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This is the Pre-Print version of our paper which has been accepted for publication

at by the International Journal of Environmental Studies [2013]

ENHANCING THE EFFICIENCY OF ABSORPTION REFRIGERATION

CYCLE BY ‘SEEDING’ NANOPARTICLES DIRECTLY IN THE WORKING

FLUID

Rahul Gulati, Ashwanth Reddy, Vikrant Khullar, Vishal Bhalla, Himanshu Tyagi*

School of Mechanical, Materials and Energy Engineering

Indian Institute of Technology Ropar

Rupnagar - 140001, Punjab, INDIA

Yuebin Zhao, Edward Law, Robert A. Taylor

School of Mechanical and Manufacturing Engineering

University of New South Wales

Sydney, NSW, 2052, Australia

*Corresponding Author

Tel: +91-1881-242119

Fax: +91-1881-223395

Email: himanshu.tyagi@iitrpr.ac.in

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ABSTRACT

The fossil fuel-driven electricity grid supplies space cooling, especially in tropical/temperate

climate regions. The timing of the peak load closely coincides with the peak of available solar

irradiance. Therefore, solar-driven space cooling can reduce the peak loads in a smart grid.

Solar-powered, direct absorption refrigeration cycles fit well. The efficiency of this cycle can be

enhanced through the use of new, engineered materials. This study analyses a model of

ammonia-water based absorption refrigeration cycle in which nanoparticles are directly ‘seeded’

in the working fluid itself. The model has been tested at various locations in India.

KEYWORDS

absorption refrigeration, nanofluids, smart grid

INTRODUCTION

In tropical areas, more than half of the total household energy is used in heating, ventilation and

air-conditioning. This is often accomplished with fossil fuels. There is a need for an alternative

energy resource for cooling and heating applications. Cooling is mostly required during warm

periods when there is a large availability of solar energy. An absorption refrigeration cycle using

solar energy can be designed to use this freely available energy. Solar energy is a lucrative

option, but it requires the use of smart grids [1]. As in the case of other renewable energy

resources such as wind, the continuous solar energy resource monitoring and forecasting will

prove to be a useful input for optimized functioning of such smart grids [2]. All this will make

the benefits of such renewable energy-based systems more tangible and overcome the problem

of intermittency.

Absorption refrigerators are usually expensive and less efficient in comparison to

conventional vapour compression refrigerators. The energy input in conventional refrigeration is

work which is high grade energy and therefore expensive [3]. In vapour compression

refrigeration system, a relatively large amount of work is required, because in compression the

vapour undergoes a large change in specific volume. Therefore, absorption refrigeration can be

both economic and ecological in meeting the cooling requirements [4, 5].

The daily average solar energy incident in India varies from 4 to 7 kWh/m2 with about

2500 sunshine hours per year [6]. The temperatures reach up to 45℃ during summers, levels

which demand high cooling requirements.

Various types of solar collectors are available in the market such as flat plate, parabolic

trough, linear Fresnel and evacuated tube. This paper examines a direct absorption flat plate

3

collector for the mathematical modelling. Usually a direct absorption flat plate collector consists

of various auxiliaries such as glazing, insulated layers etc. Glazing is used to reduce heat loss

due to convection from the collector [7].

Recent studies by Khullar et al. [8], Lenert et al. [9] and Vishwakarma et al., [10] show

that adding nanoparticles to fluid flowing in a solar collector increases the efficiency of the

collector by 5-10%. This is due to the scattering and absorption effects of nanoparticles

according to the Mie scattering theory. Kameya et al. [11] also demonstrated the increase in

absorption of solar radiation by the addition of nanoparticles in the fluid. The path followed by

light increases due to scattering by nanoparticles in the fluid, and therefore more radiation is

absorbed.

Figure 1 shows a schematic of a conventional solar based vapour absorption refrigerator.

It consists of absorber, condenser, evaporator and generator. Typically, in solar based

absorption refrigerators the generator consists of two units. In one unit, solar energy is used to

increase the temperature of a fluid flowing through the solar collector. This heat energy is

transferred to the working fluid in the second unit using a heat exchanger.

