Interaction of debris with a solid obstacle: Numerical analysis

Discrete element methods require appropriate models for particle-particle collisions. Usually, researchers use soft-sphere types of models where the collision dynamics is solved numerically. This makes the simulation computationally expensive. In this paper, however, we show a hard-sphere model that uses ready analytic formulas that relate the pre-and postcollisional velocities of the particles in contact. This hard-sphere model is an extension of an existing model that uses three input parameters. For this, we applied the linear-spring soft-sphere model, where analytic relations can be found. These relations were implemented into the standard hard-sphere model. As a result, we obtain a robust hard-sphere model that is more accurate than the standard one and is still computationally cheap.

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“…It has also been used for multiphase flow applications and is in this context mentioned in, for instance, Refs. [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Extension of the hard-sphere model for particle-flow simulations

2020

Self Cite

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“…In this research, methane-air mixtures of various compositions, and obstacles with different area blockage ratios and shapes were investigated [17]. In addition, the interaction of the cloud (debris) with a solid beam located at some distance from the explosion where the influence of different parameters like the value of the pressure of the explosion, the particle size, the number of particles and the obstacle location were investigated [18]. Experiments on the effects of flame interactions with different multiple obstacles within chambers of different L/D ratios were performed.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on Suppression of Methane Explosion Containing Obstacles with Ultra-fine Water Mist

Xue¹,

Li²,

Zhu³

et al. 2014

Fire Saf. Sci.

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The effects of obstacles on methane explosion inside a narrow space and its suppression by ultra-fine water mist are investigated experimentally. Two PCB pressure transducers and two E12-1-C-U fast response thermocouples are used to obtain the explosion pressure and temperature, respectively. A Fastcam Ultima APX high speed video camera is used to visualize the process of methane explosion with the influence of obstacles and ultra-fine water mist. The results show that the explosion would be strengthened by the obstacles, while the further strengthening occurs to the cases where the obstacles are farther from the ignition electrode. In addition, the more the obstacles are, the stronger the explosion strengthening will be. The reinforcement effect on gas explosion of the square ring is a little stronger than the column one. The suppression of methane gas explosion containing obstacles with ultra-fine water mist is very effective. KEYWORDS: gas explosion, explosion overpressure, explosion suppression, water mist

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“…Both Kosinska (97) and Kosinski (82) (83) showed that the linear motion of a rigid body even those immersed in shock waves can be found directly from Newton's second law and derivations of it. Newton's second law directly correlates force to mass and acceleration.…”

Section: Multiple Particle Cloudsmentioning

confidence: 99%

“…The theoretical number of calculations saved with the MRA approaches ½ as the number of transformed levels approached infinity. (97) This would reduce the number of iterations per level to less than one. The only way around this is to increase the number of iterations.…”

Section: Multi-resolution Augmented Annmentioning

confidence: 99%

Artificial neural network for behavior learning from meso-scale simulations, application to multi-scale multimaterial flows

Lu¹

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Interaction of debris with a solid obstacle: Numerical analysis

Cited by 6 publications

References 23 publications

Extension of the hard-sphere model for particle-flow simulations

Extension of the hard-sphere model for particle-flow simulations

Experimental Study on Suppression of Methane Explosion Containing Obstacles with Ultra-fine Water Mist

Artificial neural network for behavior learning from meso-scale simulations, application to multi-scale multimaterial flows

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