THERMAL COMFORT 3.1. INTRODUCTION.

Transcription

1 THERMAL COMFORT Contents of this chapter, extensively deals with the Thermal Comfort problem formulation and its assessment along with the respective literature survey: Introduction to thermal comfort. Fuzzy logic introduction and thermal comfort problem solving using fuzzy logic. Quantitative analysis and optimization of thermal comfort in office buildings with results. Quantitative analysis and optimization of thermal comfort in resident buildings with results INTRODUCTION. Thermal comfort is highly subjective, not only is it subject to personal preference but also to varying temperatures. Both internal and external temperatures sensing is integrated in such a way that the resulting effect would either move towards restoring deep body temperature or move away from it. A cold sensation will be pleasing when the body is overheated, but unpleasant when the core is already cold. At the same time, the temperature of the skin is by no means uniform. Besides variations caused by vasoregulation, there are variations in different parts of the body, which reflect the differences in vasculation and subcutaneous fat. The wearing of clothes also has a marked effect on the level and distribution of skin temperature. Thermal comfort for human is one of the major problems at present. Providing thermal comfort for occupants in buildings is really a challenging task because thermal comfort is not only influenced by temperature but also factors like relative humidity, air velocity, environment radiation, activity level and cloths insulation. These entire six variables play a major role in providing thermal comfort. Thermal comfort can be calculated by an equation called Fanger s Predicted Mean Vote (PMV) as given by Fanger. This equation gives the optimal thermal comfort for any activity level, clothing insulation and for all combinations of the environmental variables such as air temperature, air humidity, mean radiant temperature and relative air velocity. 65

2 Human thermal comfort is defined by ASHRAE as the state of mind that expresses satisfaction with the surrounding environment (ASHRAE Standard 55). Maintaining thermal comfort for occupants of buildings or other enclosures is one of the important goals of design engineers. Thermal comfort is maintained when the heat generated by human metabolism is allowed to dissipate, thus maintaining thermal equilibrium with the surroundings. Any heat gain or loss beyond this, generates a sensation of discomfort. It has long been recognized that the sensation of feeling hot or cold is not just dependent on air temperature alone. The problem that we are going to deal with here is the thermal comfort of offices and homes which use natural ventilation only IMPORTANCE OF THERMAL COMFORT Thermal comfort is very important to many work-related factors. It can affect the distraction levels of the workers, and in turn affect their performance and productivity of their work. Besides, thermal discomfort has been known to lead to Sick Building Syndrome symptoms. The US Environmental Protection Agency's Building Assessment Survey and Evaluation Study found that higher indoor temperatures, even within the recommended thermal comfort range, increased worker symptoms. The occurrence of symptoms increased much more with raised indoor temperatures in the winter than in the summer due to the larger difference created between indoor and outdoor temperatures FACTORS DETERMINING THERMAL COMFORT Metabolism Clothing Insulation Relative Humidity Air temperature Mean radiant temperature Air velocity 66

3 Metabolism. While measuring metabolism rates, many factors have to be taken into account. Each person has a different metabolism rate, and these rates can fluctuate when a person is performing certain activities, or when he is under certain environmental conditions. Even people who are in the same room can feel significant temperature differences due to their differing metabolic rates, which makes it very hard to find an optimal temperature for everyone in a given location. (Khodakarami, 2009); (Smolander, 2002); (Toftum, 2005) Clothing Insulation. During cold weather, layers of insulating clothing can help keep a person warm. At the same time, if the person is doing a large amount of physical activity, many layers of clothing can prevent heat loss and consequently lead to overheating. Generally, the thicker the garment is, the greater insulating abilities it has. Depending on the type of material the clothing is made out of, air movement and relative humidity can decrease the insulating ability of the material. The amount of clothing is measured against a standard amount that is roughly equivalent to a typical business suit, shirt, and undergarments. Activity level is compared to being seated quietly, as in a classroom. Clo units can be converted to R-value in SI units (m² K/W) or R SI ) by multiplying Clo by (1 Clo = R SI ). (In English units 1 clo corresponds to an R-value of 0.88 F ft² h/btu.) Relative Humidity. The human body has sensors that are fairly efficient in sensing heat and cold, but they are not very effective in detecting relative humidity. Relative humidity creates the perception of an extremely dry or extremely damp indoor environment. This can then play a part in the perceived temperature and their thermal comfort. The recommended level of indoor humidity by ASHRAE is in the range of 30-60%. A way to measure the amount of relative humidity in the air is to use a system of drybulb and wet-bulb thermometers. A dry-bulb thermometer measures the temperature not relative to moisture. This is generally the temperature reading that is used in weather reports. 67

