Re-design of an upwind rotor for a downwind configuration: design changes and cost evaluation

Within this work, an existing model of a Suzlon S111 2.1MW turbine is used to estimate potential cost savings when the conventional upwind rotor concept is changed into a downwind rotor concept. A design framework is used to get realistic design updates for the upwind configuration as well as two design updates for the downwind configuration, including a pure material cost-out on the rotor blades and a new planform design. A full design load basis according to the standard has been used to evaluate the impact of the redesigns on the loads. A detailed cost model with load scaling is used to estimate the impact 5 of the design changes on the turbine costs and the cost of energy. It is shown that generally lower blade mass can be achieved with the downwind configurations of up to 5% less than the upwind redesign. Compared to an upwind baseline, the upwind redesign shows an estimated cost of energy reduction of 2.3% where the downwind designs achieve a maximum reduction of 1.3%.

The cost-efficient design of wind turbines has been approached to a growing extent by the use of optimization frameworks.
Over the years it has been questioned that rotors designed for the maximum efficiency result in the most cost-efficient turbine designs. Optimizing a conventional 10MW upwind turbines for the lowest cost of energy (COE) Bottasso et al. (2016) showed, that designing the rotor for minimum cost instead of maximum annual energy production (AEP) results into rotors with larger chord, higher thickness and lower AEP. Higher absolute thickness could utilize higher stiffness with less material resulting in 70 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. Bottasso et al. (2015) tried therefore to design a low induction rotor for a 10MW wind turbine with an optimization framework where the blade shape was designed with the common aerodynamic parameters, such as chord, twist and airfoil thickness. Their work 75 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. Loenbaek et al. (2019) 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 lower cp than 80 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 as well as it increases the minimum blade tip to tower clearance in the upwind configuration while sacrificing AEP. This paper evaluates the specific example of the Suzlon S111 2.1MW turbine the potential of a downwind turbine configuration compared to the original upwind turbine configuration regarding mass and cost reduction. It is shown that a 5% lower rotor 85 mass can be achieved in the downwind configuration than in the upwind configuration. Despite higher tower and foundation costs the turbine specific cost model indicates lower capital expenditures (CAPEX) for the downwind configuration than the upwind configuration. Due to the difference in AEP, the upwind configuration is the most cost efficient configuration with a 1.0% lower COE.
This work aims to compare 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 2.1MW turbine, a commercial upwind turbine. The turbine is designed for wind class IIIA, with glass fiber blades, a rotor diameter of 112m on a 90m tubular tower. The turbine is pitch regulated with a variable speed generator. The shaft is tilted, the rotor is coned and the blades are prebend. All three geometrical parameters increase the blade-tip to tower clearance in the upwind configuration. 95 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 100 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 of 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 called S111dw PF. Table 1 shows a summary of the design configurations regarded as well as a name 105 indicator used throughout the study.
The rotor design procedure uses a low fidelity optimization tool to create a blade planform and stiffness distribution. The 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 Figure 1.

Baseline blade in BECAS
The baseline blade is set-up in BECAS (a 2D cross-sectional analysis tool, Blasques (2011) commercial blade to assure the baseline is reasonable and fairly close to the commercial blade. The same has been done with turbine eigenfrequencies and damping, as well as the design driving loads for blades, main bearing, and tower.

Design load basis and controller definition.
Full design load bases are simulated with HAWC2 (version 12.7) according to the IEC-standard 61400-1 Edition 3 (IEC (2014)). The interpretation of the design load basis by the Technical University of Denmark (DTU), described by Hansen et al. 130 (2015), is used. For the downwind configuration the load simulations are conducted with an inflow inclination angle of 0 • .
The combination of positive flow inclination angle and turbine tilt was seen to be beneficial in downwind configurations by Wanke et al. (2019a). It is therefore assumed to be a more realistic scenario to simulate wind fields without inclination angle for downwind configurations.
The annual energy production (AEP) is calculated for all designs. It is calculated from the normal operation load case with 135 six turbulence seeds, for all configurations without inclination or yaw angle. The turbulence intensity is according to the IECstandard class A.
For all load calculations, the controller set-up from DTU (Hansen and Henriksen (2013)) is used in this study with two additional features. The controller is for pitch regulated variable speed turbines with partial and full load regions. Optimal C ptracking is used in the partial load region and a constant torque strategy in the full load region. The detailed description of 140 the controller can be found in Hansen and Henriksen (2013) and the source code is freely available online (https://github.com/ DTUWindEnergy/BasicDTUController). The controller has been automatically tuned using a pole placement routine implemented in HAWCStab2 (Hansen (2004)), and which is described in more detail by Tibalidi et al. (2014). Different events can be initiated from the main controller, such as start-up and shut-down or failure situations.
Start-up and shut-down pitch speed in the implemented routines of the DTU-controller need different values for downwind 145 configurations than comparable upwind configurations. The moment due to thrust force and the gravity overhanging moment of the rotor nacelle assembly both increase the tower bottom bending moment. Start-up routines, especially at high wind speeds, need to have a lower pitch speed in downwind configurations than the comparable upwind configurations. Shut-down routines, especially during gusts, have to be of faster pitch speed in the downwind configuration. Both adjustments have to be made to unload the tower bottom.

