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UAB School of Engineering – Mechanical Engineering – Early Career Technical Journal, Volume 16 Page 27

SECTION 2

Energy

UAB School of Engineering – Mechanical Engineering – Early Career Technical Journal, Volume 16 8

UAB School of Engineering - Mechanical Engineering - Early Career Technical Journal, Volume 16 Page 29

Journal of UAB ECTC Volume 16, 2017

Department of Mechanical Engineering The University of Alabama, Birmingham

Birmingham, Alabama USA

REPURPOSING WASTE HEAT ENERGY FOR HIGH TEMPERATURE APPLICATIONS

Jamey Ackley, Jonathan Atkinson, Erwin Garcia, Laura Ruhala, Sathish Gurupatham Kennesaw State University

Marietta, Georgia, United States

ABSTRACT Commercial heating, ventilation and air conditioning

(HVAC) systems release large amounts of waste thermal energy into ambient outdoor spaces. This loss of energy can be repurposed and used in other applications. A Desuperheating Water Heater (DWH) heat pump system was designed to capture thermal waste heat from a restaurant’s existing HVAC system, and repurpose this energy to heat the water used in a commercial dishwasher. Design calculations and analysis performed indicate that the system will successfully heat the water to the minimum Food and Drug Administration (FDA) required 180°F. It will also reduce a restaurant’s overall power consumption, alleviate the need for chemical sterilizing agents, and will improve the efficiency of their existing HVAC system.

Restaurants within the metro-Atlanta area were surveyed regarding the types of commercial dishwashers they use and their HVAC system loads. The results of this survey provided a baseline regarding their dishwashing machine and average volumetric water flow rate requirements. Using the survey results and the known water temperature requirements, a refrigerant was selected, and the required mass flow rate of the refrigerant was analyzed. A compressor and electronic expansion valve (EEV) were chosen based on working fluid density and mass flow rate; theoretical heat transfer and CFD analyses were used to select heat exchangers for the system.

KEY WORDS: desuperheater, waste heat, hydrocarbon refrigerant

BACKGROUND With the ever-increasing costs of energy, as well as

continuing environmental restrictions on power generation and power use, it has become more evident than ever that the efficient use of all forms of energy is not just a desire but a necessity [1,2]. Commercial air conditioners and water heaters are two essential systems that require vast amounts of energy to operate; conditioning the air in large spaces, and heating water supply lines.

There has been much growth in the energy sector pushing toward more efficient, integrated systems. New laws have been

instituted requiring innovative ideas that demand less energy consumption, reduce greenhouse gas emissions, and use renewable energy sources [3]. Some existing technologies have been developed to capture waste heat from HVAC systems and repurpose it for heating water, but they are unable to efficiently output the high temperatures required for commercial dishwasher sterilization [3, 4, 5, 6].

The purpose of the newly designed Desuperheating Water Heater (DWH) system is to reduce the total energy consumption of these two systems by means of integration [7].

The DWH is a device that uses a heat pump cycle to raise the temperature of water. Heat is extracted from an existing HVAC system while in cooling mode, and captured with a refrigerant. The refrigerant is then compressed to a higher pressure, resulting in higher temperature. Heat energy is released through a heat exchanger into the water supply, and the process repeats. The type of working fluid that is chosen is of great importance. Its thermodynamic properties, including critical pressure, critical temperature, specific heat ratio, and necessary compression ratios, affect the efficiency and working envelope of the system. Applications of the DWH are extensive, including high temperature commercial dishwashing machines and boilers. They are constrained only by the amount of source energy available to the system.

Standard heat pump cycles draw heat energy from a source, and expel it into a sink. Most often, this is in the form of an air conditioner releasing heat into an outdoor ambient space. The DWH has a refrigerant-to-refrigerant heat exchanger (R2RHE) that intercepts the HVAC refrigerant before the HVAC condenser, and captures the expelled heat energy. Once the energy is recaptured by the DWH, it is converted to a higher temperature energy source via a second stage circuit, and then transferred to the water. This lessens the cost of heating water for the end user, especially end users who have both large air conditioning loads and hot water consumptions.

The report herein outlines the design of a DWH that uses expelled heat energy from a commercial HVAC system to raise the temperature of water to 180°F, allowing that water to be used to sterilize dishes in a commercial dishwashing machine. The water can also be used in similar high temperature applications. The objective and intention is that by using the


expelled heat energy from the existing HVAC system, water heating costs can be reduced, and the use of chemical sterilizing agents can be minimized or eliminated. In the process, additional refrigeration is applied to the existing HVAC system, improving its efficiency.

METHODS A survey was conducted in the Metro-Atlanta area to help

define the types of commercial dishwashing machines that were most commonly used in restaurants, how many hours they ran each day, and how much HVAC waste energy was available. The survey results showed a commonly used system that when paired with manufacturer specification, provided a volumetric flow rate requirement for hot water and an average number of working hours each year. These results are the baseline of the energy need calculations, as well as the long-term return on investment (ROI).

The volumetric flow rate of water was converted to a mass flow rate and used to calculate the total heat energy transfer needed to heat water from a nominal value of 68°F to 180°F, the value required by FDA guidelines [8]. See Equation 1 below.

�̇�𝑄 = 𝑐𝑐𝑝𝑝�̇�𝑚∆𝑇𝑇 (1)

With the heat energy requirement and temperature range known, a search was conducted for the appropriate refrigerant. Primary considerations were:

1. Working Envelope and System Efficiency 2. Cost and Availability 3. Environmental Impact

The working envelope of many refrigerants was evaluated using property tables and P-H diagrams provided by NIST’s REFPROP software. It was found that the most readily available and cost effective options that worked optimally in the temperature range established were R-134a, R-416a, and R-600a. Due to Montreal Protocol Phase-Out schedules, R-416a was dismissed from consideration [9]. While R-134a is commonly used in cascade-type systems in conjunction with R-410a, the critical pressure and critical temperature are inefficiently close to the required high pressure side temperature of the DWH. This creates higher compression ratios, higher working pressures, and less efficiency. Meanwhile, R-600a had adequate critical values, low working pressures, low cost, excellent availability, and a hydrocarbon footprint with very little environmental impact. This provided the best solution for the DWH.

Using the previously calculated heat energy requirement and the properties of R-600a at the anticipated high-side temperature, a working fluid mass flow rate and corresponding volumetric flow rate were found using a steady flow system energy transfer equation. See Equation 2 below.

�̇�𝑄 = �̇�𝑚∆ℎ𝑓𝑓𝑓𝑓 (2)

R-600a has lower density than most commercially available chemical refrigerants. For that reason, a compressor with a high volumetric displacement is required. Additionally, R-600a, otherwise known as isobutane, is a combustible refrigerant and requires the use of a hermetic compressor. It was desired that the compressor be variable speed, allowing for higher efficiency during load fluctuation and cycling. These criteria were used to select the compressor for the DWH system. The electronic expansion valve (EEV) was selected based on the volumetric flow rate of the working fluid and the working pressure differential. Both the compressor and EEV include controls and sensors that allow the system to self-regulate based upon fluctuating loads.

Heat exchangers were selected based upon iterative theoretical analyses and computational fluid dynamic (CFD) simulations. Working fluid properties were calculated and tracked throughout the system to provide the necessary temperatures at the inlet and outlet of each heat exchanger during the iterative progression. The R2WHE was analyzed using a slightly superheated working fluid entry temperature of 190°F. At the end of each simulation, the outlet water temperature was found and compared to the required 180° [8]. Selection was adjusted until the desired output temperature was ultimately attained. The R2RHE was selected following a similar technique. Additional theoretical analyses and CFD simulations were required to accommodate the two phase changes that occur simultaneously in the R2RHE. Instead of using output temperature as the requirement, the total heat energy transfer was used and tracked as a change in enthalpy.

SYSTEM COMPONENTS The system components of the DWH are comprised

primarily of off-shelf items from various manufacturers. The four primary components are the variable speed compressor, the electronic expansion valve, a R2WHE and a R2RHE, as shown in Figure 1 below.

Figure 1 – DWH System Diagram The compressor selected and analyzed was an inverter

driven, hermetic reciprocating compressor with a maximum displacement of approximately 600 ft3/hr, a maximum test


pressure of 435 psi, and a maximum discharge temperature of 275°F. The digital inverter drive controls the frequency of the compressor, which ranges from 35 Hz to 90 Hz depending on the volumetric flow rate of the water. So while this system was analyzed under steady state operating conditions, it is a dynamic system capable of changing output and power consumption. The sensor providing feedback for the closed loop control can be either a temperature sensor or thermocouple mounted to the hot water output of the R2RHE.

The EEV is used to control the mass flow rate of the system and like the compressor, is capable of responding to dynamic load conditions. It receives feedback from a pressure transducer and thermocouple between the R2RHE and the compressor. The EEV selected has a maximum pressure differential of 500 psi, and a maximum operating temperature of 221°F. It was paired with a 24V driver that is programmable, based upon the properties of the refrigerant selected.

Both heat exchangers analyzed are brazed plate type stainless steel and provide a compact design, high heat transfer, and design pressure of 435 psi. Twenty internal plates transfer heat energy through each exchanger. They are of like manufacture and like size.

RESULTS Using the volumetric flow rate of the baseline dishwashing

machine, the specific heat of water, and the required change in temperature, the total heat energy transfer required to raise the water temperature above 180°F was found using Equation 1.