Figure 2 shows the model proposed in this paper. The working fluid of the absorption

refrigeration cycle is ‘seeded’ with nanoparticles. Solar energy is directly incident on the

generator which consists of nanofluid flowing through a direct absorption flat plate collector.

This avoids the use of a heat exchanger to transfer heat between the two units of generator.

Commonly used absorption refrigeration cycles are ammonia-water and lithium bromide-

water. As compared to an ammonia water absorption refrigerator, the lithium bromide

absorption refrigerator is limited to relatively high evaporating temperatures since the refrigerant

is water [3]. For this case study ammonia-water is chosen as the working fluid.

This paper focuses on the analysis of the generator of a solar based AWAR (Ammonia

Water Absorption Refrigerator) with nanofluid flowing through it. Detailed mathematical

modelling has been carried out to optimize various parameters of the nanofluid flowing through

the generator. Radiative, conductive and convective heat transfers have been considered. A finite

difference method has been employed to calculate the intensity absorption and temperature

distribution by varying various parameters of nanoparticles. These results are used in the

analysis of the AWAR cycle to find optimum values of these parameters.

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THEORETICAL MODEL

Ammonia-Water Absorption Refrigeration

The ammonia-water absorption refrigerator uses ammonia as the refrigerant and water as the

absorber. Figure 2 shows the working cycle of a nanofluid based AWAR. It consists of four

basic components: absorber, generator, condenser and evaporator. Ammonia mixes with water

in an absorber which is then passed through a pump. This high pressure mixture then passes

through a solution heat exchanger (SHX) where it is preheated before entering the generator.

The mixture is then heated in the generator, where most of the refrigerant i.e. ammonia separates

from the absorber fluid, i.e. water. This refrigerant then passes through a condenser where the

vapours are condensed to liquid and then passed through an expansion valve to reduce the

pressure. This lower pressure refrigerant is then passed through an evaporator where the

refrigerant evaporates at low pressure and absorbs heat, providing a cooling effect. The

refrigerant then mixes with water and the cycle continues.

In the proposed design, nanoparticles are directly added to the working fluid. This

ammonia-water mixture along with the nanoparticles is passed through a direct absorption flat

plate collector in the generator where solar energy is directly incident on the working fluid. This

enhances the absorption of the incident radiation, thereby increasing the performance.

Mathematical Modelling

This section presents the mathematical equations involved in modelling the absorption

refrigeration cycle. Figure 2 shows the working fluid flowing through various states. The

assumptions made to model this system are listed below [12]. The properties of ammonia-water

binary mixture have been taken from Kuehn et al [3].

Assumptions

State 1: Saturated weak solution (liquid).

State 4: Saturated strong solution (liquid).

State 7: Saturated refrigerant (vapour).

State 8: Saturated refrigerant (liquid).

State 10: Saturated refrigerant (vapour).

At state 10, with the assumption that NH3 is in saturated vapour state, pressure can be calculated

since its temperature is known. Enthalpy of pure ammonia at state 10 can then be calculated.

Similarly, using the assumption that NH3 is in saturated liquid state at state 8, its pressure can be

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calculated. Since the upper and lower pressure values are known, pressure at every state is

known.

Next, enthalpy of the saturated NH3 vapour at state 7 can be calculated since its

temperature and pressure are known. Due to throttling, enthalpy at state 9 is equal to enthalpy at

state 8. The pressure of NH3 at state 9 is next calculated as its temperature and enthalpy are

known. Using the assumption that the mixture is saturated strong solution at state 4 and further

that the temperature and pressure are known, we can calculate the mass fraction at this state. It is

also known that the mass fraction at state 4, 5 and 6 is the same. Enthalpy at state 5 can then be

calculated since pump work is assumed to be negligible [12].

Temperature rise in the generator can be calculated by the given particle diameter, D and

volume fraction, fv of the nanoparticles. Using this temperature rise, outlet temperature at the

generator is known. Mass fraction at state 1 can then be calculated by its temperature and

pressure since it is assumed to be saturated solution. The mass fraction at state 1, 2, 3 is the same

also. Next, enthalpy of state 6 is calculated from thermodynamic tables [3].

