Articles | Volume 8, issue 7
https://doi.org/10.5194/wes-8-1201-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-8-1201-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Jul 2023
Research article |
| 20 Jul 2023
| 20 Jul 2023
A data-driven reduced-order model for rotor optimization
Nicholas Peters
CORRESPONDING AUTHOR
NASA Ames Research Center, Moffett Field, CA, USA
Department of Aerospace Engineering, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA
Christopher Silva
NASA Ames Research Center, Moffett Field, CA, USA
John Ekaterinaris
Department of Aerospace Engineering, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA
Related subject area
Thematic area: Wind technologies | Topic: Design concepts and methods for plants, turbines, and components
A novel techno-economical layout optimization tool for floating wind farm design
Hybrid-Lambda: a low-specific-rating rotor concept for offshore wind turbines
Speeding up large-wind-farm layout optimization using gradients, parallelization, and a heuristic algorithm for the initial layout
Nonlinear vibration characteristics of virtual mass systems for wind turbine blade fatigue testing
Extreme wind turbine response extrapolation with the Gaussian mixture model
The effect of site-specific wind conditions and individual pitch control on wear of blade bearings
A neighborhood search integer programming approach for wind farm layout optimization
Validation of aeroelastic dynamic model of Active Trailing Edge Flap system tested on a 4.3 MW wind turbine
Enabling control co-design of the next generation of wind power plants
Offshore wind farm optimisation: a comparison of performance between regular and irregular wind turbine layouts
Grand challenges in the design, manufacture, and operation of future wind turbine systems
Mesoscale modelling of North Sea wind resources with COSMO-CLM: model evaluation and impact assessment of future wind farm characteristics on cluster-scale wake losses
Gradient-based Wind Farm Layout Optimization With Inclusion And Exclusion Zones
Computational fluid dynamics (CFD) modeling of actual eroded wind turbine blades
Grand Challenges: wind energy research needs for a global energy transition
Current status and grand challenges for small wind turbine technology
CFD-based curved tip shape design for wind turbine blades
Impacts of wind field characteristics and non-steady deterministic wind events on time-varying main-bearing loads
Amalia Ida Hietanen, Thor Heine Snedker, Katherine Dykes, and Ilmas Bayati
Wind Energ. Sci., 9, 417–438, https://doi.org/10.5194/wes-9-417-2024, https://doi.org/10.5194/wes-9-417-2024, 2024
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The layout of a floating offshore wind farm was optimized to maximize the relative net present value (NPV). By modeling power generation, losses, inter-array cables, anchors and operational costs, an increase of EUR 34.5 million in relative NPV compared to grid-based layouts was achieved. A sensitivity analysis was conducted to examine the impact of economic factors, providing valuable insights. This study contributes to enhancing the efficiency and cost-effectiveness of floating wind farms.
Daniel Ribnitzky, Frederik Berger, Vlaho Petrović, and Martin Kühn
Wind Energ. Sci., 9, 359–383, https://doi.org/10.5194/wes-9-359-2024, https://doi.org/10.5194/wes-9-359-2024, 2024
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This paper provides an innovative blade design methodology for offshore wind turbines with very large rotors compared to their rated power, which are tailored for an increased power feed-in at low wind speeds. Rather than designing the blade for a single optimized operational point, we include the application of peak shaving in the design process and introduce a design for two tip speed ratios. We describe how enlargement of the rotor diameter can be realized to improve the value of wind power.
Rafael Valotta Rodrigues, Mads Mølgaard Pedersen, Jens Peter Schøler, Julian Quick, and Pierre-Elouan Réthoré
Wind Energ. Sci., 9, 321–341, https://doi.org/10.5194/wes-9-321-2024, https://doi.org/10.5194/wes-9-321-2024, 2024
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The use of wind energy has been growing over the last few decades, and further increase is predicted. As the wind energy industry is starting to consider larger wind farms, the existing numerical methods for analysis of small and medium wind farms need to be improved. In this article, we have explored different strategies to tackle the problem in a feasible and timely way. The final product is a set of recommendations when carrying out trade-off analysis on large wind farms.
