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Articles

Simulation of diesel spray combustion using LES and a multicomponent vapourisation model

, &
Pages 87-104 | Received 24 Oct 2017, Accepted 16 May 2018, Published online: 20 Jun 2018
 

Abstract

Diesel spray and combustion in a constant-volume engine cylinder was simulated by a large eddy simulation (LES) approach coupling with a multicomponent vapourisation (MCV) modelling. The simulation focused on the inclusion of the interaction between fuel spray and gas-phase turbulence flow at the sub-grid scale. The LES was based on the dynamic structure sub-grid model, and an additional source term was added to the filtered momentum equation to account for the effect of drop motion on the gas-phase turbulence. The multicomponent drop vapourisation modelling was based on the continuous thermodynamics approach using a gamma distribution to describe the complex diesel fuel composition and was capable of predicting a more complex drop vapourisation process. The effect of gas-phase turbulence flow on the fuel drop vapourisation process was evaluated through the solution of the gas-phase moments of the distribution in the present LES framework. A non-evaporative spray in a constant-volume engine cylinder was first simulated to examine the behaviours of LES, in comparison with a Reynolds-averaged Navier–Stokes (RANS) simulation based on the RNG kϵ model. More realistic diesel spray structures and improved agreement on liquid penetration length with the corresponding experimental data were predicted by the LES, using a grid resolution close to that of RANS. A more comprehensive simulation of diesel spray and combustion in cylindrical combustor was also performed. Predicted distributions of soot particles were compared to the experimental image, and improved agreement with the experimental data was also observed by using the present LES and MCV models. Consequently, results of the present models proved that improved overall performance of the fuel spray simulation can be achieved by the LES without a significant increase in the computational load compared to the RANS.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 51306209] and the Science Foundation of China University of Petroleum-Beijing grant number YJRC-2013-43).

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