Prediction of Soot Mass and Particle Size in a High-boosted Diesel Engine using Large Eddy Simulation 2021-01-1168
Soot mass production was investigated in high-boosted diesel engine tests by changing various operating parameters. A mixed timescale subgrid model of large eddy simulation (LES) was applied to simulate the detailed mixture formation, combustion and soot formation influenced by turbulence in diesel engine combustion. The combustion model used a direct integration approach with an explicit ordinary differential equation (ODE) solver and additional parallelization by OpenMP. Soot mass production within a computation cell was determined from a phenomenological soot formation model developed by WASEDA University. The model was combined with the LES code and included the following important steps: particle inception, in which naphthalene was assumed to grow irreversibly to form soot; surface growth with the addition of C2H2; surface oxidation due to OH radicals and O2 attack; particle coagulation; and particle agglomeration. The computational results were compared with experimental data acquired under various EGR conditions. The results showed that the in-cylinder pressure and heat release rate obtained from the engine tests were in good agreement with the calculated values. In the soot emission calculation, the simulated results showed an exponential increase with increasing EGR rate. Furthermore, the steep increase in soot mass with increasing EGR rate from 30% EGR was reproduced. Changes in the soot mass and particle size characteristics with EGR rate were analyzed, and the process and spatial distribution of soot formation were studied.
Citation: ZHOU, B., Nakatsuka, M., Wu, J., and Kusaka, J., "Prediction of Soot Mass and Particle Size in a High-boosted Diesel Engine using Large Eddy Simulation," SAE Technical Paper 2021-01-1168, 2021, https://doi.org/10.4271/2021-01-1168. Download Citation
Author(s):
Beini ZHOU, Mahoko Nakatsuka, Juibin Wu, Jin Kusaka
Affiliated:
Waseda Univ
Pages: 12
Event:
SAE Powertrains, Fuels & Lubricants Digital Summit
ISSN:
0148-7191
e-ISSN:
2688-3627
Related Topics:
Computational fluid dynamics
Combustion and combustion processes
Diesel / compression ignition engines
Particulate matter (PM)
Exhaust gas recirculation (EGR)
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