Transactions of Nanjing University of Aeronautics & Astronautics
A Monte Carlo Lagrangian Droplet Solver with Backpropagation Neural Network for Aircraft Icing Simulation
- Article
- Figures
- Metrics
- Preview PDF
- Reference
- Related
- Cited by
- Materials
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.
Keywords:
Project Supported:
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）.
Clc Number:
O359.1
Mobile website
