. In the first option

the solar input is utilized to gradually replace feedwater

heating. For this plant configuration (Fig. 1) the coal

consumption is constant but the turbine output increases

as solar energy replaces steam bled from the turbines in

order to provide feedwater heat. The second

configuration (Fig. 2) considered is one for which the

solar input replaces a portion of the boiler heat reducing

the quantity of coal as the solar input increases.

It was assumed that the first configuration would be

easier to implement at an existing facility because the

boiler does not require any modification. The only

necessary change to the existing facility is the additionof heat exchangers to the feedwater heaters. However,

it is likely to be more difficult to control because of

the complexity of the feedwater heating scenario. The

second configuration appears to be more

straightforward in terms of control and, perhaps, more

economical to implement during new construction. In

both cases the amount of CO2produced per unit of

electricity generated is reduced in comparison to the

coal-fired power plant.

2. System Analysis

The generic Rankine cycle that is selected for

analysis is a standard superheat cycle having re-heat

and two feedwater heatersone open and one closed

[6]. The temperatures and pressures at various points

in the cycle are shown in Table 1. This cycle has a

thermal efficiency (reversible) of 43.1% [6] and a

parasitic energy cost for feedwater heating

amounting to about 18%. Just less than 25% of thetotal mass flow of steam is extracted for feedwater

heating before the lowest pressure stages of the

turbine. If all of the feedwater heating is supplied by

solar energy the plant efficiency (reversible) could be

increased to 51%, based on the same coal input,

resulting in a significant increase in power output

(assuming that the turbines can accommodate the

additional steam flow rate).


19/160

Assessment of Solar-Coal Hybrid Electricity Power Generating Systems14

Fig. 1 Option 1solar thermal feedwater heating.

Fig. 2 Option 2solar thermal input at boiler.


20/160


Table 1 Characteristics of basic Rankine cycle.

Operating characteristics Value

Boiler inlet 205 oC, 8 MPa

Boiler (superheater) outlet 480 oC, 8 MPa

Closed feedwater heater inlet 2 MPa

High pressure turbine outlet 7 MPa

Low pressure turbine inlet 440 oC, 7 MPa

Open feedwater heater inletLow pressure turbine outletCondenser outletOpen feedwater heater outlet

0.3 MPa0.008 MPaSaturated, 0.008 MPaSaturated, 0.3 MPa

Power output (electric)-option 1Power output (electric)-option 2Steam rate

90-100 MW100 MW280,000 kg/hr

Boiler efficiency 90%

Generator efficiency 90%

For most of Botswana the average annual direct

normal irradiance (DNI) ranges from 6.5-7.5 kWh/m2.

For the Morupule area the DNI is approximately 6.9

kWh/m2

but a value of 6.75 kWh/m2

was used in the

current analysis to err on the conservative side. An

east-west tracking on a polar axis was considered for

parabolic trough collectors. The hourly irradiance is

illustrated in Fig. 3 [7]. Fig. 3 includes the assumption

of 75% efficiency for the parabolic trough field [7].

The results presented for both options are based onhourly calculations for one day per year which is

assumed to be representative of the annual

performance. For option 1 the solar heat is added to

the feedwater heaters. Therefore, when the hourly

solar input is less than the total heat load for the open

feedwater heater will be given by:

/ 1 (1)The fraction of steam extracted for the open

feedwater heater is given by:

(2)

When the hourly solar input is greater than the total

heat load for the open feedwater heater then a portion

of the solar heat goes to the closed feedwater heater.

For this case the enthalpy balances for the two

feedwater heaters must be solved simultaneously to

determine the fraction of steam extracted for the

closed feedwater heater. The two enthalpy balances

can be written (for 0)

Fig. 3 Hourly direct normal irradiance.

1

(3)

(4)

noting that The work output by the turbines and work input to

the pumps per total system mass flow rate are given

by: 1 (5)

1 1 (6)

1 (7)

(8)

The total heat input is:

1 (9)

The turbine total output, pump inputs, and heat

input were obtained for a 24 hour period and then theplant thermal efficiency was calculated, by dividing

the net work by the heat input, i.e.

