experimental validation RTS

8/7/2019 experimental validation RTS

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This paper has been downloaded from the Building and Environmental Thermal SystemsResearch Group at Oklahoma State University (www.hvac.okstate.edu)

The correct citation for the paper is:

Iu, I.S., D.E. Fisher, C. Chantrasrisalai, and D. Eldridge. 2003. "Experimental Validation

of Design Cooling Load Procedures: The Radiant Time Series Method", ASHRAE

Transactions. 109(2):139-150.

Reprinted by permission from ASHRAE Transactions (Vol. #109, Part 2, pp. 139-150).

2003 American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc.
http://www.hvac.okstate.edu/http://www.hvac.okstate.edu/


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Co p y r i g h t 2 0 0 3 , Ame r i c a n S o c i e t y o f H e a t i n g , R e f r i g e r a t i n g a n d A i r - C o n d i t i o n i n gE n g i n e e r s , I n c . ( www . a s h r a e . o r g ) . R e p r i n t e d b y p e r m i s s i o n f r o m ASHRAET r a n s a c t i o n s 2 0 0 3 , Vo l u me 1 0 9 , P a r t 2 . T h i s p a p e r may n o t b e c o p i e d n o rd i s t r i b u t e d i n e i t h e r p a p e r o r d i g i t a l f o r m w i t h o u t ASHRAE' s p e r mi s s i o n .

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RTSM f a c i l i t a t e s e s t i m a t i o n o f c o m p o n e n t c o n t r i b u t i o n s t o t h ec o o l i n g l o a d a n d p r o v i d e s a s p r e a d s h e e t o r i e n t e d c a l c u l a t i o np a t h t h a t i s u s e f u l f o r b o t h t e a c h i n g a n d d e s i g n .

I mme d i a t e l y f o l l o w i n g d e v e l o p me n t o f t h e RTSM, t h en e w me t h o d wa s v e r i f i e d b y c o mp a r i n g c o o l i n g l o a d sp r e d i c t e d b y t h e RTSM w i t h c o o l i n g l o a d s p r e d i c t e d b y t h eh e a t b a l a n c e me t h o d f o r a wi d e r a n g e o f z o n e c o n f i g u r a t i o n s .R e e s e t a l . ( 1 9 9 8 ) c o mp a r e d RTSM a n d h e a t b a l a n c e c o o l i n gl o a d s f o r 1 2 9 6 c o n f i g u r a t i o n s , wh i c h we r e g e n e r a t e d b y p a r a -m e t r i c a l l y v a r y i n g s i g n i f i c a n t i n p u t p a r a m e t e r s o v e r a wi d er a n g e . T h i s a n a l y s i s c o n c l u s i v e l y d e m o n s t r a t e d t wo i mp o r t a n ta t t r i b u t e s o f t h e RTSM: ( 1 ) t h e me t h o d a l w a y s p r o d u c e s ac o n s e r v a t i v e e s t i m a t e o f t h e c o o l i n g l o a d w h e n c o mp a r e d t ot h e h e a t b a l a n c e me t h o d ; ( 2 ) o v e r p r e d i c t i o n o f t h e c o o l i n gl o a d b y t h e RTSM t e n d s t o i n c r e a s e a s t h e f r a c t i o n o f wi n d o wa r e a i n t h e z o n e i n c r e a s e s .

T h e e x p e r i me n t a l r e s u l t s r e p o r t e d i n t h i s p a p e r a n d i n t woc o mp a n i o n p a p e r s p r o v i d e d i r e c t a n d i n d i r e c t v e r i f i c a t i o n o ft h e RTSM . T h e e x p e r i me n t s p r e s e n t e d i n t h e s e p a p e r s we r ed e s i g n e d t o t e s t b o t h t h e h e a t b a l a n c e me t h o d a n d t h e RTSMa t t h e e x t r e m e c o n d i t i o n s i d e n t i f i e d b y R e e s e t a l . ( 1 9 9 8 ) .U n d e r t h e s e c o n d i t i o n s ( a h i g h l y g l a z e d s p a c e w i t h n o i n t e r n a lh e a t g a i n s o r i n f i l t r a t i o n ) , t h e c o o l i n g l o a d s p r e d i c t e d b y t h eRTSM a n d t h e h e a t b a l a n c e a r e e x p e c t e d t o d i v e r g e . I n d i r e c tv e r i f i c a t i o n i s p r o v i d e d i n t h e c o mp a n i o n p a p e r ( C h a n t r a s r i s -a l a i e t a l . 2 0 0 3 ) , wh i c h d e m o n s t r a t e s t h e a c c u r a c y o f t h e h e a tb a l a n c e me t h o d . V a l i d a t i o n o f t h e h e a t b a l a n c e me t h o d s e r v e st o v a l i d a t e t h e r e s u l t s o f t h e p a r a me t r i c s t u d y d i s c u s s e d i n t h ep r e v i o u s p a r a g r a p h ( B e e s e t a l . 1 9 9 8 ) . T h i s p a p e r c o m p a r e se x p e r i me n t a l r e s u l t s d i r e c t l y w i t h c o o l i n g l o a d s p r e d i c t e d b yt h e ASHRAE L o a d s T o o l k i t ( P e d e r s e n e t a l . 2 0 0 1 ) v e r s i o n o ft h e RTSM a n d d i s c u s s e s s o u r c e s o f e r r o r i n t h e e x p e r i me n t a ld a t a a n d i n t h e RTSM i n p u t s .

