Work

Software System for Thrust Prediction and Preliminary Engineering Design of Aircraft Using Visual Recognition and Flight Parameters

Year: 2025

Type: article

Abstract: Accurate estimation of engine thrust and overload is crucial for ensuring structural integrity and optimizing the aircraft’s life-cycle design. To address this issue, this study develops an integrated thrust and load prediction framework that combines vision-based flight maneuver recognition with an improved transformer-based deep learning model (YOLO), leveraging measured flight parameters. After maneuver recognition, the model achieves a mean absolute error of 1.86 and R2 of 0.97 in prediction. The framework is implemented via a Python-based software system with a MySQL database, supporting functionalities including thrust/load prediction, trajectory visualization, and performance evaluation. Comparative experiments demonstrate that the framework achieves an average maneuver recognition accuracy of 81.06%, outperforming the existing PLR-PIP and DTW methods. This approach provides high-precision and reliable thrust data as well as tool support for real-time thrust estimation, fatigue life assessment, and flight safety risk prevention.

Source: Applied Sciences

DOI: 10.3390/app152111770

Authors: Juan Du, Senxin Mao, Rui Wang, Yue Ma, Mengchuang Zhang, Zhiping Yin

Institutions: Aviation Industry Corporation of China (China), Northwestern Polytechnical University

Language: English

Topic: Aerospace and Aviation Technology

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896