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On-line airflow pattern measurement in ventilated buildings
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Building and Environment 40 (2005) 1291–1301 www.elsevier.com/locate/buildenv
On-line airflow pattern measurement in ventilated buildings S. Van Buggenhouta, E. Vrankena, S. Schuysemansa, J. Lemairea, W. Van Malcotb, D. Berckmansa, a
Department of Agro-Engineering and Economics, Laboratory for Agricultural Building Research, K.U.Leuven, Kasteelpark Arenberg 30, 3001 Leuven, Belgium b Katholieke Hogeschool Kempen, Kleinhoefstraat 4, 2440 Geel Received 31 March 2004; received in revised form 11 October 2004; accepted 20 October 2004
Abstract Comfort, productivity and quality of life of living beings are strongly influenced by the physical properties of their microenvironment. To guarantee an optimal microenvironment, the supply and distribution of fresh air must be controlled. Therefore, on-line airflow pattern measurement and control are crucial elements. A new concept of an airflow pattern sensor, based on the measurement of the temperature distribution at air inlet, was tested and validated in two test installations at different scale. The sensor allowed a prediction of the airflow pattern centreline with a maximum deviation of 0.22 m at 1.5 m from inlet and a deviation of 0.53 m at 3.5 m from inlet. Integrating this sensor in climate control equipment will improve the distribution of fresh air within the ventilated space. r 2004 Elsevier Ltd. All rights reserved. Keywords: Airflow pattern sensor; Temperature distribution; On-line measurement; Microclimate control
1. Introduction In ventilated spaces, such as agricultural, civil and industrial process rooms, it is desirable to control the 3dimensional microenvironment around the living organisms (human, animals, plants) in order to achieve good air quality and adequate thermal comfort resulting in optimum process conditions (productivity, welfare. . .)[1–4]. Indoor air quality and thermal environment are closely related to room ventilation [5–7]. Good indoor air quality requires efficient dilution and removal of pollutants. For a comfortable thermal environment, excess heat usually needs to be removed. The microenvironment is an important parameter that covers several variables such as air temperature, air humidity and air movement. Nevertheless, the surrounding indoor environment in a room is a determining Corresponding author. Tel.: +32 16 32 17 28; fax: +32 16 32 1480.
E-mail address: [email protected] (D. Berckmans). 0360-1323/$ - see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.buildenv.2004.10.019
factor; the direct microenvironment has the most impact on living organisms. A close link between the microenvironment and climate related diseases (e.g., Sick Building Syndrome (SBS), asthma, allergic reactions. . .) is demonstrated by several authors [8–12]. To achieve optimum microclimate conditions, two basic functions of a ventilation system are to provide effective control over the ventilation rate and to guarantee an efficient control over the airflow pattern within the ventilated structure. These should be obtained simultaneously. Sensors for effective control of the ventilation rate in a building are already available and successfully used in real applications [13]. The airflow pattern is the physical link between the global control inputs (ventilation rate, inlet temperature and inlet air speed) and the 3-dimensional microenvironment around the organisms. Consequently, in order to guarantee an optimal microenvironment, it is important to control the airflow pattern in an effective way throughout the year. In that way not only the
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amount of fresh air, but also its distribution can be controlled. The concept of personalised climate control, getting the air where it is needed [14,15], becomes possible with an active airflow pattern control. A first step to make airflow pattern control possible was the development of a new sensor concept for on-line measurement of the airflow pattern. The aim of the research described in this paper was to develop and validate an airflow pattern sensor [16] that makes it possible to determine the airflow centreline. This paper outlines the first results obtained with the new airflow pattern sensor concept.
1.1. Definitions Airflow pattern: the 2-dimensional trajectory of the fresh air in a vertical plane when entering a ventilated room. Centreline: the line that connects the points in the airflow with the highest velocity (y in Fig. 1). Entering angle a1 : the angle between a horizontal line and the line connecting the point of intersection on the arc (Bðx; yÞ in Fig. 1). Drop distance: the horizontal distance from inlet wall to the intersection of the jet centre with a horizontal plane near to the floor. Deflection angle a2 : the angle between a horizontal line and the line that connects the inlet with the intersection point on an imaginary arc with radius r (cf. Fig. 1).
1.2. Airflow pattern Due to the effect of buoyancy or gravity the centreline of a non-isothermal jet is curved [17]. As shown in Fig. 1, the path of the jet centreline in a ventilated room originating from a wall mounted air inlet can be described by the trajectory equation y ¼ f ðxÞ and the deflection angle a2 : The deflection angle a2 is defined as the angle between the line AB and the initial direction of the air jet, where Að0; dÞ is a point in the air inlet and Bðx; yÞ the intersection point of the centreline with an imaginary arc of radius r around the air inlet. The momentum of incoming air, the temperature difference between indoor and outdoor air and the mixing efficiency of incoming air in the room determine the airflow pattern in a ventilated room [18]. The trajectory of an air jet also depends on the type of air inlet and its vertical distance to the ceiling and on the ratio of the thermal buoyancy to the inertial forces. Depending on the precise application and definition of the terms involved, this ratio is referred to variously as buoyancy number, a Froude number, a Richardson’s number or an Archimedes number. The different definitions of the airflow trajectory, the dependency to the dimension of the ventilated room, the inlet slot and the need for an air velocity measurement in the inlet opening make these methods such as the Archimedes number, less suitable for on-line control purposes.
2. Materials and methods A(0,d)
α1 α2
2.1. Airflow pattern sensor initial direction of the air jet
r B(x,y) centerline
velocity profile around centreline
(0,0)
y=f(x)
Fig. 1. The trajectory equation y ¼ f ðxÞ and the deflection angle y to describe the path of the centreline or axis of a non-isothermal air jet.
