Historically, the first wind turbines were dominantly downwind turbines, for which the rotor was placed behind the tower, as seen from the incoming wind. This turbine configuration was considered safer than the alternative upwind configuration with the rotor in front of the tower since the rotor blades would bend away from the tower under turbine operation. Early research, mainly by NASA and associated partners, compared the downwind rotor configuration with the upwind configuration. showed that the wake behind the tower caused significantly higher cyclic flapwise blade root loads in the downwind configuration compared to the loads of the upwind rotor configuration. However, neither for the flapwise mean blade root loads nor for the edgewise blade root loads could differences between the two configurations be shown.

Many residents living near early downwind wind turbines reported high noise levels and especially the high unsteadiness, a “thumping” sound being reported as a nuisance (). The rotor blades passing through the tower wake caused high low-frequency noise and amplitude modulation. Measurements in an anechoic wind tunnel by demonstrated that downwind rotors on a lattice tower radiated more noise than downwind rotors on a tubular tower due to the narrower and deeper tower wake. Upwind rotor configurations, on the other hand, were found to be significantly less noisy. Found to be overall advantageous, upwind rotor configurations dominated industrial applications, as well as the focus of research efforts during the 1990s and 2000s.

Cost-driven industrial designs prefer larger rotor areas to capture more energy. The rotor blades for modern-sized upwind wind turbines are designed under a constraint of maximum blade tip deflection to avoid a collision of the blades with the tower. Aiming to eliminate the tip deflection constraint for modern-sized wind turbines under normal operation, the downwind configuration is currently coming into research focus again.

Advances in wind turbine noise mitigation techniques since the 1980s, as well as airfoil design, could overcome the previously reported noise issues and bring them to an acceptable level. successfully eliminated the tower shadow effect on the fatigue loads by using a fairing. While the fairing is a rather costly device to implement, the study further showed the potential that fatigue loads can be significantly reduced by a reduced flapwise stiffness, alleviating loads by blade deformation.

used an optimization framework to compare the levelized cost of energy (COE) of land-based upwind and downwind turbines. The study included turbines of different wind classes, rated power, and rotor diameter. Modest cost savings could be achieved for the downwind configuration compared to the upwind configuration for wind turbines of wind class III. Blade mass savings had to offset the higher tower cost originating from the increased tower bottom bending moment as the gravity overhanging moment of the rotor nacelle assembly coincides with the moment from the thrust force.

In a system-level design study for large rotors, showed that coned downwind rotors significantly reduce peak blade loads during operation but have a lower annual energy production (AEP) compared to a coned upwind configuration of the same size. While the group predicts larger main bearing peak loads for the downwind configuration related to blade length, mass, and cone angle, they suggest that the increased tower loads observed by other groups could be overcome by properly balancing the nacelle on the tower.

A reduced edgewise damping for a downwind configuration compared to an upwind configuration was identified by , leading to significantly higher edgewise loads in the downwind configuration than in the upwind configuration. In a following study on a 2.1 MW turbine, showed that large downwind cone angles could reduce the edgewise damping further as the out-of-plane contribution of the edgewise mode shapes is decreased. A significantly reduced tower torsional stiffness, on the other hand, e.g., a lattice tower configuration, would benefit the downwind configuration.

Aligning the blades with the loading direction of aerodynamic forces, gravity, and centrifugal force is an opportunity for the downwind configuration to significantly reduce flapwise bending loads, instead loading the blade in axial tension. Such a load distribution is achieved by adjusting the cone angle and blade prebend. These downwind rotors with so-called “load alignment” have been suggested as an option to reduce blade mass significantly, utilizing the large cone angles and downwind prebend from for a 13.2 MW wind turbine. The study also indicated mass savings compared to the conventional upwind rotor when the blade length is increased to compensate for energy production losses.

used an optimization framework to compare the cost efficiency of an upwind configuration with a downwind configuration and a downwind configuration with “load alignment”. The analysis for a 10 MW turbine showed difficulties in reaching a more cost-efficient design for the downwind configurations than the conventional upwind configuration due to other component costs.

Downwind configurations with a passive wind direction alignment are often proposed. Such yaw systems could be cost efficient as they simplify the turbine control and reduce operation and maintenance costs as they could purely be used for cable unwinding. However, showed in an example of a 2.1 MW turbine with a tilted rotor that such systems align passively at high yaw angles for high wind speeds resulting in significant power loss. The study concluded that tilt angle, cone angle, and blade stiffness would need to be specifically designed for a free yawing downwind configuration. This would expose additional constraints on a downwind turbine design, while the benefit in terms of a cost-efficient turbine is questioned.