T h e r a d i a n t t i m e s e r i e s me t h o d ( R T S M) i s a s i m p l i f i e dc o o l i n g l o a d p r o c e d u r e t h a t wa s d e v e l o p e d b y ASHRAE t oc o mp l e me n t t h e mo r e r i g o r o u s h e a t b a l a n c e p r o c e d u r e d e v e l -o p e d b y P e d e r s e n ( P e d e r s e n e t a l . 1 9 9 7 ; P e d e r s e n 2 0 0 1 ) . T h e

I p s e ng I u a n d Ch a n v i t C h a n t r a s r i s a l a i a r e r e s e a r c h a s s i s t a n t s a n d D. E . F i s h e r i s a n a s s i s t a n t p r o f e s s o r a t Ok l a h o ma S t a t e U n i v e r s i t y , S t i l l -w a t e r , Ok l a . Da v i d E l d r i d g e i s a p r o j e c t e n g i n e e r a t Gr u mma n / B u t k u s A s s o c i a t e s , C h i c a g o , I l l .

02003 ASHRAE . 1 3 9

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Ex p e r i me n t a l V a l i d a t i o n o f

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ABSTRACTT h e r a d i a n t t i me s e r i e s me t h o d ( R T SM) i s a c o o l i n g l o ad

c a l c u l a t i o n p r o c e d ur e d e v el o p e d b y ASHRAE a s a s i m p l i f i e dc o mp a n i o n t o t h e r i g o r o u s h e a t b a l a n c e p r o c e d ur e . T h e RTSMwa s i n i t i a l l y v e r g e d f o r a l a r g e p a r a me t r i c s e t o f z o n e c o n f i g -u r a t i o n s b y c o mp a r i n g c o o l i n g l o a d s p r e d i c t e d b y t h e RTSMwi t h c o o l i n g l o a d s p r e d i c t e d b y t h e h e a t b a l a n c e me t h o d . T h ee x p e r i me n t a l r e s u l t s r e p o r t e d i n t h i s p a p e r d e mo n s t r a t e t h ev a l i d i t y o f t h e p r e v i o us p a r a me t r i c s t u d i e s a n d c o n f i r m t h es i g n i f i c a n c e o f t h e s o - c a l l e d " a d i a b a t i c z o n e a s s u mp t i o n , "wh i c h r e s u l t s i n u n d e r p r e d i c t i o n o f c o nd uc t i o n l o s s e s b y t h eRTSMf or h i g h l y g l a z e d s p a c e s :

T h e i n v e s t i g a t i o n a l s o d e mo n s t r a t e d t h e i mp o r t a n c e o fc a l c u l a t i n g t h e r e f l e c t e d s o l a r r a d i a t i o n i n c i d e n t o n t h e i n t e -r i o r s u r f a c e s o f t h e wi n d o w s . T h e ex p e c t e d e r r o r i n t h e RTSMf o r h i g h l y g l a z e d s p a c es c a n b e r e d uc e d u p t o 80%b y a c c o un t -i n g f o r r e f l e c t e d v i s i b l e r a d i a t i o n l e a v i n g t h e s p a c e . I n c l u di n ga r e f l e c t e d s o l a r r a d i a t i o n c a l c u l a t i o n i n t h e R T SMr e s u l t e d i n

p r e d i c t e d c o o l i n g l o a d s w i t h i n 1 5% o f me a s u r e d d a t a f o rp a s s i v e s u n s p a c e s wi t h w e l l - d e f i n e d z o n e c o n f i g u r a t i o np a r a me t e r s . Co n s e q u e n t l y , t h e s t u dy n o t o n l y d e mo n s t r a t e s t h ev a l i d i t y o f t h e RTSMf or s t a n da r d z o n e c o n f i g u r a t i o n s , b u t i ta l s o s u g ge s t s t h a t t h e RTSMc a n b e u s e d t o o b t a i n a f i r s t o r d e re s t i ma t e o f c o o l i n g l o a d s f o r s o l a r h e a t g a i n d o mi n a t e d s p a c e s .I NTRODUCTI ONB a c k g r o u n d

D. E . F i s h e r , Ph . D . , P. E .M e m b e r ASHRAE


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140 ASHRAE Transactions: Research

OVERVIEW OF THE

RADIANT TIME SERIES METHOD

Calculation Procedure

The radiant time series method (RTSM) is a two-stage

procedure for cooling load calculations (Spitler et al. 1997).