Previous research demonstrated a relation between 3D-temperature distribution, measured in the room, and the airflow pattern in a ventilated space [19,20]. Therefore an infrared camera (Thermovisions 570) was used to visualise the temperature distribution of the incoming air. For this purpose, a Plexiglas sheet was mounted in a vertical plane perpendicular to the slotted air inlet and at the position of the airflow pattern centre. Fig. 2 clearly demonstrates the different temperature profiles between an air jet with a high and a low inlet velocity. The left picture shows a high air inlet speed (6.8 m/s) and a narrow inlet opening (75% closed) resulting in a narrow jet with a large penetration distance, while the right picture shows the opposite case for a low air inlet speed (3.7 m/s) and a half open air inlet. These infrared pictures proved that knowledge of the temperature distribution at inlet assuming non-isothermal conditions makes it possible to obtain information on the trajectory of the incoming air.
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Fig. 2. Temperature distribution of the incoming air jet for two different inlet conditions (left: 6.8 m/s and valve position 75% closed; right: 3.7 m/s and valve position 50% closed).
y
10
r
9 8
x
Air inlet A(0,d)
α1
7 6
Cross section with arc
5 4 thermocouples
1
2
3 Centre line
Fig. 3. Prototype of the airflow pattern sensor (left: real representation, right: schematically representation of the sensor).
Consequently, a temperature method was used to quantify the air trajectory of a non-isothermal air jet [21]. A prototype (Fig. 3) of the airflow pattern sensor was developed by the research team in Laboratory of Agricultural Buildings Research (LABR) [16] in order to determine the curved trajectory of the air jet. It consisted of a set of thermocouples (type: RTD100; t ¼ 14 s), with an angle of 13.5 1 between them, more precisely 87, 73.5, 60.0, 46.5, 33.0, 19.5, 6.0, 7.5, 21.0, and 34.5 1 to the centre of the inlet and were named from T 1 to T 10 ; respectively. The thermocouples were mounted on an arc (radius ¼ 30 cm for test installation 1 and radius ¼ 50 cm for test installation 2) perpendicular to the length of the slotted air inlet. At ten measuring positions, the temperature was registered. The thermocouples on the arc had an average accuracy of 0.1 1C.
Because of the non-isothermal conditions between incoming and present air in ventilation applications, thermocouples were chosen for their low investment cost and their easiness to use, even in corrosive environments. 2.2. Reference method for airflow pattern quantification Smoke experiments were performed to visualise and to quantify the airflow pattern. Both test installations were equipped with a smoke generator, halogen lamps and a CCD camera to visualise, illuminate and film the airflow pattern. The use of image processing software allowed the calculation of the 2D-centreline to quantify the airflow pattern trajectory [21]. In Fig. 4, the output of this software, the real deflection angle a2 (average standard deviation of 10.6 1), is shown. A more detailed description of the image analysis software can be found in literature [21].
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The image processing methods are applicable to both isothermal and non-isothermal air jets, but are not usable in an on-line control system and in ventilated rooms where smoke visualisation experiments may not be conducted (for example in drying and ripening rooms for meat products,. . .), but in this research it was used as a reference method for airflow pattern quantification.
tory test chamber is a mechanically ventilated room with a length of 3 m, a height of 2 m, a width of 1.5 m. It has a slot inlet in the left sidewall 42 cm beneath the ceiling and an asymmetrically positioned circular air outlet (diameter of 0.16 m) in the right sidewall just above the floor. A controllable inlet valve (dimensions cf. Fig. 6) was mounted in front of the air inlet. The controllable inlet valve has a width of 0.615 m and a height of 0.230 m, was positioned 1.55 m above the floor. The prototype of the airflow pattern sensor is mounted in a vertical plane perpendicular to the slotted air inlet. The test room is constructed of transparent Plexiglas in order to visualise and quantify the airflow pattern by smoke experiments. The thickness of the Plexiglas walls is 0.075 m. In the room, three control inputs were used: the ventilation rate V (80–300 m3/h) bringing cold air with variable temperatures into the test room and a controllable valve at the air inlet which allows a change in the initial speed and direction of the airflow pattern. Apart from the ventilation rate and the valve position, another variable in the test room was the internal heat production Qin (300 Watt) simulating the sensible heat production of the occupants. The second test installation, at the real scale of a livestock building section (L ¼ 8 m; W ¼ 4 m; H 1 ¼ 2:75 m; H 2 ¼ 4:00 m) was built and provided with similar equipment as used in the first test installation (Fig. 7). Ventilation rate could be varied between 500 and 2000 m3/h (5–19 air changes/h) and inlet air
2.3. Test installations The first test installation was a fully instrumented ventilation chamber, illustrated in Fig. 5. This laboraVent. Rate: 170 m³/h Valve position: 0% Inlet temperature: 18˚C
Airflow pattern sensor R = 150 cm
50
Calculated centreline 100
2 150
Intersection point 3th order polynomial centreline fit
200
Calculated α2 – value: -33.4°
250 50
100
150
200
250
Fig. 4. Example of measured smoke pattern and calculation of deflection angle a2 in a steady state experiment (ventilation rate: 170 m3/h; valve position 0% closed; inlet temperature: 18 1C).
Airflow pattern sensor
θ = –10° °
25
31 34
28 32
35
1.55 m
22 26
29 33
0.4 m 36
19
23
30
14 17
21 24
8 11
15 18
1
2m
2 5
9 12
3m
4
10
16 20
27
7
13
3 6
0.8 m 0.75 m 0.4 m
0.4 m
0.6 m
1.5 m 2.5 m
3m 4m = temperature and humidity sensor = temperature sensor Fig. 5. Schematic representation of the laboratory test installation.
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Fig. 6. Schematic representation of the controllable inlet valve.
Fig. 7. Picture of the real scale test installation.
temperature between 10 and 17 1C. The same type of inlet valve was used as described in test installation 1. The room was provided with the prototype of the airflow pattern sensor in a vertical plane perpendicular to the slotted air inlet. 2.4. Experiments A total of 136 steady state experiments were conducted to measure airflow patterns in the room at
different conditions. These experiments gave insight in the relation between inputs and outputs at equilibrium and proved the potential of the proposed sensor to differentiate between different airflow patterns. During steady state conditions, different parameters were measured: 10 temperatures on the airflow pattern sensor, temperature of incoming air (T in ) and air temperature at outlet ðT out Þ: The aim of this type of experiments was to find a relation between the airflow pattern and the registered temperatures on the airflow pattern sensor.