(10)For option 2 the solar field was assumed to provide

heat up to 350oC. This allows for a much larger solar

contribution to the 100 MW (thermal) plant than that

for feedwater heating. The analysis is

straightforwardas the hourly thermal input from the

solar field increases the amount of thermal input from


21/160


coal is decreased to maintain a constant plant output.

The lower heating value for the coal is taken to be 20

MJ/kg.

For this scenario the turbine and pump work terms

remain constant but the heat input from combustion

process varies as the solar field contributes to the

thermal input from the boiler. For this case,

(11)where, again, the solar input must be summed over the

period when solar radiation is available and subtracted

from the total daily heat load, to obtain thethermal efficiency using Eq. (10).

3. Performance Analysis

In order to determine the economic performance, a

variable size of collector field was employed for both

configurations. Variable collector field costs were also

investigated. The base cost is $500/m2

which is

slightly more than that in the analysis by Kaltschmitt,

et al. [2]. The annual operating and maintenance cost

is assumed to be $10/m2

in current dollars and the cost

of coal is assumed to be 0.030$/kg. For option 1 the

base cost of construction for the solar facility is

annuitized over the assumed plant life using:

(12)

The annuitized construction costs along with the

annual operating and maintenance costs are then used

to compute the current cost per kilowatt-hour for the

additional electricity generated. The plant life (n) is

assumed to be 25 years. This leads to a determination

of the optimum size for the solar contribution to the

existing facility. In addition, it is important to

establish the sensitivity of the solar field cost and

performance assumptions. For this case the coal

consumption remains constant and the coal-fired plant

is assumed to be operational in situ.

The costs for construction and operation and

maintenance for both the coal and solar facilities are

included in the analysis for the second option. These

costs for the solar part of the facility are assumed to be

the same as those utilized in option 1. For the coal

plant the construction costs are assumed to be 1.36

106

$/MW and the annual operating and maintenance

costs are assumed to be 50,000 $/MW [2]. The coal

plant capacity factor is assumed to be 0.8 leading to

7008 operating hours per year. The various costs and

operating parameters employed in the economic

analysis are summarized in Table 2.

4. Results and Discussion

The system thermal efficiency for option 1 without

solar input is 43.1%. With solar input, as shown in

Fig. 4, the efficiency increases to slightly above 46%

as the size of the collector field is increased. The

efficiency reaches a limit when the solar field provides

all of the feedwater heating for the period when solar

radiation is available. The results shown in Fig. 4 do

not include the pumping costs for the working fluid in

the solar field or other parasitic loads like the power

used for tracking.

For option 1 the cost of the excess electricity generated

in dollars per kilowatt hour is shown in Fig. 5 as a

function of the size of the solar field in square meters.

As illustrated in the figure there is an optimum solarfield size of approximately 90,000 m

2that results in

the lowest cost for the electric power, just less than

0.108 $/kWh for the $500/m2

field cost. For this case

the solar field provides 5.8 MW in addition to the 90

MW (electric) delivered by combustion of coal. For a

6% discount rate this leads to an overall cost of

electricity of just over 0.040 $/kWh assuming the base

cost of coal generated power to be 0.036 $/kWh as

computed for option 2. It was noted that, as a result of

non-availability of solar input, particularly in the early

morning and late afternoon hours, the results contain

discontinuities. Fig. 5 shows that the cost of electricity

per unit kilowatt hour increases as the assumed cost of

the solar field increases. Therefore, augmentation of

electricity production from fossil fuel plants with solar

system will be attractive where insolation is very high.

The effect of discount rate on the cost of electricity for

this option is illustrated in Fig. 6.


22/160


Table 2 Parameters used in economic analysis.

