SELECTION OF MATERIALS WITH POTENTIAL IN THERMAL ENERGY STORAGE

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1 SELECTION OF MATERIALS WITH POTENTIAL IN THERMAL ENERGY STORAGE A. Inés Fernández 1, Mónica Martínez 1, M. Segarra 1, Luisa F. Cabeza 2 1 Department of Materials Science & Metallurgical Engineering, Universitat de Barcelona, Martí i Franqués 1, Barcelona (Spain) Phone: , Fax: GREA Innovació Concurrent Edifici CREA, Universitat de Lleida, Pere de Cabrera s/n, Lleida (Spain) Phone: , Fax: ABSTRACT Thermal energy storage is a technology under investigation from the early 70 s. Since then, numerous new applications have been found and a lot of work has been done to bring this technology to the market. Nevertheless, the materials used were mostly investigated 30 years ago, and the research has lead to improve their performance under the different conditions of the applications. In those years a significant number of new materials have been developed in many fields other than storage and energy, but a great effort to characterize and classify these materials has been done. Taking into account that thousands of materials are known and a great number of new materials are developed every year, the authors use the methodology for materials selection developed by Prof. Ashby at the Cambridge University to give an overview of other materials suitable to be used in thermal energy storage. This methodology is widely used for design purposes, as many different inputs may be considered, such as thermal properties, mechanical behaviour, price, availability, recyclability, CO 2 footprint, etc. 1. INTRODUCTION Energy storage technologies are a strategic and necessary component for the efficient utilization of renewable energy sources and energy conservation. Thermal energy storage (TES) in general has been a main topic in research for the last 30 years, but most researchers still today feel that one of the weak points of this technology is the material to be used as storage medium. When one looks at the literature, the same materials described 30 years ago as potential materials for thermal energy storage [Lane, 1983; Lane, 1986] are the materials studied today [Zalba et al. 2003; Dinçer and Rosen, 2002; Mehling and Cabeza, 2008], that is paraffins, fatty acids and salt hydrates for latent heat storage and molten salts for high temperature sensible heat storage. When authors try to find new materials, all the research is based on this type of materials [Bellettre, 1997; Vakilaltojjar, 2001; Esen, 1998]. As thousands of materials are known and are developed every year, the authors believe that these materials should be looked at, to find out if they are suitable to be used in thermal energy storage. In this paper, the methodology for materials selection developed by Prof. Ashby at the Cambridge University [Ashby, 2005; Ashby et al, 2007] is used with the CES Selector software, to give an overview of other materials suitable to be used in thermal energy storage. This methodology is widely used for design purposes as many different inputs may be

2 considered, such as thermal properties, mechanical behaviour, price, availability, recyclability, CO 2 footprint, etc. Sensible heat storage materials are defined as a group of materials which undergo no phase change in the temperature range of the storage process. The ability to store sensible heat for a given material strongly depends on the value of its energy density, that is the heat capacity per unit volume or ρ Cp. For a material, to be useful in a TES application, it must be inexpensive and have good thermal conductivity. Storage systems based on phase change materials with solid-liquid transition are considered to be an efficient alternative to sensible thermal storage systems. From an energy efficiency point of view, PCM storage systems have the advantage that they operate with small temperature differences between charging and discharging. Furthermore, these storages have high energy densities compared to sensible heat storages. 2. DESCRIPTION OF THE METHODOLOGY FOR MATERIALS SELECTION The number of available materials is more than 150,000 with more appearing every year. Materials scientists classify them in four families: metals and alloys, ceramics and glasses, polymers and elastomers, and hybrids that include composites and natural materials. Each family is divided in classes, each of which contains sub-classes containing members. Each material is characterized by a set of attributes, numeric and non-numeric, that describe its properties and behaviour. Browsing or searching in handbooks and databases is useful if we know which material or process we seek, but they give no comparison or possible relationships between properties of materials that enable selection. A simple way to compare materials is a bar-chart, were a certain property is plotted for all the families of materials. The values of most properties of engineering materials span a total range of many decades and for that reason a logarithmic, rather than a linear scale is used. Figure 1 shows the specific heat capacity values C p for a hundred of the most used engineering materials. From this chart it can be deduced that the materials with the highest c p are natural and polymeric materials such as natural rubber, or the thermoplastic copolymer ABS with a C p value around 2 kj/kg K. Other composite materials such as glass fiber reinforced epoxies GFRE and concrete, have C p values close to 1 kj/kg K. The material property charts allows mining the data for patterns. As shown in Figure 2, two properties are plotted: Specific heat capacity and Density. When this is done it is found that each family of materials occupies a particular area of the plot, for example polymer foams near the upper left, a lot of metals and alloys at the lower right, technical ceramics central, and so on, being each bubble a specific material. Even at this early stage a selection of materials with certain properties or combinations of properties can be made. The charts also give a perspective of the materials world, building knowledge of where certain material families and classes lie in material property space.

