Improving aircraft performance using machine learning: a review

• URL: http://arxiv.org/abs/2210.11481v1
• Date: Thu, 20 Oct 2022 07:16:53 GMT
• Title: Improving aircraft performance using machine learning: a review
• Authors: Soledad Le Clainche, Esteban Ferrer, Sam Gibson, Elisabeth Cross, Alessandro Parente, Ricardo Vinuesa
• Abstract summary: This review covers the new developments in machine learning (ML) that are impacting the multi-disciplinary area of aerospace engineering. We review the state of the art, gathering the advantages and challenges of ML methods across different aerospace disciplines.
• Score: 57.82442188072833
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey [51.87875066383221]
  This paper introduces fundamental concepts, traditional methods, and benchmark datasets, then examine the various roles Machine Learning plays in improving CFD. We highlight real-world applications of ML for CFD in critical scientific and engineering disciplines, including aerodynamics, combustion, atmosphere & ocean science, biology fluid, plasma, symbolic regression, and reduced order modeling. We draw the conclusion that ML is poised to significantly transform CFD research by enhancing simulation accuracy, reducing computational time, and enabling more complex analyses of fluid dynamics.
  arXiv  Detail & Related papers  (2024-08-22T07:33:11Z)
• The transformative potential of machine learning for experiments in fluid mechanics [1.9459588807364006]
  This perspective will highlight several aspects of experimental fluid mechanics that stand to benefit from progress advances in machine learning. In each case, we discuss recent success stories and ongoing challenges, along with caveats and limitations, and outline the potential for new avenues of ML-augmented and ML-enabled experimental fluid mechanics.
  arXiv  Detail & Related papers  (2023-03-28T09:03:47Z)
• Selected Trends in Artificial Intelligence for Space Applications [69.3474006357492]
  This chapter focuses on differentiable intelligence and on-board machine learning. We discuss a few selected projects originating from the European Space Agency's (ESA) Advanced Concepts Team (ACT)
  arXiv  Detail & Related papers  (2022-12-10T07:49:50Z)
• Symmetry Group Equivariant Architectures for Physics [52.784926970374556]
  In the domain of machine learning, an awareness of symmetries has driven impressive performance breakthroughs. We argue that both the physics community and the broader machine learning community have much to understand.
  arXiv  Detail & Related papers  (2022-03-11T18:27:04Z)
• Machine Learning in Aerodynamic Shape Optimization [0.0]
  We show how cutting-edge machine learning approaches can benefit aerodynamic shape optimization (ASO) Practical large-scale design optimizations remain a challenge due to the costly machine learning training expense. A deep coupling of ML model construction with ASO prior experience and knowledge is recommended to train ML models effectively.
  arXiv  Detail & Related papers  (2022-02-15T02:23:21Z)
• Which priors matter? Benchmarking models for learning latent dynamics [70.88999063639146]
  Several methods have proposed to integrate priors from classical mechanics into machine learning models. We take a sober look at the current capabilities of these models. We find that the use of continuous and time-reversible dynamics benefits models of all classes.
  arXiv  Detail & Related papers  (2021-11-09T23:48:21Z)
• Machine Learning Methods for the Design and Operation of Liquid Rocket Engines -- Research Activities at the DLR Institute of Space Propulsion [0.0]
  The paper describes current machine learning applications at the DLR Institute of Space Propulsion. The advantages and disadvantages of the presented methods as well as current and future research directions are discussed.
  arXiv  Detail & Related papers  (2021-02-14T09:09:37Z)
• Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning [49.367020832638794]
  The aerospace industry is poised to capitalize on big data and machine learning. Recent trends will be explored in context of critical challenges in design, manufacturing, verification and services.
  arXiv  Detail & Related papers  (2020-08-24T22:40:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.