Development and Testing of the Characteristic Curve Fan Model
|
|
- Warren Jordan
- 6 years ago
- Views:
Transcription
1 AN Development and Testing of the Characteristic Curve Fan Model Jeff Stein, P.E. Member ASHRAE Mark M. Hydeman, P.E. Member ASHRAE ABSTRACT This paper describes the development and testing of the characteristic curve fan model a gray-box model. This model produces fan efficiency as a function of airflow and fan static pressure. It is accurate, relatively easy to calibrate, and could be easily incorporated into commercial simulation programs. Also presented is an application of an existing model to predict fan speed from airflow and fan static pressure. These models were developed as a part of a larger research project to develop design guidelines for built-up variable air volume fan systems. The models have been successfully employed in comparative analysis of fan types, wheel diameters, fan staging, and analysis of supply pressure reset. INTRODUCTION The authors were part of a publicly funded energy efficiency research team developing design guidelines for builtup fan systems in commercial buildings. According to previous research, fan energy in new construction for commercial buildings in California accounts for 1 terawatt-hour of electric energy usage per year, representing approximately half of all HVAC energy usage (CALMAC 2003). The authors research demonstrates that up to half of that fan energy is avoidable through cost-effective design practices, including fan selection (size and type), fan sizing, fan staging, and static pressure control (Hydeman and Stein 2003). Five monitoring sites provided field data on which to test the alternative fan system designs and design techniques. These sites were selected to represent a range of climates, occupancies, and fan system configurations (Kolderup et al. 2002). As part of this work, a simulation model of a fan system was sought that had all of the following characteristics: Accurate at predicting fan system energy over the full range of actual or anticipated operating conditions. Applicable for the full range of fan types and sizes. Easy to calibrate from manufacturer s or field-monitored data. Ability to identify operation in the surge region. Relatively simple to integrate into existing simulation tools. Ability to independently model the performance of the fan system components, including the motor, the mechanical drive components, the unloading mechanism (e.g., VSD), and the fan. The purpose of this model is to evaluate design alternatives for fan selection and control through simulation. Optimally, simulation tools would directly utilize the manufacturers fan curves to evaluate fan system operation at each discrete step of evaluation. Since this is not currently available, the authors sought models that simulation tools could easily incorporate that replicated fan performance. MAIN BODY Literature on component models for fans was reviewed, including the models used in the DOE-2 simulation program (DOE 1980) and in the ASHRAE Secondary Toolkit (Brandemuehl et al. 1993; Clark, 1985). We also looked briefly at the models embedded in commercial simulation software, such as Trace and HAP, but found these suffered from the same problems as the model in DOE-2. DOE-2 uses a black-box regression model that produces the fan system power draw as a function of percent design airflow using a second-order equation as follows: Jeff Stein is a senior engineer and Mark Hydeman is a principal at Taylor Engineering, LLC, Alameda, Calif ASHRAE. 347
2 P a + b CFM CFM = + c P design CFM design CFM design 2 This model is implicitly built on several assumptions: 1. Each fan operates on a single system curve that uniquely maps airflow to static pressure. 2. Fan system efficiency is directly a function of airflow. 3. A second-order equation sufficiently models both of these effects. The DOE fan model implicitly combines the operating system curve with the models for each of the fan system components. Power is directly produced as a function of airflow only, and there is no opportunity to have different conditions of fan static pressure at a given airflow. Real VAV systems do not remain on a fixed system curve. System pressure as a function of airflow behaves differently depending on the location of the boxes that are modulating, the location of the static pressure sensor(s), and the static pressure control algorithm. Although this model is simple to use, it does not allow the user to independently model and evaluate each of the fansystem components. Thus, if designers wanted to evaluate the impact of motor oversizing, they would have to independently assemble fan and motor models to develop the DOE-2 performance curve that represented the combination of the two together. This model also does not directly account for the variation in fan system component efficiencies as the fan unloads, nor does it allow for evaluation of a multiple fan system, where fan staging will change both the operating efficiency and potentially the individual fan static as they are staged on and off. The model in the ASHRAE Secondary Toolkit is a graybox fan component model that uses the perfect fan laws through application of dimensionless flow (φ) and pressure (ψ) coefficients. This model uses a fourth-order equation to predict fan efficiency from the dimensionless flow parameter. where CFM N D ρ P C 1 and C 2 = airflow = fan speed = fan diameter CFM φ = c N D 3 P ψ = c ρ N 2 D 2 η fan = a + b φ + c φ 2 + d φ 3 + e φ 4 = average air density = fan static pressure and = constants that make the coefficients dimensionless (1) (2) (3) (4) This model allows the user to calibrate an entire family of fan curves with data from a single model. Unfortunately, this model does not permit the direct calculation of fan efficiency from airflow and pressure; rather, it correlates efficiency to the dimensionless flow term (φ), which requires both airflow and fan speed as inputs. As elaborated below, a designer (and most simulation tools) will use airflow and fan pressure as inputs to the fan system model in order to calculate fan speed and efficiency. A second problem is that this model assumes a fixed peak efficiency for fans of all sizes. This simplification reduces the applicability of the fan model for comparative analysis of fan options as peak efficiency tends to increase with fan diameter. As a result of these shortcomings, the authors set out to develop a new component model that could directly be driven by airflow and pressure. Based on the fan laws (ASHRAE 2000), the core assumption of this new characteristic curve fan model is that the efficiency of a fan is constant as the fan rides up and down on a particular characteristic system curve. Extensive testing with manufacturers fan selection software demonstrates that the manufacturers also use this simplifying assumption for developing fan performance data in both the surge and non-surge regions. ANSI/ASHRAE Standard (ANSI/AMCA Standard ) (ASHRAE 1999) explicitly permits this. For this model, a characteristic system curve is defined as a second-order equation, equating fan static pressure to airflow (cfm) with a zero constant and no first-order coefficient. For example, a VAV supply fan with a fixed duct static pressure setpoint of 1.5 in. w.c. will ride up and down on a system curve that runs through the design point and through 1.5 in. at 0 CFM. A characteristic system curve is a particular type of system curve in that it must run through the origin (0 in. at 0 CFM). A characteristic system curve is characterized by a single coefficient, SCC (system curve coefficient). The equation for any characteristic system curve is P SCC = (5) CFM 2. Using this assumption, it is only necessary to find fan performance at a single point on a characteristic system curve to define its performance along that curve at all speeds. As depicted in Figure 1, there are three characteristic system curves of particular importance: the curves at the minimum and maximum ends of the tuning data set and the curve that represents the highest efficiency for the fan. As described below and depicted in Figure 3, fans behave very differently on either side of this peak efficiency. The minimum and maximum curves represent the boundaries of the model tuning data. The triangles in Figure 1 depict points of data that were sampled from the manufacturer s fan selection software. Each point represents the fan efficiency for all points on a characteristic system curve. The fan efficiency is calculated from the fan brake horsepower (BHP), airflow (CFM), and fan static pressure ( P) reported by the software through the following equation: 348 ASHRAE Transactions: Symposia
