Optimization of gas mixing system of premixed burner based on CFD analysis

Energy Conversion and Management 85 (2014) 131–139

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Optimization of gas mixing system of premixed burner based on CFD analysis Tian-Hu Zhang a, Feng-Guo Liu a,b, Xue-Yi You a,⇑ a b

School of Environmental Science and Engineering, Tianjin University, 300072 Tianjin, China Dept. of Energy Technology and Mechanical Engineering, Tianjin Institute of Urban Construction, Tianjin 300384, China

a r t i c l e

i n f o

Article history: Received 27 January 2014 Accepted 17 May 2014

Keywords: Premixed burner Gas mixing system CFD Ejector

a b s t r a c t The optimization of gas mixing system (GMS) of premixed burner is presented by Computational Fluid Dynamics (CFD) and the uniformity at the outlet of GMS is proved experimentally to have strong influence on pollutant emission. To improve the uniformity at the outlet of GMS, the eleven distribution orifice plates and a diversion plate are introduced. The quantified analysis shows that the uniformity at the outlet of GMS is improved significantly. With applying the distribution orifice plates, the uniformity of velocity and fuel–gas mixing of single ejector is increased by 234.2% and 2.9%, respectively. With applying the diversion plate, the uniformity of flow rate and fuel–gas mixing of different ejectors is increased by 1.9% and 2.2%, respectively. The optimal measures and geometrical parameters provide an applicable guidance for the design of commercial premixed burner. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction With the development of burner, the pollutant emission reduction was always considered as a key issue [1–3]. The Computational Fluid Dynamics (CFD) method was a powerful tool that can be used to perform low cost parametric studies involving critical burner design parameters [4]. Arghode et al. [5] used CFD to capture the overall flowfield behavior in the burner and found the recirculate path of gas. Low emissions of nitric oxides (NOx) and carbonic oxide (CO) were achieved with the help of CFD. Sukumaran and Kong [6] simulated the combustion process inside the burner and found that NOx can be reduced by designing combustion conditions of fuel rich zones. The pollutant emission is closely related to the operating parameter of burner. Wei et al. [7] implemented a CFD-based Taguchi method to study the effect of eight operating parameters on NOx emission. The velocity of air and fuel injection were found to have significant effect on NOx emission. Under the optimal operating condition, the NOx emission was decreased by 25.45% compared with the original operating condition. Ko and Lin [8] studied the effect of five operating parameters on CO emission. The results showed that the CO emission can be decreased by decreasing the gas pressure to a suitable value, enlarging the primary aeration to a favorable level, selecting a proper thermal input, or adjusting the optimized heating height. Lezsovits et al. [9] used

⇑ Corresponding author. E-mail address: [email protected] (X.-Y. You). http://dx.doi.org/10.1016/j.enconman.2014.05.055 0196-8904/Ó 2014 Elsevier Ltd. All rights reserved.

the CFD modeling to investigate the factors that cause incomplete combustion: the fuel–gas outlet of the gas-turbine had significant swirl and rotation, the diffuser in between the gas-turbine and heat-recovery steam generator was too short and had a large cone angle, the velocity of flue-gas entering the duct burner was greater than expected, and the outlet direction of the flammable mixture from the ejector of the duct burner was not optimal. After reducing the flow swirl of flue-gas and modifying the nozzle, CO emission was reduced to an acceptable level. The pollutant emission can be notably reduced if the natural gas and air were well mixed before burning [10]. For premixed burners, the natural gas and air are mixed by gas mixing system (GMS) with fan and ejectors. The uniformity at the outlet of GMS is an important operating parameter which has not been considered in the other researches. Liu et al. [11] studied the optimal design of household appliance burner of low pollutant emission by improving the uniformity of velocity and fuel–gas mixing of a single ejector. The designed burner was validated experimentally to decrease greatly the emissions of nitric oxide (NO) and CO and the concentration of NO emission is less than 40 ppm. Unfortunately, in their approach, the GMS was investigated without considering the effects of fan, the assumption of uniform air flow at the ejector inlet of was applied and the uniformity of different ejectors (UDE) was not considered. This paper follows the above research to realize the optimal design of household appliance burner of low pollutant emission. The uniformity at the outlet of GMS is further studied by considering the effect of fan and multiple ejectors. After the numerical model is validated by the experimental results, the quantitative evaluations of the

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uniformity of single ejector (USE) and the uniformity of different ejectors (UDE) are both conducted. The optimal structure and geometrical parameters of GMS are outlined by CFD approach.