3 2000 Acrylonitrile butadiene styrene (ABS) Cast magnesium alloys Concrete Specific heat capacity (J/kg.K) Natural Rubber (NR) GFRP, epoxy matrix (isotropic) Medium carbon steel Silicon 200 Tin Figure 1. Bar-chart of Specific heat capacity for a hundred of the most used materials, obtained with CES Selector 2000 Thermoplastics Technical ceramics Specific heat capacity (J/kg.K) Polymer foams Ceramic composites 200 Metals and alloys Density (kg/m^3) Figure 2. Materials property chart. Specific heat capacity vs. Density. In order to select the material with the highest performance for a given application, a designled approach strategy is developed. The selection strategy involves four steps: translation, screening, ranking and documentation. The first step is that of translating the design requirements into a specification for materials selection. It is followed by a screening step,

4 where those candidates that do not meet the specifications previously established are rejected. From a criterion of excellence, the remaining materials are ranked, and finally, more detailed information about the best material is needed to ensure that the selection is successful. So, the first of all is to translate the problem to take into account the design requirements that will be expressed as: Function of the component for which the material is sought. List of the constraints it must meet: satisfy limits on thermal or electrical properties and so forth. List of objectives, the criteria by which the excellence of choice is to be judged, for example minimizing cost, minimizing mass, etc. List of free variables those that the designer is free to change: usually dimensions or shape, and, of course, the choice of material. The performance of an engineering component depends on the values of the properties of materials with which it is made of, but it usually depends not only on one property but on a combination of two or more expressed as a criteria of excellence, called material index, which maximizes the performance for a given design and is the result of the translation step. An example of objective, is to minimize cost. In this case, the cheapest solution that meets all constraints is the best choice. It is rare that a design has only one objective, and when there are two objectives to meet, a conflict arises: the choice that minimizes one metric does not generally minimize the other, and then a compromise must be sought. To reach it we need some simple ideas drawn from the field of multi-objective optimization, a technique for reaching a compromise between conflicting objectives. It lends itself to visual presentation in a way that fits well with methods developed here thus far. 3. CASE STUDY As case study we will consider materials for sensible thermal energy storage in the range of temperatures of ºC. To translate design requirements we first identify the function, which is store thermal energy. The material should meet the following constrains: minimum service temperature of 150 ºC, high energy density (or heat capacity per unit volume), good thermal conductivity (higher than 0.3 W/m K), and good thermal diffusivity. The objectives for this application are to maximize the energy storage per unit of material cost, and, additionally, to maximize the thermal diffusivity to minimize the time for energy recovery. The free variables are the material choice and the dimensions. A preliminary selection can be made by constructing material property charts and limiting properties. For example, if we look at the relation of specific heat capacity with the materials costs, the figure 2 has a new configuration (see Figure 3). Moreover, we can do a convenient combination of thermal properties and represent it in a bubble chart. In figure 4 is plotted a material property chart considering energy density (C p ρ) vs. thermal conductivity. Thus, if the aim is to look for suitable materials with the maximum energy density and thermal conductivity above 0.3 W/m K we should focus on the left upper part of the plot and, among the resulting materials limit the service temperature and the cost per unit mass.

