Prediction of Dry Powder Cleaning for Airport Navigational Lights Based on BP Neural Network
- Resource Type
- Conference
- Authors
- Hu, Lixiang; Ge, Xiaohong; Li, Jiahui; Yang, Zhao; Zhu, Qiuhua; Lv, Fuheng
- Source
- 2023 4th International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI) Intelligent Computing and Human-Computer Interaction (ICHCI), 2023 4th International Conference on. :190-193 Aug, 2023
- Subject
- Computing and Processing
Robotics and Control Systems
Powders
Atmospheric modeling
Simulation
Neural networks
Predictive models
Airports
Cleaning
Back Propagation
simulation of dry powder washing
cleaning of airport runway lights
- Language
This article uses ABAQUS software to establish a two-dimensional cleaning model based on the Yeoh theoretical model. It simulates and analyzes the dry powder cleaning process of rubber dirt on the mirror surface of airport navigational lights at different angles. The optimal washing angle is determined to be 35°. A gas-solid two-phase flow model is then simulated and analyzed using Fluent. Based on the simulation results., a BP neural network model is established to predict the washing effects under different factors such as inlet pressure., jet target distance., and abrasive flow rate. The aim is to improve the efficiency of automated cleaning and save energy consumption.