Healthy Buildings 2017 Europe July 2-5, 2017, Lublin, Poland Paper ID 0238 ISBN: 978-83-7947-232-1 Local air gap thickness model for realistic simulation of thermal effects in clothing Agnes Psikuta*, Emel Mert, Simon Annaheim, René M Rossi Empa - Swiss Federal Laboratories for Materials Science and Technology, Laboratory for Protection and Physiology, St. Gallen, Switzerland * Corresponding email: agnes.psikuta@empa.ch SUMMARY Typically comprehensive human thermoregulation models are used together with clothing models providing the detailed boundary conditions. The aim of this study was to develop an empirical regression model of the air gap thickness in typical casual and protective clothing and to demonstrate its usefulness for simulation of the thermo-physiological and sensational response in indoor environment. This study for the first time summarised the available 3D scanning results on air gap thickness and provided the basis for the development of a regression model predicting the sought parameter for a number of body regions based on the ease allowances for the given body landmarks. It was demonstrated in several selected scenarios including different clothing fit levels that the up-to-date assumptions and methods for determination of the air gap thickness can produce a substantial error, and hence, lead to false estimation of the resultant physiological state of the human body, thermal sensation and comfort. KEYWORDS air gap thickness, human thermoregulation model, clothing model 1 INTRODUCTION Modelling of the environment and human thermoregulation is a widely used method for effective design and retrofit of thermally comfortable and energy-efficient indoor and outdoor spaces. Typically these comprehensive models are used together with clothing models providing the detailed boundary conditions. The existing mathematical clothing models available in the literature assume a uniform air gap between body and fabric layers or its full contact. Such a simplification facilitates the computation process but disregards the nonuniform heat, which depend on the shape of the air layers trapped within clothing. Since the fabric thickness constitutes typically only a small portion of the entire thickness of clothing system, the air gap thickness is the greatest contributor to heat and moisture transfer processes within clothing. The literature shows the regional systematic trends in the distribution of the air gap thickness in relation to the garment ease allowance (the difference between the body and the garment girths) at the corresponding body landmarks. This fact together with the observed low standard deviation between measurement repetitions despite complete redressing of the body indicated a potential for a precise correlation model.
The aim of this study was to develop an empirical regression model of the air gap thickness in typical casual and protective clothing and to demonstrate its usefulness for simulation of the thermo-physiological and sensational response in indoor environment. 2 METHODS Model development The model of the air gap thickness was developed based on the database of the garments available in the literature (Frackiewicz-Kaczmarek et al. 2015, Mert et al. 2016, Mert et al. 2017). Several studies that used 3D body scanning technique to determine the sought parameters came up from the same laboratory, although measured on 2 different manikins (hard-shell manikin, LaRosa, Italy, and polyurethane foam agile manikin, Polyform GmbH & Co. KG., Germany) and using 2 different 3D scanners (VITUS XXL, Human Solutions GmbH, Germany, and Artec MHT, Artec Group, USA), and hence, represented high consistency of methodology and output data (Psikuta et al. 2015, Psikuta et al. 2012a). The individual repetitions were gathered in a data pool over which an exploratory data analysis was done showing linear dependence of the air gap thickness on the ease allowance at the corresponding body landmarks. In the next step the linear regression analysis was conducted including the uncertainty evaluation. Case studies In the second step the effect of assumed no air gap case and distribution in tight- and loosefitting ensembles on the human thermo-physiological and sensational response was evaluated using the model of human thermoregulation FPCm 5.3 (Ergonsim, Germany) (Fiala and Havenith, 2016, Psikuta et al. 2012b, which is the most extensively validated physiology model worldwide (Psikuta et al. 2012b, Martínez et al. 2016). The local thermal and evaporative resistances of all ensembles for covered body regions were simulated using the model described in Mert et al. (2015). The scheme of the use of models at different complexity level and their inputs and outputs is shown in Figure 1. Figure 1. The scheme of simulation order using a cascade of various models (Mert et al. 2015, Fiala and Havenith, 2016, Fiala et al. 2003) as well as required and resultant input and output parameters, where Rct, Ret, Icl and Recl are thermal and evaporative resistances of the fabric and clothing at individual body regions, respectively, fcl is the local clothing area factor determined by 3D scanning, Ta, Tr, RH and va are ambient and radiant temperatures, relative humidity and air velocity, respectively.