�̇�𝑚 = 10.743 × 10−2 𝑙𝑙𝑙𝑙𝑚𝑚 𝑠𝑠�

𝑐𝑐𝑝𝑝 = 1.00 𝐵𝐵𝑇𝑇𝐵𝐵 𝑙𝑙𝑙𝑙𝑚𝑚 °𝑅𝑅�

𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 68°𝐹𝐹

𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑜𝑜𝑜𝑜𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 184°𝐹𝐹

�̇�𝑄 = 44,861𝐵𝐵𝑇𝑇𝐵𝐵 ℎ𝑊𝑊�

With the total energy need established and the working fluid selected, the mass flow rate of R-600a under steady state operation was calculated using Equation 2.

Q̇= 44,476 BTUhr�

hfg = 102.32 BTUlbm� @190℉

�̇�𝑚= 438.4 lbmhr�

Primary heat transfer analyses were performed at two

points in the system, the R2WHE and the R2RHE. However, working fluid properties were calculated at the exit of each primary component. The system flow was analyzed under steady state operating conditions. Therefore, the starting point of the analysis matches the ending point of the analysis.

Since the mass flow rate is determined by the EEV, working fluid properties were established at this starting point. With negligible exception, enthalpy does not change across a throttling device such as an EEV. See Table 1.

Table 1 – Working Fluid Properties, EEV

EEV Inlet EEV Outlet

Mass Flow Rate (lbm/s) 0.121 0.121 Pressure (psia) 232.1 79.09

Saturation Temperature (°F) 191.0 105.0 Superheat (°F) 0.000 0.000

Total Temperature (°F) 191.0 105.0 Quality 6.084% 47.02%

Enthalpy (BTU/lbm) 191.0 191.0 Plumbing runs inside the DWH will be short in length,

therefore the properties were assumed to stay constant between the components. Note that the quality of the working fluid entering the heat exchanger is approximately 47%.

Since the working fluid is in two distinct states at various points in the R2RHE, the analysis was performed in two parts. Part 1 analyzed the heat transfer to the saturated liquid/gas mixture of the R-600a. It was also used to define the distance traveled through the heat exchanger prior to a quality of 100% being attained. Part 2 analyzed heat transfer to gas phase R-600a and provided the exit temperature of the working fluid. See Figure 2 below.

Figure 2 – Refrigerant to Refrigerant Analyses

While the total heat load of the commercial dishwasher being considered is equivalent to less than 4 tons of commercial HVAC load, the DWH is designed to work in conjunction with a 5+ ton commercial HVAC system. The oversize of the HVAC load ensures that the R-410a traveling through the existing system will not reach saturated liquid state prior to leaving the R2RHE. With this defined, a homogeneous temperature boundary condition was established at all surfaces inside the heat exchanger that are in contact with the R-410a.

While the R-600a of the DWH is undergoing phase change in Part 1, the boundaries with which it is in contact are held at a homogeneous temperature as well. This allows for use of a standard conduction equation, measuring the heat transfer through the plates instead of heat transfer between refrigerants; until R-600a reaches 100% gas state. See Equation 3.


�̇�𝑄 = −𝑘𝑘𝑘𝑘(𝑇𝑇2−𝑇𝑇1)𝐿𝐿

(3)

Using the resulting area from Equation 3, both the distance traveled through the heat exchanger and the heat transfer across the plates during Part 1 were determined. This provided the starting point for Part 2, the CFD simulation. Multiple point goals and CFD simulation using SolidWorks Flow Simulation provided the heat transfer obtained in Part 2 of the R2RHE analysis

Total heat transfer for both parts across the heat exchanger was approximately 33,575 BTU/hr. See Table 2 below for fluid properties.

Table 2 – Working Fluid Properties, R2RHE R2RHE

Inlet R2RHE outlet

Mass Flow Rate (lbm/s) 0.121 0.121 Pressure (psi) 79.09 79.09



Enthalpy (BTU/lbm) 191.0 267.9

Calculations across the compressor used a target saturation temperature of approximately 191°F and a corresponding saturation pressure of 232 psia. The exit pressure (𝑃𝑃𝑖𝑖) combined with the inlet pressure (𝑃𝑃𝑖𝑖) of the refrigerant, the mass flow rate (�̇�𝑚), the inlet density (𝜌𝜌𝑖𝑖), the specific heat ratio (𝑘𝑘), and a reasonable nominal compressor efficiency of 95% were used to determine the amount of work done by the compressor. See Equation 4 below.

�̇�𝑊 = �̇�𝑚𝑃𝑃𝑖𝑖𝑘𝑘(𝑘𝑘−1)𝜌𝜌𝑖𝑖𝜂𝜂𝑐𝑐

��𝑃𝑃𝑒𝑒𝑃𝑃𝑖𝑖�1−1𝑘𝑘 − 1� (4)

�̇�𝑊 = 11,286 𝐵𝐵𝑇𝑇𝐵𝐵 ℎ𝑊𝑊�

This work done by the compressor enters the working fluid in the form of enthalpy. The sum of the existing enthalpy (ℎ𝑖𝑖) and the enthalpy entering (ℎ𝑐𝑐) the system through the compressor provides the new total working fluid enthalpy (ℎ𝑖𝑖), as described by Equation 5.

ℎ𝑖𝑖 = ℎ𝑖𝑖 + ℎ𝑐𝑐 (5)

Using the newly defined enthalpy combined with the R-600a property tables, the remaining properties of the working fluid at the compressor outlet were defined and showed a quality of 100%, corresponding to an enthalpy greater than the vapor phase enthalpy. Using the difference between current enthalpy and vapor phase enthalpy (∆ℎ) as well as the specific heat (𝐶𝐶𝑝𝑝) and the saturation temperature (𝑇𝑇𝑠𝑠𝑠𝑠𝑖𝑖) of R-600a at 232 psia in conjunction with Equation 6, a new superheated temperature was found. See Table 3 for fluid properties across the compressor.

𝑇𝑇 = ∆ℎ𝐶𝐶𝑝𝑝

+ 𝑇𝑇𝑠𝑠𝑠𝑠𝑖𝑖 (6)

After leaving the compressor, the working fluid enters the R2WHE. Though the temperature of the working fluid is slightly superheated, the difference in heat flux between superheated vapor at 203°F and saturated vapor at 191°F is negligible [10]. Additionally, the heat flux reduces the superheated vapor to saturation temperature almost instantaneously as it enters the heat exchanger.

A fixed boundary condition representing the homogeneous temperature (191°F) of the working fluid undergoing phase change was used for CFD simulation of the R2WHE. By establishing this constant temperature boundary condition on each internal surface with which the refrigerant is in contact, and then applying the designated flow conditions of the water traveling through the opposing channels, as per the synthesized results, a computational fluid dynamic simulation was performed to obtain the output temperature of the water. See Figure 3 below.

Table 3 – Working Fluid Properties, Compressor

Compressor

Inlet Compressor

Outlet Mass Flow Rate (lbm/s) 0.121 0.121

Pressure (psia) 79.09 232.1 Saturation Temperature (°F) 105.0 191.0

Superheat (°F) 13.28 12.21 Total Temperature (°F) 118.3 203.2

Quality (x) 100.0% 100.0% Enthalpy (BTU/lbm) 268.3 294.1

Figure 3 – Refrigerant to Water Analysis

The output temperature was found to be approximately 184°F, 4°F higher than the required 180° minimum as established by the FDA Food Code 2013 [8].

Working fluid properties were calculated using a series of equations and property tables, in a manner similar to the compressor calculations. Energy transfer was found using Equation 1. The corresponding enthalpy change in the working fluid is equivalent to the enthalpy change of the water, 103.2 BTU/lbm. It was found using the energy transferred combined with the mass flow rate of the water. The exit quality of the


working fluid was defined by a modified version of the mass fraction of the vapor phase, as shown in Equation 7 below. See Table 4 for working fluid properties across the R2WHE.

𝑥𝑥 = ℎ−ℎ𝑓𝑓ℎ𝑓𝑓𝑓𝑓

(7)

INTERPRETATION The DWH is designed to work in conjunction with a

commercial dishwashing machine and existing HVAC system to heat water to the temperatures required by the FDA for commercial dish washing without the need for chemical sterilizing agents. Design simulation and analysis indicate that energy requirements are reduced greatly when compared to those needed by electric booster heaters due to the high coefficient of performance and dynamic loading capability of the DWH.

Table 4 – Working Fluid Properties, R2WHE

R2WHE

Inlet R2WHE Outlet

Mass Flow Rate (lbm/s) 0.121 0.121 Pressure (psia) 232.1 232.1



Enthalpy (BTU/lbm) 294.1 191.0

According to the analyzed results, a capital investment of approximately $3,000 and an existing energy cost of $0.108 per kW/hr shows a return on investment in nine months for the average restaurant in central Georgia using an electric booster heater. Restaurants using natural gas powered booster heaters may experience a return on investment in as little as twenty-six months.

ACKNOWLEDGMENTS Gratitude to Greg Day for his initial concept idea and

inspiration. Thanks to Eric Lemmon from NIST, for guidance on the REFPROP software. And finally, thanks to Margaret Sheppard, Team Leader at the EPA SNAP office, for help with the Significant New Alternatives Policy guidelines regarding hydrocarbon refrigerants.

