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PRJ-5764 | Examination of analytical shear stress predictions for coastal dune evolution
PI
Project TypeSimulation
Natural Hazard Type(s)Wind
Keywordsaeolian sediment transport, CFD, coastal dunes, reduced order model, symbolic regression
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Description:

Using computational fluid dynamics, we analyze the error trends of an analytical shear stress distribution model used to drive aeolian transport for coastal dunes, which are an important line of defense against storm-related flooding hazards. Additionally, two data-driven approaches (i.e., symbolic regression and projection-based, non-intrusive reduced-order modeling) are proposed for reducing the error while maintaining computational efficiency.

Simulation | 2D OpenFOAM simulations of flow over several idealistic dune profiles using the RNG k-epsilon turbulence model
Cite This Data:
Cecil, O., N. Cohn, M. Farthing, S. Dutta, A. Trautz (2024). "2D OpenFOAM simulations of flow over several idealistic dune profiles using the RNG k-epsilon turbulence model", in Examination of analytical shear stress predictions for coastal dune evolution. DesignSafe-CI. https://doi.org/10.17603/ds2-4w1m-7998

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Simulation TypeWind
Author(s); ; ; ;
Date Published2024-12-13
DOI10.17603/ds2-4w1m-7998
License
 Open Data Commons Attribution
Description:

This dataset curates a series of 2D OpenFOAM simulations for 4 idealized dune profiles and 21 height-to-characteristic length ratios where the characteristic length is taken as the half-length at half-height. This data was used to examine error trends of an analytical surface shear stress model commonly used to drive coastal dune evolution models. Additionally, this data was used in the training of symbolic regression models and projection-based, non-intrusive reduced-order models. This data accompanies a the paper published with the title "Examination of analytical shear stress predictions for coastal dune evolution".

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