Mass flow rates are then calculated as,

7

10 9

Cooling Capacitym

h h (1)

Across the generator,

1 76m m m (2)

6 1 76 1m X m X m (3)

Assuming the effectiveness of SHX to be 1, the energy balance equation can be used as,

2 1 56 6 2 1 5m h m h m h m h (4)

Heat used by the generator (Qg) can be then calculated as,

1 71 7 6 6gQ m h m h m h (5)

And then, COP can be calculated as,

e

g p

QCOP

Q W (6)

Where Qe is the heat absorbed at evaporator, Wp is the pump work and Qg is heat input to

generator.

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Extinction Coefficient

The extinction coefficient is related to the refractive index that is a measure of the amount of

light intensity absorbed by the medium. As presented in Tyagi et al. (2009) [13] and Taylor et

al. (2011) [14], the extinction coefficient has been calculated in this section. The intensity of

solar energy incident on the surface of earth has been calculated using Planck’s law (see Eq. 10);

1exp

2),(

5

2

solarB

o

o

solar

Tk

hc

hcTI

(7)

where oc is the speed of light, h is Planck’s constant and kB is the Boltzmann constant.

Using the value of spectral intensity from Eq. 7, the radiative transport equation can be

applied to determine the distribution of intensity in the ammonia-water mixture as:

IKIKKy

Iesa )(

(8)

For the fluid, the extinction coefficient can be calculated by the equation:

4a

kK (9)

Ka signifies the extent to which the fluid absorbs the incident energy. For the nanoparticle, the

size parameter can be calculated as:

D (10)

Calculation of the extinction coefficient (Keλ) requires two terms: absorption coefficient

(Kaλ) and scattering coefficient (Ksλ) as shown in equations:

2 2 2 2 4 2

2 2 2 2

12 1 1 27 38Im 1

2 15 2 2 3

va

f m D m m mK

m m m (11)

24 3 2

4 2

8 1

2

vs

D f mK

m (12)

sae KKK (13)

Once the extinction coefficient for nanofluid is obtained, spectral intensity distribution

along the thickness can then be obtained as follows:

IKIKKy

Inanofluidelesnanoparticefluida ,,, )(

(14)

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The values of n, real part of refractive index and k, complex part of refractive index for

aluminium oxide, aluminium and graphite have been taken from Brewster [15] and Palik [16].

The equations used for finite difference energy balance are next discussed. The heat

conduction through the top layer is modelled as:

1

1( ) ( )( )

i i i ii N N N N

c N p

input

K T T T Th T T I C yV

y x (15)

Similarly, heat conduction through the bottom layer is modelled as:

1

1( ) ( )i i i i

N N N Np

input

K T T T TI C yV

y x (16)

And that of in-between layers as:

1

1 1( ) ( ) ( )i i i i i i

N N N N N Np

input output

K T T K T T T TI C yV

y y x (17)

where ‘i’ is the step time interval, ‘N’ is the layer number.

For the boundary layers, heat loss by convection is considered and there is no heat input

due to conduction. For other layers, there is no convection loss since the flow through the direct

absorption flat plate is assumed to be plug flow, where the velocity is constant across the cross

section of the direct absorption flat plate.

Optical efficiency can be calculated using,

0

0

.

.

ab

opt

inc

I d

I d

(18)

Numerical Modelling

The intensity incident on direct absorption flat plate collector is assumed to be 1000 W/m2 [7].

To get the profile of the intensity distribution within the fluid using radiative transport equation,

the first step is to calculate the extinction coefficient. For calculating the extinction coefficient, a

wavelength range of 0 to 3 µm is incorporated since this is the range where more than 99% of

solar intensity is concentrated [7]. The optical properties of liquid ammonia are found to be very

close to those of water. Therefore, the optical properties of the ammonia-water mixture have

been approximated as those of pure water. It is the magnitude of the imaginary part (k) of the

complex refractive index which dictates the absorption capability of a material. As the k values

of nanoparticles are considerably higher than those of the base fluid (ammonia-water mixture),

the effective absorption capability of the nanofluid mainly depends on the nanoparticle optical

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properties. The thermo-physical properties (k, ρ, Cp) are not affected by the presence of

nanoparticles at low volume fractions [9]. The boiling point of liquid may rise upon the addition

of nanoparticles, but these effects have not been taken into account in the current study.