First, hourly heat gains in a designated building space arecalculated or estimated. The heat gains may include internal

gains from lights, equipment, or human activity, conduction

through the building envelope, convection from infiltration

and ventilation, and transmitted and absorbed solar heat gains.

Fixed fractions are used to split each internal heat gain into a

radiative and a convective component. Conductive heat gains

are split according to the relative magnitude of the radiation

and convection film resistances, as shown in Equation 1.

(1a)

(1b)

where

fr = radiative fraction of conductive heat gain

fc = convective fraction of conductive heat gain

Rr = radiation film resistance, (ft2Fh)/Btu ([m2C]/W)

Rc = convection film resistance, (ft2Fh)/Btu ([m2C]/W)

The RTSM does not use detailed surface heat balances to

model the effects of convection and radiation. Instead, it esti-

mates their combined effect on exterior surfaces using the sol-

air temperature and relies on the second stage of the two-step

procedure to handle the inside surface radiant exchange.

Conductive heat transfer is therefore calculated from the sol-

air temperature to the inside air temperature using the follow-

ing periodic response factors (PRFs) formulation:

(2)

where

q = heat flux for the current hour, Btu/(hft2) (W/m2)

Yp = air-to-air periodic response factors, Btu/(hft2F)

(W/[m

2C])

te = sol-air temperature, F (C)

trc = constant room temperature, F (C)

= current hour; = time step

The air-to-air PRFs are response factors for a one-dimen-

sional, transient conduction problem with a steady periodic

driving force. PRFs directly scale the contribution of previous

fluxes (in the form of temperature gradients) to the current

conductive heat flux, as shown in Equation 2. As a result, the

PRF series represents the transient thermal response of a wall.

The second-stage calculation procedure converts the

hourly heat gains into cooling loads. The radiative heat gains

are multiplied by the radiant time factors (RTFs) to obtain

cooling loads. The RTF represents the response of the building

space to a radiant pulse, and it is therefore dependent on the

material properties of the building elements. On the other

hand, the convective heat gains are independent of the building

materials and represent instantaneous cooling loads. The total

hourly cooling loads are the summation of the hourly radiative

and convective values.

Assumptions

Since the RTSM is a heat balance-based procedure, the

heat balance method (HBM) assumptions (Chantrasrisalai et

al. 2003) equally apply to the RTSM. In addition, the follow-

ing assumptions apply (McQuiston et al. 2000):

All external and internal driving forces are steady-peri-

odic, and the zone air temperature is constant.

The outside and inside heat transfer coefficients are

time-invariant and include the combined effect of con-

vection and radiation.

Solar transmitted beam radiation is distributed on the

floor only, while other shortwave and longwave radia-

tion is distributed uniformly on each surface in the

building space. In the Toolkit version of the RTSM,

reflected radiation also remains in the space.

Overprediction of Peak Cooling Loads By the RTSM

Rees suggested that the adiabatic zone assumption, which

is implicit in the generation of the radiant time series, coupledwith the air-to-air conduction calculation (Rees et al. 1998)

is responsible for differences between the RTSM and the heat

balance method. Applying the heat balance method to an adia-

batic zone generates the radiant time series. Heat gains, once

accounted for in the space, cannot be lost by either surface-

to-surface conduction or radiation from the space. Overpre-

diction of the cooling load occurs when radiation on the inte-

rior surfaces of the zone raise the inside surface temperature of

a lightweight, conductive surface, such as a single pane of

glazing, above the outside surface temperature. In this case,

two types of heat transfer immediately follow. First, the eleva-

tion of the inside surface temperature increases the rate of

inside convection and, as a result, increases the cooling load.This phenomenon is modeled by the air-to-air conduction

calculation in the RTSM. Second, the change in surface

temperature could cause surface-to-surface conductive heat

loss. As shown in Figure 1, the sol-air to inside air temperature

gradient (T3T4) is in the opposite direction of the inside

surface to outside surface temperature gradient (T1T2). Under

these conditions, the heat balance, which is based on the

surface temperature gradient, predicts a heat loss, while the

RTSM, which is based on the air temperature gradient,

predicts a heat gain.

r

Rc

Rr Rc+------------------=

fc 1 fr=

q

YPj te, j trc( )

j 0=

23

=


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ASHRAE Transactions: Research 141

Unaccounted for conduction losses occur during periods

of high solar heat gains when interior surface temperatures rise

to create a surface temperature gradient that is not predicted by

the RTSM. Rees et al. (1998) found that although the RTSM

would never underpredict the space loads, it could overpredict

the peak cooling load by as much as 37% for cases with a high

percentage of glazing and low internal heat gains.