(2) The influence of radiation heat and floating terms are neglected. (3) The natural gas is simplified as methane because more than 98% mole fraction of natural gas is methane.

2. Description of the GMS and experiment apparatus The present GMS consists mainly of one multi-blade centrifugal fan and eleven ejectors. Air is supplied by the fan and natural gas is injected through the nozzle of the ejectors. The multi-blade centrifugal fan is adopted to supply kinetic energy and pressure buildup. The fan has been widely used for its small size, compact structure and low noise [12–14]. The schematic diagram of GMS is shown in Fig. 1. There is a baffle at the inlet of fan to avoid the strong change of air flow when the speed of fan is adjusted. The air and natural gas are mixed in the ejector designed by Liu et al. [11] (Fig. 2). The natural gas is accelerated to a high speed by the convergent primary nozzle, which forms low pressure region at the exit of nozzle and produces the entrainment effect to entrain the secondary fluid from the suction chamber. The location of the mixing takes place in converging and diverging section. The high interfacial area of such device gives an advantage over the conventional contactor. The distribution chamber contributes to the pressure balance of mixture of natural gas and air in front of the fire holes and it is helpful for the mixing process. To meet the demand on the full mixing of reactants before entering into the fire holes, a wedge-shaped distributing chamber is designed. The experimental apparatus are shown in Fig. 3. The fan speed is controlled by DC electrical source. The pollutant emission is evaluated by the emission of NO and CO, which are measured by GXH1050 infrared analyzer and ECOM-CN gas analyzer, respectively. The analyzer measures the NO concentration in the range of 0–1000 ppm, the CO concentration in the range of 0–2000 ppm. The measuring accuracy of the two analyzers is ±1 ppm, so the uncertainty bands for the experimental results of NO and CO emissions are both ±1 ppm. The measurement conditions are set according to the Chinese standards for boiler gas water heater (GB6932-2001, Domestic gas instantaneous water heater of China). 3. Numerical model In this approach, several appropriate assumptions are made: (1) The air and natural gas are treated as Newtonian fluid. For low Mach number flow here, they are assumed to be incompressible.

The governing equations, including the equations of continuity, momentum, species transport and turbulence model are expressed as the general form [14]:

! @ ðquÞ þ r  ðq V u  Cu;eff ruÞ ¼ Su @t

! where q is density, u is dependent variable, V is velocity vector, Cu,eff is effective diffusion coefficient and Su is source term. The terms of the governing equations are shown in Table 1, where p is the static pressure, Pk is the turbulence kinetic energy generated by average velocity gradient, Yi and Di,m are the mass fraction and diffusion coefficient of species i, respectively. The source term Su describes the source/sink of mass, momentum, species, turbulence kinetic energy and turbulence kinetic energy dissipation rate in the computational domain. The standard k  e turbulence model is used to enclose Reynolds stress. The turbulent viscosity lt is computed as follows:

lt ¼

qC l k2 e

ð2Þ

The turbulent diffusivity can be obtained by the following equation [15]:

Dt ¼

lt qSct

ð3Þ

The moving reference frame approach is used for fan rotation. A coordinate system which is rotating steadily with angular velocity ! x relative to a stationary reference frame is considered. The origin ! of the rotating system is located by a position vector r0 . The computational domain for the CFD problem is defined with respect to the rotating frame such that an arbitrary point in the CFD domain ! is located by a position vector r from the origin of the rotating frame. The fluid velocities can be transformed from the stationary frame to the rotating frame using the following relation:

!

vr

¼

!

v

! ! xr

!

