Type:	Package
Title:	Models and Equations for Human Comfort Research
Version:	0.1.12
Date:	2024-01-29
Imports:	plyr, graphics, reshape, jsonlite
Maintainer:	Marcel Schweiker <mschweiker@ukaachen.de>
Description:	Calculation of various common and less common comfort indices such as predicted mean vote or the two node model. Converts physical variables such as relative to absolute humidity and evaluates the performance of comfort indices.
License:	GPL-2
LazyLoad:	yes
LazyData:	true
Suggests:	R.rsp
VignetteBuilder:	R.rsp
RoxygenNote:	7.2.3
Encoding:	UTF-8
NeedsCompilation:	no
Packaged:	2024-01-29 16:32:09 UTC; mschweiker
Author:	Marcel Schweiker [aut, cre], Sophia Mueller [aut], Michael Kleber [ctr], Boris Kingma [ctr], Masanori Shukuya [ctr], Shaomi Rahman [ctr], Shoaib Sarwar [ctr]
Repository:	CRAN
Date/Publication:	2024-01-29 17:40:02 UTC

Calculation and Evaluation of Common and Less Common Comfort Indices

Description

This package contains several functions to calculate and evaluate a series of comfort indices.

Details

To create input parameters, the function createCond, which creates a list of input parameters may be helpful. The main function of this packages is calcComfInd, which returns the desired comfort parameters. However, each index can be computed using its own function, e.g. to calculate only PMV the function calcPMV can be used.

The comfort indices calculated within this package are for example as follows. To get further information, go to the help page, which can be accessed using the index below e.g. ?pmv:

The performance criteria included in this package are presented below. Again you can get further information on the corresponding help pages:

Author(s)

Marcel Schweiker in cooperation with Sophia Mueller and many others.

Contact: mschweiker@ukaachen.de

References

See references in function descriptions.

See Also

see also createCond, calcComfInd

Comfort Indices based on the 2-Node-Model

Description

calc2Node calculates Comfort Indices based on the 2-Node-Model by Gagge et al.

Usage

calc2Node(ta, tr, vel, rh, clo = 0.5, met = 1, wme = 0, sa = NULL, pb = 760, 
ltime = 60, ht = 171, wt = 70, tu = 40, obj = "set", csw = 170, cdil = 120, 
cstr = 0.5, varOut = "else", bodyPosition = 'sitting')

Arguments

Details

All variables must have the same length 1. For the calculation of several values use function calcComfInd. The value of obj defines whether the function will use the version presented in ASHRAE 55-2013 for adjustment of pmv (obj = "pmvadj"), or the original code by Gagge to calculate set (obj = "set"). In the version presented in ASHRAE 55-2013, the lines of code related to self-generated convection is deleted. Therefore, a difference can only be seen at higher values of met.

Value

returns a data.frame with the following items:

et - Effective temperature

tsens - Predicted thermal sensation

disc - Predicted discomfort

ps - Predicted percentage satisfied with the level of air movement

pd - Predicted percentage dissatisfied due to draft

pts - Predicted thermal sensation vote based on set

pmvg - Gagge's version of Fanger's PMV

pmvstar - Same as Fanger's PMV except that dry is calculated using SET* rather than the operative temperature

The other functions return a single index, e.g. code(calcSET) returns the standard effective temperature.

Note

In case one of the variables is not given, a standard value will be taken from a list (see createCond for details.

Author(s)

The code for calc2Node is based on the code in BASIC and C++ presented by Fountain and Huizenga (1995). The translation into R-language and comparison with ASHRAE 55-2013 conducted by Marcel Schweiker.

References

ASHRAE Standard 55-2013. Thermal environmental conditions for human occupancy. American society of heating, Refrigerating and Air-Conditioning Engineering, Atlanta, USA, 2013.

Fountain & Huizenga (1995) A thermal sensation model for use by the engineering profession ASHRAE RP-781 Final report.

Gagge, Fobelets & Berglund (1986) A standard predictive index of human response to the thermal environment, ASHRAE transactions, 92 (2B), 709-731.

See Also

see also calcComfInd

Examples

## Calculation of a single set of values.
calc2Node(22, 25, .50, 50)

PPD with Ankle Draft

Description

Function to calculate ankle draft using the predicted percentage of dissatisfied.

Usage

calcAD(ta, tr, vel, rh, clo, met, vAnkle)

Arguments

Details

Calculates the percentage of thermally dissatisfied people with the ankle draft (0.1 m) above floor level.This equation is only applicable for velocity < 0.2 m/s (40 fps)

Value

Predicted Percentage of Dissatisfied occupants with ankle draft in [%]

Acceptability in [boolean]

Author(s)

Code implemented in to R by Shoaib Sarwar. Further contribution by Marcel Schweiker.

References

Original code in Python by Tartarini & Schiavon (2020) <doi:10.1016/j.softx.2020.100578>

Examples

calcAD(25,25,0.2,50,0.5,1.2,0.3) # returns Ankle_draft_ppd:18.6, Acceptability:TRUE

PMV based on Adaptive Thermal Heat Balance Framework of multiple Models

Description

calcATHBpmv calculates the PMV based on adaptive thermal heat balance framework

based on the newest version (2022)

Usage

calcATHBpmv(trm, ta, tr, vel, rh, met, 
coolingStrategyBuilding = 'naturallyventilated', 
buildingTypeSimple = 'office', seasonSimple = 'spring')

Arguments

Details

aliases athb ATHB calcATHBPmvModel1, calcATHBPmvModel2, calcATHBPmvModel3

Value

calcATHBpmv an array of PMV values of different models adapted through the ATHB appoach

Author(s)

Code implemented in to R by Shaomi Rahman. Further contribution by Marcel Schweiker.

References

Schweiker & Wagner (2015) <doi:10.1016/j.buildenv.2015.08.018> Schweiker (2022) <doi:10.1111/ina.13018>

See Also

see also calcComfInd, link{calcATHBpts}, link{calcATHBset},

link{calcATHBpmv2015}, link{calcATHBPmvModel1}, link{calcATHBPmvModel2}, link{calcATHBPmvModel3}

Examples

calcATHBpmv(20, 25, 25, .1, 50, 1.1, 'naturallyventilated','office',
'spring')

PMV based on Adaptive Thermal Heat Balance Framework

Description

calcATHBpmv2015 calculates the PMV based on adaptive thermal heat balance framework

based on the original method published 2015

Usage

calcATHBpmv2015(trm, psych, ta, tr, vel, rh, met, wme = 0)

Arguments

Details

aliases athb2015 ATHB2015 athbOld ATHBOLD

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

Value

calcATHBpmv2015 PMV value adapted through the ATHB appoach

Author(s)

Marcel Schweiker

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

See Also

see also calcComfInd, link{calcATHBpts}, link{calcATHBset}

Examples

calcATHBpmv2015(20, 0, 25, 25, .1, 50, 1.1)

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)

Arguments

Details

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

Value

calcATHBpts returns the predicted thermal sensation adapted through the ATHB approach

Author(s)

Marcel Schweiker

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

See Also

see also calcComfInd, link{calcATHBpmv}, link{calcATHBset}

Examples

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

SET based on Adaptive Thermal Heat Balance Framework

Description

Calculation of SET based on Adaptive Thermal Heat Balance framework using Gagge's 2-node model

Usage

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

Arguments

Details

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

Value

ATHBset set value adapted through the ATHB appoach

Author(s)

Marcel Schweiker

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

See Also

see also calcComfInd, link{calcATHBpmv}, link{calcATHBpts}

Examples

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

PMV based on Adaptive Thermal Heat Balance Framework for the Standard Model

Description

calcATHBstandard calculates the PMV based on adaptive thermal heat balance framework

based on the newest version (2022)

Usage

calcATHBstandard(trm, ta, tr, vel, rh, met)

Arguments

Details

aliases athb ATHB

Value

calcATHBstandard PMV value adapted through the ATHB approach with standard model

Author(s)

Code implemented in to R by Shaomi Rahman. Further contribution by Marcel Schweiker.

References

Schweiker & Wagner (2015) <doi:10.1016/j.buildenv.2015.08.018> Schweiker (2022) <doi:10.1111/ina.13018>

See Also

see also calcComfInd, link{calcATHBpts}, link{calcATHBset},

link{calcATHBpmv2015}

Examples

calcATHBstandard(20, 25, 25, .1, 50, 1.1)

PMV based on Adaptive Thermal Heat Balance Framework for the extended Model

Description

calcATHBx calculates the PMV based on adaptive thermal heat balance framework

based on the newest version (2022)

Usage

calcATHBx(trm, ta, tr, vel, rh, met, buildingTypeSimple, 
coolingStrategyBuilding, seasonSimple)

Arguments

Details

aliases athb ATHB

Value

calcATHBx PMV value adapted through the ATHB approach with extended model

Author(s)

Code implemented in to R by Shaomi Rahman. Further contribution by Marcel Schweiker.

References

Schweiker & Wagner (2015) <doi:10.1016/j.buildenv.2015.08.018> Schweiker (2022) <doi:10.1111/ina.13018>

See Also

see also calcComfInd, link{calcATHBpts}, link{calcATHBset},

link{calcATHBpmv2015}

Examples

calcATHBx(20, 25, 25, .1, 50, 1.1, 'Office', 'Mixed Mode', 
'winter')

Average Accuracy between Predicted and Actual Thermal Sensation Vote

Description

calcAvgAcc calculates the average accuracy between predicted thermal sensation votes and actual obtained sensation votes

Usage

calcAvgAcc(ref, pred)

calcavgacc(ref, pred)

AvgAcc(ref, pred)

avgacc(ref, pred)

Arguments

Value

calcAvgAcc returns a single value presenting the average accuracy between actual and predicted thermal sensation votes.

Note

The outcome heavily depends on the distribution of actual votes, i.e. in case most of the actual votes are in the same category, e.g. 'neutral', the average accuray is very high due to the fact that for the other categories the number of TRUE negative predicted votes is high as well.

Author(s)

Marcel Schweiker. Further contribution by Shoaib Sarwar.

References

Sokolova and Lapalme (2009) <doi:10.1016/j.ipm.2009.03.002>

See Also

calcTPRTSV, calcMeanBias

Examples

## Define data
ref <- rnorm(5) # actual thermal sensation votes
ref <- cutTSV(ref)

pred <- rnorm(5) # predicted thermal sensation votes
pred <- cutTSV(pred)

calcAvgAcc(ref, pred)

Bias between Predicted and Actual Thermal Sensation Vote

Description

calcMeanBias calculates the mean bias and its standard deviation and standard error between predicted thermal sensation votes and actual obtained sensation votes

Usage

calcBias(ref, pred)

calcbias(ref, pred)

calcMeanBias(ref, pred)

MeanBias(ref, pred)

meanBias(ref, pred)

meanbias(ref, pred)

bias(ref, pred)

calcSdBias(ref, pred)

calcSeBias(ref, pred)

Arguments

Value

calcMeanBias returns a dataframe with the following items:

Author(s)

Marcel Schweiker. Further contribution by Shoaib Sarwar.

