Proceedings of the ASME ASME 2010 2010 Internal Internal Combustion Combustion Engine Engine Division Division Fall Fall Technical Technical Conference Conference ICEF2010 September 12-15, 2010 San Antonio, Texas, USA September 12-15, 2010, San Antonio, Texas, USA
ICEF2010- ICEF2010-35118 NUCLEATE BOILING IDENTIFICATION AND UTILIZATION FOR IMPROVED INTERNAL COMBUSTION ENGINE EFFICIENCY Nikhil Ajotikar Michigan Technological University Houghton, Michigan, USA
Brian J. Eggart Cliffs Natural Resources Ishpeming, Michigan, USA
Scott A. Miers Michigan Technological University Houghton, Michigan, USA
ABSTRACT
INTRODUCTION
Internal combustion engines continue to become more compact and require greater heat rejection capacity. This demands research in cooling technologies and investigation into the limitations of current forced convection based cooling methods. A promising solution is the cooling strategy optimized with nucleate boiling to help meet these efficiency and emission requirements. Nucleate boiling results in an increased heat transfer coefficient, potentially an order of magnitude greater than forced convection, thereby providing improved cooling of an engine. This allows reduced coolant flow rates, increased efficiency, and reduced engine warm-up time. A study was conducted to characterize nucleate boiling occurring in the cooling passages of an IC engine cylinder head in a computational as well as experimental domain. The simulation was conducted to understand the physics of boiling occurring in an engine cooling passage and provide support for a potential boiling detection method. The computational fluid dynamics (CFD) simulation was performed for a simplified, two dimensional domain that resembled an engine cooling passage. The simulation results were followed by investigations of a pressure-based detection technique which was proven to be an effective method to detect boiling. An experimental test rig was used which consisted of a single combustion chamber section from a 5.4L V8 cylinder head. Water was used as the coolant. Results demonstrate the phase change physics involved in the boiling in an engine cooling passage, pressure variations in the coolant, heat flux data associated with the onset of nucleate boiling, and a comparison with existing boiling curves for water. Results of the simulation and experimental setup indicated that the change in energy and accompanying increase in pressure values can be related to bubble dynamics and thus provides a potential method to accurately detect nucleate boiling occurrence in an engine cooling system.
Optimizing engine cooling strategy has been an important area of research in the automotive industry in order to meet increasing performance demands and stringent environmental regulations. Use of forced convection heat transfer is the most widely used mechanism for engine cooling [1]. The cooling system efficiency can be improved by the use of nucleate boiling. Precise control of boiling is the important missing link in its application to engine cooling [2]. However, boiling is characterized by randomness and variability which causes difficulty in its application to a dynamic setting like an engine. Without a reliable system for sensing and moderating the progression of boiling in the cooling passages, the process can advance to the dangerous state of film boiling. Under such a situation, the heat flux may reach the critical heat flux value which may lead to higher metal surface temperatures and eventually complete failure of engine components, known as boiling crisis [3]. Cooling system design plays a crucial role in overall engine performance. Engine parameters such as thermal efficiency, exhaust gas emissions, and thermal stresses are linked to the performance of the cooling system. The conventional method of engine cooling utilizes forced convection heat transfer and aims to avoid boiling occurring within the engine cooling passages. The conventional system utilizes a mechanically driven coolant pump, a radiator where the coolant rejects its heat to the ambient air, a thermostat which regulates coolant temperature by adjusting flow rate between the engine and radiator, and a fan for maintaining air flow over the radiator during low vehicle velocity. Cooling systems are designed so that any vapor from boiling is condensed immediately into the coolant flow. There have been several efforts to modify the conventional cooling system to optimize the engine cooling performance. In 1993, Clough developed precision designed coolant passages which showed reduced coolant volume, reduced pump power,
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reduced HC emissions, reduced over-cooling, improved passenger comfort, and reduced thermal distortion and stress within the engine [4]. In this system, the engine block and cylinder were designed with new coolant passages for precision cooling retaining all other conventional components. As an improved approach towards precision cooling, Campbell and Hawley [5] used a variable speed pump in place of the engine driven mechanical pump and an electric flow valve in place of the wax thermostat resulting in better control of coolant flow rate and coolant temperatures. Couetouse and Gentile [6] used micro-controllers for the same function as shown below in Figure 1.
