Data-driven aerospace engineering: reframing the industry with machine learning
Data science, and machine learning in particular, is rapidly transforming the scientific and
industrial landscapes. The aerospace industry is poised to capitalize on big data and …
industrial landscapes. The aerospace industry is poised to capitalize on big data and …
Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization
Adjoint-based optimization methods are attractive for aerodynamic shape design primarily
due to their computational costs being independent of the dimensionality of the input space …
due to their computational costs being independent of the dimensionality of the input space …
[HTML][HTML] Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil
Fluid flow in the transonic regime finds relevance in aerospace engineering, particularly in
the design of commercial air transportation vehicles. Computational fluid dynamics models …
the design of commercial air transportation vehicles. Computational fluid dynamics models …
Data-driven wind turbine wake modeling via probabilistic machine learning
Wind farm design primarily depends on the variability of the wind turbine wake flows to the
atmospheric wind conditions and the interaction between wakes. Physics-based models that …
atmospheric wind conditions and the interaction between wakes. Physics-based models that …
Parameter identification and state estimation for nuclear reactor operation digital twin
Abstract Reactor Operation Digital Twin (RODT) is now receiving increasing attention and
investment in nuclear engineering domain. A prototype of a RODT was first brought out by …
investment in nuclear engineering domain. A prototype of a RODT was first brought out by …
A globally convergent method to accelerate large-scale optimization using on-the-fly model hyperreduction: application to shape optimization
We present a numerical method to efficiently solve optimization problems governed by large-
scale nonlinear systems of equations, including discretized partial differential equations …
scale nonlinear systems of equations, including discretized partial differential equations …
CAMERA: A method for cost-aware, adaptive, multifidelity, efficient reliability analysis
Estimating probability of failure in aerospace systems is a critical requirement for flight
certification and qualification. Failure probability estimation involves resolving tails of …
certification and qualification. Failure probability estimation involves resolving tails of …
Nonlinear manifold learning and model reduction for transonic flows
B Zheng, W Yao, M Xu - AIAA Journal, 2023 - arc.aiaa.org
It is aspirational to construct a nonlinear reduced-order model (ROM) with the ability to
predict computational fluid dynamics (CFD) solutions accurately and efficiently. One major …
predict computational fluid dynamics (CFD) solutions accurately and efficiently. One major …
Multi-objective, Multidisciplinary Optimization of Low-Boom Supersonic Transports Using Multifidelity Models
W Li, K Geiselhart - Journal of Aircraft, 2022 - arc.aiaa.org
A multidisciplinary optimization (MDO) method has been developed to design a
computational fluid dynamics (CFD)-based low-boom configuration that can be obtained …
computational fluid dynamics (CFD)-based low-boom configuration that can be obtained …
Multifidelity Gaussian processes for failure boundary and probability estimation
View Video Presentation: https://doi. org/10.2514/6.2022-0390. vid Estimating probability of
failure in aerospace systems is a critical requirement for flightcertification and qualification …
failure in aerospace systems is a critical requirement for flightcertification and qualification …