ð4Þ !

where v r is the relative velocity, v is the absolute velocity. At the interfaces between cell zones, a local reference frame transformation is performed to enable flow variables in one zone to be used to calculate fluxes at the boundary of the adjacent zone. Fluent permits the activation of a moving reference frame with a steady rotational speed. The no slip condition is defined in the relative frame such that the relative velocity is zero on the moving walls [15]. The inlet boundary condition of fan is fixed at an outward extended cylinder to assure that the zero static pressure boundary condition is applicable. The rational dimension of the extended cylinder is determined by the numerical simulation. It is fixed by requiring that the variation of volume flow rate at the outlet is less than 0.5% at the speed of 5000 rpm (revolutions per minute) when the extended length is increased by 5%. The inlet boundary condition of gas is mass flow. The k and e at the boundaries of inlet are calculated by the following equations [14]:

k ¼ 1:5ðuav g IÞ2

e¼

C 0:75 l k

ð5Þ

1:5

l

l ¼ 0:07Dh Fig. 1. Schematic diagram of GMS.

ð1Þ

ð6Þ ð7Þ

where Dh is the hydraulic diameter and Cl = 0.09. The turbulence intensity I is defined as follows:

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Fig. 2. Schematic diagram of ejector.

1. gas supply system, 2. pressure regulator, 3. flow meter, 4. U shape pressure gauge, 5. Electro magnetic valve, 6. thermometer, 7. pressure gauge, 8. fan, 9. finned heat exchanger, 10. infrared analyzer, 11. gas analyzer, 12. DC power supply, 13. burner. Fig. 3. Schematic drawing of the experimental apparatus. Table 1 Terms in the governing equations. Equation

u

Cu,eff

Su

Continuity Momentum

1 ! V Yi

0

0 rp

Species transport

leff qDi,m + lt/

0

Sct Turbulence kinetic energy Turbulence kinetic energy dissipation rate

k e

leff/rk leff/re

Pk  qe e(C1Pk  C2e)/ k

Note: the coefficients of turbulence model are C1 = 1.44, C2 = 1.92, Sct = 0.7, rY = 1.0,

rk = 1.0, re = 1.3.

I¼

u0 uav g

ð8Þ

where u0 is the root-mean-square of the velocity fluctuations, and uavg is the mean flow velocity.

The outlet boundary condition is the zero static pressure. Noslip conditions are used at the walls and the standard wall functions are employed at the near-wall cells. The Semi-Implicit Method for Pressure Linked Equations algorithm is adopted to solve the discrete equations. The commercial software Fluent is used for CFD simulation. The grid independence test is conducted. Due to the complex geometry, the computational zone is divided into 63 small zones when the mesh is generated. Different types and sizes of grid are used in different zones. A coarse grid with 2.1 million cells is established at first. The cell numbers of the grid independence test are 2.1, 5.4, and 13.9 million. Several sampled points are extracted in different zones, and their velocity magnitudes are compared. When the cell number is 13.9 million, the variations of velocity magnitudes are less than 5% compared to that of 5.4 million cells. The grid with 5.4 million cells is used in our approach. The grid distribution of GMS is shown in Fig. 4.

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continuum is closed to zero and the maximum is closed to the walls. This is because that there is a contraction expansion section (see Fig. 1) in the distributing chamber, which results in big loss of kinetic energy at the centre of the continuum. In addition, there is a vortex (see Fig. 6) in the downstream of contraction expansion section, which stops the high velocity at the centre of the continuum. So the velocity field at the centre of the continuum is near zero and the maximum is near the walls. Fig. 7a shows the velocity field at the outlet of the 11 ejectors obtained in the standard condition. It is found that the velocity distribution at the outlet of original GMS is not uniform. Therefore, a distribution orifice plate is introduced to cover the outlet of each ejector to improve the USE. It contains seven orifices and the geometric dimension of the distribution orifice plate is shown in Fig. 7b. The same distribution plate has been used to improve the uniformity of velocity distribution at the outlet of a single ejector [11]. Fig. 8 shows the velocity fields in four typical ejectors (Nos. 1, 4, 8 and 11) when the distribution orifice plate is used. After the mixture leaves the distribution plate, the velocity distribution at the outlet of the ejector is improved. To evaluate the optimization result, a quantitative index for uniformity is proposed as:

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u N  u 1 X /  /2 i U ¼1t N i¼1 /

Fig. 4. Grid of GMS.

