arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.14167physics.flu-dyn
收藏

非定常压力湍流燃烧室火焰关闭的神经网络研究

Neural Network Flame Closure for a Turbulent Combustor with Unsteady Pressure

Zeinab Shadram, Tuan M. Nguyen, Athanasios Sideris, William A. Sirignano

摘要:

在单喷嘴液体火箭发动机大涡模拟中,采用神经网络模型代替小火焰表进行亚网格建模。在最精确的情况下,通过将NN输出值与表中的相应值进行比较,为每个火焰变量设计并测试单独的NN。气体常数、内火焰能量和火焰热容比的估算误差分别为0.0506%、0.0852%和0.0778%。火焰温度、导热系数和热容比系数的估算误差分别为0.63%、0.68%和0.86%。进度变量反应速率的估计误差也为3.59%。根据表中所有点的均方误差计算误差。开发的NN在CFD模拟中成功实施,完全取代了火焰面表。通过与基于表格的计算流体动力学结果的比较,验证了基于神经网络的计算流体动力学方法的有效性。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