Category Archives: EuroSun2008-11

Heat exchanger model

1.1. Model structure

There are in general two possible ways to describe the thermal behaviour of a heat exchanger considering its geometry. The first one is to (numerically) solve heat transfer equations using finite element or volume methods, e. g. in FLUENT. However, these models require much computing effort and are, therefore, not appropriate for an optimization of the whole heat exchanger configuration. Depending on the optimization algorithm and the complexity of a problem, several thousand calculations can be required. The second way is to use empirical heat transfer correlations

gained from experimental investigations. Models based on empirical correlations require little computing effort, but usually have higher inaccuracies than those using finite element methods.

The model presented here (in the following simply referred to as “the model”) uses empirical heat transfer correlations. It is based on the detailed cooling coil model Type1223new[6] of the ASHRAE[7] HVAC Secondary Toolkit [3, 4], which separately considers wet and dry parts of the heat exchanger surface. Thus, the model accounts possible condensation of vapour[8] on parts or even all over the heat exchanger surface, if the surface temperature is below the air dew point temperature. In comparison to Type1223new, other heat transfer correlations have been implemented. Furthermore, pressure drops on both water and air sides are accounted and the model uses temperature-depending physical water and air properties, evaluated at the mean flow temperature, instead of constant values.

. Interconnection of subsystems via uniform interfaces

The interconnection of subsystems is done via simple EQUATION blocks acting as interfaces. They provide all needed inputs of the components collected within a subsystem and vice versa the outputs needed by connected subsystems or components. By using EQUATION pairs as input and output interfaces, variables can be transferred without a visual link between a pair (see also Fig. 3 in section 2.2). As a result the possibility of replacing subsystems without deleting or reconfiguring any visual connection in SIMULATION STUDIO is given. The principle is based on the fact that variables defined in equations can be transferred to components or other equations without linking them visually. Furthermore, in SIMULATION STUDIO outputs from components used within an equation must be defined as auxiliary variables on the left of the dialog window of an EQUATION block[12]. These auxiliary variables only appear in the graphical representation but they are not written to the input file of TRNSYS itself. Therefore, they can have the same names as the “real” variables (see Fig. 4). For the example in Fig. 4 the following section will be written to the TRNSYS input file:

EQUATIONS 2 "Inputs-right" input_T_in = [UNIT No., OUTPUT No.] input_mdot_in = [UNIT No., OUTPUT No.]

EQUATIONS 2 "Inputs-left"

Tin = input T in mdot in = input mdot in

In the graphical representation the expression [UNIT No., OUTPUT No.] corresponds to the auxiliary variables on the left of the EQUATION block dialog, where outputs of components can be connected to it (see also right dialog window of Fig. 4). Because of exactly same names of the variables and the auxiliary variables used for the inputs, the transfer within the EQUATION pair in SIMULATION STUDIO is not visible for the user while connecting subsystems to each other (see Fig.

Подпись: Fig. 4: Input EQUATION dialogs. The inputs of the right input EQUATION block are transferred to the left input EQUATION block without a visual connection in SIMULATION STUDIO. Variables can have same names, because variables on the left of the EQUATION dialog are only used in SIMULATION STUDIO as auxiliary variables but are not written to the TRNSYS input file.


Fig. 5: Connection dialogs of EQUATION pair in SIMULATION STUDIO. The user does not realize the
transfer of variables within the EQUATION pair while connecting subsystems or components because of the
same names of input (auxiliary) variables (right dialog window) and output variables (left dialog window) in
SIMULATION STUDIO. In this example the temperature and mass flow rate of a store is transferred to a pump,

which is located within a subsystem.

Mathematical model


The core of the design tool KOLEKTOR 2.2 is a mathematical model of solar flat-plate liquid collector solving one-dimensional heat transfer balances. Solar collector is defined by means of main levels: glazing exterior surface (p1), glazing interior surface (p2), absorber (abs), frame interior surface (z2) and frame exterior surface (z1). These levels are schematically outlined in Fig. 1. Detailed geometrical and physical properties of individual parts of solar collector, climatic and operation parameters are the input parameters of the model. Basic outputs of the model are usable heat gain Qu [W], efficiency ij with respect to reference collector area (gross area AG, aperture area Aa) and output heat transfer fluid temperature te.

