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Interplay between erodent concentration and impingement angle for erosion in dilute water–sand flows
Wear 332-333 (2015) 1111–1119
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Interplay between erodent concentration and impingement angle for erosion in dilute water–sand flows H S Grewal a,b, H Singh b,n, Eui-Sung Yoon a,n a b
Centre for BioMicrosystems, Korea Institute of Science and Technology, Seoul 136-791, Republic of Korea School of Mechanical, Materials and Energy Engineering, Indian Institute of Technology Ropar, Rupnagar 140001, India
art ic l e i nf o
a b s t r a c t
Article history: Received 22 September 2014 Received in revised form 21 January 2015 Accepted 4 February 2015
Slurry erosion is a complex process with number of interacting variables (operating and material parameters). We study the interaction between erodent concentration and impingement angle using experimental and computational fluid dynamics (CFD) techniques. Experiments were performed using a high velocity slurry erosion test rig at a constant velocity with dilute slurries of water and sand particles. Concentration of sand in water was varied from 0.01 wt% (100 ppm) to 0.5 wt% (5000 ppm) at three sample orientations (301, 601 and 901). Six different targets, three bulk materials (aluminum, cast iron, and stainless steel) and three thermal sprayed coatings (Ni þ 20, 40, and 60 wt% of Al2O3) were used for slurry erosion tests. Experimental results showed a significant interaction between erodent concentration and impingement angle. The concentration variation showed larger influence on erosion rate for sample held normal to the slurry jet compared to glancing angle. The degree of interaction was different for different materials. CFD simulations showed higher particle-to-particle interactions for sample at glancing angle compared to that at normal angle. This probably explains the low contribution of concentration variation at glancing angle compared to the normal angle. Further, the effect of concentration on erosion rates was also influenced by the restitution coefficient (function of material and impact parameters). For a high restitution coefficient materials, the change in slurry concentration showed minimum effect on erosion rate compared with low restitution coefficient materials. & 2015 Elsevier B.V. All rights reserved.
Keywords: Slurry erosion Wear modeling Computational fluid dynamics
1. Introduction The slurry erosion is a form of wear wherein the solid particles entrained in liquid medium impacts and deteriorates the target surface. Due to its damaging effects, slurry erosion is undesirable in fluid machineries such as turbines, pumps, and propellers as it adversely affects the durability and efficiency of the system [1–9]. On the hand, the damaging aspects of slurry erosion finds potential use in machining and cleaning processes such as abrasive jet machining, removal of damaged coatings and blast cleaning [10,11]. Although, the basic phenomenon of slurry erosion is well understood, the complexities inherent to solid–liquid interaction poses challenge to further understanding [12,13]. The non-linear mechanical properties of materials and complex microstructures further intensify the problem. The experimental and theoretical works in the past have significantly contributed to the present understanding of the
n
Corresponding authors. E-mail addresses: [email protected] (H. Singh), [email protected] (E.-S. Yoon). http://dx.doi.org/10.1016/j.wear.2015.02.039 0043-1648/& 2015 Elsevier B.V. All rights reserved.
slurry erosion process [12–18]. It is recognized that a severe interaction exists among the parameters (operating and material). These interactions have limited further understanding of the slurry erosion. As a result, an adequate theoretical framework required for the accurate predictions is still missing. Hence, further efforts are required for thorough understanding of the slurry erosion process. In general, the maximum erosion of a ductile material takes place when erodent particles impact at glancing angles (20–301), whereas, for a brittle material, the same is observed at a normal impingement angle [12]. Further, with the increase in concentration of solid particles in a liquid medium, the erosion rate increases due to increase in frequency of the damaging events which however, attains a steady state beyond a limiting value [18–22]. The steady state is a result of a balance (frequency) formed between the incident and rebounding particles (shielding phenomenon). With the increase in velocity of the erodent particles, the erosion rate increases, mostly following a power-law as a function of a target material [18]. Other than the target and erodent material properties, the interaction between the basic operating parameters (velocity, concentration, and impingement angle) plays an important role in erosion.
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In present work, attempt was made to investigate the interaction between impingement angle (sample orientation) and concentration of solid particles using a combination of experimental and computational techniques. The slurry erosion performance of different bulk materials and coatings was evaluated at different impingement angles and erodent concentrations. Simulations were performed using computational fluid dynamics (CFD) technique to further understand the erosion process.
2. Experimentation The slurry erosion experiments were performed using a highspeed slurry erosion test rig (Fig. 1). Specific details of the test rig were reported earlier elsewhere [23,24]. It is a non-recirculating type test rig with erodent particles once leaving the system are not re-used. The tungsten carbide nozzle with an inner diameter of 4 mm was used. A stand-off distance of 20 mm between the nozzle and specimen was ensured in each experiment. The experiments were conducted at a constant impingement velocity of 16 m/s at different impingement angles and concentrations of sand particles (average size: 197 mm) in water (Table 1). The maximum erodent concentration used was 0.5 wt%. These dilute erodent concentrations are similar to those found in many fluid machines such as hydroturbine, pump and other utility systems. The erosion tests were performed for a total period of 10 min with 1 min cycle time. This time was found sufficient to attain a steady state. Prior to the weight measurement, samples were washed using acetone and dried in air. The weight change was measured with an accuracy of 70.01 mg. Two samples were evaluated at each test condition and their average value is reported. Three bulk materials, aluminum (Al) (99.9%), cast iron (CI) and stainless steel, CA6NM (SS) were used as target materials. In addition, three types of coatings prepared by mixing nickel (Ni) and alumina (Al2O3) in different proportions (20, 40 and 60 wt%) were also used (Fig. 2). These coatings were coated on the CA6NM steel using a high velocity flame spray coating technique. Detailed microstructural characterization and mechanical properties of these coatings are given in Grewal et al. [25]. Slurry erosion tests were conducted according to ASTM standard G-73.
3. Modeling The computational fluid dynamics (CFD) simulation of the slurry erosion process was performed using the finite volume method. The three-dimensional meshed domain used for the CFD simulations is shown in Fig. 3 along with the used boundary conditions. This meshed domain was modeled using GAMBIT v2.0 software. The domain was discretized using tetragonal shaped elements. The CFD simulations were performed with the FLUENT 6.3 software using the discrete phase model (DPM) and volume of fluid (VOF) method. In DPM, the flow of a fluid is modeled using the Eulerian method, whereas, the motion of particles is tracked following the Lagrangian approach. The VOF method was used for simulating the un-submerged water jet condition. Further, a steady state Reynolds averaged Navier–Stokes (RANS) equations were used for the turbulence modeling. This requires solving the continuity and time averaged-momentum conservation equations for the primary phase (water) ∂ρ ∂ þ ðρvi Þ ¼ 0 ∂t ∂xi
ð1Þ
∂ ∂ ∂p ∂ ∂v ∂v 2 ∂v ∂ ρvi þ ðρvi vj Þ ¼ þ μ i þ j δij k þ ð ρv0i v0j Þ ∂t ∂xj ∂xi ∂xj ∂xj ∂xj ∂xi 3 ∂xk ð2Þ where ρ is density, v is velocity, p is pressure, and m is viscosity of the fluid phase. The additional terms ( ρv0i v0j ), the Reynolds stress, considers the effect of turbulence and must be solved for the closure of Eq. (2). These Reynolds stresses were modeled using the standard k–ε model, ∂ ∂ ∂ μ ∂k þ Gk þ Gb ρε ρk þ ρkvi ¼ μþ t ð3Þ ∂t ∂xi ∂xj σ κ ∂xj ∂ ∂ ∂ ρε þ ρεvi ¼ ∂t ∂xi ∂xj
μþ
μt ∂ε ε ε2 þ C 1ε ðGκ þ C 3ε Gb Þ C 2ε ρ σ ε ∂xj k k ð4Þ
for the turbulence kinetic energy, k and dissipation rate, ε. Here, Gk represents the turbulence kinetic energy generated due to mean
Re-circulated water line Filter unit Sand feeder Slurry Pump
Slurry containing line
. Sample holder and nozzle unit
Storage Tanks
Fig. 1. Schematic illustrating the slurry erosion test rig used for experiments [23].