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For a control routine that reflects an industrial controller, three failure scenarios are adapted. Firstly, the failure scenario of one blade getting stuck at a current pitch angle, the pitch angle of one blade is kept constant at the current pitch angle at the time of failure. The deviation of the pitch angle from the setpoint initiates a stop routine of the turbine. Secondly, the pitch run away (DLC 2.2p) is not included, since the failure mode is prevented by the type of pitch actuators used. Thirdly, for the scenario of a parked turbine with high yaw errors the wind field is interpreted as a wind direction change of 360 • over 570 seconds.

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To eliminate fault cases from the design driving loads, and to stay similar to an industrial controller, two additional control features are implemented as separate dlls manipulating the output or input from the controller to HAWC2, for practical reasons.
The first addition is a thrust control aiming to reduce fluctuations of the thrust. The second addition is a conditional stop routine avoiding operation at high yaw errors and high wind speeds. The following explains the two additions in more detail. to avoid high loads from turbulence (lower limit) and high power losses (upper limit). The filtered wind speed of the nacelle anemometer is used for wind speed dependent gain scheduling to guarantee a smooth transition between active and not active thrust control.

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The conditional stopping routine triggers the turbine stop, as soon as the filtered wind speed and the filtered wind direction are above a certain threshold. For practical reasons of implementation, the emergency stop is triggered.

STORM -Optimization
The re-design of the rotor blades is performed using the in-house code STORM (Suzlon Turbine Optimization fRaMework).
The code is aimed at fast conceptual rotor design optimization studies, and couples steady aerodynamic AEP considerations, 170 with a simplified blade structural estimation. The code, written in Matlab, is organized as a nested optimization problem. The outer optimization loop controls the blade geometrical planform, and minimizes the blade mass subject to: linear constraints on the geometrical design variables, non-linear constraints on minimum AEP, and feasibility of all the inner optimization problems (Eq. 1).
In this study, the blade geometry design variables are limited to four spline control points that set the thickness-over-chord 175 (TOC) ratios in fixed points along the blade span. The geometry at the blade root is fixed up to the point of maximum chord for all configurations. For each iteration of the outer optimization loop, six steps are taken; they are described in the following sections, and briefly consists of: 1. The blade TOC spline is defined from the control points (the four design variables).
2. The blade geometrical planform is outlined in terms of chord, twist and thickness distribution. An inner optimization 180 returns the chord distribution that minimizes the squared difference from a target axial induction distribution.
3. Steady operational loads and the power curve are retrieved from a standard steady BEM formulation. An inner optimization sets the pitch angle to maximize aerodynamic power, subject to limitation on maximum: power, thrust, aerodynamic flapwise bending moment, and angle of attack (for stall considerations).
4. The steady BEM loads are scaled to extreme loads to be used in the structural optimization.
185 5. The blade structural properties are determined solving a fast low-fidelity structural optimization problem. The blade structure is simplified to two symmetric glass fiber spar-caps joined by an ellipse, Fig. 3. The inner optimization sets the thickness and width of the spar cap, and the ellipse thicknesses to minimize the blade static mass moment, subject to constraints on: maximum strain, maximum deflection, maximum linear buckling index, and design variables range.
6. Finally, the outer loop optimization objective function is evaluated. The estimated blade mass is here taken as objective 190 function, and minimum AEP output is enforced as a non-linear constraint.