5 2000 Thermoplastics Natural Specific heat capacity (J/kg.K) Cement and concrete Ferrous alloys Price (EUR/kg) Figure 3. Specific heat capacity vs. cost per unit mass. 5e6 2e6 Energy Density J/m^3 K 1e Thermal conductivity (W/m.K) Figure 4. Material property chart with combination of properties. Energy density (C p ρ) vs Thermal conductivity. A more exhaustive selection can be performed following the selection strategy. First of all, we translate the objectives into one or more performance equations. So, taking into account that the thermal energy stored per unit volume can be expressed as:

6 where ρ is the density of material, C p its heat capacity and ΔT the temperature interval, and the cost of a mass m of material with a cost per kg of C m is: then the energy stored per unit volume and unit cost is expressed as an objective function: where V is the volume of material. If we look at this equation, it can be seen that the objective we want to reach depends on different variables, some geometrical (volume), other functional (temperature interval), and other related only to the material properties (C p /C m ). So, the material with the highest value for Q is that with the highest value for C p /C m, which is defined as the material index. In order to find the material with the highest material index, we plot both properties on a chart. Those materials with the same relation C p /C m will perform equally well, that is they give the same value of Q, located under the same line with a slope of 1. Figure 5 shows the plot of C p vs. C m, and the materials with the highest material index are those over the guideline with a slope of 1. Moreover, as another constraint is a thermal conductivity higher than 0.3 W/m K, those materials that do not meet this requirement are in grey color in the plot. Figure 5. Plot of specific heat capacity versus cost per unit mass. From Figure 5, different materials are identified: concrete, cast iron, alumina, aluminium alloys, and several glasses.

7 We now go one step beyond, and consider the other objective, a diffusivity as high as possible, but maintaining a high energy storage capacity. So we have another material index, the diffusivity, calculated from next equation: where λ is the thermal conductivity and ρ the density of the material. To identify those materials that maximize both objectives or minimize their inverse, we plot one material index in front of the other. Figure 6 shows the cost per unit of thermal energy stored (that is the inverse of the first material index, which should be minimized) versus the inverse of the diffusivity. A dotted line is also plotted, which links those materials that cominimize both indices. Figure 6. Materials that co-minimize both material indices. From Figure 6, we observe that concretes are the cheapest materials for energy storage, but their diffusivity is low, thus implying higher times to relay the stored energy. On the other hand, graphite has a high diffusivity, but it is expensive, while cast iron or aluminium alloys remain in the middle. The performance indices for those materials are an agreement between both material indices. Among these materials, an evaluation of service temperature ranges should be done to have a final/s candidate/s. 4. CONCLUSIONS A methodology to find potential materials to be used in thermal energy storage is presented with a case study that evaluates materials for sensible thermal energy storage in the range of temperatures of ºC. Two materials indices are evaluated, energy stored per unit volume and unit cost, and diffusivity. The best materials for this application are those that maximises both indices like graphite, an aluminium alloy, cast irons and concrete.

8 The proposed methodology allows, combining multiple objectives and restrictions of use, to evaluate the most used engineering materials for applications in thermal energy storage. Not only physical properties are considered but others like cost, availability or environmental aspects such as embodied energy or CO 2 footprint may also be taken in consideration to evaluate a potential material. ACKNOWLEDGEMENTS The work was partially funded by the Spanish government (project ENE C02-01/CON). REFERENCES Ashby, M.F. (2005) Materials Selection in Mechanical Design, 3rd ed, Elsevier, Oxford. Ashby, M., Shercliff, H., Cebon, D. (2007) Materials Engineering, Science, Processing and Design, Butterworth-Heinemann, Oxford. Bellettre, J., Sartre, V., Biais, F., Lallemand, A. (1997) Transient state study of electric motor heating and phase change solid liquid cooling, Applied Thermal Engineering Dinçer, I., Rosen, M.A. (2002). Thermal Energy Storage. Systems and Applications. John Wiley & Sons, England. Esen, M., Durmus, A., Durmus, A. (1998) Geometric design of solar-aided latent heat store depending on various parameters and phase change materials, Solar Energy Lane, G.A. (1983). Solar Heat Storage: Latent Heat Material, vol. I, Background and Scientific Principles, CRC Press, Florida. Lane, G.A. (1986). Solar Heat Storage: Latent Heat Material, vol. II, Technology, CRC Press, Florida. Mehling. H., Cabeza, L.F. (2008). Heat and cold storage with PCM. An up to date introduction into basics and applications. Springer, Germany. Vakilaltojjar, S.M., Saman, W. (2001) Analysis and modelling of a phase change storage system for air conditioning applications, Applied Thermal Engineering Zalba, B., Marín, J.M, Cabeza, L.F., Mehling, H. (2003). Review on thermal energy storage with phase change: materials, heat transfer analysis and applications, Applied Thermal Engineering

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