Name of variable Value

Cost of solar field, $/m2 300-700

Annual O &M for solar field, $/m2 10

Cost of coal, $/kg 0.030Plant life, yrs. 25

Construction costs for coal plant, $/MW 1.36 106

Annual O &M for coal plant, $/MW 50,000

Plant capacity factor 0.8 (7008 hr/yr)

Discount rate (dr), % 6-10

1 2 3 4 5 6 7 8 9 10 11

x 104

0.43

0.435

0.44

0.445

0.45

0.455

0.46

0.465

collector field size, m2

plantthermalefficiency

Fig. 4 Plant thermal efficiency versus collector field size

for option 1.

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

x 105

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0.22

0.24

0.26


costofelectricity,

$/kWh

$700/m2

$500/m2

$300/m2

Fig. 5 Cost of excess electricity generated versus the size

of the solar field for option 1.

For small solar fields, less than 40,000 m2, the solar

contribution affects only the open feedwater heater.

As the fraction of steam extracted to supply heat to the

feedwater heater decreases the output from the low

pressure turbine increases in proportion to the

enthalpy difference between the extraction point and

the turbine outlet. The result is a constant estimated

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

x 105

0.1

0.15

0.2

0.25


costofelectricity,

$/kW

h

dr = 10%

dr = 6%

Fig. 6 Cost of excess electricity generated for option 1 for

2 discount rates (dr).

cost for the extra power produced of just over 0.16

$/kWh. For solar fields above 40,000 m2

the solar

input begins to contribute to the closed feedwater

heater increasing the output from both the high and

low pressure turbines. This causes the cost of extra

power produced via solar to decrease significantly to

just under 0.11 $/kWh as mentioned above.

With 90,000 m2

of collector field all of the

feedwater heat is supplied by solar energy for 5 hours

of the day. Fig. 7 illustrates the quantity of excess

electricity generated as a function of the size of the

collector field. The optimum design (minimum cost of

excess power generation) occurs slightly before the

solar energy input reaches the point of supplying the

entire feedwater load for the sunshine period (11-hour

day) analyzed. For example, if the collector field is

increased to greater than 160,000 m2

all of the

feedwater load is covered (11 hours per day) resultingin 7.5 MW of extra power at a cost of 0.1439 $/kWh

(6 MW when multiplied by the plant capacity factor of

0.8). The average cost for power produced by the

hybrid facility is then 0.044 $/kWh.

For option 2 the plant thermal efficiency increases

as indicated in Fig. 8 beginning at 43.1% for no solar

contribution and peaking at 63.4% when the solar field

provides the maximum possible (350oC at 8 MPa) for

the 11 hours in which solar radiation is available. As


23/160


0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

x 105

0

1

2

3

4

5

6


additionalpower,MW

Fig. 7 Excess electricity generated by option 1 as a

function of solar field size.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

x 105

0.4

0.45

0.5

0.55

0.6

0.65

solar field area, m2

plantthermalefficiency

Fig. 8 Plant thermal efficiency as a function of solar field

size for option 2.

for the option 1 analysis the parasitic losses associated

with the solar field, pumping and tracking power, are

not considered when calculating the plant thermal

efficiency.

As indicated above, for option 2 the base cost of

electricity generated from coal at a 6% discount rate is

0.036 $/kWh. As illustrated in Fig. 9 the average cost

of electricity increases as the size of the solar

contribution is increased. However, the cost is much

less than it would be for 100% solar, particularly if

storage is included. For example, for a 200,000 m2the

estimated electricity cost is 0.050 $/kWh providing 19

MW (electricity) of the total 100 MW. With 400,000 m2

of solar resource the solar contribution is 30.9 MW at

0 0.5 1 1.5 2 2.5 3 3.5 4

x 105

0.04

0.045

0.05

0.055

0.06

0.065

0.07

solar field area, m2

costofelectricity,

$/kW

h

Fig. 9 Average cost of electricity for option 2 as a function

of solar field size.

a cost of 0.066 $/kWh. The solar heat was assumed to

be available at a maximum temperature of 350oC,

thus, if the size of the collector field is increased

significantly above 400,000 m2

the average cost of the

electricity produced increases rapidly because all of

the energy collected is not utilized.