Table 1 The experimental conditions used for conducting slurry erosion test on different bulk and coating materials. Material
Aluminum Cast iron Stainless steel (CA6NM) Coatings (Niþ20, 40,60 wt% Al2O3)
Impingement angle
Concentration (wt%)
301
601
901
0.01
0.1
0.5
√ √ √ √
√ √ √
√ √ √ √
√ √
√ √ √ √
√ √ √ √
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100 µm Fig. 2. Scanning electron microscope images showing the cross-sectional microstructure of high velocity flame sprayed (a) Ni þ20%Al2O3 (b) Ni þ40%Al2O3 (c) Niþ 60%Al2O3 coatings deposited on CA6NM steel. The scale bar shown is common for all the images [25].
was added to the force balance equation (Eq. (6)) through userdefined function. Further, the influence of turbulence on the particle trajectory was simulated using the discrete random walk model. Sand particles were distributed homogenously over the nozzle inlet cross-section. The normal and tangential restitution coefficients, e, of the particle on target surface were modeled by [27] e ? ¼ 0:988 0:78θ þ 0:19θ 0:024θ þ 0:027θ 2
3
4
e J ¼ 1 0:78θ þ 0:84θ 0:21θ þ 0:028θ 0:022θ 2
Fig. 3. Computational domain used for modeling the slurry erosion. Boundary conditions used for simulations are shown.
velocity gradient and Gb is the kinetic due to buoyancy. energy C1ε ¼1.44, C2ε ¼ 1.92 and C3ε ¼tanhv J =v ? are constants, and σk ¼ 1.0 and σε ¼ 1.3 are the turbulent Prandtl numbers for k and ε, respectively. v J and v ? are the flow velocity components parallel and perpendicular to the gravitational vector, respectively. The turbulent viscosity, mt was computed using
μt ¼ ρC μ
k
4
5
ð10Þ
using the local impingement angle, θ. Particles were injected in a converged flow field. The grid independency of the solution was assured by mesh refinement a region of high velocity gradients using the adaptive meshing. The selective refinement helped refining mesh along with optimal use of the computational resources. The refinement threshold of 1/10 was used in each step and the adaptive meshing was performed until the results became independent of the grid (further refinement did not altered the results). During the adaptive meshing, the number of elements increased from initial 1,108,900 to 1,200,578.
2
ð5Þ
ε
3.1. Model validation
with Cm ¼0.09. For calculating the particle trajectories, force balance equation is solved [26], ðρp ρf Þ dvp ¼ F D ðvf vp Þ þ g þ Fi dt ρp
ð6Þ
which equates the inertial force with the drag force, FD, the gravitational force and other forces, Fi, [26] ! 18μ C D Re ð7Þ FD ¼ ρp D2p 24
3
ð9Þ
! ρf ! v f v p Dp Re ¼ μf
ð8Þ
where subscripts f and p represents the fluid and particle phases, respectively. Further, CD and Re are the drag coefficient and Reynolds number, Dp is the particle diameter and g is the acceleration due to gravity. Other forces which includes, the body force, the virtual mass force (force required for accelerating the fluid around the particle, important only when ρf 4 ρp), thermophoretic force (due to temperature gradient) and Brownian force can be included depending upon the model. In the present work, body force induced on particles due to acceleration was considered. The body force per unit mass, F m ¼ ð1=12Þ ðdðvf vp Þ=dtÞ
The CFD model was validated using the experimental results of the Shipway and Hutching [28]. For their experiments, Shipway and Hutching [28] used a soda-lime glass particles of various size ranges (63–75 mm, 212–250 mm and 650–750 mm) entrained in air at different erodent fluxes. The inlet pressure was fixed at 0.2 bar and the inner and outer nozzle diameters were 4.7 mm and 6.35 mm, respectively. They used the opto-electronic flight timer technique to measure the particle velocities. To simulate the test rig used by Shipway and Hutching [28], a three-dimensional CFD model similar to the one in Fig. 3 was developed. For the model validation, results of the particles with size of 212–250 mm were used with flux varied from 0.008 kg/m2 s to 45.7 kg/m2 s for a total dosage of 5 g for each case. The predicted and experimental particle trajectories for the case of 45.7 kg/m2s erodent flux are shown in Fig. 4.The similarity between the predicted and actual particle trajectories is readily evident. Further, a good correlation between the predicted and measured radius of wear scars and particle velocities with the erodent flux was observed (Fig. 5). The predicted velocity (47 71 m/s) of particles exiting the nozzle was very much similar to that measured experimentally by Shipway and Hutchings [28]. The above results indicate that this CFD model is reliable and can be used for other simulations with sufficient confidence.
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Fig. 4. (a) Experimental result of Shipway and Hutchings [28] and (b) computational fluid dynamics simulation results showing the particle trajectories for lead-glass spherical particles (φ¼ 212–250 mm) in an air stream at an inlet pressure of 0.2 bar and an erodent flux of 45.7 kg/m2 s.
Fig. 6. Steady state erosion rates for the (a) bulk materials and (b) thermal spray coatings under different impingement angles and slurry concentrations at constant velocity of 16 m/s. In (a) open, half-filled and filled symbols stands for 0.01 wt%, 0.1 wt% and 0.5 wt% of slurry concentration. In (b) NixAl represents the composition of the coatings with ‘x’ representing the wt% of Al2O3, Ni for Nickel and Al for Al2O3.
Fig. 5. The comparison between the experimental (Shipway and Hutchings [28]) and predicted scar radius at different erodent flux for lead-glass spherical particles (φ¼212–250 mm) in an air stream at an inlet pressure of 0.2 bar. The measured and predicted velocity of particles leaving the nozzle was approximately 47 m/s, as shown in Fig. 4.
4. Results 4.1. Slurry erosion experiments The steady state erosion rates of the investigated test materials at different impingement angle (sample orientation) and erodent concentration are shown in Fig. 6. For the ductile Al, CA6NM steel and Ni20Al2O3 coating, the maximum erosion rate was observed at 301 angle. For other coatings and CI, the maximum erosion rates
were observed when sample was held at 901 angle. With the increase in concentration for either impingement angle, the erosion rate also increased. However, the change in erosion rates with concentration was influenced by the target material and impingement angle. Further, there was a non-linear correlation between the erosion rate and concentration. Even for 10 and 50 times increase in erodent concentration, the change in erosion rate was modest. These experimental results are in general agreement with those reported in [19,29–32]. The analysis of these results suggests that the influence of concentration on erosion rate is related to the flow field (influenced by sample orientation) and material properties. This fact is evident from Fig. 7, which presents the value of concentration exponent, m E ¼ KC m
ð11Þ 3
where E is a steady state erosion rate (m /s), K is a constant, and m is an exponent indicating the influence of concentration (kg/m3) on erosion rate. Fig. 7 shows that the value of m increases with an increase in impingement angle, irrespective of the target material.
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Fig. 7. Value of concentration exponent, m as a function of impingement angle and target material.