4 In contrast, a wet-bulb thermometer has a small wet cloth wrapped around the bulb at its base, so the reading on that thermometer takes into account water evaporation in the air. The wet-bulb reading will thus always be at least slightly lower than the dry bulb reading. The difference between these two temperatures can be used to calculate the relative humidity. The larger the temperature differences between the two thermometers, the lower the level of relative humidity. The wetness of skin in different areas also affects perceived thermal comfort. Humidity can increase wetness on different areas of the body, leading to a perception of discomfort. This is usually localized in different parts of the body. The local thermal comfort limits for local skin wetness differ between different skin locations of the body. The extremities are much more sensitive to thermal discomfort from wetness than the trunk of the body. Although local thermal discomfort can be caused from wetness, the thermal comfort of the whole body will not be affected by the wetness of certain parts. Recently, the effects of low relative humidity and high air velocity were tested on humans after bathing. Researchers found that low relative humidity engendered thermal discomfort as well as the sensation of dryness and itching. It is recommended to keep relative humidity levels higher in a bathroom than other rooms in the house for optimal conditions THERMAL STRESS The concept of thermal comfort is closely related to thermal stress. This attempts to predict the impact of air movement, and humidity for military personnel undergoing training exercises or athletes during competitive events. Values are expressed as the Wet Bulb Globe Temperature or Discomfort Index. Generally humans do not perform well under thermal stress. People s performances under thermal stress are about 11% lower than their performance at normal thermal conditions (Hancock, Ross, & Szalma, 2007)l; (Leon, 2008). Also, human performance in relation to thermal stress varies greatly by the type of task a person is completing. Some of the physiological effects of thermal heat stress include increased blood flow to the skin, sweating, and increased ventilation. 68

5 EFFECTS OF NATURAL VENTILATION. Many buildings use a HVAC (Heating Ventilation Air Conditioning) unit to control their thermal environment. Recently, with the current energy and financial situation, new methods for indoor temperature control are being used. One of these is natural ventilation. This process can make the controlled indoor air temperature more susceptible to the outdoor weather, and during the seasonal months, the temperatures inside can become too extreme. During the summer months, the temperature inside can rise too high and cause the need for open windows and fans to be used. In contrast, the winter months could call for more insulation and layered clothing to deal with the less than ideal temperatures OPERATIVE TEMPERATURE The ideal standard for thermal comfort can be defined by the operative temperature. This is the average of the air dry-bulb temperature and of the mean radiant temperature at the given place in a room. In addition, there should be low air velocities and no 'drafts,' little variation in the radiant temperatures from different directions in the room, and humidity within a comfortable range. The operative temperature intervals varied by the type of indoor location. ASHRAE has listings for suggested temperatures and air flow rates in different types of buildings and different environmental circumstances THERMAL SENSITIVITY OF INDIVIDUALS The thermal sensitivity of an individual is quantified by the descriptor F S, which takes on higher values for individuals with lower tolerance to non-ideal thermal conditions. This group includes pregnant women, the disabled, as well as individuals whose age is above 14 or below 60, which is considered the adult range. Existing literature provides consistent evidence that sensitivity to hot and cold surface declines with age and that there is also a gradual reduction in the effectiveness of the body in thermoregulation after the age of 60. This is mainly due to a more sluggish response of the counteraction mechanisms in the body that are used to maintain the core temperature of the body at ideal values. 69

6 Situational factors include the health, psychological, sociological and vocational activities of the persons. Restaurant employees often have the air-conditioner temperature to suit themselves, rather than the resting clients or incoming new customers from the temperature outside the building GENDER DIFFERENCES While thermal comfort preferences between genders seem to be small, there are some differences. Females are much more likely to be sensitive to thermal conditions. Females are also more likely to be uncomfortable with the room temperature, and will find the temperature too hot or too cold before many men would. Many times, females will prefer higher temperatures. But while females were more sensitive to temperatures, males tend to be more sensitive to relative humidity levels PREDICTED MEAN VOTE A large number of thermal comfort indices have been set up for the analysis of indoor climates and the design of HVAC systems. But only a few of them have been used to evaluate the ability of an existing room climate to create satisfactory thermal conditions for occupants. The most common and best understood one is Fanger s Predicted Mean Vote (PMV) THERMAL SENSATION INDEX For many years, it has been desirable to determine directly human s thermal sensation in a given environment condition and for a specified activity level and clothing insulation. Until the 60 s, thermal comfort calculation was limited by the lack of a well-defined unit to represent the degree of the thermal sensation. Such a unit appeared in 1970 when Fanger defined the PMV Predicted Mean Vote as the index that gives the expected degree of thermal comfort in relation to all the above-mentioned six thermal parameters. Besides that, Fanger presented a general comfort equation which describes the conditions under which the average sensation of a large group of people will feel thermal neutrality. He defined the thermal neutrality of a person as the condition of mind in which the subject would prefer neither warmer nor cooler surroundings Eq. (3.1) represents the comfort equation proposed by Fanger 70

