Applied Energy 131 (2014) 517–529

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Applied Energy

journal homepage: www.elsevier .com/locate /apenergy

Combined experimental and numerical evaluation of a prototypenano-PCM enhanced wallboard q

http://dx.doi.org/10.1016/j.apenergy.2014.02.0470306-2619/� 2014 Elsevier Ltd. All rights reserved.

q Notice: This manuscript has been authored by UT-Battelle, LLC, under ContractNo. DE-AC05-00OR22725 with the U.S. Department of Energy. The United StatesGovernment retains and the publisher, by accepting the article for publication,acknowledges that the United States Government retains a non-exclusive, paid-up,irrevocable, world-wide license to publish or reproduce the published form of thismanuscript, or allow others to do so, for United States Government purposes.⇑ Corresponding author. Address: One Bethel Valley Road, Building 3147, P.O.

Box 2008, MS-6070, Oak Ridge, TN 37831, USA. Tel.: +1 (865) 574 0917; fax: +1(865) 574 9354.

E-mail address: biswask@ornl.gov (K. Biswas).

1 http://www.eia.gov/consumption/residential/data/2009/index.cfmconsumption.

Kaushik Biswas a,⇑, Jue Lu b, Parviz Soroushian b, Som Shrestha a

a Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37831, USAb Technova Corporation, 1926 Turner Street, Lansing, MI 48906, USA

h i g h l i g h t s

� Field-testing of a nano-PCM wallboard under varying weather conditions.� Numerical model validation and annual simulations of PCM wallboard performance.� Reduced cooling electricity consumption results from PCM wallboard.� PCM wallboard reduces peak cooling loads with implications on power plant capacity.� PCM performance was sensitive to building temperature set point for cooling.

a r t i c l e i n f o

Article history:Received 8 September 2013Received in revised form 23 December 2013Accepted 11 February 2014Available online 16 June 2014

Keywords:Phase change materialsNano-PCMPCM wallboardPCM modelingFinite element analysis

a b s t r a c t

In the United States, forty-eight (48) percent of the residential end-use energy consumption is spent onspace heating and air conditioning. Reducing envelope-generated heating and cooling loads throughapplication of phase change materials (PCMs) in building envelopes can enhance the energy efficiencyof buildings and reduce energy consumption. Experimental testing and numerical modeling of PCM-enhanced envelope components are two important aspects of the evaluation of their energy benefits.An innovative phase change material (nano-PCM) was developed with PCM supported by expandedgraphite (interconnected) nanosheets, which are highly conductive and allow enhanced thermal storageand energy distribution. The nano-PCM is shape-stable for convenient incorporation into lightweightbuilding components. A wall with cellulose cavity insulation and a prototype PCM-enhanced interiorwallboard was built and tested in a natural exposure test (NET) facility in a hot-humid climate location.The test wall contained the PCM wallboard and a regular gypsum wallboard, for a side-by-side annualcomparison study. Further, numerical modeling of the wall containing the nano-PCM wallboard was per-formed to determine its actual impact on wall-generated heating and cooling loads. The model was firstvalidated using experimental data, and then used for annual simulations using typical meteorologicalyear (TMY3) weather data. This article presents the measured performance and numerical analysis eval-uating the energy-saving potential of the nano-PCM-enhanced wallboard.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

According to the 2009 residential energy consumption survey(RECS)1 of the United States Energy Information Administration(EIA), about 48% of the total residential site (end-use) energy con-sumption is due to space heating and air conditioning, which amountsto about 4.86 quadrillion BTUs (or 5.13 � 1012 MJ) annually. The U.S.Department of Energy’s (DOE) Building Technologies Office has set agoal of developing high-performance, energy efficient buildings that

?view=

Nomenclature

B(T) melt fractionD(T) Gaussian function, with an integral of 1cp specific heat (kJ/kg/K)F view factorh(T) enthalpy (kJ/kg)hext exterior surface convective heat transfer coefficient (W/

m2/K)hint interior surface heat transfer coefficient (W/m2/K)k thermal conductivity (W/m/K)Lf latent heat of melting/freezing (kJ/kg)q heat flux (W/m2)qsolar solar irradiance (W/m2)T temperature (K)a solar absorptancee infrared emittanceq density (kg/m3)

Subscriptsext exteriorint interiorl fully molten state of PCMs fully frozen state of PCM

AbbreviationsCOP coefficient of performanceEER energy efficiency ratioLWR long wave radiationNET natural exposure testOSB oriented strand boardPCM phase change materialRH relative humiditySEER seasonal energy efficiency ratio

518 K. Biswas et al. / Applied Energy 131 (2014) 517–529

require cost-effective, durable, energy efficient building envelopes.Application of phase change materials (PCMs) to building envelopesto take advantage of their high latent heat capacities in reducing theenvelope-generated heating and cooling loads has received a lot ofattention recently. Zhou et al. [1] reviewed previous research on appli-cation of PCMs in buildings for thermal energy storage. They studiedthe PCM characteristics to be considered for enhanced thermal com-fort and energy savings, and incorporation methods into building con-struction. Various approaches to PCM applications in buildingenvelopes have been investigated: PCM wallboards [2–5], PCM mixedin construction materials like concrete and brick [6,7], PCM mixedwith fibrous insulation in walls and attics [8–10], and macro-packaged PCM in plastic pouches [11].

