A preliminary assessment of machine learning algorithms for predicting CFD-simulated wind flow patterns over idealised foredunes. Issue 2 (3rd April 2021)

• Record Type: Journal Article
• Authors: Wakes, Sarah J.; Bauer, Bernard O.; Mayo, Michael
• Is Part Of: Journal of the Royal Society of New Zealand. Volume 51:Issue 2(2021)
• Page Start: 290
• Page End: 306
• Publication Date: 2021-04-03
• Subjects: Computational fluid dynamics -- machine learning -- wind flow modelling -- coastal dune geomorphology -- vortices; Science -- Periodicals; 505
• DOI: 10.1080/03036758.2020.1868541
• Languages: English
• ISSNs: 0303-6758

Abstract

ABSTRACT: Foredunes play an important role in protecting coastal communities and their assets. The use of Computational Fluid Dynamics (CFD) to simulate wind flow over foredunes has great potential because it can enable three-dimensional visualisations of the flow field, critical to predicting sediment pathways. Generalised conceptual models of foredune evolution and maintenance can then be built and revised over time as more evidence from the field becomes available. Obtaining field data is, however, time consuming, costly, and weather dependent. CFD is ideally suited to explore what happens when wind transitions across the beach and encounters the stoss face of the foredune. A simple dune shape is used in CFD simulations to tease out the influence of various dune parameters under varying wind conditions. However, it is computationally expensive to run CFD simulations for all combinations of parameters. Representative data were used to train machine learning algorithms, and the results were compared to predicted CFD simulations. The machine learning algorithms were able to identify the cases when recirculation vortices were present and to some extent their relative scales and locations, allowing the exploration and identification of key parameters related to wind flow and dune geomorphology that are associated with turbulent flow structures.