5. Conclusions

The results illustrate that solar-coal hybrid plants

can produce electricity at costs only slightly greater

than those for coal-fired power plants. Utilizing solar

thermal for feedwater heating demonstrated that by

adding solar energy system to an existing facility

potentially increased the power output by 5%-8%

while only very modestly increasing the power cost.

One of the advantages of this approach is that it

allows the construction of a small solar thermal unit

without storage or transmission that will producepower at a moderate increase over coal and increase

the total output from an existing plant. It also allows

for the establishment of local construction and

operating and maintenance costs without the

construction of a larger and potentially more risky

stand alone solar thermal facility.

Option 2 which utilizes solar thermal to replace coal

at the boiler may be more applicable to new power

plant installation. For this case providing 20% to 25%


24/160


of the total plant capacity with solar can be

accomplished with a modest increase in the cost of the

electricity generated. This may be attractive in regions

where the peak load is a result of summer air

conditioning demand and corresponds with the peak in

solar radiation.

In conclusion, integrating solar thermal power with

existing or new fossil fuel-based power plants is a

useful strategy to reduce the cost of stand-alone solar

thermal power stations, reduce CO2 emissions and

gain low risk experience necessary to operate a full

scale solar thermal electricity generation facility.

AcknowledgmentsThis work was completed while OAP was

supported by a Fulbright Award at the University of

Botswana. The authors express their appreciation to

the Fulbright Scholar Program, the University of

Botswana, and the University of Wyoming for making

this collaboration possible.

References

[1] Energy Trend Analysis, Energy Affairs Division(Planning and Documentation Unit), Energy and Water

Resources, Ministry of Minerals, Gaborone, Botswana,

2003.

[2] M. Kaltschmitt, W. Streicher, A. Wiese, RenewableEnergy-Technology, Economics and Environment,

Springer-Verlag, Berlin, Germany, 2007.

[3] K. Bullis, Mixing solar with coal to cut costs: A newstrategy could reduce coal plant emissions and cut the cost of

solar power [online], 2009, MIT Technology Review,

http://www.technologyreview.com/energy/23349/

(accessed Apr. 10, 2010).

[4] J. Guerrerio, Hybrid Power Plants Pair Solar with Coal toReduce Emissions [online], 2010,

http://www.examiner.com/x-2903-Energy-Examiner~y20

09m2d2-Hybrid-power-plants-pair-solar-with-coal-to-red

uce-emissions (accessed Apr. 10, 2010).

[5] P. Fairley, Cutting Coal Use with Sunshine [online], 2009,http://www.technologyreview.com/energy/22080/?a=f.

(accessed Apr. 10, 2010).

[6] M.J. Moran, H.N. Shapiro, Fundamentals of EngineeringThermodynamics, 5th ed.,John Wiley & Sons, NJ, 2004.

[7] S.A. Kalogirou, Solar Energy Engineering, Processes andSystems Academic Press, MA, USA, 2009.


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A Comparison of Different Communication Tools for

Distance Learning in Nuclear Education

Glenn Harvel and Wendy Hardmann

Faculty of Energy Systems and Nuclear Science, University of Ontario Institute of Technology, Oshawa, OntarioL1H 7K4, Canada

Received: December 07, 2010 / Accepted: May 05, 2011 / Published: January 31, 2012.

Abstract: Recent advancement in nuclear education learning has been through the use of computers and simulation related tasks

such as the use of industry codes. Further enhancements in nuclear education are being considered through the use of distance

learning technologies. The purpose of this work is to explore distance learning related tools to determine if they can provide an

enhanced learning environment for nuclear education. In this work, a set of tools are examined that can be used to augment or replace

the traditional lecture method. These tools are Mediasite, Adobe Connect, Elluminate, and Camtasia. All four tools have recording

capabilities that allow the students to experience the exchange of information in different ways. This paper compares recent

experiences with each of these tools in providing nuclear engineering education and assesses the various constraints and impacts on

delivery through direct feedback from students and instructors. In general, the tools were found to be useful for mature students on

the condition that the lecturer was comfortable with the tools and in some cases, adequate support from IT groups was provided.

