Work

Prediction of Temperature Distribution on an Aircraft Hot-Air Anti-Icing Surface by ROM and Neural Networks

Year: 2024

Type: article

Abstract: This study aims to address the inefficiencies and time-consuming nature of traditional hot-air anti-icing system designs by introducing reduced order models (ROM) and machine learning techniques to predict anti-icing surface temperature distributions. This study compares several classic neural networks, ultimately proposing two models: POD-AlexNet and multi-CNNs with GRU (MCG). Design variables of the hot-air anti-icing cavity are used as inputs, and the corresponding surface temperature distribution data serve as outputs. The performance of these models is evaluated on the test set. The POD-AlexNet model achieves a mean prediction accuracy of over 95%, while the MCG model reaches 96.97%. Both models significantly outperform traditional numerical simulation methods, delivering faster predictions. The proposed models achieve fast prediction of anti-icing surface temperature distribution while ensuring acceptable prediction accuracy. These models greatly enhance the prediction efficiency over existing numerical simulation approaches, contributing to the design of aircraft hot-air anti-icing systems based on optimization methods such as genetic algorithms.

Source: Aerospace

DOI: 10.3390/aerospace11110930

Authors: Ziying Chu, Ji Geng, Qian Yang, Xian Yi, Wei Dong

Institutions: University of Electronic Science and Technology of China, China Aerodynamics Research and Development Center, State Key Laboratory of Aerodynamics, Shanghai Jiao Tong University

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,728

Funders: National Natural Science Foundation of China, National Science and Technology Major Project