3 CFM P η fan = BHP The model can be used to predict the fan power for any point whose system curve is between the two extreme system curves. Figure 1 is overlaid on top of an output screen from a manufacturer s selection program. Notice that the peak efficiency line is also the boundary of the manufacturer s Do Not Select or surge region. This is typical for plenum, backward inclined, and vane-axial fans. For airfoil, mixed flow, and propeller fans, the peak efficiency is well to the right (i.e., outside) of the surge region (see Figure 5). Figure 1 (6) Tuning data for 66 in. plenum fan shown on a manufacturer s fan curve. When a fan enters the surge region, not only does the efficiency drop, but also the fan begins to vibrate, which can create audible noise and damage the fan, bearings, drive, and attached ductwork. The further the fan moves into the surge region, the greater the vibration. Catastrophic failure can occur if the fan moves well into the surge region at high power (high static). Some manufacturers appear to be more conservative than others in terms of what amount of vibration is acceptable. Moving into the surge region at low power (low static) is not likely to cause catastrophic failure or unacceptable vibration, but it will reduce fan life. From our experience, fans with variable-speed drives commonly operate for extended periods of time in the surge region, but it is usually at low power. Figure 2 shows fan efficiency plotted against system curve coefficient (SCC) for the data from this 66 in. plenum fan. The efficiency data naturally divide into two regions left and right of the peak efficiency. It is interesting to note that the surge region in Figure 2 is to the right of peak efficiency, whereas it is to the left in the manufacturer s fan curve (Figure 1). The representation in Figure 2 is also somewhat hard to read, as it condenses the normal region to a small space. The efficiency curve is easier to visualize and to fit a regression equation if plotted as a function of the negative of the log of the system curve coefficient (see Figure 3). The log causes the efficiency curves to become nearly linear, and the negative flips the surge and normal regions so that it matches manufacturer s curves (i.e., surge to the left, normal operation to the right). The base of the log does not seem to make much difference. Natural log is used here. Gamma is defined as the negative of the natural log of the system curve coefficient. Figure 2 Fan efficiency vs. system curve coefficient. ASHRAE Transactions: Symposia 349
4 Figure 3 Fan efficiency as a function of gamma. γ ln( SCC) Fan efficiency can be accurately predicted as a function of gamma. The recommended procedure is to break the function into two parts: an equation for gammas in the surge region and another for gammas in the normal region. For this reason, it is important to get an accurate indication of the critical gamma, which is the gamma that corresponds to the system curve of highest fan efficiency. This can be done with the manufacturer s software by iterating on the airflow conditions in the vicinity of the critical gamma. Figure 3 shows the R-square regression statistic for various orders of polynomials to the example 66 in. plenum fan. As demonstrated in Figure 3, a first-order polynomial (i.e., straight line) is reasonably accurate. Higher order polynomials provide a better fit, but they require more data to calibrate and can produce undesirable results between calibration data points. A third-order regression appears to provide a good balance between calibration accuracy to the tuning data set and rational function behavior between calibrating data points. These equations are of the form: η fan_left_of_peak_efficiency = S 0 + S 1 γ + S 2 γ 2 + S 3 γ 3 η fan_right_of_peak_efficiency = N 0 + N 1 γ + N 2 γ 2 + N 3 γ 3 (7) (8) (9) where S 0 S 3 and N 0 N 3 are regression coefficients developed from tuning data on the left (surge) side and right (normal) side of the peak efficiency point. Regardless of the equation order, care must be taken to provide a continuous function through the critical gamma point. Figure 4 shows the accuracy of the characteristic curve fan model across 224 points, representing a wide range of fan speeds, pressures, and airflows. This tuning data came from a manufacturer s selection program, and the results are presented against this tuning data set. We used third-order polynomials to represent efficiency of gamma, with separate equations in the surge and normal regions (Equations 8 and 9). The characteristic curve model has been found to be accurate for at least six types of fans: plenum, backward inclined, airfoil, mixed flow, propeller, and vane-axial with fixed blades. This model does not apply to fans with variable pitch blades or inlet vanes. Figure 5 shows sample gamma curves for four types of fans. The curves are divided into the surge and non-surge regions in order to illustrate the relationship between peak efficiency and the surge region. Table 1 presents the characteristic curve fan model fit results across a range of manufacturers and fan types (plenum and housed, airfoil, and flat blade) and wheel diameters. This table presents the coefficient of variation root mean square error (CVRMSE) across the 57 fans in the database. Again, this represents third-order polynomial fits against tuning data from manufacturer s selection programs. Typically a fit of 1% to 3% CVRMSE is excellent; a 5% fit is acceptable. Table ASHRAE Transactions: Symposia
5 Figure 4 Accuracy of the characteristic curve fan model against tuning data. Figure 5 Sample gamma curves for six fan types. ASHRAE Transactions: Symposia 351
6 Table 1. CVRMSE for 57 Fans Left Region Right Region Count Min Max Average Min Max Average % 5.74% 0.37% +/- 0.76% 0.17% 3.66% 1.79% +/- 0.83% Figure 6 Manufacturer A 66 in. plenum fan efficiency map using the characteristic curve fan model. Figure 7 Phi as a function of efficiency. shows that the average CVRMSE is well within the excellent region. Figure 6 depicts the predicted fan efficiency from the characteristic curve fan model for the example 66 in. plenum fan. The predicted efficiency is plotted on the Z-axis as a function of the airflow (cfm, X-axis) and fan static pressure (H 2 O, Y-axis). The efficiency is computed between the minimum and maximum characteristic system curves. When viewed from the top, this curve presents the same XY plane as the manufacturer s fan curve (Figure 1). The Z-axis goes from white at the highest efficiency to purple at the lowest efficiency. It is interesting to note that fan efficiency falls more rapidly with a change in airflow in the normal region than in the surge region. In addition to the characteristic curve fan model, which predicts fan efficiency, a second model was developed for predicting fan speed from airflow and fan static pressure. This model, referred to as the Phi model, is used to simulate the performance of fans when they are riding the fan curve (e.g., at fixed speed). With variable-speed driven fans, this occurs when the fan reaches its minimum speed for motor