REFERENCES [1] U.S. Department of Energy, “U.S. Energy Information Administration,” 2017, [Online] http://www.eia.gov. [2] United States Environmental Protection Agency, “Safer Chemical Ingredients List,” 2016, [Online], https://www.epa.gov/saferchoice/safer-ingredients.

[3] Amato, F., 2012, “High Temperature Multifunctional Heat Pump System for Better overall Energy Efficiency”, REHVA J., October, pp. 23-27. [4] Noro, S., Sakakibara, H., Kuroki, J., Kobayakawa, T., Kusakari, K., Saikawa, M., 2002, “Heat-pump water heater,” USPTO Patent Grant, US6430949-B2. [5] Robinson, G., Blackshaw, A., 1986, “Integrated Heat Pump Water Heater,” USPTO Patent Grant, US4598557-A. [6] Choi, Hwanjong, Park, Norma and Park, Heewong, 2015, “Heat Pump Type Hot Water Supply Apparatus,” USPTO Patent Grant, US9003818-B2 [7] Ackley, J., Atkinson, J., Garcia, E., “Repurposing Waste Heat Energy for Commercial Dishwashing Machines,” 2017, Undergraduate Thesis, Department of Mechanical Engineering, Kennesaw State University. [8] US Department of Health and Human Services, “Food Code U.S. Public Health Service,” 2013. pp. 133-136. [9] US Environmental Protection Agency, “Phase-out of Class II Ozone-Depleting Substances,” 2016. [10] Longo, G., 2008, “Heat Transfer and Pressure Drop During HC-600a (Isobutane) Condensation Inside a Brazed Plate Heat Exchanger”, International Refrigeration and Air Conditioning Conference, Paper 909.





COMPARISON OF HIGH BYPASS TURBOFAN ENGINE CYCLE ANALYSES – A CASE STUDY

Christopher Roper Kennesaw State University Marietta, GA, United States

Skyler Bagley Kennesaw State University Marietta, GA, United States

Dr. Adeel Khalid

Kennesaw State University Marietta, GA, United States

Alain Santos Kennesaw State University Marietta, GA, United States

ABSTRACT The study includes comparison of low bypass ratio turbo

fan engine analytical performance and experimental engine test bench results. Variations in variables such as altitude, throttle setting, and freestream velocity are explored and their effect on the engine performance at various stages is analyzed to determine thrust force, thermal efficiency, propulsion efficiency and total efficiency. Student feedback is collected and the efficacy of this student involved research is discussed.

KEY WORDS: Gas Turbine Engine, Parametric Cycle Analysis, Performance Curves

BACKGROUND AND INTRODUCTION This research involves conducting a comparison of a high-

bypass ratio turbo fan engine simulated on a test bench and parametric cycle analysis of the same engine. The results from these methods of data collection are used to analyze correlation between the two methods of performing turbine engine cycle analysis. Test bench data is acquired using the Price Induction® engine test bench and is verified with the analytical parametric cycle analysis to calculate the thrust and efficiency. The purpose of this research is to gather data on gas turbofan engines from the Price Induction test bench (simulation) and compute the corresponding parameters of the turbofan engine analytically. The test bench uses varying environmental conditions that can be used to collect engine performance data from the simulator. Some of the data collected from the simulator are thrust, efficiency, and pressures/temperatures at specific components in the turbofan engine. Computer Aided Design (CAD) models are generated using SolidWorks. As part of this on-going research, the team performed CFD analyses at the component level of the turbofan engine. The analytical aspect of this research includes solving a combination of equations that compute specific parameters for different parts of

the engine at different flight conditions with the ultimate goal of computing the overall thrust and efficiency. The gas turbofan engine that is used in the Price Induction Test Bench is the DGEN 380 engine. This engine is designed for small private aircraft. Three undergraduate students performed this research in their third and fourth years. This is one of the first time research experiences for these students taking part in this yearlong study. The intent is to introduce them to the idea of conducting research, have them complete a short and meaningful study and get them excited about conducting research in the long run with the goal of having them pursue graduate school.

The DGEN 380 is a small, lightweight, turbofan engine designed for applications in emerging personal light jet flight. Price Induction is developing this engine to compete in traditional turboshaft propulsion altitude regimes by maintaining low fuel consumption and noise level [2]. The turbofan engine has five main components that work together. The five components that are analyzed include the fan, compressor, combustion chamber, turbine, and nozzle [6].

As shown in Figure 1, the freestream air intake enters the inlet and makes direct contact with the fan. The fan helps divert the majority of the air through the bypass system and the remaining through the core of the engine. Air is then compressed through a series of compressor blades in which the area decreases to increase pressure ratio. With increased pressure, the air enters the burner (a.k.a. combustion chamber), and is mixed with jet fuel [3]. The chemical reaction at high pressure results in the generation of high energy. The high-energy fluid then turns the high and low-pressure turbines to provide power back to the compressor thus completing the propulsion engine cycle. After the extraction of energy, the remainder of fluid is directed to the nozzle to mix with the bypass stream to generate thrust force. The force body diagram is shown in Figure 1. High By-pass engine dynamics depict the


balance of forces that engine designers take into consideration in order to maximize propulsive performance.

Figure 1. High By-pass engine dynamics [8]

DESIGN SPECFICATIONS AND ALTERNATIVES Figure 2 displays the concept design of an ideal Price

Induction 4-5 passenger light sport aircraft [1]. The aircraft is designed to be optimum for Mach number of 0.35 with a service ceiling at 25,000ft [8].

Figure 2. GP Aerospace -210 concept design The DGEN 380 is one of the world’s smallest and quietest engines [1]. It produces minimal sound at 255daN output force. The jet has a rated maximum takeoff weight of 1650 to 2150kg depending on flight conditions [8]. The engine has an overall height and width of 1346mm and 511mm respectively shown with all other dimensions in Figure 3. The Price Induction Test Bench simulates the DGEN 380 engine at various flight conditions. Input parameters as well as validation of the DGEN 380 environmental conditions such as outside air temperature, wind speeds, air density etc. are all compared from the National Centers for Environmental Information [9].

Figure 3. (a) Side profile and dimensions of Price

Induction DGEN 380 engine; (b) Front Profile Figures 4 through 6 show the computer aided models

created in SolidWorks. Each major and driving component is modeled in order to recreate accurate dimensions of the DGEN 380. Figure 4 shows the Fan model and half-sectional nacelle.

(a)

(b)

Figure 4. (a) DGEN 380 prototype fan and (b) bi-sectional nacelle in Solidworks

(a)

(b)

Figure 5. (a) DGEN 380 assembled engine components and (b) components with nacelle cover

mm 1126

1346 mm

469mm

256 mm

314

mm

511 mm

352

(a)

(b)


Figure 5 represents the assembled fan, centrifugal compressor, low-pressure turbine, and high-pressure turbine components on the top while the bottom shows the assembled components with the nacelle. Figure 6 shows isometric views of the assembled designed engine of the DGEN 380 in a hidden component view

(a)

(b)

Figure 6. (a) DGEN 380 assembled engine components and (b) components with nacelle cover

METHODOLOGY The theoretical analysis is performed using the propulsion

equations to calculate the desired variables as discussed in the Theoretical Analysis section. The DGEN 380 Engine test bench, shown in Figure 7, is used to simulate the engine operation and generate the desired variables. Then experimental and analytical results are plotted. These plots show characteristic relationships of all variables studied. The experimental DGEN 380 engine data is collected at multiple stations shown in Figure 8.

Figure 1. Price Induction experimental test bench [4]

Figure 2. High bypass turbofan engine station schematic dynamics

SIMULATION BASED ANALYSIS One variable is changed at a time while keeping all the

other flight conditions constant in order to determine the optimal operating flight condition for maximum thrust and efficiency. The variables that are explored with Thrust Force output are Power Level Angle (PLA) (otherwise known as throttle setting), Speed, Temperature, and Altitude. Thrust Force and efficiency are computed for each input variable while the other input variables are held constant at their corresponding median values. The median values for input variables are as follows: Speed at Mach 0.22, Temperature: -22.75 (°C), Altitude: 3500 m, and PLA: 50%.

The control inputs can be varied, the DGEN 380 engine test bench calculates the engine performance for the corresponding control inputs / flight conditions. One variable is changed at a time. Engine performance data are collected by changing one variable at a time. These data are recorded to generate performance curves. Thrust and Efficiency versus Altitude are calculated while temperature and PLA remain at their median values. Figures 9 and 10 depict the Thrust and Efficiency versus Altitude at various PLA settings. Figure 9 displays that as altitude increases, thrust will decrease. Figure 10 shows that as altitude increases, efficiency increases correspondingly. With 100% PLA, it provides the highest thrust and at 0% PLA, it will be the most efficient.

Figure 3. Experimental thrust versus altitude with varying

PLA%.


Figure 4. Experimental efficiency versus altitude with

varying PLA% Figures 11 and 12 show Thrust and Efficiency versus

Altitude at various Mach numbers. As altitude increases, thrust decreases because of the decrease in the density of air directly correlated to the increase in altitude. Figures 11 and 12 show the response of increasing speed at various Mach increments will affect the engine’s performance. Thrust versus Altitude with change in Temperature shows interesting values referenced in Figure 13. Thrust decreases linearly with altitude with having the best overall characteristic at -0.5°C. The effect of outside air temperature on both Thrust and Efficiency seems to be less prominent than other control variables.