Al-Saadi and Zhai [12] reviewed the modeling of PCMs in build-ing enclosures and highlighted the issues needing further research.The authors noted that several models were either not validated orvalidated using analytical or comparative means, instead of exper-imental data [12]. The authors also identified the need for furtherresearch to quantify PCM modeling performance under differentclimatic and operating conditions [12]. Athienitis et al. [13] useda one-dimensional transient heat diffusion model to investigatethe thermal performance of a PCM soaked gypsum board in a pas-sive solar house and validated the calculations with data from anoutdoor test house. The PCM-gypsum board was found to increasethe daytime indoor comfort and reduce heating loads under winterconditions. However, the experimental data presented and calcula-tions were limited to a single winter day. Kuznik and Virgone [14]tested two small-scale cells in a climate chamber under periodictemperature conditions, with and without a PCM wall layer to gen-erate data for model validation. The model assumed unidirectionalheat transfer through each wall. Huang et al. [15] used a previouslyvalidated two-dimensional model to evaluate masonry walls aug-mented with PCMs. Outdoor temperatures and solar insolationon the exterior wall surface from one summer and one winter dayswere used for the model calculations. Silva et al. [16] developed atwo-dimensional model of a brick wall with and without PCMmacro-capsules. The model calculations were validated againsttesting done in a climate chamber, under 24-h cyclical temperatureconditions. In a purely numerical study, Childs and Stovall [10]found that two-dimensional modeling was necessary to capturethe effect of any wall thermal bridges (like wood or metal framing)in evaluating PCMs in walls. Shrestha et al. [8] performed whole-building modeling with PCM in wall cavities using EnergyPlus,2

2 http://apps1.eere.energy.gov/buildings/energyplus/energyplus_about.cfm.

and found the localized wall temperature and heat flux calculationresults to be in reasonable agreement with data from a test house.However, such whole-building models usually lack sufficient detailsregarding the thermal characterization of the PCM.

This study describes the testing of a prototype PCM wallboardunder natural exposure conditions for several months and detailedtwo-dimensional numerical simulations to estimate its energy-saving potential. The prototype PCM wallboard contained an inno-vative phase change material (nano-PCM) with the PCM supportedby expanded graphite nanosheets, which are highly thermally con-ductive, for enhanced thermal storage and energy distribution. Thenano-PCM is shape-stable for convenient incorporation into light-weight building components like gypsum boards, which are com-monly used as interior wallboards in residential construction.Incorporation of PCMs in wallboards is an effective and relativelylow-cost application of thermal mass in buildings [1]. The nano-PCM wallboard was field-tested in Oak Ridge National Laboratory’s(ORNL) natural exposure test (NET) building over a period of sev-eral months, under varying weather conditions. The NET buildingcan accommodate several side-by-side experimental wall assem-blies to test their hygrothermal behavior. Two-dimensional finiteelement models of wall assemblies were created and validatedagainst the data from the NET building, and then used for annualsimulations of the nano-PCM wallboard. In the following sections,the prototype wallboard is briefly described, followed by descrip-tions of the experimental testing and numerical modeling, andfinally, the performance of the nano-PCM wallboard is comparedto a regular gypsum wallboard.

2. Description of PCM wallboard

Gypsum powder was used for fabricating the gypsum boards,which were enhanced with phase change materials. A paraffin,n-heptadecane (C17H36), was used as the phase change material(PCM). High thermal conductivity is a desirable characteristic ofPCMs in energy storage applications, but the lower conductivityof paraffins decreases the rates of heat stored and released duringthe phase change processes, limiting their utility areas [17,18].Previous studies have shown that exfoliated graphite nanoplatelets(xGnP) and expanded graphite (interconnected) nanosheets areeffective in increasing the thermal conductivity of the PCM forenhanced heat transfer and better utilization of PCMs [19–21].While expanded graphite provides high liquid sorption capacity,the interconnection of nanosheets in expanded graphite benefitsits thermal conductivity and shape-stability [21]. The nano-PCM

Fig. 2. Charleston, SC NET facility. The current test wall is located on the southeastfaçade of the building, along with other adjacent test wall sections.

Fig. 3. Test wall with the PCM-enhanced wallboards.

K. Biswas et al. / Applied Energy 131 (2014) 517–529 519

incorporated in the current wallboard was a shape-stable blend of92%-by-weight n-heptadecane and 8%-by-weight expanded graph-ite nanosheets produced following the procedure described bySayyar et al. [22]. No seepage was observed above the melting tem-perature of PCM, as the molten PCM was immobilized within thepores by the capillary action and surface tension of the high spe-cific surface area of graphite nanosheets [22].

The 1.3 cm thick nano-PCM wallboard contained 20%-by-weight nano-PCM homogeneously dispersed in gypsum board. Toincorporate nano-PCM into gypsum wallboard, gypsum powderwas mixed with 65%-by-weight water with a mortar mixer for2 min, followed by addition of nano-PCM previously stored at4 �C, and further mixing for 1 min. Controlling the temperaturewell below the melting point of PCM during the mixing processis critical to prevent the melting and leaking of PCM from graphiteinterconnected nanosheets under high shear force. The slurry wascast in a plastic mold followed by curing for one week at roomtemperature. The phase change properties of the nano-PCM wall-board, such as melting and freezing points, and latent heats weremeasured using a differential scanning calorimeter (DSC 2920, TAInstruments) at a heating and cooling rate of 1 �C/minute. Fig. 1shows the heat flow data from differential scanning calorimetry(DSC) testing of the nano-PCM and the nano-PCM wallboard. Thepositive heat flows represent exothermic conditions, indicatingfreezing of the PCM; negative heat flows indicate melting. TheDSC data provide information about the active temperature rangeof the PCM (where phase changes occur) and the respective latentheats. The melting and freezing onset temperatures were deter-mined using an integration function based on a ‘tangents’ method,which was included in the DSC analysis software. The nominallatent heats of melting and freezing were 26.9 and 26.8 J/g, respec-tively, and the phase change onset temperatures were 21.1 �C formelting and 21.3 �C for freezing. Theoretically, the latent heats ofmelting and freezing should be identical; the difference of 0.1 J/gis an artifact of the measurement and calculation methods.

3. Test facility and wall details

The nano-PCM wallboard was tested in a natural exposure test(NET) facility at Charleston, South Carolina, USA, which is locatedin the hot-humid climate zone. Fig. 2 shows the southeast wallof the NET facility, which exposes side-by-side roof/attic and wallassemblies to natural weathering. The data from the NET facilityhelp the building industry in developing products to avoid adversemoisture-related impacts in buildings, and are essential in validat-ing hygrothermal and energy models. The NET building is temper-ature and humidity controlled and instrumented to measuremoisture content in materials, vapor pressure, temperature, heatflux, humidity, etc.

