Computational study of vertical-axis MHK turbines using a coupled flow-sediment-turbine modeling approach

TL;DR

The paper develops a coupled LES–bed morphodynamics framework to assess vertical-axis turbine (VAT) performance in riverine and marine environments, using an actuator surface model (ASM) for blade effects and CURVIB/IBM for bed-fluid interactions. By contrasting rigid-bed and live-bed conditions across a range of tip-speed ratios ($TSR$), the study reveals that sediment dynamics accelerate wake recovery and alter turbulence distribution, with bed deformation generating near-bed jets that enhance mixing but reduce turbine efficiency by a few percent. Sediment transport evolves toward a dynamic equilibrium, forming scour holes and downstream sand bars whose depths and heights grow with $TSR$, and the bed shear stress correlates strongly with morphodynamic changes. The approach achieves substantial computational savings ($ ext{ASM cost} ightarrow ext{≈}4 ext{%}$ of turbine-resolving simulations) while capturing key flow-sediment-turbine interactions, offering a practical tool for environmental assessment and array design of VATs.

Abstract

We present a coupled large-eddy simulation (LES) and bed morphodynamics study to investigate the influence of sediment dynamics on the performance of a utility-scale marine hydrokinetic vertical-axis turbine (VAT) parametrized by an actuator surface model. By resolving the two-way interactions between turbine-induced flow structures and bed evolution, the study offers insights into the environmental implications of VAT deployment in riverine and marine settings. A range of tip speed ratios is examined to evaluate wake recovery, power production, and bed response. The actuator surface method (ASM) is implemented to capture the effects of rotating vertical blades on the flow, while the immersed boundary method accounts for fluid interactions with the channel walls and sediment layer. The results show that higher TSRs intensify turbulence, accelerate wake recovery over rigid beds, and enhance erosion and deposition patterns beneath and downstream of the turbine under live-bed conditions. Bed deformation under live-bed conditions induces asymmetrical wake structures through jet flows, further accelerating wake recovery and decreasing turbine performance by about 2%, compared to rigid-bed conditions. Considering the computational cost of the ASM framework, which is nearly $4\%$ of the turbine-resolving approach, it provides an efficient yet robust tool for assessing flow-sediment-turbine interactions.

Figures (12)

• Figure 1: Schematic of the channel and turbine in which dimensions are normalized by the rotor diameter ($D=2m$). As seen in (a), the turbine is located at $6.25D$ downstream from the channel inlet. The flow direction aligns with the positive x-axis, while the z-axis indicates the vertical direction. The channel has a total length of $18.75D$, a width of $2.5D$, and a flow depth of $1.92D$. In (b), the turbine blade dimensions are shown along with the computational grid, where the blue mesh represents the structured grid used to discretize the flow field, and the brown unstructured triangular cells discretize the bed. For visual clarity, the former and latter grid systems are coarsened by a factor of $10$ and $5$, respectively. (c) illustrates the top view of the actuator surface embedded within the blade geometry for clarity, and (d) displays the unstructured triangular mesh cells defining the actuator surface.
• Figure 2: Color maps of hydrodynamic results under rigid-bed conditions (cases 1–3). Panels (a), (d), and (g) show the nondimensional instantaneous vorticity magnitude from the side view at the channel centerline, corresponding to TSR = 1.6, 2.0, and 2.4, respectively. Panels (b), (e), and (h) present the TKE from the side view at the channel centerline for TSR = 1.6, 2.0, and 2.4, respectively. Panels (c), (f), and (i) represent the TKE from the cross-plane located 2D downstream of the turbine for TSR = 1.6, 2.0, and 2.4, respectively. Flow direction is from left to right.
• Figure 3: Color maps of hydrodynamic results under rigid-bed conditions (i.e., cases $1$ to $3$). (a), (c), and (e) depict the nondimensional mean velocity magnitude from the top view at the mid-depth elevation of the turbine blades. (b), (d), and (f) shows the nondimensional mean velocity magnitude from the side view at the channel's centerline. The first, second, and third rows correspond to TSR $= 1.6$, $2.0$, and $2.4$, respectively. The flow is from left to right.
• Figure 4: Color maps of mean streamwise velocity magnitude normalized by the bulk velocity ($=1.5 m/s$) under rigid-bed (left column) and live-bed (right column) conditions, shown over vertical planes along the centerline of the channel. The live-bed results are presented together with the corresponding bed elevation color maps at the equilibrium state. The first, second, and third rows correspond to TSR = 1.6, 2.0, and 2.4, respectively. Flow direction is from left to right.
• Figure 5: Color maps of the computed TKE normalized by $U_{\infty}^2$ at an elevation of $z=0.35D$ above the bed and under rigid-bed (left column) and live-bed (right column) conditions. The first, second, and third rows correspond to TSR = 1.6, 2.0, and 2.4, respectively. Flow direction is from left to right.