Aiguo Zhou, Jinlei Shi, Tao Dong, Yi Ma, and Zhenhui Weng
Wind Energ. Sci., 9, 49–64, https://doi.org/10.5194/wes-9-49-2024, https://doi.org/10.5194/wes-9-49-2024, 2024
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This paper explores the nonlinear influence of the virtual mass mechanism on the test system in blade biaxial tests. The blade theory and simulation model are established to reveal the nonlinear amplitude–frequency characteristics of the blade-virtual-mass system. Increasing the amplitude of the blade or decreasing the seesaw length will lower the resonance frequency and load of the system. The virtual mass also affects the blade biaxial trajectory.
Xiaodong Zhang and Nikolay Dimitrov
Wind Energ. Sci., 8, 1613–1623, https://doi.org/10.5194/wes-8-1613-2023, https://doi.org/10.5194/wes-8-1613-2023, 2023
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Wind turbine extreme response estimation based on statistical extrapolation necessitates using a small number of simulations to calculate a low exceedance probability. This is a challenging task especially if we require small prediction error. We propose the use of a Gaussian mixture model as it is capable of estimating a low exceedance probability with minor bias error, even with limited simulation data, having flexibility in modeling the distributions of varying response variables.
Arne Bartschat, Karsten Behnke, and Matthias Stammler
Wind Energ. Sci., 8, 1495–1510, https://doi.org/10.5194/wes-8-1495-2023, https://doi.org/10.5194/wes-8-1495-2023, 2023
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Blade bearings are among the most stressed and challenging components of a wind turbine. Experimental investigations using different test rigs and real-size blade bearings have been able to show that rather short time intervals of only several hours of turbine operation can cause wear damage on the raceways of blade bearings. The proposed methods can be used to assess wear-critical operation conditions and to validate control strategies as well as lubricants for the application.
Juan-Andrés Pérez-Rúa, Mathias Stolpe, and Nicolaos Antonio Cutululis
Wind Energ. Sci., 8, 1453–1473, https://doi.org/10.5194/wes-8-1453-2023, https://doi.org/10.5194/wes-8-1453-2023, 2023
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With the challenges of ensuring secure energy supplies and meeting climate targets, wind energy is on course to become the cornerstone of decarbonized energy systems. This work proposes a new method to optimize wind farms by means of smartly placing wind turbines within a given project area, leading to more green-energy generation. This method performs satisfactorily compared to state-of-the-art approaches in terms of the resultant annual energy production and other high-level metrics.
Andrea Gamberini, Thanasis Barlas, Alejandro Gomez Gonzalez, and Helge Aagaard Madsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-63, https://doi.org/10.5194/wes-2023-63, 2023
Revised manuscript accepted for WES
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Movable surfaces on wind turbine (WT) blades, called active flaps, can reduce the cost of wind energy. However, they still need extensive testing. This study shows that the computer model used to design a WT with flaps aligns well with measurements obtained from a 3 month test on a commercial WT featuring a prototype flap. Particularly during flap actuation, there were minimal differences between simulated and measured data. These findings assure the reliability of WT designs incorporating flaps
Andrew P. J. Stanley, Christopher J. Bay, and Paul Fleming
Wind Energ. Sci., 8, 1341–1350, https://doi.org/10.5194/wes-8-1341-2023, https://doi.org/10.5194/wes-8-1341-2023, 2023
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Better wind farms can be built by simultaneously optimizing turbine locations and control, which is currently impossible or extremely challenging because of the size of the problem. The authors present a method to determine optimal wind farm control as a function of the turbine locations, which enables turbine layout and control to be optimized together by drastically reducing the size of the problem. In an example, a wind farm's performance improves by 0.8 % when optimized with the new method.
