In this paper, we present a novel model-agnostic machine learning technique to extract a reduced thermochemical model for reacting hypersonic flows simulation. A first simulation gathers all relevant thermodynamic states and the corresponding gas properties via a given model. The states are embedded in a low-dimensional space and clustered to identify regions with different levels of thermochemical (non)-equilibrium. Then, a surrogate surface from the reduced cluster-space to the output space is generated using radial-basis-function networks. The method is validated and benchmarked on a simulation of a hypersonic flat-plate boundary layer with finite-rate chemistry. The gas properties of the reactive air mixture are initially modeled using the open-source Mutation++ library. Substituting Mutation++ with the light-weight, machine-learned alternative improves the performance of the solver by 50% while maintaining overall accuracy.

翻译：本文提出了一种新颖的、与模型无关的机器学习技术，用于提取简化热化学模型，以模拟化学反应高超声速流动。首先，通过给定模型进行一次模拟，收集所有相关的热力学状态及相应的气体属性。将这些状态嵌入低维空间并进行聚类，以识别具有不同热化学（非）平衡程度的区域。随后，利用径向基函数网络，从约简的聚类空间到输出空间构建代理曲面。该方法在具有有限速率化学的高超声速平板边界层模拟中得到了验证和基准测试。反应空气混合物的气体属性最初使用开源Mutation++库进行建模。用轻量级的机器学习替代方案替换Mutation++，使求解器的性能提升了50%，同时保持了整体精度。