David Robert Verelst

Aero-Elastic Optimization of a 10 MW Wind Turbine

This article presents the multi-disciplinary wind turbine analysis and optimization tool HawtOpt2 that is based on the open-source framework OpenMDAO, and interfaces to several state-of-the art simulation codes, which allows for a wide variety of problem formulations and combinations of models. In this article simultaneous aerodynamic and structural optimization of a 10 MW wind turbine rotor is carried out with respect to material distribution and outer shape. A set of optimal designs with respect to mass and AEP are presented, which shows that an AEP biased design can increase AEP with 1.5% while a mass biased design can achieve mass savings of up to 20% compared to the baseline DTU 10MW RWT. A newly developed frequency-domain based fatigue model is used to minimise fatigue damage, which achieves up to 8% reduction in the tower bottom fore-aft fatigue damage, with only limited reductions of the aerodynamic performance or increased mass.

Wind tunnel tests of a free yawing downwind wind turbine

This research paper presents preliminary results on a behavioural study of a free yawing downwind wind turbine. A series of wind tunnel tests was performed at the TU Delft Open Jet Facility with a three bladed downwind wind turbine and a rotor radius of 0.8 meters. The setup includes an off the shelf three bladed hub, nacelle and generator on which relatively flexible blades are mounted. The tower support structure has free yawing capabilities provided at the base. A short overview on the technical details of the experiment is given as well as a brief summary of the design process. The discussed test cases show that the turbine is stable while operating in free yawing conditions. Further, the effect of the tower shadow passage on the blade flapwise strain measurement is evaluated. Finally, data from the experiment is compared with preliminary simulations using DTU Wind Energys aeroelastic simulation program HAWC2.

Optimal yaw strategy for optimized power and load in various wake situations

The interaction between nearby wind turbines in a wind farm modifies the power and loads compared to their stand-alone values. The increased turbulence intensity and the modified turbulence structure at the downstream turbines creates higher fatigue loading, which can be mitigated by wind farm and/or wind turbine control. To alleviate loads and maximize power possible strategies such as wake steering, where the turbine is yawed to redirect the wake such that it does not impinge the downstream turbine, have been studied. The work presented here focuses on situations where the wake is nevertheless affecting the downstream turbine, and more specifically how high loads can be avoided by yawing the wake-affected turbine. The analysis is conducted on a 2.3 MW machine, and the flow field is simulated using the Dynamic Wake Meandering model. The study investigates the impact on power and loads for different longitudinal interspacing and turbulence intensities. Optimal yaw strategies are defined for above rated regions where no power loss occurs. The potential load alleviation for different load sensors are studied, but the presentation is focussed on the blade root flapwise fatigue loading. For full wake at 3D interspacing and turbulence intensity of 5 %, around 35 % of load reduction on the 1 Hz Damage Equivalent Loads can be achieved at high wind speeds. Smaller reductions are achieved for higher atmospheric turbulence; the analogue case with 15 % turbulence intensity shows 17 % potential alleviation. The alleviation on the wind turbine lifetime is also calculated and compared for different turbulence intensities and mean wind speeds. Small reductions are achieved for sites with low mean wind speed and high turbulence intensity, but high reductions, of around 19 %, are accomplished in low turbulence intensity with high mean wind speed.

Wind turbine site-specific load estimation using artificial neural networks calibrated by means of high-fidelity load simulations

Previous studies have suggested the use of reduced-order models calibrated by means of high-fidelity load simulations as means for computationally inexpensive wind turbine load assessments; the so far best performing surrogate modelling approach in terms of balance between accuracy and computational cost has been the polynomial chaos expansion (PCE). Regarding the growing interest in advanced machine learning applications, the potential of using Artificial Neural-Network (ANN) based surrogate models for improved simplified load assessment is investigated in this study. Different ANN model architectures have been evaluated and compared to other types of surrogate models (PCE and quadratic response surface). The results show that a feedforward neural network with two hidden layers and 11 neurons per layer, trained with the Levenberg Marquardt backpropagation algorithm is able to estimate blade root flapwise damage-equivalent loads (DEL) more accurately and faster than a PCE trained on the same data set. Further research will focus on further model improvements by applying different training techniques, as well as expanding the work with more load components.

Optimal Aero-Elastic Design of a Rotor with Bend-Twist Coupling

Passive Bend-Twist Coupling (BTC) can be used in blades to alleviate loads and generate more Annual Energy Production (AEP). However, BTC is inherently aero-elastic, thus difficult to incorporate into the design with sequential design process. Multi-disciplinary Design Optimization (MDO) is an attractive approach for overcoming these challenges. This paper presents the re-design of a 100kW BTC rotor using the MDO rotor design package HAWTOpt2. In the preliminary design phase, MDO was used to assess the differences between elastic BTC (i.e. off-axis fibers) and geometric BTC (i.e. sweep). This work found that aero-elastic design optimization without BTC was able to achieve a 16% improvement, then with sweep a 18% improvement and with material coupling a 17% improvement. Due to the reduced stiffness of off-axis fibers, material coupled designs had more difficulty satisfying the tip deflection constraint. The geometric BTC concept was chosen for the final design. The design optimization was repeated with additional manufacturing constraints. The final design achieved a 12% improvement.

In search for a canonical design ABL stability class for wind farm turbines

Production as well as loading of wake exposed wind turbines is known to depend significantly on stability of the Atmospheric Boundary Layer (ABL), which adds a new dimension to design of wind farm turbines. Adding this new aspect in wind turbine design makes the number of design cycle computations to blow up with a factor equal to the number of representative stability bin classes. The research question to be answered in this paper is: Can an ABL stability probability distribution in a meaningful way be collapsed into a representative design stability class as based on a (predefined) confidence level.

Open Access Wind Tunnel Measurements of a Downwind Free Yawing Wind Turbine

A series of free yawing wind tunnel experiments was held in the Open Jet Facility (OJF) of the TU Delft. The ≈ 300 W turbine has three blades in a downwind configuration and is optionally free to yaw. Different 1.6m diameter rotor configurations are tested such as blade flexibility and sweep. This paper gives a brief overview of the measurement setup and challenges, and continues with presenting some key results. This wind tunnel campaign has shown that a three bladed downwind wind turbine can operate in a stable fashion under a minimal yaw error. Finally, a description of how to obtain this open access dataset, including the post-processing scripts and procedures, is made available via a publicly accessible website.