Maaike Sickler, Bart Ummels, Michiel Zaaijer, Roland Schmehl, and Katherine Dykes
Wind Energ. Sci., 8, 1225–1233, https://doi.org/10.5194/wes-8-1225-2023, https://doi.org/10.5194/wes-8-1225-2023, 2023
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This paper investigates the effect of wind farm layout on the performance of offshore wind farms. A regular farm layout is compared to optimised irregular layouts. The irregular layouts have higher annual energy production, and the power production is less sensitive to wind direction. However, turbine towers require thicker walls to counteract increased fatigue due to increased turbulence levels in the farm. The study shows that layout optimisation can be used to maintain high-yield performance.
Paul Veers, Carlo L. Bottasso, Lance Manuel, Jonathan Naughton, Lucy Pao, Joshua Paquette, Amy Robertson, Michael Robinson, Shreyas Ananthan, Thanasis Barlas, Alessandro Bianchini, Henrik Bredmose, Sergio González Horcas, Jonathan Keller, Helge Aagaard Madsen, James Manwell, Patrick Moriarty, Stephen Nolet, and Jennifer Rinker
Wind Energ. Sci., 8, 1071–1131, https://doi.org/10.5194/wes-8-1071-2023, https://doi.org/10.5194/wes-8-1071-2023, 2023
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Critical unknowns in the design, manufacturing, and operation of future wind turbine and wind plant systems are articulated, and key research activities are recommended.
Ruben Borgers, Marieke Dirksen, Ine L. Wijnant, Andrew Stepek, Ad Stoffelen, Naveed Akhtar, Jérôme Neirynck, Jonas Van de Walle, Johan Meyers, and Nicole P. M. van Lipzig
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-33, https://doi.org/10.5194/wes-2023-33, 2023
Revised manuscript accepted for WES
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Wind farms at sea are becoming more densely clustered, which means that, next to individual wind turbines interfering with each other in a single power plant, also interference between wind farms becomes important. Using a climate model, this study shows that the efficiency of wind farm clusters and the interference between the wind farms in the cluster depend strongly on the properties of the individual wind farms and is also highly sensitive to the spacing between the wind farms.
Javier Criado Risco, Rafael Valotta Rodrigues, Mikkel Friis-Møller, Julian Quick, Mads Mølgaard Pedersen, and Pierre-Elouan Réthoré
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-5, https://doi.org/10.5194/wes-2023-5, 2023
Revised manuscript accepted for WES
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Wind energy developers frequently have to face some spatial restrictions at the time of designing a new wind farm due to different reasons such as: existing of natural protected areas around the wind farm location, fishing routes, presence of buildings, etc. Wind farm design has to account for these restricted areas, but sometimes this is not straightforward to achieve. We have developed a methodology that allows for different inclusion and exclusion areas in the optimization framework.
Kisorthman Vimalakanthan, Harald van der Mijle Meijer, Iana Bakhmet, and Gerard Schepers
Wind Energ. Sci., 8, 41–69, https://doi.org/10.5194/wes-8-41-2023, https://doi.org/10.5194/wes-8-41-2023, 2023
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Leading edge erosion (LEE) is one of the most critical degradation mechanisms that occur with wind turbine blades. A detailed understanding of the LEE process and the impact on aerodynamic performance due to the damaged leading edge is required to optimize blade maintenance. Providing accurate modeling tools is therefore essential. This novel study assesses CFD approaches for modeling high-resolution scanned LE surfaces from an actual blade with LEE damages.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
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Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
Alessandro Bianchini, Galih Bangga, Ian Baring-Gould, Alessandro Croce, José Ignacio Cruz, Rick Damiani, Gareth Erfort, Carlos Simao Ferreira, David Infield, Christian Navid Nayeri, George Pechlivanoglou, Mark Runacres, Gerard Schepers, Brent Summerville, David Wood, and Alice Orrell
Wind Energ. Sci., 7, 2003–2037, https://doi.org/10.5194/wes-7-2003-2022, https://doi.org/10.5194/wes-7-2003-2022, 2022
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The paper is part of the Grand Challenges Papers for Wind Energy. It provides a status of small wind turbine technology in terms of technical maturity, diffusion, and cost. Then, five grand challenges that are thought to be key to fostering the development of the technology are proposed. To tackle these challenges, a series of unknowns and gaps are first identified and discussed. Improvement areas are highlighted, within which 10 key enabling actions are finally proposed to the wind community.