As figure 3 shows, a finite difference technique has been employed to solve heat transfer

equations numerically and determine the intensity and temperature distribution in the direct

absorption flat plate collector. The temperature gradient is significantly greater in y direction

than in x and z directions. Therefore, a one dimensional model has been considered. The flat

plate collector is divided into 20 layers in the y-direction of length dy each. Each layer is

assumed to have a constant temperature. Mass flow rate and hence the density is assumed to be

uniform throughout the direct absorption flat plate. The depth of the flat plate, H, is 2 mm and

length, l of collector is 2 m. The mass flow rate of the nanofluid is 0.00913 kg/second, which is

obtained by fixing the input parameters such as absorber temperature of the AWAR. The length

of each layer, i.e. dy, is then calculated to be 0.1 mm. The value of the convective heat transfer

coefficient between the plate and atmosphere (ambient temperature 30ºC) is approximated to be

6.43 W/m2-K and the thermal conductivity is approximated as 0.6 W/m

2 [13]. The bottom

surface of the direct absorption flat plate is assumed to be adiabatic. For calculation of various

parameters such as temperature rise and intensity distribution, the volume fraction has been

varied from 0% to 1.2% for aluminium and 0% to 0.4% for graphite nanoparticles respectively.

These values are selected since there is not much variation in the parameters such as intensity

absorbed after this value; and the particle diameter has been varied from 10 nm to 20 nm.

Results and Discussion

This section starts by presenting the properties of nanofluids and their effect on absorption of

solar energy and their temperature rise. A program has been written in MATLAB for the

theoretical formulation of these. Later on, various parameters such as particle volume fraction,

particle diameter and type of nanoparticles have been varied to study their influence on

temperature rise and intensity variation in the direct absorption flat plate collector. Further, the

effect of varying various parameters of the nanofluid on the absorption cycle has been studied.

Figures 4, 5 and 6 show the plot of extinction coefficient of ammonia-water nanofluid

inside the generator as a function of wavelength for different nanoparticles such as aluminium

oxide, graphite and aluminium at volume fractions, fv= 0%, 0.04%, 0.6% and 1.2% and at

particle diameter, D=20nm. These values of volume fraction have been chosen as there is not

much variation in parameters such as intensity absorbed after volume fraction, fv= 1.2%. These

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plots were made to compare the extinction coefficient variation for different nanoparticles and to

choose the nanoparticle for which there is a significant change in the extinction coefficient with

volume fraction. As observed from these plots, the extinction coefficient is nearly the same with

all volume fractions for aluminium oxide. This shows that aluminium oxide does not alter the

absorptivity of the fluid significantly. For the other two cases, there is a significant rise in the

extinction coefficient with volume fraction, thereby increasing the radiation absorbed by the

fluid. Thus, further results are plotted for aluminium and graphite nanoparticles only.

Figures 7 and 8 show the magnitude of intensity with depth at various volume fractions

of aluminium and graphite nanoparticles respectively. The magnitude of incident intensity

decreases exponentially as per the radiative transport equation (Equation 8). As can be seen from

the plot the intensity absorbed by pure ammonia-water mixture i.e. at fv= 0% is 113 W/m2 is

very low. As the volume fraction of aluminium nanoparticle increases, the amount of

intensity absorbed increases to 963 W/m2 at fv=0.6%. Although the intensity absorbed increases

with volume fraction, its rate of increase reduces as the volume fraction is increased. This is also

evident from Figures 9 and 10 which show the plot between the generator temperature and the

volume fraction of aluminium and graphite nanoparticles respectively.

Figures 9 and 10 also show that the change in magnitude of temperature rise is very low

after fv=0.6% for aluminium and fv=0.04% for graphite. Therefore these values can be chosen as

the optimum values. For graphite at fv=0.04%, it has been calculated that almost 985.6 W/m2 of

the intensity is absorbed. But the generator temperature increases up to a maximum of 83 ºC at

fv=0.2% which remains constant on increasing the volume fraction further. This is evident in

Figure 10.