Figure 2, which compares the heat balance and RTS meth-

ods, shows essential agreement between the methods for a

lightweight zone with 90% glazing. The agreement was

achieved by eliminating the two known sources of error

discussed in the previous paragraphs. First, for purposes of

illustration, the thermal resistance of the window was

increased to eliminate conduction that the RTSM does not

account for. Second, reflected solar radiation losses through

the windows were calculated. These two modifications result

in excellent agreement between the methods for all zone

configurations. The reflected radiation correction, though not

included in the ASHRAE Loads Toolkit, can easily be added

to future versions of the method.

Some error may also be made by assuming that surface

convection and radiation coefficients are constant. This timeinvariant assumption, which is necessary for simplified PRF

calculations, also influences the conductive heat gain split

calculations as formulated in Equation 1.

METHODOLOGY

The most recent version of the RTSM is codified in the

ASHRAE Loads Toolkit (Pedersen et al. 2001). The objective

of this investigation is to experimentally demonstrate the reli-

ability of the RTSM as a simplified cooling load calculation

procedure. This is accomplished by testing the extreme case of

the solar dominated zone. Although the heat gain splits exper-

imentally measured for this zone would not normally be

encountered in most buildings (the solar heat gain exceeded

65% of the total heat gain for the test buildings), these condi-

tions were selected in order to bound the error associated with

the RTSM procedure. For all zones with a smaller fraction of

solar to total heat gain, the expected error due to the procedure

is less, often significantly less, than reported in this paper.

Experimental Procedure

Two geometrically identical buildingsone thermally

massive and one thermally lightweightas described in a

companion paper (Eldridge et al. 2003), were constructed in

an open field in Stillwater, Oklahoma. The first floor of each

building consists of a mechanical/control room that provides

conditioned air to the test cell located directly above, as shown

Figure 3. The control room air temperature is maintained at the

same temperature as the test cell in order to minimize heat

conduction through the floor.

Elliptical flow nozzles measure the volumetric flow rate

in the air loop. The room inlet and outlet air temperatures are

also measured using thermocouple grids located in the ducts.

The cooling load can then be calculated using the following

equation:

(3)

For each set of experiments, the two test cells were iden-

tically configured. Both test cells had the same interior room

configuration, and the indoor temperatures were controlled to

the same value for each test. Four interior room parameters

were varied during the tests, as shown in Table 1. Each param-

eter was designed to test the performance of different aspects

of the RTSM algorithm.

Figure 1 Opposing temperature gradients predicted byHBM and RTSM.

Figure 2 RTSM modified to correct errors.

Q

mcp Tou t, Tin ,( )=


5/13


The experiments were designed to minimize internal and

infiltration heat gains. Consequently, the cooling load was

generated entirely by solar and conduction gains. Test condi-

tions were maintained for more than 24 consecutive hoursprior to data collection in order to allow the space to reach a

steady-periodic state. Hourly cooling loads were measured

and compared to cooling loads predicted by the RTSM under

the same conditions.

Model Development

The measured cooling loads were compared to cooling

loads predicted by two RTSM cooling load modelsthe basic

RTSM model and the modified RTSM model. Both models

were based on the ASHRAE Loads Toolkit (Pedersen 2001)

algorithms.

Basic Model Validation. The basic model uses both

Toolkit algorithms and measured input data in the calculation.

The measured input parameters are: outdoor and indoortemperatures, ground surface temperature, infiltration, globe

horizontal solar radiation, wind speed, wind direction, system

airflow rate, and surface shortwave absorptances. This valida-

tion procedure minimizes the error due to incorrectly esti-

mated inputs and provides an estimate of the ability of the

RTSM to accurately predict cooling loads.

Modifications to the Toolkit algorithms were made in

order to use the measured input parameters in the test. Table 2

summarizes the use of input parameters and the associated

heat transfer models used in the RTSM. The measured param-

eters are the hourly input parameters in the associated Toolkit

modules. In addition, in order to agree with the RTSM

assumptions, average measured indoor temperatures are used

in the calculation procedure. The PRFs and RTFs are gener-

ated using average outside and inside surface film coefficients.