Mads H. Aa. Madsen, Frederik Zahle, Sergio González Horcas, Thanasis K. Barlas, and Niels N. Sørensen
Wind Energ. Sci., 7, 1471–1501, https://doi.org/10.5194/wes-7-1471-2022, https://doi.org/10.5194/wes-7-1471-2022, 2022
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This work presents a shape optimization framework based on computational fluid dynamics. The design framework is used to optimize wind turbine blade tips for maximum power increase while avoiding that extra loading is incurred. The final results are shown to align well with related literature. The resulting tip shape could be mounted on already installed wind turbines as a sleeve-like solution or be conceived as part of a modular blade with tips designed for site-specific conditions.
Edward Hart, Adam Stock, George Elderfield, Robin Elliott, James Brasseur, Jonathan Keller, Yi Guo, and Wooyong Song
Wind Energ. Sci., 7, 1209–1226, https://doi.org/10.5194/wes-7-1209-2022, https://doi.org/10.5194/wes-7-1209-2022, 2022
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We consider characteristics and drivers of loads experienced by wind turbine main bearings using simplified models of hub and main-bearing configurations. Influences of deterministic wind characteristics are investigated for 5, 7.5, and 10 MW turbine models. Load response to gusts and wind direction changes are also considered. Cubic load scaling is observed, veer is identified as an important driver of load fluctuations, and strong links between control and main-bearing load response are shown.
Cited articles
Abhishek, A., Ananthan, S., Baeder, J., and Chopra, I.: Prediction and
Fundamental Understanding of Stall Loads in UH-60A Pull-Up Maneuver, J.
Am. Helicopt. Soc., 56, 1–14, https://doi.org/10.4050/JAHS.56.042005, 2011. a
Abras, J. and Hariharan, N. S.: Machine Learning Based Physics Inference from High-Fidelity Solutions: Vortex Classification and Localization, in: AIAA Scitech 2022 Forum, 3–7 January, San Diego, CA,
p. 310, https://doi.org/10.2514/6.2022-0310, 2022. a
Ali, N. and Cal, R. B.: Data-driven modeling of the wake behind a wind turbine array, J. Renew. Sustain. Energ., 12, 033304, https://doi.org/10.1063/5.0004393, 2020. a
Ali, N., Kadum, H. F., and Cal, R. B.: Focused-based multifractal analysis of
the wake in a wind turbine array utilizing proper orthogonal decomposition,
J. Renew. Sustain. Energ., 8, 063306, https://doi.org/10.1063/1.4968032, 2016. a
Ali, N., Cortina, G., Hamilton, N., Calaf, M., and Cal, R. B.: Turbulence
characteristics of a thermally stratified wind turbine array boundary layer
via proper orthogonal decomposition, J. Fluid Mech., 828, 175–195, https://doi.org/10.1017/jfm.2017.492, 2017. a
Ali, N., Calaf, M., and Cal, R. B.: Clustering sparse sensor placement
identification and deep learning based forecasting for wind turbine wakes,
J. Renew. Sustain. Energ., 13, 023307, https://doi.org/10.1063/5.0036281, 2021. a
Anusonti-Inthra, P.: Full Vehicle Simulations for a Coaxial Rotorcraft using
High-Fidelity CFD/CSD Coupling, in: 2018 AIAA Aerospace Sciences Meeting, 8–12 January, Kissimmee, FL, p. 0777, https://doi.org/10.2514/6.2018-0777, 2018. a