absorbing the engine heat, and condensed in the radiator. Application of boiling heat transfer in engine cooling systems has been a field of considerable research over the past 20 years. Ap and Tarquis [7] compared nucleate boiling engine cooling systems with several innovative cooling systems like THEMIS, CoolMaster and ULTIMATECOOLING. The nucleate boiling cooling system showed improved fuel economy, ability to post cool, reduced weight and packaging space, improved thermal comfort, and was the most economical system. In this paper, results from a study carried out to characterize the nucleate boiling occurring in the cooling passages of an IC engine cylinder head are presented. The paper highlights a simplified experiment and simulation aimed at understanding the phenomenon involved in boiling detection and to test a pressure-based detection and control method. NOMENCLATURE
Figure 1. Cooling system schematic for Couetouse and Gentile [6]
Another cooling system, THEMIS ™ (THErmal Management Intelligent System) utilized an electric water pump, electric flow valve, and electric fan along with computerized models to control the cooling system components resulting in improved cabin heating at idle and cooling after engine shut off to prevent heat soak and after-boil. They realized an average of a 5% reduction in fuel consumption, a 20% reduction in CO, and a 10% reduction in HC emissions. In order to address new challenges of increasing power density, greater engine efficiency and stricter emission regulations, the focus shifted from cooling system components to a cooling technique that included boiling-based cooling. This led to the introduction of the REROM system which utilized a completely filled nucleate boiling approach using an electric water pump instead of a mechanically-driven pump and a conventional wax thermostat. REROM showed a 2-3% reduction in fuel consumption, a 10% reduction in CO, and a 3% reduction in HC over the Motor Vehicle Emission Group (MVEG) cycle. The MVEG cycle is used to assess emission levels of vehicles in Europe. This cycle consists of four repeated ECE-15 driving cycles and an Extra Urban Driving cycle (EUDC). Other benefits included a 10%-15% reduction in cost, 20%-25% weight reduction, and 20%-25% coolant volume reduction [7]. With the use of conventional cooling systems in new engine designs with higher specific output, the increased coolant flow rate leads to higher pump power consumption, lower overall engine efficiency, non-uniform cooling patterns and overcooling. While virtually all internal combustion engines are designed to be cooled by forced convection, nucleate boiling remains the most efficient form of heat transfer. In nucleateboiling cooling, the coolant is locally vaporized in the engine,
BTU CFD EUDC FFT FIR HC IC MVEG MW/m2 PSIG RMS THEMIS
British Thermal Units Computational Fluid Dynamics Extra Urban Driving Cycle Fast Fourier Transform Finite Impulse Response Hydro Carbons Internal Combustion Motor Vehicle Emission Group Heat Flux (Megawatt per meter squared) Pounds per Square Inch Gage Root Mean Squared Thermal Management Intelligent System
CFD SIMULATION OF BOILING A simplified computational model for the engine cooling passage was constructed in order to further understand the physics involved in engine cooling. This model was analyzed in the CFD domain of Ansys Fluent. This section provides the background information of the simulation that was performed in the CFD domain. GEOMETRY AND MESH A small and simplified section of the engine cooling passage was considered to build the computational model. Figure 2 shows the cooling passage considered for computational purposes. The diameter of the computational domain was chosen to be 10 mm to represent an average cooling passage in the 5.4L V8 cylinder head. The length of the cooling passage was chosen to be 157 mm which corresponded to the maximum straight channel length for the engine under consideration. This domain was meshed with the quad mesh of count 12,200.