Figure 5. Experimental thrust versus altitude with varying

speed

Figure 6. Overall efficiency versus altitude with varying

speed

Thrust and Efficiency versus altitude are analyzed with varying temperatures while keeping the other parameters at their median values. Figure 14 displays Efficiency versus Altitude with change in Outside Air Temperature. The efficiency displays an increase response in slope trend for the variation in temperature.

Figure 7. Experimental thrust versus altitude with varying outside air temperature

Figure 8. Experimental efficiency versus altitude with varying outside air temperature

THEORETICAL ANALYSIS The Theoretical Analysis is the second method that is

explored with parametric analytical equations. The general thrust equation derived from Newton’s Second Law can be seen in equation 1. The Turbofan Thrust shows the difference in mass flow rate and velocity is equivalent to thrust force. The simple concept equation relationship is the baseline and foundation for the analytical equations.

𝑭𝑭 = 𝒎𝒎𝒆𝒆̇ 𝑽𝑽𝒆𝒆 −𝒎𝒎𝒐𝒐̇ 𝑽𝑽𝒐𝒐 + 𝒃𝒃𝒃𝒃𝒃𝒃𝒎𝒎𝒄𝒄̇ 𝑽𝑽𝒇𝒇 1) 𝑭𝑭𝒎𝒎𝟎𝟎̇

= 𝟏𝟏𝟏𝟏+𝛂𝛂

𝐚𝐚𝐨𝐨𝐠𝐠𝐜𝐜�(𝟏𝟏 + 𝐟𝐟) 𝐕𝐕𝟗𝟗

𝐚𝐚𝟎𝟎− 𝐌𝐌𝟎𝟎 + (𝟏𝟏 + 𝐟𝐟) 𝐑𝐑𝐭𝐭𝐓𝐓𝟗𝟗 𝐓𝐓𝟎𝟎⁄

𝐑𝐑𝐜𝐜𝐕𝐕𝟗𝟗 𝐚𝐚𝟎𝟎⁄𝟏𝟏−𝐏𝐏𝟎𝟎 𝐏𝐏𝟗𝟗⁄

𝛄𝛄𝐜𝐜� + 𝟏𝟏

𝟏𝟏+𝛂𝛂𝐚𝐚𝟎𝟎𝐠𝐠𝐜𝐜�𝐕𝐕𝟏𝟏𝟗𝟗𝐚𝐚𝟎𝟎− 𝐌𝐌𝟎𝟎 + 𝐓𝐓𝟏𝟏𝟗𝟗 𝐓𝐓𝟎𝟎⁄

𝐕𝐕𝟏𝟏𝟗𝟗 𝐚𝐚𝟎𝟎⁄𝟏𝟏−𝐏𝐏𝟎𝟎 𝐏𝐏𝟏𝟏𝟗𝟗⁄

𝛄𝛄𝐜𝐜�

2)


𝜂𝜂𝑇𝑇 = a02�(1+f)(V9 a0⁄ )2+α(V19 a0⁄ )2−(1+α)M02�

2gcfhpr 2)

𝜂𝜂𝑃𝑃 = 2M0[(1+f)(V9 a0⁄ )+α(V19 a0⁄ )−(1+α)M0]

�(1+f)(V9 a0⁄ )2+α(V19 a0⁄ )2−(1+α)M02� 3)

Various equations are programmed and analyzed using

spreadsheets. These mathematical equations help calculate Specific Thrust, Thermal Efficiency, and Propulsive Efficiency analytically. Specific thrust, represented by Equation 2, measures the amount of force per unit mass flow rate. Shown in Equation 3, Thermal efficiency is calculated by taking a ratio of work done by the engine to the heat supplied by the engine. Displayed in Equation 4, is overall propulsive efficiency.

At various flight conditions, the control variables are

changed one variable at a time and then above equations are used to determine thrust force and overall efficiency. Once calculated the results are then compared and validated with the experimental simulation based analysis. Future work includes adding computational fluid dynamic analysis at the component level.

COMPUTATIONAL FLUID ANALYSIS The engine test bench has the ability to simulate thermal

and fluid physics and dynamics. Figure 15 shows a visual representation of the fluid physics model. Figure 16 depicts the CATIA v5 baseline models housed in the engine test bench.

Figure 9. Experimental computational fluid dynamic analysis of DGEN 380 engine

(a)

(b)

Figure 10. (a) Side profile of fan blade model with assembled aircraft engine components in CATIA V5 and

(b) top profile Using SolidWorks, Computational Fluid Dynamic (CFD)

analysis is being performed. CFD will be used to visualize and validate the experimental and theoretical findings for the DGEN 380 turbofan engine. A mesh optimization process is studied to help refine the CFD techniques in order to build the foundation of discretization methods [7]. Figure 17, shows an example of the flow visualization and velocity variation at different parts of the engine.

Figure 11. (a) Side profile of computational fluid dynamic analysis of engine bypass system and (b) isometric profile

The shape of the bypass system and nacelle show that the DGEN 380 has a high bypass ratio. Fluid can be seen traveling at high speeds and proceeding through the system as shown in

(a)

(b)


Figure 17. The data analysis shows that the centrifugal compressor increases the pressure before the combustion chamber. The temperature increases significantly after the mixture of jet fuel and compressed gases. The chemical kinetics, described by optimizing the mixture of highly pressurized air and fuel rates, increase the amount of energy extracted by both the low and high-pressure turbines [5]. The turbines drive the compressors and remaining fluid proceeds through the exhaust to provide thrust force. This helps show the fluid motion interaction with the bypass system as it travels through its projected pathway. Validating these computational and numerical results, a converged and well-meshed solution is achieved by continuously running the simulation until a global convergence average value is reached [10].

RESULTS AND DISCUSSION The intersections of cross points are studied to explore the

most optimal and efficient flight conditions. Analytical and experimental analyses are compared. The theoretical calculations shown in Figures 18 through 23 display an analytical and simulation comparison analysis for Thrust versus Altitude with varying Mach number, PLA, and Temperature. The analytical approach uses parametric cycle analysis, a methodology in which imperial equations are utilized in real engine performance calculations. Figure 18 represents Thrust vs. Altitude with varying Mach number. The data depicts each set with the same color, calculated and experimental, with its corresponding Mach speed. Thrust and altitude have an inversely proportional relationship. As altitude is increased at each represented speed, output thrust force decreases. The simulation data shows a more dramatic decrease in slope when compared to the analytically calculated thrust. The analytical approach shows an ideal baseline result, whereas simulation shows more natural environmental factors that are not considered in the parametric cycle analysis calculations. Figure 19 shows Efficiency versus Altitude with varying Mach numbers. The Thrust relationship is directly proportional to altitude. As altitude increases, the overall efficiency increases respectively. This trend can be explained by breaking the overall Efficiency into its two constituents, propulsive and thermal efficiency. Taking a closer look at the two efficiencies, it can be seen that at any flight Mach number the propulsive efficiency decreases a small amount (approximately 0.2 % per 500m increase in altitude). This is because the air is less dense at higher altitudes and leads to a lower fuel to air ratio as well as a smaller thrust force produced by the engine exhaust gases and results in lower efficiency. Next, analyzing the thermal efficiency, it can be seen that efficiency increases as the altitude increases (approximately 1% per 500m). This increase in thermal efficiency is due to the fact that air breathing jet turbine engines are more efficient when the inlet temperature is colder. When these two values are multiplied together to make overall efficiency the decrease of the propulsive is less than that of the thermal efficiency and leads to an overall increase in overall efficiency.

Figure 12. Thrust vs. Altitude with varying Mach number speeds. A theoretical and experimental comparison

analysis.

Figure 13. Efficiency vs. Altitude with varying Mach numbers.

Figure 20 displays Thrust versus Altitude with change in PLA%, in which the relationship is inversely proportional. As altitude increases with constant power level angle setting, thrust force will decrease linearly. Due to altitude increase, the air molecules become less dense and it is more difficult to combust. Therefore, the amount of fuel burned due to PLA is required to be higher to compensate the low fuel to air ratio at high altitudes. In Figure 21 shows Efficiency versus Altitude with change in PLA%. It shows a directly proportional relationship. As altitude increases, efficiency increases at varying PLA% respectively. At a 50% PLA, it provides the balanced amount of fuel to provide the combustor to atomized fuel molecules. When atomized, each molecule has the ability to ignite and causes a change reaction. If too much PLA% is applied, not all fuel can become atomized. If too little is applied, there will not be sufficient fuel to combust with air. As shown in Figure 20 and 21, the theoretical calculated results show the similar trends as the experimental results and at a number of flight conditions match closely. For other flight conditions, it is expected that the rest bench results are of a higher fidelity than the analytical results. This can be attributed to factors like linearization of analytical equations and non-uniform changes in density as a function of altitude.


Figure 14. Thrust vs. Altitude with change in PLA %. A theoretical and experimental comparison analysis

Figure 15. Efficiency vs. Altitude with varying PLA % values. A theoretical and experimental comparison

analysis. Figure 22 and 23 show the comparison of Thrust and

Efficiency versus Altitude with change in Temperature values respectively. As altitude increases with each temperature set, the thrust force decrease at lower environmental temperatures, the air is denser and makes the compression and combustion process more efficient. Higher temperatures increases the kinetic energy within air molecules, when compressed and combusted, the energy output will have less efficient extraction out of the turbines. In result, Figure 23 depicts the directly proportional relationship of Efficiency, as temperature increases the efficiency also increases. The analytical and experimental results show similar trends.

