论文标题
神经网络火焰关闭，用于不稳定压力的动荡燃烧器
Neural Network Flame Closure for a Turbulent Combustor with Unsteady Pressure
论文作者
论文摘要
在本文中，基于神经网络（NN）的模型将生成以替换在单个注射液液体螺旋桨火箭发动机的大涡模拟中的亚网格建模的火焰表。在最准确的情况下，通过将NN输出值与表中的相应值进行比较，设计和测试了每个火焰变量的单独NN。气体常数，内火和火焰热量比分别为0.0506％，0.0852％和0.0778％的误差估计。火焰温度，导热率和热容量比的系数分别为0.63％，0.68％和0.86％的误差。进度可变反应速率也以3.59％的误差估计。误差是基于表中所有点的均方误差来计算的。开发的NN在CFD模拟中成功实现，完全替换了弗莱姆表。基于NN的CFD通过将其结果与基于表的CFD进行比较来验证。
In this paper, neural network (NN)-based models are generated to replace flamelet tables for sub-grid modeling in large-eddy simulations of a single-injector liquid-propellant rocket engine. In the most accurate case, separate NNs for each of the flame variables are designed and tested by comparing the NN output values with the corresponding values in the table. The gas constant, internal flame energy, and flame heat capacity ratio are estimated with 0.0506%, 0.0852%, and 0.0778% error, respectively. Flame temperature, thermal conductivity, and the coefficient of heat capacity ratio are estimated with 0.63%, 0.68%, and 0.86% error, respectively. The progress variable reaction rate is also estimated with 3.59% error. The errors are calculated based on mean square error over all points in the table. The developed NNs are successfully implemented within the CFD simulation, replacing the flamelet table entirely. The NN-based CFD is validated through comparison of its results with the table-based CFD.
