arXiv subjects

Thomas D. Dunstan

Publications and source records attributed to Thomas D. Dunstan.

3 recordsLinked to original sources

arXiv2023

An analytical model of momentum availability for predicting large wind farm power

Turbine-wake and farm-atmosphere interactions influence wind farm power production. For large offshore farms, the farm-atmosphere interaction is usually the more significant effect. This study proposes an analytical model of the `momentum availability factor' to predict the impact of farm-atmosphere interactions. It models the effects of net advection, pressure gradient forcing and turbulent entrainment, using steady quasi-1D flow assumptions. Turbulent entrainment is modelled by assuming self-similar vertical shear stress profiles. We used the model with the `two-scale momentum theory' to predict the power of large finite-sized farms. The model compared well with existing results of large-eddy simulations (LES) of finite wind farms in conventionally neutral boundary layers. The model captured most of the effects of atmospheric boundary layer (ABL) height on farm performance by considering the undisturbed vertical shear stress profile of the ABL as an input. In particular, the model predicted the power of staggered wind farms with a typical error of 5% or less. The developed model provides a novel way of instantly predicting the power of large wind farms, including the farm blockage effects. A further simplification of the model to analytically predict the 'wind extractability factor' is also presented. This study provides a novel framework for modelling farm-atmosphere interactions. Future studies can use the framework to better model large wind farms.

physics.flu-dyn↗

arXiv2023

Data-driven modelling of turbine wake interactions and flow resistance in large wind farms

Turbine wake and local blockage effects are known to alter wind farm power production in two different ways: (1) by changing the wind speed locally in front of each turbine; and (2) by changing the overall flow resistance in the farm and thus the so-called farm blockage effect. To better predict these effects with low computational costs, we develop data-driven emulators of the `local' or `internal' turbine thrust coefficient $C_T^*$ as a function of turbine layout. We train the model using a multi-fidelity Gaussian Process (GP) regression with a combination of low (engineering wake model) and high-fidelity (Large-Eddy Simulations) simulations of farms with different layouts and wind directions. A large set of low-fidelity data speeds up the learning process and the high-fidelity data ensures a high accuracy. The trained multi-fidelity GP model is shown to give more accurate predictions of $C_T^*$ compared to a standard (single-fidelity) GP regression applied only to a limited set of high-fidelity data. We also use the multi-fidelity GP model of $C_T^*$ with the two-scale momentum theory (Nishino \& Dunstan 2020, J. Fluid Mech. 894, A2) to demonstrate that the model can be used to give fast and accurate predictions of large wind farm performance under various mesoscale atmospheric conditions. This new approach could be beneficial for improving annual energy production (AEP) calculations and farm optimisation in the future.

physics.flu-dyn↗

arXiv2020

Two-scale momentum theory for time-dependent modelling of large wind farms

This paper presents a theory based on the law of momentum conservation to define and help analyse the problem of large wind farm aerodynamics. The theory splits the problem into two sub-problems; namely an 'external' (or farm-scale) problem, which is a time-dependent problem considering large-scale motions of the atmospheric boundary layer (ABL) to assess the amount of momentum available to the ABL's bottom resistance at a certain time; and an 'internal' (or turbine-scale) problem, which is a quasi-steady (in terms of large-scale motions of the ABL) problem describing the breakdown of the ABL's bottom resistance into wind turbine drag and land/sea surface friction. The two sub-problems are coupled to each other through a non-dimensional parameter called 'farm wind-speed reduction factor' or 'farm induction factor,' for which a simple analytic equation is derived that can be solved iteratively using information obtained from both sub-problems. This general form of coupling allows us to use the present theory with various types of flow models for each scale, such as a numerical weather prediction (NWP) model for the external problem and a computational fluid dynamics (CFD) model for the internal problem. The theory is presented for a simplified wind farm situation first, followed by a discussion on how the theory can be applied (in an approximate manner) to real-world problems; for example, how to estimate the power loss due to the so-called 'wind farm blockage effect' for a given large wind farm under given environmental conditions.

physics.flu-dyn↗