State and Prospects of Natural/Cross Ventilation Research in TPU
Masaaki Ohbaa, Kenji Tsukamotob, Isaac LunbabTokyo Polytechnic University, 1583 Iiyama Atsugi, JapanTokyo Polytechnic University, Wind Engineering Research Center,1583 Iiyama Atsugi, Japan

ABSTRACT: This paper reports results of physiological and psychological research on humansubjects in a cross-ventilated environment at Tokyo Polytechnic University. The physiologicalconcept of alliesthesia relating to cutaneous thermoreceptors has become an increasingly important issue recently. The cross-ventilated environment showed very different characteristics toa stagnant environment. In a recent human subject study using a climate controllable wind tunnel, it was found that the spectral peak frequency of skin surface temperature and sweat rate corresponded closely with the spectral peak frequency of sinusoidal wind flow in the range between
0.02Hz and 0.2Hz. The physiological thermoregulation models of a 2-node model and a 65-nodemodel were modified taking into consideration the evaporative heat loss by diffusion in the
cross-ventilated environment. The skin surface temperature could be predicted with high accuracy in the cross-ventilated environment. An experimental regression formula on pleasant sensation was proposed and could accurately predict the tendency of time history of pleasant sensation.
KEYWORDS: Cross-ventilation, Physiological characteristics, Human body, Thermoregulation
model, Pleasantness, Climate controllable wind tunnel
1. INTRODUCTION
Confronting the problem of energy shortage currently being experienced around the world, together with the ever-increasing threat of extreme weather events, for instance long periods of abnormally high temperatures, there exist various impacts on community cohesiveness, long-term
economic values, biodiversity, and especially human physiological comfort, health and wellbeing. Every year, the occurrence of heatwave events, prolonged periods of excessively hot
weather, has resulted in increased loss of life in Japan. Since the Fukushima accidents in 2011,
the nation has been facing a severe electricity shortfall in the summer as seasonal demand rises.
The heatwave incident in July 2012 resulted in the loss of life of 39 at home, and over 21,000
people were taken to hospitals due to heat stroke. Increasing temperatures are likely to increase
deaths from heat-related illnesses such as heat cramps, heat exhaustion and heat stroke. Higher
temperatures also increase perspiration and evaporation. Older people’s thirst responses decrease
with age and involuntary dehydration increases. Thus, the elderly are most at risk and often account for a large number of fatalities from heat stroke.
Making good use of the natural resource of wind to induce ventilation can be an attractive way
to combat the problems of energy shortage and hot weather (Lun, Ohba 20121). Increasing theavailability of adequate natural ventilation not only minimizes energy use and cost in buildings,
but also allows occupants to enjoy better indoor environmental quality and maintain or maximizea healthy, comfortable, and productive indoor climate. This paper reports results of research conducted by the Environmental Group of the Wind Engineering Research Center at the Tokyo Pol-ytechnic University on human physiological and psychological responses in a cross-ventilated
environment. It includes a concise introduction to the physiological characteristics of the humanbody in a cross-ventilated environment, a brief review of various experimental and numericalstudies on human thermoregulation, and finally focuses on results obtained from human subject
tests and numerical simulations.
2. CLIMATE CONTROLLABLE WIND TUNNEL FOR HUMAN RESPONSE STUDY
The human body is a very complex system and its responses to extreme events such as hot orcold are still fraught with uncertainties. Research investigation directly using human subjects inreal environments is still lacking, especially in non-uniform and transient indoor environments,
and thus reliable data from human subject tests are necessary. More efforts are needed tostrengthen the understanding of the relationship between naturally ventilated environments andhuman thermal responses, and to gain important knowledge of how the human thermoregulation
system responds to different environmental conditions.
A climate controllable wind tunnel was constructed in 2009 in order to systematically investigate the relationship between natural wind fluctuations and human physiological/psychologicalresponses. Figure 1 shows its layout. The complete controllable wind tunnel houses a test room
3.7m (wide)  8m (long)  2.7m (high), a pre-function room and a fan room, as shown in Figure 2. The airflow generator installed in the fan room comprises 48 plug fans driven by 400W
AC motors to increase the capability of sudden acceleration/deceleration. One air-conditioning
machine, which can supply airflow at 14000m3/h and provide cooling load and heating load of
35 kW and 20 kW, respectively, was used to prevent temperature drift of supply air occurring
when several air-conditioning machines were used. In addition, an electric steam humidifier wasused. The air-conditioning system can control air temperature from 20 to 350C ±0.50C, humidityfrom 40 to 70% ±2% and wind velocity from 0.1 to 2.0 m/s (Mizutani et al 20102). The maximum velocity was determined from field subject experiments in a detached house, which showedthat the comfortable maximum speed for subjects was 2m/s in early summer.
1800

