Chapter 5 Results and Discussions
5.3 Extended studies results
5.3.1 System identification
The dynamic model identification process is prerequisite for controller design research in the near future. All parameters in the dynamic model can be derived using experimental results for MGT performance. In Eq. 4.2, the HHV of the fuels influenced the required volume flow rate for fuel to air. As the HHV of methane fuel is 13255.95kcal/kg, kHHV factor is defining as 13.255 [51].
Correlation factor a can be derived based on experimental results with the conditions of Vfuel =0 , 0Torque= , and N =0 input into Eq. 4.2. The turbine-torque prediction function is then rewritten as0 =13.255×(0−0.4)+a×(1−0), and a =5.302 is calculated algebraically.
An experimental result for volume flow rate of fuel was input into the dynamic
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model to confirm the reliability of proposed numerical simulation model. The simulation results for variations in compressor speed in the dynamic model shows a great consistency with the experiment data (Fig. 5.14). However, simulation and experimental data differ before 100 sec and after 350 sec. In these periods, the MGT was considered unstable; this status is not discussed herein.
Variations in volume flow rate versus turbine outlet temperature indicate these variables were nonlinearly correlated. In the dynamic model illustrated in Fig. 4.1, Eq. 4.1 in this numerical simulation model should be restricted to represent only the stable status of the MGT system based on experimental results and phenomena. The required inputs for Eq. 4.1 were compressor rotation speed, fuel volume flow rate, and ambient temperature. The effect of ambient pressure on gas turbine output is not addressed herein. Correlation factor y can be calculated using experimental results with the conditions of
4 .
=0
Vfuel , Tamb =22, and N =1(p.u) input into Eq. 4.1. After correlation factor
y was calculated as 20.66, correlation factor z was calculated based on experimental results with the conditions ofVfuel =0, 22Tamb = , and N =0(p.u);
66 .
=20
y was then input into Eq. 4.1. Correlation factor z was 507.57. The simulation result for the effect of variables, including that of compressor rotation speed, fuel volume flow rate, and ambient temperature, on exhaust temperature were the same as experimental data when the MGT system is in stable status (Fig. 5.15). The completed simulation model will prove to be a helpful base structure when designing an optimal control strategy.
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Table 5.1： Simulation results for four-step reaction mechanism
R. P. M. 42000 45000 46000 47500 50000 55000 60000 65000 70000 75000 80000 85000
60% Methane
Maximum temperature in
combustor(K) 2106.2 2106.7 2109.0 2112.0 Maximum temperature on
liners(K) 1089.6 1096 1110.0 1117.5
T3 (K) 1115.5 1065 1053.1 1044.8
Outlet maximum
speed(m/s) 50.6 55.2 56.4 57.4
Average flow speed of
outlet(m/s) 38.9 42.4 43.3 44.1
Average mass flow rate of
outlet(kg/s) ×10−3 1.5907 1.7328 1.7492 1.7961
70% Methane
Maximum temperature in
combustor(K) 2108 2125 2133 2142
Maximum temperature on
liners(K) 1119 1130 1169 1207
T3 (K) 1030.0 1025.5 1017.8 1014.0
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Outlet maximum
speed(m/s) 50.3 56.6 64.0 67.6
Average flow speed of
outlet(m/s) 39.2 43.7 49.7 52.3
Average mass flow rate of
outlet(kg/s) ×10−3 1.7285 1.9956 2.3090 2.4448
80% Methane
Maximum temperature in
combustor(K) 2112.0 2129.6 2140.0 2152.0 2176.7 2188.7 2233 Maximum temperature on
liners(K) 1125.0 1146.0 1199.0 1230.0 1255.0 1289.0 1310 T3 (K) 999.9 970.0 955.1 940.5 920.0 909.0 893.0 Outlet maximum
speed(m/s) 46.7 52.9 59.0 65.0 71.5 80.4 91.6 Average flow speed of
outlet(m/s) 36.3 41.0 46.1 50.4 55.2 62.7 72.0 Average mass flow rate of
outlet(kg/s) ×10−3 1.7230 1.9882 2.2987 2.4376 2.6122 3.2178 4.0007
90% Methane
Maximum temperature in
combustor(K) 2140 2153 2159 2162 2180 2198 2253 2254 2258
65
Maximum temperature on
liners(K) 1151 1173 1221 1236 1267 1314 1315 1347 1407 T3 (K) 980.2 968.0 944.3 921.1 905.6 890.7 880.5 873.0 856.3
Outlet max. speed(m/s) 46.2 51.8 59.7 63.9 69.9 79.0 92.0 96.5 102 Average flow speed of
outlet(m/s) 35.9 40.2 46.5 49.6 54.1 61.9 72.2 75.7 80.1 Average mass flow rate of
outlet(kg/s) ×10−3 1.7191 1.9830 2.2940 2.4283 2.6075 3.2087 3.9844 4.2768 4.5711
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Chapter 6
Conclusions and Future Works
6.1 Conclusions
In this study, the effects of fuels with LHV on the performance of an annular MGT power system were assessed experimentally and numerically. The proposed system in this study has the potential to reduce the size of a distributed power supply system for biogas and enhance its popularization. The numerical analyses show that the cool air flows via dilution holes on liners were fully functioned and chamber design did not cause hot spots on the chamber liner or make the temperature of exhaust gas exceed the maximum allowable that temperature of the turbine wheels can tolerate. In the experiments, the proposed MGT power system was operated successfully under each test condition; minimum composition to the fuel with the LHV was roughly 60%
CH4 with 40% CO2. The power output was around 170W at 85,000 RPM as 90%
CH4 with 10% CO2 was used and 70W at 60,000 RPM as 70% CH4 with 30%
CO2 was used. When a critical limit of 60% CH4 was reached, the power output was extremely low. Furthermore, the theoretical Brayton cycle efficiency and electric efficiency of the MGT were calculated as 23% and 10%, respectively.
