Advanced Predictive Diesel Combustion Simulation Using Turbulence Model and Stochastic Reactor Model

Paper #:
  • 2017-01-0516

Published:
  • 2017-03-28
DOI:
  • 10.4271/2017-01-0516
Citation:
Franken, T., Sommerhoff, A., Willems, W., Matrisciano, A. et al., "Advanced Predictive Diesel Combustion Simulation Using Turbulence Model and Stochastic Reactor Model," SAE Technical Paper 2017-01-0516, 2017, doi:10.4271/2017-01-0516.
Pages:
21
Abstract:
Today numerical models are a major part of the diesel engine development. They are applied during several stages of the development process to perform extensive parameter studies and to investigate flow and combustion phenomena in detail. The models are divided by complexity and computational costs since one has to decide what the best choice for the task is. 0D models are suitable for problems with large parameter spaces and multiple operating points, e.g. engine map simulation and parameter sweeps. Therefore, it is necessary to incorporate physical models to improve the predictive capability of these models.This work focuses on turbulence and mixing modeling within a 0D direct injection stochastic reactor model. The model is based on a probability density function approach and incorporates submodels for direct fuel injection, vaporization, heat transfer, turbulent mixing and detailed chemistry. The advantage of the probability density function approach compared to mean value models is its capability to account for temperature and mixture inhomogeneities. Therefore, notional particles are introduced each with its own temperature and composition. The particle condition is changed by mixing, injection, vaporization, chemical reaction and heat transfer. Mixing is modeled using the one-dimensional Euclidean minimum spanning tree mixing model, which requires the scalar mixing frequency as input. Therefore, a turbulence model is proposed to calculate the mixing time depending on turbulent kinetic energy and its dissipation. The turbulence model accounts for density, swirl, squish and injection effects on turbulent kinetic energy within the combustion chamber. Finally, the 0D stochastic reactor model is tested for 40 different operating points distributed over the whole engine map. The results show a close match of experimental heat release rate and NOx emissions. The trends of measured CO and HC concentrations are captured qualitatively. Additionally, the 0D simulation results are compared to more detailed 3D CFD combustion simulation results for three operating points differing in engine speed and load. The comparison shows that the 0D stochastic reactor model is able to capture turbulence effects on local temperature and mixture distribution, which in turn affect NOx, CO and HC emission formation. Overall, the 0D stochastic reactor model has proven its predictive capability for the investigated diesel engine and can be assigned to tasks concerning engine map simulation and parameter sweeps.
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