4. Results 4.1. Model validation The calculated volume flow rate at the outlet of GMS is compared to the experiment result. The measured fan speeds of experiment are used in simulation. Five fan speeds (4537 rpm, 4688 rpm, 4846 rpm, 5066 rpm and 5301 rpm) are measured by speedometer Testo465. The volume flow rates of mixture obtained by numerical calculation and experiment are shown in Fig. 5. The relative error between the calculation and experiment is less than 10% for different fan speeds. It is concluded that the numerical model is feasible. 4.2. Optimization In this paper, the standard condition for GMS is adopted that the fan speed is 5000 rpm and the mass flow rate of methane is 4  105 kg/s. The velocity fields of four typical ejectors (Nos. 1, 4, 8 and 11) without the optimization measures are shown in Fig. 6. It can be seen that the velocity field at the centre of the 36 Numerical result Experimental result

Flow rate (m3/h)

33

30

27

24

4400

4600

4800

5000

5200

5400

Speed of fan (rpm) Fig. 5. Computed and experimental flow rate at the outlet of GMS.

ð9Þ

where U is the uniformity, N is the total number of the cell, /i is the value of the ith cell, / is the mean value. The value U = 1 indicates the best uniformity. This index used to evaluate the temperature uniformity of furnace in Danon’s research [16]. The simulation results are exported by Fluent, and Microsoft Excel is used to calculate the value of uniformity index. The velocity uniformity of single ejector (VUSE) and the fuel– gas mixing uniformity of single ejector (FMUSE) are studied. They represent the distribution of velocity and mole fraction of methane, separately. The VUSE and FMUSE are calculated by Eq. (9). When VUSE is calculated, /i denotes the velocity magnitude (m/s) of the ith grid at the outlet of the ejector. When FMUSE is calculated, /i denotes the mole fraction of methane (%) of the ith grid at the outlet of the ejector. Different fan speeds are studied to evaluate the effect of the distribution orifice plate. The results of four typical ejectors (1, 4, 8 and 11) at different fan speeds are shown in Figs. 9 and 10. Both of the VUSE and FMUSE are improved greatly in different fan speeds. In the standard condition, the VUSE and FMUSE of all the ejectors are increased at least by 234.2% and 2.9%, respectively. After applying the distribution orifice plates, the uniformity of USE is improved. For improving the uniformity of different ejectors (UDE), a diversion plate with eleven diversion orifices is added between the suction chamber and the converging section, see Fig. 11a. For manufacture and numerical optimization convenience, the shape of converging section inlet is changed to circle (Fig. 11c) from the rounded rectangle (Fig. 11b). The diameter of the circle is smaller than the width 12 mm of the converging section. To improve the UDE, the optimal diameter of orifices of the diversion plate is studied. For easy comparison of the performance of different diversion plate, a diversion plate with the same diameter of 12 mm for each orifice is named as the original diversion plate. The diversion plates used to study the UDE are shown in Table 2. The flow rate uniformity of different ejectors (FRUDE) and the fuel–gas mixing uniformity of different ejectors (FMUDE) are studied. They are also calculated by Eq. (9). For the calculation of FRUDE, /i is the flow rate (m3/s) at the outlet of the ith ejector.

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135

Fig. 6. Velocity fields of four typical ejectors (Nos. 1, 4, 8 and 11) without the optimization measures.

Fig. 7. (a) Velocity field at the outlet of original GMS. (b) Distribution orifice plate.

For the calculation of FMUDE, /i is the mean mole fraction of methane (%) at the outlet of the ith ejector. The relationship between the uniformity of UDE (FRUDE, FMUDE) and the pollutant emission is shown in Fig. 12. The result shows that the FMUDE keeps the same trend with the FRUDE. The emission of NO and CO is reduced if the uniformity of UDE is increased. Small improvement of UDE induces a significant decrease of pollutant emission. The CFD is used to search the optimal orifice diameters of diversion plate. Because the FMUDE keep the same trend with FRUDE (When the FRUDE is improved, the same as FMUDE), only the FRUDE is considered as the optimization objective. When the original diversion plate (same orifice diameters of 12 mm) is used, the ejector 11 has the minimum flow rate. To realize the same flow rate of different ejectors, the flow rate of the other ejectors should

be decreased. The optimization process can be considered as an iterative correction method. In the iterative process, the velocity of ejector 11 is always used to calculate the other orifice diameters for the diversion plate. If the flow rate of the ith ejector is bigger than that of ejector 11, the ith orifice diameter will be decreased, and vice versa. The orifice diameters of diversion plate for the (n + 1)th iteration are obtained by the following equation:

V nþ1 ¼ V n11 i nþ1 di

¼

n di

sffiffiffiffiffiffiffiffiffiffi V nþ1 i V ni

ð10Þ

ð11Þ n

where V ni is the flow rate of the ith ejector for the nth iteration, di is the ith orifice diameter for the nth iteration.