7 (3.1) (3.2) h c= The parameters are defined as follows: PMV: Predicted mean vote. M: Metabolism (W/m 2 ). W: External work, equal to zero for most activity (W/ m 2 ). I cl : Thermal resistance of clothing (Clo). F cl : Ratio of body s surface area when fully clothed to body s surface area when nude. T a : Air temperature ( 0 C). T mrt : Mean radiant temperature ( 0 C). V air : Relative air velocity (m/s). P a : Partial water vapour pressure (Pa). H c : Convection heat transfer coefficient (W/m 2 k) T cl : Surface temperature of clothing ( 0 C). 71

8 3.3. FUZZY THERMAL SENSATION INDEX LITERATURE SURVEY The occupants thermal comfort sensation is addressed here by the well-known comfort index (PMV - Predicted Mean Vote). In this context, different strategies for the control algorithms are proposed by using only-one-actuator system that can be associated with a cooling and/or heating system. The first strategy is related to the thermal comfort optimization and the second one includes energy consumption minimization while maintaining the indoor thermal comfort criterion in an adequate level (Freire, Roberto, H.C.Oliveira, & Nathan.Mendes, 2008). Temperature in an automobile cabin is an important factor in the occurrence of traffic accidents. A better climate control system in an automobile improves thermal comfort which results in increased driver caution and thereby improves driving performance and safety in different driving conditions. Thermal loads while minimizes energy consumption. The compressor used in a cooling system is driven by automobile engine and it therefore increases the fuel consumption. Manual control requires skill and experience about system. Automatic control frees the driver from this task. (Wang & Mendel, 1992) (Xia, R.Y, & Zhao, 1999) designed two controllers for controlling the indoor air temperature of a car, the general fuzzy controller and the state feedback with weighting fuzzy controller. By comparing the results of the two experiments, they showed that the state feedback with weighting fuzzy controller is more efficient than the other in controlling the automobile indoor air temperature. Fuzzy PMV is used instead of Fanger s PMV. PMV index is used to show thermal comfort and its variables are simplified. With this simplification, if the inside cabin temperature and air velocity are known, a good prediction of comfort can be obtained. Two fuzzy controllers with temperature feedback and PMV feedback are designed. An index for energy consumption is also suggested. It is shown that controller with PMV feedback is more effective than controller with temperature feedback. The PMV controller also better minimizes the energy consumption. A new approach based on fuzzy logic to estimate the thermal comfort level depending on the state of the following six variables: the air temperature, the mean radiant temperature, the relative humidity, the air velocity, the activity level of occupants and their clothing insulation. The new fuzzy thermal sensation index is calculated implicitly as the consequence of linguistic rules that describe human s comfort level as the result of the interaction of the environmental variables with the occupant s personal parameters. The fuzzy 72

9 comfort model is deduced on the basis of learning Fanger s Predicted Mean Vote, PMV equation. Unlike Fanger s PMV, the new fuzzy PMV calculation does not require an iterative solution and can be easily adjusted depending on the specific thermal sensation of users. These characteristics make it an attractive index for feedback control of HVAC systems. (Hamdi, Lachiver, & Michaud, 1999). Two fuzzy controllers one with temperature as its feedback and the other PMV index as its feedback are designed. Results show that the PMV feedback controller better controls the thermal comfort and energy consumption than the system with temperature feedback (YadollahFarzanth & Tootoonchi, 2008). The occupants thermal comfort sensation is addressed here by the well-known comfort index (PMV - Predicted Mean Vote). In this context, different strategies for the control algorithms are proposed by using only-one-actuator system that can be associated with a cooling and/or heating system. The first strategy is related to the thermal comfort optimization and the second one includes energy consumption minimization while maintaining the indoor thermal comfort criterion in an adequate level (Freire, Roberto, H.C.Oliveira, & Nathan.Mendes, 2008). (Soyguder & Ali, 2009)The optimal values of PID parameters were obtained by using Fuzzy sets. Fuzzy adaptive control has been performed to maximize the performance of the system. Efficiency of fuzzy adaptive control (FAC) developed method was successfully obtained (Soyguder & Ali, 2009). Fuzzy logic offers a promising solution to this conceptual design through fuzzy modelling. Numerous fuzzy logic studies are available in the nonmechanical engineering field and allied areas such as diagnostics, energy consumption analysis, maintenance, operation and its control. Relatively little exists in using fuzzy logic based systems for mechanical engineering and very little for HVAC conceptual design and control (Soyguder & Ali, 2009). During the past several years, fuzzy control has emerged as one of the most active and fruitful areas for research in the applications of fuzzy set theory, especially in the realm of industrial processes, which do not lend themselves to control by conventional methods because of a lack of quantitative data regarding the input-output relations. Fuzzy control is based on fuzzy logic a logical system which is much closer in spirit to human thinking and natural language than traditional logical systems. The fuzzy logic controller (FIX) based on fuzzy logic, provides a means of converting a linguistic control strategy, based on expert knowledge, into an automatic control strategy (MinNing & Zaheeruddin, 2010). (Hamdi, Lachiver, & Michaud, 1999). The thermal comfort of the occupants of a building depends on many factors including metabolic rates, clothing, air 73

