ASHRAE IJHVAC 16-5-2010 HVAC&R Research.pdf

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1、 VOLUME 16, NUMBER 5 HVAC accepted June 16, 2010Moving objects can disturb stratified flow and contaminant concentration gradient in an inpa-tient ward with displacement ventilation. This investigation uses computational fluid dynamics (CFD) to study the effect of objects moving, such as a visitor o

2、r caretaker walking, the changing of sheets on a patients bed, and the swinging of an entrance door for up to four seconds, on the contaminant concentration distributions in a single inpatient ward. The CFD was validated by using the measured distributions of air velocity, air temperature, and conta

3、minant concentra-tion from the mockup of an inpatient ward. The contaminant was assumed to be breathed out by the patient lying on the bed. The results show that moving objects can cause a 10 to 90 second swing in the contaminant concentration distribution. The averaged concentration change in the b

4、reathing levels in the ward was generally less than 25%, so the risk level should remain the same. The closer the location of the moving object to the contaminant source, the larger was the change in the contaminant concentration. The displacement ventilation with 4 ach in an inpa-tient ward with a

5、moving object can still produce the same air quality level as overhead mixing ventilation with 6 ach.INTRODUCTIONAir movement in indoor environments is strongly linked to the transmission and spread of air-borne infectious diseases, such as measles, tuberculosis, chickenpox, influenza, smallpox, and

6、 SARS (Li et al. 2007). The lack of knowledge of and insufficient data on ventilation require-ments in hospitals, schools, and offices make it difficult to understand the spread of airborne infectious diseases (Li et al. 2007; Beggs et al. 2008). A study by Yin et al. (2009) showed that ventilation

7、systems played a very important role in the transmission of exhaled particles from a patient to a caretaker in the same ward. They showed that displacement ventilation can provide much better indoor air quality than overhead mixing ventilation. The ventilation effectiveness of a displacement ventila

8、tion system with a reduced ventilation rate of 4 ach can be the same as that of an overhead mixing ventilation system with a ventilation rate of 6 ach. However, there were concerns about whether an object moving, such as a visitor or caretaker walking, changing Sagnik Mazumdar was a doctoral student

9、 at Purdue University, West Lafayette, IN, when this study was performed and is currently a post-doctoral fellow at the University of Medicine and Dentistry of New Jersey, Newark, NJ. Yonggao Yinis a lecturer at Southeast University, Nanjing, China. Arash Guity is a mechanical engineer and Bob Gulic

10、k is a senior principal at Mazzetti Nash Lipsey Burch, San Francisco, CA. Paul Marmion is a senior principal at Stantec, Vancouver, BC, Canada. Qingyan Chen is a Changjiang professor at Tianjin University, Tianjin, China, and a professor at Purdue University. 2010 American Society of Heating, Refrig

11、erating and Air-Conditioning Engineers, Inc. (www.ashrae.org). Published in HVAC Bjrn and Nielsen 2002; Bjrn et al. 1997). Mazumdar (2009) found that a moving passenger in an aircraft cabin could carry a contaminant in his or her wake to positions far from the contaminant source. Thus, it is essenti

12、al to assess the impact of moving objects on contaminant transmission in inpatient wards with displacement ventilation.RESEARCH METHODS An investigation of the impact of moving objects on airborne contaminant transmission inside an inpatient ward can be made experimentally or through computer simula

13、tions. Acquiring experimental data with meaningful temporal and spatial resolution is difficult and time consum-ing (Poussou 2008; Thatcher et al. 2004). Computer simulations using computational fluid dynamics (CFD) are an efficient alternative and can provide high-resolution data because CFD simula

14、tions can handle directly or indirectly the movement of an object in an inpatient ward. The indirect methods model the movement approximately, such as by using a distributed momentum source (Zhai et al. 2002) or a turbulent kinetic energy source (Brohus et al. 2006). These indirect methods need litt

15、le computing time, but they generate some uncertainties, and the results may not be accurate due to the bold assumptions used. In contrast, the direct methods use moving and dynamic grids to simulate the movement. The accuracy is greatly improved, but unfortunately, the methods are computationally d

16、emanding. To reduce the computing demand, a CFD model with combined dynamic and static mesh scheme can be used (Mazumdar and Chen 2007). The CFD model uses dynamic meshes for regions where the movement takes place and static meshes for the rest of the computational geometry. This CFD model was valid