References

Humphreys & Nicol (2002) <doi:10.1016/S0378-7788(02)00018-X>

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.

See Also

calcTPRTSV, calcAvgAcc

Examples

## Define data
ref <- rnorm(5) # actual thermal sensation votes

pred <- rnorm(5) # predicted thermal sensation votes

calcBias(ref, pred)

Cooling Effect

Description

Function to calculate cooling effect (CE) of elevated air velocities using the standard effective temperature (SET).

Usage

calcCE(ta, tr, vel, rh, clo = 0.5, met = 1, wme = 0)

Arguments

Details

The CE of the elevated air velocity is the difference in SET between conditions with given air velocities and still air. The cooling effect should be calculated only for air velocities higher than 0.2 m/s.

Value

ce - Cooling Effect in [degree C]

Author(s)

Code implemented in to R by Shoaib Sarwar. Further contribution by Marcel Schweiker.

References

Original code in Python by Tartarini & Schiavon (2020) <doi:10.1016/j.softx.2020.100578>

Examples

calcCE(25,25,0.3,50,0.5,1) # returns Cooling Effect: 1.3

Thermal Comfort Indices using a List of Climatic Conditions

Description

calcComfInd calculates one or more thermal comfort indices using a list of climatic conditions.

Usage

calcComfInd(lsCond, request = "all")

comfind(lsCond, request = "all")

Arguments

Details

The list lsCond could contain one or more of the following variables:

The vector request can contain the following elements:

Value

calcComfInd returns one or more rows with the comfort indices listed as request. For details see details above.

Note

In case one of the variables is not given, a standard value will be taken from a list (see createCond for details.

Author(s)

Sophia Mueller and Marcel Schweiker. Further contribution by Shaomi Rahman.

References

For references see individual functions.

See Also

see also calcPMVPPD, calc2Node, calcHbExSteady, calcATHBpmv2015, calcdTNZ, calcPMVadj, calcPtsa, calctAdapt

Examples

## Creating list with all values
lsCond <- createCond()
## Requesting all comfort indices
calcComfInd(lsCond, request="all")
## Requesting a single index
calcComfInd(lsCond, request="pmv")
## Requesting multiple indices
calcComfInd(lsCond, request=c("pmv", "ptse"))

Predicted Discomfort based on the 2-Node-Model

Description

calcDisc calculates Predicted Discomfort based on the 2-Node-Model by Gagge et al.

Usage

calcDisc(ta, tr, vel, rh, clo = 0.5, met = 1, wme = 0, pb = 760, ltime = 60, 
ht = 171, wt = 70, tu = 40, obj = "set", csw = 170, cdil = 120, cstr = 0.5)

Arguments

Details

All variables must have the same length 1. For the calculation of several values use function calcComfInd. The value of obj defines whether the function will use the version presented in ASHRAE 55-2013 for adjustment of pmv (obj = "pmvadj"), or the original code by Gagge to calculate set (obj = "set"). In the version presented in ASHRAE 55-2013, the lines of code related to self-generated convection is deleted. Therefore, a difference can only be seen at higher values of met.

Value

calcDisc returns the Predicted Discomfort

Note

In case one of the variables is not given, a standard value will be taken from a list (see createCond for details).

Author(s)

The code for calc2Node is based on the code in BASIC and C++ presented by Fountain and Huizenga (1995). The translation into R-language and comparison with ASHRAE 55-2013 conducted by Marcel Schweiker.

References

ASHRAE Standard 55-2013. Thermal environmental conditions for human occupancy. American society of heating, Refrigerating and Air-Conditioning Engineering, Atlanta, USA, 2013.

Fountain & Huizenga (1995) A thermal sensation model for use by the engineering profession ASHRAE RP-781 Final report.

Gagge, Fobelets & Berglund (1986) A standard predictive index of human response to the thermal environment, ASHRAE transactions, 92 (2B), 709-731.

See Also

see also calcComfInd

Examples

## Using several rows of data:
ta <- c(20,22,24)
tr <- ta
vel <- rep(.15,3)
rh <- rep(50,3)

maxLength <- max(sapply(list(ta, tr, vel, rh), length))
Disc <- sapply(seq(maxLength), function(x) { calcDisc(ta[x], tr[x], vel[x], rh[x]) } ) 

Effective Temperature based on the 2-Node-Model

Description

calcET calculates Effective temperature based on the 2-Node-Model by Gagge et al.

Usage

calcET(ta, tr, vel, rh, clo = 0.5, met = 1, wme = 0, pb = 760, ltime = 60,
ht = 171,wt = 70, tu = 40, obj = "set", csw = 170, cdil = 120, cstr = 0.5)

Arguments

Details

All variables must have the same length 1. For the calculation of several values use function calcComfInd. The value of obj defines whether the function will use the version presented in ASHRAE 55-2013 for adjustment of pmv (obj = "pmvadj"), or the original code by Gagge to calculate set (obj = "set"). In the version presented in ASHRAE 55-2013, the lines of code related to self-generated convection is deleted. Therefore, a difference can only be seen at higher values of met.

Value

calcET returns the Effective temperature

Note

In case one of the variables is not given, a standard value will be taken from a list (see createCond for details).

Author(s)

The code for calc2Node is based on the code in BASIC and C++ presented by Fountain and Huizenga (1995). The translation into R-language and comparison with ASHRAE 55-2013 conducted by Marcel Schweiker.

References

ASHRAE Standard 55-2013. Thermal environmental conditions for human occupancy. American society of heating, Refrigerating and Air-Conditioning Engineering, Atlanta, USA, 2013.

Fountain & Huizenga (1995) A thermal sensation model for use by the engineering profession ASHRAE RP-781 Final report.

Gagge, Fobelets & Berglund (1986) A standard predictive index of human response to the thermal environment, ASHRAE transactions, 92 (2B), 709-731.

See Also

see also calcComfInd

Examples

## Using several rows of data:
ta <- c(20,22,24)
tr <- ta
vel <- rep(.15,3)
rh <- rep(50,3)

maxLength <- max(sapply(list(ta, tr, vel, rh), length))
ET <- sapply(seq(maxLength), function(x) { calcET(ta[x], tr[x], vel[x], rh[x]) } ) 

Human Body Exergy Consumption Rate Using Steady State Method

Description

calcHbExSteady calculates the human body exergy consumption rate in W/m2 using steady state method based on a set of environmental variables.

Usage

calcHbExSteady(ta, tr, rh, vel, clo, met, tao, rho, frad = 0.7, eps = 0.95, 
ic = 1.085, ht = 171, wt = 70, tcr = 37, tsk = 36, basMet = 58.2, warmUp = 60, 
cdil = 100, sigmatr = 0.25)

Arguments

Value

Returns a data.frame with the following columns

Exergy input
xInmets Exergy input through metabolism [W/m2]
xInmetwcs Label warm/ cold for exergy input through metabolism [W/m2]
xInAIRwcs Exergy input through inhaled humid air [W/m2]
xInAIRwcwcs Label warm/ cold for exergy input through inhaled humid air [W/m2]
xInAIRwds Exergy input through inhaled dry air [W/m2]
xInAIRwdwds Label wet/ dry for exergy input through inhaled dry air [W/m2]
xInLUNGwcs Exergy input through water lung [W/m2]
xInLUNGwcwcs Label warm/ cold for exergy input through water lung [W/m2]
xInLUNGwds Exergy input through water lung [W/m2]
xInLUNGwdwds Label wet/ dry for exergy input through water lung [W/m2]
xInsheLLwcs Exergy input through water from sweat [W/m2]
xInsheLLwcwcs Label warm/ cold for exergy input through water from sweat [W/m2]
xInsheLLwds Exergy input through water from sweat [W/m2]
xInsheLLwdwds Label wet/ dry for exergy input through water from sweat [W/m2]
xInraDs Exergy input through radiation [W/m2]
xInraDwcs Label warm/ cold for exergy input through radiation [W/m2]
xIntotaLs total exergy input [W/m2]

Exergy output
xoutstorecores Exergy stored in core [W/m2]
xoutstoreshels Exergy stored in shell [W/m2]
xoutaIRwcs Exergy output through exhaled humid air [W/m2]
xoutaIRwcwcs Label warm/ cold for exergy output through exhaled humid air [W/m2]
xoutaIRwds Exergy output through exhaled dry air [W/m2]
xoutaIRwdwds Label wet/ dry for exergy output through exhaled dry air [W/m2]
xoutswEATwcs Exergy output through water vapour from sweat [W/m2]
xoutswEATwcwcs Label warm/ cold for exergy output through water vapour from sweat [W/m2]
xoutswEATwds Exergy output through water vapour from sweat [W/m2]
xoutswEATwdwds Label wet/ dry for exergy output through water vapour from sweat [W/m2]
xoutraDs Exergy output through radiation [W/m2]
xoutraDwcs Label warm/ cold for exergy output through radiation [W/m2]
xoutCONVs Exergy output through convection [W/m2]
xoutCONVwcs Label warm/ cold for exergy output through convection [W/m2]
xouttotaLs total exergy output [W/m2]

Exergy balance
xconss total exergy consumption [W/m2]
xConsumption total exergy consumption [W/m2]

Additional values
tsks Calculated skin temperature [degree C]
tcrs Calculated core temperature [degree C]
ws Calculated skin wettedness [degree C]

Note

According to Gagge's paper (1973), the value of 'cdil' may vary between 75 and 225 and 'sigma-tr' between 0.25 and 0.75. There is a note in the appendix of his paper saying two things: 1) whatever the values taken for cdil and sigma-tr, there must be no significant change in resulting thermal equilibrium. But, the values taken for cdil and sigmaTr do affect time to equilibrium. According to the analysis of Schweiker et al. (2016), the values of 100 and .25 lead to the best fit of calculated and observed skin temperature.

Author(s)

This function is based on a VBA code developed by Masanori Shukuya. transformation of VBA-code and Excel procedures into R syntax by Marcel Schweiker.

References

Schweiker, Kolarik, Dovjak & Shukuya (2016) <doi:10.1016/j.enbuild.2016.01.002>

Shukuya (2015) Calculation of human body-core and skin-layer temperatures under unsteady-state conditions-for unsteady-state human-body exergy analysis-, internal report of exergy-research group, Tech. rep.

