Granular Matter最新文献

Thermal energy storage (TES) systems have been proven in their capacity as a crucial component of energy grids relying on renewable sources. An established sensible heat storage technology is a packed-bed TES, employing a granular filling material as a heat storage medium, which is subjected to repeated heating-cooling cycles. As a result of the recurring particle expansion and contraction, excessive stresses and strains can develop and cause material damage. This leads to the increasing need for reliable numerical tools in order to improve the TES design and increase their durability. For this purpose, we propose a continuous thermo-mechanical approach, within the framework of the theory of hypoplasticity, that can accurately predict the single as well as cyclic loading behavior of the filling material. This work focuses on the stress–strain relations and compaction mechanisms of the granular bed in contact with a storage wall with variable inclination and friction coefficient. Furthermore, the important aspect of the wall expansion under the temperature change is also taken into account as well as the specific case when the wall expands more than the granular material. By conducting comprehensive simulations, we demonstrate that our novel numerical model adheres to existing experimental investigations and mitigates shortcomings in the predictive capabilities of previous continuous approaches.

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Existing empirical relations used to predict the porosity of gravel beds are mainly derived from laboratory-generated sediment beds with random grain packing. However, such relations could not adequately describe beds with non-random grain arrangements that occur widely in fluvial deposits. In this work, the effect of grain imbrication on gravel-bed porosity has been quantified using beds with variable strengths of imbrication generated by flume experiments. Mono-sized ellipsoids with specific shapes were used in experiments to remove particle size and sorting effects on porosity. Random bed packings were generated by settling of ellipsoids in still water whilst imbricated beds generated under flowing water. Beds were frozen using liquid nitrogen before extraction. A new relatively simple and time-saving workflow was developed to measure the orientation of particles and quantify the degree of grain imbrication in frozen beds from X-ray Computed Tomography images. Beds with the strongest grain fabric display a ca. 0.03 absolute reduction of porosity value on average (8–10% relative reduction) compared to that of random packing for undisturbed beds. Further, results were obtained for beds deposited under still-water conditions subject to disturbance by shaking, to mimic the potential effect of vibrations from currents, waves or other sources in the environment. A reduction in bed porosity of ca. 0.014–0.018 (ca. 5% relative reduction) is observed between beds with the strongest grain fabric and those with random packing that had undergone shaking after deposition. Hence, a significant proportion (> 50%) of the porosity loss observed for imbricated beds may be attributable to tighter packing due to turbulence-related vibrations from the flow. The small decrease in porosity value despite the formation of strong imbrication is considered to be due to the limited improvement in grain organization, as the results show that the flat shape of the ellipsoids and the uniformity of their size promote the formation of a stacking structure under gravity, leading to a similarly highly ordered grain organization in random packings compared to the imbricated packings.

Graphical abstract

The effects of the stress loading rate and maximum servo wall speed on the instability characteristics of granular materials were investigated using stress-controlled biaxial compression simulation. The results indicate that: (1) the stress loading rate affects the destabilization process of the specimen, with a higher stress loading rate making it easier for the sample to destabilize in a shorter time. (2) The maximum servo wall speed impacts the shear behavior of the specimen and can even lead to simulation test failure. It is therefore strongly recommended that this parameter be listed as a key parameter in future studies. (3) Based on the above analysis, a new conceptual framework was proposed to characterise the different cases of stress-controlled tests, which can guide the rational selection of rates.

Electron beam melting is a powder bed fusion process capable of manufacturing parts from a variety of high-temperature alloys. Given that the process relies on feedstock recycling for process economics, understanding process-part quality relationships is critical. This work investigates process-part quality relationships in terms of the internal and external defects and component microstructure relative to a feedstock subjected to 33 build cycles without replacement. To accomplish this, a volume of fluid mesoscale model consisting of three different powder distributions were considered: (1) Monomodal; (2) As-measured; and (3) Irregular. Particle morphology was characterized using shape factors examined via optical microscopy. To approximate the particle shapes in three-dimensions, a method is presented that utilizes a binarized domain to define low frequency, macroscale particle “base” shapes implicitly and is thus not restricted to starlike particles. The discrete element method was also used to investigate velocity distributions and packing densities of the as-measured and irregular particles with respect to deviations in the nominal layer thickness of 50 μm. In general, beam power and scan speed were found to have an appreciable effect on microstructure formation and surface roughness. Finally, correlations were found between specific classifications of irregular particles and lack of fusion defect formation.