A combination of two ambient temperatures of 20 and 30 C and two metabolic rates of 1.5 and 3 met (metabolic equivalents, 1met = 58.2 W/m 2 ) were chosen to represent moderate and hot environments and typical indoor activities (office work or light physical work), respectively. Two ensembles representing typical indoor clothing in tight and loose fit were chosen as a basis for the simulation and are depicted in Figure 2. (a) Tight-fitting shirt and trousers (b) Loose-fitting shirt and trousers Figure 2. Pictures of the garments used to simulate case studies with tight- (a) and loose-fitting ensembles (b). The air gaps in the clothing were simulated using the model developed in this study based on the ease allowances of individual garments. Finally, the simulation of all combinations of garments and exposure conditions was performed to analyse the effect of the estimated air gap thickness on human thermo-physiological and sensational response. In addition the thermophysiological effect of some typical assumptions found in literature regarding air gap thickness value, such as full contact (air gap thickness equal to zero) was compared to the effect of the realistic heterogeneous (local) air gap in tight- and loose-fitting ensembles. 3 RESULTS Model coefficients and equations Table 1 shows the linear regression coefficients for individual body regions related to ease allowance according to equation (1). Table 1. Coefficients of the linear regression analysis for air gap thickness related to ease allowance for individual body regions at upper and lower body. air gap thickness body region slope mm/c m upper chest lower chest abdomen anterior upper back lower back lumbus posterior 0.1 0.2 0.6 0.7 0 0.3 1.1 1 0.9 1.1 0.3 0.2 1.1 1.4 1.8 0.7 intercept mm 4.4 5 0.2 10.1 3.5 8.1 7.2 11.6 2.9 5.6 4.1 4.1-0.6 1.3 8.5 7.2 upper arm lower arm anterior posterior anterior thigh posterior thigh shin calf AGT = slope EA + int (1) AGT ercept AGT
where AGT is air gap thickness in mm, EA is ease allowance of the garment at corresponding body region in cm, and slope and intercept are the linear equation coefficients for individual body regions as given in Table 1. Case studies Figure 3 shows the mean skin and rectal temperatures, dynamic thermal sensation and skin wetness for assumed no air gap (air gap thickness equals zero) and tight- and loose-fitting ensembles for a combination of the environmental conditions and activity levels. Figure 3. Mean skin and rectal temperatures, dynamic thermal sensation and skin wetness for assumed no air gap case and distribution in tight and loose fitting ensembles for a combination of the environmental conditions (20 and 30 C) and activity levels (1.5 and 3met). 4 DISCUSSION Model development Since the 3D body scanning technique was applied in the clothing research area, it was possible to visualize and quantify the air gap thickness distribution in a great detail. This study for the first time summarised the available results on this parameter and provided the basis for the development of a statistical model predicting the sought parameter for a number of body regions. This information is necessary for mathematical models of heat and mass transfer in clothing to realistically predict thermal behaviour of the clothing system and its possible impact on human thermal response. Secondly, since air gap significantly affects thermal and evaporative resistances and can greatly vary over the body, detailed information about local thermal clothing properties at least corresponding to the body resolution of physiological models is needed. The studies that served as a basis for the air gap model developed in this study provided the necessary fine body resolution matching to the body division of the majority of the human thermoregulation models. The ease allowance, which is the difference in circumference between the body and the garment at corresponding landmarks, was chosen as an independent parameter representing reliably clothing fit as shown in various studies (Frackiewicz-Kaczmarek et al. 2015, Mert et al. 2016). Clear linear correlations for air gap thickness for upper and lower body garments were observed at all body parts. At some body parts such as upper and lower chest and back as well as anterior and posterior the dependence of the air gap thickness on ease allowance is
minimal approximating 0.1-0.3 mm/cm of ease allowance (Table 1). This means that regardless of the garment fit and the fabric used the air gap thickness remains nearly constant at these body parts. This is because the clothing rests on these inclined body parts due to gravity force (upper chest and back) or needs to be snugly adjusted to the body for garment to stay in place () (Psikuta et al. 2012). At the remaining body parts the air gap thickness increased proportionally with the increase of the ease allowance at the rate between 0.6-1.8 mm/cm of ease allowance (Table 1). Whenever garment is hanging at the edge of the protruding body part (e.g. below breast, shoulder blades, and buttocks) or below a fixed point (e.g. waistband), the garment fullness represented by ease allowance and originating folds increase the distance of the fabric from the skin (Frackiewicz-Kaczmarek et al. 2015, Mert et al. 2016). Case studies The majority of clothing models reported in the literature assumes full contact between the garment and the body or a certain (arbitrary) homogenous air gap. The problem of unknown air gap was addressed at first by Havenith et al. (2010) who used a tracking gas method to estimate total air volume trapped underneath the clothing. Some detailed fabric models with integrated physiological model suggested using no air gap for tight clothing assuming that the entire garment is in full contact with the body or some arbitrary values for an average air gap thickness (e.g. Fan et al. 2000). However, in reality, the air gap thickness is much larger (Frackiewicz-Kaczmarek et al. 2015, Mert et al. 2016). For example, an ensemble consisting of tight shirt and tight jeans (Figure 2a) represents an average air gap thickness of 6.8mm and another ensemble consisting of loose shirt and loose jeans (Figure 2b) represents an average air gap thickness of 16.6mm. Such a difference has a substantial impact on heat and mass transfer through the air gap and the entire clothing system. Since both thermal and evaporative resistances are greatly affected by the magnitude of the air gap thickness, it is expected that this effect will be also pronounced in human thermal response when wearing various ensembles. We evaluated several options for calculation of the air gap thickness that researchers have available at the moment to demonstrate the improvement of the simulation accuracy when using the presented air gap distribution model over practice and data available up-to-date. A frequent assumption found in literature is attributing 0 mm air gap thickness to tight fitting clothing (e.g. Fan et al. 2000, Lotens et al. 1991). As seen in Table 1 (intercept), this assumption is nearly not represented at any body region even if ease allowance was equal zero. In the simulated example shown in Figure 3 shows the mean and rectal temperatures comparison between garment with 0 mm air gap and realistic tight garment (Figure 2a). The observed differences approximated between 0.5-2.1 C, 0.02-0.26 C, 0.2-1.7 and 0.03-0.27 for mean skin and rectal temperatures, DTS and skin wetness, respectively. Such substantial differences are physiologically relevant and may lead to false estimation of the resultant thermo-physiological state of the human body. Although the differences for mean skin and body core temperatures were rather small (within experimental error in human studies), the local skin temperature differences can be critical for local and overall thermal sensation and comfort prediction. Finally, the effect of the clothing fit was evident in the thermophysiological and sensational response showing the differences of up to 0.9 C, 0.13 C, 0.6 and 0.16 for mean skin and rectal temperatures, DTS and skin wetness, respectively, in the tested scenarios (Figure 3). This comparison proves that the dedicated design of the clothing can have an influence on the human thermo-physiological response beyond the statistical error, and hence, can be used to customize and to support the desired thermal clothing effects.
