Surrogate modeling a computational fluid dynamics-based wind turbine wake simulation using machine learning

The Wind Farm Layout Optimization problem involves finding the optimal positions for wind turbines on a wind farm site. Current Metahueristic based methods make use of a combination of turbine specifications and parameters, mathematical models and empirically produced power production equations to estimate the energy output of a real wind farm [15]. The overarching variable in any optimisation function is wind speed - this is what used to determine the power generated. Therefore, accurate predictions of wind speeds at specific points across the volume of the site are needed. In this paper, Computational Fluid Dynamics (CFD) was used to simulate a full scale rotating wind turbine blade with fluid (air) at various wind speeds flowing past the turbine. The wake effect can be observed and leads to decrease in wind speeds, as expected. Wind speed at specific x,y and z (3D) coordinates were sampled and used as input to common Machine Learning regression algorithms to create different surrogate models. This was needed as each individual CFD experiment takes approximately 8 hours to complete, so it is not feasible to continuously repeat these simulations inside a metaheuristic optimiser

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 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 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 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 optimization in the future

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 optimization in the future

Reliability analysis of 15MW horizontal axis wind turbine rotor blades using fluid-structure interaction simulation and adaptive kriging model

Over the course of the last four decades, the rotor diameter of Horizontal Axis Wind Turbines (HAWTs) has undergone a substantial increase, expanding from 15 m (30 kW) to an impressive 240 m (15MW), primarily aimed at enhancing their power generation capacity. This growth in blade swept area, however, gives rise to heightened loads, stresses and deflections, imposing more rigorous demands on the structural robustness of these components. To prevent sudden failure and to plan effective inspection, maintenance, and repair activities, it is vital to estimate the reliability of the rotor blades by considering all the forces (aerodynamic and structural dynamics) acting on them over the turbine’s lifespan. This research proposes a comprehensive methodology that seamlessly combines fluid-structure interaction (FSI) simulation, Kriging model/algorithm and Adaptive Kriging Monte Carlo Simulation (AKMCS) to assess the reliability of the HAWT rotor blades. Firstly, high-fidelity FSI simulations are performed to investigate the dynamic response of the rotor blade under varying wind conditions. Recognizing the computationally intensive nature and time-consuming aspects of FSI simulations, a judicious approach involves harnessing an economical Kriging model as a surrogate. This surrogate model adeptly predicts blade deflection along its length, utilizing training and testing data derived from FSI simulations. Impressively, the Kriging model predicts blade deflection 400 times faster than the FSI simulations, showcasing its enhanced efficiency. The optimized surrogate model is then used to estimate the flap wise blade tip deflection for one million wind speed samples generated using Weibull distribution. Thereafter, to evaluate the reliability of the blades, statistical modeling using methods such as Monte Carlo Simulation (MCS), AKMCS is performed. The results demonstrate the faster convergence of AKMCS requiring only 21 samples, as opposed to 1 million samples for MCS with minimal reduction in the precision of the estimated probability of failure (Pf) and reliability index (β). Demonstrated on the backdrop of an IEA-15MW offshore reference WT rotor blade, the proposed methodology underscores its potential to be seamlessly incorporated into the creation of WT digital twins, due to its near real-time predictive capabilities for Pf and β assessments.Reliability analysis of 15MW horizontal axis wind turbine rotor blades using fluid-structure interaction simulation and adaptive kriging modelacceptedVersio