cooling. The Phi model is derived from the Transys program, as reported in the ASHRAE Secondary Toolkit (Clark 1985; Brandemuehl et al. 1993). This model produces the dimensionless flow coefficient, φ (Equation 2), from fan efficiency, as predicted by the characteristic curve fan model. φ left_of_peak_efficiency = PS 0 + PS 1 η + PS 2 η 2 + PS 3 η 3 (10) φ right_of_peak_efficiency = PN 0 + PN 1 η + PN 2 η 2 + PN 3 η 3 (11) where PS 0 PS 3 and PN 0 PN 3 are regression coefficients developed from tuning data on the left (surge) side and right (normal) side of the peak efficiency point. Figure 7 shows the model parameters and r-square statistic for the example 66 in. plenum fan. Once φ is determined from Equations 10 and 11, Equation 2 can be used to calculate fan speed (N, rpm) from φ, airflow (CFM), and fan diameter (D, in.). DISCUSSION As displayed in Table 1 and Figure 4, the characteristic curve fan model is quite accurate at predicting fan efficiency from manufacturer s data. The model presented in the ASHRAE Secondary System Toolkit provides similar results. As stated in the objective, the real issue is how to take these models and apply them to design and analysis. This includes a number of subtopics: 1. The need to model the other components in the fan system. This includes the motor, physical drive (belts, coupling, or gears), and variable-speed drive (if applicable). 2. The accuracy of the fan system model compared to fieldmeasured data. 3. A methodology to apply the model to new construction, where the airflow and pressure demand need to be estimated. 352 ASHRAE Transactions: Symposia
7 Both the characteristic curve fan model and the ASHRAE Secondary Toolkit fan model produce fan efficiency as a function of operating parameters. However, total fan energy is typically more important for design studies. Fan energy cannot be determined without component models for motors, belts, and variable-speed drives. The researchers have developed these component models and have assembled them into a fan system model. This is documented in the project reports and will be the subject of another ASHRAE paper. Resources for existing component models include the following: 1. The research project web site, at < click on link for Large HVAC Integration. 2. The Department of Energy s Motor Challenge market transformation program (< and MotorMaster+ Program (< mm3.energy.wsu.edu/mmplus/default.stm>) for models and performance data of poly phase motors. 3. Data on variable-speed drive efficiencies are reported in Gao et al. (2001). 4. AMCA Publication (AMCA 1990) for belt drives. Figure 8 presents the accuracy of an assembled fan system model against field-measured data from one of the five monitored research sites. As can be seen in this figure, the fan system model underpredicts measured fan system energy by approximately 36%. There are a number of potential reasons for this, including, but not limited to, the following: measurement error model error (for the fan and other components) variation of the fan performance in the field inaccuracy in the tuning data reported by the manufacturers The fan measurements used in this field test include fan airflow, differential pressure across the fan, and fan power. A true RMS power meter with high sampling rate was used for measurement of power; it was the most accurate of the measurements. Airflow and air pressure measurements are inherently inaccurate, particularly when the fan is operating in surge, causing the airflow and pressure to fluctuate (analysis indicated that this fan was in surge over 60% of the time [Hydeman and Stein 2003]). The fan airflow was measured with a calibrated pressure grid located in the inlet of the fan. At this site, each fan had an inlet barometric backdraft damper that was used to isolate fans for staging. The fan pressure was measured across the fan and backdraft dampers and included the pressure drop of the inlet dampers. The inlet dampers, inlet configuration, and proximity of another plenum fan at the discharge most likely also created system effects that reduced potential fan output. This is a general issue with prediction of fan performance in the field. Not only is it inherently difficult to measure fan airflow and pressure accurately, seldom, if ever, will the field conditions approximate the ANSI/ASHRAE Standard (ANSI/AMCA Standard ) test conditions used for development of manufacturers data. Field conditions will always impart inlet and discharge system effects that cause actual fan performance to deviate from test stand conditions. It is likely that these field effects will always lead to lower fan performance (higher energy and lower flow) than the manufacturer s data predicts. This is further complicated by the fact that large parts of the manufacturers reported fan data are extrapolated from actual factory test data. Data are extrapolated through speed using the assumption of fixed efficiency along a fan characteristic system curve. Data are also extrapolated between fan Figure 8 Accuracy of the fan system model on field-measured data. ASHRAE Transactions: Symposia 353
8 Figure 9 Fan efficiency vs. gamma for several of Manufacturer A s fans. Figure 10 Housed airfoil fans: peak efficiency vs. diameter. sizes within a model line using other perfect fan laws. Under ANSI/ASHRAE Standard (ANSI/AMCA Standard ), manufacturers are not required to test all fan sizes. According to the standard, test information on a single fan may be used to extrapolate the performance of larger fans that are geometrically similar using the fan laws (ASHRAE 2000). Figure 9 shows curves for several fans, including five sizes of Manufacturer A s plenum airfoil fans. The 54 to 73 in. diameter plenum airfoil fans have virtually identical curve shapes, just shifted along the x-axis (gamma). The 49 in. version has a different peak efficiency and curve shape. This suggests that Manufacturer A tested the 54 in. fan and extrapolated the performance to the 60 to 73 in. sizes. Using the fan laws, one can exactly duplicate the curves from 60 in. through 73 in. using the 54 in. fan data. Figure 9 also shows one airfoil fan and a forward curve fan. These fans have different curve shapes than the plenum fans. Figure 10 shows the highest efficiency (efficiency at critical gamma) for a number of housed airfoil fans from two manufacturers as a function of wheel diameter. By reviewing the step changes in the peak efficiency data as a function of fan 354 ASHRAE Transactions: Symposia
9 diameter, it is clear from this figure which fans the manufacturers tested and which they extrapolated. For example, both Manufacturer A and Manufacturer B tested their 30 in. fans. Manufacturer A then extrapolated the 30 in. data all the way up to 73 in. (the variability in the peak efficiency of the Manufacturer A 30 in. to 73 in. fans is due to rounding and sampling error). Manufacturer B only extrapolated the 30 in. up to 36 in., then they tested the 40 in.and extrapolated that all the way to 73 in. Manufacturer A s 30 in. is more efficient than Manufacturer B s 30 in. but not more efficient than the Manufacturer B s 40 in. Had Manufacturer A tested a 40 in. (or larger) fan, they might have found that it had higher efficiency than equally sized Manufacturer B s fans. To use the characteristic curve fan model in a design context, we suggest the following process: 1. Develop a simulation model of the facility. 2. Export the hourly demand for fan airflow cfm. 3. Bin the data by hours spent at increments of airflow (10 to 20 bins should suffice). 4. Develop a system curve that represents the coincident pressure at each fan airflow. Note: this may actually be a family of curves representing issues such as supply pressure reset control and the fixed pressure overhead for individual fans run alone or in parallel. 