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Figure 2. Geometry of the computational domain
Boundary conditions and case setup The cooling passage for the computational analysis was assumed to have a cylindrical shape. An axi-symmetric approach was followed to generate the solution and the geometry was solved in a two-dimensional domain. The inlet of the channel as shown in Figure 2 was considered as the velocity inlet at which a fully developed velocity profile corresponding to the mass flow rate of 1 kg/sec was applied. The section of the cooling passage in this simulation was located at a significant distance from the actual inlet of the cooling passage. Therefore, it was assumed that the flow is fully developed by the time it reaches the section under simulation. Inlet temperature of the flow was taken to be 373 K. The outlet of the domain was treated as a pressure outlet at the system pressure of 2 bar absolute. The wall of the channel was divided into three sections. Two sections were of length 75 mm each and had a distributed heat flux of 0.9 MW/m2 applied. A middle section of 2 mm length had a heat flux of 5 MW/m2 applied, a peak value that can be experienced in the combustion chamber [8]. The sections are shown in Figure 3. The heat flux in an engine cooling passage does not have uniform distribution. There are hot spots where the higher magnitude of heat flux is present. Such conditions are simulated by using this approach of applying heat flux by dividing the wall into three sections. 75 mm
2 mm
transfer mechanisms at these two locations are modeled in RPI boiling model. Interface mass transfer depends on several variables such as saturation temperature, interfacial heat transfer coefficient, bubble diameter, latent heat and vapor void fraction. These parameters are modeled with suitable correlations in the RPI model [9]. With the above mentioned boundary conditions and models, two cases were investigated. The first case was analyzed with no boiling by lowering the heat flux to ensure no vapor was formed in the cooling passage. The second case involved phase change i.e., the existence of boiling in an engine cooling passage. For the first case, the point heat flux was set to 1 MW/m2 while the rest of the wall was maintained at 0.9 MW/m2. For the boiling case, the point heat flux was set to 5 MW/m2 while the rest of the wall was maintained at 0.9 MW/m2. The results of these two cases were compared to identify distinct parameters to assist with identification of boiling conditions. The coolant flow rate is likely to change during engine operation and the variation in the flow rate will change the wall temperature of the coolant passages. This effect was analyzed in the computational domain using two additional cases to identify the effect of the mass flow rate on the boiling within the simplified cooling passage. SIMULATION RESULTS AND DISCUSSION Pressure study Results of the case with the reduced hot spot heat flux (1.0 MW/m2) showed no existence of a vapor phase in the computational domain. Figure 4 shows the vapor volume fraction contours in the cooling passage for the lower hot spot heat flux. The volume fraction represents the volume occupied by the vapor phase at that location.
75 mm
Figure 4. Vapor volume fraction contours for low hot spot heat flux (non-boiling case)
Figure 3. Hot spot effect under consideration (not to scale)
CFD analysis was performed on the above mentioned domain using Ansys Fluent CFD solver. A multiphase modeling approach was followed to solve this geometry. Eulerian multiphase model which is available in Ansys Fluent was used to deal with two phases which may co-exist in the computational domain under the given set of boundary conditions. Moreover, the RPI nucleate boiling model which is available with Ansys Fluent was used to solve this specific case of nucleate boiling where the flow was expected to be in the nucleate boiling regime. The RPI model has been well validated to predict boiling under the nucleate boiling regime [8]. When boiling is present, heat and mass transfer occurs at the two separate locations; at the wall which provides the required superheat and at the bubble and liquid interface. Heat and mass
The above results were recorded after 3 milliseconds of heating and showed no sign of nucleate boiling. However, even for 6 milliseconds, no phase change was observed. Thus, this case is designated as the non-boiling case in which no phase change exists. The results of the case with the 5 MW/m2 hot spot heat flux are shown in Figure 5. The results are plotted at time intervals of 1, 2, 3, 4 and 5 milliseconds. The contour plots show how the vapor volume fraction within the cooling passage varies with the time of heating. At time equal to 1 millisecond, the content of vapor within the cooling passage was quite significant. The vapor volume fraction started developing at the location of the hot spot and it strengthened as it moved along the length of the tube after the hot spot.
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Time instant - 1 milliseconds
Time instant - 2 milliseconds
Time instant - 3 milliseconds
Time instant - 4 milliseconds
Time instant – 5 milliseconds
Figure 7. Total pressure variation at monitoring point for boiling and non-boiling conditions Figure 5. Vapor volume fraction contours for 5 MW/m2 hot spot heat flux
While in absolute terms the vapor volume fraction was quite low, this small amount of vapor can be justified as nucleate boiling is defined as the inception of boiling. At subsequent time instant, the vapor formation was also observed on slight downstream side of hot spot. Vapor formation was simultaneously accompanied by condensation of those vapors in the sub-cooled, mainstream flow. Therefore, the maximum vapor volume fraction for subsequent time intervals was less than that observed at 1 millisecond. In order to compare the boiling and non-boiling cases, a cell in the computational domain close to the point heat flux wall was marked and monitored for the total pressure of liquid and vapor volume fraction. The cell location is shown in Figure 6.