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In test installation 1 (laboratory scale) 104 static experiments were conducted (cf. Table 1). Different independent combinations of eight ventilation rates in the range from 9 to 32 air changes/h (80–110–140–170– 200–230–260–290 m3/h), four different valve positions (0–25–50–75% closed) and three air temperatures at inlet (10–15–18 1C) were used. The measured parameters were the air temperature at inlet and outlet and on the 10 positions on the arc. These variables were logged every second. In each experiment the centreline of the airflow pattern was determined by means of image analysis. The drop distance was varied between 1 and 2.50 m. In test installation 1, the imaginary arc, used for the calculation of a2 ; has a radius of 1.50 m; because of the larger dimensions of test installation 2, the imaginary arc has a radius of 3.50 m. In test installation 2 (real scale) 32 experiments (cf. Table 2) were carried out with four different ventilation rates (400–900–1500–2000 m3/h), four positions of inlet valve (0–25–50–75% closed) and two inlet air temperatures (12 and 17 1C). The same variables (T 1 ; T 2 . . . T 10 ; T in ; T out ) were logged every second.
installation 1 and a distance of 3.5 m for test installation 2. From the image based centreline calculations, the real deflection angle a2 was determined and used to quantify the airflow pattern trajectory, as can be seen in Fig. 4. The deflection angle a2 was predicted as a linear combination of the 12 temperature measurements obtained with the airflow pattern sensor and the temperature of the incoming and outgoing air a2predicted ¼ f ðT 1 ; T 2 ; T 3 ; T 4 ; T 5 ; T 6 ; T 7 ; T 8 ; T 9 ; T 10 ; T in ; T out Þ:
ð1Þ
The relationship between the real deflection angle measured by the image analysis technique and the modelled values of a2 is demonstrated in Fig. 8. Table 3 shows the regression statistics for all the experiments performed in this research. It can be seen that in test installation 1 that based on the temperature distribution at inlet positions, the deflection angle a2 can be predicted and this with a standard deviation of 8.39 1 on a respective distance of 1.5 m. This corresponds to an average deviation of 0.22 m at a radius of 1.5 m from inlet. From Fig. 8, it is also clear that in test installation 2, the airflow pattern can be described using the thermocouples on the arc and in the inlet and the outlet. The prediction accuracy of this measurements has a value of 11.4 1 on a respective distance of 3.5 m from inlet position (cf. Table 3). This corresponds with an average deviation of 0.74 m at 3.5 m from inlet. The regression analysis further indicated that a reduction in temperature measurement points was possible. To find out which sensor positions were significant, a stepwise regression was performed for
3. Results 3.1. Relation temperature distribution at inlet—deflection angle a2 The aim of this analysis was to demonstrate the relation between the temperatures measured by the airflow pattern sensor and the deflection angle a2 of the incoming airflow pattern at a distance of 1.5 m for test
Table 1 Overview of experiments performed for the different ventilation rates (80–110–140–170–200–230–260–290 m3 h1) in test installation 1 Experiment number
Ventilation rate ðm3 h1 Þ
T inlet ( C)
Valve position (% closed)
Smoke experiments
Duration (min)
A001 A002 A003 A004 A005 A006 A007 A008 A009 A010 A011 A012 – A091 A092 A093 A094 A095 A096
80 80 80 80 80 80 80 80 80 80 80 80 – 290 290 290 290 290 290
10 10 10 10 15 15 15 15 18 18 18 18 – 15 15 18 18 18 18
0 25 50 75 0 25 50 75 0 25 50 75 – 50 75 0 25 50 75
4 4 4 4 4 4 4 4 4 4 4 4 – 4 4 4 4 4 4
30 30 30 30 30 30 30 30 30 30 30 30 – 30 30 30 30 30 30
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Table 2 Overview of experiments performed for the different ventilation rates (400–900–1500–2000 m3 h1) in test installation 2 Experiment number
Ventilation rate (m3 h1)
T inlet ( C)
Valve position (% closed)
Smoke experiments
Duration (min)
B001 B002 B003 B004 B005 B006 B007 B008 B009 B010 B011 B012 – B027 B028 B029 B030 B031 B032
400 400 400 400 400 400 400 400 900 900 900 900 – 2000 2000 2000 2000 2000 2000
12 12 12 12 17 17 17 17 12 12 12 12 – 12 12 17 17 17 17
0 25 50 75 0 25 50 75 0 25 50 75 – 50 75 0 25 50 75
4 4 4 4 4 4 4 4 4 4 4 4 – 4 4 4 4 4 4
30 30 30 30 30 30 30 30 30 30 30 30 – 30 30 30 30 30 30
Fig. 8. Relationship between the real (image analysis) and the modelled values of the deflection angle a2 for test installation 1 ðR2 ¼ 0:92Þ and for test installation 2 ðR2 ¼ 0:89Þ:
experiments in test room 1, which resulted in a model with only six variables a2predicted ¼ f ðT 1 ; T 3 ; T 4 ; T 6 ; T 9 ; T uit Þ:
(2)
This reduced model gave a relative good prediction of the deflection angle a2 : The predicted accuracy of this model was 8.5 1 (cf. Table 3), which corresponds with a maximum deviation of 0.22 m. In the next step, further reduction of the numbers of thermocouples was investigated: only three temperature
sensors were used in the airflow pattern sensor. Also the temperature at outlet was incorporated in the model. In test installation 1, two combinations of three sensors positions and the temperature at the outlet were found to give good predictions of the deflection angle. The regression analysis in Table 3 shows that the predicted accuracy is 9.3 1, which corresponds with a deviation of 0.25 m a2predicted ¼ f ðT 4 ; T 6 ; T 10 ; T out Þ:
(3)
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Table 3 Overview of the regression statistics for the deflection angle (real vs modelled) Experimental description
Test installation I
Test installation II
All temperature sensors on arc
Multiple R R square Adjusted R square Standard error Observations
0.95 0.90 0.89 8.89 104
Multiple R R square Adjusted R square Standard error Observations
Reduced model (6 temperature sensors on arc)
Multiple R R square Adjusted R square Standard error Observations
0.94 0.89 0.89 8.56 104
/
Reduced model (4 temperature sensors on arc)
Multiple R R square Adjusted R square Standard error Observations
0.93 0.87 0.86 9.31 104
Multiple R R square Adjusted R square Standard error Observations
0.87 0.76 0.74 16.29 41
Multiple R R square Adjusted R square Standard error Observations
0.96 0.91 0.82 15.64 20