Key words: Distance education, web based tools, nuclear engineering education.

1. Introduction

Nuclear generated electricity was first produced in

Canada in 1962, from the Nuclear Power

Demonstration (NPD) unit, the first CANDU (Canada

Deuterium Uranium) power plant. From that small

beginning grew an industry that conducts world-class

research, development, design, construction,

commissioning, operation and maintenance of nuclear

power plants, as well as research reactors and several

related technologies. The Province of Ontario, with a

population of 13 million, has been the centre of

Canadas nuclear industry. In particular approximately50% of the electricity used in Ontario is generated

from CANDU nuclear units. Two other provinces

each operate 700 MW CANDU units and other

provinces are considering to build new nuclear power

plants. The current fleet of Canadas nuclear plants

ranges in age from 15 to 37 years, with a substantial

Corresponding author: Glenn Harvel, associate professor,

research fields: nuclear education, nuclear design, and thermal

hydraulics. E-mail: [email protected].

amount of life-extension work having already been

carried out on the older units, and similar projects

being planned for the remaining ones. The continuingincrease in demand for electricity as the population

grows and its standard of living increases, and despite

strong conservation efforts and the transfer of

significant manufacturing capacity abroad, the

Government of Ontario has requested proposals for

new nuclear generation. In addition to meeting

increased demand, the new units are required to

enable the closing of the remaining coal fired

generation, so as to meet environmental targets to

reduce greenhouse gas as well as particulate

emissions.

Corresponding to the aging of the nuclear power

plants is the demographics of the workforce. About

half of the engineers and scientists in the nuclear

industry are reaching retirement age in the next five

years. A similar situation exists in several other high

technology industries in Canada, and world-wide in

the nuclear technology sector. During the design and

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A Comparison of Different Communication Tools for Distance Learning in Nuclear Education 21

construction of the early units, particularly in the

1970s and early 1980s, a combination of university

science and engineering programs and

industry-specific training produced the required

skilled personnel. As the design activities decreased in

the late 1980s and the construction of new units in

Ontario essentially stopped by mid 1990, universities

no longer had the number of students required to keep

offering nuclear-specific courses and degree programs.

At the start of the new millennium the industry

recognized the lack of specialized graduates being

produced by universities, and decided to establish the

University Network of Excellence in Nuclear

Engineering (UNENE) to address this problem [1].The specific mandates of UNENE are to fund research

chairs at the seven member universities located in

Ontario, to fund additional nuclear-specific research at

any Canadian university, and to offer a course-based

Master of Nuclear Engineering program. Unique

features of the latter are the offering of courses on

weekends, and the option to take any course at any of

the participating universities. The appointment of

professors with specialist knowledge in the nuclear

field has enhanced the offering of undergraduate

courses, and has provided new consulting services to

industry. The increased profile of nuclear research at

the universities, and the publics awareness of

employment opportunities in the nuclear sector have

also raised student interest in these courses. However,

the majority of university graduates hired into the

nuclear industry gain their nuclear-specific knowledge

after becoming employed in the industry, typically via

company or other specialized training courses. Apartfrom universities, community colleges are graduating

technicians and technologists, and are supporting

apprentice programs that produce trades people who

are in even greater demand than scientists and

engineers. With a few exceptions these graduates have

received little nuclear-specific education if any, and

will require specialist training after joining a particular

company. Industry training remains a critically

important part of ensuring that the required specialist

knowledge and skills are given to the men and women

working in the nuclear sector. Training that requires

such specialized equipment as full-scope replica

simulators, systems and components unique to a given

unit, tasks to be performed within the plant, are best

conducted by the particular company that is

responsible for the operation and maintenance of the

equipment.

