Computational Fluid Dynamics Study of Biomass Cook StoveâPart 1

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Thermodynamics, Transport, and Fluid Mechanics

Computational Fluid Dynamic (CFD) Study of Biomass Cook Stove – Part 1: Hydrodynamics and Homogeneous Combustion Zakir Husain, Shashank S. Tiwari, Aniruddha Bhalchandra Pandit, and Jyeshtharaj B Joshi Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.9b03181 • Publication Date (Web): 16 Aug 2019 Downloaded from pubs.acs.org on August 16, 2019

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Industrial & Engineering Chemistry Research

1

Computational Fluid Dynamic (CFD) Study of Biomass Cook Stove – Part 1: Hydrodynamics and Homogeneous Combustion

Zakir Husain 1, Shashank S. Tiwari 1, Aniruddha B. Pandit 1, Jyeshtharaj B. Joshi 1, 2* 1Department

of Chemical Engineering, Institute of Chemical Technology, Matunga, Mumbai 400019, India

2Homi

Bhabha National Institute, Anushaktinagar, Mumbai 400094, India

 Corresponding Authors: Tel.: +91 2225597625; Fax: +91 2233611020 Email Id: [email protected] (JBJ), [email protected] (ABP)

ACS Paragon Plus Environment

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ABSTRACT In this work, a Computational Fluid Dynamic (CFD) assisted optimization study has been carried out, implementing geometric design modifications in a biomass cook-stove (BCS) to achieve uniform airfuel distribution. The work has been divided into two parts, the first part deals with the numerical investigations of fluid phase hydrodynamics and air-fuel homogenous phase combustion. Simulations have been performed for a wide range of air velocities to predict the roles of primary and secondary airflow, in improving the spatial airflow uniformity inside the BCS. The homogeneous combustion simulations show linear dependence of power and temperature on the air velocity and air-fuel ratio. The results indicate that the most optimized power output and temperature values are achieved when the grate is placed at the height of 25 mm and the orifice plate with 15 holes 10 mm each is located at the height of 105 mm from the bottom of the cook-stove. Keywords: Cook-stove; Computational Fluid Dynamics; Reactor Design; Hydrodynamic; Homogeneous combustion


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1. INTRODUCTION Energy has a vital impact on the economic and social development of any nation. In the last three centuries, fossil fuels such as coal, oil, and gas have become the major sources of energy. It is estimated that 75% of the world’s population still relies on traditional sources of energy for various requirements. The majority of this (75%) population, is from developing nations1. Approximately 1014% of the world’s primary energy demand depends on biomass, which is the fourth essential energy source after coal, petroleum and natural gas2. Restaurants, hotels, schools and hospitals use a large amount of energy for cooking3. Despite growing access to electricity and gas fuel, consumption of solid biomass fuels continues to upsurge (particularly in the developing world) because of the everincreasing cost of fossil fuels. According to a report of the International Energy Agency (IEA), around three billion people in the world still rely on solid biomass fuel for cooking purposes4. Cooking has been one of the most ubiquitous applications of energy, constituting around 30% of total energy utilization in the developing nations. However, the rural energy demand for cooking, in developing countries are still supplied through biomass-based fuels5. In particular, the domestic sector still heavily relies on traditional sources of energy (biomass, cow-dung, agricultural residue, wood, etc.) for cooking purposes. Combustion occurs when biomass is burnt in the presence of excess air to produce heat. Biomass combustion occurs in four stages: (1) drying of biomass (evaporation of water in the biomass by providing heat), (2) devolatilization (release of volatile gases from biomass due to pyrolysis), (3) combustion of volatile gases (homogeneous/gas-phase combustion), and (4) burning of the unburnt solid mass (heterogeneous solid phase combustion). These four combustion stages may overlap with each other in real practices; however, from the perspective of developing clarity in the analysis of BCS, they have been analyzed as four separate stages6,7.



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The traditional cook-stove designs have major limitations such as unwanted dispersion of the flames and heat during windy conditions and lack of proper control of fire intensity. Bryden

8

has

reported that the energy with efficiency as high as 90% can be obtained in a traditional open fire cookstove, but only about 10% to 40%, of it, is received by a vessel placed on the flame. The major limitations of traditional cook-stove are very low thermal efficiency and high levels of smoke emission9,10. These practices lead to indoor air pollution, and exposure to smoke may cause chronic diseases such as lung cancer, asthma, chronic obstructive pulmonary diseases and cataracts11–13. World Health Organization (WHO) reported that indoor air pollution is responsible for 2.7% of the global deaths11. Moreover, most of these cook-stoves use wood in its crude form, which needs to be obtained from trees. In the past few years, massive deforestation has led to a significant scarcity in the supply of wood. Besides, cutting down trees causes hardship to the people living in rural areas, especially to women and children who invest a considerable amount of time and energy in collecting fuelwood 14,15. Also, most of the traditional cook-stoves, such as the three-stone fire, earthen chulla, cast iron stoves, Baldwin VITA are thermally as well as environmentally inefficient, thus, paving the way for developing the improvised designs of cook-stoves10,16,17. The vast applicability of biomass cook-stoves has opened several opportunities in government organizations, research institutes and industries all across the world to engage in the development and testing of newly improved biomass cook-stoves. 2. HISTORICAL DEVELOPMENT OF BIOMASS COOK-STOVES Cook-stoves have passed through a variety of modifications over the years17–19. These modifications have been in terms of technical design, fuel source, ignition mechanism, the material of construction, etc19,20. MacCarty et al10 have systematically tested fifty different types of biomass cook-


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stoves developed over the past fifty years and have provided comparative analysis. These continuous modifications and improvements improved the thermal efficiency of cook-stoves and made them easy to handle; however, the health hazards continued 22–24. In the early 1950s for the first time in history, the science and technological approaches were incorporated in the design and development of biomass-fired cook-stove and generally known as improved cook-stove (ICS)24–26,27. Fundamental scientific concepts such as combustion thermodynamics, mass and energy balances, reaction kinetics, heat transfer and hydrodynamics were employed in the analysis of the performance of cook-stove with a view to increasing their efficiencies 28.

In the late 80s, the second phase of ICS started in which the users’ comfort and preference like

smoke-free kitchens, cooking comfort, convenience, flexibility and safety of users became important objectives of research29. 2.1. Improved biomass cook-stove The main objectives of the design and development of improved biomass cook-stove are to acquire higher combustion efficiency and overall thermal efficiency30,31. To achieve these objectives, a substantial amount of research work has been performed during the past 30 years. Under controlled conditions, a 40-70% reduction in emission and a 30% increase in fuel-saving has been achieved in ICS as compared to the traditional cook-stove13,32,33. Mathematical modeling has also been performed 6,34,35.

Various parametric studies related to design, analysis, development, testing and field

performance of the biomass cook-stove have been carried out by various researchers13,32,33.



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2.2. Application of Computational Fluid Dynamics (CFD) for combustion modeling of biomass cook-stoves During the past two decades, a lot of efforts have been invested in the thermochemical conversion of biomass. The objective has been to develop reliable designs and scale-up procedures. The balanced approach of experiments and mathematical modeling has been effective to achieve the objectives. Moreover, the last decade has witnessed extensive improvements in the development of efficient and reliable numerical models for simulating hydrodynamics, mixing, combustion and related phenomena36–38. This has helped in carrying out a detailed analysis of the thermochemical reactors such as combustors, pyrolyzers, incinerators, gasifiers, etc. Continuous efforts have been put to undertake CFD simulations for the analysis and optimization of the above-mentioned thermochemical reactors17,39. The complex nature of biomass has been posing challenges for the prediction of the performance of the thermochemical conversion processes

17,39.

Over the years, various combustion

models such as the laminar flamelet model, eddy dissipation model (EDM), eddy dissipation concept (EDC), etc. have been developed for optimization of the combustion of biomass cook-stove. Thus, just like all the other chemical engineering equipment, the flow patterns inside the cook-stoves govern the mixing characteristics as well as the thermal efficiency and hence the overall performance of a biomass cook-stove. In Table S1 we have systematically analyzed some of the past CFD studies carried out for combustion of biomass29,31,44,45. The assumptions made in each of these simulations along with the solver details have been specified to have a gist of the current scenario of biomass combustion modeling using CFD approach. From the foregoing discussion, it is clear that several points need attention, before undertaking the exercise of improving efficiency.


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i)

For a variety of geometrical configurations, it is important to understand the flow and temperature distribution at all the location in the BCS.

ii)

Sensitivity analysis is needed regarding the effect of air flow rate and the air-fuel ratio.

iii)

We need to estimate quantitatively, the rate of mixing at all the locations.

