the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Large-eddy simulation of airborne wind energy farms
- 1KU Leuven, Department of Mechanical Engineering, Celestijnenlaan 300, BE-3001 Leuven, Belgium
- 2University of Freiburg, Department of Microsystems Engineering, Georges-Köhler-Allee 102, DE-79110 Freiburg, Germany
- 3University of Freiburg, Department of Mathematics, Georges-Köhler-Allee 102, DE-79110 Freiburg, Germany
- 1KU Leuven, Department of Mechanical Engineering, Celestijnenlaan 300, BE-3001 Leuven, Belgium
- 2University of Freiburg, Department of Microsystems Engineering, Georges-Köhler-Allee 102, DE-79110 Freiburg, Germany
- 3University of Freiburg, Department of Mathematics, Georges-Köhler-Allee 102, DE-79110 Freiburg, Germany
Abstract. The future utility-scale deployment of airborne wind energy technologies requires the development of large-scale multi-megawatt systems. This study aims at quantifying the interaction between the atmospheric boundary layer (ABL) and large-scale airborne wind energy systems operating in a farm. To that end, we present a virtual flight simulator combining large-eddy simulations to simulate turbulent flow conditions and optimal control techniques for flight-path generation and tracking. The two-way coupling between flow and system dynamics is achieved by implementing an actuator sector method that we pair to a model predictive controller. In this study, we consider ground-based power generation pumping-mode AWE systems (lift-mode AWES) and on-board power generation AWE systems (drag-mode AWES). For the lift-mode AWES, we additionally investigate different reel-out strategies to reduce the interaction between the tethered wing and its own wake. Further, we investigate AWE parks consisting of 25 systems organized in 5 rows of 5 systems. For both lift- and drag-mode archetypes, we consider a moderate park layout with a power density of 10 MW km−2 achieved at a rated wind speed of 12 m s−1. For the drag-mode AWES, an additional park with denser layout and power density of 28 MW km−2 is also considered. The model predictive controller achieves very satisfactory flight-path tracking despite the AWE systems operating in fully waked, turbulent flow conditions. Furthermore, we observe significant wake effects for the utility-scale AWE systems considered in the study. Wake-induced performance losses increase gradually through the downstream rows of systems and reach in the last row of the parks up to 17 % for the lift-mode AWE park and up to 25 % and 45 % for the moderate and dense drag-mode AWE parks, respectively. For an operation period of 60 minutes at a below-rated reference wind speed of 10 m s−1, the lift-mode AWE park generates about 84.4 MW of power, corresponding to 82.5 % of the power yield expected when AWE systems operate ideally and interaction with the ABL is negligible. For the drag-mode AWE parks, the moderate and dense layouts generate about 86.0 MW and 72.9 MW of power, respectively, corresponding to 89.2 % and 75.6 % of the ideal power yield.
Thomas Haas et al.
Status: closed
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RC1: 'Comment on wes-2021-141', Anonymous Referee #1, 16 Dec 2021
This paper reflects a great part of the complexity in modelling AWE systems, from the ABL over the wing to the flight dynamics and control modelling. It also explores different energy extraction methods advocated in the AWE community. They model the flow in an AWE wind farm with a simplified, pressure driven ASL and an actuator sector representation for the wing forces. The aerodynamic forces are calculated by a steady-state lifting-line, the dynamic motion by a point-mass model and the trajectory & operation is governed by model predictive control. Despite the complexity at hand, the authors have submitted a well-structured and exhaustive description of the methodology supported by high quality visualizations. The appendices and open provision of datasets also necessitates special mention.