Fig. 1. Melting and freezing characteristics (DSC curve) of the nano-PCM (left) and ‘N

The test wall was built using wood framing, with orientedstrand board (OSB) on the outside and the nano-PCM wallboardon the inside. The outer side of the OSB was covered with aweather resistant barrier and vinyl siding. Fig. 3 shows theinstalled test wall, containing the nano-PCM wallboard and a gyp-sum wallboard, with the gypsum section serving as a baseline. ThePCM section of the wall was divided into two shorter sections, onecovered by the prototype nano-PCM wallboard and the othercontaining a different wallboard incorporating a commercialmicroencapsulated PCM. This article, however, is focused on eval-uating the performance of only the nano-PCM wallboard.

Fig. 4 shows a schematic of the cross-section of the side-elevation of the test wall and also a photo showing the emptycavity, studs, exterior OSB and the partitioning material separating

ano-PCM Gypsum’ wallboard (right). DSC heating and cooling rate was 1 �C/min.

Fig. 4. Test wall side-elevation and sensor array along the cross-section. The temperature and RH sensors are labeled as: (1) wall exterior, (2) OSB interior, (3) cavity center,(4) wallboard exterior, and (5) wallboard interior.

Table 1Installed sensor accuracy.

Sensor Accuracy Sensitivity Repeatability Supply voltage

10 K ohm thermistor ±0.2 �C – ±0.2% 2.5 VdcHumidity sensor ±3.5% – ±0.5% 5 VdcHeat flux transducer ±5% (5.7 W/m2)/mV – –

3 http://www.comsol.com/.

520 K. Biswas et al. / Applied Energy 131 (2014) 517–529

the different wall sections. The smaller wall cavities were about1.1 m high and 0.4 m wide, and the taller, regular gypsum board-covered cavity was 2.2 m high and 0.4 m wide. The cavities or sec-tions were separated using wood studs and thin (1.3 cm) sheets offoam insulation to thermally isolate them from each other. Thecavity depth was 14 cm. Each wall cavity was covered with a plas-tic mesh (also shown in Fig. 4) and then cellulose insulation wasadded by cutting a slot in the mesh at the top of the cavity. The cel-lulose was allowed to settle under its own weight. The measuredcavity volume and estimated weight of cellulose insulation addedto the cavities yielded an average density of 40.9 kg/m3. It shouldbe noted that, due to the ad hoc application method, the resultingdensity was lower than the recommended density of 56.1 kg/m3

for blown-in dense-packed cellulose insulation. The area surround-ing the test cavities was filled with fiberglass insulation, to preventconvection loops in empty air spaces and minimize the thermalinteraction with the neighboring test walls. These perimeter cavi-ties were used for routing instrumentation cables.

3.1. Data acquisition system and instrumentation

Fig. 4 shows a typical instrumentation layout in the wall cavi-ties. The wall contained vinyl siding and a weather barrier overOSB on the exterior side, which was exposed to the environment(as seen on the southeast wall in Fig. 2). The interior side was cov-ered with regular gypsum and nano-PCM wallboards. Each cavitycontained a thermistor and relative humidity (RH) sensor combi-nation (T/RH sensor) on the OSB and wallboard surfaces facingthe cavity, thermistor inside the cavity (mid-depth) and on thewallboard surface facing the room interior, and heat flux transduc-ers (HFTs) on the wallboard surface facing the cavity. Within eachcavity, these sensors were located approximately in a line alongboth the vertical and horizontal mid-points of the cavity. In addi-tion, a single thermistor was attached to the wall exterior(between OSB and exterior siding) and a T/RH sensor combinationon the OSB surface facing the exterior. The T/RH sensors are indi-cated by the white packets seen in Fig. 4.

In addition to the sensors attached to the test wall, the NETfacility includes sensors and instruments to monitor the localweather conditions, including temperature, humidity, solarirradiance, wind conditions, etc. Each sensor was scanned at five

minute intervals and the data were averaged and stored at hourlyintervals. Table 1 provides the sensor specifications.

One source of errors or uncertainties in the measurements wasthe potential for formation of air gaps in the cavities due to settlingof cellulose under its own weight. Due to concerns regarding thesettling of cellulose, the test wall was inspected at the end of thetest period. The internal wallboards were removed to visuallyinspect the wall cavities. Fig. 5 shows sample images from the testwall. The number 9 was internally assigned to identify the currenttest wall section in the NET facility. Minor air gaps were observednear the sensors in the gypsum section, but not in the nano-PCMwallboard section. No gaps were observed at the top of the cavities.

4. Numerical modeling

To fully ascertain the impact of the nano-PCM wallboards onthe wall-generated heating and cooling loads, numerical modelingwas necessary. It is very difficult to isolate the individual energyimpacts of the nano-PCM and gypsum wallboards through purelyexperimental means used in this study, as both wallboards wereinteracting with the same interior conditioned space, which wasalso impacted by other test walls and attics of the NET facility.Therefore, two-dimensional (2D) finite element models werecreated using COMSOL Multiphysics (version 4.3a),3 to furtherinvestigate the nano-PCM wallboard performance.

4.1. Methodology

In this section, a brief description of the numerical analysismethodology is provided. The first task was to ensure that thenumerical models were capturing the thermal behavior of theactual test wall. For this purpose, 2D wall models were created,with identical dimensions as the test wall and using thermo-physical properties of the materials used in the test wall. Themodel calculation results were then compared to the experimen-tally measured temperatures and heat flows, for validation.

Following model validation, annual simulations were per-formed to estimate the energy benefits of using the nano-PCM

Fig. 5. Gypsum (left) and ‘Nano-PCM Gypsum’ wallboard (right) sections; the gypsum section exhibited air gaps near the sensors.

Table 2Material properties for numerical modeling.