Outer optimization loop
The outer optimization problem (Eq. 1) is solved using the Matlab Pattern Search method (G. Kolda (2014)). The algorithm is set up to perform a complete search and pooling around the current point. A list of all formula symbols can also be found in Table A1 in the appendix.
The design variables are here the four thickness-over-chord (TOC) control points ratios. Linear constraints on the design 200 variables are enforced to ensure they maintain within reasonable ranges, and that monotonically decreasing values are selected from root to tip. The objective function consists for this problem of minimizing the estimated blade mass, subject to non-linear constraints to: reach a minimum AEP output (as derived from the BEM steady power curves) and ensure feasibility in all the inner optimization problems. Once the iteration TOC control points are fixed, the TOC distribution along the blade span is outlined with a Piecewise Cubic Hermite Interpolating Polynomial. A wind speed in the below-rated variable speed range is chosen, and the target axial induction distribution for the blade at that wind speed is fixed as an input. Similarly, also the angles of attack at which the airfoils are expected to operate at that wind speed point are also fixed. Both the target induction and the target angles of attack of the airfoils could in principle also be driven by the outer optimization loop but are considered as fixed inputs in this study.

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With the given input set (TOC, target induction, target AoAs) the blade geometry is then retrieved in terms of chord, twist angle, and thickness for each section along the blade span. The chord is retrieved by solving a set of independent minimization problems (Eq. 2), one for each section along the blade span. The optimization objective is to minimize the square error between the target axial induction for that section, and the current induction, function of chord, subject to a linear constraint on the minimum and maximum chord.
In the current iteration the axial induction is retrieved from a steady BEM formulation, following Ning's implementation (Ning (2014)), where the BEM convergence is solved by minimizing a residual function of the flow angle. Once the chord is fixed, the twist angle is simply set as the difference between the converged flow angle returned by the BEM, and the input angle of attack for that section (minus eventually a chosen constant reference pitch angle). 220

Steady loads and power curves
Given the blade geometrical definition as from the step above, the steady power and loads curves are then determined running a standard steady BEM formulation, Hansen (2008), sweeping wind speeds between cut-in and cut-out. From the steady power curve, the Annual Energy Production (AEP) is retrieved accounting for the chosen wind speed distribution.
The operational pitch angle at each wind speed is retrieved from a simple optimization loop, where the objective is to 225 maximize the aerodynamic power output, subject to constraints on: maximum power (the aerodynamic rated power), maximum thrust force, maximum aerodynamic blade flapwise bending moment, and minimum "stall distance" (Eq. 3). The latter is 9 https://doi.org/10.5194/wes-2019-102 Preprint. Discussion started: 2 April 2020 c Author(s) 2020. CC BY 4.0 License.
defined as a minimum margin in degrees between the steady BEM angle of attack and the point of maximum lift for the corresponding airfoil; the stall constrained is only enforced for the outer 40 % of the blade span.
where β is the pitch angle, P is the aerodynamic power, T is the thrust force, M f lap is the flapwise bending moment. The angle of attack is α and α stall distance is the "stall distance". In the case of this study, the constraint of maximum thrust and maximum aerodynamic flapwise bending moment are not active.

Loads scaling
The maximum aerodynamic steady flapwise bending moment is retrieved from the step above, and is scaled up to an extreme 235 load using a ratio retrieved from full DLB HAWC2 simulations of the baseline blade: M extreme baseline is the extreme load distribution of the baseline rotor, extracted from full DLB simulations in HAWC2 for the baseline blade. The distribution is fitted with a fourth-order polynomial to ensure that it can be differentiated. The M BEM baseline moment is the corresponding maximum steady BEM model retrieved for the same baseline blade.

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In the case of the downwind configuration, a second flapwise design load case for cut-out wind speed is considered, as the minimum tower-blade clearance arises in different loading conditions. The load distribution for the maximum deflection towards the tower M extreme def lection is thus scaled from the baseline loads at cut-out wind speed as: For the downwind configuration, a decrease in the loading results into a larger deflection towards the tower.

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The edgewise loads remain unscaled, as they are driven by the aerodynamic torque as well as the gravity load.