5 CONCLUSIONS This paper presented an advanced model of air gap thickness able to predict the distribution of the sought parameters locally and reliably. For the first time the available results on these parameters were summarised and further compiled into a statistical model predicting the air gap thickness and contact area for 14 body regions based on the ease allowances for the given body landmarks. Such a reliable and detailed model is crucial for mathematical models of heat and mass transfer in the clothing to realistically predict thermal behaviour of the clothing system and its possible impact on human thermal and perceptual response. It was demonstrated in several selected scenarios including different clothing fit levels that the up-todate assumptions and methods for determination of the air gap thickness can produce a substantial error for all global, mean and local physiological parameters, and hence, lead to false estimation of the resultant physiological state of the human body, thermal sensation and comfort. 6 REFERENCES Fan, J.T., Z.X. Luo, and Y. Li, 2000. Heat and moisture transfer with sorption and condensation in porous clothing assemblies and numerical simulation. International Journal of Heat and Mass Transfer, 43(16): p. 2989-3000. Fiala, D., K.J. Lomas and M. Stohrer, 2003. First Principles Modelling of Thermal Sensation Responses in Steady-State and Transient Conditions. ASHRAE Transactions, 109, 179-86. Fiala D. and G. Havenith, 2016. Modelling Human Heat Transfer and Temperature Regulation, in The Mechanobiology and Mechanophysiology of Military-Related Injuries, A. Gefen and Y. Epstein, Editors. Springer International Publishing: Cham. p. 265-302. Frackiewicz-Kaczmarek, J., A. Psikuta, M-A. Bueno and R.M. Rossi 2015. Effect of garment properties on air gap thickness and the contact area distribution. Textile Research Journal, 85(18): p. 1907-1918. Havenith, G., P. Zhang, K. Hatcher, H. Daanen, 2010. Comparison of two tracer gas dilution methods for the determination of clothing ventilation and of vapour resistance. Ergonomics, 53(4): p. 548-558. Lotens, W.A. and G. Havenith, 1991. Calculation of clothing insulation and vapor resistance. Ergonomics, 34(2): p. 233-254. Martínez, N., A. Psikuta, K. Kuklane, J.J.P. Quesada, R.M. de Anda, P.P. Soriano, R.S. Palmer et al. 2016. Validation of the thermophysiological model by Fiala for prediction of local skin temperatures. International Journal of Biometeorology, 60:p. 1969-1982. Mert, E., A. Psikuta, M-A. Bueno, R.M. Rossi 2015. Effect of heterogenous and homogenous air gaps on dry heat loss through the garment. International Journal of Biometeorology, 59(11): p.1701-1710. Mert, E., S. Bönisch, A. Psikuta, M-A. Bueno, R.M. Rossi 2016. Contribution of garment fit and style to thermal comfort at the lower body. International Journal of Biometeorology, 60:p. 1995-2004. Mert, E., A. Psikuta, M-A. Bueno, R.M. Rossi 2017. The effect of body postures on the distribution of air gap thickness and contact area. International Journal of Biometeorology, 61:363 375. Psikuta, A., D. Fiala, G. Laschewski, G. Jendritzky, M. Richards, K. Błażejczyk, I. Mekjavič, H. Rintamäki, R. de Dear, G. Havenith 2012. Validation of the Fiala multi-node thermophysiological model for UTCI application. International Journal of Biometeorology, 56(3): p. 443-460. Psikuta, A., J. Frackiewicz-Kaczmarek, I. Frydrych and R.M. Rossi 2012a. Quantitative evaluation of air gap thickness and contact area between body and garment. Textile Research Journal, 82(14): p. 1405-1413. Psikuta, A., J. Frackiewicz-Kaczmarek, E. Mert, M-A. Bueno and R.M. Rossi 2015b. Validation of a novel 3D scanning method for determination of the air gap in clothing. Measurement, 67: p. 61-70.