Figure 16. Thrust vs. Altitude with varying temperature. A theoretical and experimental comparison analysis

Figure 17. Efficiency vs. Altitude with varying temperature. A theoretical and experimental comparison

analysis

STUDENT REFLECTIONS Three students were involved as part of this study. They

were asked to reflect on the work they performed. They learned the fundamental steps of conducting a systematic research study. They were exposed to multiple approaches of problem solving. These students have indicated that the lessons learned from this study have given them confidence both academically and professionally. In addition to learning the theory behind gas turbine engines in a classroom environment, they also learned how to conduct a well structured laboratory experimental study that involves multiple variables. Additionally they were able have a deeper understanding of the inner workings of a high by-pass ratio gas turbine engine. Hands on experiments gave them a certain level of confidence in their understanding of propulsion principles that they otherwise might not have acquired in as much depth. As part of this research, they also learned how to prepare the work they conducted for broader propagation in the form of writing a technical paper and presenting it to a wider audience. This study has provided the necessary tools and assets to help them become well-versed engineers. It is expected that this work will put them on a path to pursuing more research and development activities in their


engineering careers either as academics or industry professionals.

SUMMARY AND CONCLUSION/FUTURE WORK In this study, undergraduate students are involved in a

laboratory experiment. They compare analytical performance of a low by-pass ratio gas turbine engine at different flight conditions with the corresponding simulations generated using DGEN 380 Engine test bench. Thrust and efficiency are calculated by changing one variable at a time. The design variables include outside air temperature, altitude, Mach number and PLA. While one variable is changed, others are kept constant at their median values. Plots are generated to compare the results obtained from the two different methods. It is observed that the results of the two methods correlate well at various flight conditions. At other flight conditions, they show similar trends but show certain level of discrepancy. The results from both analyses are compared and discussed. As part of this continuing study, students will generate more CAD models and perform CFD analyses. This will give them a third method for comparison. It is expected that this exercise will help them pursue even more research and development studies in the future.

ACKNOWLEDGEMENTS We are highly thankful and appreciative to our faculty

advisor Dr. Adeel Khalid for his active guidance throughout the completion of the research project. Extensions of our appreciation as serves to Skyler Bagley for prior research participation and data collection.

REFERENCES [1] Berton, Jeffrey J. “System Noise Prediction of the DGEN 380 Turbofan Engine.” NASA. NASA, 30 Sept. 2015. 28 Jan. 2017. [2] Keith, Jr. A.L. “A Brief Study of the Effects of Turbofan-Engine Bypass Ratio on Short-Long-Haul Cruise Aircraft.” A Brief Study of the Effects of Turbofan-engine Bypass Ratio on Short-and Hong-haul Cruise Aircraft (n.d.): n.pag.Nrts.nasa.gov. Dec. 1975. [3] Liu, F., and W. A. Sirignano. "Turbojet and Turbofan Engine Performance Increases Through Turbine Burners." Vol. 17, No. 3, May – June 2001 Turbojet and Turbofan Engine Performance Increases Through Turbine Burners (n.d.): n. pag. Http://fliu.eng.uci.edu/Publications. May-June 2001. [4] Takikame, Carlos Eduardo Takikame. Control and Management of Motor Aeronautical Fadec. Tech. no. ISSN 2179-7625. 6th ed. Vol. 5. Taubaté: n.p., 2014. Print. Ser. 3. [5] Kestner, Brian K., Jeff S. Schutte, Jonathan C. Gladin, and Dimitri N. Mavris.ULTRA High Bypass Ratio Engine Engine Sizing and Cycle Selection Study for a Subsonic Commercial Aircaft in the N+2 Timeframe. Tech. no. GT2011-453. Vancouver: ASME, Canada. Print. [6] Vogeler, Konrad. The Potential of Sequential Combustion For High Bypass Jet Engines. Tech. no. 98-GT-311. THE

AMERICAN SOCIETY OF MECHANICAL ENGINEERS, June 1998. [7] Elfarral, Monier A., Nilay Sezer Uzol, and Sinan Akmandor NREL VI Rotor Blade: Numerical Investiagion and Winglet Design and Optimization Using CFD. Tech Journal of Fluids Engineering 2002; 124: 393-399. [8] "Price Inductions." Turbine Technologies, n.d. 31 July 2015. [9] “Wind Map | NOAA Climate.gov.” Wind Map | NOAA Climate.gov. National Centers for Environmental Information, n.d,. 25 July 2015. [10] Francesco Balduzzia, Alessandro Bianchinia, Riccardo Malecia, Giovanni Ferraraa, Lorenzo Ferrarib, ‘Critical issues in the CFD simulation of Darrieus wind turbines,’ Renewable Energy, Volume 85, January 2016, Pages 419–43





MODELING AND EVALUATION OF BUSINESS- ENGINEERING COMPLEX ENERGY PERFORMANCE

Qing Mu, Hessam Taherian Department of Mechanical Engineering

The University of Alabama at Birmingham Birmingham, Alabama, USA

ABSTRACT Distributed energy, also district or decentralized energy, is

generated or stored by a variety of small grid-connected devices referred to as distributed energy resources or distributed energy resource systems. The distributed generation systems can increase energy system reliability, reduce peak power requirements, and improve energy infrastructure resilience. The energy sources for this distributed generation system are typically photovoltaic arrays, microturbines or internal combustion engines, and/or a small wind turbines.

Before distributed generation systems are applied to a building, a simulation of the building’s energy usage is needed. In this paper, OpenStudio and SketchUp software tools are used in modeling a typical distributed generation system. The purpose of this study is to calculate the energy usage index and energy usage of a building. The result of the simulation is used in determining the optimum size of the equipment to save the cost in campus operations and reduce greenhouse gas emission. The results in this paper show that the model is working properly and accuracy meets the demand.

KEY WORDS: Distributed generation, renewable energy, Open Studio, SketchUp, Energy usage index (EUI)

INTRODUCTION The world’s energy usage is growing as well as greenhouse

gas emissions. The Paris Agreement was negotiated by representatives of 195 countries at the 21st Conference of the Parties of the UNFCCC in Paris and adopted by consensus on 12 December 2015[1]. Governments agreed to a long-term goal of keeping the increase in global average temperature to well below 2°C above pre-industrial levels and aim to limit the increase to 1.5°C since this would significantly reduce risks and the impacts of climate change. Global emissions need to be limited and reduced as soon as possible in accordance with the best available science.

Figure 1 US energy consumption by energy source, 2016

[2] Fig 1 shows that U.S. energy sources are mainly fossil

fuels including petroleum, natural gas and coal. This calls for development of technologies to achieve higher energy efficiency and promote the usage of clean renewable energy. Distributed power generation systems generate power at the location that the power is to be consumed. Comparing with traditional power plants, this has its advantages and disadvantages [3].

Advantages: • Emergency power supply • Reduce the peak demand of the electric grid • Reduce the initial investments in power generation

and transmission • Supplement for reactor power • Decrease land usage and acquisition costs • Increase infrastructure resilience and the ability to

encounter terrorism • Higher fuel conversion efficiency due to

incorporation of renewable resources Disadvantages:

• Higher maintenance costs and requirements. • Supplement of fuel (fossil fuels) is challenging. • Higher costs than centralized power plant. • Impact to grid stability of whole power system, if

grid-connected. • Potential Power Quality issues due to millions of low

quality inverters.


• Lack of availability of natural gas pipeline infrastructure at many places

• More complicated delivery system which could require maintaining and operating many small generation sets. [4], [5], [6], [14]

Energy usage intensity (EUI) is used to measure building

performance. EUI expresses the energy efficiency of a building, and it is calculated by dividing the energy usage by the property’s total area in square feet.

The energy consumption of a building depends on several factors including the construction details, orientation, occupancy patterns, local climate, building schedules, lighting system and HVAC systems, and the characteristics of other equipment loads within the building [8], [9].

UAB is the largest employer in Alabama. On UAB campus, there are 191 buildings and the cost of campus operation is very high. For example, in the year 2014, the Business-Engineering Complex (BEC) used 3,331,866 KWH electric and 44,193 CCF natural gas which cost $ 350,000 [10].

In this research project, the Business-Engineering Complex is set as a typical building. OpenStudio and SketchUp are used to simulate the energy consumption and calculate the EUI. Open studio is an open-source tool from National Renewable Energy Laboratory. There is a SketchUp plugin (extension) which enables users to view and edit 3D models of a building for energy simulation [7].

MODEL SETUP

Figure 2. Building layout in Sketchup

Figure 2 is the 3D model of the BEC building. The

blueprint is obtained from the UAB Facilities Division. In this building, the rooms are classified into 13 different space types: cafe, classrooms, computer rooms, offices, conference rooms, elevator, labs, mechanical rooms, corridor, lobby, storage rooms, washrooms and stairs (Fig 3). Each space type has its unique schedule and loads. A walk-through survey of the building is conducted before the simulation. With the help of the building manager and departmental staff, the number of devices, rated power and the hours of operation are recorded.