10 temperature, mean radiant temperature, and air velocity and humidity. In most buildings, however, only temperature and humidity can be controlled. Indeed, in many European buildings, over a wide range of humidity, only zone temperature is controlled. In such cases, the control objective is to maintain the zone temperature within a pre-defined range (Thompson & Dexter, 2009). Low operational efficiency especially under partial load conditions and poor control are some reasons for high energy consumption of heating, ventilation, air conditioning and refrigeration (HVAC&R) systems. In order to improve energy efficiency, HVAC&R systems should be efficiently operated to maintain a desired indoor environment under dynamic ambient and indoor conditions (Ning & Zaheeruddin, 2010). A new approach based on fuzzy logic is introduced to estimate the thermal comfort level, depending on the state of the following six variables: the air temperature, the mean radiant temperature, the relative humidity, the air velocity, the activity level of occupants and their clothing insulation. New fuzzy thermal sensation index is calculated implicitly as the consequence of linguistic rules that describe human s comfort level as the result of the interaction of the environmental variables with the occupant s personal parameters. The fuzzy comfort model is deduced on the basis of learning Fanger s Predicted Mean Vote (PMV) equation. The new fuzzy PMV calculation does not require an iterative solution like Fanger s PMV and can be easily adjusted depending on the specific thermal sensation of users. These characteristics make it an attractive index for feedback control of HVAC systems. Since the involved heat transfer processes are relatively complicated, the mathematical expression derived for the calculation of the PMV is complicated and not suitable for feedback control systems (Fanger.P.O, 1970) ; (Federspiel, 1882) (Int-Hout, 1990) (Sherman, 1985). In order to overcome these problems, Fanger and ISO proposed to use Tables and diagrams to simplify the determination of PMV in practical applications. Other researchers proposed to use simplified models of PMV to avoid the iterative process. Such thermal sensation indexes have been proposed in Refs. (Auliciems.A, 1984) (Coome, Gan,G, & Awbi,H.B, 1992) (Culp, Rhodes, Krafthefer, & Listvan, 1993) (Federspiel, 1882) (Fountain, Brager, Arens, Bauman, & Benton, 1994) (Gan & Croome, 1994) (Sherman, 74

11 1985) and they are deduced after significant modifications of Fanger s comfort equation. Sherman (Sherman, 1985) proposed a simplified model of thermal comfort, based on the original work of Fanger. In order to reach his objective, and to calculate the value of PMV without any iteration, he linearized the radiation exchange terms to remove the T 4 dependence on temperature. Then, he simplified the convection coefficient to eliminate the iterative solutions and finally, he used the dew point temperature instead of relative humidity to avoid its dependence on air temperature. Sherman (Sherman, 1985) indicated that these simplifications should not affect the precision of the PMV calculation only when the occupants are near the comfort zone. Based on the above mentioned assumptions, Sherman concluded that the resulting simplified index could be computed explicitly in a compact form. However, Sherman s thermal sensation index was not linearly parameterized (Federspiel, 1882) and therefore, not suitable for on-line calibration and could not be used in a control algorithm of HVAC systems. Federspiel proposed another thermal sensation index (V), that is, a modification of Fanger s PMV index (Federspiel, 1882). To simplify the derivation, he supposed that the radioactive exchange and the heat transfer coefficient are linear. In addition, it was assumed that the bodily heat production and the clothing insulation are constant. In addition, Federspiel supposed that the occupants are in a thermal neutrality condition. All these assumptions were applied to the derivation of a thermal sensation index, that is an explicit and linearly parameterized function of the four environmental variables. However, it was outlined that (V) becomes non-linear as soon as the activity level or the clothing insulation are changed. This problem limits the use of the thermal sensation index (V) in zones, where the two above-mentioned factors are changing in time. Other researchers recognized that the above-cited assumptions are difficult to reach and the simplification of the original PMV model results in a significant error when they are not respected (ASHRAE, 1989) (Federspiel, 1882). At present, the challenge is to derive a thermal sensation index, based on the original work of Fanger without any simplification and which can be used in feedback control applications with an on-line calibration. With this goal in mind, the derivation in this project results in an index that does not require any iteration solutions because it is implicitly dependent on the state of the air temperature, the mean radiant temperature, the air velocity, the relative humidity, the activity level occupants and the thermal resistance of their clothing. Since Fanger s thermal comfort model itself is 75

