Thermal comfort evaluation with virtual manikin methods

ARTICLE IN PRESS

Building and Environment 42 (2007) 4000–4005 www.elsevier.com/locate/buildenv

Thermal comfort evaluation with virtual manikin methods Ha˚kan O. Nilsson Department of Technology and Built Environment, Laboratory of Ventilation and Air Quality, University of Ga¨vle, 801 76 Ga¨vle, Sweden Received 11 January 2006; accepted 21 April 2006

Abstract Computational fluid dynamics has become an important tool in the prediction of thermal comfort in occupied spaces. Despite its ability to predict temperature and velocity fields, it is more difficult to evaluate the degree of thermal comfort experienced by an occupant. This article describes the construction of a new numerical thermal manikin, with new comfort evaluation methods based on data from thermal manikin measurements as well as subjective results from several hundred experiments. The level of thermal comfort is highly dependent on the local environment. Human beings respond differently to local heat transfer in different parts of their bodies. It is suggested for that reason that local results from manikins should be presented in new clothing independent comfort zone diagrams. The research presented in here is intended to be used to evaluate system solutions that provide improved thermal climate in many different everyday situations, e.g. all types of buildings and vehicles. r 2006 Elsevier Ltd. All rights reserved. Keywords: Thermal manikin; Mannequin; Thermal climate assessment; Clothing-independence; Comfort zone diagram

1. Introduction Modern occupants expect thermal indoor environments of continually increasing quality in many different types of situations. Thus, it has become necessary to calculate in advance the effects of different indoor climate solutions before anything permanent is built. Experimental work with thermal sensation ratings is expensive, time consuming and difficult to standardise. Simulations with computational models in virtual environments have been seen as a new way of evaluating end simulating the influence of and on human beings [1–3]. However, values from computational fluid dynamics (CFD) models still need to be further related to human reactions and verified with measurements in real environments. It is important to adjust the evaluation methods used according to clothing worn by subjects and manikins. The degree of thermal comfort depends greatly on the local environment [4–7]. Human beings usually use different types of clothing and respond differently to heat transfer from different body areas. The Tel.: +46702192492; fax: +4626648113.

E-mail address: [email protected]. URL: http://www.hig.se/hnn. 0360-1323/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.buildenv.2006.04.027

various types and amounts of clothing that manikins use today, make comparative interpretation of results from different manikins/methods very complicated. In order to facilitate comparison of results, the methods should be independent both of the manikin used as well as clothing worn. 2. Methods This method aims at combining different thermal climate investigation techniques into one evaluation scheme (Fig. 1). Results from human experiments and thermal manikin measurements are used to develop a methodology based on a virtual manikin positioned in a CFD-simulated environment. The results are presented not only as whole body influence, but also with local information on how the thermal climate varies over the human body. The development of virtual models is an efficient complement to traditional evaluation of the thermal environment. An effective and clear measure of thermal comfort is the equivalent temperature (teq). This measure comes closer to the humans experienced temperature, compared to ordinary operative temperature, with an inclusion of the effects

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Fig. 1. The climate evaluation methods associated in this article: human subjective ratings, manikin measurements and computer modelling.

Fig. 2. MANIKIN3 built with PROSTAR version 3.102.517, PRE/POST processor for STAR-CD (Star CD 3.1B Computational Dynamics Limited, 2001).

from non-evaporative heat loss from the human body [8,9]. The definition of equivalent temperature can be related to the whole body as well as local parts of a human being. In more than 500 experiments, total and local heat fluxes from full-scale thermal manikins were determined for different climatic conditions and compared with thermal sensation votes from subjects exposed to the same conditions [10]. The individual votes were averaged for each condition and reported as a Mean Thermal Vote (MTV) [11]. Equivalent temperature values obtained with different manikins in different test houses should be comparable during the same test conditions. As a consequence of methodological and individual variations, the use of climate evaluation limit lines is not the optimal solution. A new diagram should be developed affording practical support for judgement of complicated local thermal climates, this would allow the generation of general profile usable with different clothing and manikins in various environments.