See Also

see also calcComfInd, calcHbExUnsteady

Examples

## Calculation of human body exergy consumption rate 
calcHbExSteady(22, 24, 50, .1, .8, 1.2, 5, 80)

## Calculation of multiple values
dfData <- data.frame(ta=c(20:25), tr=c(20:25))
dfResult <- calcHbExSteady(22, 24, 50, .1, .8, 1.2, 5, 80) 
for(i in 1:nrow(dfData)){
dfResult[i,] <- calcHbExSteady(dfData$ta[i], dfData$tr[i], 50, .1, .5, 1.1, 5, 80)
}

Human Body Exergy Consumption Rate using Unsteady State Method

Description

calcHbExUnsteady Function calculates the human body exergy consumPtion rate using unsteady state method based on a series of environmental variables.

Usage

calcHbExUnsteady(ta, tr, rh, vel, clo, met, tao, rho, frad = 0.7, 
eps = 0.95, ic = 1.085, ht = 171, wt = 70, tcr = 37, tsk = 36, basMet = 58.2,
warmUp = 60, cdil = 100, sigmatr = 0.25, dateTime)

Arguments

Details

This function requires vectors of data including the corresponding time stamp. In case the time between two measurements is more than a minute, intermediate values are interpolated.

Value

Returns a data.frame with the following columns. Exergy input

Exergy output

Exergy balance

Additional values

Note

According to Gagge's paper (1973), the value of 'cdil' may vary between 75 and 225 and 'sigma-tr' between 0.25 and 0.75. There is a note in the appendix of his paper saying two things: 1) whatever the values taken for cdil and sigma-tr, there must be no significant change in resulting thermal equilibrium. But, the values taken for cdil and sigmaTr do affect time to equilibrium. According to the analysis of schweiker et al. (2015), the values of 100 and .25 lead to the best fit of calculated and observed skin temperature.

Author(s)

This function is based on a VBA code developed by masanori Shukuya. transformation of VBA-code and Excel procedures into R syntax by Marcel Schweiker.

References

Schweiker, Kolarik, Dovjak & Shukuya (2016) <doi:10.1016/j.enbuild.2016.01.002>

Shukuya (2015) Calculation of human body-core and skin-layer temperatures under unsteady-state conditions-for unsteady-state human-body exergy analysis-, internal report of exergy-research group, Tech. rep.

See Also

see also calcComfInd

Examples

## Define environmental parameters
ta <- seq(20,25,.1)
tr <- ta
rh <- rep(50, length(ta))
vel <- rep(.1, length(ta))
clo <- rep(.8, length(ta))
met <- rep(1.2, length(ta))
tao <- rep(5, length(ta))
rho <- rep(80, length(ta))
dateTime <- as.POSIXct(seq(0,by=60,length.out=length(ta)), origin="1970-01-01")
## Calculation of human body exergy consumPtion rate
calcHbExUnsteady(ta, tr, rh, vel, clo, met, tao, rho, dateTime = dateTime)$xconsu

IREQ and Dlim

Description

Calculate minimal and neutral values of REQUIRED CLOTHING INSULATION (IREQ) and DURATION LIMITED EXPOSURE (Dlim).

Usage

calcIREQ(M,W,ta,tr,p,w,v,rh,clo)

Arguments

Details

The function gives IREQ Insulation of clothing required to maintain thermal equilibrium of the body at specified levels of physiological stress.

Value

calcIREQ returns

Note

The authors disclaim all obligations and liabilities for damages arising from the use or attempted use of the information, including, but not limited to, direct, indirect, special and consequential damages, and attorneys' and experts' fees and court costs. Any use of the information will be at the risk of the user.

Author(s)

Developed by Ingvar Holmer and Hakan O. Nilsson, 1990 in java and transferred to R by Shoaib Sarwar. Further contribution by Marcel Schweiker.

References

ISO 11079, 2007-12-15, ERGONOMICS OF THE THERMAL ENVIRONMENT - DETERMINATION AND INTERPRETATION OF COLD STRESS WHEN USING REQUIRED CLOTHING INSULATION (IREQ) AND LOCAL COOLING EFFECTS

Examples

calcIREQ(116,0,-15,-15,8,0.3,0.4,85,2.5)

Heat Strain Indices based on ISO 7933

Description

calcISO7933 calculates Tre, SWtotg, Dlimtre, Dlimloss50 and Dlimloss95 based on ISO 7933. It additionally provides intermediate results from the calculation: Cres, Eres, Ep, SWp, Texp, Tskeq, Tsk, wp

Usage

calcIso7933(accl, posture, Ta, Pa, Tr, Va, Met, Icl, THETA, Walksp, Duration,
weight, height, DRINK, Adu, spHeat, SWp, Tre, Tcr, Tsk, Tcreq, Work, imst, 
Ap, Fr, defspeed, defdir, HR, pb)

Arguments

Details

All variables must have the same length 1.

Value

calcISO7933 returns a data.frame with the following items:

Tre final rectal temperature [degrees Celsius]

SWtotg total water loss [g]

Dlimtre time when limit for rectal temperature is reached [min]

Dlimloss50 time when limit for water loss Dmax50 (7.5 percent of body mass of an average person) is reached [min]

Dlimloss95 time when limit for water loss Dmax95 (5 percent of body mass of 95 percent of the working people) is reached [min]

Cres convective heat flow at respiration [W/(m*m)]

Eres evaporative heat flow at respiration [W/(m*m)]

Ep predicted evaporative heat flow [W/(m*m)]

SWp predicted sweating rate [W/(m*m)]

Texp temperature of the exhaled air [degrees Celsius]

Tskeq skin Temperature in equilibrium [degrees Celsius]

Tsk skin Temperature at the minute [degrees Celsius]

wp predicted skin wettedness [-]

Note

In case one of the variables is not given, a standard value according to ISO 7933 will be taken.

Author(s)

The code for calcISO7933 is based on the code in BASIC presented in Addendum E of EN ISO 7933. The translation into R-language conducted by Michael Kleber.

References

ISO 7933 (2004) Ergonomics of the thermal environment - Analytical determination and interpretation of heat stress using calculation of the predicted heat strain Malchaire, Piette, Kampmann, Mehnert, Gebhardt, Havenith, Den Hartog, Holmer, Parsons, Alfano, Griefahn (2000) <doi:10.1016/S0003-4878(00)00030-2> Malchaire, Kampmann, Havenith, Mehnert, Gebhardt (2000) <doi:10.1007/s004200050420>

Examples

## Calculation of a single set of values.
calcIso7933(accl = 100, posture = 2, Ta = 35, Pa = 4, Tr = 35, Va = 0.3, Met = 150, 
Icl = 0.5, THETA = 0, Walksp = 0, Duration = 480)
calcIso7933(100,2,35,4,35,0.3,150,0.5,0,0,480)
## Using several rows of data:
accl <- 100
posture <- 2
Ta <- c(40,35)
Pa <- c(2.5,4)
Tr <- c(40,35)
Va <- 0.3
Met <- 150
Icl <- 0.5
THETA <- 0
Walksp <- 0
Duration <- 480
maxLength <- max(sapply(list(accl, posture, Ta, Pa, Tr, Va, Met, Icl, THETA,
Walksp, Duration), length))
PHI <- sapply(seq(maxLength), function(x) {calcIso7933(accl, posture, Ta[x], 
Pa[x], Tr[x], Va, Met, Icl, THETA, Walksp, Duration) } ) 

MRT calculation based on standard and mixed convection

Description

calcMRTglobe calculates the mean radiant temperature considering mixed convection

Usage

calcMRTglobe(tg, ta, vel, x = 0.15)

Arguments

Details

aliases MRT globe

This model has only been validated from x = 0.040m (ping pong ball) to

x = 0.150m (standard globe thermometer) globes

Value

calcMRTglobe MRT with standard and mixed correction

Author(s)

code implemented into R by Shaomi Rahman and Marcel Schweiker.

References

Teitelbaum et al. (2022) <10.1038/s41598-022-10172-5> Teitelbaum (2022) <https://github.com/eteitelb/MixedConvection>

Examples

#Globe temperature [C]
tg <- 30
#Air temperature [C]
ta <- 24
#Air speed [m/s]
vel <- 0.0
calcMRTglobe(tg, ta, vel)

Various Humidity Related Values

Description

This set of functions calculates different humidity related values based on the given entities.

Usage

calcDewp(ta, rh)

calcEnth(ta, rh, pb)

calcHumx(ta, rh)

calcMixR(ta, rh, pb)

calcRH(ta, mr, pb)

calcSVP(ta)

calcVP(ta, mr, pb)

calcVapourpressure(ta, rh)

Arguments

Details

The length of the arguments must be either the same or they must have the length one and one common second length.

Value

calcDewp returns the dew point temperature in [degree C]

calcEnth returns a single value or a vector of values of enthalpy in [J]

calcHumx returns a single value or a vector of values of the humidex of air [ ]

calcMixR returns a single value or a vector of mixIng ratio in [g/kg]

calcRH returns a single value or a vector of relative humidities in [%]

calcSVP returns a single value or a vector of saturation vapor pressure in [kpa]

calcVP returns a single value or a vector of vapor pressure in [kpa]

calcVapourpressure returns a single value or a vector of vapor pressure in [kpa]

Author(s)

Michael Kleber (code and documentation), Marcel Schweiker (documentation).

References

Ranaa, Kusya, Jurdaka, Wallb & Hua (2013) <doi:10.1016/j.enbuild.2013.04.019>

Masterton & Richardson (1979) Humidex a method of quantifying humandiscomfort due to excessive heat and humidity, clI 1-79. Downsview, Ont: Environment Canada. Atmosheric Environment Service.

Examples

## Calc single value of absolute humidity
ta <- 25
rh <- 50
calcMixR(ta, rh, 760)
## Calc set of values of absolute humidity
ta <- 25:30
rh <- 50
calcMixR(ta, rh, 760)
## Calculating dew point temperature with single values for ta and rh
calcDewp(25, 50)
## Calculating dew point temperature with a vector of values for ta and a single value for rh
calcDewp(25:29, 50)
## Calc single value of enthalpy
ta <- 25
rh <- 50
calcEnth(ta, rh, 760)
## Calc set of values of enthalpy
ta <- 25:30
rh <- 50
calcEnth(ta, rh, 760)

Predicted Percentage Dissatisfied due to Draft based on the 2-Node-Model

Description

calcPD calculates Predicted Percentage Dissatisfied due to Draft based on the 2-Node-Model by Gagge et al.