10800
8000

1.0m/s

1200

3742
5092

Test room

1000

Prefunction
room

Air flow generators

3600

350

1600

Humidifier

Figure 1. Overview of climate-controllable windtunnelFigure 2. Horizontal plane distribution of thewind velocity of test room

3. PHYSIOLOGICAL CHARACTERISTICS OF HUMAN BODY IN CROSSVENTILATED ENVIRONMENT
3.1 Physiological Characteristics on Human Body
For many decades, human biometeorology has been an important topic in assessing the effects ofthe thermal environment on thermal comfort and on the thermal state of the body generally. The

human body is an extremely complicated fluid/thermal system. It is physically complex,
involving skin, tissues, blood flow, etc. Skin is the outer covering of the body and it is thephysical separator between the body’s interior structure and the exterior environment. It isreported that skin temperature studies have been the major topic among a consortium of medical
institutions in America since 2000 (Harvard Catalyst 20123). The key role of skin is to protect
the body against pathogens and to minimize unnecessary water loss as well as to maintainhomeostasis. In hot conditions, it aids the body in regulating its own temperature throughradiation, convection, conduction and evaporation. In addition, the sweat glands excrete sweat
and the evaporation of sweat from the skin surface acts as a cooling agent to regulate bodytemperature and prevent heat-related illnesses such as heat stroke.
The skin is the largest organ of the human body and one of its most important. It contains
thermal sensors that participate in thermoregulatory control. The thermal sensors affect theperson’s thermal sensation and comfort, from hot to cold, and vice versa, and all the minutevariations in between. Thus, skin temperature monitoring is an essential component whenestimating thermoregulatory responses due to heat exchange at the skin surface.
3.2 Characteristics of Sweating and Skin Temperature in Cross-Ventilated Environment
To clarify the influence of frequency of fluctuating airflow on mean skin temperature and sweat
rate, which indicate the body’s physiological response, human subject experiments were conducted in the climate controllable wind tunnel. Sinusoidal and uniform wind flows were produced inside the test room by adjusting the frequency of the sinusoid wave while keeping air
temperature at 32ºC, humidity at 70% and air velocity at 0.4 m/s (Morikami, Ohba 20114).
Figure 3 shows the power spectrum of sweat rates on the forehead/chest and wind velocity.
The sampling frequency of sweat rate was 1Hz so that Nyquist frequency was 0.5Hz. It was
found that the spectra of sweat rates comprised several sweating waves. In particular, the spectral
peak frequency of sweat rate showed a high power component at the sinusoidal frequency of the
produced wind velocity. Figure 4 shows the power spectrum of skin temperature on the chest andwind velocity. The spectral peak frequency of skin temperature corresponded closely with thespectral peak frequency of the sinusoidal wave flow.
From the results of the subject experiments, the spectral peak frequency of skin surface temperature and sweat rate corresponded closely with the spectral peak frequency of the sinusoidal
wind flow in the range between 0.02Hz and 0.2Hz. The sweat evaporation rate was higher in the
low-frequency range than in the high-frequency range, showing that the cross-ventilated airflow
had physiological influences on the human body.
1000

100

Forehead
Chest
Wind

1
0.1

10
1
0.1

0.01

0.01

0.001

0.001
0.001

0.001

Chest
Wind

100
ｆ･S(f)/σ2

f･S(f)/σ

2

10

0.01

0.1
Frequency[Hz]

1

10

0.01

0.1

1

Frequency [Hz]Figure 3. Power spectra of sweat rate on Figure 4. Power spectra of skin temperature on chest andchest/forehead and wind velocity
wind velocity

12

12

Convective heat transfer
coefficients c [W/m2･ºC]