The mathematical model of solar collector consists of external energy balance of absorber (heat transfer from absorber surface to ambient environment) and internal energy balance of absorber (heat transfer from absorber surface into heat transfer fluid). Model solves the energy balance of the solar collector under steady-state conditions according to principle Hottel-Whillier equation for usable heat gain

Qu = AaFR [ — U(tin — ta )]

Through the external energy balance of absorber (see Fig. 2) the heat transfer by radiation and by natural convection in the air gap between absorber surface and glazing (event. frame), heat conduction through glazing (event. frame) and heat transfer by convection and radiation from external glazing (event. frame) surface to ambient is solved. To calculate the heat transfer coefficients properly, temperatures for principal collector levels should be known, but on the other side the temperature distribution in the collector is dependent on the heat transfer coefficients values. Therefore, external energy balance of absorber is solved in an iteration loop starting from first temperature estimate for each main level based on given input temperature tin and ambient temperature ta. External balance loop yields in overall collector heat loss coefficient U [W/m2.K].

back side (Uz j


hp, b2-abs hpbi-a

edge side (Up)

Fig. 2. Schematic layout of external energy balance of absorber.


Internal energy balance of absorber assess the heat transfer from the absorber surface into heat transfer fluid provided by fin heat conduction, by heat conduction through the bond between absorber and pipes and by forced convection from pipe internal surface to fluid. Internal balance results in determination of collector efficiency factor F’ [-] and collector heat removal factor FR on the basis of input parameters, operational and climatic conditions and results from external balance. Main outputs from internal balance are output fluid temperature te, mean heat transfer fluid temperature tm and particularly absorber temperature tabs, which governs the calculations in the external balance. Internal balance proceeds in its own iteration loop with respect to relative dependence between mean fluid temperature tm and forced convection heat transfer coefficients in absorber pipe register.

As both external and internal balances are interdependent, superior iteration loop has been introduced to transfer the results from external balance to internal (overall collector heat loss coefficient U) and from internal balance results to external balance (absorber temperature tabs).

Geometrical description

Room’s layout is presented in figure 1. Only South and North fa? ade are glazed, height of room is 3 meters. Dimensions of each room are mentioned below. Toilets and circulation are not heated or cooled.











Conference rooms



11.5 m

108.70 m

<——————————————————————— >


Fig 1. Typical floor of type 1C building [3]

1.1. Other features

Internal gains are computed based on occupancy (maximum value is given between brackets), they include the following list of features. Note that maximum occupancy is fixed as 80 % of value in table 2.

• people (8.75 W/m2 in offices, 32.9 W/m2 in conference room, 0 W in circulations and toilets)

• appliances (15 W/m2 only in offices)

• lighting (18 W/m2 in offices, 12 W/m2 in circulations, 6 W/m2 in toilets)

• fan coil unit ventilators (117 W/Fan coil unit)

Solar protections are also modelled, they are controlled by luminance passing through windows and by occupation. Artificial lighting depends on natural light available for workers [4] , and a correlation is implemented into TRNSYS. Occupancy profile is defined for offices and meeting room [3].

Solar DHW System

Fig.1. shows the system diagram of the solar DHW heating system [5]. The solar collector is a flat plate type with selective absorption surface and one glass pane.

The total collector area is 6 m2 and the storage tank volume is 300 litters. The effectiveness of the built in heat exchanger in the tank is assumed to be 30 %. The input electric power of the motor of the collector circulation pump is 35 W. The auxiliary boiler efficiency is assumed to be 76.2 %.

