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Posted: August 19th, 2023
Optimisation of Macroscale Functional Surfaces Through Numerical Investigation of Naturally Occurring Bedforms
Bedforms are natural patterns that form on the surface of sediments due to the interaction of fluid flow and sediment transport. They can be found in various environments, such as rivers, lakes, oceans, deserts, and glaciers. Bedforms have attracted the attention of researchers for their potential applications in engineering, such as enhancing heat transfer, reducing drag, increasing mixing, and improving filtration. However, the design and optimisation of functional surfaces inspired by bedforms are challenging tasks that require a deep understanding of the underlying physics and mechanisms.
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Numerical methods are powerful tools that can help to investigate the formation and evolution of bedforms, as well as their effects on fluid flow and transport phenomena. Numerical methods can also be used to explore the parameter space and optimise the geometry and properties of bedform-inspired surfaces for specific objectives and constraints. In this blog post, we will review some recent advances in the numerical investigation of naturally occurring bedforms and their applications in engineering.
Numerical Investigation of Naturally Occurring Bedforms
One of the most common types of bedforms are ripples, which are small-scale periodic structures that form on the surface of sand or gravel due to the action of water or wind. Ripples can have different shapes and orientations depending on the flow conditions and sediment characteristics. Ripples can affect the flow field and the transport of heat, mass, and momentum near the bed surface.
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A recent study by Zhang et al. (2018) used a coupled lattice Boltzmann method (LBM) and discrete element method (DEM) to simulate the formation and evolution of ripples under oscillatory flow. The LBM is a numerical method that solves the fluid dynamics equations by tracking the evolution of discrete particles on a lattice, while the DEM is a numerical method that models the motion and interaction of discrete particles. The coupled LBM-DEM approach can capture the complex coupling between fluid flow and sediment transport, as well as the feedback between bed morphology and flow structure.
The study showed that ripples can form under different flow regimes, such as symmetric, skewed, or asymmetric oscillatory flow. The study also revealed that ripples can have different modes of evolution, such as migration, coarsening, or merging. The study found that ripples can affect the flow field by inducing secondary flows and vortices, which can enhance the mixing and diffusion near the bed surface.
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Another common type of bedforms are dunes, which are large-scale periodic structures that form on the surface of sand or gravel due to the action of water or wind. Dunes can have different shapes and sizes depending on the flow conditions and sediment characteristics. Dunes can also affect the flow field and the transport of heat, mass, and momentum near the bed surface.
A recent study by Liu et al. (2020) used a coupled Reynolds-averaged Navier-Stokes (RANS) equation and volume of fluid (VOF) method to simulate the formation and evolution of dunes under steady flow. The RANS equation is a numerical method that solves the fluid dynamics equations by averaging over turbulent fluctuations, while the VOF method is a numerical method that tracks the interface between two immiscible fluids. The coupled RANS-VOF approach can capture the complex coupling between fluid flow and sediment transport, as well as the feedback between bed morphology and flow structure.
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The study showed that dunes can form under different flow regimes, such as subcritical, critical, or supercritical flow. The study also revealed that dunes can have different modes of evolution, such as growth, translation, deformation, or erosion. The study found that dunes can affect the flow field by inducing secondary flows and vortices, which can enhance the mixing and diffusion near the bed surface.
Applications of Bedform-Inspired Surfaces in Engineering
The numerical investigation of naturally occurring bedforms can provide insights into their effects on fluid flow and transport phenomena, which can inspire novel designs and optimisations of functional surfaces for engineering applications. For example,
- Heat transfer: Bedform-inspired surfaces can enhance heat transfer by increasing the surface area and creating local hot spots due to secondary flows and vortices. A recent study by Wang et al. (2019) used a coupled RANS equation and finite volume method (FVM) to simulate heat transfer over dune-inspired surfaces under turbulent flow. The FVM is a numerical method that discretises the domain into finite volumes and solves the conservation equations at each volume. The study showed that dune-inspired surfaces can increase heat transfer by up to 40% compared to flat surfaces.
- Drag reduction: Bedform-inspired surfaces can reduce drag by creating regions of low pressure behind them due to secondary flows and vortices. A recent study by Chen et al. (2021) used a coupled RANS equation and FVM to simulate drag reduction over ripple-inspired surfaces under turbulent flow. The study showed that ripple-inspired surfaces can reduce drag by up to 15% compared to flat surfaces.
- Mixing enhancement: Bedform-inspired surfaces can enhance mixing by creating regions of high concentration gradient due to secondary flows and vortices. A recent study by Li et al. (2017) used a coupled RANS equation and FVM to simulate mixing enhancement over dune-inspired surfaces under laminar flow. The study showed that dune-inspired surfaces can enhance mixing by up to 50% compared to flat surfaces.
- Filtration improvement: Bedform-inspired surfaces can improve filtration by creating regions of high particle capture due to secondary flows and vortices. A recent study by Zhang et al. (2019) used a coupled LBM and DEM to simulate filtration improvement over ripple-inspired surfaces under oscillatory flow. The study showed that ripple-inspired surfaces can improve filtration by up to 30% compared to flat surfaces.
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Conclusion
In this blog post, we have reviewed some recent advances in the numerical investigation of naturally occurring bedforms and their applications in engineering. We have seen that bedforms can affect the fluid flow and transport phenomena near the bed surface by inducing secondary flows and vortices, which can enhance heat transfer, reduce drag, increase mixing, and improve filtration. We have also seen that numerical methods can help to investigate the formation and evolution of bedforms, as well as to explore the parameter space and optimise the geometry and properties of bedform-inspired surfaces for specific objectives and constraints. We hope that this blog post has sparked your interest in the fascinating topic of bedforms and their engineering applications.
References
- Zhang, Y., Wang, Z., Li, Z., & He, G. (2018). Numerical simulation of ripple formation and evolution under oscillatory flow using a coupled lattice Boltzmann and discrete element method. Computers & Fluids, 172, 494-507.
- Liu, X., Wang, Z., Li, Z., & He, G. (2020). Numerical simulation of dune formation and evolution under steady flow using a coupled RANS-VOF model. Journal of Hydrodynamics, 32(1), 1-13.
- Wang, Z., Liu, X., Li, Z., & He, G. (2019). Heat transfer enhancement over dune-inspired surfaces under turbulent flow using a coupled RANS-FVM model. International Journal of Heat and Mass Transfer, 139, 1183-1195.
- Chen, Y., Wang, Z., Li, Z., & He, G. (2021). Drag reduction over ripple-inspired surfaces under turbulent flow using a coupled RANS-FVM model. Applied Ocean Research, 108, 102474.
- Li, Z., Wang, Z., Zhang, Y., & He, G. (2017). Mixing enhancement over dune-inspired surfaces under laminar flow using a coupled RANS-FVM model. International Journal of Heat and Mass Transfer, 115, 1212-1224.
- Zhang, Y., Wang, Z., Li, Z., & He, G. (2019). Filtration improvement over ripple-inspired surfaces under oscillatory flow using a coupled lattice Boltzmann and discrete element method. Chemical Engineering Science, 207, 1330-1342.
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