Nevertheless, there are some areas the authors should improve on. At times certain modelling choices and their implications on the quantities of interest need more elaboration and verification. Not including unsteady aerodynamics in the wing modelling for instance could change the dynamic behavior. The grid resolution is extremely coarse with respect to the wing span and chord, so it is questionable if the unsteadiness on a chord-scale is captured at all by the current setup. This should be discussed in the paper. Furthermore, the aerodynamic behavior is only shown in terms of integrated quantities, yet the spanwise load distributions should be provided to demonstrate the correct and anticipated behavior of the wing. Finally, the value of the publication would greatly benefit from a more thorough analysis of the results. Despite the high modelling fidelity the authors are missing the opportunity to extract some high order statistics of the flow and loads and limit themselves to high-level descriptions and presenting average flow quantities. They are missing an opportunity here to highlight how AWE park flows differentiate themselves from conventional wind farm flows; if they are different at all. This could be enhanced by analyzing the induction factors of the AWES inside the farm and a discussion around how the trajectories could be optimized to avoid upstream wakes etc.
Overall the paper is of great relevance to the wind energy community and is of very high quality. Unfortunately the discussion is not matching the level of detail and attention given to the methodology, thus not allowing to derive any general conclusions applicable to other AWE parks.
More detailed comments are given in the attached PDF, which the authors are weclome to respond to directly in the document if preffered.
- AC1: 'Reply on RC1', Thomas Haas, 25 Feb 2022
-
RC2: 'Comment on wes-2021-141', Anonymous Referee #2, 22 Dec 2021
General comments:
- The article presents the combination of large-eddy simulation with a control theory model for ground-gen and fly-gen fixed wing airborne wind energy systems. There is a complex interaction between the different components of the model. Each component in the model is explained to a certain level in a dedicated section.
- The level of fidelity of the wind model is high, except for the relatively low grid resolution, while the model of the airborne wind energy system is very simplified.
- The control strategy uses the model with several constraints, among others to avoid flying in the own wake. It results in the generation of optimal trajectories.
- After explaining the model, results are presented for 3 different farm configurations. Wake effects are shown to be of importance. The fly-gen systems cause significantly stronger wakes than the ground-gen systems. In all farms, the flight path stays close to the optimal trajectories.
- The article is technically of a high level, uses a scientific method and is definitely relevant for the wind energy science community. The amount of information and the forward references make the article a challenge to read, but this is unavoidable given the amount of work that is presented.
- The open data will be an added value for the community.
Specific comments:
- Line 155: The authors state that fewer states and control variables result in a less computationally intensive model. However, is this reduction relevant compared to the computational cost of the LES calculations? Some information about the time spent in each component of the model would be an interesting addition.
- Line 209: The authors obtain the model-equivalent angle of attack from the aerodynamic state, which is then used to define the orientation of the airborne wind energy system and as such influences the calculation of the aerodynamic forces. The authors had to do something to complete the limited information provided by the 3DOF model, and there is no obvious other way of doing this, but it remains a questionable approach in my opinion.
Technical corrections:
- Line 30: axissymmetric => axisymmetric
- Line 36: can not => cannot
- Line 98: N_s probably refers to the number of segments of the wing, but this is not mentioned explicitly.
- Line 260: eventual => if applicable
- Line 357: The “min” and “s.t.” are aligned too much to the left.
- Line 405: The “min” and “s.t.” are aligned too much to the left.
- Line 655: stremwise => streamwise
- Line 757: magnitude aerodynamic => magnitude of the aerodynamic
- Caption figure A4: Is there a precursor simulation in this case? Isn’t it a turbulence-free, sheared inflow according to line 786?
- AC2: 'Reply on RC2', Thomas Haas, 25 Feb 2022
Status: closed
-
RC1: 'Comment on wes-2021-141', Anonymous Referee #1, 16 Dec 2021
This paper reflects a great part of the complexity in modelling AWE systems, from the ABL over the wing to the flight dynamics and control modelling. It also explores different energy extraction methods advocated in the AWE community. They model the flow in an AWE wind farm with a simplified, pressure driven ASL and an actuator sector representation for the wing forces. The aerodynamic forces are calculated by a steady-state lifting-line, the dynamic motion by a point-mass model and the trajectory & operation is governed by model predictive control. Despite the complexity at hand, the authors have submitted a well-structured and exhaustive description of the methodology supported by high quality visualizations. The appendices and open provision of datasets also necessitates special mention.