Multiple R R square Adjusted R square Standard error Observations
0.98 0.97 0.93 4.49 20
Reduced model (4 temperature sensors on arc) – / low ventilation rates Reduced model (4 temperature sensors on arc) – high ventilation rates
/
Also in test installation 2, the reduction to three sensors was investigated. The reduced model gave a less accurate prediction ðR2 ¼ 0:76Þ of a2 ; as can been seen in Table 3. This lower accuracy also resulted in a larger deviation from the real deflection angle, more precisely a prediction error of 16.3 1, which corresponds with a deviation of 1.02 m at 3.5 m from inlet opening a2predicted ¼ f ðT 1 ; T 4 ; T 7 ; T out Þ:
(4)
To find an explanation for this failing regression model, the stability of airflow patterns was further investigated by using the criterion of the corrected Archimedes number [19]. According to the literature, airflow patterns with an Archimedes number larger then 75 are stable and fall down towards the floor when entering the room and recirculate along the ceiling in anticlockwise direction. An Archimedes number less than 30 indicates the air jet remains horizontal after entering and the resulting airflow pattern has a clockwise direction of rotation. This was investigated with the data obtained in these experiments. Fig. 9 shows a clear distinction between experiments with high flow rate ð41000 m3 =hÞ and experiments with low flow rate ðo1000 m3 =hÞ for different positions of the inlet valve (% closed). The
0.95 0.89 0.86 11.94 41
corresponding Archimedes number can explain this phenomenon. Fig. 10 shows a clear relation between the corrected Archimedes number and the predicted deflection angle a2 : At high Arc ð475Þ; a falling airflow pattern and at low Arc ðo30Þ a horizontal airflow pattern is expected. For this reason, all experimental data of test installation 2 were divided according to the flow rates. A new regression analysis was performed for each group of experiments and better results were obtained (R2 of 0.91 for low ventilation rates and R2 of 0.97 for high ventilation rates), as can be seen in Table 3. The regression analysis shows that the predicted accuracy is 4.5 1 at high ventilation rates and 15.6 1 at low ventilation rates, which corresponds to a maximum deviation of respectively 0.28 and 0.98 m.
4. Discussion By using the information of the temperature distribution at inlet position with a new designed airflow pattern sensor in combination with the temperature of ingoing and outgoing air, it was possible to obtain a good
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Fig. 9. The predicted deflection angle in function of the position of the inlet valve; blue: high ventilation rates ð41000 m3 =hÞ; red: low ventilation rate ðo1000 m3 =hÞ:
Fig. 10. Linear relationship between the corrected Archimedes number and the predicted deflection angle a2 :
prediction of the airflow pattern trajectory. At laboratory scale (test installation 1), the deflection angle a2 ; which was varied between 45 1 and 15 1, of a nonisothermal air jet could be measured with an average accuracy of 8.39 1 (or a deviation of 0.22 m) at a distance of 150 cm from the inlet opening. At a real scale (test
installation 2) an average accuracy of 11.4 1 (deviation of 0.74 m) was obtained. Even with a reduced number of sensors, it was possible to obtain an acceptable deviation between the real deflection angle and the modelled value at a certain distance from inlet opening. With only six temperature
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measurements, an average deviation of 8.5 1 (or a difference of 0.22 m in drop distance) from the real deflection angle was obtained at laboratory scale. By further reduction of the amount of sensors (four temperature measurements) an average accuracy of 9.3 1 (0:25 m in drop distance) at laboratory scale and an average accuracy of 16.29 1 (deviation of 1:02 m) at large scale were obtained. By analysing the data, a dichotomy was discovered in the airflow patterns at real scale. A distinction could be made between horizontal and falling airflow patterns. This phenomenon supports the theory of the corresponding corrected Archimedes number. At high ventilation rates, a higher average accuracy (4.5 1 or 0:28 m in drop distance) was obtained, while on lower ventilation rates a slight improvement (15.6 1 or 0.98 m in drop distance) was possible. In the experiments, the effect of sensible heat production is already taken into account by using static heat sources on the floor. However, further research is needed that takes into account the effects on the airflow pattern of moving occupants inside the ventilated structures and the consequent effect on the accuracy of the presented sensor. The characteristics of airflow in the occupied zone of mechanically ventilated air spaces have been studied in buildings designed for humans, and also buildings designed for livestock (for example [22] and [23]). In these trials, the presence of occupants has been shown to have an complex influence on the measurements obtained. More precisely, the air velocity and temperature in the immediate surroundings of the occupants influence the flow in the occupied zone and in the entire room space [23,24]. Knowledge of this influence is essential to improve control of the indoor environment.
5. Conclusions The proposed sensor concept proved to have the potential to measure the airflow pattern on-line. This temperature sensor method, which uses a configuration of temperature sensors to detect the air jet trajectory, is a low cost and reliable method, which can be easily implemented in the hardware of existing ventilation equipment and consequently microclimate control. Further research incentives are needed in order to predict the airflow pattern in terms of drop distance, a physical meaningful and user-friendly parameter for the quantification of airflow patterns. Other nonlinear modelling techniques should be explored in order to augment the prediction accuracy of the airflow pattern sensor. In the next step, a controller can be developed based on this sensor, which offers the possibility to control the airflow trajectory in a ventilated room. The presented
concept of the airflow pattern sensor offers new possibilities for on-line control of the fresh air distribution in a ventilated space.