Usage

calcPD(ta, tr, vel, rh, clo = 0.5, met = 1, wme = 0, pb = 760, ltime = 60, 
ht = 171, wt = 70, tu = 40, obj = "set", csw = 170, cdil = 120, cstr = 0.5)

Arguments

Details

All variables must have the same length 1. For the calculation of several values use function calcComfInd. The value of obj defines whether the function will use the version presented in ASHRAE 55-2013 for adjustment of pmv (obj = "pmvadj"), or the original code by Gagge to calculate set (obj = "set"). In the version presented in ASHRAE 55-2013, the lines of code related to self-generated convection is deleted. Therefore, a difference can only be seen at higher values of met.

Value

calcPD returns the Predicted Percentage Dissatisfied due to Draft

Note

In case one of the variables is not given, a standard value will be taken from a list (see createCond for details).

Author(s)

The code for calc2Node is based on the code in BASIC and C++ presented by Fountain and Huizenga (1995). The translation into R-language and comparison with ASHRAE 55-2013 conducted by Marcel Schweiker.

References

ASHRAE Standard 55-2013. Thermal environmental conditions for human occupancy. American society of heating, Refrigerating and Air-Conditioning Engineering, Atlanta, USA, 2013.

Fountain & Huizenga (1995) A thermal sensation model for use by the engineering profession ASHRAE RP-781 Final report.

Gagge, Fobelets & Berglund (1986) A standard predictive index of human response to the thermal environment, ASHRAE transactions, 92 (2B), 709-731.

See Also

see also calcComfInd

Examples

## Using several rows of data:
ta <- c(20,22,24)
tr <- ta
vel <- rep(.15,3)
rh <- rep(50,3)

maxLength <- max(sapply(list(ta, tr, vel, rh), length))
pd <- sapply(seq(maxLength), function(x) { calcPD(ta[x], tr[x], vel[x], rh[x]) } ) 

Predicted Mean Votes (PMV)

Description

Function to calculate Predicted Mean Vote (PMV).

Usage

calcPMV(ta, tr, vel, rh, clo=.5, met=1, wme=0, basMet=58.15)

Arguments

Details

The PMV is an index that predicts the mean value of the thermal sensation of a large group of people on a sensation scale expressed from (-3) to (+3) corresponding to the categories cold, cool, slightly cool, neutral, slightly warm, warm and hot. PMV model is limited to air speeds below 0.20 m/s.

Note that the adjustments in the value for basMet need to be made with great cautiousness as the PMV calculation is an empirical model and might not be valid for other values of basMet than the one commonly used.

Value

PMV - Predicted Mean Vote

Author(s)

Code implemented in to R by Marcel Schweiker. Further contribution by Sophia Mueller and Shoaib Sarwar.

References

Fanger (1970) Thermal Comfort Analysis and Applications in Environmental Engineering McGraw-Hill, New York.

ISO 7730 (2005) Ergonomics of the thermal environment analytical determination and interpretation of thermal comfort using calculation of the pmv and ppd indices and local thermal comfort criteria.

Examples

calcPMV(25,25,0.3,50,0.5,1)

Gagge's Version of Fanger's PMV based on the 2-Node-Model

Description

calcPMVGagge calculates Gagge's Version of Fanger's PMV based on the 2-Node-Model by Gagge et al.

Usage

calcPMVGagge(ta, tr, vel, rh, clo = 0.5, met = 1, wme = 0, pb = 760, 
ltime = 60, ht = 171, wt = 70, tu = 40, obj = "set", csw = 170, cdil = 120, 
cstr = 0.5)

Arguments

Details

All variables must have the same length 1. For the calculation of several values use function calcComfInd. The value of obj defines whether the function will use the version presented in ASHRAE 55-2013 for adjustment of pmv (obj = "pmvadj"), or the original code by Gagge to calculate set (obj = "set"). In the version presented in ASHRAE 55-2013, the lines of code related to self-generated convection is deleted. Therefore, a difference can only be seen at higher values of met.

Value

calcPMVGagge returns Gagge's Version of Fanger's PMV

Note

In case one of the variables is not given, a standard value will be taken from a list (see createCond for details).

Author(s)

The code for calc2Node is based on the code in BASIC and C++ presented by Fountain and Huizenga (1995). The translation into R-language and comparison with ASHRAE 55-2013 conducted by Marcel Schweiker.

References

ASHRAE Standard 55-2013. Thermal environmental conditions for human occupancy. American society of heating, Refrigerating and Air-Conditioning Engineering, Atlanta, USA, 2013.

Fountain & Huizenga (1995) A thermal sensation model for use by the engineering profession ASHRAE RP-781 Final report.

Gagge, Fobelets & Berglund (1986) A standard predictive index of human response to the thermal environment, ASHRAE transactions, 92 (2B), 709-731.

See Also

see also calcComfInd

Examples

## Using several rows of data:
ta <- c(20,22,24)
tr <- ta
vel <- rep(.15,3)
rh <- rep(50,3)

maxLength <- max(sapply(list(ta, tr, vel, rh), length))
pmvg <- sapply(seq(maxLength), function(x) { calcPMVGagge(ta[x], tr[x], vel[x], rh[x]) } ) 

PMV and PPD

Description

Function to calculate Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD).

Usage

calcPMVPPD(ta, tr, vel, rh, clo=.5, met=1, wme=0, basMet=58.15, getLoad = FALSE)

Arguments

Details

The PMV is an index that predicts the mean value of the thermal sensation of a large group of people on a sensation scale expressed from (-3) to (+3) corresponding to the categories cold, cool, slightly cool, neutral, slightly warm, warm and hot. The PPD is an index that establishes a quantitative prediction of the percentage of thermally dissatisfied people determined from PMV.

Note that the adjustments in the value for basMet need to be made with great cautiousness as the PMV calculation is an empirical model and might not be valid for other values of basMet than the one commonly used.

Value

PMV - Predicted Mean Vote

PPD - Predicted Percentage of Dissatisfied occupants in [%]

Lraw - thermal load (only when getLoad was set to TRUE)

Author(s)

Code implemented in to R by Marcel Schweiker. Further contribution by Sophia Mueller and Shoaib Sarwar.

References

Fanger (1970) Thermal Comfort Analysis and Applications in Environmental Engineering McGraw-Hill, New York.

ISO 7730 Ergonomics of the thermal environment analytical determination and interpretation of thermal comfort using calculation of the pmv and ppd indices and local thermal comfort criteria 2005.

See Also

see also calcComfInd

Examples

calcPMVPPD(25,25,0.3,50,0.5,1)

Fanger's PMV using SET* for DRY

Description

calcPMVStar calculates Fanger's PMV based on the 2-Node-Model by Gagge et al. except that DRY is calculated using SET* rather than the operative temperature

Usage

calcPMVStar(ta, tr, vel, rh, clo = 0.5, met = 1, wme = 0, pb = 760, 
ltime = 60, ht = 171, wt = 70, tu = 40, obj = "set", csw = 170, cdil = 120, 
cstr = 0.5)

Arguments

Details

All variables must have the same length 1. For the calculation of several values use function calcComfInd. The value of obj defines whether the function will use the version presented in ASHRAE 55-2013 for adjustment of pmv (obj = "pmvadj"), or the original code by Gagge to calculate set (obj = "set"). In the version presented in ASHRAE 55-2013, the lines of code related to self-generated convection is deleted. Therefore, a difference can only be seen at higher values of met.

Value

calcPMVStar returns Fanger's PMV except that DRY is calculated using SET* rather than the operative temperature

Note

In case one of the variables is not given, a standard value will be taken from a list (see createCond for details).

Author(s)

The code for calc2Node is based on the code in BASIC and C++ presented by Fountain and Huizenga (1995). The translation into R-language and comparison with ASHRAE 55-2013 conducted by Marcel Schweiker.

References

ASHRAE Standard 55-2013. Thermal environmental conditions for human occupancy. American society of heating, Refrigerating and Air-Conditioning Engineering, Atlanta, USA, 2013.

Fountain & Huizenga (1995) A thermal sensation model for use by the engineering profession ASHRAE RP-781 Final report.

Gagge, Fobelets & Berglund (1986) A standard predictive index of human response to the thermal environment, ASHRAE transactions, 92 (2B), 709-731.

See Also

see also calcComfInd

Examples

## Using several rows of data:
ta <- c(20,22,24)
tr <- ta
vel <- rep(.15,3)
rh <- rep(50,3)

maxLength <- max(sapply(list(ta, tr, vel, rh), length))
pmvstar <- sapply(seq(maxLength), function(x) { calcPMVStar(ta[x], tr[x], vel[x], rh[x]) } ) 

Predicted Mean Votes adjusted for elevated air speed

Description

Function to calculate Predicted Mean Votes (PMV) adjusted for cooling effect of elevated air speed.

Usage

calcPMVadj(ta, tr, vel, rh, clo, met, wme = 0)

Arguments

Details

Calculated through function Two node model(calc2Node

Value

calcpmvadj returns the predicted mean vote adjusted for the cooling effect of elevated air speed.

Author(s)

Code implemented in to R by Marcel Schweiker. Further contribution by Sophia Mueller and Shoaib Sarwar.

References

pmvadj is based on ASHRAE standard 55-2013. Thermal environmental conditions for human occupancy. American society of heating, Refrigerating and Air-Conditioning Engineering, Atlanta, USA, 2013.

Examples

calcPMVadj(25,25,0.3,50,0.5,1)

Predicted Percentage of Dissatisfied (PPD)

Description

Function to calculate Predicted Percentage of Dissatisfied (PPD).

Usage

calcPPD(ta, tr, vel, rh, clo=.5, met=1, wme=0, basMet=58.15)

Arguments

Details

The PPD is an index that establishes a quantitative prediction of the percentage of thermally dissatisfied people determined from PMV.

Note that the adjustments in the value for basMet need to be made with great cautiousness as the PMV calculation is an empirical model and might not be valid for other values of basMet than the one commonly used.

Value

PPD - Predicted Percentage of Dissatisfied occupants in [%]

Author(s)

Code implemented in to R by Marcel Schweiker. Further contribution by Sophia Mueller and Shoaib Sarwar.

References

Fanger (1970) Thermal Comfort Analysis and Applications in Environmental Engineering McGraw-Hill, New York.

ISO 7730 Ergonomics of the thermal environment analytical determination and interpretation of thermal comfort using calculation of the pmv and ppd indices and local thermal comfort criteria 2005.

Examples

calcPPD(25,25,0.3,50,0.5,1)

Predicted Percentage Satisfied with the Level of Air Movement based on the 2-Node-Model

Description

calcPS calculates Predicted Percentage Satisfied with the Level of Air Movement based on the 2-Node-Model by Gagge et al.

