Human body exergy consumption and thermal comfort of an office worker in typical and extreme weather conditions in Finland

Energy and Buildings 76 (2014) 249–257

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Human body exergy consumption and thermal comfort of an office worker in typical and extreme weather conditions in Finland Mia Ala-Juusela a,∗ , Masanori Shukuya b a b

VTT Technical Research Centre of Finland, P.O. Box 1000, FI-02044 VTT, Finland Tokyo City University, Japan

a r t i c l e

i n f o

Article history: Received 23 August 2013 Received in revised form 14 February 2014 Accepted 23 February 2014 Available online 12 March 2014 Keywords: Human body exergy analysis Thermal comfort Office work

a b s t r a c t Finding the way to predict optimal thermal conditions for an office worker would contribute to sustainable building design: the environmental effects would be reduced, the economics of the organization and whole society would improve and there would be indisputable social benefits for the individual and the global community. These benefits stem from the improved productivity of the office worker in most favorable thermal environment and the possibilities to achieve this with lower energy demand. This study uses a new approach, exergy analysis, to recognise the optimal conditions by looking for the combination of mean radiant temperature and room air temperature giving the lowest human body exergy consumption rate. All of the commonly used thermal comfort prediction methods use energy analysis, and it seems that exergy analysis could give more accurate prediction of the conditions giving optimal thermal comfort. The new method is applied to the case of office worker in typical and extreme weather conditions in Finland. The results agree well with the previous analyses, and moreover, the points giving minimum human body exergy consumption rate coincide with the points usually regarded as most comfortable in summer conditions. According to recent studies, people are also most productive at these conditions. © 2014 Elsevier B.V. All rights reserved.

1. Background The energy use in buildings, accounting for 36% of the energy use and related CO2 emissions as well in Europe [1] as worldwide [2], is mainly due to the need to maintain comfortable indoor environment for us humans. Most of this energy is used for heating and cooling, aiming at thermal comfort. Finding an optimal way to control the thermal environment would help in using only the necessary amount of energy for this purpose, and nothing more. According to many studies [e.g. 3–8] people also work best in optimal thermal conditions, being most productive. Although the human performance also depends on many other things than thermal comfort, reaching the best possible thermal environment would improve possibilities in reaching the best performance or productivity. Increasing the productivity of people in the working space will cover many times the costs for any additional effort in planning or energy use [e.g. 5]. It is estimated that a half to one percent increase in productivity could cover the costs of all energy

∗ Corresponding author. Tel.: +358 50 327 0533. E-mail addresses: mia.ala-juusela@vtt.fi (M. Ala-Juusela), [email protected] (M. Shukuya). http://dx.doi.org/10.1016/j.enbuild.2014.02.067 0378-7788/© 2014 Elsevier B.V. All rights reserved.

use over the year [9]. Also on individual level, using the working hours in most productive way would release more time and personal energy for other activities, like different hobbies or spending time with family and friends. It is also claimed [10] that loss of productivity may be one of the major routes for negative economic effects of climate change. Consequently, finding the way to predict optimal thermal conditions for an office worker (and finding the right technical solutions for that) would present a promising solution for sustainable building design: the environmental effects would be reduced, the economics of the organization and whole society would improve and there would be indisputable social benefits for the individual and the global community. Many approaches to measure and calculate the optimal conditions for thermal comfort has been proposed, such as the widely used PMV method [11] or adaptive model [12], but all of them seem to have had a number of shortcomings, which will be discussed below, where some of the methods are briefly presented. A new approach is proposed by a research group at Tokyo City University and the LowEx co-operation [13], based on the idea of the human body as exergy–entropy system. According to the first results, human body exergy balance calculations seem to be a promising way to evaluate the thermal comfort provided by different

