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REDUCED CHEMICAL KINETIC MODELS USING ALTERNATE AND STOCHASTIC SPECIES ELIMINATION

机译:使用交替和随机物种消除的简化化学动力学模型

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This work extends the species sensitivity method of model reduction known as Alternate Species Elimination (ASE) to a stochastic version. The new Stochastic Species Elimination (SSE) approach allows for a linear reduction in the number of species retained in the course of reduction. It improves the computational cost and offers flexibility to the user in terminating the reduction process when an acceptable model size is attained. Larger chemical kinetic models, such as the recent literature model ofn-octanol, are approached with the SSE method coupled with multiple species sampling. This further allows for a faster model reduction process. These modified approaches are applied to the reduction of selected chemical kinetic models based on ignition simulations: the n-heptane model by Mehl et al. (654 species, 5258 reactions), reduced using the SSE method (293 species, 2792 reactions) and the ASE method (245 species, 2405 reactions); the iso-octane model by Mehl et al. (874 species, 7522 reactions), reduced to an SSE version (315 species, 3037 reactions) and an ASE version (306 species, 2732 reactions); and the n-octanol model by Cai et al. (1281 species, 5537 reactions), with a reduced SSE version (450 species, 2532 reactions). Resulting skeletal models are shown to adequately predict ignition delay times as well as flame propagation when compared to the predictions of the detailed models. Burning velocity predictions are well-captured even though the reduction is based on ignition delay simulations.
机译:这项工作扩展了所谓的模型减少的物种灵敏度方法,称为替代物种消除(ASE)到随机版本。新的随机物种消除(SSE)方法允许在减少过程中保留的物种数量的线性减少。它提高了计算成本,并为用户提供了灵活性,在获得可接受的模型尺寸时终止减少过程。较大的化学动力学模型,如最近的NON-Octanol的文献模型,并使用与多种物种采样相结合的SSE方法。这进一步允许更快的模型减少过程。这些改进的方法应用于基于点火模拟的所选化学动力学模型的减少:Mehl等人的正庚烷模型。 (654种,5258反应),使用SSE方法(293种,2792个反应)和ASE方法(245种,2405个反应)减少; Mehl等人的ISO-辛烷模型。 (874种,7522个反应),减少到SSE版本(315种,3037个反应)和ASE版本(306种,2732个反应);和N-Octanol模型由Cai等人进行。 (1281种,5537次反应),SSE版本还减少(450种,2532个反应)。与详细模型的预测相比,所产生的骨架模型被示出为充分预测点火延迟时间以及火焰传播。即使减少基于点火延迟模拟,也令人振奋的速度预测是良好的捕获。

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