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Building and Environment 40 (2005) 1291–1301 www.elsevier.com/locate/buildenv
On-line airflow pattern measurement in ventilated buildings S. Van Buggenhouta, E. Vrankena, S. Schuysemansa, J. Lemairea, W. Van Malcotb, D. Berckmansa, a
Department of Agro-Engineering and Economics, Laboratory for Agricultural Building Research, K.U.Leuven, Kasteelpark Arenberg 30, 3001 Leuven, Belgium b Katholieke Hogeschool Kempen, Kleinhoefstraat 4, 2440 Geel Received 31 March 2004; received in revised form 11 October 2004; accepted 20 October 2004
Abstract Comfort, productivity and quality of life of living beings are strongly influenced by the physical properties of their microenvironment. To guarantee an optimal microenvironment, the supply and distribution of fresh air must be controlled. Therefore, on-line airflow pattern measurement and control are crucial elements. A new concept of an airflow pattern sensor, based on the measurement of the temperature distribution at air inlet, was tested and validated in two test installations at different scale. The sensor allowed a prediction of the airflow pattern centreline with a maximum deviation of 0.22 m at 1.5 m from inlet and a deviation of 0.53 m at 3.5 m from inlet. Integrating this sensor in climate control equipment will improve the distribution of fresh air within the ventilated space. r 2004 Elsevier Ltd. All rights reserved. Keywords: Airflow pattern sensor; Temperature distribution; On-line measurement; Microclimate control
1. Introduction In ventilated spaces, such as agricultural, civil and industrial process rooms, it is desirable to control the 3dimensional microenvironment around the living organisms (human, animals, plants) in order to achieve good air quality and adequate thermal comfort resulting in optimum process conditions (productivity, welfare. . .)[1–4]. Indoor air quality and thermal environment are closely related to room ventilation [5–7]. Good indoor air quality requires efficient dilution and removal of pollutants. For a comfortable thermal environment, excess heat usually needs to be removed. The microenvironment is an important parameter that covers several variables such as air temperature, air humidity and air movement. Nevertheless, the surrounding indoor environment in a room is a determining Corresponding author. Tel.: +32 16 32 17 28; fax: +32 16 32 1480.
E-mail address: [email protected] (D. Berckmans). 0360-1323/$ - see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.buildenv.2004.10.019
factor; the direct microenvironment has the most impact on living organisms. A close link between the microenvironment and climate related diseases (e.g., Sick Building Syndrome (SBS), asthma, allergic reactions. . .) is demonstrated by several authors [8–12]. To achieve optimum microclimate conditions, two basic functions of a ventilation system are to provide effective control over the ventilation rate and to guarantee an efficient control over the airflow pattern within the ventilated structure. These should be obtained simultaneously. Sensors for effective control of the ventilation rate in a building are already available and successfully used in real applications [13]. The airflow pattern is the physical link between the global control inputs (ventilation rate, inlet temperature and inlet air speed) and the 3-dimensional microenvironment around the organisms. Consequently, in order to guarantee an optimal microenvironment, it is important to control the airflow pattern in an effective way throughout the year. In that way not only the
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amount of fresh air, but also its distribution can be controlled. The concept of personalised climate control, getting the air where it is needed [14,15], becomes possible with an active airflow pattern control. A first step to make airflow pattern control possible was the development of a new sensor concept for on-line measurement of the airflow pattern. The aim of the research described in this paper was to develop and validate an airflow pattern sensor [16] that makes it possible to determine the airflow centreline. This paper outlines the first results obtained with the new airflow pattern sensor concept.
1.1. Definitions Airflow pattern: the 2-dimensional trajectory of the fresh air in a vertical plane when entering a ventilated room. Centreline: the line that connects the points in the airflow with the highest velocity (y in Fig. 1). Entering angle a1 : the angle between a horizontal line and the line connecting the point of intersection on the arc (Bðx; yÞ in Fig. 1). Drop distance: the horizontal distance from inlet wall to the intersection of the jet centre with a horizontal plane near to the floor. Deflection angle a2 : the angle between a horizontal line and the line that connects the inlet with the intersection point on an imaginary arc with radius r (cf. Fig. 1).
1.2. Airflow pattern Due to the effect of buoyancy or gravity the centreline of a non-isothermal jet is curved [17]. As shown in Fig. 1, the path of the jet centreline in a ventilated room originating from a wall mounted air inlet can be described by the trajectory equation y ¼ f ðxÞ and the deflection angle a2 : The deflection angle a2 is defined as the angle between the line AB and the initial direction of the air jet, where Að0; dÞ is a point in the air inlet and Bðx; yÞ the intersection point of the centreline with an imaginary arc of radius r around the air inlet. The momentum of incoming air, the temperature difference between indoor and outdoor air and the mixing efficiency of incoming air in the room determine the airflow pattern in a ventilated room [18]. The trajectory of an air jet also depends on the type of air inlet and its vertical distance to the ceiling and on the ratio of the thermal buoyancy to the inertial forces. Depending on the precise application and definition of the terms involved, this ratio is referred to variously as buoyancy number, a Froude number, a Richardson’s number or an Archimedes number. The different definitions of the airflow trajectory, the dependency to the dimension of the ventilated room, the inlet slot and the need for an air velocity measurement in the inlet opening make these methods such as the Archimedes number, less suitable for on-line control purposes.
2. Materials and methods A(0,d)
α1 α2
2.1. Airflow pattern sensor initial direction of the air jet
r B(x,y) centerline
velocity profile around centreline
(0,0)
y=f(x)
Fig. 1. The trajectory equation y ¼ f ðxÞ and the deflection angle y to describe the path of the centreline or axis of a non-isothermal air jet.