5. Evaluate the performance of alternate fans across the bin data using the system curves to develop a coincident pressure. This, of course, is moot if software developers incorporate the characteristic curve fan model in their programs directly for a parametric analysis. CONCLUSIONS As shown in this paper, it is possible to accurately predict manufacturers fan performance using the characteristic curve fan model. However, it is a challenge to predict field performance due primarily to inaccuracies in instrumentation, system effects due to field conditions, and inaccuracies in the manufacturers reported performance data (due mostly from their extrapolation of test data). From a design perspective, some of these issues are moot, as biases in instrumentation, measurement, and data reporting will tend to cancel out in a comparative analysis of design alternatives. To serve as a design tool, a predictive fan model should be developed to predict brake horsepower from airflow and fan static pressure. These are the inputs that are typically provided in a simulation tool. The model should also have discrete submodels for the separate fan system components so that analysis can be done on the impact of design alternates for each of those components. Overall, the characteristic curve fan model meets all of these requirements. It is accurate at predicting manufacturers data, relatively easy to tune, and could easily be incorporated into existing simulation tools. The authors have successfully employed it in Visual Basic code. There are limitations; this model works for systems with fixed-speed fans and fans with variable-speed drives, but it will not work for fans with inlet vanes or variable-pitch blades. Those challenges are left up to future researchers. Also left to future researchers is the development of generalized fan models based on the characteristic curve fan model. The techniques described thus far require tuning data specific to each fan to be evaluated. However, there are clear patterns between gamma curves for fans of the same type (see Figure 9) A single gamma curve could be used to represent all fans of a certain type (housed airfoil, plenum airfoil, plenum flat blade, etc.). This curve could then be translated along the gamma axis using the perfect fan laws and along the efficiency axis as a function of diameter. Figure 10 shows such a function for housed airfoil fans. ACKNOWLEDGMENTS The authors would like to acknowledge the input and work of other members of our research team, including Cathy Higgins of the New Buildings Institute, Steve Taylor of Taylor Engineering, Erik Kolderup and Tianzhen Hong of Eley Associates, Lynn Qualman from SBW Consulting, Inc., and Roger Lippman from New Horizon Technologies. They would also like to recognize the contributions of our technical advisory team members. Finally, special thanks to the numerous building engineers and property managers at these sites for putting up with our intrusions at their buildings and for their significant assistance in our work. REFERENCES AMCA AMCA Publication , Field performance measurement of fan systems, 0203X90A-S. Arlington Heights, Ill.: The Air Movement and Control Association International, Inc. ASHRAE ANSI/ASHRAE Standard (ANSI/ AMCA Standard ), Laboratory Methods of Testing Fans for Aerodynamic Performance Rating. Atlanta: American Society of Heating, Refrigerating and Air- Conditioning Engineers, Inc. ASHRAE ASHRAE Handbook HVAC Systems and Equipment, Chapter 18, Fans. Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. Brandemuehl, M.J., S. Gabel, and I. Andresen HVAC 2 Toolkit: Algorithms and Subroutines for Secondary HVAC System Energy Calculations. Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. CALMAC Data from the non-residential new-construction database available from the California Measurement Advisory Council s web site, < Clark, D.R HVACSIM+ building systems and equipment simulation program: Reference Manual. NBSIR ASHRAE Transactions: Symposia 355
10 , U.S. Department of Commerce, Washington D.C. DOE (Department of Energy) DOE 2 Reference Manual, Part 1, Version 2.1. Lawrence Berkeley National Laboratories, Berkeley Calif., May. Gao, X., S.A. McInerny, and S.P. Kavanaugh Efficiencies of an 11.2 kw variable speed motor and drive. ASHRAE Transactions 107(2). Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. Hydeman, M., J. Stein A fresh look at fan selection and control. HPAC Magazine, May. Kolderup, E., M. Hydeman, M. Baker, and R.L. Qualmann Measured performance and design guidelines for large commercial HVAC systems. ACEEE Conference on Energy Efficiency, August. DISCUSSION David Yuill, Principal, Building Solutions Inc., Omaha, Neb.: We have done some similar work in which we developed a model to predict airflow through a fan using the design fan curve, fan head, and fan speed as inputs. We set up an experiment to test this model, but we found that the manufacturer s fan curves were not accurate. Have you found this, and are you aware of any data on fan curve accuracy? Jeff Stein: As noted in the paper we did not find a good correlation between measured fan energy and predicted energy (based on manufacturer's data). There are many possible reasons including: (1) The pressure and air flow sensors used in the field tests may be inaccurate. (2) Even if the sensors are accurate, field tests of fan operating static cannot match the AMCA test conditions of "fan static" in the lab: it is impossible to measure "fan static" (the Y axis on a fan curve) in the field since the installation conditions are completely different. System effects play havoc with fan performance in the field. (3) Manufacturer's do not test all sizes. The AMCA rating standard for fans allows them to test a fan and extrapolate the results to all larger fans of the same type. (4) Accuracy of the manufacturer's tests. We noticed that performance data for some fan types got worse and then better as you move to larger sizes. This suggests that there may be variability in the manufacturing or testing processes. We are not aware of any data on fan curve accuracy. 356 ASHRAE Transactions: Symposia
In typical commercial buildings, water-cooled chilled water plants use a signifi cant amount of energy.
The following article was published in ASHRAE Journal, June 2007. Copyright 2007 American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. It is presented for educational purposes
MODELING AND VALIDATION OF EXISTING VAV SYSTEM COMPONENTS
MODELING AND VALIDATION OF EXISTING VAV SYSTEM COMPONENTS Nabil Nassif Stanislaw Kajl Robert Sabourin École de technologie supérieure Montreal, Canada nnassif@mec.etsmtl.ca skajl@mec.etsmtl.ca Robert.Sabourin@etsmtl.ca
Factor. For a building owner concerned with operating costs, the clear- How it Affects Operating Cost
006, American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. (www.ashrae.org). Published in ASHRAE Journal, (Vol. 48, February 006). For personal use only. Additional distribution
I. Khoo, BEng(Hons), G.J. Levermore, BSc, ARCS, PhD, DIC, CEng, FCIBSE, MASHRAE, MInstE, K.M. Letherman, BSc, MSc, PhD, CEng, FIEE, FCIBSE.
Duct Loops and VAV Modelling and Control I. Khoo, BEng(Hons), G.J. Levermore, BSc, ARCS, PhD, DIC, CEng, FCIBSE, MASHRAE, MInstE, K.M. Letherman, BSc, MSc, PhD, CEng, FIEE, FCIBSE. Abstracts This paper
Appendix D Example M&V Plans
Appendix D: Example M&V Plans Appendix D Example M&V Plans 1 California s 2000 LNSPC Program Procedures Manual Example M&V Plans Several example M&V Plans are included in this Appendix for further guidance.
Ventilation. Demand-controlled ventilation (DCV) Demand-Controlled. With ASHRAE Standard Based
CO Demand-Controlled 2 -Based Ventilation With ASHRAE Standard 62.1 Though not as straightforward as it once was, remains a practical way to reduce costs Editor s note: Following is an update of the November
The redesign of a more complex or non-geometrically similar designed product line does not allow resource sharing across sizes (models).