Density (kg/m3) Thermal conductivity (W/m/K) Specific heat (kJ/kg/K) Latent heat (kJ/kg)

Cellulose 40.8 0.042 1.424 –Wood stud 576.7 0.144 1.633 –OSB 640.0 0.130 1.410 –Foam (Expanded Polystyrene) 24.0 0.037 1.214 –Gypsum 549.5 0.153 1.089 –Nano-PCM wallboard 658.5 0.410 (s) 2.312 (s) 26.2

0.427 (l) 2.236 (l)

K. Biswas et al. / Applied Energy 131 (2014) 517–529 521

wallboard over a regular gypsum wallboard. Following list pro-vides a synopsis of the annual simulation work:

1. Appropriate exterior and interior boundary conditions wererequired for the annual simulation models. For the exterior side,data from typical meteorological year (TMY3)4 weather fileswere used. On the interior side, a constant surface heat transfercoefficient was assumed to calculate the heat transfer betweenthe wall surface and the interior conditioned space (room).

2. Heat gains and losses at the interior wall surface were calcu-lated and used for comparing the performance of the nano-PCM and gypsum wallboards.

3. In the simulations, the room temperature was allowed to floatbetween, but was limited to, assumed heating and cooling tem-perature set points.

4. The interior cooling set point has an impact on the energy-sav-ing performance of PCM in building envelopes [10], thereforesimulations were performed with different cooling set points.

5. Time-delay in wall heat gains caused by the PCM has a potentialfor further reducing cooling electricity consumption, due tohigher equipment efficiency at lower outdoor temperatures[10]. This was also investigated by converting the wall heatgains to electricity consumption, using typical temperature-dependant energy efficiency ratios (EER) for heat pumps.

6. Finally, load factors for the cooling electricity consumptionwere calculated. Load factor is the ratio of the peak-to-averageelectricity consumption. Higher load factors and lower peakdemand are desirable as they allow potentially lower capacityand costs for electricity providers.

7. All the above are described in detail in the next subsections andin ‘Results and Discussion’ section.

4.2. Material properties

Table 2 lists the material properties used in the numericalmodel. These values were obtained from literature or throughmeasurements.

4 http://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/tmy3/.

The specific heats of the PCM wallboards in the fully frozen andfully molten states of the PCM were obtained from the DSC data.However, the effect of phase transition, or latent heat of melting/freezing, needs to be included in the specific heat (cp) of thenano-PCM wallboard. The cp during phase transition can becalculated directly from the DSC data, but sharp changes in cp asa function of temperature can result in lack of convergence in thenumerical solution [23]. To overcome this issue, following Biwoleet al. [24], a modified enthalpy (hmod) was defined, as shown inEq. (3). B(T) is the melt fraction (Eq. (4)) and D(T) is a Gaussianfunction (Eq. (5)) with an integral of 1.

hmodðTÞ ¼Z

cps þ ðcpl � cpsÞBðTÞ þ Lf DðTÞ� �

dT ð1Þ

BðTÞ ¼0;h < hs

ðh� hsÞ=Lf ;hs 6 h 6 hl

1; h > hl

8><>: ð2Þ

DðTÞ ¼ e�ðT�TmÞ2

DT2

ffiffiffiffiffiffiffiffiffiffiffiffipDT2

p.ð3Þ

‘cps’ and ‘cpl’ are the wallboard specific heats with fully frozenand fully molten PCMs, calculated using the DSC data. ‘Tm’ is themid-point and ‘DT’ the half-width of the phase change tempera-ture range. ‘hs’ and ‘hl’ are the enthalpies at the onset and end ofthe melting process and ‘Lf’ is the latent heat of melting/freezing,also calculated using the DSC data.

Finally, a modified specific heat of the nano-PCM wallboard wasdefined as,

cp;modðTÞ ¼dhmod

dTð4Þ

The thermal conductivity was defined as,

kðTÞ ¼ ks þ ðkl � ksÞBðTÞ ð5Þ

‘ks’ and ‘kl’ are the thermal conductivities of the wallboards whenthe PCM was fully frozen and fully molten, respectively.

Fig. 6 shows a comparison of the enthalpy, h (DSC), calculatedusing the DSC data, to the enthalpy, h_mod (D(T)), obtained using

Fig. 6. Nano-PCM wallboard enthalpies and melt fraction.

Fig. 7. Numerical model geometry showing the wall sections containing the ‘Nano-PCM Gypsum’ (top) and ‘Commercial PCM in Gypsum’ (bottom) wallboards.

522 K. Biswas et al. / Applied Energy 131 (2014) 517–529

Eq. (3). Since the enthalpies (h) were only used to calculate cp forthe models, and since cp is only dependent on the slope of h(T), hwas chosen to be zero at an arbitrary temperature below the phasechange temperature range of the nano-PCM and calculated up to atemperature higher than the phase change temperature range.Enthalpy from the DSC data and the modified enthalpy (from Eq.(3)) were within 10% of each other. Fig. 6 also shows the melt frac-tion of the nano-PCM as a function of temperature.

The enthalpies used in the above analysis were generated usingthe average of the heat gain/loss data from the melting and freez-ing DSC tests. In general, the phase change enthalpy is not an iden-tical function of temperature during melting and freezing of PCMs.This phenomenon in PCMs is referred to as ‘hysteresis’ and it isparticularly significant with certain types of PCM, for exampleinorganic PCMs. However, past research has shown that, at veryslow heating and cooling rates (�0.1 �C/min), the hysteresis effectin paraffinic PCMs, such as the present nano-PCM, is negligible[25]. In fact, the DSC data in Fig. 1 show that the phase change tem-peratures during cooling and heating were nearly identical. Thelocation of the wallboards in the test wall exposes the PCM to verysmall (�2–3 �C) diurnal temperature variations and, consequently,very slow heating/cooling rates. Thus, using an averaged enthalpyas a function of temperature was deemed appropriate. Once theaveraged enthalpy function was defined, the latent heat was re-calculated as the difference in enthalpy between the phase changeonset and end temperatures. The latent heat used for COMSOLmodeling was 26.2 kJ/kg (listed in Table 2), which is slightlydifferent than, but close to, the values estimated from the DSCmeasurements (26.8–26.9 kJ/kg).