Convective heat transfer
coefficients c [W/m2･ºC]

3.3 Coefficients of Convective Heat Transfer on Human Body
Coefficients of convective heat transfer on the human body are important for predicting/evaluating
the effects of airflow on thermal comfort. It is known that they take different values depending on the
airflow around a heat dissipator, its shape and size, and other factors. They are also key indicators forevaluating/predicting thermal comfort.
Convective heat transfer coefficients were measured for sitting postures of human subjects and athermal manikin under sinusoidal wave flow conditions (Morikami, Ohba 20125). Figure 5 and 6
show that they slightly decreased when the sinusoidal wind flow frequency decreased. The physical cause will be investigated by CFD analysis in future, because it is thought that the lengthscale of airflow may influence the development of the boundary layer at the skin surface.
10

10
8
6
4

c = 0.38ln( f ) + 9.02
R² = 0.26

2
0
0.001

0.01
0.1
1
Spectral peak frequency of wind f [Hz]

10

8
6
4

c = 0.27ln(f) + 7.91
R² = 0.61

2
0
0.001

0.01
0.1
1
Spectral peak frequency of wind f [Hz]10

Figure 5. Relationship between convective Figure 6. Relationship between convective
heat transfer coefficients and peak frequency heat transfer coefficients and peak frequencyof sinusoid wave flow in human subjectof sinusoid wave flow in thermal manikin
4. PHYSIOLOGICAL THERMOREGULATION MODEL
4.1 Human Physiological Thermoregulation Model(1) Review on Physiological Thermoregulation Model
The European Renaissance in the 14th century brought an increase in experimental investigation,
mostly in the field of dissection and body examination, thus advancing our knowledge of human
medicine and ergonomics. Brotton (20066) stated that the important development of the Renaissance was not any specific discovery, but rather the further development of the process for discovery, the scientific method that focused on empirical evidence and the importance of mathematics. The new scientific method led to great advances in fields such as biology, anatomy andmedicine. Besides, the invention of the thermocouple by Thomas Johann Seebeck in 1821 paved
the way to the study of body temperature. The invention of short clinical thermometers in 1866by the British physician Sir Thomas Clifford Allbutt led to the scientific, quantitative study ofthermoregulation. For instance, Lefevre (19117) used thermocouples to measure the thermal topography of the body and described whole animal and human calorimetry for determining metabolic rates.
Since the beginning of medical research, scientists have been most concerned about physiological systems and their applications. They have tried to find the most accurate model of the humantemperature regulation system for simulating reactions to different environmental conditions.
Table 1 shows the process of human thermal system modeling over the years. It should be notedthat the list of references in this table can by no means be considered complete, but it gives anindication of cutting-edge research in human thermophysiological modeling.
(2) Modification of Two-Node Model for Cross-Ventilated Environment

Mean skin temperature[ºC]

The first two-node model was proposed by Gagge et al (19718). The model subdivided the human body into two layers: skin and core, where energy balance equations of these compartments
were derived to evaluate thermal sensation of the body. The two-node thermal sensation transientmodel is employed in thermal comfort standards of ASHRAE and ISO7730.
In the thermal stagnant environment, by using the climate controllable wind tunnel, it was confirmed that mean skin temperature in thermal-transient conditions of a stagnant environment
could be predicted by modifying the human thermoregulation coefficients of sweat rate andevaporative heat loss of diffusion of the 2-node model.
In a cross-ventilated environment produced by step-wise changing of wind velocity, the accuracy of skin temperature prediction by the 2-node model deteriorated, as shown in Figure 7. Figure 8 shows the relationship between maximum evaporative potential and evaporative loss bydiffusion through the skin from subject experiments. Regardless of wind velocity, evaporative
heat loss by diffusion was kept constant. However, the two-node model showed that the evaporative heat loss by diffusion was 6% of the maximum evaporative heat loss, which is a function ofwind velocity. We modified the sweat rate and evaporative heat loss by diffusion of the 2-nodemodel. As shown in Figure 7, the time history of mean skin temperature in thermal-transientconditions in a cross ventilated situation could be predicted with high accuracy.
36
35
34
33
Experiment32

2-node model

31

Modified 2-node model

30
-60

-40

-20

0
20
Time[min.]40

60

Evaprative heat loss by diffusion [W/m2]

