A new combustion regime identification methodology using the neural networks as supervised classifiers is proposed and validated. As a first proof of concept, a binary classifier is trained with labelled thermochemical states obtained as solutions of prototypical one-dimensional models representing premixed and nonpremixed regimes. The trained classifier is then used to associate the regime to any given thermochemical state originating from a multi-dimensional reacting flow simulation that shares similar operating conditions with the training problems. The classification requires local information only, i.e. no gradients are required, and operates on reduced-dimension thermochemical states, in order to cope with experimental data as well. The validity of the approach is assessed by employing a two-dimensional laminar edge flame data as a canonical configuration exhibiting multi-regime combustion behaviour. The method is readily extendable to additional classes to identify criticality phenomena, such as local extinction and re-ignition. It is anticipated that the proposed classifier tool will be useful in the development of turbulent multi-regime combustion closure models in large scale simulations.

提出并验证了一种使用神经网络作为监督分类器的新燃烧状态识别方法。作为概念的第一个证明，使用标记的热化学状态训练二元分类器，该热化学状态作为代表预混和非预混状态的原型一维模型的解而获得。然后使用经过训练的分类器将状态与源自多维反应流模拟的任何给定热化学状态相关联，该多维反应流模拟与训练问题具有相似的操作条件。分类只需要局部信息，即不需要梯度，并且在降维热化学状态下运行，以便也处理实验数据。该方法的有效性通过采用二维层流边缘火焰数据作为表现出多态燃烧行为的规范配置来评估。该方法很容易扩展到其他类别，以识别临界现象，例如局部灭绝和重新点燃。预计所提出的分类器工具将有助于开发大规模模拟中的湍流多状态燃烧闭合模型。