1、International Journal of Heating,Ventilating, Air-conditioning and Refrigerating Research A Quarterly Publication of Ai-chiva1 Research Volume 8, Number 2, April 2002 International Journal of Heating, Ventilating, Air-conditioning and Refrigerating Research Editor John W. Mitchell, Ph.D., P.E. Profe
2、ssor of Mechanical Engineering, University of Wisconsin-Madison, USA Associate Editors Michael J. Brandemuehl, Ph.D., P.E., Professor, James E. Braun, Ph.D., P.E., Associate Professor, Ray W. Herrick Laboratories, Alberto Cavallini, Ph.D., Professor, Dipartmento di Ficicia Tecnica, University of Pad
3、ova, Italy Arthur L. Dexter, D.Phil., C.Eng., Reader in Engineering Science, Department of Leon R. Glicksman, Ph.D., Professor, Departments of Architecture and Richard R. Gonzalez, Ph.D., Director, Biophysics and Biomedical Modeling Division, Anthony M. Jacobi, Ph.D., Professor and Associate Directo
4、r ACRC, Department of Reinhard Radermacher, Ph.D., Professor and Director, Center for Environmental Energy Keith E. Starner, P.E., Engineering Consultant, York, Pennsylvania, USA Jean-Christophe Visier, Ph.D., Head, Centre Scientifique et Technique du Btiment, Energy Management Automatic Controller
5、Division, Marne La Valle, France Joint Center for Energy Management, University of Colorado, Boulder, USA School of Mechanical Engineering, Purdue University, West Lafayette, Indiana, USA Engineering Science, University of Oxford, United Kingdom Mechanical Engineering, Massachusetts Institute of Tec
6、hnology, Cambridgc, USA U.S. Army Research Institute of Environmental Medicine, Natick, Massachusetts, USA Mechanical and Industrial Engineering, University of Illinois, Urbana-Champaign, USA Engineering, Department of Mechanical Engineering, University of Maryland, College Park, USA Policy Committe
7、e Editorial Assistant Stephen W. Ivesdal, Chair, Member ASHRAE P. Ole Fanger, Fello/Lij Member ASHRAE Ken-Ich Kimura, Fellow ASHRAE John W. Mitchell, Fellow ASHRAE Frank M. Coda, Member ASHRAE W. Stephen Comstock, Associate Member ASHRAE Jennifer A. Haukohl W. Stephen Comstock Mark S. Owen, Handbook
8、 Editor Barry Kurian, Publishing Services Manager Heather E. Kennedy, Handbook Associate Editor Nancy F. Thysell, Typographer Publisher ASHRAE Staff Reviewers Dan Aarons Osman Ahmed Art Bergles Larry J. Berglund Denis Clodic Roy Crawford Dmiy B. Crawley Steven J. Emmerich Eric Gnnryd Greshon Grossma
9、n Je-Chin Han Mark Hernandez Kenneth E. Hickman John M. Iiousc Dan Int-Hout Haobo Jiang Yi Jiang Michael Kauffeld Mark Kedzierski Richard M. Kelso Min Soo Kim Sanford Klein Rich Kooy Mikkel Kristian Kragh Louis Larct Claudio Melo Majid Molki Olivier Morisot Wong Teck Neng Ron M. Nelson Ty Newell Sam
10、uel Sami Ken Schultz Timothy Shedd S.A. Sherif A.H.C. van Paassen Jon Wattclet Phillip J. Winters William Worek Samuel F. Yana Motta Felix Ziegler 112002 by the Amcrican Society of Heating. Rcfrigerating and Air-Conditiming Pcnodicals postage piid at Atldntu. Gcorgia. and additional miiiling offices
11、. HVAC and (2) during operation under partially dry and wet condi- tions (O to 0.7 kW), the latent energy rate is underestimated because of the method used to perform the calculation. This phenomenon occurs for low values of latent energy rate, and the induced error has a minimal influence on the bu
12、ilding energy consumption estimation, as explained previously. 0.61 Thermal conductuivity of fins, W/(mK) HVAC only one nominal rating point is used to characterize the coil. The model is accurate under nonnominal conditions. The noncontrolled dehumidification energy rate is estimated correctly. The
13、refore, this model allows the real operating performance of the cooling coil to be taken into account without significant computational efforts. This model has been integrated in the ConsoClim method (Morisot et al. 1997) for estimating building energy consumption. NOMENCLATURE A air-side exchange a
14、rea, m2 A, airflow fin area, m2 Aint water-side exchange area, m2 A, air-side exchange area corrected by fin Al airflow maximal area, m2 efficiency, m2 A, airflow area, m2 cpa specific heat of air, J/(kg.K) The proposed method for modeling cooling coils can be easily integrated into methods for VOLU
15、ME 8, NUMBER 2, APRIL 2002 155 specific heat of saturated air, J/(kg.K) specific heat of liquid water, J/(kg.K) specific heat of water vapor, J/(kg.K) coefficient forj factor from COLBURN correlation minimal capacity rate between air and water, kgls inside pipe diameter, m air-side hydraulic diamete
16、r, m outside tube diameter, m equivalent circular fin diameter, m fin thickness, m enthalpy of saturated air at apparatus dew-point temperature, Jkg convection heat transfer coefficient on air-side, W/(m2.K) convection heat transfer coefficient for wet coil, W/(m2.K) convection heat transfer coeffic
17、ient for dry coil, W/(m2.K) convection heat transfer coefficient on liquid-side, W/(m2.K) mass transfer coefficient, kg/(m2. s) enthalpy of air, J/kg enthalpy of saturated air at liquid temperature, Jkg heat of vaporization at O“C, Jkg mass flux (flow/area), kg/(m2. s) factor of COLBURN correlation
18、equivalent fin height, m mass air flow rate, kg/s mass water flow rate, kg/s condensation flow rate, kg/s number of rows of the coil fin thermal resistance, WW fin air-side convection resistance, KiW Nusselt number number of transfer unit fin spacing, m Prandtl number transverse tube spacing, m long