Figure 7. Comparison of measured and predicted skin temperatures in cross-ventilated environment
30
0.1m/s0.5m/s
1.0m/s20

1.5m/s
2.0m/s
10

0
0

100

200
300
400
500
Maximum evaporative heat loss [W/m2]

600

Figure 8. Relationship between maximum evaporative potential and evaporative loss by diffusion
through skin from subject experiments

4.2 CFD Physiological Thermoregulation Model(1) Review on CFD Physiological Thermoregulation Model
It can be seen that scientists, for decades, have used different types of models to represent andexplain physiological functions in order to improve our understanding of the body’s response tovarious conditions. Human temperature regulation system models have taken many forms: verbal, pictorial, mechanical, neuronal, and mathematical. Mathematical models became popular in
the 1930s when the first programmable computer was invented. Mathematical models of the human thermal system have developed from easy to more complex ones by using a variety of linear
and nonlinear equations, differential equations, integrals, trigonometric functions, and combinations of these functions. However, due to the natural complexity of human thermoregulation, it
has been difficult to determine the accuracy of these models. In addition, quantitative comparisons among models are also difficult due to the individual characteristics of each model underparticular environmental conditions (Haslam and Parsons, 19899; Crandall et al, 199410). Thus, it
is unclear which of the models would be best suited to a particular environment and application.
In recent years, human thermophysiological studies with computational models in virtual environments have been seen as a new way of evaluating and simulating the influence on human beings, as shown briefly in Table 2.
(2) Modification of 65-Node Model for Cross-Ventilated Environment
CFD simulation was done by the 65-node model, which was developed by Tanabe (200211). Thismodel, based on the Stolwijk (197112) multi-node model, is a physical model based on heat balance equations for individual body parts. Its inputs include age, sex, basal metabolic rate, and fat
rate. It considers thermal conductance between tissues, the detailed vascular system, and thethermoregulatory system consisting of perspiration, vasomotion, shivering heat production, arterio-venous anatomies, etc.
The Launder-Kato-modified linear low-Reynolds-number k-ε turbulence model was used inthis study. The QUICK scheme was applied to deal with the convection term of the equations
and the SIMPLEC algorithm was used to control the pressure-velocity coupling. The first cell
was 0.2 mm from the surface and the y+ value was equal to 1. In this fine grid region, five layers
were set with a change of thickness ratio of 1.1.
In order to verify the skin temperature and skin wettedness predicted by the modified 65-nodemodel, the subject experiment was carried out 10 times in the climate controllable wind tunnel.
The subjects were in a standing posture and they wore trunks with 0.03 clo. In these 10 tests, only young male persons were investigated. The air temperatures of the room were set at 32oC. The
wind speed was kept at 1.0 m/s and blew from the front of the subjects.
Figure 9 shows the results of CFD analysis. The 65-node model predicted that the surface
temperatures at chest and back were around 2.5 to 3oC lower than the subject experimental results. This is because the evaporative heat loss by diffusion was over-predicted in the crossventilated environment. We modified the evaporative heat loss by diffusion using the regression
equation obtained from the subject experiment. The modified 65-node model increased the prediction accuracy of skin temperature.