The cost-efficient design of wind turbines has been approached to an increasing extent by the use of optimization frameworks. Over the years, rotors designed for the maximum efficiency result in the most cost-efficient turbine designs have been questioned. Optimizing a conventional 10 MW upwind turbines for the lowest cost of energy, showed that designing the rotor for minimum cost instead of maximum annual energy production results in rotors with larger chord, higher thickness, and lower AEP. Higher absolute thickness could utilize higher stiffness with less material resulting in the lower cost compensating the AEP loss from the less efficient, thicker airfoils.

Lower rotor loads could potentially result in the possibility to increase the rotor length and therefore increase the overall power capture. This could be a more cost-efficient rotor than a traditional design approach also for upwind turbines. tried, therefore, to design a low-induction rotor for a 10 MW wind turbine with an optimization framework in which the blade shape was designed with the common aerodynamic parameters, such as chord, twist, and airfoil thickness. Their work showed that maximum AEP solutions might be achievable with low-induction rotors, but the minimum cost solutions might be different from the maximum AEP solutions. It was seen to be very dependent on the cost model if the higher AEP could pay for the increased rotor diameter.

investigated design trends by an optimization of power capture based on 1D momentum theory. Their work indicated that the maximum power capture is achievable by a larger rotor diameter and operation at a lower power coefficient (Cp) than the maximum. For a conventional upwind turbine, this is achieved by so-called thrust clipping or peak shaving. The peak shaving is a control feature that reduces extreme flapwise loads and also increases the minimum blade tip to tower clearance in the upwind configuration while sacrificing AEP.

This paper shows how rotor design trends for a downwind configuration differ from design trends for an upwind rotor configuration due to differences in design loads inherent to the configuration. Full design load bases (DLBs) are calculated to evaluate the impact of the rotor design trends on the turbine loads, AEP, and estimated cost of energy. The work in this paper is based on the specific example of the commercial S111 2.1 MW turbine. A low-fidelity optimization tool is used to redesign the commercial rotor for an upwind and a downwind configuration, generating the aerodynamic planform and flap- and edgewise stiffness distribution. High-fidelity tools are used to generate inputs for load calculations, the full load base analysis, mass, and AEP calculations. An industrial grade cost-scaling model based on the load calculations is used to estimate the impact of the designs on the cost of energy. Analyzing the impact of the redesigned rotor on load and cost allows us to discuss the potential of a downwind rotor configuration compared to an upwind rotor configuration in greater detail. The discussion shows the impact of the cost structure and other design choices like the tubular tower influencing the potential of the downwind configuration in comparison to the upwind configuration. The results show that a pure rotor redesign will hardly achieve a lower cost of energy for the downwind configuration than for the upwind configuration for the specific chosen example turbine.

This work aims to compare design trends for an upwind configuration of an existing turbine with a downwind configuration from a cost and mass perspective. The chosen example turbine is the Suzlon S111 2.1 MW turbine, a commercial upwind turbine. The turbine is designed for wind class IIIA with glass fiber blades and a rotor diameter of 112 m on a 90 m tubular tower. The turbine is pitch-regulated with a variable speed generator. The shaft is tilted, the rotor is coned, and the blades are pre-bent. All three geometrical parameters increase the blade tip to tower clearance in the upwind configuration.

For this turbine, a new baseline rotor blade is defined, inspired by the commercial blade, which is adapted to the framework. For the baseline rotor, an upwind turbine configuration is generated, called S111uw. Additionally, a downwind baseline turbine configuration is defined with the baseline rotor called S111dw. The downwind configuration utilizes the same cone and tilt angle, both increasing blade tip to tower distance. Since the blade prebend of the rotor is towards the blade pressure side, the prebend decreases the blade tip to tower distance in the downwind configuration. Three rotor redesigns are made. For the upwind configuration, a blade planform and internal structural redesign is made. The design is called S111uw PF. For the downwind configuration, two scenarios are regarded. Firstly, a pure blade material reduction is performed, called S111dw STR. This corresponds to a configuration change from an existing upwind configuration into a downwind configuration while keeping the blade molds but saving blade material. Secondly, a blade planform and structure redesign in the same manner as for the upwind redesign is called S111dw PF. Table  shows a summary of the design configurations regarded, as well as the name indicators used throughout the study.

The rotor design procedure uses a low-fidelity optimization tool to create a blade planform and stiffness distribution. The planform and stiffness distribution are afterwards matched within the HAWTOpt2 framework to create a full HAWC2 () setup for aeroelastic load calculations. The higher-fidelity tools are not used for optimization but exclusively for an evaluation of the design resulting from optimization with the low-fidelity tool. For all designs, a full design load basis is calculated. The loads are used to calculate the failure index of the blades and to evaluate if the redesigns are acceptable. From the tower loads, the required tower material is calculated. Finally, the costs of all five designs are calculated with a load and mass scaling cost model. This design procedure is conceptually outlined in Fig. .