The above mentioned three points have not been systematically addressed in the published literature; therefore, motivating us to take up these issues for the present work. An attempt has been made to obtain detailed flow, temperature and mixing patterns. Such knowledge has permitted the estimation of mixing and combustion rates at all the points within a cook-stove. The volume integration of these point values gives the overall thermal power generated by the cook-stove. 3. EXPERIMENTAL DESIGN 3.1. Various geometries Based on the limited number of CFD simulations, a cook-stove was fabricated with a capacity of 1 kg biomass per batch by Eco-Sense Appliances, Aurangabad, Maharashtra, India. A schematic diagram of the BCS has been illustrated in Figure 1. The biomass cook-stove has an inner cylinder in Figure 1 [marked as (2)], constructed of a conducting ceramic, and has an inner diameter of 190 mm. Further, the outer cylinder [marked as (3)] is made up of an insulating ceramic and has an inner diameter of 230 mm. The height of the cook-stove is 210 mm. Since the inner cylinder is made up of conducting material, the secondary air passing through the annulus of 20 mm width [marked as (5)] gets heated. Whereas, the outer cylinder, which is made up of insulating material, is used to minimize the heat losses to the atmosphere. Formerly, our group has carried out a separate study for the optimization and selection of appropriate conducting and insulating ceramic materials52. As shown in Figure 1, 34 secondary holes [marked as (7)], each of 5 mm inner diameter, have been provided on



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the inner cylinder at a distance of 195 mm from the bottom of the cook-stove. These holes aid complete combustion of wood volatiles by allowing the secondary air to pass from the annular space to the inner cylinder regions. The orifices [marked as (4)] provided in the annular region (see Figure 1) at the height of 105 mm from the bottom helps in controlling the relative distribution of secondary air passing (a) through the annular space termed as secondary air and (b) through the bottom grate [marked as (8)] located 25 mm from bottom as primary air. Also, the top view of the orifice (4) (ϕ=10 mm, 15 nos.) and grate (8) (50% opening) is shown in Figure 3, (11) and (12) respectively. The present design of biomass cook-stove is a forced draft type and the air is provided into the stove by using a fan fitted on the outer cylinder of a square shape with a dimension of 50 x 50 mm [shown as (1)], in Figure 1. Another region of 20 x 20 mm dimension [annotated as (10)] is the cut on the inner cylinder for allowing the primary air to pass below the grate plate for the further distribution through grate over the biomass. The fan is mounted at the extreme bottom of the stove which provides air to enter laterally into the cook-stove. It is worth noting that some part of this air entering the annular space hits the wall of the inner cylinder (since the square duct of the inner cylinder is smaller than the square duct of the outer cylinder) causing the air to undergo diversion to the annular space. The air which is passing in the annular space (5) has already been called as the secondary air which later enters inside the combustion/fuel chamber through the secondary holes (7). The remaining air which passes into the inner cylinder has already been named as the primary air. The relative proportion of the primary to the secondary air depends upon the relative resistances provided by orifices (4), the opening on inner cylinder, resistance provided by the grate plate (8) and the fixed bed of biomass (6). The majority of earlier works in the field of biomass cook-stoves did not investigate the following: (a) the detailed flow patterns of the primary and secondary air (b) the mixing ratio of secondary air with volatiles and subsequent combustion reaction (called homogeneous combustion)


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(c) the efficiency of contact between the primary air with the biomass fuel and the subsequent combustion (called heterogeneous combustion) (d) inefficiency of burning and the formation of smoke and CO from biomass cook-stove. The present two part paper attempts to address these limitations. Part – 1 is concerned with the flow, mixing and temperature patterns in the gas phase. This part also estimates the value of combustion at all the locations with the cook-stove, however, only the combustion of gaseous fuel and termed as homogeneous combustion.

Pressure outlet 20 mm

20 mm

15 mm

190 mm

7

210 mm

6 5

105 mm

4 3 2 1 10 9

8

25 mm

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Flat velocity profile for air (0.5 to 3 m/s), Temperature 298 K Wood volatiles (uniform velocity inlet), Temperature 298 K mass fraction of air = 1 mass fraction of wood volatiles = 1, mass fraction of wood volatiles = 0 mass fraction of air = 0,

Figure 1.

3.2. Objectives of the hydrodynamic study


No slip boundary Condition, Temperature 298 K


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The first part of this paper consists of the computational fluid dynamic simulation of the flow of primary and secondary air (cold flow studies) by considering different geometrical configurations, details of which have been illustrated in Table 1. The objective is to understand the flow patterns systematically starting from the flow generated by a fan which bifurcates the blown air into primary and secondary air. Also, the two-dimensional pictorial representation of each of these five cases (configurations) has been illustrated in Figure 2. Further top view of orifice and grate have been shown in Figure 3. Table 1. The configuration of BCS for five cases used for CFD simulations Case

Geometry

Grate plate

Orifice plate

Biomass packing

Case (a)

Empty BCS (only secondary holes)

No

No

No

Case (b)

BCS with orifice plate (orifice ϕ = 10 mm, 15 nos) in the annular space

No

Yes

No

Case (c)

BCS with a grate plate (50% opening) at the bottom

Yes

No

No

Case (d)

BCS with a grate plate (50% opening) at the bottom and orifice plate in the annular space

Yes

Yes

No

Case (e)

BCS with a grate (50% opening) at the bottom, orifice plate in the annular space and fixed bed of biomass

Yes

Yes

Yes


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20 mm

210 mm

7

5

190 mm

15 mm

15 mm

190 mm 7

210 mm

20 mm

5

3

3

2

2

1

1

105 mm

4

10

10

9

9 230 mm

230 mm

(a)

(b)

210 mm

7

5

20 mm

190 mm

15 mm

15 mm

190 mm

7

210 mm

20 mm

5

3

3

2

2

1

105 mm

4

9

8

10 9

8

230 mm

230 mm

(c)

(d) 20 mm

15 mm

190 mm

7

210 mm

6 5 4

105 mm

3 2

10 9

8 230 mm

(e) Figure 2.


25 mm

1

25 mm

1 10

25 mm

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60



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11 12

Figure 3.

3.3. Objectives of the homogeneous combustion model As stated earlier, the combustion occurs in two stages: (a) homogeneous combustion by reaction with oxygen (air) with the devolatilized gaseous fuel biomass and (b) heterogeneous combustion between oxygen and solid biomass. These subjects are being covered in this two part publication. The objective is to gain insight into the quantitative role of various operating parameters and the geometrical parameters of the biomass cook-stove. For this purpose, the parametric sensitivity


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of the geometrical parameters was systematically investigated. The operating parameters included (i) the injected fan flow rate, which was input in terms of velocity and (ii) the air to fuel ratio. Combustion of volatiles from biomass is a non-premixed combustion process. For such burning processes, the transport of the gaseous fuel, as well as air into the reaction zones, are governed by convection as well as turbulent mixing. After getting transported, the fuel in the reaction zone burns quickly. Since, turbulent transport is the rate controlling step in such non-premixed combustion processes43, the complex and often unknown chemical kinetics associated with the decomposition of fuel can be neglected. The eddy dissipation model of Magnussen and Hjertager40 has been used to model the homogenous combustion process in the present investigation. This model uses the rate of dissipation of eddies containing reactants and products to determine the reaction rates. Thus, the chemical reaction rates are determined by the large eddy mixing time scale (k/ε) and the combustion is said to occur when the flow is turbulent (k/ε>0) without the requirement of any ignition source if the local temperature is above auto-ignition temperature. In other words, the rate of a chemical reaction (combustion) is considered to be much faster than the rate of turbulent mixing of air and fuel. 4. MATHEMATICAL MODELS 4.1. Assumptions: (1) In the cook-stove, the combustible material is in the gaseous form (biomass/wood volatiles). (2) The shrinkage, movement, attrition, and agglomeration of biomass, if any, is neglected. (3) The biomass is considered to be in the spherical shape of 10 mm diameter and these are randomly packed in the inner cylinder. (4) The following correlations, which were given by Incropera et al.41, are considered for physical properties.



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  5 108 T  2 106 kg.m -1.s -1

(2)

k  8  105 T  0.0016 W.m -1.K -1

(3)

c p  0.07T  985.5 J.kg -1.K -1

(4)

(5) Preheating of secondary air has not been considered for simulation. (6) Generation of CO and fine particles are not considered. 4.2. Governing equations and simulation methodology The continuity, momentum and energy balances are shown by equations (5), (6) and (8), respectively. These equations are first discretized and then solved iteratively. This discretization process generates a set of algebraic equations relating the field variables (pressure, velocity, turbulence, temperature, etc.) at numerous node points of the mesh. With appropriate boundary conditions, it is possible to solve these equations iteratively for the flow field variables at each computational point 42 . The phase coupled SIMPLE method has been chosen for pressure-velocity coupling. Momentum and mass conservation equations were discretized using the first-order upwind scheme in space while the second-order upwind scheme was used for the discretization of turbulent kinetic energy and turbulent energy dissipation equations.

    ui   0 t xi    ui  t



   ui u j  x j

=

p  ij   Si xi x j


(5)

(6)

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Here,  ij is the shear stress tensor and can be given as shown below in:

  ui   δij   xi 

2 3

 ij  2 Dij      d  

(7)

1  ui u j  D     i.e., the rate of strain Where ij 2  x j xi 



e e   T  u j   t x j xi  xi

 ui u   ij i  S h  p xi x j 

(8)

Here, e is the internal energy, λ is the thermal conductivity and was taken equal to 0.0454 w/m-k, Sh is the source term. The turbulent kinetic energy, k and its rate of dissipation, ε are obtained from the following constitutive equations:

k t

 uj

Accumulation

 t

Accumulation

u k   ij i  x j x j x j

convective transport

Production

      t k 

viscous + trurbulent transport

  u   uj  C1 ij i  x j k x j x j convective transport

Production

 k   x j

      t  

   dissipation 

    x j

viscous + turbulent transport

 2  C 2 k  dissipation

(9)

(10)

In these equations, u j represents the velocity component in the corresponding direction. Whereas,  t is the turbulent eddy viscosity and is given, as shown below:



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 t  Cµ

k2



(11)

The values of turbulence parameters were taken as follows: Cµ=0.09, σk = 1.00, σε = 1.3, C1ε = 1.44 and C2ε = 1.92. When one chooses to solve conservation equations for chemical species, ANSYS Fluent predicts the local mass fraction of each species, Yi, through the solution of a convection-diffusion equation for the ith species. This conservation equation takes the following general form:

( Ym ) (  u jYm ) J    m  Rm  S m t x j x j

(12)

Here, Ym is the mass fraction, J m is the flux of mass-produced by molecular mechanisms, Rm is the net rate of production of species m by chemical reaction, Sm is the additional as yet unspecified source terms. 4.3. Meshing strategies ANSYS Design Modeler was used as a pre-processor to define the 3D geometry of the cookstove model. For generating the random spherical packing coordinates, the DEM randomization algorithm explained in great detail in our earlier works was used

43–45.