Previous research demonstrated a relation between 3D-temperature distribution, measured in the room, and the airflow pattern in a ventilated space [19,20]. Therefore an infrared camera (Thermovisions 570) was used to visualise the temperature distribution of the incoming air. For this purpose, a Plexiglas sheet was mounted in a vertical plane perpendicular to the slotted air inlet and at the position of the airflow pattern centre. Fig. 2 clearly demonstrates the different temperature profiles between an air jet with a high and a low inlet velocity. The left picture shows a high air inlet speed (6.8 m/s) and a narrow inlet opening (75% closed) resulting in a narrow jet with a large penetration distance, while the right picture shows the opposite case for a low air inlet speed (3.7 m/s) and a half open air inlet. These infrared pictures proved that knowledge of the temperature distribution at inlet assuming non-isothermal conditions makes it possible to obtain information on the trajectory of the incoming air.
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Fig. 2. Temperature distribution of the incoming air jet for two different inlet conditions (left: 6.8 m/s and valve position 75% closed; right: 3.7 m/s and valve position 50% closed).
y
10
r
9 8
x
Air inlet A(0,d)
α1
7 6
Cross section with arc
5 4 thermocouples
1
2
3 Centre line
Fig. 3. Prototype of the airflow pattern sensor (left: real representation, right: schematically representation of the sensor).
Consequently, a temperature method was used to quantify the air trajectory of a non-isothermal air jet [21]. A prototype (Fig. 3) of the airflow pattern sensor was developed by the research team in Laboratory of Agricultural Buildings Research (LABR) [16] in order to determine the curved trajectory of the air jet. It consisted of a set of thermocouples (type: RTD100; t ¼ 14 s), with an angle of 13.5 1 between them, more precisely 87, 73.5, 60.0, 46.5, 33.0, 19.5, 6.0, 7.5, 21.0, and 34.5 1 to the centre of the inlet and were named from T 1 to T 10 ; respectively. The thermocouples were mounted on an arc (radius ¼ 30 cm for test installation 1 and radius ¼ 50 cm for test installation 2) perpendicular to the length of the slotted air inlet. At ten measuring positions, the temperature was registered. The thermocouples on the arc had an average accuracy of 0.1 1C.
Because of the non-isothermal conditions between incoming and present air in ventilation applications, thermocouples were chosen for their low investment cost and their easiness to use, even in corrosive environments. 2.2. Reference method for airflow pattern quantification Smoke experiments were performed to visualise and to quantify the airflow pattern. Both test installations were equipped with a smoke generator, halogen lamps and a CCD camera to visualise, illuminate and film the airflow pattern. The use of image processing software allowed the calculation of the 2D-centreline to quantify the airflow pattern trajectory [21]. In Fig. 4, the output of this software, the real deflection angle a2 (average standard deviation of 10.6 1), is shown. A more detailed description of the image analysis software can be found in literature [21].
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The image processing methods are applicable to both isothermal and non-isothermal air jets, but are not usable in an on-line control system and in ventilated rooms where smoke visualisation experiments may not be conducted (for example in drying and ripening rooms for meat products,. . .), but in this research it was used as a reference method for airflow pattern quantification.
tory test chamber is a mechanically ventilated room with a length of 3 m, a height of 2 m, a width of 1.5 m. It has a slot inlet in the left sidewall 42 cm beneath the ceiling and an asymmetrically positioned circular air outlet (diameter of 0.16 m) in the right sidewall just above the floor. A controllable inlet valve (dimensions cf. Fig. 6) was mounted in front of the air inlet. The controllable inlet valve has a width of 0.615 m and a height of 0.230 m, was positioned 1.55 m above the floor. The prototype of the airflow pattern sensor is mounted in a vertical plane perpendicular to the slotted air inlet. The test room is constructed of transparent Plexiglas in order to visualise and quantify the airflow pattern by smoke experiments. The thickness of the Plexiglas walls is 0.075 m. In the room, three control inputs were used: the ventilation rate V (80–300 m3/h) bringing cold air with variable temperatures into the test room and a controllable valve at the air inlet which allows a change in the initial speed and direction of the airflow pattern. Apart from the ventilation rate and the valve position, another variable in the test room was the internal heat production Qin (300 Watt) simulating the sensible heat production of the occupants. The second test installation, at the real scale of a livestock building section (L ¼ 8 m; W ¼ 4 m; H 1 ¼ 2:75 m; H 2 ¼ 4:00 m) was built and provided with similar equipment as used in the first test installation (Fig. 7). Ventilation rate could be varied between 500 and 2000 m3/h (5–19 air changes/h) and inlet air
2.3. Test installations The first test installation was a fully instrumented ventilation chamber, illustrated in Fig. 5. This laboraVent. Rate: 170 m³/h Valve position: 0% Inlet temperature: 18˚C
Airflow pattern sensor R = 150 cm
50
Calculated centreline 100
2 150
Intersection point 3th order polynomial centreline fit
200
Calculated α2 – value: -33.4°
250 50
100
150
200
250
Fig. 4. Example of measured smoke pattern and calculation of deflection angle a2 in a steady state experiment (ventilation rate: 170 m3/h; valve position 0% closed; inlet temperature: 18 1C).
Airflow pattern sensor
θ = –10° °
25
31 34
28 32
35
1.55 m
22 26
29 33
0.4 m 36
19
23
30
14 17
21 24
8 11
15 18
1
2m
2 5
9 12
3m
4
10
16 20
27
7
13
3 6
0.8 m 0.75 m 0.4 m
0.4 m
0.6 m
1.5 m 2.5 m
3m 4m = temperature and humidity sensor = temperature sensor Fig. 5. Schematic representation of the laboratory test installation.
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Fig. 6. Schematic representation of the controllable inlet valve.
Fig. 7. Picture of the real scale test installation.
temperature between 10 and 17 1C. The same type of inlet valve was used as described in test installation 1. The room was provided with the prototype of the airflow pattern sensor in a vertical plane perpendicular to the slotted air inlet. 2.4. Experiments A total of 136 steady state experiments were conducted to measure airflow patterns in the room at
different conditions. These experiments gave insight in the relation between inputs and outputs at equilibrium and proved the potential of the proposed sensor to differentiate between different airflow patterns. During steady state conditions, different parameters were measured: 10 temperatures on the airflow pattern sensor, temperature of incoming air (T in ) and air temperature at outlet ðT out Þ: The aim of this type of experiments was to find a relation between the airflow pattern and the registered temperatures on the airflow pattern sensor.
