论文标题
基于深度学习的模型减少（DEEPMR）方法，用于简化化学动力学
A deep learning-based model reduction (DeePMR) method for simplifying chemical kinetics
论文作者
论文摘要
提出并使用高温自动点击量，完美搅拌的反应器（PSR）和一维自由传播的N甲烷/空气混合物的一维自由传播火焰，提出并验证了一种基于学习的模型还原（DEEPMR）方法。该机理还原被建模为布尔空间上的优化问题，在该问题中，布尔矢量（与物种相对应）代表了降低的机制。优化目标是在一组预先选择的基准量的误差耐受性的情况下最大程度地降低机制的大小。 DEEPMR的关键思想是采用深神网络（DNN）在优化问题中制定目标函数。为了有效地探索高维布尔空间，实施了迭代DNN辅助数据采样和DNN培训程序。结果表明，DNN-辅助可显着提高采样效率，仅选择$ 10^5 $样品中的$ 10^{34} $可能的DNN样品以实现足够的准确性。结果表明，DNN能够识别关键物种并合理预测机制性能降低。训练有素的DNN通过解决反相反优化问题来确保最佳的减少机制。通过比较点火延迟时间，层状火焰速度，PSR中的温度，所得的骨骼机制的物种较少（45种），但准确性与通过路径通量分析（PFA）方法获得的骨骼机制（56种）相同。此外，只有考虑大气，近乎杂货的条件（等效比率在0.6到1.2之间），骨骼机制就可以进一步降低至28种。 DEEPMR提供了一种创新的方法来进行模型降低，并证明了燃烧区域中数据驱动方法的巨大潜力。
A deep learning-based model reduction (DeePMR) method for simplifying chemical kinetics is proposed and validated using high-temperature auto-ignitions, perfectly stirred reactors (PSR), and one-dimensional freely propagating flames of n-heptane/air mixtures. The mechanism reduction is modeled as an optimization problem on Boolean space, where a Boolean vector, each entry corresponding to a species, represents a reduced mechanism. The optimization goal is to minimize the reduced mechanism size given the error tolerance of a group of pre-selected benchmark quantities. The key idea of the DeePMR is to employ a deep neural network (DNN) to formulate the objective function in the optimization problem. In order to explore high dimensional Boolean space efficiently, an iterative DNN-assisted data sampling and DNN training procedure are implemented. The results show that DNN-assistance improves sampling efficiency significantly, selecting only $10^5$ samples out of $10^{34}$ possible samples for DNN to achieve sufficient accuracy. The results demonstrate the capability of the DNN to recognize key species and reasonably predict reduced mechanism performance. The well-trained DNN guarantees the optimal reduced mechanism by solving an inverse optimization problem. By comparing ignition delay times, laminar flame speeds, temperatures in PSRs, the resulting skeletal mechanism has fewer species (45 species) but the same level of accuracy as the skeletal mechanism (56 species) obtained by the Path Flux Analysis (PFA) method. In addition, the skeletal mechanism can be further reduced to 28 species if only considering atmospheric, near-stoichiometric conditions (equivalence ratio between 0.6 and 1.2). The DeePMR provides an innovative way to perform model reduction and demonstrates the great potential of data-driven methods in the combustion area.