4.3. Model validation

Before calculating the reduction in wall heat flow due to thenano-PCM wallboard, the finite element numerical models devel-oped using COMSOL needed to be validated against experimentaldata. For this purpose, 2D models of the actual test walls were cre-ated. Fig. 7 shows the model geometry, which replicates a 2Dcross-section of the test wall. The section shown corresponds tothe vertical centerline of the PCM wallboard sections on the rightside of the test wall (Fig. 3). The 2D wall section was divided intotwo cavities, divided by wood studs and foam insulation in themiddle, with different internal wallboards for the top and bottomsections. A similar model geometry was created for the gypsumwallboard section, with the internal gypsum wallboard spanningthe entire height of the wall. The wall geometries were built asper the actual dimensions of the Charleston test wall.

The different components of modeled test wall section areshown in Fig. 7. Fig. 7 also shows the locations of ‘thermistors’and ‘HFTs’ in the model geometry. These were locations within

the model geometry where the simulation results were monitoredand used for comparison with the experimental data. These loca-tions were chosen to be as close as possible to the actual sensorlocations in the test wall (Fig. 4). The ‘thermistor’ near the wall-board surface facing the cavity was offset. This is because theT/RH sensors in the actual test wall were not attached to the exte-rior face of the internal wallboards, but were slightly pushed insidethe cavities once they were filled with cellulose insulation (as seenin Fig. 8).

The model solved the following time-dependent energyequation,

q@h@t¼ r � ðkrTÞ; where h ¼

ZcpdT ð6Þ

In Eq. (6), ‘q’ is the density, ‘h’ is the enthalpy, ‘k’ is the thermalconductivity and ‘cp’ is the specific heat of the different wall mate-rials. Exterior and interior wall surface temperatures from theexperimental data were used as boundary conditions on the exte-rior and interior surfaces, respectively. Insulated, or adiabatic,boundary conditions were assumed at the top and bottom of thewall model, which is appropriate since the area surrounding thecellulose-filled cavities was filled with fiberglass insulation (Fig. 3).

4.4. Annual simulation using TMY3 data

Once the COMSOL 2D finite element models were validatedagainst experimental data, they were used for annual simulations.Fig. 9 shows the simplified wall geometry that was used for theannual simulations. The wall construction used in the model was‘2 � 6’ stud construction, i.e. it contained wood studs of3.8 cm � 14.0 cm (1.5 in. � 5.5 in.), resulting in a cavity depth of14.0 cm (5.5 in.). The centerline of the studs were spaced 61 cmapart (24 in. on center), which is fairly typical of new residentialconstruction. The exterior side consisted of 1.3 cm OSB and theinterior contained 1.3 cm wallboard, either regular gypsum orthe nano-PCM wallboard. The scope of the study was limited tocalculating heat flows through the ‘clear’ section of the wall. Nowall-wall or wall-ceiling interfaces, joints and corners, windows,etc., were considered in the model. Hence, only a small two-dimen-sional (2D) horizontal cross-section of the wall was modeled,extending from the stud centerline to the cavity centerline. In

Fig. 8. Location of the T/RH sensor near the exterior (cavity-facing) surface of an internal wallboard.

Fig. 9. Simplified wall configuration used for the annual simulations.

5 http://apps1.eere.energy.gov/buildings/energyplus/energyplus_about.cfm.6 https://www.ashrae.org/resources–publications/handbook.

K. Biswas et al. / Applied Energy 131 (2014) 517–529 523

Fig. 9, exterior boundary conditions were applied to the OSB sur-face that is exposed to the outside and an interior boundary condi-tion was applied to the interior wallboard surface facing the room.Symmetric boundary conditions were assumed at the stud andcavity centerlines (Fig. 9).

Appropriate exterior and interior boundary conditions wererequired for the annual simulations. The exterior boundary condi-tions were estimated using typical meteorological year (TMY3)weather data for Charleston. Input files containing hourly valuesof outdoor and sky temperatures, solar radition and exterior sur-face convective heat transfer coefficients were generated for theannual simulation models. The following external (qext) and inter-nal (qint) heat flux boundary conditions were imposed on eachwall:

qext ¼ aqsolar þ hextðTout � Tsurf Þ

þ er ð1� FskyÞðT4out � T4

surf Þ þ FskyðT4sky � T4

surf Þh i

ð7Þ

qint ¼ hintðTroom � Tsurf Þ ð8Þ

In the above equations, a is the solar absorptance of the exteriorwall surface, assumed to be 0.6; e is infrared emittance of the exte-rior wall surface, assumed to be 0.8; qsolar is solar irradiance on theexterior wall surface (W/m2), from TMY3 data; hext is exterior sur-face convective heat transfer coefficient (W/m2/K); hint is interiorsurface heat transfer coefficient (W/m2/K); Fsky is radiation viewfactor from sky to the wall; Tout is outside ambient temperature(K), from TMY3 data; Tsky is sky temperature (K), from TMY3 data;Tsurf is wall surface temperature (K); exterior wall surface facingthe outdoor environment in Eq. (7) and interior surface facingthe room in Eq. (8), and Troom is room (interior conditioned space)temperature (K).

In Eq. (7), the first term on the right side is the solar irradiance,the second term is the convection heat transfer and the last term isthe long-wave radiation (LWR) exchange with the surroundings.

The exterior convective heat transfer coefficient (hext) was not aconstant. Hourly values of ‘hext’ were calculated using the outdoortemperature and wind velocity data from the TMY3 files. Hourlyvalues of ‘qsolar’ and ‘hext’ for the different wall orientations weregenerated with the help of EnergyPlus,5 a whole-building modelingtool.

The LWR heat transfer consists of radiation exchange betweenthe exterior wall surface and the outside environment (‘out’),ground and sky, and is given by:

qLWR ¼ er½FoutðT4out � T4

surf Þ þ FgroundðT4ground � T4

surf Þ þ FskyðT4sky � T4

surf Þ�ð9Þ

The outside air temperatures (Tout) were obtained from TMY3data. Fsky, Fout and Fground are the view factors between the exteriorwall surface and the sky, outside and ground, respectively. For sim-plicity, the ground temperature (Tground) is assumed to be the sameas the outside air temperature (Tout), reducing the above equationto:

qLWR ¼ er½ðFout þ FgroundÞðT4out � T4

surf Þ þ FskyðT4sky � T4

surf Þ� ð10Þ

The sum of the three view factors (Fsky, Fout and Fground) for a ver-tical exterior wall surface is unity.

