Improving aircraft performance using machine learning: a review
This review covers the new developments in machine learning (ML) that are impacting the multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics (experimental and numerical), aerodynamics, acoustics, combustion and structural health monitoring. We review the state of the art, gathering the advantages and challenges of ML methods across different aerospace disciplines and provide our view on future opportunities. The basic concepts and the most relevant strategies for ML are presented together with the most relevant applications in aerospace engineering, revealing that ML is improving aircraft performance and that these techniques will have a large impact in the near future.
Code (0)
등록된 구현이 없습니다.
Tasks
Structural Health MonitoringSimilar Papers 제목 키워드 기반
An Interpretable Systematic Review of Machine Learning Models for Predictive Maintenance of Aircraft Engine
This paper presents an interpretable review of various machine learning and deep learning models to predict the maintenance of aircraft engine to avoid any kind of disaster. One of the advantages of the strategy is that …
Deep LearningOn the Generalization Properties of Deep Learning for Aircraft Fuel Flow Estimation Models
Accurately estimating aircraft fuel flow is essential for evaluating new procedures, designing next-generation aircraft, and monitoring the environmental impact of current aviation practices. This paper investigates the …
Domain GeneralizationFrom industry-wide parameters to aircraft-centric on-flight inference: improving aeronautics performance prediction with machine learning
Aircraft performance models play a key role in airline operations, especially in planning a fuel-efficient flight. In practice, manufacturers provide guidelines which are slightly modified throughout the aircraft life cy…
BIG-bench Machine LearningThe AeroSonicDB (YPAD-0523) Dataset for Acoustic Detection and Classification of Aircraft
The time and expense required to collect and label audio data has been a prohibitive factor in the availability of domain specific audio datasets. As the predictive specificity of a classifier depends on the specificity …
Binary ClassificationSpecificityUAS in the Airspace: A Review on Integration, Simulation, Optimization, and Open Challenges
Air transportation is essential for society, and it is increasing gradually due to its importance. To improve the airspace operation, new technologies are under development, such as Unmanned Aircraft Systems (UAS). In fa…