12 proposed to use, it was not necessary at the expense of the accuracy to simplify the comfort model that makes calculations easier. It is difficult to justify control schemes, based on a simplified model of thermal comfort, where it is necessary to verify the credibility of the model simplifications. However, the proposed thermal comfort model can be used as a new indoor climate high-level performance control variable of the HVAC systems without any simplification of the original work of Fanger, and it does not require iterative solutions. At present, such a thermal sensation index has not yet been developed PROBLEM FORMULATION The aim is to derive a thermal sensation index, based on the original work of Fanger that can be used in feedback HVAC control applications with an on-line calibration and without requiring any simplification. This work presents a new strategy for the design of an accurate thermal sensation index that does not require any iteration solutions and that can be used as a high level performance variable in the control of HVAC systems. According to the state of the six parameters that affect human s thermal comfort, the proposed thermal sensation index can be calculated directly on the basis of knowledge gleaned from the original work of Fanger. In order to reach this objective, fuzzy logic modelling, which is defined as a method of describing characteristics of systems, using fuzzy reasoning, is used to approximate the human s comfort/discomfort level in a given indoor climate. By analysing the influence of the individual variables on the thermal sensation index, it becomes possible to evaluate linguistically, how each variable influences the thermal sensation. It was shown that it is impossible to consider the effect of the six variables on the human thermal sensation independently, as the effect of each of them depends on the level and the state of the other variables; the thermal comfort level is a complicated result of the interaction of the six variables (Fanger.P.O, 1970). On the basis of this analysis, a new fuzzy PMV is designed and a general fuzzy rule base is derived to describe the state of human s thermal sensation. For any activity level and any clothing, the fuzzy rule base is able to calculate all combinations of the four environmental variables which will create optimal thermal comfort. The newly designed thermal sensation index can then be easily used in feedback control of HVAC systems. 76

13 FUZZY PMV Ensuring thermal comfort of occupants and reducing energy consumption are the control challenges in the development of modern industrial HVAC systems. For thermal comfort and energy efficiency, it is desirable to design a HVAC control system that can guarantee high performance and good robustness with regard to the variation of the environmental variables as well as the activity level of occupants and their clothing insulation. Research has shown that it is possible to reach these objectives if HVAC control strategies are based on the thermal sensation index instead of air temperature alone (Auliciems.A, 1984) ; (Fanger.P.O, 1970) ; (Federspiel, 1882) ; (Fountain, Brager, Arens, Bauman, & Benton, 1994) ; (MacArthur, 1986) ; (Sherman, 1985). Presently, the non-linear behaviour of human s thermal sensation and the unavailability of a direct quantitative PMV regarding the inputs output relations make it very difficult or impossible to design a direct control strategy of HVAC systems that regulate thermal comfort levels. Fig.3.1. PMV and thermal sensation. To overcome this problem, the thermal sensation index as shown in fig.3.1 should be calculated as an implicit result of the six previously mentioned variables influencing human s thermal sensation. Fuzzy logic theory was proposed to make quick and direct calculation of the thermal comfort level in a given indoor climate. The new fuzzy thermal sensation index (fuzzy PMV) can be designed by extracting knowledge from Fanger s comfort equation and by transforming it into rules and membership functions. The basic design idea is to transform all possible combinations of the variables that affect thermal comfort into linguistic fuzzy 77

14 implications to describe the thermal sensation index. This is done that, the input output relationships are transformed into a set of fuzzy rules and the human thermal sensation is evaluated as a result of a fuzzy evaluation of the state of the six input variables that affect thermal comfort. Instead of using Eq. (3.1) to calculate a PMV value, it becomes possible to calculate it directly by using some linguistic rules such as: IF the air temperature (T a ) is High, AND relative air velocity (V air ) is Very small, AND radiant mean temperature (T mrt ) is Close to air temperature, AND the activity level (MADu) is Low, AND the clothing (I c1 ) is Very light, THEN PMV is near zero (the indoor climate is comfortable). While the six input variables are described by a set of fuzzy terms, the abovepresented design strategy requires a high number of fuzzy rules and thus, a large amount of time calculation. This number of rules can be reduced significantly by considering that the fuzzy PMV model is composed of two subsystems: the personal-dependent model and the environmental model. Their interconnection is shown in Fig.3.2. On one side, the personal-dependent model evaluates the air temperature range ( T a ), in which the predicted mean vote is found to be close to zero. T a is evaluated, depending on the state of the occupants activity level and their clothing insulation. On the other side, the environmental model calculates the PMV value according to the state of T and the four environmental variables. 78

15 Fig. 3.2 Architecture of the fuzzy thermal sensation index THE FUZZY PMV CALCULATION The design strategy of the fuzzy thermal sensation index is achieved in four steps process. First, the input and output variables are chosen. As shown in Fig. 3.2, the personaldependent subsystem input variables are the activity level of occupants and their clothing insulation. Its output variable is the ambient temperature range in which the predicted mean vote is close to zero. The environmental-dependent subsystem input variables are air temperature (T a ), air velocity (V air ), mean radiant temperature (T mrt ) and relative air humidity (RH). The output variable is the value of the predicted mean vote (fuzzy PMV). The second design step is to derive the fuzzy rule base that should be used to evaluate the PMV, depending on the state of the input variables. The general method developed by (Wang & Mendel, 1992) is used to generate an accurate fuzzy rule base by extracting knowledge from Fanger s thermal sensation vote equation. To generate all fuzzy rules that represent all possible combinations of the six variables, each of the input and the output spaces are divided into symmetric triangular membership functions as shown in Fig