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heating and cooling systems (e.g. in [14–18]). The new method uses the outdoor conditions as environmental conditions for the calculations, and therefore it is interesting to compare the results of the thermal comfort calculations with the human body exergy consumption (HBEC) rate in some chosen weather conditions. This paper first reviews some work from other researchers to find out to what extent the human body exergy balance calculations could tell us where to find the optimal conditions for thermal comfort, compared to some other ways to predict thermal comfort. Calculations on the human body exergy consumption have been conducted in some typical and extreme weather conditions in Finland (which has characteristics of both a maritime and a continental climate), and these are discussed and compared with the results in similar cases in other countries with different climates calculated by other researchers. 2. Thermal comfort and methods to predict or assess it 2.1. Thermal comfort Human thermal comfort is defined by ANSI/ASHRAE Standard 55-2010 [19] as “that condition of mind which expresses satisfaction with the thermal environment and is assessed by subjective evaluation”. The perceived thermal comfort is not only a result of the thermal environment, but a combination of many physical, physiological and psychological factors related to the environmental and personal qualities. Environmental factors include e.g. noise level, IAQ (Indoor Air Quality), air temperature and velocity, radiant temperature, visual environment, availability of daylight and humidity or enthalpy of the air. Personal factors include not only the clothing insulation or metabolic rate, but also age, gender, thermal sensitivity and ability to control the thermal environment [20–22]. According to many studies [e.g. 12,17,23] also the short and long term thermal history has an effect on the way people perceive their thermal environment. Many of these factors are also interrelated, and they seem to affect the perceived intensity of each other [3], although not many studies have been conducted on this field. 2.2. PMV method to predict thermal comfort in artificially conditioned spaces Fanger [11] created a mathematical model to predict the way people perceive their thermal environment. It is called the Predicted Mean Vote or PMV method. The method is based on the human body heat balance equation which was adjusted according to considerable amount of measurement results from large group of human subjects. It predicts the mean value of the Thermal Sensation Votes (TSV) given by a large group of people in certain environmental conditions. PMV method is by far the most applied method for assessing the thermal comfort provided by environmental conditions of an indoor space, although it is sometimes severely criticized and its applicability is claimed to be limited [24]. It is used in national and international standards as basic method for the indoor thermal comfort calculations, e.g. in ISO Standard 7730 [25] and ANSI/ASHRAE Standard 55-2010 [19]. In these standards, it is stressed that also the local thermal discomfort must be considered in determining conditions for acceptable thermal comfort. Fanger’s PMV model was initially intended for application by HVAC industry in creation of artificial climates in controlled spaces [11]. However, it has been widely applied to all kinds of buildings in many types of environments. In his analysis of the applicability of PMV method [24], van Hoof assessed the results of several studies which seem to show that significant number of people prefer

conditions that are in the non-neutral vote area, and even the TSV votes outside the three central categories do not necessarily mean that people would feel discomfort. The preferred conditions are often influenced by the season. It also seems that PMV model is not very well applicable to naturally ventilated buildings. [12] Many experienced designers and scientists have noticed that the best thermal comfort in winter situation is usually found in conditions, where the mean radiant temperature is slightly higher than the air temperature (e.g. [26]). However, this is not shown by the PMV method, as it predicts thermal neutrality with many combinations of mean radiant temperature and air temperature. 2.3. Adaptive model to predict the thermal comfort in naturally ventilated spaces Due to the limited ability of PMV model to predict the thermal comfort in naturally ventilated buildings, de Dear and Brager developed an Adaptive model of Thermal Comfort and Preference [12], which is currently presented as an alternative method in ASHRAE Standard 55-2010 [19] for evaluating the thermal comfort in naturally ventilated buildings. They also noted the results that the preferred temperatures are on slightly cool side of neutral vote in summer and slightly warm side in winter. The Adaptive model uses the outdoor temperature for predicting the optimum thermal comfort temperature. This model is based on the assumption that the contextual factors and person’s thermal history affect her expectations and preferences. It is derived from an impressive amount of raw data from field-experiments around the world. Based on this data de Dear and Brager concluded that occupants in naturally ventilated buildings tend to be tolerant to wider range of temperatures, due to both behavioral adjustment and physiological adaptation. This observation is supported by the findings by Tokunaga and Shukuya in their experiments [17]. Their results show that the subjects in a group who are accustomed to take passive strategies became comfortable effectively with a smaller sweat secretion rate than the subjects in a group usually exposed to convective cooling. From point of view of the current paper it is interesting that the humanbody exergy consumption rate of the subjects in the former group was generally smaller than that of the subjects in the latter group in both everyday life and in the experimental room. Both the advantages and disadvantages of the adaptive model seem to basically be due to the limited number of input values: while it is very simple and straight forward to use and the result is easily understandable, it ignores many of the central parameters of the heat balance equation, especially the air velocity. This limits its applicability to different situations, and therefore it should only be used for offices and workspaces and for regular levels of metabolism and clothing insulation. Local discomfort should also be assessed separately. [27–29] 2.4. Other ways for predictive assessment of thermal comfort In addition to the above mentioned methods, there are many other ways suggested to predict the thermal comfort, but they are not as widely used as the PMV method or adaptive model. Many of the new methods are based on PMV, like the method which uses the Standard Effective Temperature (SET*) instead of operative temperature in the calculation of PMV, resulting in calculation of PMV* [30]. This method was claimed to improve the responsiveness to relative humidity and vapor pressure changes as well as the vapor permeability of the clothing, and this is why it is chosen for the comparisons of PMV method vs. Human Body Exergy Consumption rate method in this paper. To improve the applicability of PMV method for warm climates, Fanger and Toftum suggested the use of expectancy factor ‘e’ in