65‐node model

Modified 65‐node model

Whole body

Foot

Leg

Thigh

Hand

Arm

ShoulderPelvis

Back

Chest

Neck

36
35
34
33
32
31
30
29
28

Head

Surface temperature [℃]Experiment

Figure 9. Prediction accuracy of body skin temperatures at 32oC in early summer environment bymodified 65-node model
5. EXPERIMENTAL PREDICTION OF PLEASANT SENSATION IN CROSSVENTILATED ENVIRONMENT(1) Review on Thermal Comfort and Pleasant Sensation
Thermal comfort indexes were proposed by Fanger and Gagge et al. in 1984 and 1986, respectively. The PMV index of Fanger was adopted by the ISO7730 (198413). It was derived from subject
experiments in a stagnant environment, and succeeded in directly combining the heat balance ofthe human body with the thermal comfort vote. This index is being used worldwide because of its
easy measurement and innovative idea. However, the comfort equation can only be established ina stagnant environment and not in a cross-ventilated environment (Fountain et al. 199414).
Richard de Dear et al (199815) suggested the Adaptive Model, which takes into consideration
the effect of natural ventilation on human comfort. The Adaptive Model indicates that the indoor
thermal comfort zone changes according to outside air temperature, if behavioral thermoregulation such as clothing adjustment or window opening by residents can be allowed to improve theindoor thermal environment. However, the influences of cross-ventilation on thermal comfort
have not been quantitatively analyzed. Therefore, it is thought to be difficult to derive a universal
thermal environment index that includes the evaluation of transient thermal comfort on crossventilated environments.
Cabanac (198116) defined “pleasant sensation” as a positive comfort against comfort that is regarded as a lack of discomfort in steady state proposed by Gagge et al. A pleasant sensation index
for the thermal environment has not been established yet. Pleasantness occurs when there is asudden change from a discomfort environment to a neutral environment. Kuno (198717) has investigated a pleasantness of a thermal comfort environment by subject experiments. However, apredictive method for determining pleasantness is not yet established. Nagano et al (200518) investigated the unsteady-state thermal environment by using the movement of human subjects outside and inside rooms, and reported that the transient thermal sensation in unsteady-state could bewell predicted by using an experimental regression formula based on mean skin temperature.
Pleasantness achieved by cross-ventilation can improve the comfort sensation of a residential environment.

(2) Experimental Regression Formula for Pleasant Sensation in Cross-ventilated Environment
Air temperature was kept constant at 28°C during the experiment, while wind velocity waschanged step-wise from 0.1 m/s to 1.0 m/s. Figure10 shows the time history of the psychologicalresponse of a subject. Cold-hot sensation showed variation between “slightly hot” and "neutral" inPhase 1 and "neutral" in Phase 2 for wind velocities of 0.1 m/s and 1.0 m/s, respectively. The
thermal comfort sensation showed “neutral” in Phase 1, and it was kept approximately constant at“slightly comfort” in Phase 2. However, the time history of pleasant sensation showed a different
transient tendency with the cold-hot and thermal comfort sensation. Pleasant sensation showed“pleasant” for a short time in Phase 2 after the wind velocity was changed step-wise, but it to"neutral" with time. While pleasantness was occurring, comfort sensation moved to a more comfortable level.
The phenomenon that pleasantness decreases with time was approximated by an exponentialfunction. Mean skin temperature and a differential mean skin temperature as explanatory parameters were selected to correlate the psychological response with the physiological response in thissubject experiment.
Figure 11 compares the time histories of pleasant sensation of prediction and experiment. The
pleasant sensation values were averaged by three subjects. The predicted pleasantness was slightly
lower than the experimental pleasantness from the time just after the step-wise change of wind velocity. The tendency of time history of pleasant sensation could be predicted with an accuracy
within a standard deviation of 0.08 (Morikami,Ohba 201319).
Phase 13

Phase 2

2
1

2

0
-1

1

Cold-Hot sensation
Thermal comfort sensation
Pleasant sensation-2-3

Pleasant sensation

Cold-Hot sensation
Thermal comfort sensation

3

0
-60

-40

-20

0
Time [min.]

20

40

60

Figure 10. Time history of the psychological responses of a subject in cool-biz environment
3
Pleasant sensation