Fsky þ Fout þ Fground ¼ 1 ð11Þ

Following Walton [26], the view factors were calculated as:

Fsky ¼ b½0:5ð1þ cos/Þ�; b ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi0:5ð1þ cos/Þ

pð12Þ

where / is the tilt angle of the surface from the horizontal, which is90� for the walls being modeled. The following common assump-tions were made for the radiation calculations:� Each surface emits or reflects diffusely and is gray and opaque.� Each surface is at a uniform temperature.� Energy flux incident on or leaving a surface is evenly distributed

across the surface.

The interior heat transfer coefficient (hint) was assumed to be8.29 W/m2/K, following ASHRAE Handbook of Fundamentals,6 fora non-reflective vertical surface. It was assumed that the heatingand cooling systems could exactly match the instantaneous loads,so that the interior room temperature (Troom) floated between theheating and cooling set points but never went outside that range.No internal loads were considered.

Fig. 11. Experimentally measured heat flows through the different test wallsections during three summer days.

524 K. Biswas et al. / Applied Energy 131 (2014) 517–529

5. Results and discussion

5.1. Sample temperature and heat flux data

The data from the test wall were compiled into weekly filescontaining hourly-averaged data. The test period extended fromJuly 2012 to March 2013, covering the summer, winter and ‘swing’(fall or autumn) seasons. The focus of the discussion is on summerand winter periods, when the wall heat gains and losses are typi-cally at their peak. The section containing the nano-PCM wallboardis referred to as ‘NP-Gyp’ and the other section as ‘Gypsum’ in theforthcoming discussions.

The temperature distributions in the different sections of thetest walls are shown in Fig. 10. Hourly-averaged temperature dataare shown for three typical hot and sunny summer days (July 22–25) at the wall exterior, the OSB interior (facing the cavity), thecavity center, the wallboard exterior (facing the cavity) and thewallboard interior (facing the room). Fig. 11 compares the heat fluxdata from the ‘NP-Gyp’ and ‘Gypsum’ sections of the test wall.

The room interior was maintained at about 20–22 �C during themonitoring period. A clear trend is seen in the temperatures fromthe exterior to the interior in both ‘NP-Gyp’ and ‘Gypsum’ sections.Some differences were seen in the cavity center and the wallboardexterior between the different test sections. However, the temper-ature differences were fairly insignificant and did not reveal muchabout the behavior and impact of the nano-PCM wallboard. Theheat gains, as measured by the HFTs, were discernibly lowerthrough the nano-PCM wallboard compared to the regular gypsumwallboard. Similar trends were observed during the other seasons,depending on the outside conditions.

Since both the nano-PCM and gypsum wallboards were inter-acting with the same interior space, it was difficult to isolate theirenergy impacts through purely experimental means. For furtherevaluation and estimation of energy savings due to the nano-PCM wallboard, numerical modeling was utilized.

5.2. Model validation using measured data

In Figs. 12 and 13, the model calculations are compared to thetemperature data from the test wall during three summer andthree winter days, respectively. ‘[C]’ indicates the model calcula-tions using COMSOL. The monitored temperature locations wereas shown in Figs. 4 and 7. Overall, there was excellent agreementbetween model calculations and experimental data. The only loca-tion where major differences were observed was the ‘WallboardExterior’ in the ‘Gypsum’ section. These differences can partly be

Fig. 10. Experimentally measured temperatures at different l

explained by the air gaps observed near the sensors in the ‘Gyp-sum’ section of the test wall (Fig. 5) potentially resulting in errorsin the measured temperatures.

Fig. 14 compares the calculated and measured heat fluxes at thenano-PCM (‘NP-Gyp’) wallboard exterior. The HFTs were incorpo-rated in the model geometry using the same dimensions and mate-rial properties as the HFTs installed in the test wall. There wasreasonable agreement between the calculations and experimentaldata for the nano-PCM wallboard section. At peak heat flux condi-tions (positive and negative), the calculations were within 5–15%of the measurements. The calculations of the ‘Gypsum’ sectiondid not match the measurements very well and are not shown.These differences were possibly, at least in part, the result of errorsin measurements caused by the air gaps in the ‘Gypsum’ cavity.

In summary, the numerical models adequately simulated theCharleston test wall, and can be utilized for estimating the annualheat flows through walls with and without the nano-PCMwallboard.

5.3. Annual simulations

5.3.1. Heat gains and lossesTable 3 lists the total annual heat gains and losses through the

different modeled wall sections with ‘Gypsum’ and ‘NP-Gyp’ wall-boards, using the exterior and interior boundary conditions

ocations within the test wall during three summer days.

Fig. 12. Comparison of model calculations (indicated by [C]) with measured temperature data (summer).

Fig. 13. Comparison of model calculations (indicated by [C]) with measured temperature data (winter).

Fig. 14. Comparison of model calculations (indicated by [C]) with measured heat flux data.

K. Biswas et al. / Applied Energy 131 (2014) 517–529 525

described in Section 4.4. The heating and cooling set points forthe room were assumed to be 20 �C (68 �F) and 23.3 �C (74 �F),respectively, which is fairly typical for buildings in the US. Differ-ent wall orientations receive different amounts of annual solarirradiance, which is reflected in the total heat gains through thewalls. The annual heat losses through the different walls also

varied substantially, with the highest heat loss associated withthe north wall.

Since Charleston is a cooling-dominated climate, the heat gainswere higher than the heat losses through all walls, except the northwall. The nano-PCM wallboard (‘NP-Gyp’) reduced both annualheat gains and losses through each wall, although by different

Table 3Annual heat gains and losses through the wall section with different wallboards and at different orientations, using 20–23.3 �C (68–74 �F) room temperature set points.