Figure 6. Monitoring cell location
The graph of the total pressure variation at the monitoring point for up to 45 seconds for both boiling and non-boiling conditions is shown in Figure 7. The total pressure of the liquid shows a clear distinction between the two cases of boiling and non-boiling. The case with no boiling displays no fluctuations in the pressure value. The boiling case pressure plot involved large pressure fluctuations as the solution developed over time. Both boiling and non boiling cases were simulated until the steady state condition was observed after approximately 20 seconds.
When the boiling occurs within a flow, the phenomenon is accompanied with changes in the pressure at that location. These pressure changes result from the exchange of mass and momentum from the liquid to the vapor phase and vice-versa. The nucleate boiling regime requires the bubbles to collapse back to the sub-cooled flow after inception. Such inception and collapse cause significant pressure fluctuations in the liquid which are evident from the above plot. The vapor phase formation was more rapid and the pressure fluctuations were quite high during the first five seconds of the results. Over this time interval, the vapor volume fraction within the cooling passage was high and subsequently the bubbles were collapsing back into the liquid phase. As the time progressed, the pressure fluctuations were reduced as the generation of the vapor volume fraction within the flow gradually reduced. Figure 8 shows the difference in pressure fluctuations between the boiling and non-boiling cases for the first 10 milliseconds. It takes approximately 7 milliseconds to distinguish the pressure difference between the two cases. This time delay is an important parameter to track as the control mechanism is developed.
Figure 8. Pressure difference between boiling and non-boiling for first 10 milliseconds
The generation of vapor volume fraction at the monitored location from 0 seconds to 45 seconds is shown in Figure 9. Vapor generation was rapid during the initial time and hence the vapor volume fraction at the monitored location was quite
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high. The pressure difference indicated the same trend in vapor formation early in the analysis. As the time progressed, the vapor volume fraction at the monitored location reduced, thereby reducing the pressure variation at the point.
of vapor volume fraction at the monitoring location during the initial time.
Figure 11. Vapor volume fraction for different mass flow rates during initial time Figure 9. Vapor volume fraction variation at monitoring point
Based on the results of this simplified analysis, the detection of nucleate boiling in the cooling passage based on the pressure fluctuations appear promising. Mass flow study The second simulation was focused on studying the effect of mass flow rate on nucleate boiling. The prime focus of mass flow variation was to ensure that the nucleate boiling phenomenon exists for the range of mass flow rates through the channel. The flow rates were selected in the range of 0.6 to 1 kg/sec. Figure 10 shows the vapor volume fraction generation for varying magnitudes of coolant mass flow rate. Note the color scale is different for each flow rate to improve resolution. The mass flow rate of 0.6 kg/sec displayed the highest fraction of vapor within the channel. As the flow rate was increased, the vapor volume fraction systematically reduced, yet spread further downstream due to the higher flow rate.
The variation of the vapor volume fraction at the monitoring point, from 0 seconds to 40 seconds, is shown in Figure 12. As expected, the highest flow rate produced the lowest vapor volume fraction over the entire time domain while the lowest mass flow rate resulted in the highest average vapor volume fraction over the entire domain.