1. DOE generated formulae for manufacturer production cost (MPC) as a function of subgroup and diameter (which DOE believes can be used as a general proxy for airflow). DOE requests comments on whether
Dynamic Reset for Multiple-Zone Systems
This article was published in ASHRAE Journal, March 2010. Copyright 2010 American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. Posted at www.ashrae.org. This article may not be
Fundamentals of Test & Balance for Engineers, Cx & Energy Providers Course Number: CXENERGY1619
AABC Commissioning Group AIA Provider Number 50111116 Fundamentals of Test & Balance for Engineers, Cx & Energy Providers Course Number: CXENERGY1619 Jim Hall, PE, TBE, CxA Systems Management & Balancing,
Meeting the Variable Needs of Energy- Efficient Mechanical Ventilation When and Where You Need It
Meeting the Variable Needs of Energy- Efficient Mechanical Ventilation When and Where You Need It Charles Withers Jr. 1 Introduction Effectively providing mechanical ventilation in commercial buildings
Performance Characteristics and Mapping for a Variable-Speed Ductless Heat Pump
Purdue University Purdue e-pubs Publications of the Ray W. Herrick Laboratories School of Mechanical Engineering Performance Characteristics and Mapping for a Variable-Speed Ductless Heat Pump Howard Cheung
Airflow Measurement for HVAC Systems Comparing Technologies June 11, 2003
Airflow Measurement for HVAC Systems Comparing Technologies June 11, 2003 INTRODUCTION The proper selection of airflow measurement devices can be critical to the performance of today s state-of-the-art
Analysis of Different Methods for Computing Source Energy in the Context of Zero Energy Buildings
Analysis of Different Methods for Computing Source Energy in the Context of Zero Energy Buildings ABSTRACT Paul A. Torcellini, National Renewable Energy Laboratory Eric Bonnema, National Renewable Energy
ARE YOUR VARIABLE SPEED PUMPING APPLICATIONS DELIVERING THE PREDICTED SAVINGS? IMPROVING CONTROL TO MAXIMIZE RESULTS
ARE YOUR VARIABLE SPEED PUMPING APPLICATIONS DELIVERING THE PREDICTED SAVINGS? IMPROVING CONTROL TO MAXIMIZE RESULTS University of Dayton, Dayton, Ohio Vijay Gopalakrishnan, Lead Engineer Alexandra Brogan,
The impact of air filter pressure drop on the performance of typical air-conditioning systems
BUILD SIMUL (2012) 5: 345 350 DOI 10.1007/s12273-012-0091-6 The impact of air filter drop on the performance of typical air-conditioning systems Nabil Nassif ( ) Department of CAAE Engineering, North Carolina
Fan Industry: Facing Up to the Energy Challenges
Fan Industry: Facing Up to the Energy Challenges AIR MOVEMENT AND CONTROL ASSOCIATION INTERNATIONAL, INC. Dr. Michael Brendel Vice President, Engineering and Marketing Lau Industries / Ruskin Company Dayton,
Using Annual Building Energy Analysis for the Sizing of Cooling Tower for Optimal Chiller-Cooling Tower Energy Performance
Proceedings: ISHVAC 007 Using Annual Building Energy Analysis for the Sizing of Cooling Tower for Optimal Chiller-Cooling Tower Energy Performance Chia-Wei Liu, Yew-Khoy Chuah Institute of Mechanical and
Performance Mapping for Variable Ductless Heat Pump Systems in Heating, Cooling and Defrost Operation
Purdue University Purdue e-pubs International Refrigeration and Air Conditioning Conference School of Mechanical Engineering 2012 Performance Mapping for Variable Ductless Heat Pump Systems in Heating,
Energy Modeling Applications for Existing Buildings
Energy Modeling Applications for Existing Buildings Presented by: Clark Denson PE, CEM, BEMP, LEED AP BD+C 4/27/12 Learning Objectives 1. List available methods and tools for energy modeling 2. Explain
A NEW POLYNOMIAL BASED MODEL FOR DETERMINING COOLING TOWER EVAPORATION WATER LOSS. Aaron Powers Johnson Controls, Inc.
ASHRAE and IBPSA-USA SimBuild 2016 Building Performance Modeling Conference Salt Lake City, UT August 8-12, 2016 A NEW POLYNOMIAL BASED MODEL FOR DETERMINING COOLING TOWER EVAPORATION WATER LOSS Aaron
Fan Efficiency Grade Classification for Fans
Fan Efficiency Grade Classification for Fans Dr. Michael Brendel Lau Industries/Ruskin Company Dayton, OH USA ASHRAE Annual Meeting 2012 San Antonio, TX Learning Objectives 1. Describe Fan Efficiency Grade
VAV System Design Tips
AMCA International VAV System Design Tips Steve Taylor, PE, FASHRAE Principal, Taylor Engineering LLC, Alameda, CA staylor@taylor-engineering.com Air System Engineering & Technology (ASET) Conference US
Pump Performance Curves and Matching a Pump to a Piping System
Pump Performance Curves and Matching a Pump to a Piping System Fundamental Parameters Some fundamental parameters are used to analyze the performance of a pump Mass flow rate (Volumetric Flow rate in case
Pump Performance Curves and Matching a Pump to a Piping System
Pump Performance Curves and Matching a Pump to a Piping System Fundamental Parameters Some fundamental parameters are used to analyze the performance of a pump Mass flow rate (Volumetric Flow rate in case
2. The requirements of Sections 502, 503, 504 and 505. In addition, commercial buildings shall comply with either Section 506.2, or
Public Comment 3: Dave Hewitt, representing New Buildings Institute, Jessyca Henderson, representing American Institute of Architects, Ron Majette, representing U.S. Department of Energy requests Approval
CENTRAL STATION AIR-HANDLING UNIT SUPPLY FANS CERTIFICATION PROGRAM
faoo CENTRAL STATION AIR-HANDLING UNIT SUPPLY FANS CERTIFICATION PROGRAM AHRI AHU OM JANUARY 2018 2111 Wilson Blvd, Suite 500 Arlington, Virginia 22201 (703) 524-8800 Sponsored and administered by: PREFACE
Field Accuracy of Temperature Measurements in TAB Work Andrew P. Nolfo, PE
Field Accuracy of Temperature Measurements in TAB Work Andrew P. Nolfo, PE The purpose of this article is to discuss the measuring and reporting of temperatures at heat transfer equipment during the Testing
Advantages of Staged Implementation of Efficiency Upgrades
Advantages of Staged Implementation of Efficiency Upgrades Christopher O Brien and Drury B. Crawley, U.S. Environmental Protection Agency As an integral part of its new Energy Star Buildings Program, EPA
Off-Peak Building Control Considerations Utilizing CO 2 Based Demand-Controlled Ventilation (DCV) with Large Packaged Rooftop Units