2. UOITs Nuclear Degree Programs

A somewhat unique combination of events led to

the establishment of nuclear degree programs at the

University of Ontario Institute of Technology (UOIT)[2]. These events included the aspects of plant aging

and demographics described above, the location of 12

CANDU units within a 25 km radius of UOIT, the

decision by the operator of these units (Ontario Power

Generation) to move their nuclear head office staff

from downtown Toronto to within the above

mentioned 25 km radius of UOIT, and the recognition

that the combination of demographics, plant life

extension and new build projects will require an

unprecedented number of nuclear science and

engineering graduates.

The first intake of students into the undergraduate

nuclear programs took place in September 2003, with

110 students selected from over 400 applicants. The

first graduates of these programs joined the work force

in 2007. The education program is centered around a

nuclear plant engineer. Courses in reactor physics,

thermal hydraulics, health and radiation physics,

materials and chemistry, and heavy emphasis ondesign are included in the program [2]. To date, over

100 engineers have graduated and either entered the

work force or graduate school. Initial feedback

indicates that the nuclear specific education they

received has resulted in enabling them to become

more productive sooner than graduates of the more

typical science and engineering programs.

The Master of Nuclear Engineering program


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A Comparison of Different Communication Tools for Distance Learning in Nuclear Education22

consisting of a both research and a course based

option was initiated in the 2008-2009 academic year.

The Ph.D. in Nuclear Engineering program was

approved in 2010. The graduate program has

developed a set of advanced course topics that extend

the knowledge obtained in the undergraduate program

to include more detailed information useful for the

working environment. The course based program is

designed specifically to enhance the student skill set

for industry while the thesis based program is

designed for those interested in research and further

advanced studies.

In addition to the graduate program, a diploma

program has been initiated where students can takeonly four courses and establish a specialty in such

areas as reactor systems, radiological applications, and

safety, licensing, and regulatory affairs.

While classroom lectures remain an essential part of

the education provided at UOIT, the use of computers

to enhance learning has been a key aspect of the

success of the programs and our graduates. The

CANDU reactors, by their unique characteristics,

required the use of computer monitoring as early as

the 1960s, and the use of computers was extended to

control and safety systems with each subsequent

power plant. As well, Canada was an early developer

of full-scope nuclear plant replica training simulators,

with the first such training tool for the Pickering A

units becoming operational in 1976. The unique

synergy of the use of computers in the CANDU

reactors and operator training programs made the

extensive use of computers in UOITs nuclear degree

programs a logical outcome.

3. Distance Learning for Nuclear Education

UOITs goal from the outset was to be both a

research-intensive and a student-centric institution. An

important additional mission of the university was to

investigate how strategies for college-university

transition could be facilitated through co-location and

the application of information and communication

technologies (ICT) to facilitate movement of students

from one level of education to the other. Consequently,

the university was designed as a fully laptop

university, the second in Canada, and the first in

Ontario. Planning included building both an online

infrastructure and a purpose-built physical

infrastructure, to facilitate learning both on and off

campus, from any location and at any time in lecture

halls, research laboratories, the library, and in public

areas such as study halls, cafeterias and on-campus

restaurants. The combination of ubiquitous Internet

access and the provision of standards-based laptop

hardware has allowed UOIT to establish a teaching

and learning environment that addresses total studentaccess to learning technologies, creating common

access to a unique web-centric learning environment.

The web-centric learning environment at UOIT can

be defined as the strategic integration of information

and communication technologies into all aspects of

the teaching and learning processes. The adoption of

digital technologies within higher education

institutions requires investment in both hardware and

software, and in the necessary support systems to

provide a learning environment where students

experience both a high-touch and a high-tech

educational experience, and where faculty are

supported in their use of both new network

technologies and new pedagogical approaches to

instruction [3, 4] .