Usage

calcPS(ta, tr, vel, rh, clo = 0.5, met = 1, wme = 0, pb = 760, ltime = 60, 
ht = 171, wt = 70, tu = 40, obj = "set", csw = 170, cdil = 120, cstr = 0.5)

Arguments

Details

All variables must have the same length 1. For the calculation of several values use function calcComfInd. The value of obj defines whether the function will use the version presented in ASHRAE 55-2013 for adjustment of pmv (obj = "pmvadj"), or the original code by Gagge to calculate set (obj = "set"). In the version presented in ASHRAE 55-2013, the lines of code related to self-generated convection is deleted. Therefore, a difference can only be seen at higher values of met.

Value

calcPS returns the Predicted Percentage Satisfied with the Level of Air Movement

Note

In case one of the variables is not given, a standard value will be taken from a list (see createCond for details).

Author(s)

The code for calc2Node is based on the code in BASIC and C++ presented by Fountain and Huizenga (1995). The translation into R-language and comparison with ASHRAE 55-2013 conducted by Marcel Schweiker.

References

ASHRAE Standard 55-2013. Thermal environmental conditions for human occupancy. American society of heating, Refrigerating and Air-Conditioning Engineering, Atlanta, USA, 2013.

Fountain & Huizenga (1995) A thermal sensation model for use by the engineering profession ASHRAE RP-781 Final report.

Gagge, Fobelets & Berglund (1986) A standard predictive index of human response to the thermal environment, ASHRAE transactions, 92 (2B), 709-731.

See Also

see also calcComfInd

Examples

## Using several rows of data:
ta <- c(20,22,24)
tr <- ta
vel <- rep(.15,3)
rh <- rep(50,3)

maxLength <- max(sapply(list(ta, tr, vel, rh), length))
ps <- sapply(seq(maxLength), function(x) { calcPS(ta[x], tr[x], vel[x], rh[x]) } ) 

Predicted Thermal Sensation Vote based on SET

Description

calcPTS calculates Predicted Thermal Sensation Vote based on SET by the 2-Node-Model by Gagge et al.

Usage

calcPTS(ta, tr, vel, rh, clo = 0.5, met = 1, wme = 0, pb = 760, ltime = 60, 
ht = 171, wt = 70, tu = 40, obj = "set", csw = 170, cdil = 120, cstr = 0.5)

Arguments

Details

All variables must have the same length 1. For the calculation of several values use function calcComfInd. The value of obj defines whether the function will use the version presented in ASHRAE 55-2013 for adjustment of pmv (obj = "pmvadj"), or the original code by Gagge to calculate set (obj = "set"). In the version presented in ASHRAE 55-2013, the lines of code related to self-generated convection is deleted. Therefore, a difference can only be seen at higher values of met.

Value

calcPTS returns the Predicted Thermal Sensation Vote based on SET

Note

In case one of the variables is not given, a standard value will be taken from a list (see createCond for details).

Author(s)

The code for calc2Node is based on the code in BASIC and C++ presented by Fountain and Huizenga (1995). The translation into R-language and comparison with ASHRAE 55-2013 conducted by Marcel Schweiker.

References

ASHRAE Standard 55-2013. Thermal environmental conditions for human occupancy. American society of heating, Refrigerating and Air-Conditioning Engineering, Atlanta, USA, 2013.

Fountain, M. & Huizenga, C. A thermal sensation model for use by the engineering profession ASHRAE RP-781 Final report, 1995

Gagge, A. P., Fobelets, A. P. and Berglund, L. G. A standard predictive index of human response to the thermal environment, ASHRAE transactions, 1986, 92 (2B), 709-731.

See Also

see also calcComfInd

Examples

## Using several rows of data:
ta <- c(20,22,24)
tr <- ta
vel <- rep(.15,3)
rh <- rep(50,3)

maxLength <- max(sapply(list(ta, tr, vel, rh), length))
pts <- sapply(seq(maxLength), function(x) { calcPTS(ta[x], tr[x], vel[x], rh[x]) } )

Predicted Thermal Sensation based on 2-Node Model adjusted for Adaptation

Description

calcPtsa calculates Predicted Thermal Sensation based on the 2-Node-Model by Gagge et al. and adjusts its output according to adaptive coefficient

Usage

calcPtsa(ta, tr, vel, rh, clo = .5, met = 1, wme = 0, pb = 760, 
                     ltime = 60, ht = 171, wt = 70, tu = 40, asCoeff)

Arguments

Details

All variables must have the same length 1. For the calculation of several values use function calcComfInd. The value of obj defines whether the function will use the version presented in ASHRAE 55-2013 for adjustment of pmv (obj = "pmvadj"), or the original code by Gagge to calculate set (obj = "set"). In the version presented in ASHRAE 55-2013, the lines of code related to self-generated convection is deleted. Therefore, a difference can only be seen at higher values of met.

Value

calcPtsa returns a dataframe containing the Predicted Thermal Sensation value

Note

In case one of the variables is not given, a standard value will be taken from a list (see createCond for details).

Author(s)

The code for calc2Node is based on the code in BASIC and C++ presented by Fountain and Huizenga (1995). The translation into R-language and comparison with ASHRAE 55-2013 conducted by Marcel Schweiker.

References

ASHRAE Standard 55-2013. Thermal environmental conditions for human occupancy. American society of heating, Refrigerating and Air-Conditioning Engineering, Atlanta, USA, 2013. Fountain & Huizenga (1995) A thermal sensation model for use by the engineering profession ASHRAE RP-781 Final report.

Gagge, Fobelets & Berglund (1986) A standard predictive index of human response to the thermal environment, ASHRAE transactions, 92 (2B), 709-731. Coefficients are calculated based on Gao, Wang & Wargocki (2015) <doi:10.1016/j.buildenv.2015.04.030> The aPMV concept was introduced by Yao, Li & Liu (2009) <doi:10.1016/j.buildenv.2009.02.014> The ePMV concept was introudced by Fanger & Toftum (2002) <doi:10.1016/S0378-7788(02)00003-8>

See Also

see also calcComfInd and calc2Node

Examples

## Using several rows of data:
ta <- c(20,22,24)
tr <- ta
vel <- rep(.15,3)
rh <- rep(50,3)
asCoeff <- 0.5

maxLength <- max(sapply(list(ta, tr, vel, rh,asCoeff), length))
ptsa <- sapply(seq(maxLength), function(x) { calcPtsa(ta[x], tr[x], vel[x],
rh[x], asCoeff=asCoeff) } )

Predicted Thermal Sensation based on 2-Node Model adjusted for Expectancy

Description

calcPtse calculates Predicted Thermal Sensation based on the 2-Node-Model by Gagge et al. and adjusts its output according to expectancy factor

Usage

calcPtse(ta, tr, vel, rh, clo = .5, met = 1, wme = 0, pb = 760, 
                     ltime = 60, ht = 171, wt = 70, tu = 40, esCoeff)

Arguments

Details

All variables must have the same length 1. For the calculation of several values use function calcComfInd. The value of obj defines whether the function will use the version presented in ASHRAE 55-2013 for adjustment of pmv (obj = "pmvadj"), or the original code by Gagge to calculate set (obj = "set"). In the version presented in ASHRAE 55-2013, the lines of code related to self-generated convection is deleted. Therefore, a difference can only be seen at higher values of met.

Value

calcPtse returns a dataframe containing the Predicted Thermal Sensation value

Note

In case one of the variables is not given, a standard value will be taken from a list (see createCond for details).

Author(s)

The code for calc2Node is based on the code in BASIC and C++ presented by Fountain and Huizenga (1995). The translation into R-language and comparison with ASHRAE 55-2013 conducted by Marcel Schweiker.

References

ASHRAE Standard 55-2013. Thermal environmental conditions for human occupancy. American society of heating, Refrigerating and Air-Conditioning Engineering, Atlanta, USA, 2013. Fountain & Huizenga (1995) A thermal sensation model for use by the engineering profession ASHRAE RP-781 Final report.

Gagge, Fobelets & Berglund (1986) A standard predictive index of human response to the thermal environment, ASHRAE transactions, 92 (2B), 709-731. Coefficients are calculated based on Gao, Wang & Wargocki (2015) <doi:10.1016/j.buildenv.2015.04.030> The aPMV concept was introduced by Yao, Li & Liu (2009) <doi:10.1016/j.buildenv.2009.02.014> The ePMV concept was introudced by Fanger & Toftum (2002) <doi:10.1016/S0378-7788(02)00003-8>

See Also

see also calcComfInd and calc2Node

Examples

## Using several rows of data:
ta <- c(20,22,24)
tr <- ta
vel <- rep(.15,3)
rh <- rep(50,3)
esCoeff <- 0.5

maxLength <- max(sapply(list(ta, tr, vel, rh), length))
ptse <- sapply(seq(maxLength), function(x) { calcPtse(ta[x], tr[x], vel[x], 
rh[x], esCoeff=esCoeff) } )

Required recovery time

Description

Function to calculate Required recovery time, RT in hours.

Usage

calcRT(M,W,ta,tr,p,w,v,rh,clo)

Arguments

Value

returns required recovery time in [hours]

Note

The authors disclaim all obligations and liabilities for damages arising from the use or attempted use of the information, including, but not limited to, direct, indirect, special and consequential damages, and attorneys' and experts' fees and court costs. Any use of the information will be at the risk of the user.

Author(s)

Developed by Ingvar Holmer and Hakan O. Nilsson, 1990 in java and transferred to R by Shoaib Sarwar. Further contribution by Marcel Schweiker.

References

ISO 11079, 2007-12-15, ERGONOMICS OF THE THERMAL ENVIRONMENT - DETERMINATION AND INTERPRETATION OF COLD STRESS WHEN USING REQUIRED CLOTHING INSULATION (IREQ) AND LOCAL COOLING EFFECTS

Examples

calcRT(90,0,25,25,8,0.2,0.4,50,1.5)

Standard Effective Temperature (SET)

Description

calcSET calculates Standard Effective Temperature based on the 2-Node-Model by Gagge et al.

Usage

calcSET(ta, tr, vel, rh, clo = .5, met = 1, wme = 0, sa = NULL, pb = 760, 
ltime = 60, ht = 171, wt = 70, tu = 40, obj = "set", csw = 170, cdil = 120, 
cstr = .5, bodyPosition = 'sitting')

Arguments

Details

All variables must have the same length 1. For the calculation of several values use function calcComfInd. The value of obj defines whether the function will use the version presented in ASHRAE 55-2013 for adjustment of pmv (obj = "pmvadj"), or the original code by Gagge to calculate set (obj = "set"). In the version presented in ASHRAE 55-2013, the lines of code related to self-generated convection is deleted. Therefore, a difference can only be seen at higher values of met.

Value

calcSET returns the Standard Effective Temperature (SET)

Note

In case one of the variables is not given, a standard value will be taken from a list (see createCond for details).