Experimental
Prediciton
2

1

0
-60

-40

-20

0
Time [min.]

20

40

60

Figure 11. Comparison of time history of pleasant sensation of prediction and subject
experiment6. FUTURE PROSPECTS IN NATURAL/CROSS VENTILATION
Today, environmental concerns have increased and sustainable design has become more desirable. Hence, natural ventilation has become the preferred system for meeting occupants’ requirements. The energy used by natural ventilation is minimal. It thus enables considerable cost savings through reduced construction costs as well as maintenance and running costs. It can also
help alleviate the prevalence of indoor air quality (IAQ) problems in buildings, such as sickbuilding syndrome (SBS) and concentration rates. Decreasing cooling demand improves comfort
conditions and reduces indoor pollution levels, and these are problems that have direct impacts
on human beings. It is necessary to take immediate action to seek a solution before it is too late.
It is a real challenge for building designers, architects, engineers and researchers to present thepossibilities of alternative cooling and ventilation strategies.
References[1] Isaac Lun and Masaaki Ohba: An overview of the cause of energy shortage and building energy strategy after Fukushima disaster in Tohoku District of Japan, Advances in Building
Energy Research, Vol.6, No.2, pp.272-309, November 2012.
[2] K. Mizutani, M. Ohba, H. Sato, “Characteristics of fluctuating airflow produced by climate
controllable wind tunnel”, Proceedings of 21st National Symposium on Wind Engineering,
2010.12, 125-130[3] Harvard Catalyst (2012): Skin Temperature
http://connects.catalyst.harvard.edu/profiles/profile/concept/skin+temperature, Retrieved on
Feb 2 2013[4] Shinya Morikami, Masaaki Ohba, Kenji Tsukamoto: Human-subject experiments on thermal
sweating for evaporative cooling by fluctuating wind, ICWE13, 1-8, 2011.7[5] Shinya Morikami, Masaaki Ohba, Kenji Tsukamoto and Lun Yu-Fat: Experiment study ofthe influence of fluctuation air flow on mean skin temperature and sweat rate of human
body, Symposium of Wind Engineering, Tokyo, Japan, pp.43-48, 5-7, Dec., 2012.
[6] J. Brotton: The Renaissance: A very short introduction, Oxford University Press, 2006,
ISBN 0-19-280163-5.
[7] J. Lefevre: Chaleur Animale et Bioe´nerge´tique, Paris: Masson, 1911.
[8] A. P. Gagge, A. P. Fobelets and L. G. Berglund, A standard predicted index of human response to the thermal environment. ASHRAE Transactions, Vol.92, Part2, pp.709-731, 1986
[9] R. A. Haslam and K. C. Parsons: Computer-based models of human responses to the thermal
environment: Are their predictions accurate enough for practical use, in Thermal Physiology,
J.B. Mercer, Ed., 1989, pp. 763-768.
[10] C.G. Crandall, J.M. Johnson, V.A. Convertino, P.B. Raven, and K.A. Engelke: Alteredthermoregulatory responses after 15 Days of head-down tilt, J. Appl. Physiol., 77(4), 1994,
pp. 1863-1867.
[11] S. Tanabe, K. Kobayashi, J. Nakano, Y. Ozeki, and M. Konishi: Evaluation of thermal comfort using combined multi-node thermoregulation (65MN) and radiation models and computational fluid dynamics (CFD), Energy and Buildings, 34, 2002, 637–646.
[12] J.A.J. Stolwijk, A mathematical model of physiological temperature regulation in man,
NASA-Langley, Report No.CR-1855, 1971
[13] International standard 7730, Moderate thermal environment-determination of PMV and PPD
indices and specification of the conditions for thermal comfort. ISO, 1984.

[14] Marc Fountain, Edward Arens, Richard de Dear, Fred Bauman and Katsuhiro Miura, Local-

ly controlled air movement preferred in warm isothermal environments. ASHRAE, 100(2),pp.937-952, 1994
[15] Richard de Dear, Gail Brager and Donna Cooper, Developing an adaptive model of thermal
comfort and preference, ASHRAE Transactions 104 (1998) (1A), pp. 145–167.
[16] M. Cabanac, Physiological Signals for Thermal Comfort, Chapter12, Studies in Environmental Science, 10, 1981, pp.181-192[17] S. Kuno, H. Ohno, and N. Nakahara, A two-dimensional model expressing thermal sensationin transitional conditions. ASHRAE Transaction, 93(2), pp. 396-406(1987)[18] K. Nagano, A. Takaki, M. Hirakawa and Y. Tochihara, Effects of ambient temperature steps
on thermal comfort requirements. International Journal of Biometeorology, September 2005,
Volume 50, Issue 1, pp. 33-39[19] Shinya Morikami, Masaaki Ohba, Proposal of Experimental Regression Formula on PleasantSensation in Cross-ventilated Environment Produced by Step-wisely Changing Wind Velocity, Submitted to CLIMA 2013 (Accepted).