Mass flow rate 0.6 kg/sec; Time instant 3 milliseconds
Figure 12. Vapor volume fraction variation at monitoring point
Mass flow rate 0.75 kg/sec; Time instant 3 milliseconds
Mass flow rate 1 kg/sec; Time instant 3 milliseconds
Figure 10. Vapor volume fraction for different mass flow rates
For the range of mass flow rates considered, the existence of nucleate boiling was identified. Figure 11 shows the variation
A key result of this work is the determination of coolant flow rate as a primary control mechanism for nucleate boiling. The amount of the heat removed from the channel walls correlated with the amount of vapor generation in the computational domain. As the vapor generation increased, the heat transfer also increased. This was evident from the higher temperature of the liquid (coolant) at the lower mass flow rates. At the low mass flow rates, heat extracted by the coolant was higher, which resulted in higher coolant temperatures. Higher coolant temperatures can be interpreted to mean lower surface temperatures for the same amount of heat flux input; thereby providing an improved cooling effect. Thus, more heat extraction was observed with the existence of nucleate boiling. Coolant temperatures adjacent to the channel walls are shown in Figure 13. A coolant flow rate of 0.6 kg/sec resulted in the
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maximum coolant temperatures whereas the coolant flow rate of 1 kg/sec resulted in the lowest coolant temperature. This indicates that for the same amount of heat flux supplied to the fluid, coolant with lower flow rate resulted in the lower channel surface temperatures. In Figure 13, it is observed that the temperatures of the liquid at the location close to the hot spot location were very high. At this location, the liquid is converted to the vapor phase and thus explains why the sudden and significant increase in temperature exists. Mass flow rate – 0.6 kg/sec
Figure 13. Temperature plots of liquid surrounding the cooling passage walls
EXPERIMENTAL SETUP FOR BOILING DETECTION
Mass flow rate – 0.75 kg/sec
Simulation results showed that a pressure based technique can be used to detect boiling in the cooling passage. The idea of the pressure based boiling detection was then tested on the actual engine geometry in an experimental test rig. The following section describes the experimental procedures and results to validate the pressure-based identification technique for pool boiling. FIXTURE DESIGN AND SPECIFICATIONS
Mass flow rate – 1 kg/sec
The test rig fixture was constructed from a single combustion chamber section taken from a 5.4L V8 Ford cylinder head. By using a piece of the actual engine to be studied, the complex geometry of the head and cooling passages was preserved. A Plexiglas window was added for visual observation of the cooling passages, and a liquid reservoir was attached to the side of the head with a 2” long galvanized steel pipe of 0.625” inside diameter. The Plexiglas window was replaced with an aluminum plate for pressurized testing. The reservoir was insulated with 2” thick mineral wool to help prevent heat loss. The entire test fixture was mounted on a steel plate that sat on top of a 55,000 BTU propane burner. The burner was run in two configurations: standard (as purchased), and with a converging cone added to focus the flame on the center of the combustion chamber. The purpose of the cone was to attain higher heat flux values. Distilled water was used for all the experimental runs. Figure 14 is a picture of the test rig fixture with the Plexiglas window installed.
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Figure 15. Cross section of test rig fixture showing instrumentation locations
DATA ACQUISITION Figure 14. Nucleate boiling test rig fixture
INSTRUMENTATION A high sensitivity pressure transducer, three thermocouples, and an accelerometer were installed in the test rig fixture. The pressure transducer, PT1, was mounted in the side wall of the cylinder head section. The characteristics of the pressure transducer which were used in the experiment are shown in Table 1. Table 1. Pressure transducer specifications
PT1
Sensitivity
Linearity
Uncertainty
[mV/kPa] 14.7
[%] 0.1
[%] -0.64
Natural Frequency [kHz] 350
The first thermocouple, TC1, measured bulk coolant temperature. The second thermocouple, TC2, measured the combustion chamber side embedded surface temperature. The third thermocouple, TC3, measured the coolant passage side embedded surface temperature. TC2 and TC3 were used to calculate the heat flux applied to the head. They were installed in the coolant passage “hot spot,” located between the intake port and spark plug boss, in the coolant passage. The onset of boiling was first observed at this location. The accelerometer, A1, was mounted on the top of the cylinder head section, and measured acceleration in the vertical direction. The locations of various instruments are shown in Figure 15. CALIBRATION Pressure transducer PT1 and Accelerometer A1 were provided with factory calibrations. The thermocouples were calibrated after being installed in the fixture by holding the entire fixture at known temperature points and recording the output. A correction factor was then determined for each thermocouple and applied to the acquired data.
Data was taken using a laptop computer connected to a Siglab unit and a National Instruments thermocouple input module. The Siglab unit recorded signals from the pressure transducers and accelerometer. A sampling rate of 12,800 Hz was used with a sample length of 6.4 seconds. Voltage outputs from the thermocouples were input to the thermocouple module, which then applied an electronic, cold point compensation. The sampling rate for temperature measurements was 5 Hz, with a similar sample length of 6.4 seconds. The following block diagram, Figure 16, shows the layout of the transducers and acquisition system. It also includes the post processing that was used for data analysis.