Figures 11 and 12 show the variation of generator temperature at various nodes along the

depth at different volume fractions varying from fv=0% to fv=1% for aluminium and fv=0% to

fv=0.4% for graphite nanoparticles. The range of volume fraction for aluminium and graphite

nanoparticles is different in figures 11 and 12 since the value of the critical fv - after which there

is no significant change in intensity absorbed - is lower in case of graphite. In the case of

Aluminium nanoparticles, the critical value is 0.6% as can be seen from figures 9 and 11. In the

case of graphite nanoparticles, the critical value is 0.04%; see figures 10 and 12.

From these two plots it is evident that graphite enhances the absorption more effectively

in comparison to aluminium nanoparticles as comparatively more intensity is absorbed per

volume fraction of particles added. A similar conclusion can also be derived from figures 7 and

8, 9 and 10. By the addition of relatively smaller amount of graphite nanoparticles, there is

greater change in intensity.

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Figure 13 depicts the plot of incident intensity with depth at particle diameters = 10 nm,

15 nm, 20 nm at constant volume fraction, fv= 0.2% of graphite nanoparticles. This value of

volume fraction has been chosen as it is higher than the optimum value of fv=0.04%. Although

the intensity absorbed increases by increasing particle diameter, its effect is not as significant as

that of volume fraction. A similar trend can also be seen in figure 14 where generator

temperature is plotted along various depths for these particle diameters. Similarly, there is no

significant effect of particle diameter in the case of aluminium (not shown).

So far, the nanofluids were studied for their intensity absorption and temperature rise in

the collector. From here onwards, this collector is embedded in an absorption chiller and the

results of a theoretical model are plotted. To calculate the COP of the cycle, various parameters

that were considered are shown in Table 1. Table 2 gives the thermodynamic properties at

various states, at volume fraction, fv=0.2% for graphite nanoparticles in ammonia-water mixture.

Table 1 Parameters known for the calculation of the COP of the AWAR cycle.

Parameter Value

Tevap 0℃

Tcond 30℃

Tabs 25℃

Tg,in 45℃

4m

0.00913 kg/s

Table 2 Thermodynamic properties at various states in the AWAR cycle for graphite

nanoparticles of 20 nm and at fv=0.2%.

State Temperature(℃) Pressure(MPa) Enthalpy(KJ/kg) Mass

fraction

Mass flow

rate(kg/s)

1 83 1.167 210 0.41 0.00649

2 - 1.167 - 0.41 0.00649

3 - 0.429 - 0.41 0.00649

4 25 0.429 -30 0.58 0.00913

5 25 1.167 -30 0.58 0.00913

6 45 1.167 70 0.58 0.00913

7 83 1.167 1616 1 0.00263

8 30 1.167 322.4 1 0.00263

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9 0 0.4296 322.4 1 0.00263

10 0 0.4296 1442 1 0.00263

In general, the value of COP increases initially, reaches a peak and then reduces due to

increase in irreversibility at higher temperatures, as found by Sathyabhama and Ashok Babu

[12]. Irreversibility may be attributed to the mixing process of the absorbent and the refrigerant.

As the generator temperature increases, this irreversibility of mixing also increases, resulting in

decrease in COP [17].

Figure 15 shows the plot of COP as a function of volume fraction of graphite

nanoparticles. As the volume fraction increases, the temperature rise in the generator also

increases; thus, the COP increases. In this plot, initially COP increases rapidly and reaches a

peak of 0.592 for graphite nanoparticles at volume fraction of fv= 0.02%. COP then remains

almost constant. These plots can be used to calculate the amount of nanoparticles to be added in

the working fluid for maximum COP.

Figure 16 shows the temperature rise of nanofluid flowing through the flat plate of the

model presented for graphite nanoparticles volume fraction, fv=0.2% at different locations all

over India. Each of these locations belong to different climatic zones; namely, tropical wet (Goa,

South India), semi-arid (New Delhi, North Central India), arid (Jaipur, West India), mountain

(Srinagar, North India), humid subtropical (Lucknow, South India), tropical wet and dry

(Hyderabad, South-East India). These plots were made using the data of annual global horizontal

incidence [18] at different locations to check the feasibility of the model presented.