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non-air-conditioned buildings in warm climates. The PMV is simply multiplied with this factor varying between 0.5 and 1 depending on the air-conditioning strategy of the building in question. [31] In the PMV methods as well as the adaptive model, the human body is presented as one point in relatively uniform and steadystate space. In the search of more precise models of the human body, first the two-nodes models were developed (e.g. the Pierce Two-Node Model [30]), along with different multi-node models (e.g. [32,33]). The recently developed three-dimensional models are able to give very detailed views on the interaction of the thermal environment and human physiology, like the NREL Human Thermal Physiological Model with its 40 000 nodes and elements [34] or the UC Berkeley Comfort Model described by Zhang et al. [35] with its advanced heat transfer models. A research group at VTT developed a human thermal model (HTM), which allows estimating the effect of different building systems (building structures combined with different types of building services systems) to the thermal sensations of the occupants [36]. 2.5. A new approach to thermal comfort: calculation of human body exergy consumption rate All the above mentioned methods are based on the use of pure energy analysis and the first law of thermodynamics, which states that energy is always conserved in a closed system, even if it can transform from one form to another. Energy analysis gives us only a partial image of the thermodynamic systems, as it does not articulate the direction of the energy flows. A recent study [13] suggests that human body exergy balance analysis could give more accurate indication of thermal comfort. The exergy analysis will give further information compared to sheer energy analysis, as it takes into account the second law of thermodynamics, which gives indication of the spontaneous direction of the conversion process: towards the state with bigger entropy content. The amount of entropy generation is proportional to the exergy consumption of the system. The generation of entropy and consumption of exergy are two steps in the exergy–entropy process, which allows any thermodynamic system to continue the cyclic operation. The other two steps in this process are the exergy supply and the entropy disposal. [13] Exergy is the part of energy that has the potential for doing work or to be changed to another form of energy. Exergy content is dependent on the environmental conditions. For example electricity is pure exergy and can be totally transformed to another form of energy (e.g. heat) in any environment, but heat close to environmental temperature contains only small amount of exergy. Once the exergy content of the energy is used, it is no longer useful in those environmental conditions. Human body, as any other biological system, consumes exergy to perform its tasks and to maintain the static core temperature. Recent studies ([14–18], the first of which is presented as an example in [13]) have shown indications of correlation between human body exergy consumption and thermal comfort: Isawa et al. concluded that from the exergetic view point, there is an optimal combination of room air temperature and mean radiant temperature, which gives thermally neutral condition (PMV = 0), while, from the conventional energetic viewpoint, there are many combinations of room air temperature and mean radiant temperature resulting in same PMV value [14]. Prek found that while there is a correlation between minimum exergy consumption rate and thermal sensations where minimum exergy consumption rate coincides with the thermal sensation vote near neutrality, there also seems to be a limited number of combinations of air temperature and mean radiant temperature giving this minimum value [15]. Tokunaga and Shukuya found in their experimental study that human body exergy consumption seems to be related to the need

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to get rid of the extra heat, to reduce the exergy storage rate. Increasing the storage rate too much would result in higher core temperature, which in turn is what the thermoregulatory system tries to keep constant. Increased storage rate correlated with lower thermal comfort rates better than the calculated PMV*. [17] Simone et al. [18] made a study using several sets of data to relate the thermal sensation data from previous studies to the human body exergy consumption. They found that the lowest human body exergy consumption rate occurred when the thermal sensation votes were close to neutrality, on slightly cool side. Also, a second-order polynomial relationship between the thermal sensation votes and human-body exergy consumption rate could be established. According to this equation, human body exergy consumption rate has the lowest value when the thermal sensation vote is −0.61. But they concluded that due to the small sample size and many uncertainties in the available data, more studies must be conducted and analyzed to get a better comprehension of how the human body exergy consumption varies as a function of thermal sensation. 3. Methods used in current study 3.1. Human body exergy consumption (HBEC) rate calculation Calculations on human body exergy balance for this study are based on the human body exergy balance principle presented by Shukuya et al. in [13]. According to this principle, the exergy supply to the body must equal the sum of exergy consumption within the human body, the exergy stored in the body and the exergy output as presented in Eq. (1), giving the general form of the exergy balance equation for human body. Einput = Econsumed + Estored + Eoutput

(1)