The following section presents the resulting design configurations regarding the planforms and resulting blade masses. Further, the design-driving loads and the resulting changes in turbine costs and COE are presented. All results are shown relative to the S111uw design configuration as the data are confidential.

Within this study, the COE reduction potential for the Suzlon S111 2.1 MW turbine has been estimated for changing the original upwind configuration into a downwind configuration. A design framework including a low-fidelity in-house optimization tool has been used to redesign rotors for upwind and downwind configurations. A full design load basis has been simulated for every design configuration. The design configurations have been evaluated by a COE estimation.

New planforms were optimized for upwind and downwind configurations for minimum blade mass under the constraint of a minimum AEP. The new planforms were shown to have higher thickness over chord ratios inboard, utilizing higher stiffness with less material. This design trend agrees well with findings by and .

The downwind designs were generally subject to lower flapwise blade root moments than the comparable upwind designs due to the coning direction, as also proposed by, for example, and . As a result, lower blade mass could be achieved for downwind configurations than for upwind configurations. The S111dw PF design showed, for example, 4.6 % lower blade mass than the S111uw PF design.

The load saving on the blade in the downwind configuration is offset by an increase in the tower bottom bending moment as the gravity overhanging moment of the rotor nacelle assembly is aligned with the thrust force, as also shown by . As a result, around 10.5 % higher tower masses were seen in the direct comparison of the S111uw PF design and the S111dw PF design.

The downwind configurations are subject to a lower AEP production due to the coning direction. This effect has also been observed by, for example, and . In the direct comparison, the AEP of the S111dw PF is 2.04 % lower than the AEP of the comparable S111uw PF.

Lower rotor and nacelle costs can be achieved by the downwind designs. However, the downwind designs also come with higher tower and foundation costs. Overall, the downwind configurations of comparable rotor size achieve a lower total turbine cost than the upwind design configuration. The difference in cost is due to the lower OPEX cost and does heavily depend on the cost model. Overall, the lower turbine cost does not compensate for the loss in AEP. The lowest COE level is achieved by the S111uw PF design configuration which achieves a significant mass and load reduction for a small sacrifice in AEP compared to the baseline.

This study has shown, for the example of the Suzlon S111 2.1 MW turbine, that a downwind rotor configuration could be achieved with lower total turbine costs than the comparable upwind configuration. Due to a lower AEP of the downwind configurations, the upwind configuration, on the other hand, showed overall the lowest COE. A downwind configuration would, therefore, be the configuration to choose on a cost-driven turbine market, while for COE-driven markets, the upwind configuration would be chosen.

These results depend on the very baseline-specific cost model. Scaling the OPEX with the AEP has been the only cost driver for the OPEX which results in the lower turbine costs for the downwind configuration. It could be expected that the higher fatigue load of the downwind configuration would increase the material wear, but this does not enter the OPEX model.

It should also be highlighted that the costs are effected by the chosen optimization approach, namely a mass minimization under AEP constraint. This does not give the true optimal solution in the sense of cost of energy. However, it does show the influence of the observed design trends on the turbine cost and the cost of energy.

The cost model generally depends on the loads simulated. This comes with uncertainty due to the seed number, the seeds themselves, and the assumptions of the wind field inclination angle. In the case of the downwind configuration, additionally, the dynamic effect of the tower shadow is not captured correctly within the HAWC2 simulations. Within HAWC2 the tower shadow model for downwind configurations is a pure deficit model and the increased vorticity behind the tower is not reflected. It can be expected that especially flapwise blade root and tilt-related fatigue loads are underpredicted. Further research would need to be done to quantify the impact of this effect.

Generally, fatigue loads should be part of the design process in future work. In the chosen approach, fatigue loads are not regarded in the design process and hardly reflected in the cost model. This might be a valid assumption in the upwind configurations, but for downwind configurations, this approach needs to be proven. Due to the tower shadow effect, as well as a possible decrease in edgewise damping, it might be possible that rotors of downwind configurations are driven by edgewise fatigue loads rather than flapwise extreme loads.

Prescribing the induction distribution in the optimization is a major restriction of the chosen design approach. The resulting chord and twist distributions are therefore very similar. The induction should be a design variable in future work as unloading the tip might allow for increases in rotor diameter and therefore AEP increase. The latter does not just hold for the downwind configuration but also for the upwind configuration.

Prescribing the induction distribution did, however, have the advantage that the load scaling approach was possible. Scaling loads from the BEM code loads to the extreme loads has decreased computation time significantly. A drawback of the load scaling approach is that a change in aerodynamic damping is not reflected. For the downwind configuration, the flapwise stiffness could be significantly reduced, while the edgewise stiffness had to be increased; the edgewise whirl modes can, therefore, be expected to increase in damping due to the frequency placement of the edgewise frequency compared to the second yaw frequency. An increase in damping decreases the blade extreme loads. The effect of the loads has been observed in the downwind designs, but there is no feedback within the optimization reflecting the change in damping. In future work, the framework would need to be enhanced with either time consuming load calculations or with a set of transfer functions that can transfer a wind field to extreme loads from a linearized turbine model. Such a linearized turbine model could be extracted, for example, from HawcStab2, which uses these models for eigenvalue analysis. In this case, a representative wind field could be used that represents extreme loads from a simulation set with a much larger seed number and with known uncertainty. This would decrease the computational time drastically while achieving reasonable results.