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In test installation 1 (laboratory scale) 104 static experiments were conducted (cf. Table 1). Different independent combinations of eight ventilation rates in the range from 9 to 32 air changes/h (80–110–140–170– 200–230–260–290 m3/h), four different valve positions (0–25–50–75% closed) and three air temperatures at inlet (10–15–18 1C) were used. The measured parameters were the air temperature at inlet and outlet and on the 10 positions on the arc. These variables were logged every second. In each experiment the centreline of the airflow pattern was determined by means of image analysis. The drop distance was varied between 1 and 2.50 m. In test installation 1, the imaginary arc, used for the calculation of a2 ; has a radius of 1.50 m; because of the larger dimensions of test installation 2, the imaginary arc has a radius of 3.50 m. In test installation 2 (real scale) 32 experiments (cf. Table 2) were carried out with four different ventilation rates (400–900–1500–2000 m3/h), four positions of inlet valve (0–25–50–75% closed) and two inlet air temperatures (12 and 17 1C). The same variables (T 1 ; T 2 . . . T 10 ; T in ; T out ) were logged every second.
installation 1 and a distance of 3.5 m for test installation 2. From the image based centreline calculations, the real deflection angle a2 was determined and used to quantify the airflow pattern trajectory, as can be seen in Fig. 4. The deflection angle a2 was predicted as a linear combination of the 12 temperature measurements obtained with the airflow pattern sensor and the temperature of the incoming and outgoing air a2predicted ¼ f ðT 1 ; T 2 ; T 3 ; T 4 ; T 5 ; T 6 ; T 7 ; T 8 ; T 9 ; T 10 ; T in ; T out Þ:
ð1Þ
The relationship between the real deflection angle measured by the image analysis technique and the modelled values of a2 is demonstrated in Fig. 8. Table 3 shows the regression statistics for all the experiments performed in this research. It can be seen that in test installation 1 that based on the temperature distribution at inlet positions, the deflection angle a2 can be predicted and this with a standard deviation of 8.39 1 on a respective distance of 1.5 m. This corresponds to an average deviation of 0.22 m at a radius of 1.5 m from inlet. From Fig. 8, it is also clear that in test installation 2, the airflow pattern can be described using the thermocouples on the arc and in the inlet and the outlet. The prediction accuracy of this measurements has a value of 11.4 1 on a respective distance of 3.5 m from inlet position (cf. Table 3). This corresponds with an average deviation of 0.74 m at 3.5 m from inlet. The regression analysis further indicated that a reduction in temperature measurement points was possible. To find out which sensor positions were significant, a stepwise regression was performed for
3. Results 3.1. Relation temperature distribution at inlet—deflection angle a2 The aim of this analysis was to demonstrate the relation between the temperatures measured by the airflow pattern sensor and the deflection angle a2 of the incoming airflow pattern at a distance of 1.5 m for test
Table 1 Overview of experiments performed for the different ventilation rates (80–110–140–170–200–230–260–290 m3 h1) in test installation 1 Experiment number
Ventilation rate ðm3 h1 Þ
T inlet ( C)
Valve position (% closed)
Smoke experiments
Duration (min)
A001 A002 A003 A004 A005 A006 A007 A008 A009 A010 A011 A012 – A091 A092 A093 A094 A095 A096
80 80 80 80 80 80 80 80 80 80 80 80 – 290 290 290 290 290 290
10 10 10 10 15 15 15 15 18 18 18 18 – 15 15 18 18 18 18
0 25 50 75 0 25 50 75 0 25 50 75 – 50 75 0 25 50 75
4 4 4 4 4 4 4 4 4 4 4 4 – 4 4 4 4 4 4
30 30 30 30 30 30 30 30 30 30 30 30 – 30 30 30 30 30 30
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Table 2 Overview of experiments performed for the different ventilation rates (400–900–1500–2000 m3 h1) in test installation 2 Experiment number
Ventilation rate (m3 h1)
T inlet ( C)
Valve position (% closed)
Smoke experiments
Duration (min)
B001 B002 B003 B004 B005 B006 B007 B008 B009 B010 B011 B012 – B027 B028 B029 B030 B031 B032
400 400 400 400 400 400 400 400 900 900 900 900 – 2000 2000 2000 2000 2000 2000
12 12 12 12 17 17 17 17 12 12 12 12 – 12 12 17 17 17 17
0 25 50 75 0 25 50 75 0 25 50 75 – 50 75 0 25 50 75
4 4 4 4 4 4 4 4 4 4 4 4 – 4 4 4 4 4 4
30 30 30 30 30 30 30 30 30 30 30 30 – 30 30 30 30 30 30
Fig. 8. Relationship between the real (image analysis) and the modelled values of the deflection angle a2 for test installation 1 ðR2 ¼ 0:92Þ and for test installation 2 ðR2 ¼ 0:89Þ:
experiments in test room 1, which resulted in a model with only six variables a2predicted ¼ f ðT 1 ; T 3 ; T 4 ; T 6 ; T 9 ; T uit Þ:
(2)
This reduced model gave a relative good prediction of the deflection angle a2 : The predicted accuracy of this model was 8.5 1 (cf. Table 3), which corresponds with a maximum deviation of 0.22 m. In the next step, further reduction of the numbers of thermocouples was investigated: only three temperature
sensors were used in the airflow pattern sensor. Also the temperature at outlet was incorporated in the model. In test installation 1, two combinations of three sensors positions and the temperature at the outlet were found to give good predictions of the deflection angle. The regression analysis in Table 3 shows that the predicted accuracy is 9.3 1, which corresponds with a deviation of 0.25 m a2predicted ¼ f ðT 4 ; T 6 ; T 10 ; T out Þ:
(3)
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Table 3 Overview of the regression statistics for the deflection angle (real vs modelled) Experimental description
Test installation I
Test installation II
All temperature sensors on arc
Multiple R R square Adjusted R square Standard error Observations
0.95 0.90 0.89 8.89 104
Multiple R R square Adjusted R square Standard error Observations
Reduced model (6 temperature sensors on arc)
Multiple R R square Adjusted R square Standard error Observations
0.94 0.89 0.89 8.56 104
/
Reduced model (4 temperature sensors on arc)
Multiple R R square Adjusted R square Standard error Observations
0.93 0.87 0.86 9.31 104
Multiple R R square Adjusted R square Standard error Observations
0.87 0.76 0.74 16.29 41
Multiple R R square Adjusted R square Standard error Observations
0.96 0.91 0.82 15.64 20