Ashwin Renganathan, S., Maulik, R., Letizia, S., and Iungo, G. V.: Data-driven wind turbine wake modeling via probabilistic machine learning, Neural Comput. Appl., 34, 6171–6186, https://doi.org/10.1007/s00521-021-06799-6, 2022. a
Benek, J. A., Steger, J. L., and Dougherty, F. C.: Chimera: a grid-embedding technique, Vol. 85., No. 64, Arnold Engineering Development Center, Air Force Systems Command, United States Air Force, https://apps.dtic.mil/sti/pdfs/ADA167466.pdf (last access: November 2021), 1986. a
Brunton, S. L. and Kutz, J. N.: Data-Driven Science and Engineering: Machine
Learning, Dynamical Systems, and Control, Cambridge University Press,
https://doi.org/10.1017/9781108380690, 2019. a
Buning, P., Chiu, I., Obayashi, S., Rizk, Y., and Steger, J.: Numerical
Simulation of the Integrated Space Shuttle Vehicle in Ascent, in: 15th Atmospheric Flight Mechanics Conference, 15–17 August 1988, Minneapolis, MN, USA, Minneapolis, MN, 4359, https://doi.org/10.2514/6.1988-4359, 1988. a
Camp, E. H. and Cal, R. B.: Low-dimensional representations and anisotropy of
model rotor versus porous disk wind turbine arrays, Phys. Rev. Fluids, 4, 024610, https://doi.org/10.1103/PhysRevFluids.4.024610, 2019. a
Chan, W. M., Chiu, I. T., and Buning, P. G.: User's Manual for the HYPGEN Hyperbolic Grid Generator and the HGUI Graphical User Interface, NASA TM-108791, NASA, https://ntrs.nasa.gov/citations/19940011311 (last access: November 2021), 1993. a
Chan, W. M.: Advances in Software Tools for Pre-Processing and Post-Processing of Overset Grid Computations, in: Proceedings of the 9th International Conference on Numerical Grid Generation in Computational Field Simulations, 11–18 June 2005, San Jose, California, 2005. a
Chang, Y.-H., Zhang, L., Wang, X., Yeh, S.-T., Mak, S., Sung, C.-L., Jeff Wu,
C., and Yang, V.: Kernel-Smoothed Proper Orthogonal Decomposition–Based
Emulation for Spatiotemporally Evolving Flow Dynamics Prediction, AIAA J., 57, 5269–5280, https://doi.org/10.2514/1.J057803, 2019. a
Chau, R.: Process and Packaging Innovations for Moore's Law Continuation and
Beyond, in: 2019 IEEE International Electron Devices Meeting (IEDM), 7–11 December, San Francisco, CA, 1–1, https://doi.org/10.1109/IEDM19573.2019.8993462, 2019. a
Cinquegrana, D. and Vitagliano, P. L.: A Reduced Order Model for Boundary
Layer Ingestion Map Prediction at Fan Inlet of Rear-Mounted Engine Nacelle,
in: AIAA Scitech 2021 Forum, 11–15 January 2021, p. 0993, https://doi.org/10.2514/6.2021-0993, 2021. a
Cizmas, P. G. and Palacios, A.: Proper Orthogonal Decomposition of Turbine
Rotor-Stator Interaction, J. Propuls. Power, 19, 268–281, https://doi.org/10.2514/2.6108, 2003. a
Colella, M., Saltari, F., Pizzoli, M., and Mastroddi, F.: Sloshing
reduced-order models for aeroelastic analyses of innovative aircraft
configurations, Aerosp. Sci. Technol., 118, 107075, https://doi.org/10.1016/j.ast.2021.107075, 2021. a
Conley, S. and Shirazi, D.: Comparing Simulation Results from CHARM and RotCFD to the Multirotor Test Bed Experimental Data, in: AIAA Aviation 2021 Forum, virtual event, p. 2540, https://doi.org/10.2514/6.2021-2540, 2021. a
Crozon, C., Steijl, R., and Barakos, G.: Coupled Flight Dynamics and
