论文标题
减少输入/输出查找表的数据驱动框架：应用于化学非平衡的高超音速流
Data-driven framework for input/output lookup tables reduction: Application to hypersonic flows in chemical non-equilibrium
论文作者
论文摘要
在本文中，我们提出了一种新型的模型无形机器学习技术，用于提取简化的热化学模型，以反应高超音速流量模拟。第一个模拟通过给定模型聚集了所有相关的热力学状态和相应的气体性能。将状态嵌入低维空间中，并聚集以识别具有不同水平的热化学（非）平衡区域的区域。然后，使用radial-BASIS功能网络生成了从降低的集群空间到输出空间的替代表面。该方法在具有有限速率化学的高超音速平板边界层的模拟上进行了验证和基准测试。最初使用开源突变++库对反应性空气混合物的气体性质进行建模。用轻巧的机器学习替代方案将突变++取代可提高求解器的性能50％，同时保持整体准确性。
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.
