Prediction of abrasion wear for slurry pump materials

Elkholy, Ahmed

doi:10.1016/0043-1648(83)90117-5

Cited by 122 publications

(23 citation statements)

References 5 publications

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“…In the case of metals and, especially, of rather common cast irons [5][6][7], the shared opinion is that their properties are quite predictable (e.g., fatigue [8], abrasion [9], fracture [10], strength [11]). This is certainly the case when compared to other families, such as organic or composite materials.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Tensile Behaviour of Cast Alloys by a Pattern Recognition Analysis on Experimental Data

Fragassa

Babič

Bergmann

et al. 2019

Metals

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The ability to accurately predict the mechanical properties of metals is essential for their correct use in the design of structures and components. This is even more important in the presence of materials, such as metal cast alloys, whose properties can vary significantly in relation to their constituent elements, microstructures, process parameters or treatments. This study shows how a machine learning approach, based on pattern recognition analysis on experimental data, is able to offer acceptable precision predictions with respect to the main mechanical properties of metals, as in the case of ductile cast iron and compact graphite cast iron. The metallographic properties, such as graphite, ferrite and perlite content, extrapolated through macro indicators from micrographs by image analysis, are used as inputs for the machine learning algorithms, while the mechanical properties, such as yield strength, ultimate strength, ultimate strain and Young’s modulus, are derived as output. In particular, 3 different machine learning algorithms are trained starting from a dataset of 20–30 data for each material and the results offer high accuracy, often better than other predictive techniques. Concerns regarding the applicability of these predictive techniques in material design and product/process quality control are also discussed.

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Section: Introductionmentioning

confidence: 99%

Predicting the Tensile Behaviour of Cast Alloys by a Pattern Recognition Analysis on Experimental Data

Fragassa

Babič

Bergmann

et al. 2019

Metals

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“…In defiance of their excellent mechanical and thermal properties, the presence of entrained sand in accelerated carrier fluids enhances erosion-induced damage, which depresses the integrity of the transportation pipelines under field operating conditions. Much work [1,2,3,4,5,6,7,8,9,10,11,12,13] has been directed to understanding the failure mode of ductile material and proposing models and mechanisms to decouple the relationship between different physical parameters and erosion rates under specific experimental conditions. In solid particle erosion–corrosion, an increase of impact velocity results in catastrophic damage and greater mass transfer and escalates the erosion–corrosion rate [12,14,15].…”

Section: Introductionmentioning

confidence: 99%

Erosion–Corrosion of 30°, 60°, and 90° Carbon Steel Elbows in a Multiphase Flow Containing Sand Particles

Khan

Pao

et al. 2019

Materials

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Erosion–corrosion in flow changing devices as a result of sand transportation is a serious concern in the hydrocarbon and mineral processing industry. In this work, the flow accelerated erosion–corrosion mechanism of 90°, 60°, and 30° long radius horizontal–horizontal (H–H) carbon steel elbows with an inner diameter of 50.8 mm were investigated in an experimental closed-flow loop. For these geometrical configurations, erosion–corrosion was elucidated for erosive slug flow regimes and the extent of material degradation is reported in detail. Qualitative techniques such as multilayer paint modeling and microscopic surface imaging were used to scrutinize the flow accelerated erosion–corrosion mechanism. The 3D roughness characterization of the surface indicates that maximum roughness appears in downstream adjacent to the outlet of the 90° elbow. Microscopic surface imaging of eroded elbow surfaces disseminates the presence of corrosion pits on the exit regions of the 90° and 60° elbows, but erosion scars were formed on the entry regions of the 30° elbow. Surface characterization and mass loss results indicated that changing the elbow geometrical configuration from a small angle to wide angle significantly changed the mechanical wear mechanism of the tested elbows. Moreover, the maximum erosive location was identified at the top of the horizontally-oriented elbow for slug flow.

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“…Elkholy [6] has carried out a systematic study on the phenomenon of abrasion wear in slurry pumps and has shown that wear is dependent on velocity, concentration, particle diameter, impingement angle and relative hardness.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Hydroabrasion in a Slurry Tank

Azimian¹,

Bart²

2013

JEPE

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Abstract:A slurry erosion tank test rig was designed and built to investigate the erosion rates of different materials and effects of the influencing parameters on material loss and erosion profiles. A CFD (computational fluid dynamics) tool is applied to study the flow impact velocity, solid concentration and particle size effects on the erosion rate of sample plates in the liquid-solid mixture in a cylindrical tank. The MRF (multiple reference frames) method is applied to model the rotating parts inside the tank. The flow behavior and liquid-solid interactions in the slurry tank test rig are simulated and the results are validated with the experimental data. It was approved that changing the height and diameter of each rotating zone (MRF zones) have a negligible effect on simulation results. It was observed that the erosion mass losses are increasing with increase in flow velocity and sand concentration. Both variations can be predicted with a logarithmic dependence of mass loss to rotational velocity and sand concentration. The increase in erosion rate by increase in particle size was also observed for three various particle size distributions.

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Prediction of abrasion wear for slurry pump materials

Cited by 122 publications

References 5 publications

Predicting the Tensile Behaviour of Cast Alloys by a Pattern Recognition Analysis on Experimental Data

Predicting the Tensile Behaviour of Cast Alloys by a Pattern Recognition Analysis on Experimental Data

Erosion–Corrosion of 30°, 60°, and 90° Carbon Steel Elbows in a Multiphase Flow Containing Sand Particles

Evaluation of Hydroabrasion in a Slurry Tank

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