The electric efficiency might be improved by replacing the generator as suggested in Future Works section. This study presents a novel distributed power supply system that can utilize renewable biogas. The completed micro biogas power supply system is small, low cost, easy to maintain and suited to household use.
67
6.2 Future Works
To reduce fuel supply pressure and enhance safety, fuel pipe diameter of the proposed annular MGT system can be enlarged to deliver additional volume of fuel for future studies. Moreover, the data-acquisition facilities of the MGT system can be enhanced to determine gas exhaust components. The CO concentration profile in exhaust gas can help researchers determine whether complete combustion is achieved. Furthermore, in some fields, a brushless DC motor can be utilized as a generator and actuator simultaneously. Thus, if such a generator is applied to the proposed MGT, power loss is avoidable compared to that when transmissions are used to connect the power turbine and generator.
68
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Gas Turbine and Aeroengine Congress and Exposition, Stockholm, Sweden, 1998.
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73
Figure 1.1: The structure of project.
Figure 1.2: The basic structure of the MGT generator.
74
(a) Picture of MW54. (b) Picture of fuel supply system.
Figure 1.3: Pictures of the adopted micro gas turbine.
75
.
(a) (b) (c)
Figure 2.1: Pictures of the major parts in MGT. (a) Compressor (b) Combustion chamber (c) Turbine wheel
(a) Picture of transmission system. (b) Picture of 20 Kg steel cylinder.
(c) Picture of blower (d) Picture of Mobil Jet Oil Figure 2.2: Pictures of experimental facilities in proposed MGT system.
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(a)Schematic of the experimental configuration for proposed MGT system.
(b) Picture of the suspended test stand.
Figure 2.3: Schematic of experiment layout.
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(a) Picture of the CompactDAQ Chassis.
(b) Picture of Analog Input Module. (c) Picture of Analog Output Module.
(d) Picture of Thermocouple Differential Analog Input Module.
(e) Picture of Simultaneous Analog Input Module.
Figure 2.4: Pictures of data acquisition devices.
78
(a) Picture of thermal couple (K-type).
(b) Picture of the hall effect sensor.
(c) Picture of pressure sensor. (b) Picture of mass flow controller.
Figure 2.5: Pictures of sensors and actuators for the proposed MGT system.
79
0 200 400 600 800
temperature(0C)
0 0.2 0.4 0.6 0.8
error (%)
Figure 2.6: The relationship of temperature and error.
80
Figure 2.7: Experimental error bars for CH4:CO2 mixing ratios: (a) 60% CH4 to 40% CO2, (b) 70% CH4 to 30% CO2, (c) 80% CH4 to 20% CO2,,and (d) 90%
CH4 to 10% CO2.
81
(a) Annulus combustion chamber (b) The lateral view of (a)
(c) One sub-chamber (one-third) of annulus combustion chamber
(d) The lateral view of (c)
Figure 3.1: The simplifying procedure of model domain illustrated by software Solid Works.
82
(a) The components configuration of sub-chamber
(b) The flow configuration of air inflow and fuel inflow
Fig. 3.2 The configurations of a sub-chamber.
(a) The front view of sub-chamber (b) The lateral view of sub-chamber Fig. 3.3 The size specification of sub-chamber.
83
(a) Model domain of combustor
(b) Grids generation Fig. 3.4 Grids generation for numerical computation.
84
Fig. 4.1 Simulation model of micro gas turbine in SIMULINK.
85
Figure 5.1: Volume flow rate for fuels with LHV fuels at various RPMs.
86
Figure 5.2: Compressor speed vs. volume flow rate of CH4 with different concentrations of methane fuels.