CFD-Demonstration for Helicopters in Shipborne Environment, Aeronaut. J., 122, 42–82, https://doi.org/10.1017/aer.2017.112, 2018. a
De Cillis, G., Cherubini, S., Semeraro, O., Leonardi, S., and De Palma, P.:
Data Driven Modal Decomposition of the Wake Behind an NREL 5-MW Wind Turbine,
in: 14th European Conference on Turbomachinery Fluid dynamics &
Thermodynamics, European Turbomachinery Society,
https://doi.org/10.3390/ijtpp6040044, 2021. a
De Cillis, G., Cherubini, S., Semeraro, O., Leonardi, S., and De Palma, P.:
Stability and optimal forcing analysis of a wind turbine wake: Comparison
with POD, Renew. Energy, 181, 765–785, https://doi.org/10.1016/j.renene.2021.09.025, 2022a. a
De Cillis, G., Semeraro, O., Leonardi, S., De Palma, P., and Cherubini, S.:
Dynamic-mode-decomposition of the wake of the NREL-5 MW wind turbine impinged by a laminar inflow, Renew, Energy, 199, 1–10,
https://doi.org/10.1016/j.renene.2022.08.113, 2022b. a
DNV: Bladed User Manual: Version 4.9, Gerrad Hassan & Partners Ltd.,
Bristol, UK, https://media.oiipdf.com/pdf/f867bf2b-1102-4cbd-a5e4-b8704b373d91.pdf (last access: February 2022), 2018. a
Dreyer, E. R., Grier, B. J., McNamara, J. J., and Orr, B. C.: Rapid
Steady-State Hypersonic Aerothermodynamic Loads Prediction Using Reduced
Fidelity Models, J. Aircraft, 58, 663–676, https://doi.org/10.2514/1.C035969, 2021. a
Fitzgibbon, T., Woodgate, M., and Barakos, G.: Assessment of Current Rotor
Design Comparison Practices based on High-Fidelity CFD Methods, Aeronaut. J., 124, 731–766, https://doi.org/10.1017/aer.2019.162, 2020. a
Hamilton, N., Viggiano, B., Calaf, M., Tutkun, M., and Cal, R. B.: A
generalized framework for reduced-order modeling of a wind turbine wake, Wind
Energy, 21, 373–390, https://doi.org/10.1002/we.2167, 2018. a
Ho, J. C., Jayaraman, B., and Yeo, H.: Coupled Computational Fluid Dynamics
and Comprehensive Analysis Calculations of a Gimballed Tiltrotor, AIAA J., 57, 4433–4446, https://doi.org/10.2514/1.J057394, 2019. a
Holmes, P., Lumley, J. L., and Berkooz, G.: Turbulence, Coherent Structures,
Dynamical Systems and Symmetry, in: Cambridge Monographs on Mechanics, Cambridge University Press, https://doi.org/10.1017/CBO9780511622700, 1996. a, b
Houck, D., deVelder, N., and Kelley, C.: Comparison of a mid-fidelity free
vortex wake method to a high-fidelity actuator line model large eddy
simulation for wind turbine wake simulations, J. Phys.: Conf. Ser., 2265, 042044, https://doi.org/10.1088/1742-6596/2265/4/042044, 2022. a
Jin, Y., Lu, K., Hou, L., and Chen, Y.: An adaptive proper orthogonal
decomposition method for model order reduction of multi-disc rotor system,
J. Sound Vibrat., 411, 210–231, https://doi.org/10.1016/j.jsv.2017.09.001, 2017. a
Johnson, W.: CAMRAD II, Comprehensive Analytical Model of Rotorcraft
Aerodynamics and Dynamics, Johnson Aeronautics, Palo Alto, California, https://apps.dtic.mil/sti/pdfs/ADA090513.pdf (last access: November 2021), 1992. a
Johnson, W.: NDARC-NASA Design and Analysis of Rotorcraft, Tech. rep., https://ntrs.nasa.gov/citations/20170011656 (last access: November 2021), NASA Technical Report Number ARC-EDAA-TN46522, 2015. a
Jonkman, J.: The New Modularization Framework for the FAST Wind Turbine CAE