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Overview of the discrete element method simulation domain for the electron beam melting powder bed fusion process

To address the limited consideration of coarse particles in existing studies on gravity flow of caved ore and rock, this study conducted physical and numerical draw tests. The purpose was to investigate the flow characteristics of caved ore and rock under the influence of different numbers, relative positions, and spacing of coarse particles. The analysis focused on the perspective of interparticle interaction, considering the evolution laws of unbalanced force, void fraction, and force chains. The main research findings are as follows: (1) As the number of coarse particles increases from one to four, the size ratio of coarse and fine particles, which has similar effects on the shape of IMZ, decreases from 6.0 to 4.0. (2) When two coarse particles are arranged vertically and in contact, there is no significant separation between them, and the agglomeration effect of the force chain is the most prominent.

Particle Tracking Velocimetry (PTV) is a Lagrange-based flow visualization technique that tracks the motion of multiple particles or granules simultaneously. With the widespread application of three-dimensional (3D) particle imaging systems, 3D PTV algorithms have attracted considerable interest, whereas many 3D algorithms are developed from the corresponding 2D algorithms; moreover, compared with 3D algorithms, 2D algorithms are more suitable for real-time flow monitoring in industry. This paper proposes a 2D PTV algorithm based on the Voronoi diagram (VD) that is optimized by the minimum enclosing ellipse (MEE); then a re-matching process based on a homemade method called Quasi-Parallel Correction (QPC) is developed to correct the abnormal results produced by PTV at large inter-frame particle displacement. This PTV is thereby named MQ-PTV. MQ-PTV is then employed for reconstructing a granular flow made of dense polypropylene particles along a declined chute, an aeolian sand flow over sand bed, the migration of a barchans swarm and the motion of stars, thus confirming its practicability in a wide variety of particle motion reconstruction.

Investigations into the various properties of granular matter composed of particles with defined shapes have gained increasing attention. Additive manufacturing, with its freedom of shape and rapid prototyping capabilities, has significantly contributed to these studies. However, this technique may introduce defects in the manufactured particles, which can significantly affect the properties of granular materials. The extent of these defects on particles of different shapes is investigated here. Particles of various shapes (cube, octahedron, quatropod, stellated octahedron, tetrahedron, and tetrapod) were manufactured and subsequently imaged using micro-Computed Tomography. The surface roughness, solidity, and convexity of the particles were quantified. Discrete element simulations of granular bed porosity, utilizing both idealized and real particle shapes, were conducted with different surface mesh resolutions and frictional parameters. A clear influence of the manufacturing process on the packing properties of 3D printed particles was identified. This influence is not uniform across all shapes and is directly correlated with the particle convexity. For numerical simulations, a shape-dependent correction of particle density and surface characteristics are imperative for each shape under consideration, despite the fact that the particles were manufactured using the same technique and material.

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Existing image analysis algorithms cannot achieve consistency with human visual classification results when classifying particles based on angular levels. To address this issue, this paper proposes an image analysis method based on triangle side ratio to quantify particle angularity, referred to as a TSR method. The proposed method utilizes a primary parameter, Mean Angularity, to assess the mean angularity level, and employs three auxiliary parameters to offer insights into the Sharpest Angularity, the Flat Proportion, and the Number of Angularity. When quantifying the angularity, the method further provides the count of convex angles. Each parameter can reflect different characteristic information of the angularity. When using the mean angularity level to order particles, the TSR method achieves the same results as visual classification, and furthermore introduces a range of values for the main parameter corresponding to the different angularity levels. The TSR method is simpler and more stable, since the particle parameters can be calculated directly without contour smoothing, and consistent results are achieved for different shapes with the same degree of angular sharpness. The results of the study on lunar soil, volcanic rock, mechanism stone, and stream stone, show that the TSR method can objectively and comprehensively analyze and quantify the particle angularity.

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