2. Simulation method

The simulation tool EESLISM [6] was used. Usually, the time increment of one hour is used in the simulation. However, smaller time increment was examined in this study. Table1. shows the simulation cases examined in this study. The time increments used to examine are Case 1: 1 hour, Case 2: 10 minutes and Case 3: 1 minute.

The standard weather data prepared as the Expanded AMeDAS Weather Data [7] and the supplied city water temperature data prepared by the Solar System Development Association were used in this simulation.


Fig.3. Comparison of simulation results by three time increments for temperature for the collector tilts angle 40 degrees and azimuth 0 degrees in Tokyo

Model calibration and boundary conditions

The modelling process was carried out for 3D analysis using CFD-Fluent 6.2 software. Gambit 2.2 was used to mesh the model. The simple and regular geometry of the model suggested the use of a quad structured mesh of size 1mm. Two different boundary condition types were applied on the absorber plate for tests, depending on the purpose of the simulation. Optimisation simulations used constant heat flux of 300W/m2 as the boundary condition on the absorber plate. Constant absorber plate temperature boundary condition was taken when comparing the heat absorbed by the two ICS-SWH designs. According to those conditions the program calculated the temperature field in the geometry and as a result values at the nodes were displayed.

3. Modelling results

Stratification is essential for the good performance of a solar collector and is significant when trying to optimise the collector for draw off. However better mixing in the tank results in higher rates of heat transmission when trying to get solar energy into the water. Then the addition of fins might give a differential of temperature lower from the top to the bottom of the ICS-SHW but will optimise the transfer of energy into water. A CFD stratification analysis was developed for the four fin ICS-SWH design and then undertaken for a second design using five fins to compare both water stratification and velocity magnitude for a given time and heat transfer through time.

3.1. Extended heat transfer fins

A good understanding of the influence of extended surfaces is important when seeking performance enhancement of the ICS-SWH. Figure 2 represents the temperature of the fins with height after a lapse time of 60 minutes and a simulated heat flux on the absorber plate of 300 W/m2. Heat is transferred through the fins from the top of the collector to the bottom where the water temperature is cooler. This results from gravity and buoyancy effects. Water with different temperatures will settle at a corresponding height in the ICS-SWH according to the density of the fluid. Hot water of low density will naturally settle in upper layers while cold water of high density will fall to the bottom layers. During the first three hours of the charging process, high temperature stratification occurs in the ICS-SWH. With time, temperature in the upper layers get fully established and reach an equilibrium influencing the lower layer to increase in temperature and therefore decrease the density gradient of water inside the ICS-SWH.

Подпись: Contours of Static Temperature (k) (Time=3.6010e+03) Nov 02, 2007 FLUENT 6.2 (3d, segregated, lam, unsteady) Figure 2: Temperature stratification of fins and middle line of water collector

In order to ease the comprehension of the analysis process a discussion of the original four fin collector design is discussed below.

Energy balance model

The energy balance is considered for each component of the CPV receiver:

Tc, Tvb and the fluid, Tf and Tfo. Some geometrical characteristics of the CPV receiver and the silicone oil properties are given in Table 1.

3.1. Solar energy model

The irradiance absorbed by the vessel top, solar cells and vessel bottom are given by:

Ib = CR x I x a

vb g

As input for the model, the optical properties of the materials are used (Table 2).

Table 2. CPV receiver optical parameters









Solar cells



Silicone oil



3.2. Electrical model

A basic and wide spread method is used to estimate the photovoltaic power output [18]:

E = CRX- Ere, [1 + T(T. — Trf)] (9)

1 ref

Where I is the incident irradiance, E is the power output, T is the temperature, subscript ‘ref refers to standard testing conditions, r is the power correction factor for temperature(I = 800^^2 ,

Подпись: / m

Подпись: / m

If = 1000W/ 2 , Ef = 246.8W/ 2 , Tf = 298K, y = -0.65%/K ).

4. Results and discussion

The models above are analyzed to simulate solar cells temperature (Tc), vessel top temperature (Tvt), vessel bottom temperature (Tvb) and fluid average temperature (Tf).