Author(s)

The code for calc2Node is based on the code in BASIC and C++ presented by Fountain and Huizenga (1995). The translation into R-language and comparison with ASHRAE 55-2013 conducted by Marcel Schweiker.

References

ASHRAE Standard 55-2013. Thermal environmental conditions for human occupancy. American society of heating, Refrigerating and Air-Conditioning Engineering, Atlanta, USA, 2013.

Fountain & Huizenga (1995) A thermal sensation model for use by the engineering profession ASHRAE RP-781 Final report.

Gagge, Fobelets & Berglund (1986) A standard predictive index of human response to the thermal environment, ASHRAE transactions, 92 (2B), 709-731.

See Also

see also calcComfInd

Examples

## Using several rows of data:
ta <- c(20,22,24)
tr <- ta
vel <- rep(.15,3)
rh <- rep(50,3)

maxLength <- max(sapply(list(ta, tr, vel, rh), length))
SET <- sapply(seq(maxLength), function(x) { calcSET(ta[x], tr[x], vel[x], rh[x]) } ) 

Skin Wettedness based on the 2-Node-Model

Description

calcSkinWettedness calculates Skin Wettedness based on the 2-Node-Model by Gagge et al.

Usage

calcSkinWettedness(ta, tr, vel, rh, clo = 0.5, met = 1, wme = 0, pb = 760, 
ltime = 60, ht = 171, wt = 70, tu = 40, obj = "set", csw = 170, cdil = 120, 
cstr = 0.5, varOut="skinWet")

Arguments

Details

All variables must have the same length 1. For the calculation of several values use function calcComfInd. The value of obj defines whether the function will use the version presented in ASHRAE 55-2013 for adjustment of pmv (obj = "pmvadj"), or the original code by Gagge to calculate set (obj = "set"). In the version presented in ASHRAE 55-2013, the lines of code related to self-generated convection is deleted. Therefore, a difference can only be seen at higher values of met.

Value

calcSkinWettedness returns the Skin Wettedness

Note

In case one of the variables is not given, a standard value will be taken from a list (see createCond for details).

Author(s)

The code for calc2Node is based on the code in BASIC and C++ presented by Fountain and Huizenga (1995). The translation into R-language and comparison with ASHRAE 55-2013 conducted by Marcel Schweiker.

References

ASHRAE Standard 55-2013. Thermal environmental conditions for human occupancy. American society of heating, Refrigerating and Air-Conditioning Engineering, Atlanta, USA, 2013.

Fountain & Huizenga (1995) A thermal sensation model for use by the engineering profession ASHRAE RP-781 Final report.

Gagge, Fobelets & Berglund (1986) A standard predictive index of human response to the thermal environment, ASHRAE transactions, 92 (2B), 709-731.

See Also

see also calcComfInd

Examples

## Using several rows of data:
ta <- c(20,22,24)
tr <- ta
vel <- rep(.15,3)
rh <- rep(50,3)

maxLength <- max(sapply(list(ta, tr, vel, rh), length))
skinWet <- sapply(seq(maxLength), function(x) { calcSkinWettedness(ta[x], tr[x], vel[x], rh[x]) } )

Solar Gain

Description

Function to calculate effective radiant field and delta mean radiant temperature.

Usage

calcSolarGain(solAlt, solAzi, solRadDir, solTrans,
 fSvv, fBes, asw=0.7, posture="seated", floorRef=0.6)

Arguments

Value

An array of two values First values represents erf - Net energy flux to or from the human body using the Effective Radiant Field [W/m2] Second value represents delMrt - Delta mean radiant temperature, the increase in radiant temperature required without solar radiation [Degree C]

Author(s)

Code implemented in to R by Shaomi Rahman. Further contribution by Marcel Schweiker.

References

Original code in Python by Tartarini & Schiavon (2020) <doi:10.1016/j.softx.2020.100578>

See Also

see also calcComfInd

Examples

calcSolarGain(0, 120, 800, 0.5, 0.5, 0.5, asw=0.7, posture="seated") # Returns [42.9, 10.3]

Values related to TNZ approach

Description

calcTNZPDF calculates the distance from the thermoneutral zone, either skin temperature or room air related. Also calculates the probability function (PDF) of the thermoneutral zone.

Usage

calcTNZPDF(ht, wt, age, gender, clo, vel, tskObs, taObs, met, rh,
fBasMet = "rosa", fSA = "duBois", percCov = 0, TcMin = 36, TcMax = 38,
plotZone = FALSE, gridTaMin = 20, gridTaMax = 30, gridTskMin = 30, gridTskMax = 42,
gridTa = 1000, gridTsk = 1000, sa = 1.86, IbMax = 0.124, IbMin = 0.03, alphaIn = 0.08,
metMin = 55.3, metMax = 57.3, metDiff = .1, forPDF = FALSE, metAdapt = "none", 
trm = 15, TcPreAdapt = 37.2)

Arguments

Details

The percentage of the body covered by clothes can be estimated e.g. based on ISO 9920 Appendix H (Figure H.1). A typical winter case leads to a value of around .86, in the summer case this goes down to values around .68.

Value

calcTNZPDF returns either a dataframe suitbale to draw the pdf of TNZ (by setting forPDF to TURE) or a dataframe with the columns dTNZ, dTNZTs, dTNZTa and others. Thereby

Note

This function was used for the review paper by Schweiker et al. (2018) (see reference above). Some of the equations implemented are still to be validated further - therefore, use this function and its parameters with great care.

This function is not (yet) implemented in calcComfInd, calcdTNZ is applied there.

Author(s)

Marcel Schweiker and Boris Kingma

References

Schweiker, Huebner, Kingma, Kramer & Pallubinsky (2018) <doi:10.1080/23328940.2018.1534490> Kingma, Schweiker, Wagner & van Marken Lichtenbelt Exploring the potential of a biophysical model to understand thermal sensation Proceedings of 9th Windsor Conference: Making Comfort Relevant Cumberland Lodge, Windsor, UK, 2016. Kingma & van Marken Lichtenbelt (2015) <doi:10.1038/nclimate2741> Kingma, Frijns, Schellen & van Marken Lichtenbelt (2014) <doi:10.4161/temp.29702>

See Also

calcdTNZ

Examples

## Calculate and draw pdf of TNZ for a young non-obese male
longTcYoungMale <- calcTNZPDF(ht = 178, wt = 70, age = 30, gender = 2, clo = 0.5,
           vel = 0.2, tskObs = 36.2, taObs = 26, met = 1,
           rh = 50, fBasMet = "rosa", fSA = "duBois", percCov = 0.6,
           TcMin = 36, TcMax = 38, plotZone = FALSE, gridTaMin = 20, gridTaMax = 30,
           gridTskMin = 20, gridTskMax = 42, gridTa = 1000, gridTsk = 1000, 
           sa = 2.0335, IbMax = 0.124, IbMin = 0.03, alphaIn = 0, metMin = 55.3, 
           metMax = 57.3, metDiff = 0.1, forPDF = TRUE)

plot(density(longTcYoungMale$X2), main="", xlim=c(14,36), ylim=c(0,.50),
    xlab="Operative temperature [degree C]")

True Positive Rate between Predicted and Actual Thermal Sensation Vote

Description

calcTPRTSV calculates the true positive rate between predicted thermal sensation votes and actual obtained sensation votes

Usage

calcTPRTSV(ref, pred)

Arguments

Value

calcTPRTSV returns a single value presenting the true positive rate between actual and predicted thermal sensation votes.

Author(s)

Marcel Schweiker

References

Schweiker & Wagner (2015) <doi:10.1016/j.buildenv.2015.08.018>

See Also

see also calcMeanBias, calcAvgAcc

Examples

## Define data
ref <- rnorm(5) # actual thermal sensation votes
ref <- cutTSV(ref)
pred <- rnorm(5) # predicted thermal sensation votes
pred <- cutTSV(pred)
calcTPRTSV(ref, pred)

Predicted Thermal Sensation based on the 2-Node-Model

Description

calcTSens calculates Predicted Thermal Sensation based on the 2-Node-Model by Gagge et al.

Usage

calcTSens(ta, tr, vel, rh, clo = 0.5, met = 1, wme = 0, pb = 760, ltime = 60, 
ht = 171, wt = 70, tu = 40, obj = "set", csw = 170, cdil = 120, cstr = 0.5)

Arguments

Details

All variables must have the same length 1. For the calculation of several values use function calcComfInd. The value of obj defines whether the function will use the version presented in ASHRAE 55-2013 for adjustment of pmv (obj = "pmvadj"), or the original code by Gagge to calculate set (obj = "set"). In the version presented in ASHRAE 55-2013, the lines of code related to self-generated convection is deleted. Therefore, a difference can only be seen at higher values of met.

Value

calcTSens returns the Predicted Thermal Sensation

Note

In case one of the variables is not given, a standard value will be taken from a list (see createCond for details).

Author(s)

The code for calc2Node is based on the code in BASIC and C++ presented by Fountain and Huizenga (1995). The translation into R-language and comparison with ASHRAE 55-2013 conducted by Marcel Schweiker.

References

ASHRAE Standard 55-2013. Thermal environmental conditions for human occupancy. American society of heating, Refrigerating and Air-Conditioning Engineering, Atlanta, USA, 2013.

Fountain & Huizenga (1995) A thermal sensation model for use by the engineering profession ASHRAE RP-781 Final report.

Gagge, Fobelets & Berglund (1986) A standard predictive index of human response to the thermal environment, ASHRAE transactions, 92 (2B), 709-731.

See Also

see also calcComfInd

Examples

## Using several rows of data:
ta <- c(20,22,24)
tr <- ta
vel <- rep(.15,3)
rh <- rep(50,3)

maxLength <- max(sapply(list(ta, tr, vel, rh), length))
TSens <- sapply(seq(maxLength), function(x) { calcTSens(ta[x], tr[x], vel[x], rh[x]) } ) 

Windchill temperature (TWC)

Description

Function to calculate windchill temperature, TWC, in Degrees.

Usage

calcTWC(v,ta)

windchill(v,ta)

Arguments

Details

The function returns the temperature that considers the cooling effect on a localized skin segment.

Value

returns (twc) Wind chill temperature in [Degree C]

Note

The authors disclaim all obligations and liabilities for damages arising from the use or attempted use of the information, including, but not limited to, direct, indirect, special and consequential damages, and attorneys' and experts' fees and court costs. Any use of the information will be at the risk of the user.

Author(s)

Developed by Ingvar Holmer and Hakan O. Nilsson, 1990 in java and transferred to R by Shoaib Sarwar. Further contribution by Marcel Schweiker.