The generated 3D geometry

was imported from ANSYS Design Modeler and meshed into small control volumes employing the cut cell meshing method for obtaining grids of high orthogonal quality with a minimum skewness. The meshed geometries for the critical locations in the model have been shown in Figure 4. As shown in Figure 4, fine grid elements were selectively generated around critical areas such as around secondary holes (see Figure 4 (a) and (b)), particle interstices (Figure 4 (d) and (e)), the orifice in


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annular region (Figure 4 (e)) and the grate rings (Figure 4 (e) and (f)). A mesh configuration of 5 Million cells was employed for simulating the empty BCS. This mesh configuration was selected based on the mesh independency studies carried out for Case (a) operating under 2 m/s air inlet velocity (which is equivalent to a fan speed of 4000 RPM). Four different grid counts were employed for testing the mesh independencies as shown in Figure 5. These four grid configurations have been denoted as M1: 0.625 Million cells with a minimum mesh size of 2mm, M2: 1.25 Million cells with a minimum mesh size of 1.5mm, M3: 2.5 Million cells with a minimum cell size of 1mm and M4: 5 Million cells with a minimum cell size of 0.5 m.


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(a)

(b) Figure 4.


(c)

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(d)

(e) Figure 4.


(f)


20

5 4.5 4

Velocity Magnitude (m/s)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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3.5 3 2.5 2 1.5 1 0.5 0 0

0.05

0.1 0.15 Axial Distance (m) M1

M2

M3

0.2

M4

Figure 5: 4.4. Homogenous combustion modeling It has been stated before (section 3.3) that the rate of the combustion reaction is far greater than the rate of mixing of gaseous fuel and oxygen from the air. In turn, the rate of mixing is controlled by turbulent dispersion for the estimation of turbulent dispersion rate, we have employed the eddy diffusion model proposed by Magnussen and Hjertager40, For modeling of the homogeneous combustion, the following reaction of wood volatiles is assumed.


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Fuel (wood volatiles)  air (oxidizer)  product of combustion + Energy

y y y   C x H y O z +  x    O 2  3.76N 2   xCO 2  H 2 O   x   N 2 4 2 4  

The net rate of production of species i due to reaction r, Ri,r, is given by the smaller (i.e., limiting value) of the two expressions below:

Ri ,r  v'i ,r M w,i A

 YR minR  k  v'R ,r M w,R





 Ri ,r  v'i ,r M w,i AB   k 

Y

p p



N

v" j ,r M w, j j

  

(13)

   

(14)

Also, the presence of resistance/obstructions in the way of air flow in terms of orifice, grates and inert spherical particles in BCS makes the flow turbulent. The flow conditions due to the forced draft effect, also render the flow to be turbulent. In order to have an accurate representation of the flow distribution of air, it is important to account for these turbulence effects as accurate as possible. A variety of turbulence models are available to model the turbulence effects, and there exists a lot of dilemmas to choose an appropriate turbulence model. One such model is the standard k-ε model. The standard k-ε model is a two-equation, semi-empirical, eddy viscosity based Reynolds Average Navier Stokes (RANS) model, derived using phenomenological considerations and pragmatism. It is the most commonly used turbulence model which has been successfully used to simulate a wide variety of flows ranging from some fundamental flow configurations to multiscale industrial flows

46–51.

The standard k-ε model gives considerably

accurate results in a time-averaged sense 52–54. Moreover, along with reasonable accuracy, the k-ε



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22

model is also robust, computationally less intensive and numerically stable as compared to higher order turbulence models55,50. As per the physics of turbulence involved in cook-stoves and by previously reported studies, it can be concluded that the standard k-ε model would be appropriate for serving our purpose of optimizing the hydrodynamic parameters of BCS. The two-equation, k-ε turbulence model, allows the determination of both, a turbulent length and time scale by solving two separate transport equations k (is the turbulent kinetic energy of the flow) and ε (is the turbulent energy dissipation rate). In common with all other eddy viscosity models, in the k-ε model, the Reynolds stresses are obtained from the Boussinesq eddy viscosity assumption, and the turbulent kinematic viscosity takes the Prandtl-Kolmogorov form. These equations appear as equations 9 and 10. In the derivation of the standard k-ε model, several assumptions are made which have been described in detail by Mathpati et al47. 4.5. Boundary conditions The numerical solution of governing equations can be iteratively computed for any given geometrical configuration by specifying correct boundary conditions. For all configurations of BCS, fuel in the form of wood volatiles was specified as a uniform velocity inlet at the bottom of the inner cylinder of the cook-stove (see Figure 1). The inlet air velocity boundary condition was prescribed at the side square cut. This velocity was varied from 0.5 to 3 m/s, which is equivalent to 1700 to 4600 RPM created by a forced draft fan. The pressure was not specified at the inlet because of the incompressible air assumption (relatively low-pressure drop system). A pressure outlet boundary condition was specified at the top of the inner cylinder of the BCS which was kept at atmospheric pressure (as done for simulating a usual fixed bed 43). The boundary condition at the grates, orifice plates, spherical packings, and walls was prescribed a no-slip boundary condition


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23

(see Figure 1). The under-relaxation factors for density, body force, and turbulent viscosity was set to unity while that of turbulent kinetic energy and turbulent dissipation was maintained at 0.8. Pressure and momentum were ascribed an under relation factor of 0.3 and 0.7, respectively. Details of all the simulation parameters used for the simulations have been shown in Table 2. After meshing the geometry into small control volumes, simulations were run on the ANSYS Fluent platform by setting up convergence criteria of 10-4 for all of the above mentioned governing equations. Steady-state simulations were performed, and once the convergence criteria were met, the corresponding post-processing step was computed. Table 2. Simulation parameters prescribed to the solver Inlet velocity

0.5 - 3 (m/s)

Air to fuel ratio for each inlet velocity

1:1, 3:1, 5:1

Turbulent intensity for inlet

5%

Hydraulic diameter for inlet

50 mm

Pressure outlet

1 atm

Turbulent intensity

5%

Hydraulic diameter for an outlet

186 mm

Wall and particles

No slip condition

Average Voidage (for Case (e))

0.60

Column Diameter (D mm)

190

Particle Diameter (dp mm)

15

Fuel Inlet Diameter

190

Air Inlet Diameter

50

Outlet Diameter

190

Number of the particle (for Case (e))

145

The height of the column (H)

210

Fluid

Air

Fuel

Wood volatiles



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5. RESULTS AND DISCUSSION 5.1. Comparison and validation of results with literature Our validation study for the present simulations is very preliminary as we did not use the temperature measurements for water boiling in a pot which is conventionally used for testing the thermal efficiency of a cook-stoves. In additions, all the simulations in the present study were performed assuming a steady-state condition. Whereas for accurately validating an unsteady phenomenon such as combustion we need to measure experimentally parameters such as the rate of devolatilization, burning as well as thermal efficiency with respective time. A more robust validation study is being currently performed which will be reported in a future publication which is under preparation. In the present work, we have followed the approach of Agenbroad et al. 56 of comparing the range of power output predicted from our simulations to that reported experimentally as well as with numerical studies from published literature for a variety of cookstove configurations. Here, the power output is the same as the heat release rate for the fuel and is thus defined as the rate of energy generated due to the combustion of the fuel. Further, we have not included the heat losses due to radiation and the heterogeneous combustion of oxygen and the solid fuel which will be the subject of Part II of this paper. One more important aspect that we have noticed, is that most of the available CFD studies in the literature consider natural convection arising out of by a chimney effect in contrast to the present case wherein forced convection cookstoves are modeled 56–58. Pande et al. 57 did CFD simulations for an elbow type natural-draft biomass cook-stove and investigated the effect of inlet area ratio (ratio of area unoccupied by the fuel to the total crosssection area of the inlet) on the temperatures inside the cook-stoves. They reported the maximum temperature of around 980˚C to be attained in the middle region of the cook-stove for the optimized


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25

inlet air ratio which was further found to reach a temperature of 1203 K at the top. They compared their results with the experimental results and found that there exists a variation of around 2-18% for all the comparison made for all the configurations under consideration. The present (of this work) cook-stove configuration is a cross-flow forced draft. The maximum temperature was found to be in the range of 1073 to 2073 K inside the cook-stove. Recently, Núñez et al.58 used CFD to simulate a Plancha-type natural draft cook-stove and found the power output for their natural-draft cook-stoves to be in the range 2000 – 7000 W for the range of velocity of wood fuel injected through an area of 0.01m2. This in turn is within the range of the power output values obtained experimentally for the Patsari wood-burning cook-stove by Berrueta et al.