The inside heat balance parameters have a major impact

on the resulting cooling load. This is particularly true of the

inside convection because it results in a direct contribution to

the cooling load. Table 3 compares the ASHRAE natural

convection coefficients to the ceiling-diffuser convection

coefficients (Fisher and Pedersen 1997) used in this valida-

tion. Note that the convection coefficients in the ASHRAE

model are based on reduced convection at the ceiling due to

thermal stratification. As a result, the ASHRAE model under-

estimates the inside convection for the high ventilation ratespresent in the test buildings.

Modified Model Validation. The modified model uses

the same Toolkit algorithms and input data used by the basic

model. In addition, the modified model accounts for short-

wave radiation heat loss through glazed surfaces. The short-

wave radiation heat loss is due to shortwave radiation from

internal gains, diffuse solar radiation, and the portions of

transmitted solar radiation heat gains that are reflected from

the floor and transmitted out of the space through the

windows.

TABLE 1

Test Cell Configurations

Test Base Ceiling Carpet Blinds Mass Office

Suspended Ceiling: 2 ft 4 ftlay-in ceiling below bar joists

No Yes No No No Yes

White VenetianBlinds

No No No Yes(45 slats)

No Yes(Horizontal slats)

Carpet with 0.25 in. pad No No Yes No Yes Yes

Thermal Mass: office furniture

including desks, tables, chairs,

and bookshelves filled with

books

No No No No Yes Yes

Figure 3 Terminal reheat system used in the RTSM

validation (not to scale).


6/13


The calculation procedure of the modified RTSM

includes the following algorithm to determine the amount of

shortwave radiation heat gain that is transmitted out of the

building space through the windows. First, the total diffuse

shortwave heat gain is calculated as

(4)

where Qr,beam is the portion of the beam radiation that is

reflected diffusely from the floor, Qdiffuse is the diffuse solar

radiation transmitted into the building space, andQsw,Intis the

shortwave radiation from internal loads.By assuming that the beam solar radiation is distributed

only on the floor, while the diffuse solar radiation is distributed

uniformly on each interior surface, Qr,beam can be calculated as

follows:

(5)

where sfloor is the solar fraction andt,floor is the shortwavereflectance of the floor. Note that the values of beam radiation

Qbeam and diffuse radiation Qdiffuse are available in the stan-

dard RTSM calculation procedures. The total diffuse short-

wave radiation heat loss through the windows is related to the

total shortwave heat gain in the zone, as shown in Equation 6.

(6)

where s is the solar fraction andtis the transmittance of thewindows. In the RTSM, the solar and radiant gains are oper-

ated on by RTFs to obtain cooling loads. In order to properly

account for the loss of solar radiation that is reflected from

interior surfaces and leaves the space through the windows,

the shortwave radiation heat loss must be calculated andsubtracted from the hourly total radiant heat gain before the

radiant heat gain is processed by the RTFs.

RESULTS

Predicted Heat Gains

For a 40C (104F) design day outside dry-bulb and

constant 25C (77F) inside air boundary temperatures, Table

4 shows the individual heat gain contribution to the total heat

gain for the lightweight building with the base configuration.

TABLE 2

Summary of Input Parameters and Heat Transfer Models for the RTSM Validation

Input Data / Heat Transfer Models Methods

Indoor air temperature Measured data

Sol-air temperature Detailed sol-air temperature model

Measured outdoor air temperature Measured shortwave absorptances: 0.9 for roofs, 0.7 for heavy building, and 0.6 for light build-

ing exterior surfaces

Measured ground surface temperature

BLAST sky temperature model

ASHRAE surface view factor model

Sky (solar) radiation Modified ASHRAE clear sky model with measured global horizontal solar radiation

Ground surface temperature Measured data

Conduction State-space conduction (Seem 1987)

Surface convection coefficients Outside: MoWitt model with hourly measured wind speed and direction

Inside: Fisher and Pedersen (1997) model

Surface radiation coefficients Outside: Walton (1983) model

Inside: Walton (1980) modelInfiltration BLAST model with 0.25 ACH hourly measured values

Conductive heat gain splits Detailed splits, Equation 1

TABLE 3

Comparison of Interior Convection Models

Convection Coefficients ASHRAE Fisher and Pedersen (1997)*

Ceiling 1.250 20.497

Wall 4.679 7.294

Floor 4.370 5.380

* Based on 19.5 ACH

Qsw , Qr,beam, Qdiffuse, Qsw ,In t, ,+ +=

Qr,beam, sf loort,f loorQbeam, ,=

QSW,loss, Qsw , skt,kk 0=

#windows

=


7/13


The heat gain fractions were calculated from the followingformulation:

(7)

where

Q* = heat gain fraction

Q = component heat gain, W or Btu/h

Qtot= total heat gain, W or Btu/hThe following component heat gains are shown in the

table:

Q*infil = infiltration heat gain fraction

Q*cond = air-to-air conductive heat gain fraction

Q*solar = transmitted solar heat gain fraction

Q*internal= internal heat gain fraction

For the test building, the cooling load is dominated by the

solar heat gain with a significant contribution from the

conductive heat gain. Infiltration is insignificant, and internal

gains are nonexistent. Since the air-to-air conductive heat gain

is calculated using the sol-air temperature and air-to-air PRFs,

the conductive heat gain includes convection, longwave radi-

ation, and surface incident solar radiation in addition to

surface-to-surface conduction. As a result, the contribution of

air-to-air conduction to the total heat gain is relatively high.

The heat gain splits shown in Table 4 represent extreme

operating conditions, and the procedural error associated with

these conditions is significant. Rees et al. (1998) calculated the

deviation of the peak RTSM cooling load from the peak HBM

cooling load for 1296 cases. Figure 4 shows the experimental

test case data predicted by the modified RTSM (117-RP)

superimposed on the Rees data (RP-942). As shown in the

figure, the experimental test cases are well within the expected

range of uncertainty for the RTSM even though they representextreme operating conditions.

Figure 4 shows a deviation from the heat balance of less

than 23% for all cases, even when the peak cooling load is rela-

tively low. For cooling loads greater than 300 W/m2 (95.13

Btu/hft2), the modified RTSM deviates from the HBM by less

than 17%.

Modeling Thermal Mass Effect

The thermally massive test cell was constructed of 8 in.

(20.32 cm), filled, heavyweight concrete blocks with a brick

veneer, a 5 in. (12.7 cm) concrete roof, and a 5 in. (12.7 cm)concrete floor. The lightweight test cell consisted of wood-

framed walls with an exterior insulated finish system (EIFS)

veneer, a built-up insulated roof, and a 3.5 in. (8.89 cm)

concrete floor. Both test cells had large windows (50% glaz-

ing) on the south and west walls. The periodic response factors

shown in Figure 5 illustrate the different thermal responses of

the building elements.

The abrupt change of the wall and roof PRFs for the light

building means that the thermal responses of these surfaces are

faster than the heavy building wall and roof. Based on the

surface response factors, one might expect the zone thermal

response to be significantly different for the two buildings.

However, the dominance of the solar heat gain ensures that the

RTFs rather than the PRFs will largely determine the zone

response. Figure 6 shows the radiant time factors for diffuse

and beam solar radiation. The difference between diffuse RTF

and beam RTF is due to different radiation distributions. The

diffuse RTF assumes that radiation is distributed uniformly on

all interior surfaces; the fast thermal response of the light-

weight walls and roof causes the diffuse RTF to decrease

faster. The beam RTF assumes that radiation is distributed

TABLE 4

Typical Light Building Heat Gain Splits

Component Heat Gain Q*infil Q*cond Q

*solar Q

*internal

Heat Gain Fraction 0.65% 31.85% 67.50% 0.0%

Q

Q

1=

24

Qto t, 1=

24

-------------------------=

Figure 4 Comparison of RP-1117 results with Rees

parametric study.


8/13


Figure 5 Periodic response factors for test buildings surfaces.

Figure 6Radiant time factors for test buildings.


9/13


only on the floor, and since the building floor constructions are

similar, the zone RTFs are also similar.

The overall effect of the building thermal mass is shown

in Figure 7. The figure shows the measured cooling load for

the two buildings under identical operating conditions. As

shown by the load plot, the thermal mass of the heavy build-

ing damps the peak load by 25% but shows no discernible shiftin the peak hour. The fraction of peak load plot shows the

same cooling load profiles normalized as follows:

(8)

where QNis the normalized cooling load, Qestis the estimated

cooling load, andQmin,exp andQmax,exp are the minimum and

maximum measured cooling loads, respectively. Presenting

the measured data in this way shows that the thermal response

times of the two buildings are very similar in spite of signifi-

cant differences in the building thermal mass.

As reported in a companion paper (Chantrasrisalai et al.2003), the HBM correctly predicts both the peak load reduc-

tion and the peak time. The HBM models were therefore used

in a small parametric study to gain additional insight into the

thermal response of the experimental buildings. As a result of

this simulation study, the following conclusions are reached:

The high percentage of glazing on the south and west

walls (50% by outside surface area) effectively short-

circuits the building thermal mass. Removing the win-

dows in the simulation study results in a significant time

shift.