More detailed description of the theory is presented in [13], and the limited description here only gives a very short introduction to the underlying theory of the balance which is derived starting from the exergy–entropy process. As the very purpose of the thermoregulatory system is to keep the body core temperature at constant level, the Estored component in Eq. (1) is very small compared to the other components. Einput and Eoutput can be further allocated to different components depending on the routes of the exergy transfer across the boundaries of human body. The exergy input consists of following five components: (1) exergy generated by metabolism; (2) exergy contained in the inhaled humid air; (3) exergy contained in the liquid water generated in the body core by metabolism; (4) exergy contained in sum of liquid water generated in the shell by metabolism (as sweat) and dry air to let the liquid water disperse and (5) radiant exergy absorbed through the surface (skin and clothing). The exergy output can be divided into following four components: (1) exergy contained in the exhaled humid air; (2) exergy contained in the humid air leaving the body surface, containing the evaporated water from the sweat, (3) radiant exergy discharged through the surface (skin and clothing) and (4) exergy transferred by convection from the surface to the surrounding air. [13] The eight input values needed for the calculation of human body exergy balance are: (1) outdoor air temperature, tout ; (2) relative humidity of outdoor air, ϕout ; (3) room air temperature, troom ; (4) mean radiant temperature, tr,mean ; (5) relative humidity of room air, ϕroom ; (6) relative velocity of room air, vrair ; (7) the activity level (metabolic rate), M; and (8) the clothing insulation value, clo. The environmental temperature used for the calculation is the outdoor temperature, to facilitate the simultaneous analysis of the heating and cooling systems. [13] The calculations in current work are automated with a humanbody exergy balance contour calculation tool developed by Asada

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et al. [37], which uses as input only six of the above mentioned input values and calculates the output values with different combinations of room air temperature and mean radiant temperature, both varying from 15 to 35 ◦ C in 0.5 ◦ C steps to create a set of values for a contour plot. It first calculates the body core, skin and clothing surface temperatures (tcr , tsk , tcl ) and sweat secretion rate with Gagge’s model [30]. Then these are used as input to exergy balance equation, which is then solved. Final output is the exergy consumption rate which satisfies the exergy balance equation. The different components constituting the exergy consumption are also displayed in the output file. These include, as described above as exergy input and output components (in W/m2 , with indication of the necessary choice of the above mentioned eight input values and the three calculated values for the calculation of the component) [37]: - Warm and cool radiant exergies absorbed by skin and clothing surfaces; Exr,in = f(tr,mean , tout ). - Warm and cool exergies transferred by convection from skin and clothing surfaces into the surrounding air; Exc = f(tcl , troom , tout ). - Warm/cool and wet/dry exergies of the inhaled humid air Exair,in = f(troom , tout , ϕroom , ϕout ). - Warm exergy generated by metabolism; Exm = f(M, tout , tcr ). - Warm and wet exergies of the liquid water generated in the core by metabolism; Exw,cr = f(tcr , tout , ϕout ). - Warm/cool and wet/dry exergies of the sum of liquid water generated in the shell by metabolism and dry air to let the liquid water disperse; Exw,sh = f(tsk , tout , ϕroom , ϕout ). - Thermal exergy stored/released in/from the core and the shell; Exstore = f(tout , tcr , tsk ). - Warm and cool radiant exergies discharged from skin and clothing surfaces; Exr,out = f(tcl , tout ). - Warm and wet exergies of the exhaled humid air; Exair,out = f(tcr , tout , ϕout ). - Warm/cool exergy of the water vapor originating from the sweat and wet/dry exergy of the humid air containing the evaporated water from the sweat; Exsw = f(tcl , tout , ϕroom , ϕout ). The human body exergy consumption (HBEC) rate (W/m2 ) and PMV* values for an office worker were calculated with the human-body exergy balance contour calculation tool for typical and extreme winter conditions at two locations in Finland: in Helsinki (Southern Finland) and in Sodankylä (Northern Finland), as function of mean radiant temperature (tr,mean ) and room air temperature (troom ). Calculations for typical and extreme summer conditions were also conducted. A summary of the input values including the values for outdoor temperature (tout ) are presented in Table 1 and further explanations on the choice of the values are provided in chapters 3.1.1–3.1.3. Finland belongs to the temperate coniferous-mixed forest zone with cold, wet winters. 3.1.1. Outdoor air temperatures To find evidence on the typical daytime temperatures and the typical extreme temperatures, the Test reference year (TRY) data was studied [38] in addition to the statistical information from the Finnish Meteorological Institute [39]. TRY is the hourly weather data used for calculation of the energy certificate in Finland, and it represents a typical year in Finland. Typically in summer, the office-hour (8–17) mean temperature is about 2◦ higher than the daily (24-h) mean temperature, which in July is on average 17.2 ◦ C for Helsinki and 14.1 ◦ C for Sodankylä [39]. Based on this information, 20 ◦ C was chosen as the value for outdoor air temperature in typical summer conditions in Helsinki, and 16 ◦ C for Sodankylä. For the calculations, a “typical extreme” temperature is more useful than the highest ever temperatures (being 31.6 ◦ C in Helsinki