Blade structural design
The simplified blade structural model is based on the work of Blasques and Stolpe (2012), also presented in the thesis work of Carstensen (2017) and Andersen (2018). The blade is described as a sequence of beam elements, each with a cross-section simplified to the elements shown in Fig. 3. The main load-carrying structure is simplified as a symmetric girder with two glass-250 reinforced-plastic (GRP) spar caps, connected by a GRP ellipse. The ellipse major axis is taken equal to the section chord, and with the tip deflection for the deformed blade δ, the strain levels on caps and ellipses ε and the buckling coefficient η.

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The buckling coefficient is added to the optimization problem compared to the references. The buckling coefficient is calculated under the assumption of an orthotropic plate under compression load. The compression load N z is obtained from the bending moment, assuming that the internal flapwise bending moment M x can be distributed as two forces acting on one girder side as compression forces and on the other girder side as tension forces. 265 The buckling coefficient η is then obtained via with the elastic modulus E, the Poisson ration ν, and the shear modulus G.
The optimization is solved with the Interior Point Optimizer Ipopt (Wächter and Biegler (2006)), and analytical gradients 270 are given for objective and the constraints functions, thus speeding up considerably the process (Blasques and Stolpe (2012)).
The solution returns a reliable estimate of the overall blade mass (and hence cost), which is here taken as the objective for the outer optimization loop.

Design evaluation
The optimized planform (chord, twist, and thickness distribution), as well as the changes in the structural geometry (spar 275 cap width, thickness of the spar and trailing edge caps), are applied in HAWTOpt2 according to the planform calculated by STORM. All thickness distributions are fitted by hand at 5 control points and a spline fit is applied in between the control points. The HAWC2 inputs are extracted from HAWTOpt2 and a DLB is calculated for each redesign. From the DLB the maximum load at each blade cross-section is extracted. The failure index is calculated with BECAS for each cross-section.
The design is accepted if the failure index i f is −1 < i f < 1. The failure index calculated by BECAS is not used in the design 280 process.
The DLB calculation is further used to calculate the tower wall thickness w for a tubular tower of given outer wall diameter D.
The tower is divided into 50 cross-sections and the outer diameter, as well as the load distribution, are varied linearly between tower top and tower bottom. Within a for-loop, the wall thickness is increased until the stress σ steel reaches the allowed stress of the tower steel material.
Where the bending moment M is the bending moment of the cross section, SF is the safety factor for steel material and the W b is the section modulus calculated as The iteration is done twice, once for the extreme loads and the according stress limit for steel, and once for the lifetime 290 equivalent load from the fatigue calculation and the fatigue stress limit for steel. From the two resulting wall thicknesses, the maximum thickness is picked for each cross-section. Constant masses for the tower interior are added and kept the same as for the baseline. The new tower mass distribution, as well as the stiffness redistribution, do not enter the DLB calculations.

Cost estimation
The cost model used for the cost evaluation consists of costs that scale with the mass, such as tower and blade costs. For other 295 components, the costs scale with a design driving load or measure called cost driver CD. The cost driver is scaled with a factor f CD to mass to the component mass Cost component . A second factor f mass to cost is defined to scale component masses to component costs.
Cost component = f mass to cost (f CD to mass CD) Other cost components e.g. logistics or operation and maintenance costs are scaled directly with the factor f CD to mass from 300 a cost driver to the cost. The component costs and total turbine costs (CAPEX+OPEX) of the baseline have been compared to the commercial turbine to assure a reasonable cost scaling and cost distribution within the present study.

Results
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 310 S111uw design configuration, as the data is confidential. Figure 4 shows the planforms resulting from the design workflow. All values are normalized with the maximum chord. The figure shows that the chord and the twist distribution change only slightly, while larger differences can be observed for the thickness over chord distribution, which is likely primarily due to the induction distribution being kept fixed during the optimization, 315 while the larger changes in thickness are due to the direct coupling between AEP constraint, blade structural constraints and blade mass. For the S111uw PF and S111dw PF, the thickness over chord ratio increases from the 70% span and inboard compared to the baseline (S111uw). The S111dw PF has a slightly lower thickness than the S111uw PF design in this area up to the tip. From 40% span and inboard to the displayed region the S111dw PF design shows a larger thickness over chord ratio than the S111uw PF design. In the outer 8% of the blade span, the PF redesigns show a larger thickness than the baseline blade.