Figure 16. Block diagram of data acquisition system
TEST PROCEDURE As previously stated, distilled water was used in all experiments. The head and reservoir were filled to within approximately 2.5 cm of the top of the reservoir prior to each set of runs. The water level was checked periodically to ensure that adequate liquid was present to fill the coolant passages in the head section. Heat was applied with a propane burner to the combustion chamber area of the cylinder head. The exhaust valve was left partially open to simulate the exhaust port heating present in a running engine. The fixture was heated with the propane burner until all the water reached the saturation temperature, 100 ºC, including the liquid in the reservoir. The system was vented to the atmosphere by an
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adjustable pressure relief valve which was set to either the open position, atmospheric testing, or adjusted for a system pressure of 10 psig. The heat flux from the burner was adjusted to the desired level, and all temperatures were allowed to reach steady state before data was taken. TEST MATRIX A total of 100 data sets were collected for the test rig research. They were separated into three main categories: I. Initial data - Seven data sets were used to develop the post processing technique for the pressure signal. Data was collected over a range of excess temperatures to represent various boiling and non-boiling conditions. Excess temperature, ∆Te, is the amount of superheat at the metal’s surface above the liquid’s saturation temperature i.e. difference between surface temperature and saturation temperature of coolant. (100 ºC for distilled water at atmospheric pressure.) II. Atmospheric pressure data - The bulk of the data was taken at atmospheric pressure. The goal was to develop a pool boiling curve for the fixture, and examine the pressure signal. For this purpose, 80 runs were performed over as broad of a range of heat flux values as the fixture would allow. III. 10 psig pressure data - 13 runs were collected for the 10 psig pressure condition. They were used to examine the effect of system average pressure on the boiling signature.
The initial data was separated into signal and calibrated data files. Fast Fourier Transform's (FFT's) were calculated for PT1 using a Hanning window and block size of 4096 which produced 20 averages. The Hanning window was selected because it works well in situations with a large amount of extraneous noise. The equation for the Fourier Transform is shown in Equation 1.
/ ∑ ∆
EQ. 1
for: m = 0, 1, 2, …N/(2-1) After examining the initial FFT's, it was decided to reduce the block size to 2048 for 40 averages. This decreased the frequency resolution, but since boiling is a broadband signal, it did not hinder the analysis. Additionally, it was decided to overlap each window by 50% in order to utilize the data being lost at the beginning and end of each block due to the use of the Hanning window. This raised the number of averages to 79. Using the new processing parameters, FFT's and auto powers were calculated for each run. The auto power equation is shown in Equation 2. G ω = G∗ ωG ω
The Autopower eliminated the chance that the frequency bins could average to zero. Figure 17 is a plot of the Autopower of transducer PT1. Run 5 represents the boiling test while the other traces (runs 1, 2, 3, and 6) represent the non-boiling conditions. This plot clearly shows the difference in frequency content between the boiling and non-boiling conditions.
Figure 17. Auto power of cylinder head pressure PT1
IDENTIFICATION OF BOILING FREQUENCY RANGE
EXPERIMENTAL RESULTS AND DISCUSSION
= ∆ =
where: Gx is the linear spectrum (Fourier Transform) of a time history Gx* is the complex conjugate of Gx
EQ. 2
Auto power data was generated for PT1 and a cumulative summation was calculated for each of the five initial runs using Equation 3. ∑ ! = ∑ × ∆/
EQ. 3
The purpose was to determine the optimum frequency range for boiling detection and design a digital filter that could be applied to the real time pressure signal before computing an instantaneous RMS value. The RMS value can then be monitored for a threshold that indicates the presence of boiling. The cumulative summations show the change in the total energy content of the pressure signal verses frequency. Figure 18 begins summing at 25 Hz and Figure 19 begins summing at 800 Hz. The 25 Hz plot was used to determine where the boiling frequency band begins. The bulk of the energy content was located in the lower frequencies, so the start value was varied until a frequency was found that gave maximum offset between the boiling and non boiling signals. The 800 Hz plot was used for determining the upper boundary of boiling. Eliminating the large initial energy content improved the slope resolution at higher frequencies. At 2,000 Hz, the slopes of the boiling and non-boiling signals were approximately equal, so the relative difference in the total energy content was maximized.