This indicates an increase in influence of the concentration at higher impingement angles. For a given increase in the erodent concentration, the proportional increase in erosion rate is higher for a sample held normal to the slurry jet in comparison to that at glancing angle. However, it is normally expected that due to shielding effect, the influence of concentration on erosion rate will be marginal in the former case (sample at normal orientation). This, although holds true for the solid particle erosion (carrier fluid is a gas instead of liquid), will apparently be influenced by the carrier fluid properties such as viscosity and density. In comparison to air, water is almost 50 times more viscous and approximately 800 times denser. The difference in these physical properties will surely influence the flow field and hence the particle trajectories. Further discussion in alliance with CFD results is provided in Section 5. 4.2. Computational fluid dynamics simulations The velocity profiles for both 301 and 901 impingement angles are shown in Fig. 8. The magnitude of velocity indicated by the color map shows its distribution in the flow domain. The velocity of a water jet issuing from the nozzle at 16 m/s reduced to around one-fourth to one-tenth at the center of impact zone. This is due to transformation of the fluid kinetic energy into dynamic pressure leading to the formation of squeeze film (Fig. 9). Further, a symmetrical distribution of velocity was observed around the impact zone for the sample held at 901. However, for the 301 sample orientation, velocity distribution was highly skewed. In this case, the shape of squeeze film was distorted and stretched along the flow direction. This non-symmetrical distribution of pressure and velocity influenced the particle trajectories (Fig. 10). The particle trajectories for the 901 sample orientation were symmetrical compared with 301 case. In the latter case, almost all the particles were moving in an identical direction over the sample confirming to the flow field. This leads to increase in interaction among the particles. This becomes further evident from Fig. 11, which shows the trajectory of individual particle streams. The starting position of particle stream before been ejected from the nozzle is also shown. A closer look at Fig. 11 (a) shows a highly chaotic motion of the particles when sample is oriented at 301 resulting in a significant interaction between particle streams. This is mainly due to the flow field following which all the particles are moving in the identical direction. As a
Fig. 8. Velocity distribution plot of water jet issuing from the nozzle and impacting the samples oriented at (a) 901 and (b) 301 to the nozzle axis. The color map is common for both the cases.
result, the probability of particle-to-particle interaction also increases. On the other hand, the impact zone for 901 case (Fig. 11(b)) shows a smooth flow of the particles. The flow field in the case of 901 impingement angle (sample orientation) is symmetrical around the impact zone. Thus, particles flow in an individual direction after impact. This reduces the chance of interaction with other particles. These results are further discussed in the following section.
5. Discussion The slurry erosion experiment showed that the change in erosion rate with concentration depends on the impingement angle (sample orientation) and the target material. The increase in concentration exponent, m with impingement angle (sample orientation) indicated an increase in influence of concentration at higher impingement angles. The change in erosion rate with the change in concentration is highest when sample is held normal to the slurry jet compared to glancing angle. Effect of impingement angle (sample orientation) on concentration was thoroughly
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investigated by Uuemo and Kleis [19] and Anand et al. [29]. Uuemo and Kleis [19] observed that the effect of concentration is more prominent at normal impingement angle in comparison to that at glancing angle. This is in agreement with present observations. Similar results were also reported by Anand et al. [29] and Zhou and Bahadur [30]. However, the reason for this trend was not
Fig. 9. The contour plot of static pressure inside the modeling domain for the samples oriented at (a) 901 and (b) 301 to the nozzle axis. The color map is same for both the figures.
discussed. At first glance, it appears that the shielding effect, which is caused by the interaction between incoming and rebounding particles, will be more dominant when a sample is oriented at 901 in comparison to glancing angles. This is because the rebound angle of most of the particles impacting at 901 will be around 1801. This will increase the chance of collision with the incoming particles. However, the CFD results (Figs. 8,10 and 11) showed that when sample is held normal to the jet of liquid and erodent particles, only small fraction of particles at the jet center actually impacts at 901 or so. Similar results have also been reported by Sugiyama [33,34]. The particles away from the jet center readily follows the fluid path lines due to dominance of the drag force over the inertia force of particle. This will reduce the strength of shielding layer. Had it been air in place of water, the drag and viscous forces induced by the carrier fluid on particles would have been nominal. Thus, most of the particles would have retained their original trajectory and impinged at a pre-set impingement angles due to the dominance of particle inertia force over the drag and viscous forces (Fig. 4). Similar experimental results were reported by Shipway and Hutching [28]. Therefore, shielding effect will predominate when carrier fluid with low viscosity and density such as air is used. In this case, the influence of increase in erodent concentration on erosion rate is reduced. However, the situation becomes completely different when a dense and viscous carrier medium is used. The effect of density and viscosity of carrier medium on the particle impingement angles is shown in Fig. 12. These results indicate that for the fluid with low viscosity and density, most of the particles impact the target surface at a pre-set impingement angle. However, as the density/viscosity of the fluid is increased, the impingent angle of the particles is significantly influenced. The fraction of particles impinging at a pre-set angle is reduced. This is due to dominance of drag and viscous forces in comparison to inertia force. Similar observations were also reported by Hojo et al. [35] and Clark [36,37] for the pot-type test rig. Therefore, particles entrained in a fluid with comparatively higher density and viscosity (water in comparison to air) will move along the path lines of the fluid rather than retaining their original trajectories. This reduces the shielding effect for sample oriented at normal angle, whereas, the shielding effect increases at glancing angles due to rise in frequency of particle-to-particle collisions (Fig. 11). Further, the results in Fig. 7 also showed that the value of concentration exponent, m was influenced by the target materials. For soft materials such as Al, the value of m was high compared to harder CI or SS. The particle impact on a soft material can induce plastic deformations. This in turn will affects the rebound characteristic and hence the shielding effect. This postulation was
Fig. 10. Particle trajectories for samples oriented at 901 and 301 colored by velocity magnitude.
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Fig. 11. Particle trajectories colored by the particle identity along with individual particle streams issuing from different regions of the nozzle for the sample oriented at (a) 301 and (b) 901.
examined by investigating the correlation between the exponent, m and the restitution coefficient, e using [38] e ¼ const:
H 0:625
ρ
0:125 V 0:25 E0:5 e in
ð12Þ
According to this model, the restitution coefficient, e is a function of material and operating parameters. It is directly proportional to hardness, H and inversely related to the equivalent Young's modulus, Ee of the elastic contact. The correlation between restitution coefficient (as a function of hardness and equivalent Young's modulus) and concentration exponent, m is shown in Fig. 13. For both bulk and coating materials, concentration exponent, m showed an inverse correlation with the restitution coefficient. This result suggests that the concentration effect is also influenced by material properties, particularly hardness and Young's modulus. High value of m indicates the erosion rate is highly influence by the change in concentration. The dependence of concentration effect on restitution coefficient behavior can be explained as below: For a soft material with low restitution coefficient, impacting particle will penetrate to a greater depth (and induce plastic deformation), during which, large fraction of its kinetic energy is consumed. It will rebound back with a small velocity and is easily
flushed away by the carrier liquid (in present case, water). However, in case of a hard target surface with comparatively high restitution coefficient, the particle will rebound with a higher velocity. This rebounded particle interferes with incoming particles and contributes to the shielding effect. As a result, influence of increase in concentration on erosion rate is reduced for hard materials. This is because the rebounded particle traveling at a high velocity will enhance the strength of the shielding layer. From the above discussion, it can be concluded that for a given system, concentration and impingement angle are highly interrelated with each other. The effect of concentration on erosion rates is significantly influenced by orientation of the sample. In the case of slurry erosion at a low or glancing angle, the increase in concentration produces small effect on erosion rate in comparison to a similar concentration change at a normal impingement angle. This is due to influence of sample orientation on the flow field which affects the particle trajectories. The density and viscosity of carrier fluid and the inertia of particles also influences the trajectories. In the case of glancing angle (sample orientation), most of particles flow in a same direction (along the flow direction) leading to a high particle-to particle interactions. Many particles passes-out without even impacting the target surface. On the other hand, the symmetrical nature of flow field decreases the probability of particle-
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Fig. 13. The relation between concentration index m and function of hardness and equivalent elastic modulus as per Eq. (12) representing restitution coefficient for impingement angles of 301 and 901.
compared to when sample is held at a normal angle. This is related to the flow field influenced by the sample orientation and fluid properties. Experiments also showed that the effect of change in concentration on erosion rate is influenced by the target material properties. The effect of increase in concentration (on erosion rate) was more evident for a soft material (aluminum) than for hard materials (cast iron and stainless steel). A direct correlation between the restitution coefficient and the concentration effect (the rate at which the erosion rates increases with the increase in concentration) was observed. This might be due to the high rebound velocity of particles (when impacting the target surface with high restitution coefficient), which leads to formation of an effective shielding layer. The rebounded particle traveling at a high velocity will enhance the strength of the shielding layer. Fig. 12. Effect of viscosity and density of carrier fluid on the impingement angle (particle trajectories) of particle at 0.1 wt% sand concentration and 16 m/s velocity for CA6NM target surface. For studying the effect of viscosity, density was kept constant at 1000 kg/m3 and for studying the effect of density, viscosity was held constant at 1 mPa s.
to-particle collision when sample is oriented at 901. Therefore, it is a nature of flow field, fluid and particle properties that dictate the particle-to-particle collision and hence controls the effect of concentration on erosion rate. The material properties also influence the extent to which the concentration affects the erosion rates at a given impingement angle. Concentration effect is usually low for an impact system with high restitution coefficient.