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The latter is an artifact of the combination of the spline type chosen and the fixed airfoil thickness at the blade tip. For none of 13 https://doi.org/10.5194/wes-2019-102 Preprint. Discussion started: 2 April 2020 c Author(s) 2020. CC BY 4.0 License.  the redesigns the constraint on thickness over chord is active.
While the S111uw PF design is constrained in blade deflection, in none of the downwind designs the blade deflection constraint is active. All the resulting redesigns are generally utilizing the maximum strain of the material over a larger blade span than the S111uw and S111dw design configurations. All downwind redesigns are fully strain constrained in the spar caps. In the 325 structural module of the optimization, the buckling constraint is active along the full blade span. The downwind configurations generally show larger shell thickness than the upwind configuration.
For all redesigns of the rotor blade, significant savings could be achieved of at least 12%. The lowest blade mass savings are achieved by the upwind configuration. For the S111uw PF 12.5% of blade mass could be saved. For the S111dw STR 14.5% mass savings are achieved and 17.1% blade mass reduction was observed for S111dw PF. part thinner, more efficient airfoils compensate for a production loss of the inboard part of the blade. This effect is amplified as a small AEP penalty was allowed in the design procedure. From the S111dw STR, it can be seen that the downwind configuration benefits from lower flapwise loads, and a release of the tower clearance constraint resulting in a reduced blade mass. A 335 higher shell thickness is required to carry the higher edgewise loads in the downwind configurations. Comparing the S111dw PF design to the S111uw PF design a further effect of the edgewise load increase can be seen. To carry the increased edgewise loads there are two options. The first one is to increase the shell thickness as for the S111dw STR design. The second option is to increase the stiffness by using airfoils with higher relative thickness. The solution found in the optimization routine for the S111dw PF is a combination of both, showing slightly thicker airfoils on the inboard part for the S111dw Pf than for the 340 S111uw PF. Another solution to carrying the increased edgewise loads is an increased chord, but since the variation in chord is limited due to a fixed induction and tip speed ratio, this design freedom is not utilized. The lower flapwise loads in the S111uw PF design allow, on the other hand, to compensate for a power loss with slightly thinner airfoils in the outboard part. The chord distribution is hardly changing as the AEP is constrained to not deviate from the baseline AEP and the induction distribution is frozen. The twist is simply adjusting the given operational point of the airfoils at the given spanwise position.

Cost driving loads from full DLB calculation
The following section shows the loads driving either the cost components in Table 2 Table 3 shows the loads influencing the cost estimation of the designs relative to the S111uw configuration. It can be seen, that the S111uw PF design clearly benefits, from the reduced blade mass on the edgewise extreme and fatigue blade root bending moment, as well as on the tower bottom bending moment. The only disadvantage is an increase in the tower top yaw moment.
The table also shows that the downwind designs generally benefit on the flapwise mean, flapwise extreme blade root moment 355 and the related tower top yaw moment, mainly from the alignment of the rotor cone and the rotor forces ("load alignment").
In the tower top tilt moment, the influence of the tower shadow, as well as the alignment of the rotor overhanging gravity moment and the moment due to thrust force is observed. Due to the latter also an increase in the extreme tower bottom bending moment is seen compared to the S111uw design. The gravity-related loads, e.g. tower top tilt moment and longitudinal tower bottom bending moment are reduced for each configuration by the reduction of mass due to the redesign. With the reduced 360 flapwise stiffness of the S111dw PF design, the fatigue load is reduced to the level of the S111uw and the tower shadow effect is overcome. A relative reduction of the flapwise stiffness compared to the edgewise stiffness increases the edgewise damping which results in the load decrease for edgewise extreme and fatigue loads of the S111dw STR and S111dw PF compared to the S111dw. load sensor ∆ normalized load relative to S111uw configuration S111uw S111dw S111dw S111dw This section shows the estimated costs resulting from the load and mass difference of the design configurations. Figure 5 shows the summary of the main cost components of the turbine with an indication of the cost that is not affected by the design process (constant cost). The costs sum up to the total CAPEX. All results are normalized by the CAPEX of the S111uw design. It can be seen that the nacelle is the main cost component, followed by the blades, the tower, and the costs for electrical equipment.
The figure shows that more than a third of the CAPEX is not affected by the chosen redesigns. In the CAPEX distribution of 370 the nacelle, major cost differences are associated with the pitch bearing, the mainframe and the pitch drives. The blade costs reduce significantly with the redesign of the blades. Where the S111dw PF shows the lowest blade costs associated with the lowest blade mass. The tower and foundation costs are for the downwind configurations generally higher than for the upwind configurations, as the associated extreme loads and also the tower top fatigue loads are significantly higher. The costs of the electrical components reflect the change in hub panel costs as these scale with the extreme blade root torsion. Only small 375 differences in the logistics costs are observed due to the change in nacelle mass. The balance of the plant is achieved for the same estimated costs while the installation reflects the changes in total main frame mass, driven by the extreme tilt moment.
Overall, the total CAPEX costs of the turbine vary only marginally between all the redesigns. The OPEX costs, on the other hand, are lower for all the downwind designs since the OPEX costs scale with the lower AEP. As a result, the combined turbine costs of the redesigned downwind configurations (S111dw STR and S111dw PF) are lower than for the S111uw PF design.
380 Table 4 summarizes the achieved blade and tower mass, as well as the AEP and the estimated COE differences compared to the S111uw design. With a COE reduction of -2.3% the S111uw PF shows the lowest COE, as the CAPEX is low, while the AEP the COE -1.3% below the S111uw baseline turbine. Overall, the S111uw PF remains of lowest COE, since the rotor mass is only 5% above the S111dw PF while the tower is 10% lighter and the AEP is 2% higher.