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100
Atmospheric 10 psig Nukiyama PBC
Heat Flux (kW/m^2)
80
Rohsenow PBC 60
40
20
Figure 18. Cumulative summation of cylinder heat pressure auto power pool boiling (25 – 5,000 Hz)
0 0
2
4 6 Excess Temperature (deg C)
8
10
Figure 20. Comparison of test data with pool boiling curves
RMS PROCESSING OF FILTERED TEST DATA
Figure 19. Cumulative summation of cylinder head pressure auto power pool boiling (800 Hz – 5,000 Hz)
Based on the cumulative summations, two digital bandpass filters were designed utilizing the filter tool in Matlab. Both filters used FIR Kaiser windows. An FIR filter was selected because it is always stable and easy to implement. The first filter was for the discussed 25-2,000 Hz range and the second one was for the 300-2,000 Hz range. The decision to investigate the second filter came from examining Figure 17, the auto power of PT1. There is a bulge in the energy content of the non-boiling signal that drops off at 300 Hz. The 300-2,000 Hz filter eliminated this region.
Figure 21 is a plot of the RMS values of filtered p' versus their respective heat flux values. A logarithmic curve fit with an R2 value of 0.802 was added along with +/-20% and +/-30% confidence interval lines. The bulk of the outliers appeared to be on the lower side of the RMS range. Upon examining the data broken up and plotted as individual measurement groups, it was found that the bulk of these lower level outliers appeared in measurement group number 1. Based on the work of Kutepov et al., it was safe to say that the boiling surface was not fully seasoned during the initial run [12]. The fixture had been modified for improved performance, and the surface unavoidably disturbed. Figure 22 is a plot of the RMS versus heat flux again, but with the measurement 1 data removed, except for three of the non-boiling points. The non-boiling points were needed to provide a reference for the fitting of a trend line. Since the only non-boiling data available was from measurement 1 there was not another option. This should not affect the outcome, however, since the seasoning of the surface was only a factor for the nucleate boiling conditions. 0.03
All measurements were run through the post processing procedure, developed with the initial data, which consisted of calibration, filtering, and RMS calculation. Additionally, the heat flux, excess temperature, and the heat transfer coefficient h were calculated for each data set. The data from experimental pool boiling was then plotted along with the Nukiyama pool boiling curve and Rohsenow pool boiling curve for comparison as shown in Figure 20. Nukiyama used a platinum wire for his experiments [10] while Rohsenow used a flat plate [11]. There were geometric differences between both cases, but the data followed the trend of the classic Nukiyama curve more closely. There was a shift in the magnitude of heat flux for a given excess temperature, but again, with the differences in geometry and physical properties of the experiment [10] [11], this was expected. Unfortunately, the maximum heat flux attained was limited by the propane burner, so the extended trend as excess temperature increased could not be determined.
RMS of Pressure, p' (psi)
VALIDATION WITH BOILING CURVES
y = 0.0113Ln(x) - 0.0164 0.02
Atmospheric
0.01
10 psig +/- 20% +/- 30% Log. (Atmospheric)
0.00 0
10
20 Heat Flux (kW/m^2)
30
40
Figure 21. RMS value of pressure fluctuation versus heat flux
With the revised data plot in Figure 22, a logarithmic trend line was again fitted and +/- 20% and +/-30% bounds were added. The new curve fit had an R2 value of 0.918 and most of the outliers were eliminated. The bulk of the data fell within +/-
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20% which was excellent considering the random nature of boiling data. A control system for boiling detection and control requires high accuracy data. Therefore the use of data with +/20% curve fit is justified over +/-30% curve fit data. Referring back to Figure 20, it is important to note that there was no significant difference between the atmospheric pressure data and 10 psig data as far as RMS was concerned. This is an important feature of the boiling detection technique since a typical automotive cooling system will typically experience pressure ranging from 0 psig to 15 psig during normal operation.
by the fluctuating component of pressure can be correlated with heat flux. These values of heat flux from the experimental pool boiling results were compared with Nukiyama and Rohsenow pool boiling curves. The results from the test setup were comparable with some deviation due to differences in test conditions between the experiments. The study is an initial step toward implementation of a practical, robust control technique for the detection and utilization of boiling in an engine cooling system. REFERENCES 1.