As designed for fast conceptual rotor design studies, the chosen design approach with STORM is limited in finding truly optimal solutions in the sense of the lowest cost of energy. The reasons are, on the one hand, the simplified structural model. On the other hand, the aerodynamic planform is limited by the assumption of a prescribed target induction, thus, allowing hardly any freedom of chord variations. Further, fixing the rotor diameter limits the investigations of the cost of energy for the different designs as it is fixed to one point of investigation. With the chosen approach and the AEP constraint, it is only possible to find the lowest blade mass for a certain targeted AEP at a specific blade length. With these limitations the resulting designs are not truly optimal in the sense of the lowest cost of energy. However, the chosen approach does allow for the comparison of design trends between the upwind and the downwind configurations.

The COE estimation and therefore success criteria of the downwind concept do also depend on the cost share between the different components. Since in the chosen example turbine the rotor and the tower are similar in the CAPEX share, it is difficult in the downwind configuration to offset the increased tower cost with savings on the rotor. If the baseline had a comparably more expensive rotor and a cheaper tower, the downwind configuration would be more competitive. Possible scenarios could be lower steel prices or higher blade material prices.

Another possibility to increase the competitiveness of the downwind configuration would be a change in the tower configuration, such as a wired tower for which wires are a cheap measure to take the bending loads. Alternatively, a low-labor-cost market could give the options of low tower costs with lattice or hybrid-lattice towers which generate bending stiffness from the increased footprint of the tower rather than large tower wall thicknesses for a tubular tower. These options could make the downwind configuration competitive as the cost share of the tower decreases. However, the cost model with the chosen baseline is not able to reflect such significant design changes.

Compensating the AEP loss in the downwind configuration with a larger rotor area could be an option to decrease the COE. Nevertheless, this does also increase the turbine cost not just due to an increased rotor diameter and therefore rotor mass but also to mass-related loads such as tilt loads and tower base loads. The rotor diameter has not been part of the rotor design as the cost model is very specific and does not reflect large differences from the baseline. Especially for components such as generator or gearbox which are not available in any possible configuration but are bought as “off-the-shelf” components, the linear cost scaling is insufficient. A rotor diameter increase of 4 % has been investigated, indicating the potential to decrease the COE for the downwind configurations further with an increase in rotor diameter. With the investigated increase of 4 %, the cost of energy could only be reduced to -1.4 % compared to the baseline cost. The increase in AEP was offset by the higher costs associated with increased rotor and tower mass, as well as increasing nacelle costs. Generally, future investigations should be performed more thoroughly that include the rotor diameter in the optimization. However, it is not expected that the conclusions for the example turbine will change significantly as margins for loads and tower clearance are low.

Future work should also consider a redesign of the nacelle for the better balancing of the rotor mass on the tower for the downwind configuration, as suggested by . However, it should be kept in mind that the upwind configuration will always be beneficial in terms of tower bottom bending moment. Masses that can not be relocated for balancing such as rotor, hub, pitch, and yaw-system-related masses account for around 50 % of the mass of the rotor nacelle assembly. Extending the lengthwise dimensions of the remaining components to relocate the center of gravity might be more expensive than the higher tower costs of downwind configurations.

It should not be forgotten in the discussion of the cost efficiency of downwind configurations that simple control features such as peak shaving, as suggested by , might benefit the upwind configuration in the same manner as the configuration change: the tower clearance is increased, and the flapwise blade root moment is decreased with a penalty on AEP. Since the tower bottom load does not in this case increase, as in the case of the downwind configuration, such an upwind configuration might outperform a downwind configuration in terms of COE.

Overall, the study shows that a downwind configuration of the chosen example 2.1 MW turbine would need to be pushed to much larger rotor sizes than investigated. Further, low-cost measures would need to be chosen to carry the increased tower loads if the downwind configuration should become competitive in terms of COE with the comparable upwind configuration.

The optimization framework would need to be extended to be able to capture the design changes regarding the rotor, but also different tower configurations need to be included. To be able to evaluate such changes, a more comprehensive cost model is required to do a fair comparison of the designs.

It can be concluded from the study that it will be difficult to design a downwind configuration in the 2 MW range which can show significant economic benefits unless the design targets a different market than the upwind configuration or more drastic changes are made than just a rotor redesign and a structural redesign of the tower.