Off-Peak Building Control Considerations Utilizing CO 2 Based Demand-Controlled Ventilation (DCV) with Large Packaged Rooftop Units By: Bill Timmons Mark Tozzi Carrier Corporation Syracuse, New York September,
LESSONS LEARNED IN MODELING UNDERFLOOR AIR DISTRIBUTION SYSTEM
LESSONS LEARNED IN MODELING UNDERFLOOR AIR DISTRIBUTION SYSTEM Kwang Ho Lee 1, 2, Stefano Schiavon 1, Tom Webster 1, Fred Bauman 1, Jingjuan Feng 1, Tyler Hoyt 1 1 Center for the Built Environment, University
APPENDIX B: Example Lab Preparation Guide and Manual. The University of Texas at Austin. Mechanical Engineering Department
APPENDIX B: Example Lab Preparation Guide and Manual ME 130L 17905 17990 The University of Texas at Austin Mechanical Engineering Department Spring 12 Dr. Hidrovo Lab #4 Preparation Guide - Dimensional
Doubling Down on Not Balancing Variable Flow Hydronic Systems BY STEVEN T. TAYLOR, P.E., FELLOW ASHRAE
This article was published in ASHRAE Journal, December 2017. Copyright 2017 ASHRAE. Posted at www.ashrae.org. This article may not be copied and/or distributed electronically or in paper form without permission
in UFAD Systems The control of room-air stratification is Design Guidelines for Stratification
Design Guidelines for Stratification in UFAD Systems Guidance for sizing interior and perimeter zones of underfloor-air-distribution systems Editor s note: This is the first of two articles based largely
Underf loor For Schools
The following article was published in ASHRAE Journal, May 2008. Copyright 2008 American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. It is presented for educational purposes
Energy Efficiency in Building Active Design Part II
Energy Efficiency in Building Active Design Part II Presented by: CK Tang BSEEP Component 4 Manager Veritas Enviornment Sdn Bhd ck.tang@veritas.com.my Air Conditioning System System Sizing 1 Current Industry
Project Information. Measure Description
Project Information Project ID#: CE07M016 Measure: Multiple Electric : 452,593 kwh, 8.84 kw, 39.26 kw Facility Type School Technology: Multiple Measure Description CH-1 - Chiller Plant Optimization and
Lawrence Berkeley National Laboratory Lawrence Berkeley National Laboratory
Lawrence Berkeley National Laboratory Lawrence Berkeley National Laboratory Title Real-Time Measurement of Rates of Outdoor Airflow into HVAC Systems: A Field Study of Three Technologies Permalink https://escholarship.org/uc/item/3rf221fw
A Twin City Fan Company COMPANYPROFILE
A Twin City Fan Company COMPANYPROFILE Who We Are Clarage is the heavy duty division of Twin City Fan Companies Ltd. that specializes in designing and manufacturing custom heavy duty fans. And with nearly
A Revolutionary Method of Saving Energy for Commercial and Industrial Fan Systems
A Revolutionary Method of Saving Energy for Commercial and Industrial Fan Systems Michael Ivanovich, AMCA International ABSTRACT Fan energy index (FEI) and fan electrical power (FEP) are design-point metrics
PROPOSAL OF JAPANESE STANDARD OF THE MEASURING METHOD OF AIRFLOW RATES OF BUILDING EQUIPMENTS
PROPOSAL OF JAPANESE STANDARD OF THE MEASURING METHOD OF AIRFLOW RATES OF BUILDING EQUIPMENTS Y. Utsumi 1, S. Hayakawa 2, T. Kurabuchi 3, and H. Yoshino 4 1 Department of Architecture, Miyagi National
OPTIMAL OPERATION OF VAV AIR-CONDITIONING SYSTEM
Nabil NASSIF *, Stanislaw KAJ *, Robert SABOURIN * Key words: VAV system, controller set point, optimization, genetic algorithm OPTIMAL OPERATION OF VAV AIR-CONDITIONING SYSTEM The detailed and simplified
Project Measurement and Verification Procedures
Project and Verification Procedures 1) Introduction The objective of measurement and verification (M&V) activities at the Project level is to confirm that the Measures that are supported by the Retrofit
Therma-Fuser Systems. Energy Modeling Guide for Therma-Fuser systems. Acutherm was spec d here
Therma-Fuser s Energy Modeling Guide for Therma-Fuser systems Acutherm was spec d here Proven Energy Savings Energy Savings Comfort Therma-Fuser VAV diffusers allow for all low-pressure system designs.
Supply Fan Energy Use in Pressurized Underfloor Air Distribution Systems
CBE Summary Report October 2002 Supply Fan Energy Use in Pressurized Underfloor Air Distribution Systems Tom Webster, P.E., Fred Bauman, P.E., and Erik Ring,. P.E. EXECUTIVE SUMMARY This preliminary study
Mechanical Technical Report Three Mechanical Systems Existing Conditions Evaluation
Mechanical Technical Report Three Mechanical Systems Existing Conditions Evaluation 2900 Fairview Park Drive, Prepared By: Charles Haack Mechancial Option November 21, 2008 Prepared For: Dr. William P.
Modular Active Chilled Beams
Modular Active Chilled Beams Presented by: Darren Alexander, P.Eng. Modular Active Chilled Beams Current System Solutions Fan Coil Units - Medium/High energy, medium noise solution. Output = 100-200 w/m
COOLING TECHNOLOGY INSTITUTE
PAPER NO: TP08-16 CATEGORY: DRY COOLING COOLING TECHNOLOGY INSTITUTE THE COST OF NOISE ROBERT GIAMMARUTI HUDSON PRODUCTS CORPORATION JESS SEAWELL COMPOSITE COOLING SOLUTIONS, LLC The studies and conclusions
Two New Metrics for Fan System Efficiency: Fan Energy Index and Fan Electrical Power
Two New Metrics for Fan System Efficiency: Fan Energy Index and Fan Electrical Power Michael Ivanovich*, Mark Stevens*, Michael Wolf** *Air Movement and Control Association (AMCA) International, ** Greenheck
Variable Frequency Drive Retrofit Opportunities. Michael Kuk CERx Solutions LLC
Variable Frequency Drive Retrofit Opportunities Michael Kuk CERx Solutions LLC Speaker Introduction» Founder / owner of CERx Solutions LLC» Veteran Owned Small Business Enterprise founded in 2013» 27+
Modeling and Simulation of Air Compressor Energy Use
Modeling and Simulation of Air Compressor Energy Use Chris Schmidt, Energy & Resource Solutions, Inc. Kelly Kissock, Department of Mechanical Engineering, University of Dayton ABSTRACT This paper describes
MEASUREMENT AND VERIFICATION GUIDELINES FOR RETROFIT AND NEW CONSTRUCTION PROJECTS
EL PASO ELECTRIC COMPANY S LARGE COMMERCIAL AND INDUSTRIAL STANDARD OFFER PROGRAM MEASUREMENT AND VERIFICATION GUIDELINES FOR RETROFIT AND NEW CONSTRUCTION PROJECTS This document includes detailed information
Leveraging Smart Meter Data & Expanding Services BY ELLEN FRANCONI, PH.D., BEMP, MEMBER ASHRAE; DAVID JUMP, PH.D., P.E.