6. Conclusion The interaction between the impingement angle and erodent concentration was studied in the case of slurry erosion. Experiments showed that erosion rates are less influenced by the change in concentration at glancing angle in comparison to that at normal angle (sample held at 901 to slurry jet). This suggests a high level of interaction exists between the impingement angle (sample orientation) and the slurry concentration. CFD simulations showed that for water–sand slurries, high particle-to-particle interactions takes place when sample is oriented at the glancing angles
Acknowledgment Authors thanks the financial assistance provided by Council of Scientific and Industrial Research (CSIR), India, under Project title “Development of Slurry Erosion Resistant Coatings for Hydroturbines”, File no.: 22(0604)/12/EMR-II. This research was also supported by the KIST Institutional program (2E25590, 2E25474). References [1] A. Elkholy, Prediction of abrasion wear for slurry pump materials, Wear 84 (1983) 39–49. [2] J.J. Tuzson, Laboratory Slurry Erosion Tests and Pump Wear Rate Calculations, J. Fluids Eng. 106 (1984) 135–140. [3] M.C. Roco, G.R. Addie, Erosion wear in slurry pumps and pipes, Powder Technol. 50 (1987) 35–46. [4] Y. Iwai, K. Nambu, Slurry wear properties of pump lining materials, Wear 210 (1997) 211–219. [5] S. Ariely, A. Khentov, Erosion corrosion of pump impeller of cyclic cooling water system, Eng. Fail. Anal. 13 (2006) 925–932. [6] X.-G. Song, J.-H. Park, S.-G. Kim, Y.-C. Park, Performance comparison and erosion prediction of jet pumps by using a numerical method, Math. Comput. Model. 57 (2013) 245–253. [7] S. Khurana, V. Kumar, A. Kumar, The effect of nozzle angle on erosion and the performance of turgo impulse turbines, Int. J. Hydropower Dams 20 (2013) 97–101. [8] H.S. Grewal, S. Bhandari, H. Singh, Parametric study of slurry-erosion of hydroturbine steels with and without detonation gun spray coatings using taguchi technique, Metall. Mater. Trans. A 43 (2012) 3387–3401.
HS Grewal et al. / Wear 332-333 (2015) 1111–1119
[9] M.K. Padhy, R.P. Saini, A review on silt erosion in hydro turbines, Renew. Sustain. Energy Rev. 12 (2008) 1974–1987. [10] M. Barletta, Progress in fluidized bed assisted abrasive jet machining (FBAJM):Internal polishing of aluminium tubes, Int. J. Mach. Tools Manuf. 47 (2007) 483–495. [11] A. Raykowski, M. Hader, B. Maragno, J.K. Spelt, Blast cleaning of gas turbine components: deposit removal and substrate deformation, Wear 249 (2001) 126–131. [12] I. Finnie, Some reflections on the past and future of erosion, Wear 186–187 (1995) 1–10. [13] R.W. Lyczkowski, J.X. Bouillard, State-of-the-art review of erosion modeling in fluid/solids systems, Prog. Energy Combust. Sci. 28 (2002) 543–602. [14] Bitter, A study of erosion phenomena – part I, Wear 6 (1963) 5–21. [15] I.M. Hutchings, Tribology: Friction and Wear of Engineering Materials, Edward Arnold, London, 1992. [16] A.V. Levy, The platelet mechanism of erosion of ductile metals, Wear 108 (1986) 1–21. [17] R. Bellman Jr, A. Levy, Erosion mechanism in ductile metals, Wear 70 (1981) 1–27. [18] I. Kleis, P. Kulu, Solid Particle Erosion: Occurrence Predictio and Control, Springer Verlag, London, 2008. [19] H. Uuemo, I. Kleis, A critical analysis of erosion problems which have been little studied, Wear 31 (1975) 359–371. [20] R. Gupta, S.N. Singh, V. Sehadri, Prediction of uneven wear in a slurry pipeline on the basis of measurements in a pot tester, Wear 184 (1995) 169–178. [21] A. Neville, F. Reza, S. Chiovelli, T. Revega, Erosion–corrosion behaviour of WCbased MMCs in liquid–solid slurries, Wear 259 (2005) 181–195. [22] M.-H. Wang, C. Huang, K. Nandakumar, P. Minev, J. Luo, S. Chiovelli, Computational fluid dynamics modelling and experimental study of erosion in slurry jet flows, Int. J. Comput. Fluid Dyn. 23 (2009) 155–172. [23] H.S. Grewal, A. Agrawal, H. Singh, Design and development of high-velocity slurry erosion test rig using CFD, J. Mater. Eng. Perform. 22 (2013) 152–161.
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[24] H.S. Grewal, A. Agrawal, H. Singh, Slurry erosion performance of Ni–Al2O3 based composite coatings, Tribol. Int. 66 (2013) 296–306. [25] H.S. Grewal, H. Singh, A. Agrawal, Microstructural and mechanical characterization of thermal sprayed nickel–alumina composite coatings, Surf. Coat. Technol. 216 (2013) 78–92. [26] Fluent Manual 6.3.1, Fluent, Inc., 1998. [27] A. Forder, M. Thew, D. Harrison, A numerical investigation of solid particle erosion experienced within oilfield control valves, Wear 216 (1998) 184–193. [28] P.H. Shipway, I.M. Hutchings, A method for optimizing the particle flux in erosion testing with a gas-blast apparatus, Wear 174 (1994) 169–175. [29] K. Anand, S.K. Hovis, H. Conrad, R.O. Scattergood, Flux effects in solid particle erosion, Wear 118 (1987) 243–257. [30] J. Zhou, S. Bahadur, The effect of material composition and operational variables on the erosion of alumina ceramics, Wear 150 (1991) 343–354. [31] R. Brown, E.J. Jun, J.W. Edington, Erosion of α-Fe by spherical glass particles, Wear 70 (1981) 347–363. [32] S. Das, D.P. Mondal, O.P. Modi, R. Dasgupta, Influence of experimental parameters on the erosive–corrosive wear of Al–SiC particle composite, Wear 231 (1999) 195–205. [33] K. Sugiyama, K. Harada, S. Hattori, Influence of impact angle of solid particles on erosion by slurry jet, Wear 265 (2008) 713–720. [34] K. Sugiyama, K. Harada, S. Hattori, Prediction of the volume loss by using slury jet test on SCS6, J. Solid Mech. Mater. Eng. 2 (2008) 955–966. [35] H. Hojo, K. Tsuda, T. Yabu, Erosion damage of polymeric material by slurry, Wear 112 (1986) 17–28. [36] K.K. Wong, H.M. Clark, A model of particle velocities and trajectories in a slurry pot erosion tester, Wear 160 (1993) 95–104. [37] H.M. Clark, The influence of the flow field in slurry erosion, Wear 152 (1992) 223–240. [38] M.M. Stack, N. Corlett, S. Zhou, Some thoughts on the effect of elastic rebounds on the boundaries of the aqueous erosion–corrosion map, Wear 214 (1998) 175–185.