Four support components and/or services systems

form the core of the web-centric environment at UOIT:

Learning Infrastructure, Online Educational Services,

the Mobile Learning Program, and Research Support.Each component provides a foundational element for

the strategic use of information and communication

technology at UOIT. Rather than merely applying ICT

to enable distance learning or distributed education,

UOIT has developed a unique learning model,

whereby digital technologies enhance face-to-face

(F2F) education through the application of digital

technologies to enhance student engagement, both


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A Comparison of Different Communication Tools for Distance Learning in Nuclear Education 23

online and in F2F classroom settings, in a coordinated

strategy. All learning spaces are equipped with smart

podiums to facilitate F2F instruction using laptop

computers, data projectors, DVD players, and Internet

access. As well, students in the Faculty of Energy

Systems and Nuclear Science have access to nuclear

plant simulation programs and other nuclear specific

software on their laptop computers. Research indicates

that students and faculty experience frustration with

the introduction of new technology without also

having the appropriate support structures in place [5].

To successfully merge traditional F2F lectures with

the development of an online learning community,

UOIT recognized that it is imperative to developstrategic support systems that can provide a seamless

environment whereby students and faculty could

receive assistance when required. By empowering

both faculty and students with right-fit technologies,

the institutional focus was to enhance students

education/experiences. The UOIT institutional support

centers are depicted in Fig. 1 [6].

4. Education Requirements for DistanceStudents

Distance education is changing with the times. It has

shifted from a somewhat solitary self study experience,

which Wedemeyer [7] described as the independence

of the student, to a rich dynamic teaching and learning

environment supported by web-based resources and

multiple interactions between individual students

and/or an instructor. Increasingly the format of

distance education has become online or web-basedlearning enabling personal relationships and a stronger

sense of engagement as in fact the actual distance

between students and teachers has in some cases nearly

disappeared [8].

Fig. 1 Schematic of UOIT webcentric learning environment [6].


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A Comparison of Different Communication Tools for Distance Learning in Nuclear Education24

Simonson [9] described distance education as being

institution-based, formal education where a student is

separate from teacher, and interactions occur between

learner, resources and instructors. This sharing oflearning and experiences can occur via many different

asynchronous (at different times) and synchronous (at

the same time) means. With the advancement of

technological tools to be lower cost, easier to install

and use, and very little if any special equipment

required, communication between the student,

instructor and other students is commonplace in most

distance education courses. Asynchronous

communication is considered the most flexible form

of communicating [10], enabling contributions and

responses at any time by participants. This form of

communication has its drawbacks, and some students

and instructors view it as cumbersome and struggle to

cope with the slow pace of the dialogue. Synchronous

communication offers opportunities for immediate

dialogue between the learner and instructor or the

learner and other learners. Synchronous format

imposes a fixed time on the participants which,

depending on the number of time zones involved, canbe onerous. Synchronous communication can include

video, audio and text interactions which gives

participants familiar cues that can build rapport and

community more quickly and asynchronous dialogue.

Distance education has evolved, influenced by

technological tools and capabilities from being a

one-dimensional type of learning with the student

interacting solely with learning materials in a situation

of absent instruction and independent student or as

Holmberg [11] described it guided didactic

interaction to being a multidimensional learning

experience with interaction between student, materials,

instructor and other students.

Traditionally, the distance student has been a person

who could not attend school because they were

located in a different city or region. They

communicated with the school/lecturer via mail and

essentially self-taught the material. In todays world,

the nuclear student falls into several categories. There

are those that can and do attend school because they

live close enough and can afford transportation. There

are those students who live close enough, but due towork commitments are not available for study at the

same time the classes are being delivered, and hence a

fixed structured program does not work. The third

group of students are those that are at a distance and

also have significant work commitments. As such,

distance education is no longer just for students that

live outside the region of the University. Universities

report that the majority (85%) of the students

enrolling in distance education courses are within

commuting distance of campus [12]. With the

technological tools available and used in courses, the

flexibility of place and time is very attractive to

on-campus students.

The majority of the students able to attend class are

those in the undergraduat

Documents

Journal of Energy and Power Engineering-2012.01