Table 1 Physiological thermoregulation models
ModelsVerbal:Authors
Ott (1887)
Rubner (1902)
Barbour (1921)
Bazett (1949)

Pictorial:

DuBois (1948)
Chatonnet and
Cabanac (1965)

Mechanical:
Physical

MacDonald andWyndham (1950)

Electrical

Crosbie et al (1963)

Neuronal:

Nakayama et al (1961)
Hammel (1965)

Mekjavic andMorrison (1985)
Mathematical:

Burton (1934)
Machle and Hatch(1947)
Pennes (1948)
Wyndham and Atkins(1960)
Wissler (1961, 1964)
Crosbie et al (1963)
Stolwijk and Hardy

Description
Studied the heat center in the brain from the concept of thermotaxiccentre which
composed thermogenic, thermoinhibitory, and thermolytic centers involved intemperature regulation.
Showed the difference made by a protecting layer of adipose tissue on animals
when thin has a much higher metabolism at various temperatures than when fat.
Implanted fine silver tubes into the hypothalamus of laboratory animals to test
the effect of heat and cold applied directly to this temperature-responsive regionof the brain.
Studied two sets of antagonistic functions with separate but integrated centersdesignated as warm (thermogenic) and cold (thermolytic).
Depicted the neurophysiological components of the temperature regulatory system and its connection to the control elements, sweat glands, blood vessels, andskeletal muscles
Proposed a model which assumed that the motivation for behavioral temperature
regulation originated in feelings of discomfort related to central and peripheral
thermal stimulation and physiological responses.
A physical analog used an electrical circuit to model temperature regulation thatthe temperature was represented by electrical potential, and heat flow was defined by the electrical current.
An electrical analog constructed to simulate the physiological responses to heatand cold in a nude man by using the basic equations for heat balance and taking
into account heat losses by radiation, convection and evaporation.
Studied the temperature-responsive neurons in the anterior hypothalamus andpreoptic area
Proposed a neuronal model to explain set-point thermoregulation based on asynaptic network encompassing four different types of hypothalamic neurons:i.e., warm-sensitive and temperature-insensitive neurons and heat loss and heatproduction effector neurons.
Used stimulus response expressions to predict thermogenesis based on static peripheral, core, and central temperatures and incorporated the characteristics ofthermosensitive neural structures of the body.
Investigated the skin temperature/blood flow relationship using mathematicalmodeling.
Applied the physical laws of heat transfer to the analysis of thermal responses ofa human body to his environment.
Modeled the single element of a human body based on quantitative analysis of
the relationship between arterial blood temperature and tissue temperature.
Developed a model to predict the transient responses of the human body by approximating the human body by a cylinder which consists of a number of thinconcentric shells.
Extended Pennes’ model of the forearm to obtain the temperature distribution of
the entire body.
Studied a simple proportional regulator which gave effector actions proportional
to deviations in the core temperature from a set point and to deviations from theskin temperature from a set point.
Contributed a simpler 8-node mathematical model based on a simplified config-

Remarks
The brain was identified as important in temperature regulation on
warm-blooded animals
First to relate the first law of thermodynamics to animal metabolism.
Demonstrated for the first time that the hypothalamus actually functioned as a thermostat.
Explained human temperature regulation and used widely in medical
schools
The model contained the basic knowledge of what is known today of
the neurophysiology of temperature regulation.
This simple model stimulated considerable research in behavioral
temperature regulation.
One of the first predecessors to the later analog computer simulationsof temperature regulation.
This model closely predicted steady-state conditions in rectal temperature, skin temperature, metabolic rate, vasomotor state, andevaporative heat loss at rest and during exercise.
Details of inter-neuronal connections of temperature-sensitive units
in the preoptic area of the hypothalamus were first outlined.
This model offered some unique concepts that were not present inother feedback control systems and understanding of the neuralcounterparts of error comparators, reference signals, and set points.
The model was successful in describing the body's temperature response to environmental changes
Early mathematical model opened the door to new developments intemperature regulatory research.
First nonsteady-state model of the core and shell concept using
measurements of rectal and mean skin temperatures, respectively.
One of the earliest workers to analyze heat transfer in human arm,with the cylindrical concept was introduced.
First to introduce a transient model in predicting transient responseof human body.
First multi-element steady-state model of the entire human body, andit widely used for prediction of temperature elevation during hyperthermia.
First successful computer simulation of human temperature regulation.
These models have become the standard anatomical approach to(1966)
Stolwijk (1971)
Gagge et al (1971)
Huizenga et al (2001)
Werner and Webb
(1993):
Kohri and Mochida(2002)