Figure 3. Graphical rendering by space types

After the survey, the loads and schedules are set in the

model. The heating/cooling calculation for the whole building is set to be considered as ideal air loads, which means an “ideal” heating/cooling source is assumed to provide energy to the building in order to keep comfort level inside the building within the set range.

The BEC is located in Jefferson County, AL which is classified as ASHRAE climate zone Warm-Humid (3A) [11]. The engineering wing is in the direction of north to south, with the main entrance facing east, and the azimuth angle of the business wing is 60° west of south.

METHODOLOGY There are two general methodologies to model energy use

by the building sector: Top-Down models: energy-use data from power

companies is compared with climate variables, census results, and statistical surveys. This is usually used to determine the average energy consumption for existing buildings. These models can compare different variables, but cannot distinguish spatial variations in energy consumption of a municipality or a territory.

Bottom-Up models: these models are used to simulate the energy consumption of a single building with high details. These models can be combined to evaluate the energy consumption of a group of buildings, districts or cities. These models can also be used to evaluate an energy savings model after building retrofits. [12]

RESULTS AND DISSCUSSION Bottom-Up model result:

Table 1. Building summary

Table 1 shows the basic information about the Business -

Engineering Complex. There are 3 floors and 150 spaces in this building.

Information Value Units Building Name BEC Net Site Energy 11,244,466 kBtu

3,295,428 kWh

Total Building Area 131,590 ft2

EUI 85.45 kBtu/ft2


Figure 4 Base surface constructions

Figure 5 Window-Wall Ratio and Skylight-Roof Ratio

Fig 4 and Fig 5 are the calculated input values from

OpenStudio. The construction and window - wall ratio have significant influences on the energy consumption of a building. The R value of the envelope is determined by the construction materials.

Figure 6 Energy End Use

Fig 6 shows the end use energy distribution of the BEC:

Interior Equipment 55%, Cooling 22%, Interior Lighting 18% and Heating 6%. The reason the Interior Equipment is shown as the largest portion of the pie is that the water chiller and all air handling units, pumps and fans are included in this portion. Figure 7 Building Space Types Breakdown

There are 13 space types in BEC building. The floor area of

each space type is described in Fig 7.


Figure 8 HVAC Monthly Load Profiles

Fig 8 shows the monthly energy consumption for cooling

load and heating load. The highest cooling load is approximately 305 MMBtu (89,386 kWh).

Figure 9. Natural Gas Consumption


Figure 10. Electricity Consumption

Fig 9 and Fig 10 are the results in OpenStudio simulation for natural gas and electricity use, respectively. The total consumption of natural gas in one year is 46,187 MBtu and consumption of electricity is 3,122,665 KWh. BEC uses natural gas for heating and hot water supply, therefore there is a significant decrease of natural gas usage in the summer. As for the electricity consumption, highest usage is in July and August which are usually the months with hot weather and highest demand for cooling. Cooling is provided to the building by vapor-compression electric water chillers. The electricity use for lighting is fairly uniform. Other end uses such as interior and exterior equipment are not reported because they are not significantly affected by weather conditions. However, the values of all end use points are accounted for in calculating the EUI of the building. Top-Down model result:

The BEC building utility bills for several years were obtained from the UAB Facilities division.

Figure 11. Electricity use report

Figure 12. Natural Gas consumption report

Fig 11 and Fig 12 are generated from Building Operating

Cost Report [10]. They are the utility bills of the year 2014-2015. From the utility bills we can see the highest demand for electricity is in August and highest demand for natural gas is in February. There is a slight difference between the simulated value and actual bill. This is because human activity is unpredictable during summer and winter.

Table 2. Comparison between the result and cost report OS result Cost Report Accuracy Electricity (KWh)

3,122,665.56 3,331,866 93.7%

Natural gas (MBtu)

46,187.67 44,193 95.1%

EUI (kBtu/ft2)

85.45 82.23 96.2%


In Table 2, the simulation result is compared with actual bills. The accuracy of the model’s result is quite high considering the fact that there are several loads in a building as large as BEC that cannot be predicted with high accuracy. One example is prediction of occupancy, which varies from semester to semester. The National Median EUI of school buildings is 114 kBtu/ft2 [13] which is 38.6% higher than seen in the BEC. There are a variety of reasons for the discrepancy whose study and analysis are beyond the scope of this paper.

CONCLUSION AND FUTURE STUDY By comparing the results from two models, we can see the

OpenStudio simulation is accurate, and it is suitable for distributed generation system analysis. However, there are some other types of buildings on campus and the simulation in OpenStudio of other types of buildings should be done. Once the simulation of the entire UAB campus is finished, a renewable- integrated distributed generation system for UAB could be proposed by which the cost of campus operating is expected to be reduced together with a reduced greenhouse gas emission through achieving higher conversion efficiency.

ACKNOWLEDGEMENT The authors would like to thank the assistance provided by

the UAB Department of Energy Management and specifically Mr. Matthew Winslett for providing energy use data.

REFERENCES [1] United Nations Treaty section. 2015. Paris Agreement. Retrieved on 9/24/2017 from: https://treaties.un.org/Pages/ViewDetails.aspx?src=TREATY&mtdsg_no=XXVII-7-d&chapter=27&lang=_en&clang=_en [2] U.S. Energy Information Administration. 2017. Monthly Energy Review. Table 1.3 and 10.1 Retrieved on 9/27/2017 from: https://www.eia.gov/energyexplained/?page=us_energy_home [3] Virginia Tech. 2007. Distributed Generation—Educational Module. Retrieved July 2017 from: http://www.dg.history.vt.edu/ [4] Saleh, M. S., Althaibani, A., Esa, Y., Mhandi, Y., Mohamed, A. A., 2015. "Impact of clustering microgrids on their stability and resilience during blackouts". International Conference on Smart Grid and Clean Energy Technologies (ICSGCE). 195–200. [5] Kuang, H., Li, S., Wu, Z., 2011. Discussion on advantages and disadvantages of distributed generation connected to the grid. International Conference on Electrical and Control Engineering (ICECE). [6] Girgis A. Brahma S. 2001. Effect of Distributed Generation on Protective Device Coordination in Distribution System[C]. Proceedings of Large Engineering Systems Conference on Power Engineering. Halifax (Canada). 115-119 [7] Cnet news. 2009. SketchUp 'plug-in' offers energy analysis. Retrieved on 9/27/2017 from: https://www.cnet.com/news/google-sketchup-plug-in-offers-energy-analysis/

[8] Fung, A.S., Taherian, H., Hossain, M., Rahman, Md. Z. and M.M. Selim. 2015. Energy Audit and Base Case Simulation of Ryerson University Buildings. ASHRAE Transactions, 121(2):84-98. [9] Li, Z., Taherian, H., Robert, P. 2017 TFEC-IWHT2017-18059. Energy Use and Chilled Water Storage System Studies for an Educational Facility through High-resolution Modeling. [10] UAB Facilities Management Division. 2015. “FY1415 Building Operating Cost Report – Utilities Summary”, Birmingham, AL, The University of Alabama at Birmingham. Received directly from the source. [11] ANSI/ASHRAE/IESNA Standard 90.1-2007 Normative Appendix B – Building Envelope Climate Criteria. [12] Torabi, S., Mutani, G., Lombardi, P. 2016, GIS-Based Energy Consumption Model at the Urban Scale for the Building Stock. 9th International Conference Improving Energy Efficiency in Commercial Buildings and Smart Communities. [13] US Department of Energy, 2017, Building performance database. Retrieved on: September 2017 from: https://energy.gov/eere/buildings/building-performance-database [14] Borges, C. L. T. 2012. An overview of reliability models and methods for distribution systems with renewable energy distributed generation. Renewable and Sustainable Energy Reviews, 16(6). 4008-4015.





THE EFFECT OF TANK VOLUME ON PERFORMANCE OF AN EVACUATED TUBE SOLAR WATER HEATER

Azadeh Sohrabi University of Alabama at Birmingham

Birmingham, AL, USA

Farzad Veysi Razi University of Kermanshah

Kermanshah, Iran

Hessam Taherian University of Alabama at Birmingham

Birmingham, AL, USA

ABSTRACT In this study, the effect of tank-volume on thermal

performance of the evacuated tube solar water heater is investigated. These heaters are based on low-temperature thermal collectors whose performance is dependent on several factors, such as the collector tilt angle, aspect ratio of the tube, the space between vacuum tubes, use or non-use of reflective plane, storage tank size and geographic location. Because of the limitation of sunlight to daytime hours, energy must be stored in other forms to be used when sunlight is not available. Therefore, the optimization of such systems to absorb and store the maximum energy during sunlight is very important. To achieve this goal, the optimum size of the storage tank is determined to design more efficient evacuated tube solar water heating systems. Eleven different ratios of tank volume to collector area are considered. Our numerical approach is validated using data obtained through an experimental setup under the climatic conditions in Kermanshah, Iran.

The results show that by increasing the tank volume, the thermal performance increases by 16%; however, the tank water temperature decreases by 14.5%. Consequently, our results show there is an optimal ratio of tank volume to collector.