Table 1 The input parameters and their values used in the calculations. Variable

Abbreviation

Values in calculations

Outdoor air temperature

tout

Relative humidity of outdoor air

ϕout

Room air temperature Mean radiant temperature Relative humidity of room air Relative velocity of room air The activity level (metabolic rate) Clothing insulation value

troom

ϕroom

Typical winter in Helsinki −5 ◦ C Typical winter in Sodankylä −15 ◦ C Extreme winter in Helsinki −15 ◦ C Extreme winter in Sodankylä −28 ◦ C Typical summer in Helsinki 20 ◦ C Typical summer in Sodankylä 16 ◦ C Extreme summer in Helsinki 25 ◦ C Extreme summer in Sodankylä 20 ◦ C Typical winter 88% (both Helsinki and Sodankylä) Extreme winter in Helsinki 80% Extreme winter in Sodankylä 78% Typical summer 70% (both Helsinki and Sodankylä) Extreme summer in Helsinki 40% Extreme summer in Sodankylä 45% varying from 15 to 35 ◦ C, with 0.5 ◦ C intervals varying from 15 to 35 ◦ C, with 0.5 ◦ C intervals 40%

vrair

0.1 m/s

M

1.2 met (normal for office worker in Finland) 0.6 clo (summer), 1 case with value 1.0 clo 1.0 clo (winter)

tr,mean

clo

and 31.7 ◦ C in Sodankylä) [40]. For Helsinki, 25 ◦ C was chosen as a representative for this typical extreme, which would occur practically every summer for about one or two weeks. In Sodankylä the hourly temperature exceeds 20 ◦ C for about 240 h/year [38], so this is chosen as the typical extreme summer temperature case for Sodankylä. Typical daytime temperatures in Helsinki can vary a lot in February (the coldest month), between some plus degrees to −15 ◦ C. There are, on average, 6–10 days, when the temperature is above zero, in each winter month in the south of the country [41]. During very mild winters this can happen even in Lapland (Sodankylä), too. Since a typical winter daytime temperature is hard to define, the average monthly temperature, −5 ◦ C in Helsinki and −15 ◦ C in Sodankylä were chosen as the representatives for such typical daytime temperatures. The office-hour temperatures (from 8 to 17) are typically almost the same as the mean daily (24h) temperatures in winter. They typically stay in 1◦ difference to either direction, although more often to positive direction (which means that day is then warmer than night, and office-hour mean temperature is slightly higher than daily mean). Again, the lowest measured temperatures (lowest ever −51.5 ◦ C in Northern Finland, Kittilä, −34.3 ◦ C in Helsinki and −49.5 ◦ C in Sodankylä, [40]) do not represent an average situation, so a similar “typical extreme” temperatures as for the summer cases were needed for winter cases, too. In the TRY data for Helsinki, the temperature is below −15 ◦ C only for 164 h (corresponding to 6.8 days), and below −12 ◦ C about 2 weeks. In Sodankylä, the temperature occurring for about one week’s time (157 h) is −28 ◦ C, which is chosen as the representative for typical extreme temperature for Sodankylä. 3.1.2. Humidity Because the relative humidity varies a lot depending on the conditions (tout and ␸out ) during the previous time step, it was challenging to find a typical value for outdoor relative humidity

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Fig. 1. Human body exergy consumption rate [W/m2 ] of an average office worker in typical winter conditions in Helsinki.

to occur with the typical and extreme temperatures Also, according to analysis of the TRY data [38], there seemed to be no strong correlation between the temperature and relative humidity. After thorough analysis of the hourly data in [38] and statistical information in [40], the mean values for relative humidity were evaluated to be representative enough for the typical winter and summer cases, while for the extreme cases, values were hand-picked from the TRY data (Table 1). The indoor relative humidity has different recommended values in Finland, depending on the source. Most of them recommend limiting the indoor relative humidity to maximum 45%, especially during winter [42–46]. As 40% seems to fall in most of the recommended areas, this was used as the value for indoor relative humidity in all calculations. This requires the use of mechanical supply ventilation or other humidity control techniques, as the stable indoor humidity cannot otherwise be guaranteed. 3.1.3. Other values (air velocity, metabolic rate and clothing) The recommended target value for room air velocity in the Finnish indoor air class S1 (best class), is below 0.2 m/s [47]. To be on the safe side, 0.1 m/s is used in the calculations. The metabolic rate normally used for calculations in office work cases is 1.2 met, and this was used also for the analyses in this case. In Finland, the normal office clothing in winter and summer on one hand and for men and women on the other hand varies a bit, but a mean value of 0.6 clo for summer and 1 clo for winter is normally used, and was also chosen for these calculations. For comparison, a clo value of 1 was used for one summer case. 4. Results The results are presented here (two examples in Figs. 1 and 2) and in the Appendix (Figs. A1–A6) as graphs of Human Body Exergy Consumption (HBEC) rate and PMV* as function of tr,mean and troom . Because Gagge’s model tends to underestimate the sweat secretion rate in higher temperatures (e.g. [48]), the graphs are only presented for temperature ranges of 15–30 ◦ C, although the calculations were made for values between 15 and 35 ◦ C. The equi-HBEC rate and equi-PMV* curves were drawn with help of IGOR Pro software [49] which is a program for visualizing, analyzing, transforming and presenting experimental data. In Fig. 1, the results for the typical winter conditions in Helsinki are presented as graph as an example of the winter case and in Fig. 2 the results for typical summer conditions in Sodankylä are presented as an example of the summer case. The rest of the results are presented as graphs in Appendix A and the results are summarized in Table 2, presenting the input and result values at the points where minimum human body exergy consumption (HBEC)