Summary
Within this study, the COE reduction potential for the Suzlon S111 2.1MW turbine has been estimated changing the original upwind configuration into a downwind configuration. A design framework including a low fidelity in-house optimization tool 390 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 Bottasso et al. (2016) and Zahle et al. (2016).

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The downwind design 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 Ichter et al. (2016) and Bortolotti et al. (2019). 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 400 the gravity overhanging moment of the rotor nacelle assembly is aligned with the thrust force, as also shown by Ning and Petch (2016). 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 Zalkind et al. (2019) or Ning and Petch (2016). In the direct comparison, the AEP of the S111dw PF 405 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 410 S111uw PF design configuration which achieves a significant mass and load reduction for a small sacrifice in AEP compared to the baseline.

Discussion and future work
This study has shown, for the example of the Suzlon S111 2.1MW 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 con-415 figurations, the upwind configuration, on the other hand, showed overall the lowest COE. A downwind configuration would, therefore, be the configuration to choose on a turbine cost-driven 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 420 fatigue load of the downwind configuration would increase the material wear, but this does not enter the OPEX model.
The cost model generally depends on the loads simulated. This comes with uncertainty due to the seed number, the seeds themselves as well as and the assumptions of 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.

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It can be expected that especially flapwise blade root and tilt related fatigue loads are under-predicted. 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 430 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 435 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, 440 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 445 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.
The COE estimation and therefore success criteria of the downwind concept does 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 450 in the downwind configuration to offset the increased tower cost with savings on the rotor. If the baseline had a comparably 20 https://doi.org/10.5194/wes-2019-102 Preprint. Discussion started: 2 April 2020 c Author(s) 2020. CC BY 4.0 License. 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, where wires are a cheap measure to take the bending loads. Alternatively, a low labor cost 455 market could give the options of low tower costs with lattice or hybrid-lattice towers which generate bending stiffness from an increased foot-print 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 drastic design changes.
Compensating the AEP loss in the downwind configuration with a larger rotor area could be an option to decrease the COE.

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Nevertheless, this does also increase the turbine cost, not just due to an increased rotor diameter and therefore rotor mass, but also 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 465 the downwind configurations further with an increase of rotor diameter.
Future work should also consider a redesign of the nacelle for better balancing of the rotor mass on the tower for the downwind configuration, as suggested by Zalkind et al. (2019). 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 470 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 Loenbaek et al. (2019) might benefit the upwind configuration in the same manner as the configuration change: the tower clearance is increased, the flapwise blade root moment is decreased with a penalty on AEP.

475
Since the tower bottom load does in this case not increase as in the case of the downwind configuration, such an upwind configuration might out-perform a downwind configuration in terms of COE.

Conclusion
Overall, the study shows, that a downwind configuration of the chosen example 2.1MW 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 480 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.
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.
Data availability. The data is not publicly accessible, since the research is based on a commercial turbine and the data is not available for disclosure by Suzlon.