Sparse-Lagrangian simulation based on improved multiple mapping conditioning

作者：Wei Wei; Xu Dong; Cao Chang Min; Ye Tao Hong

来源：Journal of Propulsion Technology, 2016, 37(7): 1312-1319.

DOI：10.13675/j.cnki.tjjs.2016.07.015

• Characteristic value
• Computational costs
• Generalized models
• Lagrangian simulation
• Multiple mapping conditioning
• Probability density pdf)
• Small-scale mixing
• Turbulent combustion

Sparse-Lagrangian simulation based on Multiple Mapping Conditioning (MMC) can dramatically decrease the computational costs for Probability Density PDF) methods. In the present research, a series of round jets is simulated to investigate an improved MMC model and analyze the stochastic noise in RMS statistical results of Sparse-Lagrangian simulation. Based on the analysis of the effects of particles number on the performance of characteristic value in the reference space, and the effect of MMC model is more significant in the shear layer where the scalar gradient is large, a generalized model of sparse-Lagrangian MMC is developed to model the small-scale mixing process. Large eddy simulations of several round jet cases are performed by the improved model. The predicted results by the improved model have the same accuracy as previous study using the specific constant characteristic value in the reference space. The production and variation of stochastic noise of RMS statistics are also investigated in the sparse particle field. The results indicate that the stochastic noise depends on the access method from the scalar value of particles to the corresponding LES grid, and the more particles are selected the smaller noise is produced.

• 出版日期2016
• 单位中国科学技术大学