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Interplay between erodent concentration and impingement angle for erosion in dilute water–sand flows H S Grewal a,b, H Singh b,n, Eui-Sung Yoon a,n a b
Centre for BioMicrosystems, Korea Institute of Science and Technology, Seoul 136-791, Republic of Korea School of Mechanical, Materials and Energy Engineering, Indian Institute of Technology Ropar, Rupnagar 140001, India
art ic l e i nf o
a b s t r a c t
Article history: Received 22 September 2014 Received in revised form 21 January 2015 Accepted 4 February 2015
Slurry erosion is a complex process with number of interacting variables (operating and material parameters). We study the interaction between erodent concentration and impingement angle using experimental and computational fluid dynamics (CFD) techniques. Experiments were performed using a high velocity slurry erosion test rig at a constant velocity with dilute slurries of water and sand particles. Concentration of sand in water was varied from 0.01 wt% (100 ppm) to 0.5 wt% (5000 ppm) at three sample orientations (301, 601 and 901). Six different targets, three bulk materials (aluminum, cast iron, and stainless steel) and three thermal sprayed coatings (Ni þ 20, 40, and 60 wt% of Al2O3) were used for slurry erosion tests. Experimental results showed a significant interaction between erodent concentration and impingement angle. The concentration variation showed larger influence on erosion rate for sample held normal to the slurry jet compared to glancing angle. The degree of interaction was different for different materials. CFD simulations showed higher particle-to-particle interactions for sample at glancing angle compared to that at normal angle. This probably explains the low contribution of concentration variation at glancing angle compared to the normal angle. Further, the effect of concentration on erosion rates was also influenced by the restitution coefficient (function of material and impact parameters). For a high restitution coefficient materials, the change in slurry concentration showed minimum effect on erosion rate compared with low restitution coefficient materials. & 2015 Elsevier B.V. All rights reserved.
Keywords: Slurry erosion Wear modeling Computational fluid dynamics
1. Introduction The slurry erosion is a form of wear wherein the solid particles entrained in liquid medium impacts and deteriorates the target surface. Due to its damaging effects, slurry erosion is undesirable in fluid machineries such as turbines, pumps, and propellers as it adversely affects the durability and efficiency of the system [1–9]. On the hand, the damaging aspects of slurry erosion finds potential use in machining and cleaning processes such as abrasive jet machining, removal of damaged coatings and blast cleaning [10,11]. Although, the basic phenomenon of slurry erosion is well understood, the complexities inherent to solid–liquid interaction poses challenge to further understanding [12,13]. The non-linear mechanical properties of materials and complex microstructures further intensify the problem. The experimental and theoretical works in the past have significantly contributed to the present understanding of the
n
Corresponding authors. E-mail addresses: [email protected] (H. Singh), [email protected] (E.-S. Yoon). http://dx.doi.org/10.1016/j.wear.2015.02.039 0043-1648/& 2015 Elsevier B.V. All rights reserved.
slurry erosion process [12–18]. It is recognized that a severe interaction exists among the parameters (operating and material). These interactions have limited further understanding of the slurry erosion. As a result, an adequate theoretical framework required for the accurate predictions is still missing. Hence, further efforts are required for thorough understanding of the slurry erosion process. In general, the maximum erosion of a ductile material takes place when erodent particles impact at glancing angles (20–301), whereas, for a brittle material, the same is observed at a normal impingement angle [12]. Further, with the increase in concentration of solid particles in a liquid medium, the erosion rate increases due to increase in frequency of the damaging events which however, attains a steady state beyond a limiting value [18–22]. The steady state is a result of a balance (frequency) formed between the incident and rebounding particles (shielding phenomenon). With the increase in velocity of the erodent particles, the erosion rate increases, mostly following a power-law as a function of a target material [18]. Other than the target and erodent material properties, the interaction between the basic operating parameters (velocity, concentration, and impingement angle) plays an important role in erosion.
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In present work, attempt was made to investigate the interaction between impingement angle (sample orientation) and concentration of solid particles using a combination of experimental and computational techniques. The slurry erosion performance of different bulk materials and coatings was evaluated at different impingement angles and erodent concentrations. Simulations were performed using computational fluid dynamics (CFD) technique to further understand the erosion process.
2. Experimentation The slurry erosion experiments were performed using a highspeed slurry erosion test rig (Fig. 1). Specific details of the test rig were reported earlier elsewhere [23,24]. It is a non-recirculating type test rig with erodent particles once leaving the system are not re-used. The tungsten carbide nozzle with an inner diameter of 4 mm was used. A stand-off distance of 20 mm between the nozzle and specimen was ensured in each experiment. The experiments were conducted at a constant impingement velocity of 16 m/s at different impingement angles and concentrations of sand particles (average size: 197 mm) in water (Table 1). The maximum erodent concentration used was 0.5 wt%. These dilute erodent concentrations are similar to those found in many fluid machines such as hydroturbine, pump and other utility systems. The erosion tests were performed for a total period of 10 min with 1 min cycle time. This time was found sufficient to attain a steady state. Prior to the weight measurement, samples were washed using acetone and dried in air. The weight change was measured with an accuracy of 70.01 mg. Two samples were evaluated at each test condition and their average value is reported. Three bulk materials, aluminum (Al) (99.9%), cast iron (CI) and stainless steel, CA6NM (SS) were used as target materials. In addition, three types of coatings prepared by mixing nickel (Ni) and alumina (Al2O3) in different proportions (20, 40 and 60 wt%) were also used (Fig. 2). These coatings were coated on the CA6NM steel using a high velocity flame spray coating technique. Detailed microstructural characterization and mechanical properties of these coatings are given in Grewal et al. [25]. Slurry erosion tests were conducted according to ASTM standard G-73.
3. Modeling The computational fluid dynamics (CFD) simulation of the slurry erosion process was performed using the finite volume method. The three-dimensional meshed domain used for the CFD simulations is shown in Fig. 3 along with the used boundary conditions. This meshed domain was modeled using GAMBIT v2.0 software. The domain was discretized using tetragonal shaped elements. The CFD simulations were performed with the FLUENT 6.3 software using the discrete phase model (DPM) and volume of fluid (VOF) method. In DPM, the flow of a fluid is modeled using the Eulerian method, whereas, the motion of particles is tracked following the Lagrangian approach. The VOF method was used for simulating the un-submerged water jet condition. Further, a steady state Reynolds averaged Navier–Stokes (RANS) equations were used for the turbulence modeling. This requires solving the continuity and time averaged-momentum conservation equations for the primary phase (water) ∂ρ ∂ þ ðρvi Þ ¼ 0 ∂t ∂xi
ð1Þ
∂ ∂ ∂p ∂ ∂v ∂v 2 ∂v ∂ ρvi þ ðρvi vj Þ ¼ þ μ i þ j δij k þ ð ρv0i v0j Þ ∂t ∂xj ∂xi ∂xj ∂xj ∂xj ∂xi 3 ∂xk ð2Þ where ρ is density, v is velocity, p is pressure, and m is viscosity of the fluid phase. The additional terms ( ρv0i v0j ), the Reynolds stress, considers the effect of turbulence and must be solved for the closure of Eq. (2). These Reynolds stresses were modeled using the standard k–ε model, ∂ ∂ ∂ μ ∂k þ Gk þ Gb ρε ρk þ ρkvi ¼ μþ t ð3Þ ∂t ∂xi ∂xj σ κ ∂xj ∂ ∂ ∂ ρε þ ρεvi ¼ ∂t ∂xi ∂xj
μþ
μt ∂ε ε ε2 þ C 1ε ðGκ þ C 3ε Gb Þ C 2ε ρ σ ε ∂xj k k ð4Þ
for the turbulence kinetic energy, k and dissipation rate, ε. Here, Gk represents the turbulence kinetic energy generated due to mean
Re-circulated water line Filter unit Sand feeder Slurry Pump
Slurry containing line
. Sample holder and nozzle unit
Storage Tanks
Fig. 1. Schematic illustrating the slurry erosion test rig used for experiments [23].