Tool, in: 51st AIAA aerospace sciences meeting including the new horizons
forum and aerospace exposition, 7–10 January 2013, Dallas Texas, p. 202, https://doi.org/10.2514/6.2013-202, 2013. a
Jonkman, J. M. and Buhl Jr., M. L.: Fast user's guide-updated august 2005,
report NREL/TP-500-38230, NREL – National Renewable Energy Lab., Golden, CO, USA, https://doi.org/10.2172/15020796, 2005. a
Kecskemety, K. M. and McNamara, J. J.: Influence of wake dynamics on the
performance and aeroelasticity of wind turbines, Renew. Energy, 88, 333–345, https://doi.org/10.1016/j.renene.2015.11.031, 2016. a
Lakshminarayan, V. K., Sitaraman, J., and Wissink, A. M.: Application of
Strand Grid Framework to Complex Rotorcraft Simulations, J. Am. Helicopt. Soc., 62, 1–16, https://doi.org/10.4050/JAHS.62.012008, 2017. a
Larsen, T. J. and Hansen, A. M.: How 2 HAWC2, the user's manual, Risø
National Laboratory, https://www.hawc2.dk/-/media/sites/hawc2/hawc2-download/hawc2-manual/manual_version_12-7.pdf?la=da&hash=43585868F8F94FC4EC38A2F719CE02E224CA2B38 (last access: November 2021), 2007. a
Liew, J., Urbán, A. M., and Andersen, S. J.: Analytical model for the
power–yaw sensitivity of wind turbines operating in full wake, Wind Energ.
Sci., 5, 427–437, https://doi.org/10.5194/wes-5-427-2020, 2020. a
Liu, H., Gao, X., Chen, Z., and Yang, F.: Efficient reduced-order aerodynamic
modeling in low-Reynolds-number incompressible flows, Aerosp. Sci. Technol., 119, 107199, https://doi.org/10.1016/j.ast.2021.107199, 2021. a
Liu, Y., Lu, Y., Wang, Y., Sun, D., Deng, L., Wang, F., and Lei, Y.: A
CNN-based shock detection method in flow visualization, Comput. Fluids, 184, 1–9, https://doi.org/10.1016/j.compfluid.2019.03.022, 2019. a
Ma, X., Karamanos, G., and Karniadakis, G.: Dynamics and low-dimensionality of a turbulent near wake, J. Fluid Mech., 410, 29–65,
https://doi.org/10.1017/S0022112099007934, 2000. a
Morelli, M., Bellosta, T., and Guardone, A.: Development and preliminary
assessment of the open-source CFD toolkit SU2 for rotorcraft flows, J. Comput. Appl. Math., 389, 113340, https://doi.org/10.1016/j.cam.2020.113340, 2021. a
Neerarambam, S., Bowles, P. O., Min, B.-Y., Lamb, D., Dunn, A. F., Frydman, J., Harrington, G., Lian, C., Kazlauskas, M., Wake, B. E., Joshi, N., Becker, N., Forsythe, J. R., Powers, R. W., Stratton, Z., Collins, C., Spyropoulos, J., Jayaraman, B., Simonetti, J., Foti, C., and Axtell, J.: An Overview of the Exhaust Gas Reingestion Challenges on the CH 53K King Stallion, in: AIAA Scitech 2021 Forum, 11–15 January, p. 0028, https://doi.org/10.2514/6.2021-0028, 2021. a
Nuernberg, M. and Tao, L.: Three dimensional tidal turbine array simulations
using OpenFOAM with dynamic mesh, Ocean Eng., 147, 629–646,
https://doi.org/10.1016/j.oceaneng.2017.10.053, 2018. a
Peters, N., Ekaterinaris, J. A., and Wissink, A. M.: A Mode Based Reduced
Order Model for Supersonic Store Separation, in: AIAA Aviation 2021 Forum,
virtual event, https://doi.org/10.2514/6.2021-2548, 2021. a
Peters, N., Ekaterinaris, J., and Wissink, A.: A Mode Based Reduced Order
Model for Rotorcraft Separation, in: AIAA Scitech 2022 Forum, 3–7 January, San Diego, CA,