Multiple R R square Adjusted R square Standard error Observations
0.98 0.97 0.93 4.49 20
Reduced model (4 temperature sensors on arc) – / low ventilation rates Reduced model (4 temperature sensors on arc) – high ventilation rates
/
Also in test installation 2, the reduction to three sensors was investigated. The reduced model gave a less accurate prediction ðR2 ¼ 0:76Þ of a2 ; as can been seen in Table 3. This lower accuracy also resulted in a larger deviation from the real deflection angle, more precisely a prediction error of 16.3 1, which corresponds with a deviation of 1.02 m at 3.5 m from inlet opening a2predicted ¼ f ðT 1 ; T 4 ; T 7 ; T out Þ:
(4)
To find an explanation for this failing regression model, the stability of airflow patterns was further investigated by using the criterion of the corrected Archimedes number [19]. According to the literature, airflow patterns with an Archimedes number larger then 75 are stable and fall down towards the floor when entering the room and recirculate along the ceiling in anticlockwise direction. An Archimedes number less than 30 indicates the air jet remains horizontal after entering and the resulting airflow pattern has a clockwise direction of rotation. This was investigated with the data obtained in these experiments. Fig. 9 shows a clear distinction between experiments with high flow rate ð41000 m3 =hÞ and experiments with low flow rate ðo1000 m3 =hÞ for different positions of the inlet valve (% closed). The
0.95 0.89 0.86 11.94 41
corresponding Archimedes number can explain this phenomenon. Fig. 10 shows a clear relation between the corrected Archimedes number and the predicted deflection angle a2 : At high Arc ð475Þ; a falling airflow pattern and at low Arc ðo30Þ a horizontal airflow pattern is expected. For this reason, all experimental data of test installation 2 were divided according to the flow rates. A new regression analysis was performed for each group of experiments and better results were obtained (R2 of 0.91 for low ventilation rates and R2 of 0.97 for high ventilation rates), as can be seen in Table 3. The regression analysis shows that the predicted accuracy is 4.5 1 at high ventilation rates and 15.6 1 at low ventilation rates, which corresponds to a maximum deviation of respectively 0.28 and 0.98 m.
4. Discussion By using the information of the temperature distribution at inlet position with a new designed airflow pattern sensor in combination with the temperature of ingoing and outgoing air, it was possible to obtain a good
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Fig. 9. The predicted deflection angle in function of the position of the inlet valve; blue: high ventilation rates ð41000 m3 =hÞ; red: low ventilation rate ðo1000 m3 =hÞ:
Fig. 10. Linear relationship between the corrected Archimedes number and the predicted deflection angle a2 :
prediction of the airflow pattern trajectory. At laboratory scale (test installation 1), the deflection angle a2 ; which was varied between 45 1 and 15 1, of a nonisothermal air jet could be measured with an average accuracy of 8.39 1 (or a deviation of 0.22 m) at a distance of 150 cm from the inlet opening. At a real scale (test
installation 2) an average accuracy of 11.4 1 (deviation of 0.74 m) was obtained. Even with a reduced number of sensors, it was possible to obtain an acceptable deviation between the real deflection angle and the modelled value at a certain distance from inlet opening. With only six temperature
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measurements, an average deviation of 8.5 1 (or a difference of 0.22 m in drop distance) from the real deflection angle was obtained at laboratory scale. By further reduction of the amount of sensors (four temperature measurements) an average accuracy of 9.3 1 (0:25 m in drop distance) at laboratory scale and an average accuracy of 16.29 1 (deviation of 1:02 m) at large scale were obtained. By analysing the data, a dichotomy was discovered in the airflow patterns at real scale. A distinction could be made between horizontal and falling airflow patterns. This phenomenon supports the theory of the corresponding corrected Archimedes number. At high ventilation rates, a higher average accuracy (4.5 1 or 0:28 m in drop distance) was obtained, while on lower ventilation rates a slight improvement (15.6 1 or 0.98 m in drop distance) was possible. In the experiments, the effect of sensible heat production is already taken into account by using static heat sources on the floor. However, further research is needed that takes into account the effects on the airflow pattern of moving occupants inside the ventilated structures and the consequent effect on the accuracy of the presented sensor. The characteristics of airflow in the occupied zone of mechanically ventilated air spaces have been studied in buildings designed for humans, and also buildings designed for livestock (for example [22] and [23]). In these trials, the presence of occupants has been shown to have an complex influence on the measurements obtained. More precisely, the air velocity and temperature in the immediate surroundings of the occupants influence the flow in the occupied zone and in the entire room space [23,24]. Knowledge of this influence is essential to improve control of the indoor environment.
5. Conclusions The proposed sensor concept proved to have the potential to measure the airflow pattern on-line. This temperature sensor method, which uses a configuration of temperature sensors to detect the air jet trajectory, is a low cost and reliable method, which can be easily implemented in the hardware of existing ventilation equipment and consequently microclimate control. Further research incentives are needed in order to predict the airflow pattern in terms of drop distance, a physical meaningful and user-friendly parameter for the quantification of airflow patterns. Other nonlinear modelling techniques should be explored in order to augment the prediction accuracy of the airflow pattern sensor. In the next step, a controller can be developed based on this sensor, which offers the possibility to control the airflow trajectory in a ventilated room. The presented
concept of the airflow pattern sensor offers new possibilities for on-line control of the fresh air distribution in a ventilated space.
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