59

which was in the

range of 2000 - 9000 W. The values of power output obtained from the simulations of Núñez et al.58 are somewhat less than the values predicted from our simulations (our simulations predict power output in the range 8300 to 8484 W) where in the air enters into the cook-stove through an area of 0.0025m2. Thus, we see that a cross-flow air entry system with a forced draft design of a cook-stove gives better power output as compared to that obtained in the natural draft. It is however worth noting that Núñez et al.58 used the finite rate single step Arrhenius expression for modeling the reaction of wood-volatiles and air. In the present work, combustion has been modeled by an eddy dissipation model. Besides, the power output, the maximum temperatures has been found to be in the range of 800-1800˚C as mentioned earlier. This temperature range is much higher than that typically obtained for the mixed/ forced draft cook-stove which gives the temperature range of 1023 to 1573 K as reported by Macqueron 60. The over-predictions of temperatures obtained are again due to the simplifying assumption of neglecting the radiation effects and also due to the pure homogenous combustion.



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5.2. Hydrodynamic study The combustion efficiency inside the cook-stove is highly dependent on the air-fuel availability and its spatial variation. Unavailability of oxygen in the cook-stove is responsible for the incomplete combustion of gases (biomass/wood volatiles) followed by the formation of smoke. Therefore, it is important to achieve uniform air flow distribution. So that every spatial location in the stove become effective. This results in an improvement of the overall cook-stove efficiency. However, due to geometrically complex cook-stove design, chances for incomplete combustion are always possible. Hence, it was considered appropriate to monitor the effects design variables such as the grate opening, the number and sizes of the orifices (in the annular region) and particle packing on the airflow pattern, the temperature of the flame and the overall heat (power) output of the system for different design configurations described in Figure 2. It is known that macro-mixing occurs via two parallel actions: (a) convective motion and (b) turbulent or eddy motion. Amongst these two, the convective motion is known to control the macro-mixing process as compared to the turbulent diffusion. Further, the micro-mixing (or homogeneity at the molecular level) occurs by three actions: (a) convective motion (b) turbulent diffusion and (c) molecular diffusion. As far as the quality of mixing in cook-stoves is concerned the rate of macro-mixing is considered to be the rate controlling step61. In addition, for macromixing, though the convective motion is relative more important, the value of eddy diffusivity can be optimized for efficient macro-mixing. It may be worth noting at this stage that in the absence of turbulence, the homogenization (micro-mixing) occurs by the combined effect of convective motion and molecular diffusion. Generally, micro-mixing rates are unacceptably low. From the forgoing discussion, the rate of combustion mainly depends upon the rate of mixing.


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The objective of the hydrodynamic study was to understand the spatial air flow distribution in the biomass cook-stove followed by a design of a cook-stove configuration that enables proper mixing of air and fuel. This evaluation of air flow distribution was done by assessing the air flow quality due to the presence of orifice plate, grate and particle packing inside the biomass cookstove. The air flow parameters have been quantified to understand the underlying physical significance of these design modifications of cook-stove. 5.2.1. Flow distribution in the system To evaluate the distribution of incoming air into primary and secondary, the velocity contours on the XZ – plane at the height of 20 mm from the bottom of the stove have been shown in Figure 6. This plot reveals the flow distribution of the inlet flow (generated by a fan) into the annular space (secondary flow) and inside the inner cylinder (primary flow). Figure 6 shows the velocity contours for the five different cook-stove configurations (Figure 2) at an inlet velocity of 0.5 m/s. It can be seen from Figure 6 that, for the case (a), the length of the jet of entering air does not reach the opposite wall of the inner cylinder. The jet lengths for the five configurations is shown in Figure 7. Further, the relative flow distribution of primary and secondary is given in Table S7. The length of the jet in case (b) is observed to get extended up to the inner region of the inner cylinder, as shown in Figure 6 (b). The increase in the length of the jet can be explained because of the presence of orifice which provides resistance to secondary flow and enables the anhancement in primary flow. Therfore, the jet length in case (b) is longer than that for case (a). It is interesting to see the case (c) also has longer jet length than that of case (a). The provision of grate, in fact, decreases the primary air. However, the provision of grate results into uniformity of



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axial flow across the grate. This exceeds availability of air below and across the plate. In case (a), the flow become upward near the inlet (which means non-uniformity). Further, it is found that the velocities in the inner cylinder are highest for case (d) as compared to any of the previous cases again due to the enhanced uniformity above the grate plate (as compared to case (c)) because of the presence of grate. Further, case (d) also has orifice in the annular region which results in an increase in the primary flow. The relative comparison of jet lengths is shown in Figure 7. In an attempt to make our CFD geometry more realistic, we introduced spherical particles representative of biomass pellets, inside the inner cylinder. The geometry of the cook-stove containing the spherical packing is referred to as case (e). The void fraction inside the bed was kept 0.60. For CFD simulations, at the walls of all the of the particles were prescribed no-slip boundary condition and were modeled as inert to avoid the complex chemical reactions involved. Thus very close to particle surface (a region of turbulence decay), on contrast the presence of particles enhances the turbulence (like in static mixture). Thus the presence of particles offer two opposite effects and the net results depends upon the relative quantities of turbulence enhancement in the bulk gas and the turbulence decay near the wall. The quantification of the primary and secondary air for all the design configurations (a) to (e) is given in Table S2 (provided in the supplementary information). The percentage of mass flow rates reported in Table S2 has been estimated at three different cross-section located at a height 65 mm, 105 mm and 200 mm from the bottom of the cook-stove. Such a qualitative information has been given for different inlet air velocities (0.5 – 3 m/s) and for different cook-stove configurations (case (a) to case (e)). The mass flow rate for all the planes were calculated considering the area and axial velocity of that particular plane, which showed that 63% of the total air entering the stove


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29

gets into the annular space and the remaining 37% of the air passes through the inner cylinder for the case (a) and case (c). It is also shown in the Table S2 that for the cases (b), (d) and (e) the total mass flow rate of air gets distributed as 40% primary air and 60% a secondary air. This decrease in the percentage of secondary air or increase in the percentage of primary air is due to the resistance provided by the orifice in the annular region for the cases (b), (d) and (e) which were absent in case (a) and (c). Further, the annular space is closed at the top. Therefore, all the secondary air passes through secondary holes located at 195 mm from the bottom. Therefore, at 195 mm and above, the primary air become 100 % which is also equal to that at the outlet of inner cylinder (210 mm from bottom).


Industrial & Engineering Chemistry Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

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30

(a)

(b)

(d)

(c)

(e) Figure 6.


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31

200

Jet Length (mm)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47


150 100 50 0 Case (a)

Case (b) Case (c) Case (d) Cook-Stove Configuration Figure 7.


Case (e)


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5.2.2. Uniformity index (γ) The distribution of air at any given cross-section can be well defined using the term ‘uniformity index,’ which is mathematically represented by the following equation62. n

  1.0 

   i 1

i

 Ai   n

A

2

i 1

(15)

i

n

Where,



 A i 1 n

i

i

A i 1

i

i here represents the index of each node on the selected surface while Ai is the differential area of node i and  i is the velocity at node i. It is evident from equation (15) that the uniformity index (γ=1) indicates that the magnitudes of local velocity are the same as that of the average velocity. Thus, any value (which is always less than 1) indicates the extent of uniformity of the axial velocity at a plane under consideration. Table 3 gives the uniformity index (γ) using equation 14 at different planes at 65 mm, 105 mm, 200 mm and outlet. For the plane at 65 mm height from the bottom, a uniformity index of 0.7 can be observed for the first two cases (case (a) and (b)). This shows that the orifice plate (case (b)) situated at the height of 105 mm from the bottom of the cook-stove, does not have a significant effect on uniformity at 65 mm height plane. However, for the case (c), we found the uniformity index (0.71) to increase slightly. This slight increase (0.71) is due to the provision of the grate plate, which was absent in the first two cases (case (a) and (b)).


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33

Further, with an inclusion of both the grate plate and the orifice plate, i.e. case (d), the uniformity index was found to go up to 0.76. Thus, providing an orifice plate and grate plate gave around 6.5% better flow distribution as compared to that achieved for the previous three cases (case (a), (b) and (c)) at the height of 65 mm from the bottom. The same trend as mentioned above for plane 65 mm can be seen for the plane at height 105 mm from the bottom of cook-stove. The constant value of uniformity index (0.73) can be observed for the case (a) and (b) which further increased due to the inclusion of orifice and grate (case (c) (0.75), case (d) (0.77)). The uniformity index on the 200 mm plane does not get much affected due to geometrical modifications. The uniformity index remains in the range 0.77-0.79 for these plane for all the five cases. This can be understood by the fact that the 200 mm plane is just near to the secondary holes and modifications of the geometry in the lower regions do not have any significant effect near the upper region. A low but constant uniformity index value (0.58) was observed at the outlet plane for all the cases. This may be due to boundary condition (pressure outlet) provided at the outlet plane for all the cases. For instance, the radial profile of axial velocity for case (a) and (d) are shown in Figure S1 and S2. For case (e) (see Table 3 case (e)) the uniformity index (at all planes) is relatively less than the case (d). This is because of the zero slip boundary condition at the surface of the fuel particles considered. Thus the velocity distribution in the packed bed becomes very wide and in the range of zero to interstitial velocity. Such a wide variation gives a low uniformity index as per equation (14). Further, to justify the results of the uniformity index, we calculated the standard deviation for each of the planes at 65 mm, 105 mm and 200 mm heights from the bottom of the cook-stove. We found that for all the three planes, the standard deviation for case (a) and case (b) was high



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(see Table 5). For case (c), the standard deviation decreased on all the three planes. This decrease in the standard deviation illustrates that providing a grate plate at the bottom helps in achieving a better uniformity as was also observed from the uniformity index analysis. The standard deviation values for the case (d) and case (e) were found to reduce further even more than that for case (c). Thus, providing a grate and an orifice indeed helped in achieving a better air flow distribution. We also carried out hydrodynamic simulations for inlet velocities in the higher ranges, i.e. 0.5 m/s to 3 m/s too for all the cases. The results from these high inlet velocity show a similar trend of uniformity index as well as standard deviation as illustrated for 0.5 m/s inlet velocity. The results are given in Tables S3 and S4 of supplementary information show the uniformity index and standard deviation obtained at higher inlet velocities.