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Fig. 2. Human body exergy consumption rate [W/m2 ] of an average office worker in typical summer conditions in Sodankylä.

rate occurs in each case. Fig. 3 is a graphical presentation of the same. In the figures (1 and 2 and A1–A6 in Appendix A), the relevant equi-PMV* lines are presented to enable the comparison with PMV* method. The adaptive method is only recommended for use in environmental conditions where the mean outdoor temperature remains between 10 and 33 ◦ C [19], and therefore only the calculation results for the summer conditions can be compared with this method. The adaptive method gives the allowable indoor operative temperature based on air- and mean radiant temperature. The model states 90% acceptability within ±5 ◦ C and 80% acceptability within ±7 ◦ C from this allowable temperature. Interestingly, calculation of operative temperature as stated in the adaptive model gives exactly the same value for all points giving the minimum HBEC rate, i.e. 22.25 ◦ C, while the Adaptive model gives different values for each case, varying between 22.8 and 25.6 ◦ C with outdoor temperatures of 16–25 ◦ C, giving a lot of options for combinations of room air temperature and mean radiant temperature. In Fig. 4, the calculation results of different combinations of mean radiant temperature and room air temperature are presented: HBE min: The combinations where minimum HBEC rate occurred in the calculations with the HBEC rate calculation model used in the current work. ACS tr (ta HBE): Combination giving the allowable indoor operative temperature calculated with the adaptive model, where room air temperature was calculated from the operative temperature equation using the mean radiant temperature of the point where minimum HBEC rate occurred in each case ACS ta (tr HBE): Same as above, but now the mean radiant temperature was calculated from the operative temperature equation

Table 2 Points of minimum HBEC rate in the calculated cases. Case 123a

tout ◦ C

HBEC W/m2

ϕout %

troom ◦ C

tr,mean ◦ C

M met

clo clo

PMV*

TWH TWS EWH EWS TSH TSS ESH ESS TSHclo1

−5 −15 −15 −28 20 16 25 20 20

3.415 3.41 3.41 3.32 2.85 2.812 2.896 2.849 3.457

88 88 80 78 70 70 40 45 70

15 15 15 15 19 18.5 19 19 15.5

23 24 24 28.5 25.5 26 25.5 25.5 22.5

1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

1 1 1 1 0.6 0.6 0.6 0.6 1

−0.19 −0.12 −0.12 0.27 −0.28 −0.29 −0.28 −0.28 −0.19

a 1: T = typical, E = extreme; 2: W = winter, S = summer; 3: H = Helsinki, S = Sodankylä; clo1 = case with clo value 1 in summer.

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2

Human body exergy consumption rate [W/m ]

4.0

3.5

HBEC; Troom; Tr, mean; clo 2

3,457 W/m ; 15,5 °C; 22,5 °C; 1

3.0

2

2,896 W/m ; 19 °C; 25,5 °C; 0,6 2

2,85 W/m ; 19 °C; 25,5 °C; 0,6 2

2,812 W/m ; 18,5 °C; 26 °C; 0,6 2

3,415 W/m ; 15 °C; 23 °C; 1 2

3,41 W/m ; 15 °C; 24 °C; 1

2.5

2

3,32 W/m ; 15 °C; 28,5 °C; 1 linear_HBEC Summer, clo = 0,6 linear_HBEC Winter, clo = 1 2.0 -30

-25

-20

-15

-10

-5

0

5

10

15

20

25

30

Outdoor temperature [°C] Fig. 3. The results as graph of minimum Human Body Exergy Consumption rate (HBEC) as function of outdoor temperature (tout ). dHBECsummer /dtout = 0.0093 W/m2 /◦ C, dHBECwinter /dtout = 0.0043 W/m2 /◦ C.