87
(a)
(b)
88
(c)
(d)
Figure 5.3: Temperatures at different positions in the MGT (a) Fuel with 90%
CH4; (b) Fuel with 80% CH4; (c) Fuel with 70% CH4; (d) Fuel with 60% CH4.
89
Figure 5.4: (a) Combustion chamber (oil). (b) Combustion chamber using 70%
CH4 (gas).
90
Figure 5.5: Brayton cycle efficiency of different compressors speed with different concentrations of CH4.
91
Figure 5.6: Output power for different generator rotation speeds with different LHV fuel.
92
Primary Zone
Intermediate Zone
Dilution Zone
Primary Zone
Intermediate Zone
Dilution Zone
(a) Temperature distribution in combustor. (b) Mass fraction distribution of methane.
Primary Zone
Intermediate
Zone Zone
Dilution
(c) Mass fraction distribution of oxygen. (d) Distributions of velocity flow field.
Figure 5.7: Simulation results of applying 60% methane fuel at rotation speed 45000RPM.
93
(a) 90% methane concentration. (b) 80% methane concentration.
(c) 70% methane concentration. (d) 60% methane concentration.
Figure 5.8: Distribution of temperaturefor different concentration of methane at 45000 RPM.
94
(a) 90% methane concentration. (b) 80% methane concentration.
(c) 70% methane concentration. (d) 60% methane concentration.
Figure 5.9: Distributions of CH4 mass fraction for different concentration of methane at 45000 RPM.
95
(a) 90% methane concentration. (b) 80% methane concentration.
(c) 70% methane concentration. (d) 60% methane concentration.
Figure 5.10: Distributions of O2 mass fraction for different concentration of methane at 45000 RPM.
96
(a) 90% methane concentration. (b) 80% methane concentration.
(c) 70% methane concentration. (d) 60% methane concentration.
Figure 5.11: Velocity vector of flow field for different concentration of methane at 45000 RPM.
97
(a) Maximum temperature in combustor (b) Maximum temperature on liner walls
(c) Outlet maximum temperature (d) Outlet maximum velocity
Figure 5.12: Curved chart with different methane concentration and different compressor speed
98
(a) 90% CH4 to 10% CO2 (b) 80% CH4 to 20% CO2;
(c) 70% CH4 to 30% CO2; (d) 60% CH4 to 40% CO2
Figure 5.13: Maximum temperature at combustor outlet for different fuel
99
Figure 5.14: Numerical result of output power for different generator rotation speeds with 90% CH4.
100
Figure 5.15: Numerical result of turbine outlet temperature versus experimental time.
101
Publications
1. R. F. Fung, C. H. Yang, and J. L. Ha, Hysteresis Identification and Dynamic Responses of the Impact Drive Mechanism, Journal of Sound and Vibration, Vol.
283, pp. 943-956, 2005.
2. C. H. Yang, M. T. Yu, C. H. Chen, and C. H. Chen, Performance Simulation and Thermal Stress Analysis of Ceramic Recuperators by SiC and MAS, Numerical Heat Transfer, Part A: Applications, Vol.53, pp.709-725, 2008.
3. C. H. Yang, D. H. Wu, and C. H. Chen, Numerical Performance Analysis of an Annular Miniature Gas Turbine Power System using Fuels with Low Heating Values, International Journal of Numerical Methods for Heat and Fluid Flow, 2009. (Accepted)
4. C. H. Yang, C. C. Lee, J. H. Hsiao, and C. H. Chen, Numerical Analysis and Experiment Investigations to an Annular Micro Gas Turbine Power System using Fuels with Low Heating Values, Science in China Series E-Technological Sciences, 2009. (Accepted)
5. R. F. Fung, C. H. Yang, S. C. Hsien, and J. L. Ha, Identification of the Piezoelectric Actuator with Hysteresis Based on Real-Coded Genetic Algorithm, 第十三屆自動化技術研討會
6. R. F. Fung, and C. H. Yang, Hysteresis Identification and Dynamic
102
Simulations of an Impact Drive Mechanism, 中國機械工程學會第二十屆全國 學術研討會.
7. 馮榮豐、楊竣翔、韓長富，磁滯與摩擦力考慮的奈米定位，物理雙月刊。
8. C. H. Yang., D. H. Wu, and C. H. Chen, Optimal Design of a methane-used combustor: applying CFD Modeling, The 3rd International Green Conference, June 17-21, Vasteras, Sweden, 2007.
9. C. H. Yang, R. H. Hsiao, and C. H. Chen, An Improved Numerical Model Analysis and Experiment Investigations to a Methane-used Annular-type Micro Gas Turbine Power System, The Tenth World Renewable Energy Congress, July 20-23, Glasgow, Scotland, UK, 2008.