comf (version 0.1.7)

calcPtsadj: Calculating the Predicted Thermal Sensation based on 2-Node Model adjusted for Adaptation or Expectancy

Description

calcpts are two functions calculating predicted thermal sensation votes (pts) based on Gagges two-node model and adjusted either through the adaptive coefficient lambda or the expectancy factor e.

Usage

calcPtsa(ta, tr, vel, rh, clo = 0.5, met = 1, wme = 0, pb = 760, ltime = 60, ht = 171,
 wt = 70, tu = 40, asCoeff)
calcPtse(ta, tr, vel, rh, clo = 0.5, met = 1, wme = 0, pb = 760, ltime = 60, ht = 171,
 wt = 70, tu = 40, esCoeff)

Arguments

ta

a numeric value presenting air temperature in [degree C]

tr

a numeric value presenting mean radiant temperature in [degree C]

vel

a numeric value presenting air velocity in [m/s]

rh

a numeric value presenting relative humidity [%]

clo

a numeric value presenting clothing insulation level in [clo]

met

a numeric value presenting metabolic rate in [met]

wme

a numeric value presenting external work in [met]

pb

a numeric value presenting barometric pressure in [torr] or [mmHg]

ltime

a numeric value presenting exposure time in [minutes]

ht

a numeric value presenting body height in [cm]

wt

a numeric value presenting body weight in [kg]

tu

a numeric value presenting the turbulence intensity (value does not affect outcome)

asCoeff

adaptive coefficient lambda

esCoeff

expectancy factor e

Value

calcPtsa returns the predicted thermal sensation vote adjusted through the adaptive coefficients.

calcPtse returns the predicted thermal sensation vote adjusted through the expectancy factor.

Details

asCoeff can be derived using calcasCoeff.

esCoeff can be derived using calcesCoeff.

calcPtse requires the actual sensation vote related to the physical data as it is required to alter the metabolic rate.

References

Gao, J., Wang, Y. and Wargocki, P. Comparative analysis of modified pmv models and set models to predict human thermal sensation in naturally ventilated buildings, Building and Environment, 2015, 92, 200-208.

See Also

see also calcComfInd, calcasCoeff, calcesCoeff

Examples

# NOT RUN {
## Note. Due to random generated asv values. The values for the coefficients will not be meaningful.
## Create sample data
ta  <- 20:24     # vector with air temperature values
tr  <- ta         # vector with radiant temperature values
vel <- rep(.1,5)  # vector with air velocities
rh  <- rep(50,5)  # vector with relative humidity values
clo <- rep(1.0,5) # vector with clo values
met <- rep(1.1,5) # vector with metabolic rates
asv <- rnorm(5)   # vector with actual sensation votes

lsCond <- as.list(data.frame(ta,tr,vel,rh,clo,met,asv))

## Calculate coefficient asCoeff for data set
asCoeff <- calcasCoeff(lsCond)

## calculate ptsa
ptsa <- NULL
for (i in 1:length(ta)){
ptsa[i] <- calcPtsa(ta[i], tr[i], vel[i], rh[i], clo[i], met[i], asCoeff = asCoeff)$ptsa
}
ptsa

# }