KEY WORDS: Solar energy, Solar water heater, Storage tank volume, Thermosyphon flow

INTRODUCTION Both widespread use of energy and the problems of limited

fossil fuels lead to an increased use of renewable energy resources [1]. During recent years, research about renewable energies, such as solar energy, has increased. Providing hot water for buildings is one of the most economic ways of solar-energy application. One of the earliest studies on solar water

heaters was done by Whillier and Saluja to determine some factors influencing solar water heating systems (SWHS) [2]. M. Raisul Islam investigated the efficiency of different types of SWHS and economic factors of these systems [3]. SWHS are also characterized as either active or passive. There are numerous studies evaluating the thermal performance of different types of SWHS. Electrical or mechanical equipment, such as pumps and fans, is used in active solar systems to increase the usable heat in such systems. However, passive solar systems do not use external energy or active mechanical devices [4]. There are several studies on passive solar systems. Chan et al. conducted a comprehensive review of passive solar heating [5]. Roonprasang et al. investigated a specific active solar water heater using a water pump to circulate water [6].

In SWHS, the thermosyphon effect uses natural convection to circulate water between a solar collector and a storage tank placed higher than the collector [7]. Thermosyphon heaters are widely used in the design of solar water heaters because of the low investment and maintenance cost of these systems [8]. Probably the first investigation of a thermosyphon solar water heater circuit was by Close [9]. The simplest of solar water heaters is composed of a flat-plate collector and a storage tank in which water or antifreeze fluid circulates by natural convection flow due to the temperature gradient. In this system, the water inside the tubes absorbs energy and warms up, its density reduces and it heads upwards. The warm water moves from the upper half of the tube into the tank and is replaced by cold water in the tubes. The open end of a vacuum tube is placed in a well-insulated tank in order to preserve the energy and hot water [10]. Due to the popularity of this system and the multiple variables that augment its performance, like tank volume, collector surface area and geographical position, optimization of such systems is quite important. In order to serve this purpose, a wide range of investigations have been done through several numerical and experimental studies to


increase the performance of solar water heaters [11-14]. For instance, Morrison et al. investigated some of the factors that influence performance of vacuum tube collectors. They conducted a numerical study of water circulation inside vacuum tubes that showed a non–active area in single-ended tubes, which can affect collector performance [11]. Budihardjo et al. evaluated the performance of evacuated tube solar water heaters by using experimental data and a simulation model of the thermosyphon circulation in vacuum tube. Furthermore, in their study, comparing between a solar water heater having 30 water-in-glass evacuated tubes and a two panel flat plate system; they showed that the performance of the 30 tube water heater was lower [12]. Hayek et al. compared thermal performance of heat-pipe-based collectors with water-in-glass collectors. They found that the efficiency of the heat-pipe collectors is almost 15-20% better than water-in-glass collectors [13]. Li et al. presented a heat-transfer model for all-glass-vacuum-tube collectors which have been used in forced circulation solar water heaters [14]. Hasan performed experiments on solar water heaters with vertical as well as horizontal storage tanks and concluded that both tank systems give similar efficiencies [15].

There are only a few studies on the optimization of the water tank volume of evacuated tube solar water heaters, which are also geographically dependent. Çomakli et al. indicated that the performance of a flat-plate solar water heater is highly dependent on the storage tank size. They found that a larger storage volume than needed does not necessarily increase the efficiency [16]. In contrast, Bello et al. investigated the effect of two different storage tank sizes, namely 20 L and 100 L, on a system equipped with flat surface collector, and found out that efficiency in the 100 L capacity tank is higher compared to the 20 L one. This is mainly because the higher temperature of the 20 L tank causes energy loss and less efficiency [17]. Zhang et al. tested the performance of a water-in-glass evacuated tube based on Chinese standards and provided the optimal values for the ratio of tank volume to collector area to reach the desired temperature [18].

In this study, through numerical simulation, the effect of tank-volume on performance of the evacuated solar water heater is investigated using several sizes of the storage tanks, and the optimized tank volume for each desired temperature is presented. An experimental setup that was tested under weather conditions in Kermanshah, Iran, is used for validating the numerical results.

NOMENCLATURE A Total area absorber

Cpw Specific heating capacity of water

D Tube diameter

�̅�𝐺 Average radiation per unit area on the surface of

the absorber (or just Irradiation)

Gr Grashof number

Ra Rayleigh number

U Heat loss coefficient

V Volume

W Watt

G Acceleration of gravity

K Thermal conductivity

M Mass of water

T Average temperature

𝑢𝑢� Average velocity

𝑢𝑢�⃗ Velocity

𝛼𝛼 Thermal diffusion coefficient

𝛽𝛽 Thermal expansion coefficient of water

𝜂𝜂 Thermal performance

𝜌𝜌 Density

𝜗𝜗 Kinematic viscosity

𝜃𝜃 Solar collector slope

𝜏𝜏 � Shear stress

∆𝑇𝑇 Temperature difference between tube wall and

fluid

∆𝑡𝑡 Duration of test

( )D Diameter

( )as(av) Ambient air temperature adjacent

( )f Final

( )i Initial

( )s Storage tank

( )w Water

METHODS The schematic description of the solar water heater

operation is illustrated in Fig. 1. The tubes absorb energy from solar radiation and transfer it to the cold water coming from the storage tank. This raises the water temperature and leads the heated water back to the storage tank.


Figure 1. Solar Water Heater Operation

Fig. 2. shows the general characteristics of the solar water heater which is investigated in this research. Based on the variation of the ratio of tank volume to the constant area of collectors, eleven different models for a water heater were considered.

Figure 2. General characteristics of the explored water heater

NUMERICAL SIMULATION The water heater contains three tubes, which have a

diameter of 4.5cm, length of 180cm and are inclined at 45o to the vertical. The radius of the storage tank changes in different models. However, the width is constant at 36 cm.

After a grid independency study, each model is discretized using 50000-100000 tetrahedral control volume elements, varying based on the size of the tank. Laminar and incompressible flow conditions were assumed, and a computational Fluid Dynamics (CFD) simulation was conducted on all 11 solar water heater models. Virtual solar radiation was adopted on the surface of tubes, and no-slip boundary conditions are considered.

The heat loss coefficient of the tubes is adopted as 0.71W m2k ⁄ , based on the study by Tang et al. [19]. The storage tank heat loss was calculated using equation presented in ISO 9459-2:1995(E) [20]:

𝑈𝑈𝑠𝑠 =𝜌𝜌𝑤𝑤𝑐𝑐𝑝𝑝𝑤𝑤𝑉𝑉𝑠𝑠

∆𝑡𝑡𝑙𝑙𝑙𝑙 �

𝑡𝑡𝑖𝑖 − 𝑡𝑡𝑎𝑎𝑠𝑠(𝑎𝑎𝑎𝑎)

𝑡𝑡𝑓𝑓 − 𝑡𝑡𝑎𝑎𝑠𝑠(𝑎𝑎𝑎𝑎)� (1)

in which, 𝜌𝜌𝑤𝑤 is water density, 𝑐𝑐𝑝𝑝𝑤𝑤 is specific heat capacity of water, 𝑉𝑉𝑠𝑠 is tank volume, 𝑡𝑡𝑖𝑖 is initial average temperature of water in tank, 𝑡𝑡𝑓𝑓 final average temperature of water in tank, 𝑡𝑡𝑎𝑎𝑠𝑠(𝑎𝑎𝑎𝑎) average ambient air temperature adjacent to store during test and ∆𝑡𝑡 is the duration of test. The Boussinesq model was used for simulating thermosyphon flow. Governing equations of the fluid include energy, momentum and continuity: Continuity equation:

𝜕𝜕𝑢𝑢𝜕𝜕𝑡𝑡

+ 𝛻𝛻(𝜌𝜌𝑢𝑢�) = 0 (2)

Momentum equation

𝜌𝜌𝜕𝜕𝑢𝑢�⃗𝜕𝜕𝑡𝑡

+ 𝜌𝜌𝑢𝑢�⃗ 𝛻𝛻(𝑢𝑢�⃗ ) = −𝛻𝛻𝛻𝛻 +𝑢𝑢𝜌𝜌𝛻𝛻2𝑢𝑢�⃗ + 𝜌𝜌𝑓𝑓 ̅ (3)

Energy equation

𝜌𝜌𝜕𝜕𝐶𝐶𝑝𝑝𝑇𝑇𝜕𝜕𝑡𝑡

+ 𝜌𝜌𝑢𝑢�⃗ 𝛻𝛻�𝐶𝐶𝑝𝑝𝑇𝑇� = −𝑝𝑝𝛻𝛻𝑢𝑢�⃗ + 𝜏𝜏 �:𝛻𝛻𝑢𝑢�⃗ + 𝛻𝛻(𝑘𝑘𝛻𝛻𝑇𝑇) (4)

A transient simulation is conducted on all models by exposing the tubes to solar radiation for 60 days. The temperature inside the storage tank and tubes was monitored over time.

EXPERIMENTAL VALIDATION An experimental setup with the same characteristics as

described in the numerical section was designed. The size of the tank storage is the same as that considered for model 8. Fig. 3a shows the experimental setup and the thermocouples location, which is located at Razi University of Kermanshah. Table 1 provides the specific characteristics of the experimental setup. Solar radiation is estimated between 1800 to 2200 kWh/m2 per year throughout the country, which is more than the average global amount.