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35

Table 3. Uniformity index (γ) at different planes for different BCS configurations (Inlet air velocity U0 = 0.5 m/s) Planes

Configuration γ for Case (a)

γ for Case (b)

γ for Case (c)

γ for Case (d)

γ for Case (e)

Plane 65 mm

0.7

0.7

0.71

0.76

0.7

Plane 105 mm

0.73

0.73

0.75

0.77

0.75

Plane 200 mm

0.78

0.77

0.78

0.79

0.78

Outlet plane

0.58

0.58

0.58

0.58

0.58

Table 4. Standard deviation at different planes for different BCS configurations (Inlet air velocity U0 = 0.5 m/s) Planes

Configuration Case (a)

Case (b)

Case (c)

Case (d)

Case (e)

Plane 65 mm

0.14

0.18

0.07

0.08

0.05

Plane 105 mm

0.13

0.13

0.04

0.07

0.04

Plane 200 mm

0.08

0.06

0.03

0.04

0.03

Outlet plane

0.1

0.1

0.1

0.09

0.08



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5.2.3. Effect of grate opening To determine the grate openings that would result in optimized conditions, we performed simulations at three different percentage openings of the grate for case (c) and case (d). The three openings selected were 50%, 25%, and 12.5%. Table 5 shows the values of uniformity index (γ) for these three conditions of the openings. It can be seen from Table 5 that the values of uniformity index (γ for 50%>25%>12.5%) marginally decreases (at all planes of cases (c) and (d)) as the free area decreases. It seems that the value of γ decreases with a decrease in primary air. This shows that inclusion of grate and orifice plate in the BCS design helps in achieving better air distribution uniformity throughout the BCS. Table 5. Uniformity index (γ) for case (c) and (d) under three different grate opening conditions (50%, 25% and12.5%) at 1 m/s Configuration

Planes

γ for 50% grate opening

γ for 25% grate opening

γ for 12.5% grate opening

Plane 65 mm

0.71

0.69

0.68

Plane 105 mm

0.75

0.73

0.71

Plane 200 mm

0.78

0.76

0.75

Outlet

0.58

0.56

0.56

Plane 65 mm

0.76

0.74

0.72

Plane 105 mm

0.77

0.75

0.73

Plane 200 mm

0.79

0.76

0.76

Outlet

0.58

0.57

0.56

Case (c)

Case (d)


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37

5.3. Homogeneous combustion: Steady-state 3D homogeneous combustion simulations of BCS were performed for all the configurations of the cook-stove (Figure 2). The main objective of performing a sequential study was to evaluate the mixing of air and wood volatiles and thereby to assess the heat of reaction/power output. This was followed by the adiabatic flame temperature predictions of cookstove. In this regards, simulations were carried out for different inlet air velocities providing various combinations of air to fuel ratio. In order to understand the effects of various geometrical modifications made in the cook-stove in terms of grates, orifice plate, secondary holes and spherical packing, the predictions from the simulations have been reported in terms of the heat of reaction/ stove power output. 5.3.1. Effect of inlet air velocity To evaluate the power output, homogeneous combustion simulations were carried out at different inlet air velocities (1, 2, 3 m/s), for all the five configurations (i.e., cases a to e) of the BCS with fixed air to fuel ratio (AFR 1:1) (see Table 6). An increase in power output (heat of combustion/reaction) of (8350, 8360, 8422 and 8484 W) was observed for case (a), case (b), case (c), and case (d) respectively. As discussed in section 5.2.1 for case (e), the walls of each of the particles were prescribed no-slip boundary condition and were modeled as inert to avoid the complex chemical reactions involved. This adversely affected the power output, causing power output to reduce for case (e) as compared to that reported for case (d). Further, for case (a), an increase in power output (heat of combustion/reaction) of (8350, 16577 and 24213 W) was observed at 1, 2 and 3 m/s respectively for 1:1. It is seen from Table 6, that increasing the air velocity increases the power output for all the five configurations of BCS. This behavior of increasing power output with an increase in velocity is evident since increasing



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velocity (with constant air to fuel ratio) means an increase in the fuel combustion rate resulting in higher power output. A similar trend can also be observed for all the cases (case (a)-(e)). In other words, the observations from Table 6 are in good agreement with the results shown in section 5.2.1. Table 6. Power output for case (a)-(e) have been evaluated at three different inlet air velocities for 1:1 air-to-fuel ratio

Configuration

Power

Thermal

Power

Thermal

Power

Thermal

output (W)

efficiency

output (W)

efficiency

output (W)

efficiency

U0 = 1 m/s

(%)

U0 = 2 m/s

(%)

U0 = 3 m/s

(%)

AFR = 1:1

AFR = 1:1

AFR = 1:1

Case (a)

8350

99

16577

99

24213

99

Case (b)

8360

99

16593

98

24660

98

Case (c)

8422

99

16978

99

25417

98

Case (d)

8484

98

16990

97

25600

96

Case (e)

8300

98

16717

98

24685

97

Also, to evaluate the homogeneous combustion in BCS, the temperature contours on the XZ – plane at the height of 20 mm (created horizontally which consist inner and annular part of BCS) from the bottom of the stove have been shown in Figure 8. This plane was selected to be 20 mm above from the bottom of the stove to enable us to visualize the effect of flow distribution (section 5.2.1) on combustion. Figure 8 shows the temperature contours for five different cookstove configurations at an inlet velocity of 1 m/s. A comparison of temperature profiles for the different cases shows that for case (a), the area weighted temperature is 611 K. An increase in area weighted temperature can be seen for case (b) (624 K), case (c) (634 K), case (d) (648 K) and case (e) (628 K). The highest temperature can be observed in case (d) (648 K) followed by that of case


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39

(c) (634 K). The results obtained from this section is validating the flow distribution in the system (section 5.2.1).

(a)

(b)

(d)

(c)

(e)

Figure 8.



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5.3.2. Effect of air to fuel (w/w) ratio Quantum of the power output of a combustion system is dependent on the air-fuel ratio. As far as combustion is concerned, it is a fact that obtaining high power output simultaneously with low fuel consumption is not possible. The lowest fuel consumption (best fuel economy) is obtained with lean air-fuel mixtures, with an AFR of 5:1. The maximum power is produced with rich airfuel mixtures, with an AFR of 1:1. With a stoichiometric air-fuel mixture, there is always a compromise between maximum power produced and minimum fuel consumption. Figure 9 (a-f) illustrates that the maximum power of the system and the lowest fuel consumption cannot be simultaneously obtained with the same air-fuel ratio. Similarly, for a constant air velocity of 1, 2 and 3 m/s, the effect of AFR (1:1, 3:1 and 5:1) is given in Tables S5.1, S5.2 and S5.3, respectively. It can be seen that, an increase in AFR from 1:1 to 3:1 decreases the power output in the range of 6 to 10 percent. A further increase in AFR to 5:1 decreases the power output in the range of 15 to 18 percent. In addition, on an increase in AFR also reduces the thermal efficiency (though marginally). This is because, with an increase in AFR, the energy having BCS also increases because of the increase in total mass flow rate of fuel and air. This results in a net reduction of power output (power output 1:1>3:1>5:1) with an increase in AFR.


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7800

8450

7700

8400 8350 8300

7100

7600 7500 7400

7200

Case Case(a) 1 Case Case(b) 2 Case Case(c) 3 Case Case(d) 4 Case Case(e) 5

(a)

7050 7000 6950 6900 6850 6800 6750

Case Case (a) 1 Case Case(b) 2 Case Case(c) 3 Case Case(d) 4 Case Case(e) 5

(b) 1920

2000

1960

1900

1940

1880

1920 1900 1880 1860 1840 1820

Case Case(a) 1 Case Case(b) 2 Case Case(c) 3 Case Case(d) 4 Case Case(e) 5

(d)

Temperature (⁰C)

Temperature C) Temperature(⁰(˚C)

1940

Temperature (˚C)

1980

1960

1920 1900 1880

1860 1840 1820

1860

1800

1840

1780

1820

Case (e)5 Case(a) 1 Case Case(b) 2 Case Case(c)3 Case Case(d)4 Case Case

(e)

Figure 9.