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Fig. 8. Velocity fields in four typical ejectors (Nos. 1, 4, 8 and 11) with the distribution orifice plate.

1.0

1.0 case A case B

0.8

0.8

0.6

0.6

0.4

0.4 0.2

0.2 0.0

4000

4500

5000

5500

0.0

6000

4000

4500

Speed of fan (rpm)

5500

6000

1.0 case A case B

Ejector 8

case A case B

Ejector 11

0.8

0.8

0.6

0.6

VUSE

VUSE

5000

Speed of fan (rpm)

1.0

0.4 0.2 0.0

case A case B

Ejector 4

VUSE

VUSE

Ejector 1

0.4 0.2

4000

4500

5000

5500

6000

Speed of fan (rpm)

0.0

4000

4500

5000

5500

6000

Speed of fan (rpm)

Fig. 9. Velocity uniformity of single ejector (VUSE) for four typical ejectors at different fan speeds. Case A and B are corresponding to the outlet of original ejector and that of ejector with the distribution orifice plate, respectively.

The new orifice diameters generated by Eq. (11) might lead to worse FRUDE due to the complex interrelationship of different orifices. If the FRUDE obtained by latest three iterations is not better than that obtained by the previous iterations, the iteration

process will be stopped. The FRUDEn is specified as the FRUDE in the nth iteration. FRUDE1 is the FRUDE of original diversion plate. When n > 1, the optimization process of FRUDE is shown as follows:

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T.-H. Zhang et al. / Energy Conversion and Management 85 (2014) 131–139

0.98 0.97

0.97

0.96

0.96

0.95 0.94 0.93

case A case B

Ejector 4

FMUSE

FMUSE

0.98

case A case B

Ejector 1

0.95 0.94

4000

4500

5000

5500

0.93

6000

4000

4500

0.98 0.97

6000

case A case B

Ejector 11 0.95 0.94

FMUSE

FMUSE

5500

0.96

case A case B

Ejector 8

0.96 0.95

0.93

0.94

0.92

0.93

0.91

0.92

5000

Speed of fan (rpm)

Speed of fan (rpm)

4000

4500

5000

5500

0.90

6000

4000

Speed of fan (rpm)

4500

5000

5500

6000

Speed of fan (rpm)

Fig. 10. Fuel–gas mixing uniformity of single ejector (FMUSE) for four typical ejectors at different fan speeds. Cases A and B are corresponding to the outlet of original ejector and that of ejector with the distribution orifice plate, respectively.

Fig. 11. The description of adding diversion plate in uniformity of different ejectors. (a) The diversion plate; (b) original inlet shape of converging section and (c) inlet shape of converging section after adding the diversion plate.

Table 2 The diversion plates used to study the uniformity of ejectors. No.

d1 (mm)

d2 (mm)

d3 (mm)

d4 (mm)

d5 (mm)

d6 (mm)

d7 (mm)

d8 (mm)

d9 (mm)

d10 (mm)

d11 (mm)

A1 A2 A3 A4 A5

12.0 10.0 11.0 10.0 10.0

12.0 10.0 11.0 10.0 11.0

12.0 11.0 10.0 11.0 11.0

12.0 11.0 11.0 10.0 10.0

12.0 11.0 10.0 11.0 11.0

12.0 11.0 11.0 10.0 10.0

12.0 10.0 11.0 11.0 11.0

12.0 11.0 10.0 11.0 11.0

12.0 11.0 11.0 12.0 11.0

12.0 11.0 11.0 12.0 11.0

12.0 12.0 12.0 12.0 12.0

Step 1: calculate the orifice diameters for the nth iteration generated by Eq. (11). Step 2: calculate the velocity for each ejector at the nth iteration by CFD.