16 The activity level and the clothing insulation are described by three and four triangular membership functions, respectively for accuracy purposes. On the other side, the air velocity, the air temperature and the predicted mean vote are transferred into fuzzy subsets by using seven triangular membership functions to describe each of them. For instance, the relative humidity is supposed to be 50% and the mean radiant temperature is supposed to be close to the ambient air temperature. Since thermal comfort can be obtained by many different combinations of the six above-mentioned variables, a conflict among the generated rules appears. This is due to the interdependence between thermal comfort influencing factors as the effect of each of them depends on the level and the conditions of the other factors. In order to resolve this conflict, we assigned a degree to each of the generated rules to keep only the rule from a conflict group that has maximum degree. In this way, not only the conflict problem is resolved, but also the number of rules is greatly reduced (Wang & Mendel, 1992).. Fig. 3.3 Initial membership functions. 80

17 THE PERSONAL-DEPENDENT MODEL RULES. Studies of the influence of the input variables on the thermal sensation index [15, 19] demonstrated the powerful dependence between the personal-dependent variables and the environmental variables. Since the variation of I cl and MADu affects the body heat production and consequently, the mean temperature of the outer surface of the clothed body (T cl ), the relative influence of the activity level and the clothing insulation on the thermal comfort is assembled in the personal-dependent model. This is done to evaluate the air temperature range that should ensure thermal comfort. This is realized by a fuzzy reasoning that uses three and five membership functions to describe the activity level and the clothing insulation states respectively. Table 3.1 shows the 15 fuzzy rules used to evaluate the temperature range in which the predicted mean vote is close to zero. This rule base can be expressed linguistically as: IF the occupant has Light clothing AND he or she is sedentary THEN the ambient temperature should be Very high (in [ C] range) IF the occupant has Medium clothing AND his or her activity level is Medium THEN the ambient temperature should be normal (in [ C C] range) IF the occupant has Very heavy clothing AND his or her activity level is Medium THEN the ambient temperature should be low (in [ C 14 0 C] range) Table 3.1 Fuzzy evaluation of the temperature range in which the thermal sensation is neutral The clothing The activity level insulation Low Medium High Light [ C C] [24 0 C C] [ C C] Medium [ C C] [ C C] [13 0 C C] Heavy [23 0 C C] [15 0 C 24 0 C] [7.0 0 C 12 0 C] Very heavy [ C C] [ C 14 0 C] [0.0 0 C C] 81

18 THE ENVIRONMENTAL MODEL RULES. From a practical point of view, the personal-dependent model is used to adjust the environmental model which starts with the evaluation of the air velocity to determine the operative temperature that ensure a predicted mean vote equal to zero. To this end, seven membership functions are used to describe the state of both the air velocity Vair and the air operative temperature T o. These variables generate a maximum of seven fuzzy rules such as the following examples: IF the air velocity is V1 THEN the operative temperature is T 1 IF the air velocity is V2 THEN the operative temperature is T 3 IF the air velocity is V7 THEN the operative temperature is T7 etc. Where (V1...V 7) and (T1...T7) are fuzzy terms that describe the air velocity and the operative temperature respectively that correspond to an optimal sensation of thermal. Once the desired ambient air temperature is calculated, it is compared to the measured air temperature Ta to determine the state of the predicted mean vote (PMV) MATLAB AND LabVIEW SIMULATION IMPLEMENTATION OF PMV IN LabVIEW. On the basis of the analysis, a new fuzzy PMV is designed and a general fuzzy rule base is derived to describe the state of human s thermal sensation. Fuzzy PMV code is written using mfile in Matlab and it is interfaced with LabVIEW as shown in fig

19 Fig. 3.4 Block diagram for PMV calculation. 83

20 Fig. 3.5 Front panel for PMV Calculation (1) Fig. 3.6 Membership function (1) 84

21 Fig. 3.7 Front panel for PMV Calculation (2) Fig. 3.8 Membership function (2). 85

22 RESULT AND DISCUSSION SIMULATION RESULT FOR FUZZY PMV PMV index is found out for different values of activity level and clothing insulation using fuzzy PMV technique as discussed earlier. Table.3.2. Fuzzy PMV values. Activity Clo-value V air Room temperature(t a ) PMV The PMV index which is found using fuzzy PMV varies from -3 to +3 as Fanger s PMV index without any iteration. It will be acceptable only when PMV lies between -0.5 and Therefore the optimum values of PMV using fuzzy logic is Table 3.3 PMV values which are in acceptable range from table.3.2. Activityclo-value V air Room temperature(t a ) PMV From the above table the minimum PMV value is 0.129, the corresponding Percentage of people dissatisfied is CONCLUSION Poor interior conditions contribute to discomfort. Thermal comfort is one of the most important comfort factors. Important parameters that affect thermal comfort are air temperature, relative humidity, air velocity, environment radiation, activity level of passengers and clothing insulation. PMV index which combines the above parameters is used 86