Case (b)2 Case (c)3 Case Case(a)1 Case Case Case Case(d)4 Case Case(e)5

(c)

2020

1980

Temperature (˚C)

7150

7300

8250 8200

7200

Power Output (W)

8500

Power Output (W)

Power Output (W)

41

Temperature (⁰C)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47


1760

Case Case (a) 1 Case Case(b) 2 Case Case(c) 3 Case Case (d) 4 Case Case (e) 5

(f)


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5.3.3. Effect of grate opening We also evaluated the effect of percentage opening of grate on the area-weighted average temperature at different planes (65 mm, 105 mm, 200 mm and outlet). To determine the grate openings that would result in optimized conditions for homogeneous combustion of wood volatiles, we performed simulations at three different percentage openings of the grate for case (c) and case (d) similar to section 5.2.3. Table 7 shows the values of area weighted average temperature ( T ) for these three conditions of the openings. It can be seen that the highest values of T for both case (c) and case (d) are obtained for the 50% opening case. This is evident since the opening area available for the air to flow inside the fuel chamber of the cookstoves is highest for the 50% opening case. Further, the value of the uniformity index (γ for 50%>25%>12.5%) for velocity increases with an increase in the % grate opening. Moreover, the area-weighted average temperature on the 65 mm and 105mm planes are significantly higher for case (d) as compared to that for case (c). This shows that inclusion of and grate and orifice plate in the BCS design helps in achieving better uniformity and temperature throughout the BCS. The observations explained above were found to be consistent for the three openings (50%, 25%, and 12.5%) for case (c) and case (d)) as shown in Table 7 and it is also a validation of the results of the section 5.2.3. The homogenous combustion simulations showed that the spatial distribution of temperature inside the cook-stoves is interrelated to the uniformity of air predicted from the hydrodynamic simulations. The temperatures were found to be maximum for the case where the uniformity of air-fuel mixture was the highest. Achieving proper air distribution inside the cook-stove has been one of the major challenges in designing thermally efficient cookstoves. The present CFD study confirms that using two concentric cylinders with the provision of secondary holes in the inner cylinder along with an orifice plate fitted in the annular space and a


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grate system at the bottom of the cylinder to hold the fuel particles helps in achieving higher air velocity distribution leading to much uniform air-fuel mixing. Further power output for case (c) and (d), at 1 (Table 6.1), 2 (Table 6.2) and 3 m/s (Table 6.3) for Air-Fuel ratio (a) 1:1 (b) 3:1 (c) 5:1 are mentioned in Table S6 of the supplementary information. Table 7. Area-weighted average temperature for case (c) and (d) under three different grate opening conditions (12.5%, 25% and 50%) at 1 m/s Temperature K Configuration

Planes

50% grate

25% grate

12.5% grate

Plane 65 mm

opening 464

opening 456

opening 436

Plane 105 mm

506

489

433

Plane 200 mm

1064

1056

1048

Outlet

948

944

943

Plane 65 mm

492

472

469

Plane 105 mm

547

510

504

Plane 200 mm

1053

1038

1035

Outlet

952

933

922

Case (c)

Case (d)

5.3.4. Effect of orifice holes opening and orifice number Further, we have also implemented the role of air distribution in BCS. Accordingly, Table S7 (provided in the supplementary information) gives the relative distribution of primary to secondary air. We have also estimated the power output and the thermal efficiency in the fourth and fifth columns of Table S7, respectively. These respective flows have been estimated for various



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geometrical variations in case (b) (Figure 2). The effect of the ratio of primary to secondary air has been investigated at different inlet velocities (1, 2 and 3 m/s) and the air to fuel ratio (1:1, 3:1 and 5:1) ( see Tables S7.1 to Tables S7.9 in supplementary information). The results are shown in Table S7 of the supplementary information. It can be seen that the power increases with an increase in air velocity. This is because the amount of fuel also increases with air velocity. However, it may be noted that the thermal efficiency decreases (though marginally) with an increase in air velocity. A similar observation can be made for the other AFR (for instance AFR = 3:1, Tables S7.2, S7.5, S7.8 and for AFR = 5:1, Tables S7.3, S7.6 and S7.9). Also, for a constant air velocity of 1 m/s, the effect of AFR (1:1, 3:1 and 5:1) is given in Tables S7.1, S7.2 and S7.3, respectively. It can be seen that, an increase in AFR from 1:1 to 3:1 decreases the power output in the range of 10 to 13 percent. A further increase in AFR to 5:1 decreases the power output in the range of 14 to 18 percent. In addition, an increase in AFR also reduces the thermal efficiency (though marginally). This is because, with an increase in AFR, the energy having BCS also increases because of the increase in total mass flow rate. This results in a net reduction of power output with an increase in AFR. We also evaluated the effect of opening (orifice diameters of 10, 5 and 2 mm) and also the number of orifices (15, 10 and 5) on the power output as well as on thermal efficiency. It can be seen from Table S7.1 that the change in orifice diameter and number has a substantial influence on the ratio of primary to secondary air. Thus, for d0 of 10 mm and 15 orifices, the primary air is 36 percent. It increase to 96 percent when orifice resistance increased (d0 = 2 mm, n = 5). It can be seen from Table S7.1 that an increase in orifice resistance decreases the power output as well as the thermal efficiency. Similar results can be seen for all the other air velocities as well as different AFR (Tables S7.2 to S7.9).


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6. CONCLUSIONS This study presents CFD simulations of hydrodynamic and homogeneous combustion for five design configurations of biomass cook-stoves (Figure 2). The optimized hydrodynamic parameters and the corresponding predictions from the simulations of homogenous combustion enabled us to arrive at the following conclusions. (1) The inclusion of both the grate plate and the orifice plate (case (d)) exhibited relatively better air flow uniformity as compared to cases (a), (b), (c) and (e). (2) The orifice plate and grate help in obtaining uniform mixing between the fuel and the air which when undergoes combustion gives good power output. (3) An increase in the grate resistance (50%, 25% and 12.5%) to the primary air slightly decreases the uniformity index. As a consequence, the power output and thermal efficiency are maximum for case (d) where the grate resistance was the least in the range covered in this work. (4) The power output was found to be strongly dependent on the fuel flow rate. However, for a given fuel flow rate, the power output, the flame temperature and the thermal efficiency were found to be maximum at air to fuel ratio of 1:1. An increase in AFR from 1:1 to 3:1 decreases the power output in the range of 10 to 13 percent. A further increase in AFR to 5:1 decreases the power output in the range of 14 to 18 percent for all the cases. In addition, on an increase in AFR also reduces the thermal efficiency (though marginally). (5) The same trend (as in conclusion - 4) was also observed for orifice sensitivity (orifice hole diameter (ϕ = 10, 5, 2 mm) and orifice number (15, 10 and 5)), where increasing the resistance to the secondary flow decrease the power output and thermal efficiency (though marginally).



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(6) The present study serves as a cost-effective solution to improve the uniformity in air flow distribution across the cook-stove, the CFD simulations. (7) The resistance to the secondary flow was investigated by changing the diameter and the number of holes. It was found that the power output as well as thermal efficiency improved by reducing the secondary resistance. (8) The homogenous combustion simulations showed that the spatial distribution of temperature inside the cook-stoves is interrelated to the uniformity of air predicted from the hydrodynamic simulations. The temperatures were found to be maximum for the case where the uniformity of air-fuel mixture was the highest. This verifies that although the Eddy Dissipation Model (EDM) does over predict the temperatures, it can be reasonably used to describe the homogenous combustion. (9) The simulation strategy explained in this work is useful for the further optimization of biomass cook-stoves. SUPPLEMENTARY INFORMATION Tables for a historical review of CFD studies for biomass-based cook-stoves, mass flow rates, uniformity indices, standard deviation and power output predicted from simulations for all the air flow velocities, air-fuel ratios and cook-stove configurations have been presented in the supplementary information. This information is available free of charge via the Internet at http://pubs.acs.org/. ACKNOWLEDGMENTS Zakir Husain and Shashank S. Tiwari gratefully acknowledge the financial support from the ICTDAE center for Chemical Engineering Education and Research.


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NOMENCLATURE A: Area, m2 Cp: Specific heat capacity e: Total energy, activation energy h: Sensible enthalpy k: Thermal conductivity Ji: Mass flux; diffusion flux, Mass diffusion coefficient

J m : Flux of mass-produced by molecular mechanisms keff: Effective conductivity kt: Turbulent thermal conductivity

M w,i : Molecular weight P: Pressure

Ri ,r : Reaction rate Rm: Net rate of production of species m by chemical reaction S: Total (species) entropy Sc: Schmidt number, the ratio of momentum diffusivity to mass diffusivity Sh: Rate of reaction

Sm : Additional as yet unspecified source T: Temperature of the flow T0: Operating temperature u, v, w: Velocity magnitude in x, y and z-direction respectively,