Table 1 The experimental conditions used for conducting slurry erosion test on different bulk and coating materials. Material
Aluminum Cast iron Stainless steel (CA6NM) Coatings (Niþ20, 40,60 wt% Al2O3)
Impingement angle
Concentration (wt%)
301
601
901
0.01
0.1
0.5
√ √ √ √
√ √ √
√ √ √ √
√ √
√ √ √ √
√ √ √ √
HS Grewal et al. / Wear 332-333 (2015) 1111–1119
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100 µm Fig. 2. Scanning electron microscope images showing the cross-sectional microstructure of high velocity flame sprayed (a) Ni þ20%Al2O3 (b) Ni þ40%Al2O3 (c) Niþ 60%Al2O3 coatings deposited on CA6NM steel. The scale bar shown is common for all the images [25].
was added to the force balance equation (Eq. (6)) through userdefined function. Further, the influence of turbulence on the particle trajectory was simulated using the discrete random walk model. Sand particles were distributed homogenously over the nozzle inlet cross-section. The normal and tangential restitution coefficients, e, of the particle on target surface were modeled by [27] e ? ¼ 0:988 0:78θ þ 0:19θ 0:024θ þ 0:027θ 2
3
4
e J ¼ 1 0:78θ þ 0:84θ 0:21θ þ 0:028θ 0:022θ 2
Fig. 3. Computational domain used for modeling the slurry erosion. Boundary conditions used for simulations are shown.
velocity gradient and Gb is the kinetic due to buoyancy. energy C1ε ¼1.44, C2ε ¼ 1.92 and C3ε ¼tanhv J =v ? are constants, and σk ¼ 1.0 and σε ¼ 1.3 are the turbulent Prandtl numbers for k and ε, respectively. v J and v ? are the flow velocity components parallel and perpendicular to the gravitational vector, respectively. The turbulent viscosity, mt was computed using
μt ¼ ρC μ
k
4
5
ð10Þ
using the local impingement angle, θ. Particles were injected in a converged flow field. The grid independency of the solution was assured by mesh refinement a region of high velocity gradients using the adaptive meshing. The selective refinement helped refining mesh along with optimal use of the computational resources. The refinement threshold of 1/10 was used in each step and the adaptive meshing was performed until the results became independent of the grid (further refinement did not altered the results). During the adaptive meshing, the number of elements increased from initial 1,108,900 to 1,200,578.
2
ð5Þ
ε
3.1. Model validation
with Cm ¼0.09. For calculating the particle trajectories, force balance equation is solved [26], ðρp ρf Þ dvp ¼ F D ðvf vp Þ þ g þ Fi dt ρp
ð6Þ
which equates the inertial force with the drag force, FD, the gravitational force and other forces, Fi, [26] ! 18μ C D Re ð7Þ FD ¼ ρp D2p 24
3
ð9Þ
! ρf ! v f v p Dp Re ¼ μf
ð8Þ
where subscripts f and p represents the fluid and particle phases, respectively. Further, CD and Re are the drag coefficient and Reynolds number, Dp is the particle diameter and g is the acceleration due to gravity. Other forces which includes, the body force, the virtual mass force (force required for accelerating the fluid around the particle, important only when ρf 4 ρp), thermophoretic force (due to temperature gradient) and Brownian force can be included depending upon the model. In the present work, body force induced on particles due to acceleration was considered. The body force per unit mass, F m ¼ ð1=12Þ ðdðvf vp Þ=dtÞ
The CFD model was validated using the experimental results of the Shipway and Hutching [28]. For their experiments, Shipway and Hutching [28] used a soda-lime glass particles of various size ranges (63–75 mm, 212–250 mm and 650–750 mm) entrained in air at different erodent fluxes. The inlet pressure was fixed at 0.2 bar and the inner and outer nozzle diameters were 4.7 mm and 6.35 mm, respectively. They used the opto-electronic flight timer technique to measure the particle velocities. To simulate the test rig used by Shipway and Hutching [28], a three-dimensional CFD model similar to the one in Fig. 3 was developed. For the model validation, results of the particles with size of 212–250 mm were used with flux varied from 0.008 kg/m2 s to 45.7 kg/m2 s for a total dosage of 5 g for each case. The predicted and experimental particle trajectories for the case of 45.7 kg/m2s erodent flux are shown in Fig. 4.The similarity between the predicted and actual particle trajectories is readily evident. Further, a good correlation between the predicted and measured radius of wear scars and particle velocities with the erodent flux was observed (Fig. 5). The predicted velocity (47 71 m/s) of particles exiting the nozzle was very much similar to that measured experimentally by Shipway and Hutchings [28]. The above results indicate that this CFD model is reliable and can be used for other simulations with sufficient confidence.
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Fig. 4. (a) Experimental result of Shipway and Hutchings [28] and (b) computational fluid dynamics simulation results showing the particle trajectories for lead-glass spherical particles (φ¼ 212–250 mm) in an air stream at an inlet pressure of 0.2 bar and an erodent flux of 45.7 kg/m2 s.
Fig. 6. Steady state erosion rates for the (a) bulk materials and (b) thermal spray coatings under different impingement angles and slurry concentrations at constant velocity of 16 m/s. In (a) open, half-filled and filled symbols stands for 0.01 wt%, 0.1 wt% and 0.5 wt% of slurry concentration. In (b) NixAl represents the composition of the coatings with ‘x’ representing the wt% of Al2O3, Ni for Nickel and Al for Al2O3.
Fig. 5. The comparison between the experimental (Shipway and Hutchings [28]) and predicted scar radius at different erodent flux for lead-glass spherical particles (φ¼212–250 mm) in an air stream at an inlet pressure of 0.2 bar. The measured and predicted velocity of particles leaving the nozzle was approximately 47 m/s, as shown in Fig. 4.
4. Results 4.1. Slurry erosion experiments The steady state erosion rates of the investigated test materials at different impingement angle (sample orientation) and erodent concentration are shown in Fig. 6. For the ductile Al, CA6NM steel and Ni20Al2O3 coating, the maximum erosion rate was observed at 301 angle. For other coatings and CI, the maximum erosion rates
were observed when sample was held at 901 angle. With the increase in concentration for either impingement angle, the erosion rate also increased. However, the change in erosion rates with concentration was influenced by the target material and impingement angle. Further, there was a non-linear correlation between the erosion rate and concentration. Even for 10 and 50 times increase in erodent concentration, the change in erosion rate was modest. These experimental results are in general agreement with those reported in [19,29–32]. The analysis of these results suggests that the influence of concentration on erosion rate is related to the flow field (influenced by sample orientation) and material properties. This fact is evident from Fig. 7, which presents the value of concentration exponent, m E ¼ KC m
ð11Þ 3
where E is a steady state erosion rate (m /s), K is a constant, and m is an exponent indicating the influence of concentration (kg/m3) on erosion rate. Fig. 7 shows that the value of m increases with an increase in impingement angle, irrespective of the target material.
HS Grewal et al. / Wear 332-333 (2015) 1111–1119
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Fig. 7. Value of concentration exponent, m as a function of impingement angle and target material.