19、itudinal tube spacing, m total energy rate, W Reynolds number temperature, K overall enthalpy heat transfer Coefficient, W/K air-side heat transfer coefficient, W/K liquid-side heat transfer coefficient, WK front face velocity, mis front water velocity, mis specific dry air volumetric flow rate, m3/
20、s humidity ratio, kgkg dry air variable average value of the population of X variable log mean enthalpy difference, J/kg constant coil effectiveness or fin factor Usoro et al. 1985; Stylianou and Nikanpour 1996; Lee et al. 1997; Dexter 1999). Soft sensor faults, such as biases or drifts, are among t
21、he typical faults found in building HVAC systems. A conventional engineering method to find and correct the faults is to follow procedures that check and recalibrate the sensors periodically (Pike and Pennycook 1992). This does not satisfi the requirements of modem HVAC systems, which need reliable
22、measurements for continuous online automated schemes. It has also been recognized that it is very difficult, if not impossible, to recalibrate water flow meters after they have been installed into pipelines (Phelan et al. 1996). Therefore, automated online sensor fault detection and diagnosis (FDD)
23、methods that not only indicate when and where a sensor is faulty, but also evaluate the magni- tude of the fault, are highly desirable. Many advanced methods have been proposed for the detection and diagnosis of sensor faults. The model-based method (Patton 1994) is most commonly used in modem FDD s
24、chemes. The Jin-Bo Wang, currently an associate professor in the Faculty of Environmental Science and Engineering, Huazhong Uni- versity of Science and Technology, Wuhan, is a Ph.D. graduate, Shengwei Wang is an associate professor, and John Burnett is head and chair professor of the Department of B
25、uilding Services Engineering, The Hong Kong Polytechnic University, Hong Kong, China. 159 160 HVAC Clarke and Fraher 1996; Yung and Clarke 1989). This approach does not use system-level relationships among different variables that may be simple, easy to establish, and useful in certain situations. T
26、he difficulty in distinguishing soft sensor faults from plant performance degradation or changes in working conditions is another problem with the model-based FDD method. Usually, both soft sensor faults and plant performance degradation occur naturally and simultaneously. It is difficult to separat
27、e them with a model-based method because the models used become invalid due to the presence of component faults. Recently, Wang and Wang (1999) presented a conservation-law-based sensor fault detection, diagnosis, and evaluation (FDD&E) strategy. They developed several schemes to detect the existenc
28、e, identi the location, and evaluate the magnitudes of sensor faults in the chilled water flow meters and temperature sensors in a typical chilling plant. The values of the sensor biases were estimated (Wang and Wang 1999, 2000). The strategy uses the relationships that are directly based on the uni
29、versally valid steady-state mass and energy conservation laws. Such relationships are easy to set up and are not affected by the presence or the occurrence of most component faults, including equipment or system performance degradation or changes in plant working conditions. Only sensor faults can c
30、ause the apparent imbalances of mass or energy. Therefore, the law-based strategy not only avoids the model validity problem, but also can dis- tinguish intrinsically sensor faults from component faults. Sensor biases are estimated by mini- mizing the sum of the squares of the associated balance res
31、iduals. The estimates successfully produced by the FDD&E schemes make it possible for building management systems (BMS) to automatically correct the faulty measurements. This paper presents an integrated robust FDD&E strategy for the flow meters and temperature sensors in central chilling plants. Th
32、e integrated strategy builds on the basic scheme described in Wang and Wang (1999) that improves the robustness of bias estimation in cooling water sen- sors. The robust scheme estimates the bias magnitudes of several chilled water sensors by mini- mizing systematically the sums of the squares of th
33、e associated energy balance residuals. A genetic algorithm is used to solve the corresponding multimodal minimization problem, which is difficult to solve by conventional gradient-directed searching methods. For the cooling water sensor: FDD&E scheme, a correlation cancellation method is developed t
34、o estimate the cooling water flow meter bias. The integrated robust strategy is validated using data generated by a dynamic simulation pro- gram for an existing chilling system. It is also applied to an existing building chilling system. The results of the simulation tests and the field application
35、are presented and analyzed. OVERVIEW OF INTEGRATED ROBUST STRATEGY The system studied is a typical primary-secondary chilling plant commonly used in large building HVAC systems, as shown schematically in Figure 1. The sensors illustrated are neces- sary to facilitate different schemes of control, management, and condition monitoring in the plant. The building flow meter Mb and the supply and return temperature sensors (Tsb, Trb) are necessary for measuring the building cooling load. The chilled water flow meter M(j), the
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