A Whole Explosive Dispersion Process Prediction Model for Fuel Clouds

Chen, Xing; Wang, Zhongqi; Liu, Yun

doi:10.1002/prep.201900318

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…The motion process of the shell can be tracked by eq 5 ( 15 ) where w b represents the acceleration of the inner wall of the shell, which is equal to the acceleration of the outer wall of the fuel in the expansion phase; P 1 is the pressure on the outer wall of the shell caused by the air; P 2 is the pressure at the inner wall of the shell passed by the fuel; b and c represent the inner radius and outer radius of the shell in the deformation process, respectively; u b and u c represent the particle velocities of the inner wall and outer wall of the shell, respectively, which are equal to the velocity of the outer wall of the fuel in the expansion phase; u c represent the particle velocity of the outer wall of the shell; and σ Y D is the dynamic yield stress for the material.…”

Section: Modeling and Analyzing The Concentration Distribution Of Dismentioning

confidence: 99%

“…In the destruction process of the fuel, the fuel compressed by shock waves is considered an incompressible medium. The fuel breaks under the condition that the compressive stress zone of σ θ disappears and the crack penetrates the inner wall. In eq , w a represents the acceleration of the inner wall of the fuel, w b represents the acceleration of the outer wall of the fuel, P 2 represents the pressure on the outer wall of the fuel generated by the air, P 3 represents the pressure on the inner wall of the fuel ring generated by the central charge explosion, a represents the inner radius of the fuel ring, b represents the outer radius of the fuel ring, u a represents the velocity of the inner wall of the fuel ring, u b represents the velocity of the outer wall of the fuel ring, Δ t is the time, and u 0 represents the initial velocity of the fuel.…”

Section: Modeling and Analyzing The Concentration Distribution Of Dis...mentioning

confidence: 99%

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Predictive Model for Concentration Distribution of Explosive Dispersal

Chen

Wang

et al. 2021

ACS Omega

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At present, concentration of explosive dispersal is very difficult and uncertain to measure. Numerical experimentation can avoid this deficiency. Data of particles during dispersal are readily available, including velocity, displacement, and mass. However, there is minimal research on the concentration of explosive dispersal. Existing models used for the calculation of particle concentration neglect measuring the initial condition of particles and cannot, therefore, accurately describe the whole particle dispersion process. Moreover, existing concentration models do not take into account the continuous decrease in the size of particles caused by stripping and evaporation effects during flight, resulting in inaccurate descriptions of the concentration distribution. Consequently, this work derives a model to predict the concentration distribution of liquid and granular material dispersal, considering the two questions above. Concentration can be calculated based on the condensed-phase distribution and gas-phase distribution of the fuel cloud at different times by the model. This model was validated using experimental data on the mean concentration of dispersal and was well fitted. Therefore, it can be used as a tool to predict the dispersal of liquid and granular material, an explosion suppressant in coal mine accidents, and an aerosol fire extinguishant in remote forest fire extinguishers. Moreover, being able to predict the concentration of large-scale dispersal can significantly improve the accuracy and efficiency of secondary detonation.

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Section: Modeling and Analyzing The Concentration Distribution Of Dismentioning

confidence: 99%

Section: Modeling and Analyzing The Concentration Distribution Of Dis...mentioning

confidence: 99%

Predictive Model for Concentration Distribution of Explosive Dispersal

Chen

Wang

et al. 2021

ACS Omega

Self Cite

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“…Currently, most cloud dispersal devices adopt cylindrical shell structures [7], and the actual cloud dispersal is a dynamic process, resulting in an umbrella-shaped cloud. However, the cloud size resulting from dynamic dispersal is smaller than that from static dispersal [8], resulting in the concentrated distribution of the cloud, which is not only detrimental to expanding the area of fire extinguishing but also increase the risk of dust explosions. Introducing a frustoconical cloud dispersal device can increase the cloud size [9], compensating for the drawbacks of dynamic cloud dispersal.…”

Section: Introductionmentioning

confidence: 99%

Study on Multi-Scale Cloud Growth Characteristics of Frustoconical Dispersal Devices

Zhou,

Li,

Jiang

et al. 2024

Processes

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This study aims to understand cloud growth behavior and enhance cloud safety and reliability by investigating the design of cloud dispersal devices. Based on the experimental results and simulation results, this study analyzes the dispersion characteristics of cloud materials within a frustoconical device with a semi-cone angle ranging from 0° to 10° across multiple scales. The collision aggregation model for cloud particles and the multi-scale coupling mechanism for cloud growth are established. The research shows that the semi-cone angle of the device extends the effective cloud growth duration and enlarges the cloud macroscopic size. At the mesoscopic scale, vortex phenomena are observed, causing particles to converge within the cloud, resulting in collisions and aggregation. The vortices enhance the continuity of the cloud concentration. The magnitude of these vortices demonstrates a positive correlation with the magnitude of the semi-cone angle of the dispersal device. For a macroscopically stable cloud, the high-concentration area within the cloud moves outward radially with an increase in the semi-cone angle. This study provides a theoretical foundation for cloud morphology control technology, contributing to enhancing the safety and reliability of cloud systems.

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“…As early as 1990s, the modeling of near-field and far-field disperse process of liquid fuel was studied by David R. Gardner [19] and Michael W. Glass [1] respectively. Recently, a prediction model for dispersion process was built by Xing Chen et al [23], in which several factors were taken into consideration. Yunhua Li et al [2] selected Taylor condition as the fracture criterion of shell and deduced differential variation scheme of kinetic model.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a prediction model for dispersion process was built by Xing Chen et al. [23], in which several factors were taken into consideration. Yunhua Li et al.…”

Section: Introductionmentioning

confidence: 99%

Numerical simulation of disperse process of fuel‐air explosive based on the computational fluid dynamics

Ruipeng,

Xianzhen,

Wengang

et al. 2023

Propellants Explo Pyrotec

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The formation process of fuel cloud is easily affected by the material properties of fuel mixture and the structural qualities of manufacture. The spread region of fuel cloud plays an important role in the damage performance of FAE bomb. However, there are not sufficient simulation study on the disperse process, lacking a useful model for calculating the fuel cloud size due to the physical complexity in disperse process. In this work, a FAE bomb was designated at first and an experiment was carried out to measure the fuel cloud diameters. And then a computational model was established to illustrate the formation process of fuel cloud. This computational model consisted of an explosion dynamics model in the near‐field stage and a fluid dynamics model in the far‐field stage. Furthermore, multiphase turbulence flow, discrete phase and species chemical transportation were incorporated in the fluid dynamics model. The results showed that good agreements were yielded by comparison of the simulation results and experimental data for the fuel cloud diameters. The fuel cloud diameter at 220 ms was 9.11 m for simulation and 9.37 m for experiment. Additionally, it was also discussed the influence of particle size distribution on the formation process of fuel cloud. Larger particles were more likely to aggregate at the outside region of fuel cloud due to individual inertial influence. This study can provide an effective computation model for evaluation of the fuel cloud region, which can be worthwhile in understanding the physical mechanism of disperse process.

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A Whole Explosive Dispersion Process Prediction Model for Fuel Clouds

Cited by 7 publications

References 22 publications

Predictive Model for Concentration Distribution of Explosive Dispersal

Predictive Model for Concentration Distribution of Explosive Dispersal

Study on Multi-Scale Cloud Growth Characteristics of Frustoconical Dispersal Devices

Numerical simulation of disperse process of fuel‐air explosive based on the computational fluid dynamics

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