23 to indicate thermal comfort. It will be acceptable only when PMV lies between -0.5 and +0.5 By Fuzzy logic method it was found that the minimum PMV value is 0.129, the corresponding Percentage of people dissatisfied is DEFINITIONS OF THERMAL COMFORT AND ITS PARAMETERS: a) Thermal comfort. That condition of mind which expresses satisfaction with the thermal environment and is assessed by subjective evaluation b) Thermal sensation: A Conscious feeing commonly graded into the categories cold, cool, slightly cool, neutral, slightly warm, warm and hot; it requires subjective evaluation. c) Predicted mean vote (PMV): An index that predicts the mean value of the votes of a large group of persons on the seven point thermal sensation scale is the PMV. d) Percent dissatisfied. Percentage of people predicted to be dissatisfied due to local discomfort. e) Predicted percentage of Dissatisfied. (PPD): An index that establishes a quantitative prediction of the percentage of thermally dissatisfied people determined from PMV f) Air Temperature: The temperature of the air surrounding the occupant g) Mean radiant temperature: The uniform surface temperature of an imaginary black enclosure in which an occupant would exchange the same amount of radiant heat as in the actual non uniform space is known as mean radiant temperature. The MRT affects the rate of radiant heat loss from the body. Since the surrounding surface temperatures may vary widely, the MRT is a weighted average of all radiating surface temperatures within line of sight. In winter, levels of wall, roof, and floor insulation together with window treatments such as double glazing, blinds, and drapes contribute to Mean Radiant temperature. h) Air speed. The rate of air movement at a point, without regard to direction 87

24 i) Velocity of Air. Air motion significantly affects body heat transfer by convection and evaporation. Air Movement results from free convection from the occupants body movements. The faster the motion, the greater the rate of heat flow by both convection and evaporation. When ambient temperatures are within acceptable limits, there is no minimum air movement that must be provided for thermal comfort. The natural convection of air over the surface of the body allows for the continuous dissipation of body heat. When ambient temperatures rise, however, natural air flow velocity is no longer sufficient and must be artificially increased, such as by the use of fans. j) Clothing insulation/ensemble (I cl ) The resistance to sensible heat transfer provided by a clothing ensemble and is expressed in Clo units. The definition of clothing insulation relates to heat transfer from the whole body and thus also includes the uncovered parts of the body, such as head and hands. k) Clo. A unit used to express the thermal insulation provided by garments and clothing ensembles where 1 clo = m 2 0 C/ W (0.88 ft 2 0 F/Btu) l) Humidity ratio. It is the ratio of the mass of water vapour to the mass of dry air in a given volume. m) Relative humidity (RH) It is the ratio of the partial pressure of the water vapour in the air to the saturation pressure of water vapour at the same temperature and the same total pressure. n) Metabolic rate (M) The rate of transformation of chemical energy into heat and mechanical work by metabolic activities within an organism, usually expressed in terms of unit area of the total body surface. Here it is expressed in met units. o) Met A unit used to describe the energy generated inside the body due to metabolic activity. This is also equal to 58.2 W/m 2, which is equal to the energy produced per unit surface area of an average person, seated at rest. The surface area of an average person is 1.8 m 2 (19ft). 88

25 3.4. THERMAL COMFORT IN AN OFFICE BUILDING LITERATURE SURVEY Human perception of air movement depends on environmental factors such as air velocity, air velocity fluctuations, air temperature, and personal factors such as overall thermal sensation, clothing insulation and physical activity level (metabolic rate) (Toftum, 2004). Air velocity affects both convective and evaporative heat losses from the human body, and thus determines thermal comfort conditions (Tanabe, 1988; Mallick, 1996). If we agree that thermal environments that are slightly warmer than preferred or neutral, can be still acceptable to building occupants as the adaptive comfort model suggests (dedear, Brager, 2002; Nicol, 2004), then the introduction of elevated air motion into such environments should be universally regarded as desirable. This is because the effect will be to remove sensible and latent heat from the body, so body temperatures will be restored to their comfort set-points. This hypothesis can be deduced from the physiological principle of alliesthesia (Cabanac, 1971). In hot and humid climates, elevated indoor air velocity increases the indoor temperature that building occupants find most comfortable. Nevertheless, the distribution of air velocities measured during these field studies was skewed towards rather low values. Many previous studies have attempted to define when and where air movement is either desirable or not desirable (i.e. draft) (Mallick, 1996; Santamouris, 2004). Thermal comfort research literature indicates that indoor air speed in hot climates should be set between m/s, yet 0.2 m/s has been deemed in ASHRAE Standard 55 to be the threshold upper limit of draft perception allowed inside air-conditioned buildings, where occupants have no direct control over their environment (de Dear, 2004) The new standard 55 is based on Fanger s (1988) draft risk formula, which has an even lower limit in practice than 0.2 m/s. None of the previous research has explicitly addressed air movement acceptability. Instead it has focused mostly on overall thermal sensation and comfort (Toftum, 2002) Research methods Outdoor Climatic environment. Under the Koppen climate classification, the Coimbatore city has a tropical wet and dry climate. It has mild winters and moderate summers. Karunya University office buildings 89