17、ated with experimental data for airflow in an airliner cabin to be reliable and, therefore, appropriate for the present study.The CFD model used a second-order upwind scheme and the SIMPLE algorithm. The renor-malization group (RNG) k- model was used to model the turbulent flow inside an inpatient w

18、ard. Compared to other turbulence models, the RNG k- model was one of the best in terms of accuracy, computing efficiency, and robustness for modeling indoor environments (Zhang et al. 2007). This study used a commercial CFD program, FLUENT (Ansys 2003). The CFD model was used to calculate the distr

19、ibutions of air velocity, air temperature, gaseous contaminant con-centration, air pressure, and turbulence parameters. The contaminant concentration was normal-ized using(1)where Cpis the contaminant concentration at the point of interest. Csand Ceare the steady state contaminant concentrations at

20、the supply inlet and at the exhaust outlet, respectively.VALIDATION OF THE CFD METHODIn order to verify that the CFD model can also be used in an inpatient ward with displacement ventilation, this investigation first used the measured data of air velocity, air temperature, and contaminant concentrat

21、ion as simulated by a tracer gas (Yin et al. 2009) from a mockup of an inpatient ward as shown in Figure 1 for the validation. Figure 1 shows that the ward was fur-nished with one bed, a TV set, and a piece of medical equipment. This ward had one patient lying on the bed and one caretaker standing o

22、n the right side of the patient. The air was supplied at 4 ach from the diffuser located near the floor on the opposite wall from the patient. The Normalized CCpCsCeCs-= 2010 American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. (www.ashrae.org). Published in HVAC did not v

23、ary the walking speed or the speed of the changing sheet; did not simulate the actual exhalation with a variable momentum; and did not simulate other exhaling activities, such as talking, sneez-ing, and coughing. Thus, the subject deserves further study. One should not extend the conclusions obtaine

24、d from this study to other conditions that were not studied. For example, Qian et al. (2006) studied a two-patient award with different postures and found that the contaminant could be locked in the breathing zone of a person lying down. Since this study was only for one person lying down, the pheno

25、menon was not produced. CONCLUSIONThis study investigated the effects of moving objects on contaminant concentration levels in a single inpatient ward with displacement ventilation. The investigation was conducted by assum-ing a constant contaminant generated from the mouth of a patient lying on the

26、 bed and by using a validated CFD program. The study assumed a 4 ach ventilation rate for the displacement ven-tilation. To compare with a conventional design, this investigation also used a case of overhead mixing ventilation with a 6 ach ventilation rate. This study considered four different movin

27、g objects in the ward for up to four seconds of movement: a visitor walking near the foot of the bed, a caretaker walking alongside the bed, the Figure 13. Area fraction in the ward with the displacement ventilation at 4 ach where the contaminant concentration at breathing levels was less than that

28、in perfect mixing venti-lation at 6 ach. 2010 American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. (www.ashrae.org). Published in HVAC accepted June 14, 2010This paper presents a model-based feedback control scheme for energy minimization in an air-conditioning system to m

29、aintain a desired level of thermal comfort. The proposed control scheme consists of two layers of controlsetpoint optimizing control and dynamical optimizing control. In setpoint optimizing control, the optimum setpoints for dry-bulb temperature, relative humidity and relative velocity of air, evapo

30、rator pressure, and condenser pressure are com-puted on-line under operational and performance constraints of the system. Dynamical optimiz-ing control deals with the design of centralized continuous control based on output feedback to track the optimum setpoints. This control law is designed for th

31、e complete dynamical system, i.e., the refrigeration circuit and air circuit simultaneously. The constraints on control inputs were also considered while designing the proposed optimizing control scheme, which has been validated on an air-conditioning unit provided in an Indian Railway passenger coa

32、ch. The result shows that the proposed control scheme saves substantial energy as compared to the con-stant-setpoints-based control scheme to maintain the same level of thermal comfort.INTRODUCTIONAir-conditioning systems are major consumers of energy in all developed as well as develop-ing countrie

33、s. With a steady rise in energy demand worldwide, every effort to improve energy efficiency counts. With the increasing complexity of air-conditioning systems, the modeling, control, and monitoring issues have become a challenge for improving energy efficiency. The control of an air-conditioning sys