Fig. 3shows the effect of concentration ratio on temperature when wind speed, ambient temperature, fluid inlet temperature and its volume flow rate are constant. The solar cells temperature is increased from 312K to 343K as concentration ratio is increased from 100X to 300X. So, if solar cells are operated in more than 300X, other parameters need be changed. Fig. 4 shows the four component temperature as a function of fluid volume flow rate at fixed wind speed, ambient temperature, fluid inlet temperature and concentration ratio. It is obvious that solar cells temperature and vessel bottom temperature varied with great ranges at beginning, then changed smoothly. A optimal fluid volume flow rate must be existed at a certain condition. Fig. 5 represents the variation of the four component temperature as a function of fluid inlet temperature at fixed wind speed, ambient temperature, fluid volume flow rate and concentration ratio. As an important parameter, the four component temperature is increased with its increased. The effect of environment variables including ambient temperature and wind speed on the four component temperature are shown in Fig. 6 and Fig. 7. The temperature of the four components has little change when ambient temperature and wind speed increased.

5. Conclusion

The main novelties of the HCPV system are the combination of a dish concentrator with solar cells immersed in dielectric liquid. The developed model predicts the effect of concentration ratio, fluid volume flow rate, fluid inlet temperature, ambient temperature and wind speed on solar cells temperature (Tc), vessel top temperature (Tvt), vessel bottom temperature (Tvb) and fluid average temperature (Tf). Results show that this kind of immersion operation of solar cells is an effective

method of cooling solar cells under concentrated light. Based on the experience achieved in the present work about high concentrated photovoltaic system with solar cells immersed in a dielectric liquid, the following topics need to be investigated: (1) construction of HCPV prototype, (2) the material stability, (3) determination the validation of the model and reliability of solar cells immersed in a dielectric liquid under concentrated light through experiments, (4) evaluation on feasibility and cost of the HCPV system.



Fig. 5. Temperature vs. fluid inlet temperature


Concentration Ratio( CR)

Fig. 3. Temperature vs. concentration ratio


w=3m, T =298K, V,=6m, CR=300X

s’ a ’ f h ’



— Tvt — a — Tvb Tf









-A————— A


A————— A————— A-


•——- •——- •——- • ‘…………………………… ‘

275 280 285 290 295 300 30б

Ambient Temperature( K)


Fig. 6. Temperature vs. ambient temperature

Vf =6m , T=293K, w=3m, CR=300X

f h ’ f s’


Fig. 4. Temperature vs. volume flow rate w=3m T =298K, Tf=293K, CR=300X

s a f





[1] Antonio Luque, Gabriel Sala, Ignacio Luque-Heredia. Photovoltaic Concentration at the Onset of its Commercial Deployment. Progress in Photovoltaics: Research and Applications. 2006, 14:413-428

[2] Martin A. Green, Keith Emery, Yoshihiro Hishikawa, et al. Solar cells Efficiency Tables (Version 32). Progress in Photovoltaics: Research and Applications. 2008, 16:435-440

[3] G. Martinelli, M. Stefancich. Chp.7: Solar Cell Cooling, in: Concentrator Photovoltaics. Springer Berlin Heidelberg New York. 2007, 130: 133-149

[4] Pierre J. Verlinden, Allan Lewandowski, Carl Bingham, et al. Performance and Reliability of Multijunction lll-VModules for Concentrator Dish and Central Receiver Applications. 2006 IEEE: 592­597

[5] Anja Royne, Christopher J. Dey, David R. Mills. Cooling of Photovoltaic Cells under Concentrated Illumination: a Critical Review. Solar Energy Materials & Solar Cells. 2005, 86: 451-483

[6] Welford, R. Winston. The Optics of Non-imaging Concentrators, Academic Press, New York, 1978

[7] A. Luque. Solar Cells and Optics for Photovoltaic Concentration, Adam Hilger, Bristol, 1989

[8] Blanco M, Alarcon D, Lopez T, et al. Computing the Solar Vector. Solar Energy. 2001,70(5):431-441