ASHRAE www.ashrae.org. Used with permission from ASHRAE Journal. This article may not be copied nor distributed in either paper or digital form without ASHRAE s permission. For more information about ASHRAE,
Limitations of Using Uniform Heat Flux Assumptions in Sizing Vertical Borehole Heat Exchanger Fields
Limitations of Using Uniform Heat Flux Assumptions in Sizing Vertical Borehole Heat Exchanger Fields Veera Malayappan 1, Jeffrey D. Spitler 2 1,2 Mechanical and Aerospace Engineering, Oklahoma State University
DRAFT METHODOLOGY FOR CALCULATING COOLING AND HEATING ENERGY-INPUT-RATIO (EIR) FROM THE RATED SEASONAL PERFORMANCE EFFICIENCY (SEER OR HSPF)
ESL-TR-13-04-01 DRAFT METHODOLOGY FOR CALCULATING COOLING AND HEATING ENERGY-INPUT-RATIO (EIR) FROM THE RATED SEASONAL PERFORMANCE EFFICIENCY (SEER OR HSPF) A Project for Texas Senate Bill 5 Legislation
TWO DOE-2 FUNCTIONS. Tianzhen Hong, Charles Eley, and Erik Kolderup Architectural Energy Corporation 142 Minna Street, San Francisco, CA 94105, USA
Ninth International IBPSA Conference Montréal, Canada August 15-18, 2005 TWO DOE-2 FUNCTIONS Tianzhen Hong, Charles Eley, and Erik Kolderup Architectural Energy Corporation 142 Minna Street, San Francisco,
A NEW ALGORITHM FOR SENSORS VERIFICATION AND CORRECTION IN AIR HANDLING UNITS
A NEW ALGORITHM FOR SENSORS VERIFICATION AND CORRECTION IN AIR HANDLING UNITS Nunzio Cotrufo, Radu Zmeureanu Department of Building, Civil and Environmental Engineering Concordia University Montréal Quebec,
Evaluating Alternative Fume Hood System Technologies Using Advanced Building Simulation
Evaluating Alternative Fume Hood System Technologies Using Advanced Building Simulation William J. Kosik, P.E., C.E.M. 1 Karl Aveard, AIA Associate 2 Heather R. Beaudoin, P.E., C.E.M. 3 Erik L. Olsen 3
Rebel commercial packaged rooftop systems
Rebel commercial packaged rooftop systems Advanced technologies propel performance and energy savings to uncommon levels 3-28 tons Rebel features and benefits 18 13 6 5 3 6 4 43% Energy Savings 5 11 12
Rebel commercial packaged rooftop systems
Rebel commercial packaged rooftop systems Advanced technologies propel performance and energy savings to uncommon levels 3-28 tons Capitalize with 43% more energy savings If you are a commercial building
Accelerated Capital Allowances Eligibility Criteria. Category: Process and Heating, Ventilation and Air conditioning (HVAC) Control Systems
Accelerated Capital Allowances Eligibility Criteria Ref. No.: FAN C2.1 Category: Process and Heating, Ventilation and Air conditioning (HVAC) Control Systems Technology: Fans A fan is defined as a rotary
Demonstration of Load Shifting and Peak Load Reduction with Control of Building Thermal Mass
Demonstration of Load Shifting and Peak Load Reduction with Control of Building Thermal Mass J. E. Braun and T. M. Lawrence, Ray W. Herrick Laboratories, Purdue University C. J. Klaassen and John M. House,
Effects of shaft supporting structure on performance test of axial flow fan
IOP Conference Series: Materials Science and Engineering PAPER OPEN ACCESS Effects of shaft supporting structure on performance test of axial flow fan To cite this article: R Ma et al 2016 IOP Conf. Ser.:
Therma-Fuser Systems. Energy Modeling Guide for Therma-Fuser systems. Acutherm was spec d here
Therma-Fuser s Energy Modeling Guide for Therma-Fuser systems Acutherm was spec d here Proven Energy Savings Energy Savings Comfort Therma-Fuser VAV diffusers allow for all low-pressure system designs.
CASE STUDIES IN USING WHOLE BUILDING INTERVAL DATA TO DETERMINE ANNUALIZED ELECTRICAL SAVINGS
CAS STUDIS IN USING WHOL BUILDING INTRVAL DATA TO DTRMIN ANNUALIZD LCTRICAL SAVINGS Mark ffinger James Anthony Lia Webster ngineer ngineer Senior ngineer Portland nergy Conservation, Inc Portland, OR USA
7 th NATIONAL CERTIFICATION EXAMINATION Nov FOR ENERGY MANAGERS & ENERGY AUDITORS
Regn No: Name: (To be written by the candidates) 7 th NATIONAL CERTIFICATION EXAMINATION Nov. 2008 FOR ENERGY MANAGERS & ENERGY AUDITORS PAPER 3: Energy Efficiency in Electrical Utilities Date: 23.11.2008
AHU Models Using Whole Building Cooling and Heating Energy Consumption Data
Calibrating AHU Models Using Whole Building Cooling and Heating Energy Consumption Data Mingsheng Liu, Ph.D., P. E. ; Guanghua Wei; and D. E. Claridge, Ph.D., P. E. Energy Systems Laboratory Texas A&k?
EVALUATION OF BUILDING ENERGY CONSUMPTION BASED ON FUZZY LOGIC AND NEURAL NETWORKS APPLICATIONS
P P (1) EVALUATION OF BUILDING ENERGY CONSUMPTION BASED ON FUZZY LOGIC AND NEURAL NETWORKS APPLICATIONS Kajl, SP (1) P., Roberge, M-AP (1) P., Lamarche, LP (1) P., Malinowski, PP PEcole de technologie
ANSI/ASHRAE STANDARD , METHODS OF TESTING CHILLED BEAMS
ANSI/ASHRAE STANDARD 200-2015, METHODS OF TESTING CHILLED BEAMS NEMIC 2017 Agenda 1. Foreword 2. Purpose & Scope 3. Definitions 4. Instrumentation & Facilities 5. Test Methods 6. Reporting 7. Normative
A MODEL-BASED METHOD FOR THE INTEGRATION OF NATURAL VENTILATION IN INDOOR CLIMATE SYSTEMS OPERATION
Ninth International IBPSA Conference Montréal, Canada August 15-18, 2005 A MODEL-BASED METHOD FOR THE INTEGRATION OF NATURAL VENTILATION IN INDOOR CLIMATE SYSTEMS OPERATION Ardeshir Mahdavi and Claus Pröglhöf
Fundamentals of Acoustics
providing insights for today s hvac system designer Engineers Newsletter volume 47 4 Understanding VAV Sound Standards Acoustics are an important and often overlooked topic in HVAC&R. Many pieces of equipment
IMPLEMENTATION OF ANALYTICAL MODELS FOR PASSIVE DOWN-DRAFT EVAPORATIVE COOLING (PDEC) TOWER WITH SPRAY SYSTEMS
IMPLEMENTATION OF ANALYTICAL MODELS FOR PASSIVE DOWN-DRAFT EVAPORATIVE COOLING (PDEC) TOWER WITH SPRAY SYSTEMS Daeho Kang 1, Richard K. Strand 2 1 Department of Environmental Control Technology, New York
Commercial Building Performance Evaluation System for HVAC Diagnostics and Commissioning
Commercial Building Performance Evaluation System for HVAC Diagnostics and Commissioning Stuart S. Waterbury and Donald J. Frey, Architectural Energy Corporation Karl F. Johnson, Electric Power Research
NJ AIHA CIH Review Industrial Ventilation
NJ AIHA CIH Review Industrial Ventilation Thomas Block, CIH MPH tblock@aps.rutgers.edu 848-445-2550 2016 American Industrial Hygiene Association, New Jersey Section, Inc. Course Objectives At the end of
Energy Efficiency Impact Study
Energy Efficiency Impact Study for the Preferred Resources Pilot February, 2016 For further information, contact PreferredResources@sce.com 2 1. Executive Summary Southern California Edison (SCE) is interested
Using System Curves to Avoid Problems in VAV Systems
AMCA International Using System Curves to Avoid Problems in VAV Systems Ronald Wroblewski Owner, Productive Energy Solutions, LLC, Madison, Wisconsin, USA Ron@ProductiveEnergy.com Air System Engineering
Application of Near-Optimal Tower Control and Free Cooling on the Condenser Water Side for Optimization of Central Cooling Systems
Purdue University Purdue e-pubs International High Performance Buildings Conference School of Mechanical Engineering 2014 Application of Near-Optimal Tower Control and Free Cooling on the Condenser Water
Development of New Self-Comparison Test Suites for EnergyPlus
Development of New Self-Comparison Test Suites for EnergyPlus Michael J. Witte Robert H. Henninger GARD Analytics, Inc., Arlington Hts., IL Drury B. Crawley U.S. Department of Energy, Washington, DC IBPSA-USA
Chapter 3 Passive Protective Schemes for Rail- Counterweight System
Chapter 3 Passive Protective Schemes for Rail- Counterweight System The analyses in the previous chapter have shown that the rail-counterweight systems are vulnerable to overstressing of the guide rails
E MIT (Energy Model Input Translator) User s Guide
E MIT (Energy Model Input Translator) User s Guide 1820 Folsom Street Boulder, CO 80302 303.245.1003 www.rmi.org May 2011 1 ACKNOWLEDGEMENTS The Energy Model Input Translator was developed with funding
