Evaluation of Pressure Drop and Particle Sphericity for an Air-rock Bed Thermal Energy Storage System

Trahan, Jamie; Graziani, Augusto; Goswami, D. Yogi; Stefanakos, Elias; Jotshi, C.K.; Goel, Neerja

doi:10.1016/j.egypro.2014.10.218

Cited by 28 publications

(6 citation statements)

References 15 publications

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“…The image processing tool used measured the perimeter of random particles and defined them by a ratio of sphere area to particle area. 15 The sphericity of particles was predicted by using the following correlation 16 …”

Section: Resultsmentioning

confidence: 99%

Study on role of particle shape in erosion wear of austenitic steel using image processing analysis technique

Singh

Kumar

Mohapatra

2018

Proceedings of the Institution of Mechanical Engineers, Part J:

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Properties of flowing media (e.g. such as fly ash, bottom ash, coal, mineral tailings, sand slurries, etc.) play a crucial role in the service life of centrifugal slurry pump components. Generally, these solid particles vary in shape and size. In literature, a limited number of studies have been carried out to investigate the influence of particle size and shape on erosion wear. Stainless steel (SS 316L) is the most commonly used material for the fabrication of slurry pump components namely, casing, impeller, shafts, and sealing columns. In the present study, the influence of particle type and circularity factor on erosion wear of austenitic steel has been studied. A slurry pot tester (Ducom TR-41) was used to perform the experiments that established the erosion wear of slurry pump austenitic steel under the influence of the parameters noted above. Abrasives used in the current study are fly ash, bottom ash, and sand. Surface smoothness, circularity factor, coefficient of variance, sphericity, and solidity of solid particles were also analyzed prior to performing the experiments. The circularity factor value and erosion wear rate hold a power law relationship. Three-dimensional surface plots were plotted to explain the underlying mechanism of erosion wear.

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Section: Resultsmentioning

confidence: 99%

Study on role of particle shape in erosion wear of austenitic steel using image processing analysis technique

Singh

Kumar

Mohapatra

2018

Proceedings of the Institution of Mechanical Engineers, Part J:

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“…A similar equation was derived for plug flow by Trahan et al [37], who replaced the tensor cij by a scalar. The resistance tensor cij accounts for the particle orientation relative to the flow direction.…”

Section: Theorymentioning

confidence: 90%

Flow Resistance of Randomly Packed Beds of Crushed Rock and Ellipsoidal Particles using CFD

Hoffmann¹,

Manatsa²,

Houtappels³

2022

JFFHMT

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Rock bed thermal energy storage is a cost-effective solution to store waste heat from a solarized Brayton cycle for use in a Rankine cycle after sunset. However, rock bed thermal energy storage systems for utility scale concentrated solar power are huge and require multiple air inlets and outlets. As a result, the flow inside the bed is fully three dimensional and deviates considerably from plug flow conditions usually encountered in chemical reactors. Designing a rock bed thermal energy storage system for the minimum capital cost and pumping power depend on reliable predictions of the fluid flow paths and temperature profiles in the bed. Particle size and shape have a significant influence on how the particles will pack down, which in turn influences the flow pattern in the bed, and hence the pressure drop and heat transfer characteristics of the bed. In this work, we discuss the characterization of crushed rock particles and concluded that there are benefits in approximating particles by mono-dispersed ellipsoids. We used discrete element modelling to generate packed beds of the ellipsoidal particles, and computational fluid dynamics to model the flow in the interstitial voids. This way, we successfully captured the directional effect of the flow resistance for ellipsoidal particles in terms of sphericity, , porosity , particle diameter Dve, and particle Reynolds number ReOur current model under-predicted the pressure drop across a packed bed of crushed rock particles.

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“…Permeability is also connected with other aspects, such as, particle roughness, tank to particle ratio, and gas flow speed (Trahana et al, 2014).…”

Section: Resultsmentioning

confidence: 99%

“…The gas flow is dependent on the strength and gas permeability of the bed, and highly influenced by the physical consistency of the pellets. The most adopted equation to solve gas flow through packed beds is Ergun's equation (Hinkley et al, 1994;Luckos and Bunt, 2011;Trahana et al, 2014;Koekemoer and Luckos, 2015;Erdin et al, 2015):…”

Section: Introductionmentioning

confidence: 99%

Use of an Artificial Neural Network in determination of iron ore pellet bed permeability

et al. 2017

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The thermal processing of iron ore pellets in pelletizing plants is a decisive stage regarding final product quality and knowledge of its characteristics has a fundamental importance in its process optimization. This study evaluated the variable sensitivity involved in pellet bed formations and their permeability using the artificial neural networks method. The model stated that standard diameter deviation, sphericity and pellet bed height mostly affect bed permeability. The computational model was able to predict pellet bed backpressure by means of pellet geometrical features, thus allowing improving green pellet generation, in order to ensure fuel and energy consumption reduction, final quality improvement and better productivity.

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Evaluation of Pressure Drop and Particle Sphericity for an Air-rock Bed Thermal Energy Storage System

Cited by 28 publications

References 15 publications

Study on role of particle shape in erosion wear of austenitic steel using image processing analysis technique

Study on role of particle shape in erosion wear of austenitic steel using image processing analysis technique

Flow Resistance of Randomly Packed Beds of Crushed Rock and Ellipsoidal Particles using CFD

Use of an Artificial Neural Network in determination of iron ore pellet bed permeability

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