Machine learning in aerodynamic shape optimization
Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …
optimization (ASO), thanks to the availability of aerodynamic data and continued …
Active manifold and model-order reduction to accelerate multidisciplinary analysis and optimization
G Boncoraglio, C Farhat - AIAA Journal, 2021 - arc.aiaa.org
A computational framework is proposed for efficiently solving multidisciplinary analysis and
optimization (MDAO) problems in a relatively high-dimensional design parameter space. It …
optimization (MDAO) problems in a relatively high-dimensional design parameter space. It …
Nonlinear reduced order modeling using domain decomposition
View Video Presentation: https://doi. org/10.2514/6.2022-1250. vid As designers become
increasingly reliant upon expensive, high-fidelity numerical modeling and simulation …
increasingly reliant upon expensive, high-fidelity numerical modeling and simulation …
Shock wave prediction in transonic flow fields using domain-informed probabilistic deep learning
Transonic flow fields are marked by shock waves of varying strength and location and are
crucial for the aerodynamic design and optimization of high-speed transport aircraft. While …
crucial for the aerodynamic design and optimization of high-speed transport aircraft. While …
Comparison of Overwing and Underwing Nacelle Aeropropulsion Optimization for Subsonic Transport Aircraft
This research compares a forward-mounted overwing nacelle configuration to a
conventional underwing nacelle for a single-aisle transport aircraft. We focus on …
conventional underwing nacelle for a single-aisle transport aircraft. We focus on …
Manifold alignment-based nonintrusive and nonlinear multifidelity reduced-order modeling
This study presents the development of a methodology for the construction of data-driven,
parametric, multifidelity reduced-order models to emulate aerodynamic flowfields with …
parametric, multifidelity reduced-order models to emulate aerodynamic flowfields with …
Multi-fidelity non-intrusive reduced-order modelling based on manifold alignment
This work presents the development of a multi-fidelity, parametric and non-intrusive reduced-
order modelling method to tackle the problem of achieving an acceptable predictive …
order modelling method to tackle the problem of achieving an acceptable predictive …
Nonlinear Multi-Fidelity Reduced Order Modeling Method using Manifold Alignment
View Video Presentation: https://doi. org/10.2514/6.2021-3050. vid This study presents the
development of a methodology for the construction of data-driven, parametric, multi-fidelity …
development of a methodology for the construction of data-driven, parametric, multi-fidelity …
A multi-fidelity approximation of the active subspace method for surrogate models with high-dimensional inputs
View Video Presentation: https://doi. org/10.2514/6.2022-3488. vid Modern design problems
routinely involve high-dimensional inputs and the active subspace has been recognized as …
routinely involve high-dimensional inputs and the active subspace has been recognized as …
Automated optimal experimental design strategy for reduced order modeling of aerodynamic flow fields
J Wang, JRRA Martins, X Du - Aerospace Science and Technology, 2024 - Elsevier
Aerodynamic flow fields reveal essential physical insights (such as shocks) that substantially
affect aerodynamic performance. However, conventional flow field computations require …
affect aerodynamic performance. However, conventional flow field computations require …