uration of the human body but took into account the physiological thermoregulation mechanism.
Developed a 25-node model of thermoregulation to evaluate local blood flow
rate, metabolic rate, evaporation rate.
Subdivided the human body into two layers: skin and core, where energy balance
equations of these compartments have been given to evaluate thermal sensationof the body.
Improved human comfort model from six body segments to unlimited segmentation to assess blood flow, sweating, metabolism and comfort of sedentary humansubjects in complex thermal environments.
Described the basics of a 6-cylinder model of human thermoregulation for use
on personal computers
Developed a dispersed two-node model to predict steady-state physiological responses of a human in a vehicle.

modeling human temperature regulation, and many other multi-nodemodels were based on these models with significant improvements
such as body segments and layers.
The two-node thermal sensation transient model has been employedin thermal comfort standards of ASHRAE and ISO7730.
The model was found to be accurate in predicting blood flow in awide range of applications and environments.
One of the earliest models applicable to water immersion conditionsand validated with human data in cold water.
This model could give practical precision when considering the environment where radiative heat load was uneven.

Table 2 CFD physiological thermoregulation models
Authors
Gan (1994)
Kato et al (1996)
Murakami
1998)

et

al

(1997,

Bjorn et al (2000)
Han et al (2001)

Description
Developed a CFD code for the indoor environment in ventilated rooms. The program produces thermal sensation indices, PMV and PPD for evaluation.
The flow and temperature fields around a computational manikin and the age ofthe supplied air as well as the residual lifetime were analyzed by CFD.
Developed a computational thermal manikin on the basis of coupled simulation of
CFD, radiation, moisture transport, and heat transfer inside the human body as a
new resolution method with regard to thermal sensation.
Numerical simulation of the effects of respiration measured in displacement ventilated rooms. The model geometry is well-defined geometrical primitives, easy toconvert into an accurate virtual geometry for CFD simulations.
Numerical prediction of occupant thermal comfort to support automotive climate
control systems by using a model of the human thermal regulatory system basedon the Stolwijk 16-segment model.

Tanabe et al (2002)

Integrated a 65-node human thermoregulatory model with a 3D model of a nude
male body in CFD which incorporated radiation heat transfer.

Zhu et al (2002)

More complicated unsteady-state simulations taking account of inhalation andexhalation processes
Investigated the perceived thermal climate using subject and manikin correlated
model. Numerical simulation adopted zero-equation with dynamic boundaries.
Studied the indoor climate with CFD in combination with a detailed thermal comfort model describing the human thermoregulation

Nilsson (2004)
Croooer et al (2009)
van Treeck et al (2011)

Presented a co-simulation procedure for scale-adaptive indoor thermal climate
simulation which comprises a middleware platform for coupling heterogeneouscomputational codes for distributed simulations.

Features of work
Early use of numerical thermal occupants for assessment of indoor thermal comfort,
steady-state airflow simulations, two-equation k- turbulence model.
Steady-state simulations, low Re k- turbulence model, three different positions of
the contaminant generation analyses.
Physiological model, low Re k- turbulence model, coupled simulations, and the
dynamic effects of air flow around the body is demonstrated.
Steady-state simulations, RNG k- turbulence model with buoyancy effects and second order discretisation scheme, geometry of the manikins with a body fitted, unstructured grid.
Ability to predict local thermal comfort level of an occupant in a non-uniform thermal environment as a function of air temperature, surrounding surface temperatures,
air velocity, humidity, direct solar flux, as well as the level of activity and clothingused.
The physiological model uses anthropometric data of an average man; body weightand body surface area, for radiation and CFD analyses, maintaining sufficient accuracy.
CFD model with a complex shape is used to evaluate the influence of local effects
of real human body by low Reynolds number k- model.
Develop a general comfort zone diagram, which is applicable to different manikins
and instruments, evaluating different comfort climate situations.
Demonstrate the application of the coupled system on heat and moisture transfer at
the surface of a human body for naturally ventilated buildings and provide guidelines for modeling CFD and human-environment interactions.
Provides a framework and a documented class library for weak coupling of heterogeneous computational codes in distributed environments.