B. ASHRAE 1991 HVAC Applications Handbook: Chapter 34--Testing, Adjusting and Balancing.
PART 1 GENERAL 1.1 RELATED DOCUMENTS SECTION 230593 TESTING, ADJUSTING, AND BALANCING A. Drawings and general provisions of Contract, including General and Supplementary Conditions and Division 1 Specification
Transient and Succession-of-Steady-States Pipeline Flow Models
Transient and Succession-of-Steady-States Pipeline Flow Models Jerry L. Modisette, PhD, Consultant Jason P. Modisette, PhD, Energy Solutions International This paper is copyrighted to the Pipeline Simulation
Investigation on A Ground Source Heat Pump System Integrated With Renewable Sources
Purdue University Purdue e-pubs International High Performance Buildings Conference School of Mechanical Engineering 2016 Investigation on A Ground Source Heat Pump System Integrated With Renewable Sources
Verification of Chiller Performance Promotion and Energy Saving
Engineering, 2013, 5, 141-145 http://dx.doi.org/10.4236/eng.2013.51a020 Published Online January 2013 (http://www.scirp.org/journal/eng) Verification of Chiller Performance Promotion and Energy Saving
EER & SEER AS PREDICTORS OF SEASONAL COOLING PERFORMANCE
EER & SEER AS PREDICTORS OF SEASONAL COOLING PERFORMANCE Developed by: Southern California Edison Design & Engineering Services 6042 N. Irwindale Avenue, Suite B Irwindale, California 91702 December 15,
Analysis of the Energy Savings Potential in K-5 Schools in Hot and Humid Climates. Jeff S. Haberl, Ph.D., P.E.
Analysis of the Energy Savings Potential in K-5 Schools in Hot and Humid Climates Piljae Im Graduate Student Jeff S. Haberl, Ph.D., P.E. Professor Energy Systems Laboratory Texas Engineering Experiment
Section 2: Estimating Energy Savings
Section 2: Estimating Energy Savings 2.1 Estimating Methods... 2-2 2.2 Estimation Software... 2-2 2.2.1 Comprehensive Lighting... 2-3 2.2.2 Lighting Controls... 2-3 2.2.3 High-Efficiency Packaged A/C or
ESTIMATING ENERGY SAVINGS IN COMPRESSED AIR SYSTEMS
ESTIMATING ENERGY SAVINGS IN COMPRESSED AIR SYSTEMS Chris Schmidt Project Engineer Energy and Resource Solutions Haverhill, Massachusetts Kelly Kissock, Ph.D., P.E. Associate Professor / Director Industrial
6. PUMPS AND PUMPING SYSTEM
6. PUMPS AND PUMPING SYSTEM Syllabus Pumps and Pumping System: Types, Performance evaluation, Efficient system operation, Flow control strategies and energy conservation opportunities 6.1 Pump Types Pumps
A Primer on Recovery Efficiency Ratio
A Primer on Recovery Efficiency Ratio By Tom Brooke PE, CEM 2/29/12 INTRODUCTION As well it should be, probably the most used metric in the applied HVAC equipment industry is the concept of Efficiency.
Improvement of Building Energy Simulation Accuracy with Occupancy Schedules Derived from Hourly Building Electricity Consumption
CH-15-030 Improvement of Building Energy Simulation Accuracy with Occupancy Schedules Derived from Hourly Building Electricity Consumption Yang-Seon Kim Student Member ASHRAE Jelena Srebric, PhD Member
Core Technologies for Developing Wind Power Generation Systems
Hitachi Review Vol. 63 (2014), No. 7 422 Featured Articles Core Technologies for Developing Wind Power Generation Systems Shigehisa Funabashi Kohei Tanaka, Dr. Eng. Mamoru Kimura, Dr. Eng. Masatoshi Watanabe,
Improving Energy Retrofit Decisions by Including Uncertainty in the Energy Modelling Process
Improving Energy Retrofit Decisions by Including Uncertainty in the Energy Modelling Process Alireza Bozorgi 1, James R. Jones 2 1 ICF International, Atlanta, GA 2 Virginia Tech, Blacksburg, VA ABSTRACT:
Content. 0 Questionnaire 87 from Max Frisch
Content 0 Questionnaire 87 from Max Frisch 1 Introduction to Wind Energy... 1 1.1 Wind Energy in the year 2010... 1 1.2 The Demand for Electricity... 4 1.3 Energy Policy and Governmental Instruments...
TO Stanley A. Mumma, Ph.D., P.E.
TO-98-21-3 Extension of the Multiple Spaces Concept of ASHRAE Standard 62 to Include Infiltration, Exhaust/Exfiltration, Interzonal Transfer, and Additional Short-Circuit Paths Stanley A. Mumma, Ph.D.,
Measurement and Analysis of Vitiation of Secondary Air in Air Distribution Systems (RP-1276)
University of Nebraska - Lincoln From the SelectedWorks of David Yuill Spring May, 2008 Measurement and Analysis of Vitiation of Secondary Air in Air Distribution Systems (RP-1276) David P Yuill, University
Computational Fluid Dynamic Analysis in De-staging of Centrifugal Pumps
Computational Fluid Dynamic Analysis in De-staging of Centrifugal Pumps Vishnu R Nair 1, Shinas K V 2, Souganth Sugathan Manjhiparambil 3 Student, Department of Mechanical Engineering, IES College of Engineering,
Mechanical Depth. Redesign and Analysis
Depth Redesign and Analysis 14 Analysis Scope The scope of my mechanical redesign will include the 2 nd and 3 rd floors of the SW hospital wing which includes Semi-Private, Private and Isolations rooms.
Calculating the Uncertainty of Building Simulation Estimates
Calculating the Uncertainty of Building Simulation Estimates Franklin D. Stern, Robert E. Ciliano, and William Kallock, RCG/Hagler, Bailly, Inc. Providing confidence intervals on savings estimates is becoming
Motor Systems Optimization Case Studies in the Peoples Republic of China
Motor Systems Optimization Case Studies in the Peoples Republic of China Vestal Tutterow, Lawrence Berkeley National Laboratory Qin Hongbo, Shanghai Energy Conservation Service Center Hugh Falkner, Future
Energy Performance Platform: Revealing and Maintaining Efficiency With a Customized Energy Information System
Energy Performance Platform: Revealing and Maintaining Efficiency With a Customized Energy Information System Andrea Mercado, Lawrence Berkeley National Laboratory, John Elliott, University of California,