using the room air temperature of the point where minimum HBEC rate occurred in each case ACS top = ta = tr: Same as above, using the actual outdoor temperatures of the calculation points. 5. Discussion 5.1. HBEC rate in different weather conditions The results indicate that the human body exergy consumption (HBEC) rate is in general higher in winter than in summer (Fig. 3), probably due to more insulation provided by the clothing, as the minimum HBEC rate in summer with the same clothing value (1 clo) does not differ much from those in winter. The minimum HBEC

rate seems to be more sensitive to outdoor temperature changes in summer than in winter (Fig. 3: summer slope 0.0093 vs. winter slope 0.0043), although the dependence is to same direction: the lower the outdoor temperature, the lower the minimum HBEC rate. Although the minimum HBEC rate does not change much in winter with different outdoor temperatures, and the indoor air temperature giving the lowest rate remains the same, the mean radiant temperature giving the lowest rate does change, getting higher at lower outdoor temperatures. (Table 2 and Fig. 3) For summer conditions, the combination giving lowest HBEC rate is always such where the room air temperature is slightly lower than the mean radiant temperature (Table 2 and Fig. 3). This might be taken as an indication that natural ventilation could be an effective cooling strategy in typical Finnish summer conditions, and it is indeed very often applied, especially in residential buildings. Office buildings with high internal loads are more often cooled with mechanical supply and exhaust ventilation, which are coming also more popular in residential buildings due to the new energy efficiency requirements because they also allow heat recovery during cold periods. Older office buildings are typically equipped with mechanical exhaust ventilation. Other ways of cooling are not yet often applied in Finnish residential or office buildings. The outdoor relative humidity seems to have minor effect on the results in winter case, as the human body exergy consumption rate is the same for winter conditions, where relative humidity is 80% (extreme case in Helsinki) or 88% (typical case for Sodankylä) (Table 2). This is quite natural, as the absolute amount of water in the air in these cases is not that different, when the temperature is −15 ◦ C in both cases, and the relative difference in these two cases is small. 5.2. Comparison with PMV* method and adaptive model

Fig. 4. Comparison with adaptive model.

It seems that in Finnish summer conditions, the minimum human body exergy consumption rate occurs in conditions giving a

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PMV* value of slightly over −0.3 (Table 2). In winter conditions, the minimum HBEC rate point gets closer to PMV* = 0 line, but remains on the slightly cool side in most cases (Table 2). This is an interesting result considering the findings of de Dear and Brager [12] who noted during the development of the adaptive model that the preferred temperatures are on slightly cool side of neutral vote in summer and slightly warm side in winter. It is also interesting to notice that Kosonen and Tan [50] found that the optimal productivity was found at a predicted PMV value of −0.21 (when room air temperature was 24 ◦ C). This was supported by Lan et al. [51], who estimated the best performance level at TSV = −0.25 and Wu et al. [52], who predicted best performance at TSV = −0.34. The minimum human body exergy consumption rate in summer conditions not only coincides with the PMV* = −0.3 line, but gives a narrow range on that line. It would be interesting to study if this has something to do with the relative proportions of the exergy input and output processes occurring in different temperature combinations, and if the human body has some natural preferences regarding the routes of exergy transfer. The minimum human body exergy consumption rate area in winter conditions does coincide well with the PMV* = 0 line or slightly cooler (Table 2), but it is not in the area usually regarded as the most comfortable room air temperature, according to every-day experience. This aspect should be further studied. The every-day experience may stem from the local thermal discomfort situations. Adaptive model gives consistently higher operative temperatures for the thermal comfort point than the minimum HBEC rate points (difference from 0.5 to 3.3 ◦ C). The minimum HBEC rate in the calculated summer cases is located at the area within the 90% acceptability limit given by the adaptive model. For two of the calculated cases, the adaptive model gives ranges where the minimum allowable operative temperature for 90% acceptability is the same as the room air temperature giving the minimum HBEC rate, while the respective maximum operative temperature is some degrees higher than the mean radiant temperature giving the minimum HBEC rate (due to the difference in the operative temperature given by the adaptive model and the operative temperature given by the HBEC rate calculations). (Figure 4) 5.3. Comparison with other HBEC studies The same contour calculation program as in the calculations here, was used by Isawa et al. [14] to calculate the HBEC rate in Japanese winter conditions (of outdoor air temperature at 0 ◦ C and relative humidity at 40%). The results, although obtained with slightly different input values (clo = 0.9; M = 1 met) are very similar to those presented in this study: The minimum point of HBEC rate occurred also in conditions where room air temperature is slightly lower than the mean radiant temperature (troom = 18 ◦ C, tr,mean = 25 ◦ C). Prek [15] calculated the HBEC rate and compared it to the PMV* calculation results. The air velocity, relative air humidity, metabolic rate and clothing insulation value were set to 0.1 m/s, 50%, 1 met and 1 clo, respectively. Again, the HBEC rate gets a minimum value in conditions where the mean radiant temperature is slightly higher (24 ◦ C) than the room air temperature (19 ◦ C). Also, it is worth noticing that the minimum point for HBEC rate was very close to the PMV* = 0 line. Simone et al. studied the relation between calculated HBEC rate and TSV [18]. Again, the results were consistent with this study and the previous examples, and the minimum HBEC rate occurred in conditions where the mean radiant temperature was slightly higher than the mean air temperature. An interesting result was that the Thermal Sensation Votes (TSV) given by the human subjects in the experimental conditions moved towards “slightly