[9] Radziemska Ewa. Thermal Performance of Si and GaAs Based Solar Cells and Modules: a Review. Progress in Energy and Combustion Science. 2003, 29:407-424

[10] Jones A. D. Underwood C. P. A thermal Model for Photovoltaic Systems. Solar Energy.2001, 70(4):349- 359

[11] Davis M. W., Dougherty B. P., Fanney A. H. Prediction of Building Integrated Photovoltaic Cell Temperatures. Journal of Solar Energy Engineering. 2001, 123:200-210

[12] Garg H. P. Adhikari, R. S. Conventional Hybrid Photovoltaic/Thermal (PV/T) Air Heating Collectors

Steady State Simulation. Renewable Energy, 1997,11(3):363-385

[13] Garg H. P. Adhikari R. S. Transient Simulation of Conventional Hybrid Photovoltaic/Thermal (PV/T) air heating collectors. International journal of energy research, 1998,22:547-562

[14] Lee W. M. Infield D. G. Gottschalg R. Thermal Modelling of Building Integrated PV Systems. REMIC 2001.

[15] Duffie JA, Beckman WA. Solar Engineering of Thermal Processes. 2nd ed. New York: Wiley & Sons; 1991

[16] Biehl FA. Test Results and Analysis of a Convective Loop Solar Air Collector. Proc. 6th National Passive Solar Conf., Portland, Oregon, 1981: cited in [17]

[17] Altfeld K, Leiner W, Fiebig M. Second law Optimization of Flat-plate Solar Air Heaters. Part 1: The Concept of Net Exergy Flow and the Modeling of Solar Air Heaters. Solar Energy 1988;41(2):127±32

[18] Menicucci D, Fernandez JP. User’s manual for PVFORM: a Photovoltaic System Simulation Program for Stand-alone and Grid-interactive Applications; 1988. Sandia National Laboratories, SAND85-0376, Albuquerque, USA

What is GIS and why?

The GIS (Geographic Information System) is a tool for computational treatment of geographical data and their associated data banks. It can be seen as a system of support for decision that unites spatially referenced data in an ambient of replies to problems. SIG groups, brings together and unites information. Through this available information becomes more accessible, and old information is put into a new context. In this project, the GIS is used as a tool that permits the integration and processing of information from diverse sources. From this it is possib

Подпись: Figure 3 - Productive applications in community with photovoltaic solar energy: electric fence and school.

the elaboration of strategies for implantation and management of rural electrification with renewable energy.

The GIS is a valuable tool for evaluation and development of the use of renewable energy resources in large regions, because it is a tool that is specially adequate for analyzing the spatial variabilities of the resource as well as also for resolving problems of management and planning of installation programs of decentralized systems, that are characterized by a great spatial dispersion.

The solar resource is strongly influenced by altitude, latitude and phytogeographical conditions; the aeolic resource for rugosity and topography of land and finally the biomass for its soil characteristic and pluviometric conditions.

In its turn, management and planning of renewable energy systems, is a task that besides knowledge on energy resource needs knowledge on the nearest technical assistance center, a diagnosis of the most frequent defects, information from installed systems, the proximity to electric transmission lines, the energy demand, the laws that regulate land use, the indices of human development and non electrification and tools for dimensioning the systems for the given local demand, among others. All these interelationships can be collected, quickly quantified and visualized spatially by the intrinsic management capacity of a GIS system.