Step 3: calculate the FRUDEn. Step 4: if FRUDEn > max (FRUDE1, FRUDE2, . . .FRUDEn1), tn = 0. Else tn = tn1 + 1(t1 = 0). If tn < 3, n = n + 1, turn to step 1, else stop the iteration process.

T.-H. Zhang et al. / Energy Conversion and Management 85 (2014) 131–139

FRUDE

0.99 36 34 0.98 32

1

2

3

4

5

0.99

400

0.98

200

0.97

30

1

2

Diversion plate number

FMUDE

36 34 0.98 32

3

5

0

4

5

600 FMUDE Emmision of CO

d

0.99

400

0.98

200

FMUDE

38

Emmision of NO (ppm)

c

0.99

2

4

1.00

40 FMUDE Emmision of NO

1

3

Diversion plate number

1.00

0.97

b

Emmision of CO (ppm)

38

600 FRUDE Emmision of CO

FRUDE

a

Emmision of NO (ppm)

FRUDE Emmision of NO

0.97

1.00

40

1.00

30

0.97

1

2

Diversion plate number

3

4

5

Emmision of CO (ppm)

138

0

Diversion plate number

Fig. 12. Pollutant emission with respect to the UDE (uniformity of different ejectors). (a and b) FRUDE (flow rate uniformity of different ejectors); (c and d) FMUDE (fuel–gas mixing uniformity of different ejectors).

Table 3 The five diversion plates used to validate the universality of optimal diversion plate. No.

d1 (mm)

d2 (mm)

d3 (mm)

d4 (mm)

d5 (mm)

d6 (mm)

d7 (mm)

d8 (mm)

d9 (mm)

d10 (mm)

d11 (mm)

B1 B2 B3 B4 B5

12.0 10.4 10.4 10.3 10.4

12.0 10.8 10.7 10.6 10.7

12.0 11.2 11.3 11.4 11.4

12.0 11.1 11.0 10.8 10.5

12.0 10.8 10.9 11.1 10.9

12.0 10.2 10.2 10.3 10.4

12.0 10.5 10.4 10.5 10.5

12.0 10.8 10.8 10.9 11.0

12.0 11.5 11.5 11.4 11.3

12.0 11.2 11.3 11.3 11.2

12.0 12.0 12.0 12.0 12.0

1.00

1.00 B1

B3

B4

B1

B5

a

0.99

B2

B3

B4

B5

b

FRUDE

FMUDE

0.99

B2

0.98

0.97

0.98

4000

4500

5000

5500

6000

Speed of fan (rpm)

0.97

4000

4500

5000

5500

6000

Speed of fan (rpm)

Fig. 13. FRUDE (flow rate uniformity of different ejectors) and FMUDE (fuel–gas mixing uniformity of different ejectors) at different fan speeds with different diversion plates.

The value of t is the convergence parameter. If the FRUDE of new orifice diameters is worse than that of last step, 1 is added to t value; otherwise the value of t is assign with 0. When the value t is up to 3, the process of iteration is stopped. For our case, the iteration is stopped at n = 20. With the optimal diversion plate, the FRUDE is increased by 1.9% (from 0.9713 to 0.9897) and the FMUDE is increased by 2.2% (from 0.9695 to 0.9907) compared with that of the original diversion plate. For the commercial applications, the universality is a key point for the optimization. The optimal diversion plate is obtained when

the fan speed is 5000 rpm. Whether the obtained diversion plate is optimal at all fan speeds should be discussed. Five different diversion plates generated in the above optimization iteration are chosen to study the universality of optimal diversion plate. The five diversion plates are: B1 is the original diversion plate, B5 is the optimal diversion plate, and the other three diversion plates are selected arbitrarily. The geometrical parameters of the five chosen diversion plates are shown in Table 3. Fig. 13 shows the FRUDE and FMUDE with the five diversion plates at different fan speeds. It is found that the dispersion plate

