南京航空航天大学学报（英文版）2023，Vol.40Issue(5)：566-577,12.DOI:10.16356/j.1005-1120.2023.05.006
一种使用BP神经网络加速的蒙特卡洛拉格朗日水滴求解器
A Monte Carlo Lagrangian Droplet Solver with Backpropagation Neural Network for Aircraft Icing Simulation
摘要
Abstract
In-flight icing is threatening aviation safety.The Lagrangian method is widely used in aircraft icing simulation to solve water collection efficiency,the development of which has been impeded by robustness issues and high computational cost.To resolve these disadvantages,two critical algorithms are employed in this study.The Monte Carlo integral method is applied to calculate collection efficiency,which makes the Lagrangian method unconditionally robust for an arbitrary situation.The backpropagation(BP)neural network is also implanted to make a rapid prediction of droplet impingement.Additionally,these two algorithms are deeply coupled in an asynchronous parallelism that allows un-interfered parallel for each procedure respectively.The current study is implemented in NNW-ICE software platform.The asynchronous solver is evaluated with a 3D GLC-305 airfoil and a jet engine nacelle model.The result shows that the BP network contributes a significant acceleration to the Monte Carlo method,saving about 27%running time to achieve equal accurate result.The study is a first attempt for coupling the neural network art and numerical simulation in aircraft icing,providing strong support for the improvement of Lagrangian method and aircraft icing.关键词
飞机结冰/拉格朗日方法/蒙特卡洛方法/BP神经网络/异步并行Key words
aircraft icing/Lagrangian method/Monte Carlo method/backpropagation(BP)neural network/asynchronous parallelism分类
数理科学引用本文复制引用
刘宇,屈经国,易贤,王强..一种使用BP神经网络加速的蒙特卡洛拉格朗日水滴求解器[J].南京航空航天大学学报（英文版）,2023,40(5):566-577,12.基金项目
This work was supported in part by the National Natural Science Foundation of China(Nos.12172372,12132019)and the National Major Science and Technology Project(No.J2019-III-0010-0054). （Nos.12172372,12132019）
