Learning Mappings from Iced Airfoils to Aerodynamic Coefficients Using a Deep Operator Network
Sign Up to like & getrecommendations! Published in 2023 at "Journal of Aerospace Engineering"
DOI: 10.1061/jaeeez.aseng-4508
Abstract: , This study abstracted the prediction of the aerodynamic coefficients of an iced airfoil as a mapping from the iced airfoil space to the aerodynamic coefficient space. Thus, a deep network called Airfoils2AeroNet that can… read more here.
Keywords: network; operator network; deep operator; iced airfoils ... See more keywords
Learning mappings of thermal updraft fields under unknown operating conditions using a deep operator network
Sign Up to like & getrecommendations! Published in 2024 at "Physics of Fluids"
DOI: 10.1063/5.0205695
Abstract: Precise estimation of the thermal updraft environment is important for the effective exploration of wind resources in long-endurance drones. Nevertheless, previous regression algorithms exhibit limitations in accurately evaluating updrafts under new operating conditions, and traditional… read more here.
Keywords: operating conditions; thermal updraft; deep operator; network ... See more keywords
A deep operator network-based method for fast predicting arc quenching performance of eco-friendly gases
Sign Up to like & getrecommendations! Published in 2025 at "Journal of Physics D: Applied Physics"
DOI: 10.1088/1361-6463/ae23e0
Abstract: Sulfur hexafluoride (SF6) has been widely employed in the power industry as an arc quenching medium. However, its extremely high global warming potential has led to urgent efforts to reduce or replace its use. Identifying… read more here.
Keywords: arc deeponet; quenching performance; eco friendly; performance ... See more keywords