0.03
RMS of Pressure, p' (psi)
y = 0.0125Ln(x) - 0.0179
0.02
Atmospheric 10 psig
0.01
+/- 20% +/- 30% Log. (Atmospheric)
0.00 0
10
20 Heat Flux (kW/m^2)
30
40
Figure 22. RMS value of pressure fluctuations versus heat flux (data set 1 removed)
SUMMARY AND CONCLUSION The goal of this experiment was to gain experience with collection of nucleate boiling data and test a new technique for identifying and quantifying boiling before adding in the additional challenges of such work in a full scale engine. The technique of boiling detection proved to be accurate for the experiment. Success at test rig level builds up the confidence for applying this technique to a full scale engine experiment. The simulation results indicated the presence of nucleate boiling in the cooling passage for the typical operating range of coolant mass flow rates. With the existence of nucleate boiling, desired cooling can be achieved with lower mass flow rates of coolant thereby significantly reducing the amount of coolant pump power. This will ultimately result in improving overall engine efficiency. Results of the simulation correlated with an experimental pressure-based boiling detection technique. This study demonstrated that the fluctuating pressure within the engine cooling system experienced a large increase in magnitude at the onset of nucleate boiling. Therefore it is possible to detect boiling using the fluctuating component of pressure. An auto power calculation of the fluctuating pressure provides successful differentiation between boiling and nonboiling conditions. It was also possible to predict the range of frequency within which boiling occurs. RMS processing provides better estimates of pressure magnitudes and heat flux data. The increase in energy and accompanying increase in filtered RMS was tied to the generation and collapse of vapor bubble. The RMS value given
Kanefsky, P., Nelson, V.A., Ranger, M., “A Systems Engineering Approach to Engine Cooling Design,” SAE SP-1541, Nov 1999. 2. Robinson, K., Campbell, N.A.F., Hawley, J.G., Tilley, D.G., “A Review of Precision Engine Cooling,” SAE 1999-01-0578, Mar 1999. 3. Incropera, F. P., DeWitt, D.P., “Fundamentals of Heat and Mass Transfer,” Chapter 10, fifth edition, 2002. 4. Clough, M.J., “Precision Cooling of a Four Valve per Cylinder Engine,” SAE 931123, 1993. 5. Campbell, N.A.F., Hawley, J.G., “Predicting Critical Heat Flux as a Precursor to a Boiling-Based IC Engine Cooling Strategy,” Journal of the Institute of Energy, March 2003. 6. Couetouse, H., Gentile, D., “Cooling System Control in Automotive Engines,” SAE 920788, 1992. 7. Ap, N.S., Tarquis, M., “Innovative Engine Cooling Systems Comparison,” SAE 2005-01-1378, 2005. 8. Heywood, J.B., “Internal Combustion Engine Fundamentals,” McGraw-Hill Inc., 1988. 9. Kurul, N., Podowski, M.Z., “9th International Heat Transfer Conference,” Jerusalem, pp. 21-26, 1990. 10. Nukiyama, S., “The Maximum and Minimum Values of Heat Transmitted from Metal to Boiling Water Under Atmospheric Pressure,” Journal Japan Soc. Mech. Eng., Vol. 37, pp. 367-374, 1934. (Translation: Int. J. Heat Mass Transfer, 9, pp. 14-19, 1966). 11. Rohesnow,W.M., “Handbook of Heat Transfer,” McGrawHill Inc., New York, 1973 12. Kutepov, A.M., Sterman, L.S., Styushin, N.G., “Hydrodynamic and Heat Transfer under Vapor Generation,” Section 6, Vysshaya Shkola Publishing House, Moscow, 1977. CONTACT Scott A. Miers Assistant Professor Michigan Technological University Houghton, MI 49931 Email:
[email protected] Phone: (906) 487-2709
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