T.-H. Zhang et al. / Energy Conversion and Management 85 (2014) 131–139

from good to bad is also B5 > B4 > B3 > B2 > B1 for different fan speeds. It is found that the optimal diversion plate obtained at fan speed 5000 rpm is also optimal at other fan speeds. This indicates that the optimal diversion plate can be obtained at an operation fan speed. This property is good for commercial applications. Although the uniformity of flow and mixing is improved by the distribution orifice plate and the diversion plate, the gas flow is blocked. When the diversion plate and the diversion plate are added, the air flow rate of optimal model is decreased by 18.7% compared with that of the original one at the fan speed of 5000 rpm. 5. Conclusions The uniformity at the outlet of gas mixing system (GMS) is studied by considering the effect of fan and multi ejectors. The pollutant emission is studied by experiment. After the numerical model is validated by the experimental results, the CFD is used to optimize the GMS by improving the uniformity at the outlet of GMS. The optimal structure and geometrical parameters of GMS are obtained. The main results are:  The distribution orifice plate improves the uniformity of velocity and the fuel–gas mixing at the outlet of ejector at different fan speeds. When the fan speed is 5000 rpm, the uniformity of velocity and fuel–gas mixing of single ejector is at least increased by 234.2% and 2.9%, respectively.  The proposed diversion plate improves the uniformity of ejectors. The optimal diameter of orifices of the diversion plate is obtained and the optimal diversion plate performs well for all fan speeds studied. For a fan speed of 5000 rpm, the uniformity of flow rate and fuel–gas mixing of ejectors is increased by 1.9% and 2.2% with the optimal diversion plate, respectively.  The improvement of the uniformity of GMS results in the reduction of pollutant emission of premixed burner.

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Acknowledgement The research is financially supported by the special technology fund of Tianjin University-Wuqing District. References [1] Wu C, Chen K, Yang SY. Experimental study of porous metal burners for domestic stove applications. Energy Convers Manage 2014;2014(77):380–8. [2] Kesgin U. Study on prediction of the effects of design and operating parameters on NOx emissions from a leanburn natural gas engine. Energy Convers Manage 2003;44(6):907–21. [3] Keramiotis C, Stelzner B, Trimis D, Founti M. Porous burners for low emission combustion: an experimental investigation. Energy 2012;45(1):213–9. [4] Yapıcı H, Kayatasß N, Albayrak B, Basßtürk G. Numerical calculation of local entropy generation in a methane-air burner. Energy Convers Manage 2005;46(11):1885–919. [5] Arghode VK, Gupta AK, Bryden KM. High intensity colorless distributed combustion for ultra low emissions and enhanced performance. Appl Energy 2012;2012(92):822–30. [6] Sukumaran S, Kong S. Modeling fuel NOx formation from combustion of biomass-derived producer gas in a large-scale burner. Combust Flame 2013;160(10):2159–68. [7] Wei Z, Li X, Xu L, Tan C. Optimization of operating parameters for low NOx emission in high-temperature air combustion. Energy Fuel 2012;26(5): 2821–9. [8] Ko Y, Lin T. Emissions and efficiency of a domestic gas stove burning natural gases with various compositions. Energy Convers Manage 2003;44(19):3001–14. [9] Lezsovits F, Koenczoel S, Sztanko K. CO emission reduction of a HRSG duct burner. Thermal Science 2010;14(3):845–54. [10] Docquier N, Candel S. Combustion control and sensors: a review. Prog Energy Combust 2002;28(2):107–50. [11] Liu F, You X, Wang Q, Zhang R. On optimal design and experimental validation of household appliance burner of low pollutant emission. Energy Convers Manage 2013;2013(76):837–45. [12] Lin SC, Huang CL. An integrated experimental and numerical study of forward-curved centrifugal fan. Exp Therm Fluid Sci 2002;2002(26):421–34. [13] Moon YJ, Cho Y, Nam HS. Computation of unsteady viscous flow and aeroacoustic noise of cross flow fans. Comput Fluids 2003;2003(32): 995–1015. [14] Shih Y, Hou H, Chiang H. On similitude of the cross flow fan in a split-type airconditioner. Appl Therm Eng 2008;28(14–15):1853–64. [15] FLUENT Inc., Fluent user’s guide. Version 6.3, Lebanon, NH, USA; 2006. [16] Danon B, Cho ES, de Jong W, Roekaerts DJEM. Numerical investigation of burner positioning effects in a multi-burner flameless combustion furnace. Appl Therm Eng 2011;2011(31):3885–96.