calcATHBpts: Predicted Thermal Sensation by Gagge using Adaptive Thermal Heat Balance approach

Description

calcATHB calculates predicted thermal sensation based on the adaptive thermal heat balance approach using Gagge's 2 Node Model

Usage

calcATHBpts(trm, psych, ta, tr, vel, rh, met, wme = 0, pb = 760, 
                      ltime = 60, ht = 171, wt = 69.9)

Value

calcATHBpts returns the predicted thermal sensation adapted through the ATHB approach

Arguments

trm: - Running mean outdoor temperature in [degree C]
psych: - factor related to fixed effect on perceived control
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 [%]
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]

Author

Marcel Schweiker

Details

All variables must have the same length 1. For the calculation of several values use function calcComfInd.

References

Schweiker & Wagner (2015) <doi:10.1016/j.buildenv.2015.08.018> Schweiker & Wagner (2016) Exploring potentials and limitations of the adaptive thermal heat balance framework Proceedings of 9th Windsor Conference: making comfort relevant Cumberland Lodge, Windsor, UK, 2016

Examples

Run this code

calcATHBpts(20, 0, 25, 25, .1, 50, 1.1, 0, 760, 60, 171, 70)

Run the code above in your browser using DataLab