Predicting Species Composition From Negative Valve Overlap Reforming using a Stochastic Reactor Model

Paper #:
  • 2017-01-0529

Published:
  • 2017-03-28
Abstract:
Stochastic reactor models (SRMs) can be used to accurately model combustion phenomena without the time or computational expense required for multi-dimensional fluid dynamics simulations. SRM uses discrete particles without spatial coordinates that react independently and subsequently mix at each time step. Generally, SRM is used to simulate engine pressure, temperature and heat release on a crank angle basis. With added complexity, SRM can also be used to predict species concentrations when coupled to a detailed chemical mechanism. In this work we developed a SRM approach for simulating the gas composition resulting from negative valve overlap (NVO) reforming of surrogate gasoline fuel components. NVO reforming is a strategy for increasing the reactivity of gasoline-like fuels to better control low temperature gasoline combustion at low loads. It requires direct injection of fuel into the cylinder after early exhaust valve closing and allowing it to react through moderate recompression before intake valve opening (IVO). Previous work has experimentally speciated NVO reforming products and examined their impact on main-period combustion. Efforts to model the output NVO reformate have largely failed because they either employed conventional multi-dimensional simulations that accurately capture mixing but had overly simplistic chemistry mechanisms, or used detailed chemistry mechanisms in homogeneous reactors that neglected mixing behavior. In this work, we developed a SRM that takes into account heat transfer, compression, thermal stratification and turbulent mixing along with detailed chemistry of the reforming process. It also includes a spray model to predict the equivalence ratio distribution in the cylinder early in the NVO period after fuel injection. NVO reforming of three single component fuels, iso-octane, n-heptane and ethanol were considered in this study. Results show excellent agreement between modeled and measured mean concentrations at IVO. The modeling results provide further insight into the experiments by revealing several fuel decomposition steps during the NVO period driven by partial oxidation and pyrolysis reactions.
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