Wall orientation Heat gain (Wh/m2) % Difference Heat loss (Wh/m2) % Difference

Gypsum NP-Gyp Gypsum NP-Gyp

East 10453.7 9551.2 �8.63 �7822.5 �6572.9 �15.97West 10145.5 9004.0 �11.25 �8141.5 �6734.6 �17.28North 5762.2 5479.9 �4.90 �10112.1 �9622.1 �4.85South 11292.2 8639.5 �23.49 �6525.9 �3605.1 �44.76

Fig. 16. Cooling set points (21, 22 and 23.3 �C), shown by the dashed red lines, usedfor annual simulations with respect to the phase change range of the nano-PCM,indicated by the shaded portion of the enthalpy function [h(T)].

526 K. Biswas et al. / Applied Energy 131 (2014) 517–529

percent amounts. For further investigation, the monthly heat gainsand losses through the south wall are shown in Fig. 15. It is inter-esting to note that the reductions in heat gain, due to the inclusionof the nano-PCM in the wallboard, were greatest during the winterand ‘swing’ (spring and autumn) months. In fact, the heat gainsthrough the nano-PCM wallboard were marginally higher duringthe peak summer months (Jun-Aug).

5.3.2. Impact of interior temperature set pointsChilds and Stovall [10] performed an optimization study of the

impact of PCM incorporated in the cavity insulation in walls andinvestigated various parameters with respect to their effect onenergy-savings resulting from the PCM. An important findingwas that the savings were sensitive to the room cooling set pointwith respect to the PCM phase change temperature. Therefore,annual simulations were performed at two additional pairs of inte-rior set points, 19–21 �C (66–70 �F) and 20–22 �C (68–72 �F). Thelower temperature indicates the heating set point and the highertemperature is the cooling set point. Fig. 16 shows the three cool-ing set points with respect to the phase change temperature rangeof the nano-PCM, which is indicated by the steep changes in thegradient of the enthalpy function (shaded portion). 23.3 �C iswell-above the phase change temperature range, 22 �C is nearthe higher end of the melting range and 21 �C is at about the centerof the melting range.

Only the south wall was chosen for further investigation. Fur-ther, only the heat gains were considered for this analysis, asCharleston lies in a cooling-dominated climate zone. Fig. 17 showsthe hourly heat gain variations during three typical summer days,with hot and sunny conditions (based on the TMY3 data), with thedifferent room temperature set points. The calculated heat gainswith the gypsum and ‘NP-Gyp’ wallboards were practically identi-cal at the 20–23.3 �C and 20–22 �C set points. However, with 19–21 �C set point, the ‘NP-Gyp’ wallboard resulted in lower peak heatgains. A time-delay was also observed in the diurnal evolution ofthe wall heat gain with the ‘NP-Gyp’ wallboard, compared to thegypsum wallboard. During the late-night/early-morning hours,the heat gains through the ‘NP-Gyp’ wallboard were higher thanthe gypsum wallboard, presumably due to the freezing of the

Fig. 15. Calculated monthly heat gains and losses through the

PCM releasing latent heat into the room. If some means of remov-ing this latent heat of freezing can be devised, so it is not releasedinto the room, higher energy savings can be realized.

5.3.3. Annual electricity consumptionThe time-delay in peak heat gains due to the nano-PCM wall-

board has the added benefit of reduction in electricity use as thecooling equipment efficiency is related to the ambient tempera-ture. The cooling equipment is very often placed in unconditionedspace, and operates more efficiently, i.e. provides the same coolingpeformance while consuming lower electricity, when the outsidetemperatures are lower. To determine the reduction in electricityconsumption, a typical heat pump is considered, with a tempera-ture-dependent energy efficiency ratio (EER) as shown in Fig. 18.The temperature-dependent EER values were calculated based onpublished data [27,28] for a 3-ton heat pump unit with a seasonalenergy efficiency ratio (SEER) rating of 13. Both the SEER rating andtonnage chosen are typical values for heat pumps currently used inbuildings. The energy efficiency ratio (EER, Btu/Wh), or coefficientof performance (COP, Wh/Wh), shown in Fig. 18 was used to con-vert the calculated heat gains into electricity consumption. It was

south wall, with 20–23.3 �C room temperature set points.

Fig. 17. Calculated heat gains through the south wall during three summer days,with different cooling set points.

1.8

2.3

2.9

3.5

4.1

4.7

6

8

10

12

14

16

20 25 30 35 40 45 50

CO

P (W

h/W

h)

EER

(Btu

/Wh)

Outdoor Temperature ( C)

Fig. 18. Energy efficiency ratio (EER, Btu/Wh)/coefficient of performance (COP, Wh/Wh) of the cooling system as a function of outdoor temperature.

Table 4Annual heat gains and cooling electricity consumption through the south wall section wit

Set points Heat gain (Wh/m2) % Differen

Gypsum NP-Gyp

19–21 �C (66–70 �F) 15379.2 12191.2 �20.7320–22 �C (68–72 �F) 13768.6 10375.2 �24.6520–23.3 �C (68–74 �F) 11292.2 8639.5 �23.49

K. Biswas et al. / Applied Energy 131 (2014) 517–529 527

assumed that the cooling equipment operated only when the roomtemperature tended to exceed the cooling set point. In other words,if the room temperature remained below the cooling set point,there was no electricity consumption even if there was some heatgain through the walls. Table 4 lists the calculated annual heatgains and electricity consumption with the different room temper-ature set points. It was interesting to note that, while at 19–21 �Cset points the summer peak heat gains were reduced by usingthe nano-PCM wallboard and not at the other set points (Fig. 17),the percentage reduction in overall annual heat gain was the low-est at 19–21 �C set points. On the other hand, at 19–21 �C, the per-cent reduction in electricity consumption was higher compared tothe corresponing reduction in actual heat gain, presumably due tothe time-delays in heat gains through the nano-PCM wallboard. Atboth 20–22 �C and 20–23.3 �C, the reductions in electricity con-sumption were lower than the respective reductions in heat gain.

Another factor to be considered while evaluating the impact ofPCM wallboards is the ‘load factor (LF)’. Load factor is defined asthe ratio of the peak electricity consumption to average electricityconsumption over a certain time period. Lower load factors implyhigher peaks, but lower average usage, and since the power plantcapacities need to be designed to be able to meet peak loads, theadded capacity may remain idle for extended time periods leadingto under-utilization. Higher load factors and lower peak demandare beneficial for electricity providers, allowing potentially lowercapacity and costs.