Ym : Mass fraction



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Yp: Mass fraction of any product species, P YR: Mass fraction of a particular reactant, R µ: Dynamic viscosity ρ: Density of the flow ρ0: Operating density β: Coefficient of thermal expansion τij: Shear stress δ: Delta function λ: Molecular mean free path ε: Turbulent dissipation rate

 t : Turbulent eddy viscosity  : Uniformity index

 i : Index of each of the nodes on the selected surface


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REFERENCES (1) Joshi, J. B.; Pandit, A. B.; Patel, S. B.; Singhal, R. S.; Bhide, Govind. K.; Mariwala, K. V.; Devidayal, B. A.; Danao, S. P.; Ganguli, A. A.; Gudekar, A. S.; et al. Development of Efficient Designs of Cooking Systems. II. Computational Fluid Dynamics and Optimization. Ind. Eng. Chem. Res. 2012, 51, 1897 (2) Ladanai, S.; Vinterbäck, J. Global potential of sustainable biomass for energy; Report; Uppsala, 2009. (3) Shinde, Y. H.; Gudekar, A. S.; Chavan, P. V.; Pandit, A. B.; Joshi, J. B. Design and Development of Energy Efficient Continuous Cooking System. J. Food Eng. 2016, 168, 231. (4) Weyant, C. L.; Chen, P.; Vaidya, A.; Li, C.; Zhang, Q.; Thompson, R.; Ellis, J.; Chen, Y.; Kang, S.; Shrestha, G. R.; et al. Emission Measurements from Traditional Biomass Cookstoves in South Asia and Tibet. Environ. Sci. Technol. 2019, 53, 3306. (5) Singhal, R. S.; Pandit, A. B.; Joshi, J. B.; Patel, S. B.; Danao, S. P.; Shinde, Y. H.; Gudekar, A. S.; Bineesh, N. P.; Tarade, K. M. Development of Efficient Designs of Cooking Systems. III. Kinetics of Cooking and Quality of Cooked Food, Including Nutrients, Anti-Nutrients, Taste, and Flavor. Ind. Eng. Chem. Res. 2012, 51, 1923. (6) Bryden, K. M.; Ragland, K. W. Numerical Modeling of a Deep, Fixed Bed Combustor. Energy Fuels 1996, 10, 269. (7) Arce, M. E.; Saavedra, Á.; Míguez, J. L.; Granada, E.; Cacabelos, A. Biomass Fuel and Combustion Conditions Selection in a Fixed Bed Combustor. Energies 2013, 6, 5973. (8) Bryden, M. Design Principles for Wood Burning Cook Stoves; U.S. Environmental Protection Agency, Office of Air and Radiation: Washington, DC, 2005. (9) Raiyani, C. V.; Shah, S. H.; Desai, N. M.; Venkaiah, K.; Patel, J. S.; Parikh, D. J.; Kashyap, S. K. Characterization and Problems of Indoor Pollution Due to Cooking Stove Smoke. Atmos. Environ., Part A 1993, 27, 1643. (10) MacCarty, N.; Still, D.; Ogle, D. Fuel Use and Emissions Performance of Fifty Cooking Stoves in the Laboratory and Related Benchmarks of Performance. Energy for Sustainable Dev. 2010, 14, 161. (11) Menghini, D.; Marra, F. S.; Allouis, C.; Beretta, F. Effect of Excess Air on the Optimization of Heating Appliances for Biomass Combustion. Exp. Therm. Fluid Sci. 2008, 32, 1371. (12) Yang, Y. B.; Sharifi, V. N.; Swithenbank, J.; Ma, L.; Darvell, L. I.; Jones, J. M.; Pourkashanian, M.; Williams, A. Combustion of a Single Particle of Biomass. Energy Fuels 2008, 22, 306.



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(13) Kshirsagar, M. P.; Kalamkar, V. R. A Comprehensive Review on Biomass Cookstoves and a Systematic Approach for Modern Cookstove Design. Renewable and Sustainable Energy Rev. 2014, 30, 580. (14) Venkataraman, C.; Sagar, A. D.; Habib, G.; Lam, N.; Smith, K. R. The Indian National Initiative for Advanced Biomass Cookstoves: The Benefits of Clean Combustion. Energy Sustainable Dev. 2010, 14, 63. (15) Khandelwal, M.; Hill, M. E.; Greenough, P.; Anthony, J.; Quill, M.; Linderman, M.; Udaykumar, H. S. Why Have Improved Cook-Stove Initiatives in India Failed? World Dev. 2017, 92, 13. (16) Lewis, J. J.; Pattanayak, S. K. Who Adopts Improved Fuels and Cookstoves? A Systematic Review. Environ. Health Perspect. 2012, 120, 637. (17) Sedighi, M.; Salarian, H. A Comprehensive Review of Technical Aspects of Biomass Cookstoves. Renewable and Sustainable Energy Rev. 2017, 70, 656. (18) Smith, K. R.; Uma, R.; Kishore, V. v. n.; Zhang, J.; Joshi, V.; Khalil, M. a. k. Greenhouse Implications of Household Stoves: An Analysis for India. Annu. Rev. Energy. Environ. 2000, 25, 741. (19) Sutar, K. B.; Kohli, S.; Ravi, M. R.; Ray, A. Biomass Cookstoves: A Review of Technical Aspects. Renewable and Sustainable Energy Rev. 2015, 41, 1128. (20) Ruiz-Mercado, I.; Masera, O.; Zamora, H.; Smith, K. R. Adoption and Sustained Use of Improved Cookstoves. Energy Policy 2011, 39, 7557. (21) Azad, K.; Sharma, S. Clean Energy for Sustainable Development: Comparisons and Contrasts of New Approaches; Academic Press: New York, USA, 2016. (22) Abolhosseini, S.; Heshmati, A.; Altmann, J. A Review of Renewable Energy Supply and Energy Efficiency Technologies; IZA Discussion Papers; 8145; Institute for the Study of Labor (IZA), 2014. (23) Muller, C.; Yan, H. Household Fuel Use in Developing Countries: Review of Theory and Evidence. Energy Economics 2018, 70, 429. (24) Ravindra, K.; Kaur-Sidhu, M.; Mor, S.; John, S. Trend in Household Energy Consumption Pattern in India: A Case Study on the Influence of Socio-Cultural Factors for the Choice of Clean Fuel Use. J. Cleaner Prod. 2019, 213, 1024. (25) Suresh, R.; Singh, V. K.; Malik, J. K.; Datta, A.; Pal, R. C. Evaluation of the Performance of Improved Biomass Cooking Stoves with Different Solid Biomass Fuel Types. Biomass Bioenergy 2016, 95, 27.


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(26) Mehetre, S. A.; Panwar, N. L.; Sharma, D.; Kumar, H. Improved Biomass Cookstoves for Sustainable Development: A Review. Renewable and Sustainable Energy Rev. 2017, 73, 672. (27) Barnes, D. F.; Openshaw, K.; Smith, K. R.; Plas, R. van der. The Design and Diffusion of Improved Cooking Stoves. The World Bank Research Observer 1993, 8, 119. (28) Jagger, P.; Jumbe, C. Stoves or Sugar? Willingness to Adopt Improved Cookstoves in Malawi. Energy Policy 2016, 92, 409. (29) Rhodes, E. L.; Dreibelbis, R.; Klasen, E.; Naithani, N.; Baliddawa, J.; Menya, D.; Khatry, S.; Levy, S.; Tielsch, J. M.; Miranda, J. J.; et al. Behavioral Attitudes and Preferences in Cooking Practices with Traditional Open-Fire Stoves in Peru, Nepal, and Kenya: Implications for Improved Cookstove Interventions. Int. J. Environ. Res. Public Health 2014, 11, 10310. (30) Clark, M. L.; Peel, J. L.; Burch, J. B.; Nelson, T. L.; Robinson, M. M.; Conway, S.; Bachand, A. M.; Reynolds, S. J. Impact of Improved Cookstoves on Indoor Air Pollution and Adverse Health Effects among Honduran Women. Int. J. Environ. Health Res. 2009, 19, 357. (31) Ansari, K. B.; Arora, J. S.; Chew, J. W.; Dauenhauer, P. J.; Mushrif, S. H. Effect of Temperature and Transport on the Yield and Composition of Pyrolysis-Derived Bio-Oil from Glucose. Energy Fuels 2018, 32, 6008. (32) Mukhopadhyay, R.; Sambandam, S.; Pillarisetti, A.; Jack, D.; Mukhopadhyay, K.; Balakrishnan, K.; Vaswani, M.; Bates, M. N.; Kinney, P.; Arora, N.; et al. Cooking Practices, Air Quality, and the Acceptability of Advanced Cookstoves in Haryana, India: An Exploratory Study to Inform Large-Scale Interventions. Glob. Health Action 2012, 5, 19016. (33) Adkins, E.; Tyler, E.; Wang, J.; Siriri, D.; Modi, V. Field Testing and Survey Evaluation of Household Biomass Cookstoves in Rural Sub-Saharan Africa. Energy for Sustainable Dev. 2010, 14, 172. (34) MacCarty, N. A.; Bryden, K. M. Modeling of Household Biomass Cookstoves: A Review. Energy for Sustainable Dev. 2015, 26, 1. (35) Pradhan, P.; Arora, A.; Mahajani, S. M. A Semi-Empirical Approach towards Predicting Producer Gas Composition in Biomass Gasification. Bioresour. Technol. 2019, 272, 535. (36) Ravi, M. R.; Kohli, S.; Ray, A. Use of CFD Simulation as a Design Tool for Biomass Stoves. Energy for Sustainable Dev. 2002, 6, 20. (37) Deshmukh, S. R.; Vlachos, D. G. CFD Simulations of Coupled, Countercurrent Combustor/Reformer Microdevices for Hydrogen Production. Ind. Eng. Chem. Res. 2005, 44, 4982. (38) Rajika, J. K. A. T.; Narayana, M. Modelling and Simulation of Wood Chip Combustion in a Hot Air Generator System. SpringerPlus 2016, 5, 1166.