References

ISO 11079, 2007-12-15, ERGONOMICS OF THE THERMAL ENVIRONMENT - DETERMINATION AND INTERPRETATION OF COLD STRESS WHEN USING REQUIRED CLOTHING INSULATION (IREQ) AND LOCAL COOLING EFFECTS

Examples

calcTWC(6.8,-25)

Radiative and Operative Temperature

Description

The functions calcTroin calculates radiative and operative temperature based on air temperature, globe temperature, air velocity and metabolic rate. Globe temperature needs to be measured using a standard globe with a diameter of 0.15m and an emissivity of .95 (black coloured).

Usage

calcTroin(vel, tg, ta, met)

calctroin(vel, tg, ta, met)

Troin(vel, tg, ta, met)

troin(vel, tg, ta, met)

Arguments

Details

Calculation of the radiative temperature is based on ISO 7726:2001, equation (9). Calculation of operative temperature is based on ISO 7726:2001, Appendix G.3. The adjustment of air velocity to present relative air velocity based on metabolic rate is based on ISO 7730:2005 Appendix C.2.

Value

calcTroin returns a data.frame with radiative and operative temperature.

Author(s)

Marcel Schweiker. Further contribution by Shoaib Sarwar.

References

ISO 7726 Ergonomics of the Thermal Environment, Instruments for measuring Physical Quantities Geneva: International standard Organization, 1998.

ISO 7730 Ergonomics of the thermal environment - analytical determination and interpretation of thermal comfort using calculation of the pmv and ppd indices and local thermal comfort criteria 2005.

Examples

## 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
vel <- rep(.1,5)  # vector with air velocities
met <- rep(1.1,5) # vector with metabolic rates
tg <- 25:29       # vector with globe temperature values

calcTroin(vel, tg, ta, met)

Universal Thermal Comfort Index (UTCI)

Description

Functions to calculate UTCI.

Usage

calcUTCI(ta, tr, vel, rh)

Arguments

Details

air temperature and mean radiant temperature should be in Degree C unit. Air velocity has to be in m/s unit and relative humidity has to be put in percentage value.

Value

the utciValue value rounded to one decimal

Author(s)

Code implemented in to R by Shaomi Rahman. Further contribution by Marcel Schweiker.

References

UTCI project page on http://www.utci.org/ Original code in Python by Tartarini & Schiavon (2020) <doi:10.1016/j.softx.2020.100578>

See Also

see also calcComfInd

Examples

calcUTCI(25, 25, 1.0, 50) # Returns 24.6

PPD with Vertical Air Temperature Gradient

Description

Function to calculate vertical air temperature gradient using the predicted percentage of dissatisfied.

Usage

calcVTG(ta, tr, vel, rh, clo, met, v_tmp_grad)

Arguments

Details

Calculates the percentage of thermally dissatisfied persons with a vertical temperature gradient between feet and head. Applicable only for velocity(vel) < 0.2 m/s

Value

Predicted Percentage of Dissatisfied with vertical temperature gradient in [%]

Acceptability in [boolean]

Author(s)

Code implemented in to R by Shoaib Sarwar. Further contribution by Marcel Schweiker.

References

Original code in Python by Tartarini & Schiavon (2020) <doi:10.1016/j.softx.2020.100578>

Examples

calcVTG(25,25,0.1,50,0.5,1.2,7) 
# returns Vertical Air Temperature Gradient:12.4, Acceptability:FALSE

Adaptive Predicted Mean Votes

Description

Function to calculate adaptive Predicted Mean Vote (aPMV) adjusted through the adaptive coefficient.

Usage

calcaPMV(ta, tr, vel, rh, clo = 0.5, met = 1, wme = 0, apCoeff)

Arguments

Details

apCoeff can be derived using calcapCoeff.

Value

calcaPMV returns the predicted mean vote adjusted through the adaptive coefficients.

Note

In case one of apCoeff is not given, a standard value will be taken from a list (see createCond for details.

Author(s)

Code implemented in to R by Marcel Schweiker. Further contribution by Sophia Mueller and Shoaib Sarwar.

References

aPMV is based on Yao, Li and Liu (2009) <doi:10.1016/j.buildenv.2009.02.014>

See Also

calcComfInd, calcapCoeff

Examples

## 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 apCoeff for data set
apCoeff <- calcapCoeff(lsCond)
## calculate apmv
apmv <- NULL
for (i in 1:length(ta)){
 apmv[i] <- calcaPMV(ta[i], tr[i], vel[i], rh[i], clo[i], met[i], apCoeff = apCoeff)$apmv}
apmv

dTNZ, the Distance from the Thermoneutral Zone

Description

calcdTNZ calculates the distance from the thermoneutral zone, either skin temperature or room air related.

Usage

calcdTNZ(ht, wt, age, gender, clo, vel, tskObs, taObs, met, rh, deltaT =.1, 
fBasMet = "rosa", fSA = "duBois", percCov = 0, TcMin = 36, TcMax = 38, 
plotZone = FALSE)

Arguments

Details

The percentage of the body covered by clothes can be estimated e.g. based on ISO 9920 Appendix H (Figure H.1). A typical winter case leads to a value of around .86, in the summer case this goes down to values around .68.

Value

calcdTNZ returns a dataframe with the columns dTNZ, dTNZTs, dTNZTa. Thereby
dTNZ The absolute distance to the centroid of the thermoneutral zone
dTNZTs Relative value of distance assuming skin temperature to be dominant for sensation
dTNZTa Relative value of distance assuming ambient temperature to be dominant for sensation

Note

This function was used in earlier versions of TNZ calculation (see references above). The newest version is calcTNZPDF.In case one of the variables is not given, a standard value will be taken from a list (see createCond for details.

Author(s)

Marcel Schweiker and Boris Kingma

References

Kingma, Schweiker, Wagner & van Marken Lichtenbelt Exploring the potential of a biophysical model to understand thermal sensation Proceedings of 9th Windsor Conference: Making Comfort Relevant Cumberland Lodge, Windsor, UK, 2016. Kingma & van Marken Lichtenbelt (2015) <doi:10.1038/nclimate2741> Kingma, Frijns, Schellen & van Marken Lichtenbelt (2014) <doi:10.4161/temp.29702>

See Also

see also calcTNZPDF and calcComfInd

Examples

## Calculate all values
calcdTNZ(171, 71, 45, 1, .6, .12, 37.8, 25.3, 1.1, 50) 

Adjusted Predicted Mean Votes with Expectancy Factor

Description

Function to calculate Predicted Mean Votes (PMV) adjusted by the expectancy factor.

Usage

calcePMV(ta, tr, vel, rh, clo = 0.5, met = 1, wme = 0, epCoeff)

ePMV(ta, tr, vel, rh, clo = 0.5, met = 1, wme = 0, epCoeff)

epmv(ta, tr, vel, rh, clo = 0.5, met = 1, wme = 0, epCoeff)

Arguments

Details

epCoeff can be derived using calcepCoeff.

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

Value

calcePMV returns the predicted mean vote adjusted by the expectancy factor.

Note

In case one of epCoeff is not given, a standard value will be taken from a list (see createCond for details.

Author(s)

Code implemented in to R by Marcel Schweiker. Further contribution by Sophia Mueller and Shoaib Sarwar.

References

epmv is based on Fanger & Toftum (2002) <doi:10.1016/S0378-7788(02)00003-8>

See Also

calcComfInd, calcepCoeff

Examples

## 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 epCoeff for data set
epCoeff <- calcepCoeff(lsCond)
## calculate epmv
epmv <- NULL
for (i in 1:length(ta)){
 epmv[i] <- calcePMV(ta[i], tr[i], vel[i], rh[i], clo[i], met[i], epCoeff = epCoeff)$epmv}
epmv

Coefficients for aPMV, ePMV, aPTS, ePTS

Description

The functions calcCOEFF calculate the coefficients necessary for apmv, epmv, apts, and epts based on a given dataset with actual comfort votes. calcapCoeff calculates lambda the adaptive coefficients for apmv, calcepCoeff calculates e the expectancy factor for epmv, calcasCoeff calculates lambda the adaptive coefficients for apts, calcesCoeff calculates e the expectancy factor for epts.

Usage

calcapCoeff(lsCond)

calcepCoeff(lsCond)

calcasCoeff(lsCond)

calcesCoeff(lsCond)

Arguments

Value

calcCOEFF returns the adaptive coefficient lambda or expectancy factor depending on its call.

Note

For calcapCoeff and calcepCoeff, lsCond should contain the following variables: ta, tr, vel, rh, clo, met, wme, asv (see createCond for details). In case one or more of these variables are not included in the list, standard values will be used.

For calcasCoeff and calcesCoeff, lsCond should contain the following variables: ta, tr, vel, rh, clo, met, wme, pb, ltime, ht, wt, asv (see createCond for details). In case one or more of these variables are not included in the list, standard values will be used.

Author(s)

Marcel Schweiker.

References

Coefficients are calculated based on 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.

The aPMV concept was introduced by Yao, Li & Liu (2009) <doi:10.1016/j.buildenv.2009.02.014>

The epmv concept was introudced by Fanger & Toftum (2002) <doi:10.1016/S0378-7788(02)00003-8>

See Also

see also calcaPMV, calcePMV, calcPtsa, calcPtse

Examples

## 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 coefficients

calcapCoeff(lsCond)
calcepCoeff(lsCond)
calcasCoeff(lsCond)
calcesCoeff(lsCond)

## use coefficients to calculate apmv
lsCond$apCoeff[1] <- calcapCoeff(lsCond)$apCoeff
calcComfInd(lsCond, request="apmv")

Adaptive Comfort or Neutral Temperatures

Description

calctadapt are three functions to calculate adaptive comfort or neutral temperatures based on a given outdoor temperature value.

Usage

calctAdapt15251(trm = 20)

calctAdaptASHRAE(tmmo)

calctnAuliciems(ta, tmmo)

calctnHumphreysNV(tmmo)

calctnHumphreysAC(tmmo)

Arguments

Value

returns the adaptive comfort or neutral temperature with respect to the given outdoor temperature value

Note

The difference between calctnHumphreysNV and calctnHumphreysAC is that the former was found for natural ventilated buildings (NV), while the latter was found for climate-controlled buildings (AC).

Author(s)

Code implemented in to R by Marcel Schweiker. Further contribution by Sophia Mueller and Shoaib Sarwar.

References

calctAdapt15251 is based on DIN EN 15251 Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics; German version EN 15251:2012 2012.

calcAdaptASHRAE is based on Brager & de Dear (2001) Climate, comfort, & natural ventilation: a new adaptive comfort standard for ASHRAE standard 55.

calctnAuliciems is based on Auliciems (1981) Psycho-physiological criteria for global thermal zones of building design.

calctnHumphreysNV and calctnHumphreysAC are based on Humphreys (1978) Outdoor temperatures and comfort indoors. Batiment International, Building Research and Practice, Taylor and Francis.