CONCLUSION

These results clearly suggest that by ‘seeding’ nanoparticles directly in the generator, the

performance of the cycle improves because of the absorption and scattering effects of

nanoparticles. An optimum value of volume fraction fv=0.6% for aluminium and fv=0.04% for

graphite can be observed. Particle diameter does not play an important role for graphite

nanoparticles. Comparing the nanoparticles, it is observed that graphite is the best choice,

followed by aluminium and then aluminium oxide. This study can be carried out for many more

types of nanoparticles. In a tropical country like India, these absorption refrigerators can reduce

the peak loads. A detailed grid management policy needs to be devised to integrate these solar

systems with the dominant fossil fuel energy systems. .

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ACKNOWLEDGEMENTS

The authors based in India (R.G., A.K.R., V.K., V.B. and H.T.) wish to acknowledge the

support provided by the School of Mechanical, Materials & Energy Engineering at IIT Ropar.

The Australia based authors (Y.Z., E.L. and R.A.T.) gladly acknowledge support provided by

UNSW. Many thanks also to the anonymous reviewers for their work, and to the Editor for his

unfailing patience..

REFERENCES

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as a Means of Adiabatic Underwater Compressed Air Energy Storage, International Journal of

Environmental Studies, Vol. 69(4), pp. 566-577.

[2] Diesendorf, M. 2006, Wind Power in Australia, International Journal of Environmental

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[3] Kuehn, T.H., Ramsey, J.W., and Threlkeld, J.L., 1998, Thermal Environmental Engineering,

Prentice Hall, New Jersey.

[4] Khullar, V., and Tyagi, H., 2012, A Study on Environmental Impact of Nanofluid-Based

Concentrating Solar Water Heating System, International Journal of Environmental Studies,

Vol. 69(2), pp. 220-232.

[5] Otanicar, T.P., Taylor, R.A., and Phelan, P.E., 2012, Prospects for Solar Cooling – An

Economic and Environmental Assessment, Solar Energy, Vol. 86(5), pp. 1287-1299.

[6] National Renewable Energy Laboratory (NREL),

http://www.nrel.gov/international/ra_india.html, (accessed in January 2013).

[7] Duffie, J. A., and Beckman, W. A., 1980, Solar Engineering of Thermal Processes, Wiley,

New York.

[8] Khullar, V., Tyagi, H., Phelan, P. E., Otanicar, T. P., Singh, H., and Taylor, R. A., 2012,

Solar Energy Harvesting Using Nanofluids-Based Concentrating Solar Collector, Journal of

Nanotechnology in Engineering and Medicine, Vol. 3 (3).

[9] Lenert, A., and Wang, E.N., 2012, Optimization of Nanofluid Volumetric Receivers for

Solar Thermal Energy Conversions, Solar Energy, Vol. 86(1), pp. 253-265.

[10] Vishwakarma, V., Singhal, N., Khullar, V., Tyagi, H., Taylor, R.A., Otanicar, T.P., and

Jain. A., 2012, Space Cooling Using the Concept of Nanofluids-Based Direct Absorption Solar

Collectors, Paper No. IMECE2012-87726, Proceedings of ASME 2012 International

Mechanical Engineering Congress & Exposition, November, Houston, Texas, USA.

13

[11] Kameya, Y., and Hanamura, K., 2011, Enhancement of Solar Radiation Absorption Using

Nanoparticle Suspension, Solar Energy, Vol. 85(2), pp. 299-307.

[12] Sathyabhama, A., and Ashok Babu, T.P., 2008, Thermodynamic Simulation of Ammonia-

Water Absorption Refrigeration System, Thermal Science, Vol. 12(3), pp. 45-53.

[13] Tyagi, H., Phelan, P., and Prasher, R., 2009, Predicted Efficiency of a Low Temperature

Nanofluid-based Direct Absorption Solar Collector, Journal of Solar Energy Engineering, Vol.