This indicates an increase in influence of the concentration at higher impingement angles. For a given increase in the erodent concentration, the proportional increase in erosion rate is higher for a sample held normal to the slurry jet in comparison to that at glancing angle. However, it is normally expected that due to shielding effect, the influence of concentration on erosion rate will be marginal in the former case (sample at normal orientation). This, although holds true for the solid particle erosion (carrier fluid is a gas instead of liquid), will apparently be influenced by the carrier fluid properties such as viscosity and density. In comparison to air, water is almost 50 times more viscous and approximately 800 times denser. The difference in these physical properties will surely influence the flow field and hence the particle trajectories. Further discussion in alliance with CFD results is provided in Section 5. 4.2. Computational fluid dynamics simulations The velocity profiles for both 301 and 901 impingement angles are shown in Fig. 8. The magnitude of velocity indicated by the color map shows its distribution in the flow domain. The velocity of a water jet issuing from the nozzle at 16 m/s reduced to around one-fourth to one-tenth at the center of impact zone. This is due to transformation of the fluid kinetic energy into dynamic pressure leading to the formation of squeeze film (Fig. 9). Further, a symmetrical distribution of velocity was observed around the impact zone for the sample held at 901. However, for the 301 sample orientation, velocity distribution was highly skewed. In this case, the shape of squeeze film was distorted and stretched along the flow direction. This non-symmetrical distribution of pressure and velocity influenced the particle trajectories (Fig. 10). The particle trajectories for the 901 sample orientation were symmetrical compared with 301 case. In the latter case, almost all the particles were moving in an identical direction over the sample confirming to the flow field. This leads to increase in interaction among the particles. This becomes further evident from Fig. 11, which shows the trajectory of individual particle streams. The starting position of particle stream before been ejected from the nozzle is also shown. A closer look at Fig. 11 (a) shows a highly chaotic motion of the particles when sample is oriented at 301 resulting in a significant interaction between particle streams. This is mainly due to the flow field following which all the particles are moving in the identical direction. As a
Fig. 8. Velocity distribution plot of water jet issuing from the nozzle and impacting the samples oriented at (a) 901 and (b) 301 to the nozzle axis. The color map is common for both the cases.
result, the probability of particle-to-particle interaction also increases. On the other hand, the impact zone for 901 case (Fig. 11(b)) shows a smooth flow of the particles. The flow field in the case of 901 impingement angle (sample orientation) is symmetrical around the impact zone. Thus, particles flow in an individual direction after impact. This reduces the chance of interaction with other particles. These results are further discussed in the following section.
5. Discussion The slurry erosion experiment showed that the change in erosion rate with concentration depends on the impingement angle (sample orientation) and the target material. The increase in concentration exponent, m with impingement angle (sample orientation) indicated an increase in influence of concentration at higher impingement angles. The change in erosion rate with the change in concentration is highest when sample is held normal to the slurry jet compared to glancing angle. Effect of impingement angle (sample orientation) on concentration was thoroughly
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investigated by Uuemo and Kleis [19] and Anand et al. [29]. Uuemo and Kleis [19] observed that the effect of concentration is more prominent at normal impingement angle in comparison to that at glancing angle. This is in agreement with present observations. Similar results were also reported by Anand et al. [29] and Zhou and Bahadur [30]. However, the reason for this trend was not
Fig. 9. The contour plot of static pressure inside the modeling domain for the samples oriented at (a) 901 and (b) 301 to the nozzle axis. The color map is same for both the figures.
discussed. At first glance, it appears that the shielding effect, which is caused by the interaction between incoming and rebounding particles, will be more dominant when a sample is oriented at 901 in comparison to glancing angles. This is because the rebound angle of most of the particles impacting at 901 will be around 1801. This will increase the chance of collision with the incoming particles. However, the CFD results (Figs. 8,10 and 11) showed that when sample is held normal to the jet of liquid and erodent particles, only small fraction of particles at the jet center actually impacts at 901 or so. Similar results have also been reported by Sugiyama [33,34]. The particles away from the jet center readily follows the fluid path lines due to dominance of the drag force over the inertia force of particle. This will reduce the strength of shielding layer. Had it been air in place of water, the drag and viscous forces induced by the carrier fluid on particles would have been nominal. Thus, most of the particles would have retained their original trajectory and impinged at a pre-set impingement angles due to the dominance of particle inertia force over the drag and viscous forces (Fig. 4). Similar experimental results were reported by Shipway and Hutching [28]. Therefore, shielding effect will predominate when carrier fluid with low viscosity and density such as air is used. In this case, the influence of increase in erodent concentration on erosion rate is reduced. However, the situation becomes completely different when a dense and viscous carrier medium is used. The effect of density and viscosity of carrier medium on the particle impingement angles is shown in Fig. 12. These results indicate that for the fluid with low viscosity and density, most of the particles impact the target surface at a pre-set impingement angle. However, as the density/viscosity of the fluid is increased, the impingent angle of the particles is significantly influenced. The fraction of particles impinging at a pre-set angle is reduced. This is due to dominance of drag and viscous forces in comparison to inertia force. Similar observations were also reported by Hojo et al. [35] and Clark [36,37] for the pot-type test rig. Therefore, particles entrained in a fluid with comparatively higher density and viscosity (water in comparison to air) will move along the path lines of the fluid rather than retaining their original trajectories. This reduces the shielding effect for sample oriented at normal angle, whereas, the shielding effect increases at glancing angles due to rise in frequency of particle-to-particle collisions (Fig. 11). Further, the results in Fig. 7 also showed that the value of concentration exponent, m was influenced by the target materials. For soft materials such as Al, the value of m was high compared to harder CI or SS. The particle impact on a soft material can induce plastic deformations. This in turn will affects the rebound characteristic and hence the shielding effect. This postulation was
Fig. 10. Particle trajectories for samples oriented at 901 and 301 colored by velocity magnitude.
HS Grewal et al. / Wear 332-333 (2015) 1111–1119
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Fig. 11. Particle trajectories colored by the particle identity along with individual particle streams issuing from different regions of the nozzle for the sample oriented at (a) 301 and (b) 901.
examined by investigating the correlation between the exponent, m and the restitution coefficient, e using [38] e ¼ const:
H 0:625
ρ
0:125 V 0:25 E0:5 e in
ð12Þ
According to this model, the restitution coefficient, e is a function of material and operating parameters. It is directly proportional to hardness, H and inversely related to the equivalent Young's modulus, Ee of the elastic contact. The correlation between restitution coefficient (as a function of hardness and equivalent Young's modulus) and concentration exponent, m is shown in Fig. 13. For both bulk and coating materials, concentration exponent, m showed an inverse correlation with the restitution coefficient. This result suggests that the concentration effect is also influenced by material properties, particularly hardness and Young's modulus. High value of m indicates the erosion rate is highly influence by the change in concentration. The dependence of concentration effect on restitution coefficient behavior can be explained as below: For a soft material with low restitution coefficient, impacting particle will penetrate to a greater depth (and induce plastic deformation), during which, large fraction of its kinetic energy is consumed. It will rebound back with a small velocity and is easily
flushed away by the carrier liquid (in present case, water). However, in case of a hard target surface with comparatively high restitution coefficient, the particle will rebound with a higher velocity. This rebounded particle interferes with incoming particles and contributes to the shielding effect. As a result, influence of increase in concentration on erosion rate is reduced for hard materials. This is because the rebounded particle traveling at a high velocity will enhance the strength of the shielding layer. From the above discussion, it can be concluded that for a given system, concentration and impingement angle are highly interrelated with each other. The effect of concentration on erosion rates is significantly influenced by orientation of the sample. In the case of slurry erosion at a low or glancing angle, the increase in concentration produces small effect on erosion rate in comparison to a similar concentration change at a normal impingement angle. This is due to influence of sample orientation on the flow field which affects the particle trajectories. The density and viscosity of carrier fluid and the inertia of particles also influences the trajectories. In the case of glancing angle (sample orientation), most of particles flow in a same direction (along the flow direction) leading to a high particle-to particle interactions. Many particles passes-out without even impacting the target surface. On the other hand, the symmetrical nature of flow field decreases the probability of particle-
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Fig. 13. The relation between concentration index m and function of hardness and equivalent elastic modulus as per Eq. (12) representing restitution coefficient for impingement angles of 301 and 901.
compared to when sample is held at a normal angle. This is related to the flow field influenced by the sample orientation and fluid properties. Experiments also showed that the effect of change in concentration on erosion rate is influenced by the target material properties. The effect of increase in concentration (on erosion rate) was more evident for a soft material (aluminum) than for hard materials (cast iron and stainless steel). A direct correlation between the restitution coefficient and the concentration effect (the rate at which the erosion rates increases with the increase in concentration) was observed. This might be due to the high rebound velocity of particles (when impacting the target surface with high restitution coefficient), which leads to formation of an effective shielding layer. The rebounded particle traveling at a high velocity will enhance the strength of the shielding layer. Fig. 12. Effect of viscosity and density of carrier fluid on the impingement angle (particle trajectories) of particle at 0.1 wt% sand concentration and 16 m/s velocity for CA6NM target surface. For studying the effect of viscosity, density was kept constant at 1000 kg/m3 and for studying the effect of density, viscosity was held constant at 1 mPa s.
to-particle collision when sample is oriented at 901. Therefore, it is a nature of flow field, fluid and particle properties that dictate the particle-to-particle collision and hence controls the effect of concentration on erosion rate. The material properties also influence the extent to which the concentration affects the erosion rates at a given impingement angle. Concentration effect is usually low for an impact system with high restitution coefficient.