See Also

see also calcComfInd

Examples

## define variable
trm <- 21.2
## calculate adaptive comfort temperature
calctAdapt15251(trm)

Internal Functions Used by Other Functions of this Package

Description

A few internal functions called by other functions in this package

Usage

bisect(fn, lower, upper, tol = 1e-07, ...)
hcvF(v, tcl, ta)
hcvG(va, met, basMet)
hcvM(v)
mrsw1(tcrSet, tskSet, tcr, tsk)
mrsw2(tcrSet, tskSet, tcr, tsk, Qbl)
metaTherm(met, basMet)
mshiv(tcrSet, tskSet, tcr, tsk)
pVapor(tCel, phi)
QLoad(met, pmv, basMet)
QperReq(qmet)
Qskin_evap(qmet, pa)
tskReq(qmet)
vbl(tcrSet, tskSet, tcr, tsk)
vblCdilStr(cdil, str1, tcrSet, tskSet, tcr, tsk)
wcEx(cp, t1, too)
wcXCheck(t1, too)
wdEx(t1, too, pv1, pvo)
wdExLw(too, pvso, pv1, pvo)
wdXCheck(p1, poo)
listOfRequests()
pckgCheck()
solarGain(solAlt, solAzi, solRadDir, solTrans, fSvv, fBes, asw=0.7, 
                      posture="seated", floorRef=0.6)
findSpan(arr, x)					  
utciApprox(ta, tr, vel, rh)
es(ta)
validateUTCI(ta, tr, vel, rh)
validateSolarGainRange(solAlt, solAzi, solRadDir, solTrans, fSvv, 
                              fBes, asw=0.7, posture) 
validatePMVPPD(pmv_ppd_data)
validateSET(set_data)
validateAllFunctions()
checkRange(parameter, lower_bound, upper_bound) 
calcCoolingStrategyBuilding(coolingStrategyBuilding)
calcBuildingType(buildingType)
calcBuildingTypeSimple(buildingTypeSimple)
calcSeason(season)

Arguments

depending on the function

Value

depending on the function

Author(s)

Most functions were coded by Masanori Shukuya and translated into R-language by Marcel Schweiker. The function bisect is taken from a forum entry and with permission by Ravi Varadhan. Functions listOfRequests and pckgCheck were written by Marcel Schweiker. Other functions were coded by Shaomi Rahman.

Examples

## none

Creating a List with Standard Values

Description

createCond creates a list with standard variables to be used as an input parameter for calculating comfort indices using the function calcComfInd.

Usage

createCond(a = TRUE)

createcond(a = TRUE)

Arguments

Details

lsstrd and lsEmpty contain the following elements

Value

indices listed as request. For details see details above.

Author(s)

Sophia Mueller and Marcel Schweiker.

References

For references see individual functions.

See Also

see also calcComfInd

Examples

## Creating list with standard variables
createCond()
## Creating list with empty values
createCond(a = FALSE)

Categorizing Thermal Sensation Votes

Description

cutTSV converts continuous thermal sensation votes to categorical ones.

Usage

cutTSV(pred)

Arguments

Details

Categorization is realized with intervals closed on the right, e.g. setting all values lower and equal then -2.5 to a value of -3, higher than -2.5 and lower or equal -1.5 to -2, and so on.

Value

cutTSV returns an item or a vector with categorical thermal sensation votes.

Author(s)

Marcel Schweiker

Examples

## define example data
pred <- rnorm(5)
## bin values
cutTSV(pred)

Calibration data for SET

Description

Data from ASHRAE 55-2013 to calibrate values given by SET model

Usage

data(dfASHRAETableG11)

Format

A data frame with 22 rows and 11 variables:

ta

a numeric vector of air temperature [degree C]

taF

a numeric vector of air temperature [degree F]

tr

a numeric vector of radiant temperature [degree C]

trF

a numeric vector of radiant temperature [degree F]

vel

a numeric vector of indoor air velocity [m/s]

velFPM

a numeric vector of indoor air velocity [fpm]

rh

a numeric vector of relative humidity [%]

met

a numeric vector of metabolic rate [MET]

clo

a numeric vector of clothing insulation level [CLO]

set

a numeric vector of standard effective temperature (SET) [degree C]

setF

a numeric vector of standard effective temperature (SET) [degree F]

References

ASHRAE Standard 55-2013. Thermal environmental conditions for human occupancy. American society of heating, Refrigerating and Air-Conditioning Engineering, Atlanta, USA, 2013.

Examples

data(dfASHRAETableG11)
head(dfASHRAETableG11)

Field data example

Description

Randomly sampled data from a field study campaign with data from 156 samples. For further description, see the reference given.

Usage

data(dfField)

Format

A data frame with 156 rows and 9 variables:

ta

air temperature [degree C]

tr

radiant temperature [degree C] - same as ta

rh

relative humidity [%]

trm

running mean outdoor temperature [degree C]

clo

clothing insulation level [CLO]

tao

outdoor air temperature [degree C]

vel

indoor air velocity [m/s]

met

metabolic rate [MET]

asv

actual thermal sensation vote on ASHRAE scale [ ]

References

Schweiker, M. and Wagner, A. Exploring potentials and limitations of the adaptive thermal heat balance framework. Proceedings of 9th Windsor Conference: Making Comfort Relevant Cumberland Lodge, Windsor, UK, 2016. <https://windsorconference.com/>

Examples

data(dfField)
head(dfField)

Calibration data for PMV

Description

Data from ISO 7730 Appendix E to calibrate values given by PMV model

Usage

data(dfISO7730AppE)

Format

A data frame with 13 rows and 8 variables:

top

a numeric vector of operative temperature [degree C]

vel

a numeric vector of indoor air velocity [m/s]

rh

a numeric vector of relative humidity [%]

met

a numeric vector of metabolic rate [MET]

clo

a numeric vector of clothing insulation level [CLO]

pmv

a numeric vector of Predicted mean vote (PMV)

References

ISO 7730 Ergonomics of the thermal environment analytical determination and interpretation of thermal comfort using calculation of the pmv and ppd indices and local thermal comfort criteria 2005.

Examples

data(dfISO7730AppE)
head(dfISO7730AppE)

Calibration data for PMV and PPD

Description

Data from ISO 7730 to calibrate values given by PMV / PPD model

Usage

data(dfISO7730TableD1)

Format

A data frame with 13 rows and 8 variables:

ta

a numeric vector of air temperature [degree C]

tr

a numeric vector of radiant temperature [degree C]

vel

a numeric vector of indoor air velocity [m/s]

rh

a numeric vector of relative humidity [%]

met

a numeric vector of metabolic rate [MET]

clo

a numeric vector of clothing insulation level [CLO]

pmv

a numeric vector of Predicted mean vote (PMV)

ppd

a numeric vector of Predicted percentage dissatisfied (PPD)

References

ISO 7730 Ergonomics of the thermal environment analytical determination and interpretation of thermal comfort using calculation of the pmv and ppd indices and local thermal comfort criteria 2005.

Examples

data(dfISO7730TableD1)
head(dfISO7730TableD1)

Calibration data for Tre, SWtotg, Dlimtre, Dlimloss50, Dlimloss95

Description

Data from ISO 7933 Appendix F to calibrate values given by the proposed model

Usage

data(dfISO7933AppF)

Format

A data frame with 10 rows and 16 variables:

accl

a numeric vector of state of acclimatised subject, 100 if acclimatised, 0 otherwise [-]

posture

a numeric vector of posture of subject, posture = 1 sitting, =2 standing, =3 crouching [-]

Ta

a numeric vector of air temperature [degree C]

Pa

a numeric vector of partial water vapour pressure [kPa]

Tr

a numeric vector of mean radiant temperature [degree C]

Va

a numeric vector of air velocity (m/s)

Met

a numeric vector of metabolic rate (W/(m*m))

Icl

a numeric vector of static thermal insulation (clo)

THETA

a numeric vector of angle between walking direction and wind direction (degrees)

Walksp

a numeric vector of walking speed (m/s)

Duration

a numeric vector of the duration of the work sequence (min)

Tre

a numeric vector of final rectal temperature (degree C)

SWtotg

a numeric vector of total water loss (g)

Dlimtre

a numeric vector of maximum allowed exposition time for heat storage (min)

Dlimloss50

a numeric vector of maximum water loss for protection of an average person (g)

Dlimloss95

a numeric vector of maximum water loss for protection of 95% of the working people (g)

References

ISO 7933 Ergonomics of the thermal environment - Analytical determination and interpretation of heat stress using calculation of the predicted heat strain 2004

Examples

data(dfISO7933AppF)
head(dfISO7933AppF)

Dataset with Different Combinations of Inputs to Calculate Solar Gain

Description

Dataset with Different Combinations of Inputs to Calculate Solar Gain

Usage

data(dfSolarGainValues)

Format

A data frame with 384 rows and 10 variables:

solAlt

a numeric value presenting solar altitude, degrees from horizontal in [degree C]

solAzi

a numeric value presenting solar azimuth, degrees clockwise from North in [degree C]

solRadDir

a numeric value presenting direct-beam solar radiation [W/m2]

solTrans

a numeric value presenting total solar transmittance. Ranges from 0 to 1

fSvv

a numeric value presenting fraction of sky vault exposed to body, ranges from 0 to 1

fBes

a numeric value presenting fraction of the possible body surface exposed to sun, ranges from 0 to 1

asw

a numeric value presenting the average short-wave absorptivity of the occupant. Set to 0.7 by default

posture

Default 'seated' list of available options 'standing', 'supine' or 'seated'

erf

Solar gain to the human body using the Effective Radiant Field [W/m2]

delMrt

Delta mean radiant temperature [Degree C]

Examples

data(dfSolarGainValues)
head(dfSolarGainValues)

Dataset with Different Combinations of Inputs to Calculate UTCI

Description

Dataset with Different Combinations of Inputs to Calculate UTCI

Usage

data(dfUTCIValues)

Format

A data frame with 81 rows and 5 variables:

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 [%]

utci

a numeric value presenting the UTCI value

See Also

see also calcUTCI

Examples

data(dfUTCIValues)
head(dfUTCIValues)

List of all the variables used in this package

Description

List of all the variables used in this package

Usage

data(dfVariables)

Format

A data frame with 13 rows and 5 variables:

Name

a string representing name of the variable

Unit

a string representing Unit of the variable

Min

a numeric value representing the minimum value of the variable

Max

a numeric value representing the maximum value of the variable

Definition

a sentence defining the variable with Unit

Examples

data(dfVariables)
head(dfVariables)