131(4), pp. 0410041-0410047

[14] Taylor, R.A., Phelan, P.E., Otanicar, T.P., Adrian, R., and Prasher, R., 2011, Nanofluid

Optical Property Characterization Towards Efficient Direct Absorption Solar Collectors,

Nanoscale Research Letter, Vol. 6(1), pp.X1-11.

[15] Brewster, M. Q., 1992, Thermal Radiative Transfer and Properties, Wiley, New York.

[16] Palik, E, Handbook of Optical Constants of Solids, London, Elsevier, 1998.

[17] Arora, C. P., 2009, Refrigeration and Air Conditioning, Tata McGraw-Hill, New Delhi.

[18] Ministry of New and Renewable Energy (MNRE), Government of India,

http://mnre.gov.in/sec/GHI_Data.xls, (accessed in February 2013).

NOMENCLATURE

English Symbols

A Area of direct absorption flat plate collector, [m]

co Speed of light, [m/s]

D Mean particle diameter, [nm]

dy Thickness of spatial node along y-direction, [m]

fv Particle volume fraction

H Depth of Collector, [m]

h Planck’s constant

hc Convection coefficient, [W/m2K]

h Enthalpy, [kJ/Kg]

I Intensity, [W m-2

str-1

µm-1

]

K Radiative coefficients, [m-1

]

k Complex part of refractive index

kB Boltzmann constant

l Length of Collector, [m]

m Normalized refractive index

m Mass flow rate, [Kg/s]

14

N Node number

n Real part of refractive index

P Pressure, [kPa]

Qe Refrigeration effect, [kW]

Qgen Heat input to generator, [kW]

T Temperature, [˚C]

Tsolar Black body temperature of sun, Tsolar= 5800 K

V Velocity of the nanofluid through the direct absorption flat plate collector.

w Width of Collector, [m]

X Mass fraction of ammonia in water

Greek symbols

η Collector efficiency

λ Wavelength, µm

Subscripts

a Absorption

ab Absorbed

abs Absorber

cond Condenser

e Evaporator

evap Evaporator

g Generator

in Inlet

inc Incident

opt Optical

s Scattered

λ Spectral

Abbreviations

AWAR Ammonia Water Absorption Refrigeration

SHX Solution heat exchanger

TOR Tons of Refrigeration

15

LIST OF FIGURES

Fig. 1: Schematics of Conventional Solar based Vapour Absorption Refrigerator.

Fig. 2: Schematics of proposed Solar based Vapour Absorption Refrigerator with the direct

absorption flat plate collector directly embedded into the generator.

Fig. 3: Schematic of the nanofluid based direct absorption flat plate solar collector.

Fig.4: Plot of Extinction Coefficient of aluminium oxide nanoparticles in ammonia-water

mixture as a function of wavelength at volume fraction, fv=0%, 0.04%, 0.6% and 1.2%.

Fig.5: Plot of Extinction Coefficient of graphite nanoparticles in ammonia-water mixture as a

function of wavelength at volume fraction, fv=0%, 0.04%, 0.6% and 1.2%.

Fig.6: Plot of extinction coefficient of aluminium nanoparticles in ammonia-water mixture as a

function of wavelength at volume fractions, fv=0%, 0.04%, 0.6% and 1.2%.

Fig.7: Plot of intensity as a function of depth at fv = 0, 0.004, 0.01, 0.04, 0.08, 0.14, 0.2, 0.4, 0.6,

0.8, 1, 1.2% for aluminium nanoparticles at particle diameter (D) = 20nm.

Fig. 8: Plot of intensity as a function of depth at fv = 0, 0.002, 0.004, 0.006, 0.008, 0.01, 0.02,

0.04, 0.2, 0.4% for graphite nanoparticles at particle diameter (D) = 20nm.

Fig. 9: Plot of generator temperature as a function of fv for aluminium nanoparticles at particle

diameter (D) = 20nm.

Fig. 10: Plot of generator temperature as a function of fv for graphite nanoparticles at particle

diameter (D) = 20nm.