Fig. 19 shows the monthly electricity consumption and load fac-tors associated with the heat gains through the south wall at thedifferent temperature set points. Again, the highest percentagereductions in electricity consumption were observed in the winterand ‘swing’ months. During the peak summer months (June–Sep-tember), with the 20–23.3 �C and 20–22 �C set points, the electric-ity consumption with the ‘NP-Gyp’ wallboard was similar to ormarginally greater than with the regular gypsum wallboard; theload factors were also similar. With the 19–21 �C set points, how-ever, the calculated electricity consumption was lower and theload factors were discernibly higher during the peak summermonths with the ‘NP-Gyp’ wallboard. Table 5 lists the annual heatgains and cooling electricity use, and associated reductions withthe nano-PCM wallboard with different wall orientations at 19–21 �C temperature set points. It is again noted that, for all wall ori-entations, the percent electricity savings were higher than the cor-responding percent reductions in annual heat gain.

In summary, the annual simulations showed that the nano-PCMwallboard can potentially reduce the heat gains and losses throughthe building envelope and result in reduced electricity consump-tion. The annual vs. peak summer behavior of the nano-PCM wall-board was dictated by the interior cooling set point relative to thephase change temperature range of the nano-PCM. The savings canbe optimized through proper choice of the phase change tempera-ture range of the PCM with respect to the interior heating and cool-ing set points, or vice versa. Additional factors, such as thethickness of the PCM wallboard and its location within the enve-lope (interior or exterior side), should be evaluated and optimizedfor greater energy (electricity) savings, but they were outside thescope of this article.

h different wallboards, using variable room temperature set points.

ce Cooling electricity use (Wh/m2) % Difference

Gypsum NP-Gyp

3815.8 3010.1 �21.123427.3 2676.6 �21.902836.5 2246.9 �20.78

Fig. 19. Calculated monthly cooling electricity use and load factors for the south wall heat gain at different cooling set points.

Table 5Annual heat gains and cooling electricity consumption with different wallboards for different wall orientations and using 19–21 �C (66–70 �F) room temperature set points.

Wall orientation Heat gain (Wh/m2) % Difference Cooling electricity use (Wh/m2) % Difference

Gypsum NP-Gyp Gypsum NP-Gyp

East 14047.77 12747.19 �9.26 3636.82 3253.01 �10.55West 13698.06 12059.71 �11.96 3467.21 3001.98 �13.42North 8643.72 8035.13 �7.04 2249.39 2035.91 �9.49South 15379.19 12191.17 �20.73 3815.78 3010.05 �21.12

528 K. Biswas et al. / Applied Energy 131 (2014) 517–529

6. Summary and conclusions

This article describes the experimental testing in a naturalexposure test facility in a cooling-dominated climate and numeri-cal simulations of a nano-PCM-enhanced interior wallboard. Theexperimental data were used to validate finite element models ofthe test wall. The simulation results of the nano-PCM wallboardsection were in excellent agreement with measurements. Next,the numerical models were used to evaluate the annual impactof the nano-PCM wallboard on the wall-generated heating andcooling loads. Annual simulations were performed using TMY3weather data. Models of wood framed walls with both nano-PCMand regular gypsum wallboards were created.

Different heating and cooling set points were used in the simu-lations to evaluate their impact on the performance of the nano-PCM wallboard. The modeled reductions in heat flow resultingfrom the use of the nano-PCM wallboard were sensitive to thechoice of interior cooling set points. Three pairs of temperatureset points were investigated. The three cooling setpoints chosenwere: 23.3 �C that was well-above the phase change temperaturerange, 22 �C that was near the higher end of the melting tempera-ture range and 21 �C that was at about the center of the meltingrange. The modeled results for a south-oriented wall indicated thatthe highest annual reduction in wall heat gain was obtained with a22 �C cooling set point. However, at 22 and 23.3 �C, no reductionsin heat gain were observed during peak summer conditions with

K. Biswas et al. / Applied Energy 131 (2014) 517–529 529

the use of the nano-PCM wallboard. At 21 �C cooling set point, dur-ing peak summer, the nano-PCM wallboard reduced the peak heatgains and also delayed the heat flowing into the interior space.Thus, annual vs. peak summer behavior of the nano-PCM wall-board was dictated by the interior cooling set point relative tothe phase change temperature range of the nano-PCM. The time-delay in heat gains has an added advantage of allowing the air con-ditioning equipment to operate at higher efficiencies for coolingthe interior space. The nano-PCM wallboard also yielded higherload factors, which can potentially allow electricity providers andpower plants to lower capacity and costs. Overall, based on theannual simulations, the nano-PCM wallboard did show the poten-tial for reducing electricity consumption for space-conditioning.

Further work is needed to determine an optimum set of condi-tions to maximize the energy savings resulting from the use ofPCM-enhanced wallboards. Some of the factors to be consideredare removal of the latent heat of freezing so it is not released tothe building interior, thickness of the wallboards and their place-ment within the building envelope assembly, etc. In addition, test-ing and simulations need to be performed to evaluate theperformance of the PCM wallboards in other climate types. Further,the current annual simulations only considered a clear wall sec-tion, with no construction details like corners, joints with ceilingand other walls, etc. Also, internal loads, solar gain and heat flowthrough windows, roof and ceiling loads, infiltration, etc. werenot considered. To get a more realistic estimate of energy savingsfrom the PCM wallboads, whole-house modeling that considersall the above factors is required.

Acknowledgements

The authors would like to gratefully acknowledge the fundingsupport for this work from the Department of Energy Small Busi-ness Innovation and Strategies Program (Recovery Act) AwardNo. DE-SC0003309. The authors are also thankful to Jerald Atchleyand Phillip Childs of ORNL for their contributions in installing andinstrumenting the test wall, data gathering and troubleshooting,Dr. Pedro Otaduy (formerly of ORNL) for his guidance with COM-SOL modeling, and Dr. Keith Rice (ORNL) for the energy efficiencyratio trend calculations.

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