The turbulence model used for MANIKIN3 calculates the turbulent viscosity empirically with

2.1. Computational manikin with new surface temperature regulation MANIKIN3 is built inside the computer with a heat flow interface to a CFD code (Fig. 2). The virtual manikin is built up of cubes and has a similar size, area and number of zones as the real MANIKIN2. Both manikins have a free surface area of 1.6 m2, giving the same constant heat flux to the surrounding air. The surface temperature of MANIKIN3 is regulated during the iteration process, using the adaptive boundary conditions at the first grid cell. This procedure together with a virtual calibration is implemented in order to even out the geometrical differences. When the general mixing of the air is the main interest, it is possible to use a constant turbulent (eddy) viscosity mt instead of the molecular viscosity [12]. This zero-equation model uses a constant to express the turbulent viscosity. This does not require the solution of any additional differential equations beyond the Navier–Stokes equations.

mt ¼ 0:04ru0 H,

(1)

where mt is the turbulent (eddy) viscosity [Pa s], r the fluid density [kg/m3], u0 the characteristic velocity, inlet velocity [m/s], and H the characteristic length, inlet min length [m]. The length scale is a characteristic length; normally the minium length of the air inlet should be used. In the same way, the inlet velocity is used as the characteristic velocity. The empirical constant suitable for different indoor airflows is a number between 0.038 and 0.040. Although this model is found to be sufficient for predicting the total characteristic of a turbulent flow, it may not always be suitable for predicting local details. One benefit of this method is that the time used for calculations is much less compared to more complicated models. Furthermore, the use of this model does not need extensive grid refinement or the use of special wall functions, two factors that significantly speed up the working and iteration process. Consequently the computer power needed to calculate the airflow is less and can quite easily be realised using an ordinary personal computer. The STAR-CD CFD code [13] supports continuous modification boundary conditions during the flow and temperature field calculations. Functions have been designed that regulate the numerical manikin surface temperature and calculate the equivalent temperature. These functions are introduced in the CFD calculations with the results of the flow and temperature fields as input. The subroutines enable the user to calculate new surface temperatures and heat transfer for each zone on the manikin. In order to minimise the computational load, the surface temperatures and equivalent temperatures are calculated in a user subroutine that is programmed to be called only once in every iteration step. These new Fortran 77 user subroutines are written so that the surface temperature data and current equivalent temperature data for comfort diagram output are continuously updated

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for real time monitoring. This method is much faster compared to previously published methods involving two or three separate codes for thermoregulation, radiation, and CFD. No thermoregulation model has to be used as the routines are based on correlation results from extensive studies of subjective responses in different radiative and convective environments. The confirmed hypothesis [4,10] is that human beings do not differentiate between various avenues of heat loss, just experience an increased or decreased general heat loss from any zone of the body.

2.2. Clothing independent comfort zone diagram evaluation output In order to make the comfort evaluation clothing independent, the construction of new comfort zone diagrams can be made by inserting any seated total insulation available. Eq. (2) shows how a relationship between the equivalent temperature level and MTV can be established for each manikin body part. The heat loss corresponding to a certain level of comfort, or discomfort, in the diagram is consequently considered to be the same.

Table 1 Linear regressions and calculated ‘‘neutral’’ values for clothing combinations (LS) ‘‘Light summer clothing’’ (1.0 clo) and (EW) ‘‘Enhanced winter clothing’’ (1.9 clo) Zone

a

b

r2

RT (m2K/W)LS

RT (m2K/W)EW

IT (clo) LS

IT (clo) EW

teq (1C) neutral LS

teq (1C) neutral EW

Whole body Scalp Face Chest Up. back L U arm R U arm L L arm R L arm L hand R hand L thigh R thigh L calf R calf L foot R foot Lo. back Seat

43.8 65.5 65.5 36.1 36.1 43.0 43.0 43.0 43.0 84.9 84.9 46.7 46.7 46.7 46.7 46.7 46.7 39.5 39.5

13.3 33.9 33.9 20.5 20.5 21.1 21.1 21.1 21.1 57.2 57.2 20.3 20.3 20.3 20.3 20.3 20.3 19.5 19.5

0.97 0.89 0.89 0.95 0.95 0.94 0.94 0.94 0.94 0.98 0.98 0.97 0.97 0.97 0.97 0.97 0.97 0.93 0.93