The peak time is relatively insensitive to the magnitude

of interior convection coefficient. High convection coef-ficients associated with 19.5 ACH and natural convec-

tion coefficients for both high glazing and no glazing

cases result in less than a 0.5 hour change in the peak

hour.

Although the convection coefficient has little effect on the

peak time, it has a significant effect on the magnitude of the

peak load. This is illustrated in a companion paper (Chantras-

risalai et al. 2003), which shows that significant error can be

introduced in the peak load calculation by not adjusting the

convection coefficients to match the ventilative flow rate

required to meet the hourly cooling load. For example, using

the ASHRAE default (natural convection) correlations to

model the heavyweight buildings (which required 20 ACH to

meeting the cooling load) results in the HBM underpredicting

the peak load by more than 20%.

Prediction of Peak Cooling Loads

Cooling load data for each of the test configurations were

collected and compared with the RTSM predicted results as

shown in Figure 8 and Figures 10 through 14. Each plot shows

both measured and predicted hourly cooling loads, which are

defined in the figures as follows:

Measuredmeasured cooling load

HBMcooling load predicted by heat balance method

with measured input data

RTSMcooling load predicted by the basic RTSM

model

RTSM-Modifiedcooling load predicted by the modi-

fied RTSM model

All cooling loads are shown as a fraction of the measured

load range. The actual load range (peak load and minimum

load) used to calculate the fraction of full load is also shown

in each figure. Showing the data as a fraction of measured load

facilitates estimation of the peak load error associated with theRTSM and the HBM for each case. The peak error associated

with each method can be read directly from the graphs.

Base Configuration. This test compares the measured

cooling loads for an empty room with the RTSM predicted

Figure 7Thermal mass effects.

QN

Qes t Qmin,exp

Qma x,exp Qmi n,exp------------------------------------------------%=


10/13


loads. The comparison is shown in Figure 8. Although most of

the input uncertainties are eliminated in the basic model, theRTSM overpredicts the peak cooling load as expected for the

solar-dominated experimental rooms. This is due to two

uncorrected errors in the Toolkit RTSM, as discussed above.

Although the conductive heat loss error was not corrected, the

radiation correction was implemented. This correction

reduces the peak load errors in both buildings by more than

50%, as shown by the RTSM-modified curves.

The damping effect of the thermally massive heavy build-

ing is also shown in Figure 8. The cooling load is shifted off

peak by the heavy building, resulting in a 25% reduction in the

peak cooling load.Suspended Ceiling Configuration. This test configura-

tion required installation of suspended ceilings in the test cells,

as shown in Figure 9. Note that the diffuser remained fixed at

the same level for both the suspended ceiling and the base case

configuration. The correlation used to obtain the ceiling

convection coefficient was a better match for the suspended

ceiling case than for the base case. That is, the correlations

were developed for an attached radial ceiling jet (Fisher et al.

1997). As a result, the error in the cooling load predicted by the

Figure 8 Base configuration.

Figure 9 Ceiling diffuser configurations.


11/13


RTSM was less for the suspended ceiling case, as shown in

Figure 10. The basic trends, however, were the same, with the

radiation loss correction significantly improving the results

and the remaining peak errors about the same for the both

buildings.

Mass Configuration. Thermal mass was added to the

carpet configuration in the form of office furniture and books.

Office Configuration.Figure 11 shows cooling load

comparisons for an office configuration that includes blinds,

carpet, and thermal mass. Thermal mass was added to the

carpet configuration in the form of office furniture and books.

The blind slats are in a horizontal position, resulting in higher

beam solar radiation heat gains compared to the blind config-

uration. The blinds may also influence the surface convection

coefficients in this test. The thermal mass in the test cells alle-

viates the unaccounted for conduction error, and the

suspended ceiling results in a better approximation of the inte-

rior convection coefficient at the ceiling level. The discrep-

ancy between the RTSM and the HMB for this configuration

is primarily due to the fact that a sophisticated blind model was

implemented in the heat balance procedure in order to achievegood agreement with measured data, as discussed in the

companion paper (Chantrasrisalai et al. 2003).

Summary of Results. Table 5 summarizes the peak

errors predicted by the basic and modified RTSM models for

all test cases. In order to improve the overall performance of

the RTSM for these cases, the HBM must first be improved.

The peak errors of the tests change depending on the uncer-

tainties associated with the interior configurations.

The shortwave correction significantly improves the

RTSM results for all test configurations. For the experimental

Figure 10 Suspended ceiling.

Figure 11 Office configuration.


12/13


rooms, maximum deviation from the heat balance is 20% and

maximum deviation from the measured data is 29%. As previ-

ously explained, this represents the maximum expected error

for a zone with a transmitted solar heat gain contribution of

67.5% of the total heat gain. For typical applications, the erroris expected to be much less.