Measurement of water temperature was done using 6 type K thermocouples located inside the storage tank and the middle tube. Fig.3b & c show the locations of these thermocouples. The first thermocouple is located at a distance of 5 cm from the inlet tube, and other thermocouples have a 50 cm distance from each other. Additionally, one thermocouple is located outside the water heater to measure the ambient temperature. The experimental setup was exposed to the same boundary conditions as the numerical simulation.


Figure 3. Experimental model and the location of thermocouples

Table 1. Experimental model characteristics

21 L Storage tank volume

27 L/m2 Ratio of storage tank volume to collectors’ surface

area

3 Number of tubes

4.5 cm Inner diameter of tubes

5.6 cm Outer diameter of tubes

180 cm Tube length

45o Tilt angle

9.8 cm Distance between center of tubes

7 Number of thermocouples

The numerical data obtained in this study is compared with

the average temperatures of thermocouples inside the tank and tube for validation of the numerical model. This comparison showed that the present data for the numerical model is quite reasonable as can be seen in Fig. 4 and Fig. 5. The maximum error for a duration of one hour is 3.5% and 2.7% for the average temperature of fluid inside the tank and fluid inside the tube, respectively. Moreover, the average error is 2.3% and 1.3% for the average temperature of water inside the tank and inside the tube, respectively.

Figure 4. Comparison of numerical solution results and

experiments for the average water tube temperature

Figure 5. Comparison of numerical solution results and

experiments for the average water tank temperature

RESULTS Fig. 6 shows the water circulation inside the tank and tube.

As shown in Fig. 7, by approaching the end of the tube, the velocity decreases. In fact, there is an inactive area at the end of the tube that conforms with previous results [11]. In addition, in Fig. 8 the stagnation region at the bottom of the tank can be observed.

In order to investigate the effect of tank volume on the SWHS performance, four different solar radiation magnitudes are used. The amount of this radiation varies between 85-154.5 W.


Figure 6. Circulation in tube and tank, displaying

temperature in K

Figure 7. The velocity vectors inside tube and tank,

showing velocity magnitude in m/s

Figure 8. stagnation region in the tank

As shown in Fig. 9, the water temperature in the tank decreases by increasing the storage tank volume. The amount of this reduction for ratios less than 50 L⁄m2 is more considerable

and reaches 16% of the temperature of model 1. Moreover, the temperature change for ratios more than 50 L⁄m2 is almost constant, less than 3%.

Figure 9. The effect of tank volume on tank water

temperature Fig. 10 shows that the average water temperature in the

tubes also decreases by increasing tank size. The amount of this reduction for ratios less than 50 L⁄m2 reaches 14.5% in comparison with model 1; however, for ratios higher than 50 L⁄m2 it is only about 2.5%.

Figure 10. The effect of tank volume on tube water temperature

The thermal performance of the SWHS is considered as a ratio of useful heat absorbed by the SWHS to the incoming solar energy absorbed by the vacuum tubes, which is given by:

𝜂𝜂 =𝑚𝑚𝐶𝐶𝑝𝑝∆𝑇𝑇�̅�𝐺𝐴𝐴𝛥𝛥𝑡𝑡

(5)


Fig. 11 shows the thermal performance with increasing tank volume. It shows that the thermal performance increases, especially for ratios less than 50 L⁄m2 this increase reaches 23%. Furthermore, it shows that additional increase of the tank volume to 100 L/m2 can increase the performance by 3 %.

Figure 11. The effect of tank volume on SWHS thermal

performance These results can be interpreted by considering that the

higher temperature in a smaller tank results in more heat loss and thermal performance reduction. Besides, tank volume influences the circulation of the water. The ratio of the stagnation region in the tube to the tank volume is depicted in Fig. 12. It shows that by increasing the tank volume the stagnation region in the tube decreases, which means better thermosyphon circulation and consequently better performance.

Figure 12. The effect of tank volume on the stagnant

region in the tube The thermosyphon circulation inside the tank is also

important for the performance of the water heater. For example, there are regions in the tank where temperature changes are

much less than other regions. Figure 13 depicts the effect of tank volume variations on the volume ratio of the stagnant region in the tank.

Figure 13. The effect of tank volume on stagnant region in

tank Grashof and Rayleigh numbers are usually used in

evaluation of the water heater performance. Regarding free convection, the Rayleigh number is a criterion for determining the flow type. In addition, the dimensionless Grashof number, which indicates the ratio of buoyancy force to viscosity force, is significantly important.

𝑅𝑅𝑅𝑅𝐷𝐷 =𝑔𝑔 𝑐𝑐𝑐𝑐𝑐𝑐 𝜃𝜃 𝛽𝛽∆𝑇𝑇𝐷𝐷3

𝛼𝛼𝜗𝜗 (6)

𝐺𝐺𝐺𝐺𝐷𝐷 =

𝑔𝑔 𝑐𝑐𝑐𝑐𝑐𝑐 𝜃𝜃 𝛽𝛽∆𝑇𝑇𝐷𝐷3

𝜗𝜗2 (7)

Figures 14 and 15 show that the Rayleigh and Grashof numbers decrease by increasing tank volume. The range of Rayleigh numbers in Fig. 14 confirms the existence of a laminar regime in the boundary layer. The amount of reduction for ratios less than 50 L m2⁄ is much more, so that the average reduction percent for Rayleigh number to 50 L m2⁄ is about 18.5% and for ratios more than 50 L m2⁄ it is only 6.5%. Also, the average reduction percentage regarding Grashof for ratios less than 50 L m2⁄ is about 19% and considering for ratios more than 50 L m2⁄ it is only 7%.


Figure 14. The effect of tank volume on Raleigh number

Figure 15. The effect of tank volume on Grashof number

To investigate the effect of solar radiation on SWH thermal performance, radiation on four different days in a year was investigated. The amount of this radiation varies between 154.5-85 W. The results are depicted in Fig. 16. As seen by increasing tank volume, SWH thermal efficiency increases. Moreover, for each of the ratios, thermal efficiency increases by increasing the average radiation. The amount of the increase is negligible for the ratios less than 20 L⁄m2 which is less than 5%, for the ratios 27-50 L⁄m2, it is 5-15 % and for ratios higher than 50 L⁄m2 it can reach 18%.

Figure 16. The effect of average radiation on SWH

thermal performance

DISCUSSION The numerical setup is validated by comparison of the

average water temperature inside the tank and tubes with the experimental model. The maximum errors for the average temperature of fluid inside tank and tube for a duration of one hour are 3.5% and 2.7% respectively, indicating a good agreement between the numerical and experimental setup.

Our results show that when the storage tank volume increases, the performance of the solar heating system increases; however, the average temperature of water in the storage tank decreases by increasing the storage tank volume. Therefore, for best optimization of the water heater, both the tank volume and the final temperature of the water should be considered. The results for our specific solar water heater show that, when the storage volume is at least (5 L/m2), tank temperature is the highest. The amount of reduction for ratios less than 50 L/m2 is more remarkable and could reach 16 percent, and for ratios more than 50 L/m2, it is almost constant, approximately 3%. The average temperature of water inside the tubes decreases by increasing tank size. The amount of reduction for ratios less than 50 L/m2 is more considerable and reaches 14.5%, and for ratios above 50 L/m2, it is only 2.5%. The collector’s thermal performance increases by increasing the storage tank volume.

When the ratio of the storage tank volume to the collector area is 100 L/m2, thermal performance of the vacuum tubes is the highest, and when it is 5 L/m2, thermal performance was the lowest. The amount of increase for ratios less than 50 L/m2 is more considerable, and the average is about 23% and for ratios higher than 50 L/m2 it is constant and equals about 3%. A stagnant region is observed at the end of tubes. By increasing the tank volume, the length of the stagnant region at the end of tubes decreases. The shortest length of the stagnant region, 8.7 cm, corresponds to the ratio of 100 L/m2. An area is observed in


the bottom of the tank which has negligible temperature variations, and by increasing tank volume, the ratio of this region to tank total volume decreases. In addition, by increasing the tank volume, the Graco and Rayleigh numbers decrease.

It should be mentioned that the amount of reduction for ratios higher than 50 L⁄m2 is about 6-7%. Furthermore, the range of Rayleigh numbers confirms the existence of a laminar regime in the boundary layer. Thermal performance increases by increasing average radiation for each of the different tank volumes. An amount of increase is negligible for ratios less than 20 L⁄m2 with values less than 5%, 5-15 % for ratios 27-50 L m2⁄ and can reach 18% for ratios higher than 50 L m2⁄ . In fact, all models in the study have the same collector area (receiving energy) and the volume inside the model increases, so clearly a model with smaller volume has higher temperature gradients. Additionally, increasing the tank volume increases its area, which results in more heat losses, which in turn leads to further temperature reduction.

As can be seen, the trend of decrease and increase in plots is constant at ratios higher than 50 L/m2, so increasing tank volume more than 50 L/m2 raises costs. According to the thermal performance and tank temperature, the best ratio for this kind of solar water heater is 40-50 L⁄m2. Hence, the collector volume should be 18-23 % of tank volume.

CONCLUSION

This study has investigated the performance of solar water heaters by optimization of the size of storage tank, through numerical simulation. The study was performed for 60 days under four different intensities of solar radiation. Our main conclusion is that increasing the storage tank size will improve the performance of the water heater. Our results also show that there is an optimal ratio of tank volume to collector, and further increase of tank volume will not change the performance of the water heater.

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