34、tem plays a major role in its energy consumption. The chal-lenge in controlling an air-conditioning system lies in providing the desired indoor thermal com-fort with the least energy input under dynamic outdoor and indoor conditions. The thermal comfort can be measured with the help of an index know

35、n as predicted mean vote (PMV). The PMV is a thermal comfort index proposed by Fanger (1972) and is internationally standardized. The energy consumption in air-conditioning systems is mainly energy consumed in compressor motors, condenser fan motors, and evaporator fan motors. In the existing contro

36、l structure, the human operator is essential to adjust the setpoints for optimizing the performance of air-condi-tioning systems under varying operational conditions, such as changing ambient temperature and humidity, changes in number of occupants, and other indoor loads. However, in order to ensur

37、e even a remotely close to optimal operation of the air-conditioning system, the interven-tion frequency from the operator has to be quite high. It is therefore not realistic that an operator does these adjustments in practice; thus, most air-conditioning systems operate with constant setpoints. Mah

38、endra Kumar is a research student in the Department of Electrical Engineering, Indian Institute of Technology Del-hi, and Deputy Chief Electrical Engineer with Indian Railway, New Delhi, India. I.N. Kar is a professor in the Depart-ment of Electrical Engineering, Indian Institute of Technology Delhi

39、. 2010 American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. (www.ashrae.org). Published in HVAC Larsen et al. 2004; House and Smith 1995; He et al. 1998; Wang and Jin 2000; Zaheer-uddin and Zheng 2001) have contrib-uted to the development of model-based control of an air-c

40、onditioning and refrigeration sys-tem. He et al. (1998) designed a multivariable controller to control a vapor-compression cycle using the linear-quadratic Gaussian technique with an integrator. Arguello-Serrano and Velez-Reyes (1999) designed a controller for an air circuit that consists of a regul

41、ator and a dis-turbance rejection component. In existing control schemes, air-conditioning systems are oper-ated at constant setpoints. Larsen et al. (2004) have contributed in optimizing the setpoints for refrigeration systems. Wang and Jin (2000) computed optimal settings of air-handling unit (AHU

42、) supply temperature, outdoor ventilation rate, and chilled water temperature by mini-mizing the cost function using a genetic algorithm. The feedback control law was designed in these papers by considering either a refrigeration circuit or an air circuit. The proposed study is an attempt to design

43、an energy-efficient multi-input multi-output (MIMO) feedback control based on a physical model for an air-conditioning system in which the refrigeration circuit and the air circuit have been considered simultaneously. Work related to feedback control law design by considering refrigeration and air c

44、ircuit simultaneously has not been identified in the existing literature. The main contributions of this paper are as follows:1. An optimization algorithm has been developed for on-line computation of setpoints for air temperature, relative humidity, relative air velocity, evaporator pressure, and c

45、ondenser pres-sure under constraints to minimize energy consumption at a desired level of thermal comfort. These setpoints will vary with environmental and indoor conditions. 2. The dynamical optimizing control has been designed by using output feedback control law to track the optimal setpoints. Th

46、e static output feedback-based control law for the constrained control input has also been developed. 3. The proposed control scheme has been validated on a non-linear model of an air-conditioning unit provided in an Indian Railway passenger coach. A comparison has been made between the constant set

47、point control scheme and the proposed control scheme in terms of energy con-sumption for maintaining the same level of thermal comfort.SYSTEM OPERATIONThe air-conditioning system can be divided into two main subsystems or circuits: (i) the refrig-eration circuit and (ii) the air circuit or variable-

48、air-volume (VAV) air distribution subsystem.The refrigeration circuit of a direct-expansion (DX) refrigeration plant consists of an evapora-tor coil, a condenser coil, a compressor, and an expansion valve. An air circuit consists mainly of the following components: circulating or blower fan, thermal

49、 space (passenger area), connect-ing ductwork, dampers, and a mixing air component.The layout of an air-conditioning system with a basic control structure is shown in Figure 1. The proposed control structure has two layers of control. The first layer is setpoint optimizing 2010 American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. (www.ashrae.org). Published in HVAC He et al. 1998) of an air-conditioning system. The feedback control law is designed by using ou