0.160 0.199 0.199 0.229 0.229 0.215 0.215 0.122 0.122 0.117 0.117 0.128 0.128 0.128 0.128 0.128 0.128 0.247 0.247

0.300 0.193 0.193 0.464 0.464 0.432 0.432 0.432 0.432 0.146 0.146 0.292 0.292 0.292 0.292 0.215 0.215 0.381 0.381

1.03 1.28 1.28 1.48 1.48 1.39 1.39 0.79 0.79 0.75 0.75 0.83 0.83 0.83 0.83 0.83 0.83 1.59 1.59

1.94 1.25 1.25 2.99 2.99 2.79 2.79 2.79 2.79 0.94 0.94 1.88 1.88 1.88 1.88 1.39 1.39 2.46 2.46

21.0 21.0 25.7 25.7 24.8 24.8 28.8 28.8 24.1 24.1 28.0 28.0 28.0 28.0 28.0 28.0 24.2 24.2 21.0

21.4 21.4 17.2 17.2 15.4 15.4 15.4 15.4 21.6 21.6 20.4 20.4 20.4 20.4 24.0 24.0 19.0 19.0 21.4

"Light summer clothing" Comfort Zones

-10

0

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20

Equivalent temperature, teq (°C)

30

40

-10

0

10

20

too hot

hot but comfortable

neutral

cold but comfortable

too cold

Whole body Scalp Face Chest Up. back L U arm R U arm L L arm R L arm L hand R hand L thigh R thigh L calf R calf L foot R foot Lo. back Seat

too hot

cold but comfortable neutral hot but comfortable

too cold

Whole body Scalp Face Chest Up. back L U arm R U arm L L arm R L arm L hand R hand L thigh R thigh L calf R calf L foot R foot Lo. back Seat

"Enhanced winter clothing" Comfort Zones

30

40

Equivalent temperature, teq (°C)

Fig. 3. Comfort zone diagrams constructed for ‘‘light summer’’ (LS) and ‘‘enhanced winter’’ (EW) clothing ensembles. Note that the comfort zones of acceptance are much narrower for the summer clothing, except for the ‘‘less sensitive’’ head and hands.

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The shape of the zones is however changed according to the clothing used: teq;zone ¼ ts  RT ða þ b MTVzone Þ,

(2)

where teq;zone is the equivalent temperature in the zone [1C], ts the manikin surface temperature (here 34 1C) [1C], RT the total insulation, seated [m2 K/W], a, b the linear regression constants [W/m2], MTVzone the MTV in the zone [n.d.]. The equation is valid for an interval of seated whole body total insulation (IT) between 0.9 and 1.9 clo. With Eq. (2) and the information in Table 1, it is possible for the first time to make a comfort zone diagram (Fig. 3) that applies to a specific clothing combination used in a given situation. Insert corresponding local values of the surface temperature (ts, usually 34 1C), a and b (from Table 1) and

local total insulation value (RT) of the clothing and air layer together with a MTVzone for the zone borders (1.5 left blue, 0.5 right blue, 0.5 left red, 1.5 right red) in Eq. (2). Now teq,zone can be calculated for the four borders of the three shaded comfort zones (blue–green–red) for all zones and the whole body. The result, plotted in a diagram, forms the evaluation background in the clothing independent comfort zone diagram. This is only done once for each clothing combination, and reflects the insulation distribution of the clothing used. As could be expected, diagrams for clothing with less insulation show an increased sensitivity in most zones, except the normally unclothed head and hands. The opposite, decreased sensitivity for heat loss variations, can be observed for diagrams with increased clothing insulation.

Mixing ventilation office case

Displacement ventilation office case

10

15

20

25

Equivalent temperature, teq (°C)

35

40

10

15

20

odis cfd

25

too hot

hot but comfortable

neutral

too hot 30

odis meas

cold but comfortable

omix cfd

hot but comfortable

neutral

toocold

cold but comfortable

omix meas

Whole body Scalp Face Chest Back U Arm LU Arm RU Arm LL Arm RL Hand L Hand R Thigh L Thigh R Calf L Calf R Foot L Foot R Back L Seat

too cold

Whole body Scalp Face Chest Back U Arm LU Arm RU Arm LL Arm RL Hand L Hand R Thigh L Thigh R Calf L Calf R Foot L Foot R Back L Seat

4003

30

35

40

Equivalent temperature, teq (°C)

Fig. 4. The virtual and real office investigated with MANIKIN3 and MANIKIN2. Below the case with mixing ventilation (omix—office mixing) showing good agreement between measurements and simulation and the displacement ventilation simulation (odis—office displacement) producing slightly lower equivalent temperatures compared to the measurements due to an overall lower simulated air temperature. The comfort zone diagrams are drawn on the basis of the 1.3 clo summer clothing that MANIKIN2 and MANIKIN3 wore.

