A model to predict soot evolution during the combustion of complex fuels is presented. On one hand, gas phase, \(\hbox {polycyclic aromatic hydrocarbon (PAH)}\) and soot chemistry are kept large enough to cover all relevant processes in aero engines. On the other hand, the mechanisms are reduced as far as possible, to enable complex computational fluid dynamics (CFD) combustion simulations. This is important because all species transport equations are solved directly in the \(\hbox {CFD}\). Moreover, emphasis is placed on the applicability of the model for a variety of fuels and operating conditions without adjusting it. A kinetic scheme is derived to describe the chemical breakdown of short- and long-chain hydrocarbon fuels and even blends of them. \(\hbox {PAHs}\) are the primary soot precursors which are modeled by a sectional approach. The reversibility of the interaction between different \(\hbox {PAH}\) classes is achieved by the introduction of \(\hbox {PAH}\) radicals. Soot particles are captured by a detailed sectional approach too, which takes a non-spherical growth of particles into account. In this way the modeling of surface processes is improved. The applicability and validity of the gas phase, \(\hbox {PAH}\), and soot model is demonstrated by a large number of shock tube experiments, as well as in atmospheric laminar sooting flames. The presented model achieves excellent results for a wide range of operating conditions and fuels. One set of model constants is used for all simulations and no case-dependent optimization is required.

提出了一种预测复杂燃料燃烧过程中烟灰演化的模型。一方面，气相、\(\hbox {多环芳烃 (PAH)}\)和烟灰化学保持足够大以涵盖航空发动机中的所有相关过程。另一方面，尽可能减少机制，以实现复杂的计算流体动力学 (CFD) 燃烧模拟。这很重要，因为所有物种传输方程都直接在\(\hbox {CFD}\)中求解。此外，强调模型对各种燃料和运行条件的适用性，无需对其进行调整。推导出一个动力学方案来描述短链和长链碳氢化合物燃料甚至它们的混合物的化学分解。\(\hbox {PAHs}\)是通过分段方法建模的主要烟灰前体。不同\(\hbox {PAH}\)类之间相互作用的可逆性是通过引入\(\hbox {PAH}\)部首来实现的。烟灰颗粒也通过详细的截面方法捕获，该方法考虑了颗粒的非球形生长。通过这种方式，表面过程的建模得到了改进。气相的适用性和有效性，\(\hbox {PAH}\), 并且烟尘模型通过大量激波管实验以及大气层流烟尘火焰进行了验证。所提出的模型在广泛的操作条件和燃料范围内取得了出色的结果。一组模型常数用于所有模拟，不需要依赖于案例的优化。