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cool” sensation when the HBEC rate decreased. Taking into account the earlier mentioned findings from other researchers [12] that people often prefer slightly cool conditions in summer, this could be regarded as one further confirmation to the assumption that HBEC rate calculation better predicts the human thermal comfort zone than the traditional energy based methods. 5.4. Other constraints for optimal thermal comfort In addition to HBEC rate minimization, to achieve optimal thermal comfort, it is also important to assure that the requirements for local thermal comfort are met. Local thermal discomfort may be caused by a vertical air temperature difference between the feet and the head, by an asymmetric radiant field, by a local convection cooling (draft), or by contact with a hot or cold floor [19]. Although the HBEC rate minimization seems to give an optimal combination of mean air temperature and mean radiant temperature for predefined static conditions of clothing, metabolic rate, air humidities and indoor air velocity, it only gives the set points for these temperatures. For environments where the occupant has more opportunities to affect the variables (e.g. changing the clothing insulation by putting on more clothes or air velocity by opening a window) than in office environment, some method to take into account this freedom of choices could be developed to assess the range of optimal thermal environment with HBEC rate calculation. It should include the effect of the psychological phenomenon that people tend to be more satisfied if they believe they can affect the temperature (described e.g. in [22]). 6. Conclusions The calculations confirm the findings of some earlier studies that minimum HBEC rate usually occurs in conditions where the mean radiant temperature is slightly higher than the mean air temperature. This would mean that in winter conditions, the room could be heated to a lower temperature than currently is usual, provided that a suitable radiant temperature can be maintained. It is estimated [53] that, as a general rule, lowering room temperature by 1 ◦ C gives 5% savings in energy. In summer case, if natural ventilation could provide optimal thermal comfort, the energy need for cooling would be minimized. For the summer conditions, human body exergy analysis results seem to provide an explanation for the everyday experience of some experienced designers and researchers that some conditions on the PMV* = 0 line seem to give more satisfaction than others. Furthermore, the fact that the minimum human body exergy consumption coincides better with the PMV* = −0.3 line in summer conditions, where people’s preferred temperatures usually occur in summer, gives indications that human body exergy analysis might present a better way to predict the optimal thermal conditions than PMV* method. A test series with human subjects would give further light to this aspect. The adaptive model gives quite a wide range of allowable combinations of mean radiant temperature and room air temperature. The minimum HBEC rate occurs consistently at lower operative temperature points than the comfort temperature given by adaptive model, but they all fall into the range of 90% acceptability given by the adaptive model. More calculations are needed to study the sensitivity of the HBEC rate to different input variables, but in the light of the results obtained in this study, it can be concluded that the HBEC rate is more sensitive to outdoor temperature changes in summer than in winter conditions, in the case of the average office worker studied here.

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Acknowledgement The work of the corresponding author was supported by VTT Technical Research Centre of Finland, enabling e.g. two extended visits to Tokyo City University. Appendix A. Graphical Figs. A1–A6.

presentations

of

the

calculation

results.

See

Fig. A4. Human body exergy consumption rate [W/m2 ] of an average office worker in typical summer conditions in Helsinki.

Fig. A1. Human body exergy consumption rate [W/m2 ] of an average office worker in typical winter conditions in Sodankylä.

Fig. A5. Human body exergy consumption rate [W/m2 ] of an average office worker in typical summer conditions in Helsinki, when the clothing insulation value is 1.0 instead of 0.6 clo.

Fig. A2. Human body exergy consumption rate [W/m2 ] of an average office worker in extreme winter conditions in Helsinki.

Fig. A6. Human body exergy consumption rate [W/m2 ] of an average office worker in extreme summer conditions in Helsinki. The result was almost the same in the extreme summer conditions in Sodankylä.

References

Fig. A3. Human body exergy consumption rate [W/m2 ] of an average office worker in extreme winter conditions in Sodankylä.

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