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3. Results 3.1. Full-scale measurements vs. CFD simulations Results from measurements with the full-scale MANIKIN2 (meas) have been compared to CFD simulations with MANIKIN3 (CFD). The heat loss and temperature of the two manikins influenced the air movements around the body in a realistic way. Thermal interaction with walls, ventilation and the seat influenced each manikin’s thermal situation. The evaluated office room could alternatively be ventilated with mixing (omix—office mixing) or displacement ventilation (odis—office displacement). MANIKIN3 had no problem simulating the increased insulation of the seat zones supplied by the virtual office chair (Fig. 4). In relatively homogeneous environments, as in this case, the

simulations with MANIKIN3 made good predictions of how a human will perceive the thermal climate. The cabin is build with an option to change the glazing used in order to test the different possibilities to improve the thermal climate. Fig. 5 shows few differences between simulation and measurement in the case with clear cabin glass and no sun (cclearg—cabin clear glass). But in the case with solar radiation and reflective glass (creflg—cabin reflective glass) does a lower air temperature increase the heat loss from the hands and thighs for the measured data not compensated by the radiation from the windows caused by the sun as in the simulation. 4. Discussion This work is intended to complement full-scale investigations in order to determine human thermal comfort in

10

15

20 25 30 35 Equivalent temperature, teq (°C)

40

10

15

creflg meas creflg cfd

too h ot

hot but com fortable

cclearg cfd

too cold

cclearg meas

Whole body Scalp Face Chest Back U Arm LU Arm RU Arm LL Arm RL Hand L Hand R Thigh L Thigh R Calf L Calf R Foot L Foot R Back L Seat

neu tral

Reflective glass and sun cabin case

too h ot

hot but com fortable

neu tral

too cold

Whole body Scalp Face Chest Back U Arm LU Arm RU Arm LL Arm RL Hand L Hand R Thigh L Thigh R Calf L Calf R Foot L Foot R Back L Seat

cold b ut com fortable

Clear glass no sun cabin case

cold b ut com fortable

4004

20 25 30 35 Equivalent temperature, teq (°C)

40

Fig. 5. This figure shows pictures of the virtual and real cabin environment with MANIKIN3 and MANIKIN2 in driver position. The boxes to the right of MANIKIN3 are the shielded air temperature probes always connected to the manikin measurements, as seen with MANIKIN2 in the picture to the right. Below clothing independent comfort zone diagrams with measurements and CFD results with MANIKN3 presenting good agreement between measurements and simulations for the case with clear cabin glass and no sun (cclearg—cabin clear glass). The reflective glass with sun (creflg—cabin reflective glass) produced higher equivalent temperatures at the middle zones in the simulation due to higher air temperatures around the manikin caused by slightly different inlet and hand positions.

