What question did this study set out to answer?

This research aims to develop a physics-based framework for efficient simulation of steady transonic potential flows.

Non‐Linear Reduced Order Modelling of Transonic Potential Flows for Fast Aerodynamic Analysis

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This work demonstrates an efficient framework for simulating transonic flows in aerodynamics, suggesting robust aerodynamic analysis.

This research aims to develop a physics-based framework for efficient simulation of steady transonic potential flows.
Developed a reduced order modelling framework for aerodynamic analysis.
Utilized proper orthogonal decomposition and least-squares Petrov-Galerkin projection.
Introduced a spatially weighted LSPG formulation for improved accuracy in shock-affected regions.
Implemented the methodology in the open-source Kratos Multiphysics framework.
Validated on benchmark configurations like the 2D NACA 0012 airfoil and 3D ONERA M6 wing.
Achieved accurate reconstructions of aerodynamic fields with relative errors on the order of .
Reduced dimensionality of full order models by approximately three orders of magnitude.
Demonstrated more accurate solutions in shock-dominated regions compared to standard data-driven surrogates.
Provided potential speed-ups in model evaluation for transonic flows, despite modest results in current settings.

Zuñiga et al. (2026) studied this question.

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View Full Paper

Ask AI

Bookmark

Authors

This work demonstrates an efficient framework for simulating transonic flows in aerodynamics, suggesting robust aerodynamic analysis.

This research aims to develop a physics-based framework for efficient simulation of steady transonic potential flows.
Developed a reduced order modelling framework for aerodynamic analysis.
Utilized proper orthogonal decomposition and least-squares Petrov-Galerkin projection.
Introduced a spatially weighted LSPG formulation for improved accuracy in shock-affected regions.
Implemented the methodology in the open-source Kratos Multiphysics framework.
Validated on benchmark configurations like the 2D NACA 0012 airfoil and 3D ONERA M6 wing.
Achieved accurate reconstructions of aerodynamic fields with relative errors on the order of .
Reduced dimensionality of full order models by approximately three orders of magnitude.
Demonstrated more accurate solutions in shock-dominated regions compared to standard data-driven surrogates.
Provided potential speed-ups in model evaluation for transonic flows, despite modest results in current settings.

Zuñiga et al. (2026) studied this question.

View Full Paper

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