Heat losses investigation

1.1. Problem description

When solar radiation come to an absorber layer, the spectral response of the ideal material should allow an absorbance on the bandwidth of incoming radiation and an emittance completely shifted in a different bandwidth. In this case all incoming radiation would be transferred into thermal radiation. Part of this radiation is lost under the form of emittance of the absorbing layer to the environment, part becomes thermal energy and is transferred to the below layers. The structure of the solar tube avoids conduction and convection losses to the environment due to the evacuated area between two concentric glass cylinders inside which area the absorbing material is deposited, usually on the external surface of the inner tube. The consequence is that, all conduction and convection losses concentrate in the various layers from the absorber to the vector fluid. The investigation of the problem starts with the evaluation of the efficiency for the analysed solar collector as certified according to EU certification EN.12975-2:2006. The solar collector efficiency is the ratio between the energy Q (energy density) absorbed from the vector fluid and the energy (solar energy density) incident on its external surface.


and similarly, as usually reported, from the relation:


where tjq is the solar collector efficiency at TM = 0, ai and a2 are heat transfer coefficients, G is the total solar radiation and:


where tm is the panel average temperature and is the ambient temperature. The relation (2) is

the main instrument to evaluate the quality of a solar collector, and is within the certification reports. The efficiency of the solar collector investigated in the actual work can be viewed in the Fig. 1. It starts at 71,8 % at ambient temperature and it’s referred to an incident radiation of 1000 W/m2. In the same graph are represented the various heat losses calculated from optical and geometric factors, tube materials, reflector characteristics and cermet thermal properties. "Other losses", the orange area, resume some other factors, the most relevant of whose is the thermal resistance between the cermet layer and the vector fluid.

Подпись: Efficiency and thermal loss on a vacuum solar collectorПодпись: ■ Other Losses ■ Conduction Loss to External ■ CPC Concentrator ■ Cermet Emissivity ■ Borosilicate Glass ■ Collector Efficiency Подпись: 0 0.025 0.075 0.125 0.175 0.275 tm-ta / G image233100%











Fig. 1. Evaluation of the thermal losses on the real solar collector.

The Fig. 2 represents a section of the Evacuated Solar Tube (EST), selected for the modelling and used in the experimental prototype.





Borosilicate Glass (W)



















Table 1. Main contribution to radiation heat losses due to different optical behaviour and properties of the evacuated solar tube.

* surface reflectance = 0.9

Подпись: Fig. 2. Sectional view of the analysed evacuated solar tube (EST)

From external to internal the different layers of the EST are:

• borosilicate glass 3.3 tube — Glass TubeEXT ^EXT = 58mm; tb = 0.92, p = 0.062, a = є = 0.018 );

• vacuum (pINT~10-7 Torr, фЕХ1- = 56.4mm, фют = 47mm);

• cermet layer (a > 0.93);

• borosilicate glass tube — GlassTubeINT (фЕХЇ = 47mm, 5 = 1.5mm);

• computed air layer for materials tolerance between GTINT and aluminium profile (5 = 0.7mm);

• aluminium foil;

• copper tube (фЕХт = 7mm, фШт = 6mm);

• water (v = 0.556 m/s).

The problem of the evaluation of the heat loss and the development of a modified object with higher thermal efficiency is the first step to begin the research for a new device able to produce electrical, thermal and cooling power, with an efficiency higher than 70%. The first step is to build an evacuated solar tube with the capability to work at 250°C and more, without loosing thermal energy with consequences on its efficiency. The actual evacuated solar tube, at a vector fluid temperature of 523 K, has a thermal efficiency n = 0.23.

Morris method

Подпись: d, (x) Подпись: [ y ( x,.. .Xj_ і, Xj + A, Xj+1,..., xk ) - y (x)] A Подпись: (1)

This method is qualitative and only ranks parameters by its importance which means influence on the target function [2]. However, it also allows to determine whether the parameters have linear or non­linear effects on the target function or if they are involved in interactions with other parameters. The Morris method is based on the so-called elementary effects, defined for the ith parameter as follows:

The elementary effects dt(xm) are calculated at different parameter configurations xm, m = 1,…,M and then the distribution F of the elementary effects d; or the distribution G; of their absolute values is examined. The most informative sensitivity measures are the mean ц of the distribution G; and the standard deviation a of the distribution F. The mean ц is used to detect overall influence of the ith parameter on the target function y. The deviation a is used to detect the parameters involved in interaction with other parameters or those, whose influence on y is non-linear.