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different environments. For engineering purposes, the ‘‘comfort’’ sensation needs to be converted to and expressed in measurable, physical quantities. This is facilitated using the concept of clothing independent comfort zone diagrams. The model of clothing independence assumes that the human being is equally sensitive to different heat losses independent of the insulation of the clothing worn. This may not always be the case, especially at the borders of no clothing or heavy clothing and should be investigated further. In the future, more data from validation experiments with subjects and different methods will contribute to the development of this general evaluation criterion. The numerical methods used need further development. The computer should become a professional tool for visualising thermal comfort problems, developing new ventilation strategies and designing new systems. With more research and experience the tools and methods for simulation of thermal environment conditions can be continuously improved. Unfortunately too little of the theory behind CFD simulations is available in the public domain. Many researchers and companies still use in-house codes for all or essential parts of their calculations. A remaining issue that requires more investigation is how to simulate the correct heat transfer from different types of wall boundaries to the fluid, in order to carry out CFD modelling with better accuracy. A lot of research today uses high-resolution calculations with refined grids. Research is also needed at the other end of the scale, where the whole cell is substituted for one or a number of wall functions specially adapted for the environment studied. It is important that research concerning the relationships for simulation of transient situations continues. The development of thermal manikins to enable the handling of thermal transient conditions has started. However, there is still a need for more tests with human subjects in order to investigate the correlation between subjective feelings and equivalent temperature transients. 5. Conclusions Results from subjective panels and measurements with full-scale thermal manikins have been used to develop a new type of virtual thermal manikin, MANIKIN3, to be used in CFD simulations. The surface temperature of the computational manikin is regulated continuously through an iteration process. This procedure together with a new model for virtual calibration forms the basis of this new numerical manikin concept. Results from measurements as well as simulations are visualised in new ‘‘clothing independent comfort zone diagrams’’, showing how an average human being would perceive the whole body as well as local climate. The results shows that the set of equations used in the simulations give good agreement with real life measurements in the different environments. The use of input data from CFD calculations produced reasonable results,

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especially in situations with relatively homogeneous climate, like the office case, but still have to be further developed in order to be reliable in more complicated climate situations like the cabin case. Today, CFD calculation methods have developed further and a growing field of research is working to establish the methods for simulation of the human thermal environment. Taking this into account, there are still too many unexplained differences in the results within and in between simulation methods, pointing out the limitations of currently available CFD methods. There is consequently a need for continued validation of CFD results with real life measurements and benchmark tests. Acknowledgements The author sincerely appreciates the travel grant from the research-funding agency Formas (Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning) that made it possible to present this research at the 10th Indoor Air conference in China 2005. I am also most grateful to the Journal Paper Selection Committee for selecting my paper for publication. References [1] Murakami S, Kato S, Zeng J. Flow and temperature fields around human body with various room air distribution. CFD Study on Computational Thermal Manikin-Part I 1997;103(part 1):3–15. [2] Brohus H. Personal exposure to contaminant sources in ventilated rooms. Thesis, Aalborg University, Aalborg, Denmark, 1997. [3] Tanabe S. Numerical comfort simulator for evaluating thermal environment. In: Proceedings of the 10th International Conference on Environmental Ergonomics, Fukuoka, Japan, 2002. p. 435–38. [4] Nilsson H, Holme´r I, Bohm M, Nore´n O. Equivalent temperature and thermal sensation—comparison with subjective responses. In: Proceedings of Comfort in the Automotive Industry, Bologna, Italy, ATA vol 1, 1997. p. 157–62, 97A3018. [5] Huizenga C, Hui Z, Arens E. A model of human physiology and comfort for assesing complex thermal environments. Building and Environment 2001(36):691–9 /elsevier.comS. [6] Bureau C, Kampf H, Taxis-Reischl B, Traebert A, Mayer E, Schwab R. MARCO – BEHRs Method to assess thermal comfort. In: Vehicle Thermal Management Systems Conference Proceedings (VTMS 6), SAE Paper No. C599/005/2003, 2003. p. 223–33. [7] Han T, Huang L. A model for relating a thermal comfort scale to EHT Comfort Index. SAE Paper 2004-01-0919, March 2004. [8] Dufton AF. The equivalent temperature of a warmed room. vol 4, 1936. p. 227–29. [9] Madsen TL, Olesen BW, Kristensen NK. Comparison between operative and equivalent temperature under typical indoor conditions. vol 90, Part 1, 1984. p. 1077–90. [10] Nilsson HO, Holme´r I. Comfort climate evaluation with thermal manikin methods and computer simulation models. International Journal of Indoor Air Quality and Climate (Munksgaard, Denmark) vol. 13 2003:28–37. [11] Bedford T. The warmth factor in comfort at work. MRC Industrial Health Board Report, vol. 76. London, UK: HMSO; 1936. [12] Nielsen PV. The selection of turbulence models for prediction of room airflow. ASHRAE Transactions 1998;104(part 1B):1119–27. [13] Star CD 3.1B. User guide and methology. Computational Dynamics Limited, /cd-adapco.comS, Manual, 2001.