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(39) Yin, C.; Rosendahl, L.; Kær, S. K.; Clausen, S.; Hvid, S. L.; Hille, T. Mathematical Modeling and Experimental Study of Biomass Combustion in a Thermal 108 MW Grate-Fired Boiler. Energy Fuels 2008, 22, 1380. (40) Magnussen, B. F.; Hjertager, B. H. On Mathematical Modeling of Turbulent Combustion with Special Emphasis on Soot Formation and Combustion. Symposium (International) on Combustion 1977, 16, 719. (41) Incropera, F. P.; DeWitt, D. P.; Bergman, T. L.; Lavine, A. S. Fundamentals of Heat and Mass Transfer, 6th Edition edition.; Wiley: Hoboken, NJ, 2006. (42) Joshi, J. B.; Nandakumar, K.; Patwardhan, A. W.; Nayak, A. K.; Pareek, V. K.; Gumulya, M. M.; Wu, C.; Minocha, N.; Pal, E.; Kumar, M.; et al. Computational Fluid Dynamics. In Advances of Computational Fluid Dynamics in Nuclear Reactor Design and Safety Assessment; Woodhead Publishing, Cambridge, United Kingdom, 2019. (43) Bale, S.; Sathe, M.; Ayeni, O.; Berrouk, A. S.; Joshi, J. B.; Nandakumar, K. Spatially Resolved Mass Transfer Coefficient for Moderate Reynolds Number Flows in Packed Beds: Wall Effects. Int. J. Heat Mass Transfer 2017, 110, 406. (44) Bale, S.; Tiwari, S.; Sathe, M.; Berrouk, A. S.; Nandakumar, K.; Joshi, J. Direct Numerical Simulation Study of End Effects and D/d Ratio on Mass Transfer in Packed Beds. Int. J. Heat Mass Transfer 2018, 127, 234. (45) Bale, S.; Tiwari, S. S.; Nandakumar, K.; Joshi, J. B. Effect of Schmidt Number and D/d Ratio on Mass Transfer through Gas-Solid and Liquid-Solid Packed Beds: Direct Numerical Simulations. Powder Technol. 2019, 354, 529. (46) Murthy, B. N.; Joshi, J. B. Assessment of Standard k–ε, RSM and LES Turbulence Models in a Baffled Stirred Vessel Agitated by Various Impeller Designs. Chem. Eng. Sci. 2008, 63, 5468. (47) Mathpati, C. S.; Deshpande, S. S.; Joshi, J. B. Computational and Experimental Fluid Dynamics of Jet Loop Reactor. AIChE J. 2009, 55, 2526. (48) Rane, C. V.; Ganguli, A. A.; Kalekudithi, E.; Patil, R. N.; Joshi, J. B.; Ramkrishna, D. CFD Simulation and Comparison of Industrial Crystallizers. Can. J. Chem. Eng. 2014, 92, 2138. (49) Bale, S.; Clavin, K.; Sathe, M.; Berrouk, A. S.; Knopf, F. C.; Nandakumar, K. Mixing in Oscillating Columns: Experimental and Numerical Studies. Chem. Eng. Sci. 2017, 168, 78. (50) Tiwari, S. S.; Pal, E.; Bale, S.; Minocha, N.; Patwardhan, A. W.; Nandakumar, K.; Joshi, J. B. Flow Past a Single Stationary Sphere, 1. Experimental and Numerical Techniques. Powder Technol. 2019.


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(51) Tiwari, S. S.; Pal, E.; Bale, S.; Minocha, N.; Patwardhan, A. W.; Nandakumar, K.; Joshi, J. B. Flow Past a Single Stationary Sphere, 2. Regime Mapping and Effect of External Disturbances. Powder Technol. 2019. (52) Ranade, V.; Joshi, J. B.; Marathe, A. G. Flow Generated by Pitched Blade Turbines II: Simulation Using k-ε Model. Chem. Eng. Commun. 1989, 81, 225. (53) Kumaresan, T.; Nere, N. K.; Joshi, J. B. Effect of Internals on the Flow Pattern and Mixing in Stirred Tanks. Ind. Eng. Chem. Res. 2005, 44, 9951. (54) Kumaresan, T.; Joshi, J. B. Effect of Impeller Design on the Flow Pattern and Mixing in Stirred Tanks. Chem. Eng. J. 2006, 115, 173. (55) Joshi, J. B.; Nandakumar, K. Computational Modeling of Multiphase Reactors. Annu. Rev. Chem. Biomol. Eng. 2015, 6, 347. (56) Agenbroad, J.; DeFoort, M.; Kirkpatrick, A.; Kreutzer, C. A Simplified Model for Understanding Natural Convection Driven Biomass Cooking Stoves—Part 1: Setup and Baseline Validation. Energy for Sustainable Dev. 2011, 15, 160. (57) Pande, R. R.; Kshirsagar, M. P.; Kalamkar, V. R. Experimental and CFD Analysis to Study the Effect of Inlet Area Ratio in a Natural Draft Biomass Cookstove. Environ Dev Sustain 2018. (58) Núñez, J.; Moctezuma-Sánchez, M. F.; Fisher, E. M.; Berrueta, V. M.; Masera, O. R.; Beltrán, A. Natural-Draft Flow and Heat Transfer in a Plancha-Type Biomass Cookstove. Renewable Energy 2020, 146, 727. (59) Berrueta, V. M.; Edwards, R. D.; Masera, O. R. Energy Performance of Wood-Burning Cookstoves in Michoacan, Mexico. Renewable Energy 2008, 33, 859. (60) Macqueron, C. Computational Fluid Dynamics Modeling of a Wood-Burning Stove-Heated Sauna Using NIST’s Fire Dynamics Simulator. arXiv:1404.6774 [physics] 2014. (61) Patwardhan, A. W.; Joshi, J. B. Relation between Flow Pattern and Blending in Stirred Tanks. Ind. Eng. Chem. Res. 1999, 38, 3131. (62) Ansys Fluent Theory Guide. ANSYS Inc., USA 2011, 15317, 724.



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Table and Figure Legends List of Tables Table 1. The configuration of BCS for five cases used for CFD simulations Table 2. Simulation parameters prescribed to the solver Table 3. Uniformity index (γ) at different planes for different configuration at 0.5 m/s Table 4. Standard deviation at different planes for different configuration at 0.5 m/s Table 5. Uniformity index (γ) for case (c) and (d) under three different grate opening conditions (50%, 25% and12.5%) at 1 m/s Table 6. Power output for Case ((a)-(e)) have been evaluated at three different inlet air velocities for 1:1 air-to-fuel ratio Table 7. Area-weighted average temperature for case (c) and (d) under three different grate opening conditions (12.5%, 25% and 50%) at 1 m/s


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List of Figures Figure 1. Schematic representation of BCS, (1) Fan, (2) Inner conducting cylinder, (3) Outer insulating cylinder, (4) Orifice in the annular space, (5) Annular space, (6) Biomass packing, (7) Secondary holes, (8) Grate/supporting plate, (9) Ash collector, (10) Opening on the inner cylinder as the inlet for primary air. Figure 2. Two-dimensional pictorial representation of the different configuration of BCS a: Case (a), b: Case (b), c: Case (c), d: Case (d), e: Case (e) Labels: [1] Fan, [2] Inner cylinder (ϕ=190 mm, H=210 mm), [3] Outer cylinder (ϕ=230 mm, H=210 mm), [4] Orifice plate in annular space at 105 mm from the bottom (ϕ=10 mm, 15 nos), [5] Annular space (20 mm), [6] Particle packed bed (ϕ=20 mm, void=0.60), [7] Secondary holes (ϕ=5 mm, 34 nos) 15mm from top, [8] Grate plate at the bottom (50% opening), [9] Ash collector, [10] Square cut on inner cylinder body (20 mm x 20 mm). Figure 3. Top view of BCS [11] Orifice (ϕ=10 mm, 15 nos) and [12] Grate (50% opening) Figure 4. Mesh configuration for (a) YZ- Center plane, (b) XZ- plane at H=195mm, (c) XY-Center plane for Case (a) (d) XZ- plane at H=105mm, (e) XY- Centre plane for Case (e), (f) XZ- plane at H=25mm Figure 5: Mesh Sensitivity analysis for Case (a) at an inlet velocity of 2 m/s Figure 6. Velocity contours on XZ plane at 20 mm height from the base of the cook-stove (a) Case (a) (b) Case (b) (c) Case (c) (d) Case (d) (e) Case (e)



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Figure 7. Air jet length based on the velocity distribution predicted from the hydrodynamic simulations for inlet velocity of 0.5 m/s Figure 8. Temperature contours on XZ plane at 20 mm height from the base of the cook-stove (a) (a) Case (a) (b) Case (b) (c) Case (c) (d) Case (d) (e) Case (e) Figure 9. Power output at 1 m/s for Air-Fuel ratio (a) 1:1 (b) 3:1 (c) 5:1 and highest temperature attained for Air-Fuel ratio (d) 1:1 (e) 3:1 (f) 5:1


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For Table of Contents Only Secondary holes Orifice (15 holes, 10 mm) Grate plate

Biomass Cook-stove with orifice and grate Velocity distribution on a plane at Temperature profile on a plane at 20mm height 20mm height


Computational Fluid Dynamics Study of Biomass Cook StoveâPart 1

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Computational Fluid Dynamics Study of Biomass Cook StoveâPart 1