Nevertheless, there are some areas the authors should improve on. At times certain modelling choices and their implications on the quantities of interest need more elaboration and verification. Not including unsteady aerodynamics in the wing modelling for instance could change the dynamic behavior. The grid resolution is extremely coarse with respect to the wing span and chord, so it is questionable if the unsteadiness on a chord-scale is captured at all by the current setup. This should be discussed in the paper. Furthermore, the aerodynamic behavior is only shown in terms of integrated quantities, yet the spanwise load distributions should be provided to demonstrate the correct and anticipated behavior of the wing. Finally, the value of the publication would greatly benefit from a more thorough analysis of the results. Despite the high modelling fidelity the authors are missing the opportunity to extract some high order statistics of the flow and loads and limit themselves to high-level descriptions and presenting average flow quantities. They are missing an opportunity here to highlight how AWE park flows differentiate themselves from conventional wind farm flows; if they are different at all. This could be enhanced by analyzing the induction factors of the AWES inside the farm and a discussion around how the trajectories could be optimized to avoid upstream wakes etc.
Overall the paper is of great relevance to the wind energy community and is of very high quality. Unfortunately the discussion is not matching the level of detail and attention given to the methodology, thus not allowing to derive any general conclusions applicable to other AWE parks.
More detailed comments are given in the attached PDF, which the authors are weclome to respond to directly in the document if preffered.
- AC1: 'Reply on RC1', Thomas Haas, 25 Feb 2022
-
RC2: 'Comment on wes-2021-141', Anonymous Referee #2, 22 Dec 2021
General comments:
- The article presents the combination of large-eddy simulation with a control theory model for ground-gen and fly-gen fixed wing airborne wind energy systems. There is a complex interaction between the different components of the model. Each component in the model is explained to a certain level in a dedicated section.
- The level of fidelity of the wind model is high, except for the relatively low grid resolution, while the model of the airborne wind energy system is very simplified.
- The control strategy uses the model with several constraints, among others to avoid flying in the own wake. It results in the generation of optimal trajectories.
- After explaining the model, results are presented for 3 different farm configurations. Wake effects are shown to be of importance. The fly-gen systems cause significantly stronger wakes than the ground-gen systems. In all farms, the flight path stays close to the optimal trajectories.
- The article is technically of a high level, uses a scientific method and is definitely relevant for the wind energy science community. The amount of information and the forward references make the article a challenge to read, but this is unavoidable given the amount of work that is presented.
- The open data will be an added value for the community.
Specific comments:
- Line 155: The authors state that fewer states and control variables result in a less computationally intensive model. However, is this reduction relevant compared to the computational cost of the LES calculations? Some information about the time spent in each component of the model would be an interesting addition.
- Line 209: The authors obtain the model-equivalent angle of attack from the aerodynamic state, which is then used to define the orientation of the airborne wind energy system and as such influences the calculation of the aerodynamic forces. The authors had to do something to complete the limited information provided by the 3DOF model, and there is no obvious other way of doing this, but it remains a questionable approach in my opinion.
Technical corrections:
- Line 30: axissymmetric => axisymmetric
- Line 36: can not => cannot
- Line 98: N_s probably refers to the number of segments of the wing, but this is not mentioned explicitly.
- Line 260: eventual => if applicable
- Line 357: The “min” and “s.t.” are aligned too much to the left.
- Line 405: The “min” and “s.t.” are aligned too much to the left.
- Line 655: stremwise => streamwise
- Line 757: magnitude aerodynamic => magnitude of the aerodynamic
- Caption figure A4: Is there a precursor simulation in this case? Isn’t it a turbulence-free, sheared inflow according to line 786?
- AC2: 'Reply on RC2', Thomas Haas, 25 Feb 2022
Thomas Haas et al.
Data sets
Large-eddy simulation of airborne wind energy farms: AWES virtual flight data Thomas Haas and Johan Meyers https://doi.org/10.5281/zenodo.5705563
Thomas Haas et al.
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