26 lie in the latitude of N and longitude of E with elevation 1551 ft. The surveys in this study were performed in the May 2009 and September Subjects. A Sample size of 220 subjects in 8 different office buildings in the Karunya University was collected in survey and field measurements. The offices interviewed are multi-story buildings. The volunteers participating in the study comprised both men and women. The average age of all respondents was 33.2 years, ranging from 23 to 57 years. All the participants were in good health. The questionnaire covered several areas including personal factors (name, gender, age, etc.), years of living in their current cities and personnel environmental control. The questionnaire also included the traditional scales of thermal sensation and thermal preferences, current clothing garment and metabolic activity. The thermal sensation scale was the ASHRAE seven point scale ranging from cold (-3) to hot (3) with neutral (0) in the middle. The three point thermal preference scale asked whether the respondents would like to change their present thermal environment. Possible responses were want warmer, no change, or want cooler. Clothing garment check list were compiled from the extensive lists published in ASHRAE -55, Metabolic rates were assessed by a check of activities databases published in ASHRAE-55, The summary of the background characteristics of the subjects are presented. Table.3.4 Summary of the sample of residents and personal thermal variables Sample size 220 Mean 33.2 Age (year) Maximum 23 years Minimum 5 months Metabolic rate 75(W/m 2 ) Clothing insulation 1.5 Clo 90

27 Data collection. Both physical and subjective questionnaires were obtained simultaneously in the visit of the field survey. This study investigates thermal environment and comfort of office buildings in the Karunya University. A total of 220 subjects in naturally ventilated 11 office buildings ( with occupant operable windows) provided 220 sets of cross-sectional thermal comfort data, first field campaign from Mar 15, 2010 to Mar24,2010 and second field campaign from Sep10,2010 to Sep 19, 2010 in Karunya University, Coimbatore. In both the set of data collections the same buildings were taken into account for data collection. Indoor climatic data were collected using instruments, with accuracies and response times in accordance the recommendations of ANSI/ASHRAE 55. All the measurements were carried out between 10:00 hours and 16:00 hours. A number of instruments were used to measure the thermal environment conditions, while the respondents filled in the subjective thermal comfort questionnaire. The instruments were standard thermometer for air temperature, whirling hygrometer for humidity, globe thermometer for radiant heat, kata thermometer for air velocity. Metabolic rate can be estimated using standard Table found in ISO Among the residential respondents, air temperature readings were taken at a minimum of two locations in each space and at two different levels corresponding to the body level and the ankle level corresponding to approximately 0.1 m and 1.2 m above the floor level, respectively. Instruments used in this study met the ASHRAE standards requirements for accuracy. During the survey period, there were no significant sources of radiant heat in residents apartments. Therefore the operative temperature is close to the air temperature. The insulation of clothing ensembles was determined using the Olsen s 1985 summation formula: I cl = I clu,i where I cl is the insulation of the entire ensemble and I clu,i represents the effective insulation of the garment i. The garments values published in the ANSI/ASHRAE Stand card was the basis for the estimation of clothing ensemble insulation. The general mean clothing-insulation value of 1.5 clo was recorded among all the respondents. The majority of the respondents were seated on partly or fully upholstered chairs at the time of survey. This appears to have been reflected in the generally higher mean value of 1.1 clo recorded among the subjects at home. 91

28 The metabolic rates were determined from the activities filled by the subjects and as observed at the time of the survey. Uniform value of 75 W/m 2 was assumed for respondents of the residential buildings. This assumption is based on the ISO 7730 Table of metabolic rates for provisions for relaxed seating which was assumed to be the case with all subjects in their homes Subjective questionnaire. The subjective questionnaire consists of the following areas. All the surveys are right now surveys. It takes 15 minutes in average for a participant to answer those questions. 92

29 MBA CST MECH CIVIL EEE, EIE ECE,MEDIA,BIO TECH,BIO INFO, FOOD S and H,MCA, B.Ed Indoor climate. Table.3.5 Summary of indoor climatic conditions in the first session for office thermal comfort ROOM date SAMPLE Size Ta(0c) Vair Tmrt Pa Tcl 15-Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar MEAN MAX MIN AVERAGE