Fig. 11: Plot of temperature as a function of depth for aluminium nanoparticles at volume

fraction, fv=0%, 0.004%, 0.01%, 0.02%, 0.04%, 0.08%, 0.14%, 0.2%, 0.4%, 0.6%, 0.8%, 1.2%

at particle diameter (D) = 20nm.

Fig. 12: Plot of temperature as a function of depth for graphite nanoparticles at volume fraction,

fv=0%, 0.001%, 0.004%, 0.006%, 0.008%, 0.01%, 0.02%, 0.04%, 0.2%, 0.4% at particle

diameter (D) = 20nm.

Fig. 13: Plot of intensity as a function of depth at particle diameter (D) = 10nm, 15 nm, 20 nm

and volume fraction, fv= 0.2% for graphite nanoparticles.

Fig. 14: Plot of generator temperature as a function of particle diameter for graphite

nanoparticles at volume fraction, fv= 0.2%.

Fig. 15: Plot of COP as a function of fv for graphite nanoparticles at particle diameter (D) =

20nm.

Fig. 16: Plot of temperature rise for graphite nanoparticles of particle diameter 20nm, at volume

fraction, fv= 0.2%for the model proposed at different locations.

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Fig. 1: Schematics of Conventional Solar based Vapour Absorption Refrigerator.

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Fig. 2: Schematics of proposed Solar based Vapour Absorption Refrigerator with the direct

absorption flat plate collector directly embedded into the generator.

Fig. 3: Schematic of the nanofluid based direct absorption flat plate solar collector.

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Fig.4: Plot of Extinction Coefficient of aluminium oxide nanoparticles in ammonia-water

mixture as a function of wavelength at volume fraction, fv=0%, 0.04%, 0.6% and 1.2%.

Fig.5: Plot of Extinction Coefficient of graphite nanoparticles in ammonia-water mixture as a

function of wavelength at volume fraction, fv=0%, 0.04%, 0.6% and 1.2%.

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Fig.6: Plot of extinction coefficient of aluminium nanoparticles in ammonia-water mixture as a

function of wavelength at volume fractions, fv=0%, 0.04%, 0.6% and 1.2%.

Fig.7: Plot of intensity as a function of depth at fv =0, 0.004, 0.01, 0.04, 0.08, 0.14, 0.2, 0.4, 0.6,

0.8, 1, 1.2% for aluminium nanoparticles at particle diameter (D) = 20nm.

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Fig. 8: Plot of intensity as a function of depth at fv =0, 0.002, 0.004, 0.006, 0.008, 0.01, 0.02,

0.04, 0.2, 0.4% for graphite nanoparticles at particle diameter (D) = 20nm.

Fig. 9: Plot of generator temperature as a function of fv for aluminium nanoparticles at particle

diameter (D) = 20nm.

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Fig. 10: Plot of generator temperature as a function of fv for graphite nanoparticles at particle

diameter (D) = 20nm.

Fig. 11: Plot of temperature as a function of depth for aluminium nanoparticles at volume

fraction, fv=0%, 0.004%, 0.01%, 0.02%, 0.04%, 0.08%, 0.14%, 0.2%, 0.4%, 0.6%, 0.8%, 1.2%

at particle diameter (D) = 20nm.

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Fig. 12: Plot of temperature as a function of depth for graphite nanoparticles at volume fraction,

fv=0%, 0.001%, 0.004%, 0.006%, 0.008%, 0.01%, 0.02%, 0.04%, 0.2%, 0.4% at particle

diameter (D) = 20nm.

Fig. 13: Plot of intensity as a function of depth at particle diameter (D) = 10nm, 15 nm, 20 nm

and volume fraction, fv= 0.2% for graphite nanoparticles.

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Fig. 14: Plot of generator temperature as a function of particle diameter for graphite

nanoparticles at volume fraction, fv= 0.2%.

Fig. 15: Plot of COP as a function of fv for graphite nanoparticles at particle diameter (D) =

20nm.

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Fig. 16: Plot of temperature rise for graphite nanoparticles of particle diameter 20nm, at volume

fraction, fv= 0.2%for the model proposed at different locations.