In the era of global space industry’s rapid expansion,reusable launch technology has the advantage of cost reduction,but makes launch frequency and flight reliability remain critical.This study proposes that Artifici...In the era of global space industry’s rapid expansion,reusable launch technology has the advantage of cost reduction,but makes launch frequency and flight reliability remain critical.This study proposes that Artificial Intelligence(AI)would be the potential disruptive technology to solve these challenges.AI enables transformative capabilities for launch vehicles which are pointed out in four domains:Agile launch operations enabling automate testing,fault diagnosis,and decision-making for targeting hour-level launch cycles and minute-level fault resolution;high-reliability flight enabling real-time autonomous fault diagnosis,mission replanning,and fault-tolerant control within seconds during anomalies,potentially improving reliability by 1–2 orders of magnitude;rapid maintenance enabling real-time health monitoring and lifespan prediction for swift relaunch decisions;efficient space traffic management enabling predict/resolve orbital conflicts amid growing congestion from satellites and debris.The key challenges for AI applications are analyzed as well,including multi-system coupling,uncertain failure modes and narrow flight corridors,limited sensor data,and massive heterogeneous data processing.Finally,the study also proposes that AI promises substantial efficiency gains in launch vehicle design,manufacturing,and testing through multidisciplinary optimization and reduced reliance on physical testing.展开更多
In the aerospace field, residual stress directly affects the strength, fatigue life and dimensional stability of thin-walled structural components, and is a key factor to ensure flight safety and reliability. At prese...In the aerospace field, residual stress directly affects the strength, fatigue life and dimensional stability of thin-walled structural components, and is a key factor to ensure flight safety and reliability. At present, research on residual stress at home and abroad mainly focuses on the optimization of traditional detection technology, stress control of manufacturing process and service performance evaluation, among which research on residual stress detection methods mainly focuses on the improvement of the accuracy, sensitivity, reliability and other performance of existing detection methods, but it still faces many challenges such as extremely small detection range, low efficiency, large error and limited application range.展开更多
基金supported by the National Natural Science Foundation of China(Nos.52495000 and 52332012).
文摘In the era of global space industry’s rapid expansion,reusable launch technology has the advantage of cost reduction,but makes launch frequency and flight reliability remain critical.This study proposes that Artificial Intelligence(AI)would be the potential disruptive technology to solve these challenges.AI enables transformative capabilities for launch vehicles which are pointed out in four domains:Agile launch operations enabling automate testing,fault diagnosis,and decision-making for targeting hour-level launch cycles and minute-level fault resolution;high-reliability flight enabling real-time autonomous fault diagnosis,mission replanning,and fault-tolerant control within seconds during anomalies,potentially improving reliability by 1–2 orders of magnitude;rapid maintenance enabling real-time health monitoring and lifespan prediction for swift relaunch decisions;efficient space traffic management enabling predict/resolve orbital conflicts amid growing congestion from satellites and debris.The key challenges for AI applications are analyzed as well,including multi-system coupling,uncertain failure modes and narrow flight corridors,limited sensor data,and massive heterogeneous data processing.Finally,the study also proposes that AI promises substantial efficiency gains in launch vehicle design,manufacturing,and testing through multidisciplinary optimization and reduced reliance on physical testing.
文摘In the aerospace field, residual stress directly affects the strength, fatigue life and dimensional stability of thin-walled structural components, and is a key factor to ensure flight safety and reliability. At present, research on residual stress at home and abroad mainly focuses on the optimization of traditional detection technology, stress control of manufacturing process and service performance evaluation, among which research on residual stress detection methods mainly focuses on the improvement of the accuracy, sensitivity, reliability and other performance of existing detection methods, but it still faces many challenges such as extremely small detection range, low efficiency, large error and limited application range.