6. Conclusion The interaction between the impingement angle and erodent concentration was studied in the case of slurry erosion. Experiments showed that erosion rates are less influenced by the change in concentration at glancing angle in comparison to that at normal angle (sample held at 901 to slurry jet). This suggests a high level of interaction exists between the impingement angle (sample orientation) and the slurry concentration. CFD simulations showed that for water–sand slurries, high particle-to-particle interactions takes place when sample is oriented at the glancing angles
Acknowledgment Authors thanks the financial assistance provided by Council of Scientific and Industrial Research (CSIR), India, under Project title “Development of Slurry Erosion Resistant Coatings for Hydroturbines”, File no.: 22(0604)/12/EMR-II. This research was also supported by the KIST Institutional program (2E25590, 2E25474). References [1] A. Elkholy, Prediction of abrasion wear for slurry pump materials, Wear 84 (1983) 39–49. [2] J.J. Tuzson, Laboratory Slurry Erosion Tests and Pump Wear Rate Calculations, J. Fluids Eng. 106 (1984) 135–140. [3] M.C. Roco, G.R. Addie, Erosion wear in slurry pumps and pipes, Powder Technol. 50 (1987) 35–46. [4] Y. Iwai, K. Nambu, Slurry wear properties of pump lining materials, Wear 210 (1997) 211–219. [5] S. Ariely, A. Khentov, Erosion corrosion of pump impeller of cyclic cooling water system, Eng. Fail. Anal. 13 (2006) 925–932. [6] X.-G. Song, J.-H. Park, S.-G. Kim, Y.-C. Park, Performance comparison and erosion prediction of jet pumps by using a numerical method, Math. Comput. Model. 57 (2013) 245–253. [7] S. Khurana, V. Kumar, A. Kumar, The effect of nozzle angle on erosion and the performance of turgo impulse turbines, Int. J. Hydropower Dams 20 (2013) 97–101. [8] H.S. Grewal, S. Bhandari, H. Singh, Parametric study of slurry-erosion of hydroturbine steels with and without detonation gun spray coatings using taguchi technique, Metall. Mater. Trans. A 43 (2012) 3387–3401.
HS Grewal et al. / Wear 332-333 (2015) 1111–1119
[9] M.K. Padhy, R.P. Saini, A review on silt erosion in hydro turbines, Renew. Sustain. Energy Rev. 12 (2008) 1974–1987. [10] M. Barletta, Progress in fluidized bed assisted abrasive jet machining (FBAJM):Internal polishing of aluminium tubes, Int. J. Mach. Tools Manuf. 47 (2007) 483–495. [11] A. Raykowski, M. Hader, B. Maragno, J.K. Spelt, Blast cleaning of gas turbine components: deposit removal and substrate deformation, Wear 249 (2001) 126–131. [12] I. Finnie, Some reflections on the past and future of erosion, Wear 186–187 (1995) 1–10. [13] R.W. Lyczkowski, J.X. Bouillard, State-of-the-art review of erosion modeling in fluid/solids systems, Prog. Energy Combust. Sci. 28 (2002) 543–602. [14] Bitter, A study of erosion phenomena – part I, Wear 6 (1963) 5–21. [15] I.M. Hutchings, Tribology: Friction and Wear of Engineering Materials, Edward Arnold, London, 1992. [16] A.V. Levy, The platelet mechanism of erosion of ductile metals, Wear 108 (1986) 1–21. [17] R. Bellman Jr, A. Levy, Erosion mechanism in ductile metals, Wear 70 (1981) 1–27. [18] I. Kleis, P. Kulu, Solid Particle Erosion: Occurrence Predictio and Control, Springer Verlag, London, 2008. [19] H. Uuemo, I. Kleis, A critical analysis of erosion problems which have been little studied, Wear 31 (1975) 359–371. [20] R. Gupta, S.N. Singh, V. Sehadri, Prediction of uneven wear in a slurry pipeline on the basis of measurements in a pot tester, Wear 184 (1995) 169–178. [21] A. Neville, F. Reza, S. Chiovelli, T. Revega, Erosion–corrosion behaviour of WCbased MMCs in liquid–solid slurries, Wear 259 (2005) 181–195. [22] M.-H. Wang, C. Huang, K. Nandakumar, P. Minev, J. Luo, S. Chiovelli, Computational fluid dynamics modelling and experimental study of erosion in slurry jet flows, Int. J. Comput. Fluid Dyn. 23 (2009) 155–172. [23] H.S. Grewal, A. Agrawal, H. Singh, Design and development of high-velocity slurry erosion test rig using CFD, J. Mater. Eng. Perform. 22 (2013) 152–161.
1119
[24] H.S. Grewal, A. Agrawal, H. Singh, Slurry erosion performance of Ni–Al2O3 based composite coatings, Tribol. Int. 66 (2013) 296–306. [25] H.S. Grewal, H. Singh, A. Agrawal, Microstructural and mechanical characterization of thermal sprayed nickel–alumina composite coatings, Surf. Coat. Technol. 216 (2013) 78–92. [26] Fluent Manual 6.3.1, Fluent, Inc., 1998. [27] A. Forder, M. Thew, D. Harrison, A numerical investigation of solid particle erosion experienced within oilfield control valves, Wear 216 (1998) 184–193. [28] P.H. Shipway, I.M. Hutchings, A method for optimizing the particle flux in erosion testing with a gas-blast apparatus, Wear 174 (1994) 169–175. [29] K. Anand, S.K. Hovis, H. Conrad, R.O. Scattergood, Flux effects in solid particle erosion, Wear 118 (1987) 243–257. [30] J. Zhou, S. Bahadur, The effect of material composition and operational variables on the erosion of alumina ceramics, Wear 150 (1991) 343–354. [31] R. Brown, E.J. Jun, J.W. Edington, Erosion of α-Fe by spherical glass particles, Wear 70 (1981) 347–363. [32] S. Das, D.P. Mondal, O.P. Modi, R. Dasgupta, Influence of experimental parameters on the erosive–corrosive wear of Al–SiC particle composite, Wear 231 (1999) 195–205. [33] K. Sugiyama, K. Harada, S. Hattori, Influence of impact angle of solid particles on erosion by slurry jet, Wear 265 (2008) 713–720. [34] K. Sugiyama, K. Harada, S. Hattori, Prediction of the volume loss by using slury jet test on SCS6, J. Solid Mech. Mater. Eng. 2 (2008) 955–966. [35] H. Hojo, K. Tsuda, T. Yabu, Erosion damage of polymeric material by slurry, Wear 112 (1986) 17–28. [36] K.K. Wong, H.M. Clark, A model of particle velocities and trajectories in a slurry pot erosion tester, Wear 160 (1993) 95–104. [37] H.M. Clark, The influence of the flow field in slurry erosion, Wear 152 (1992) 223–240. [38] M.M. Stack, N. Corlett, S. Zhou, Some thoughts on the effect of elastic rebounds on the boundaries of the aqueous erosion–corrosion map, Wear 214 (1998) 175–185.