Figure 12 shows the cooling load predicted by the RTSM

and HBM for an office configuration with a 1.52 m 1.52 m(5 ft 5 ft) sized window on the west-facing wall. The windowis a double-pane, low emissivity glass and is shaded at all times

with a 45 venetian blind. The boundary conditions are 40C(104F) design day outside dry-bulb and constant 25C (77F)

inside air temperatures. Internal heat gains were also consid-

ered in this simulation: two people are working in each build-

ing during office hours; two computers are used continuously

and are in saver mode in the evening until the people come

back the next morning; two 40 W lamps are on when the

people are there. This configuration represents one of the low

solar heat gain cases that would fall on the 45 degree lineshown in Figure 4. For this case, the RTSM results are almost

identical to the HBM results, as shown in Figure 12. The off-

peak load difference is due to the constant film coefficients

used in the RTSM. Table 6 summarizes the individual heat

gain contribution to the total cooling load for the light build-

ing. Comparing these heat gains to the experimental heat gains

(Table 4), shows that the air-to-air conductive heat gain domi-

nates the thermal processes. Although the window area is

smaller, the transmitted solar heat gain still contributes signif-

TABLE 5

Summary of Peak Load Errors Predicted by RTSM

RTSM Basic Model

Test Base Ceiling Blinds Carpet Mass Office

Light Bldg 41.93% 36.98% 37.86% 44.45% 35.59% 28.08%

Heavy Bldg 37.36% 31.49% 19.4% 42.22% NA 12.49%

RTSM Modified Model

Test Base Ceiling Blinds Carpet Mass Office

Light Bldg 16.89% 12.66% 26.61% 29.83% 24.41% 21.05%

Heavy Bldg 11.14% 6.4% 8.65% 28.17% NA 6.63%

NA = Not available

Figure 12 Simulations for typical building configurations.

TABLE 6

Heat Gain Fractions for Typical Building Configurations

Component Heat

GainQ*infil Q

*cond Q

*solar Q

*internal

Heat Gain Fraction 2.78% 55.81% 27.42% 13.99%


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icantly to the total cooling load. The internal heat gains are

about 50% of the transmitted solar heat gains. The infiltration

rate was not changed for the typical office simulation. Rees et

al. (1998) provide a detailed discussion of the expected devi-

ation of the RTSM from the HBM over the expected range of

input parameters.

CONCLUSIONS AND RECOMMENDATIONS

The experimental results illustrate the utility of the

RTSM, even for the extreme case of a conditioned sun space

with no internal gains. The modified RTSM overpredicts the

heat balance by less than 20% for all cases. This is quite

reasonable for the heat gain split (67.5% solar) observed in the

experiments. Simulation of a typical building configuration

with internal heat gains and less transmitted solar radiation

shows that the RTSM can be expected to match the HBM

predicted peak cooling load for typical configurations.

The experimental results also highlight the importance of

the reflected solar radiation correction for highly glazed

zones. This correction can be easily implemented using infor-

mation already available in the RTSM algorithms. It is recom-

mended that the correction be included in the next version of

the ASHRAE Loads Toolkit. Additional work to correct for

the conduction losses from the zone is not recommended. The

adiabatic zone assumption is an implicit simplification in the

RTSM; derivation of additional correction factors unnecessar-

ily complicates the method. Rather, future research should be

aimed at improving the heat balance models and refining and

cataloging model input parameters. Improvements in theHBM will be propagated to the RTSM and result in the

improved accuracy of both methods. Implementation of

adequate blind models in both procedures is of particular

importance.

The research also illustrated the importance of choosing

interior convective heat transfer coefficients on the basis of the

ventilative flow rate. Using natural convection-based correla-

tions for high ventilative flow rates can result in significant

error in the calculated cooling load. Finally, the dominant

effect of windows in determining the peak hour for the exper-

imental buildings was illustrated by the research results. Fifty

percent glazing on west and south walls completely eliminated

any peak hour shift between the heavy and light buildings.

Additional research is required to determine the effect of

advanced glazing systems on the peak cooling load.

ACKNOWLEDGMENTS

This research project (RP-1117) was funded by ASHRAE

and sponsored by ASHRAE TC 4.1. Tom Romine and Steve

Bruning guided the project with a practiced eye and the invalu-

able perspective gained by many years of experience in the

field. Jeff Spitler and Simon Rees also contributed substan-

tially to the research effort.

REFERENCES

Chantrasrisalai, C., D.E. Fisher, I.S. Iu, and D.S. Eldridge.2003. Experimental validation of design cooling load

procedures: The heat balance method. ASHRAE Trans-

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