https://doi.org/10.2514/6.2022-0312, 2022a. a
Peters, N., Wissink, A., and Ekaterinaris, J.: Machine learning-based surrogate modeling approaches for fixed-wing store
separation, Aerosp. Sci. Technol., 133, 108150, https://doi.org/10.1016/j.ast.2023.108150, 2022b. a
Peters, N., Wissink, A., and Ekaterinaris, J.: On the construction of a
mode based reduced order model for a moving store, Aerosp. Sci. Technol., 123, 107484, https://doi.org/10.1016/j.ast.2022.107484, 2022c. a
Peters, N., Ekaterinaris, J., and Wissink, A.: A Data-Driven Modeling Approach for Rotorcraft Store Separation, in: AIAA Scitech 2023 Forum, 23–27 January, Nartional Harbor, MD, https://doi.org/10.2514/6.2023-0233, 2023. a
Quackenbush, T., Wachspress, D., Boschitsch, A., and Curbishley, T.: A
Comprehensive Hierarchical Aeromechanics Rotorcraft Model (CHARM) for General
Rotor/Surface Interaction, CDI Report, Continuum Dynamics, https://www.worldcat.org/title/comprehensive-hierarchical-aeromechanics-rotorcraft-model-charm-for-general-rotorsurface-interaction-sbir-phase-ii-final-report/oclc/248058260 (last access: November 2021), 99–03, 1999. a
Raissi, M., Perdikaris, P., and Karniadakis, G.: Physics-informed neural
networks: A deep learning framework for solving forward and inverse problems
involving nonlinear partial differential equations, J. Comput. Phys., 378, 686–707, https://doi.org/10.1016/j.jcp.2018.10.045, 2019. a
Ramasamy, M., Johnson, B., and Leishman, J.: Turbulent Tip Vortex Measurements Using Dual-Plane Stereoscopic Particle Image Velocimetry, AIAA J., 478, 1826–1840, https://doi.org/10.2514/1.39202, 2009. a
Rogers, S., Cao, H., and Su, T.: Grid Generation for Complex High-Lift
Configurations, in: 29th AIAA, Fluid Dynamics Conference, 15–18 June, Albuquerque, NM, p. 3011, https://doi.org/10.2514/6.1998-3011, 1998. a
Saberi, H., Khoshlahjeh, M., Ormiston, R. A., and Rutkowski, M. J.: Overview
of RCAS and Application to Advanced Rotorcraft Problems, in: American
Helicopter Society 4th Decennial Specialists' Conference on Aaeromechanics, 21–23 January, San Francisco, CA, 2004. a
Sankaran, V., Sitaraman, J., Wissink, A., Datta, A., Jayaraman, B., Potsdam,
M., Mavriplis, D., Yang, Z., O'Brien, D., Saberi, H., et al.: Application of
the Helios Computational Platform to Rotorcraft Flow Fields, AIAA J.
1230, 2010, https://doi.org/10.2514/6.2010-1230, 2010. a
Schmid, P. J.: Dynamic mode decomposition of numerical and experimental data,
J. Fluid Mech., 656, 5–28, https://doi.org/10.1017/S0022112010001217, 2010. a
Sengers, B. A. M., Zech, M., Jacobs, P., Steinfeld, G., and Kühn, M.: A
physically interpretable data-driven surrogate model for wake steering, Wind
Energ. Sci., 7, 1455–1470, https://doi.org/10.5194/wes-7-1455-2022, 2022. a
Sieber, M., Paschereit, C. O., and Oberleithner, K.: Spectral Proper
Orthogonal Decomposition, J. Fluid Mech., 792, 798–828, https://doi.org/10.1017/jfm.2016.103, 2016. a
Sirovich, L.: Turbulence and the dynamics of coherent structures. I. Coherent structures, Q. Appl. Math., 45, 561–571, https://doi.org/10.1090/qam/910462, 1987. a
Smagorinsky, J.: General Circulation Experiments with the Primitive Equations: I. The Basic Experiment, Mon. Weather Rev., 91, 99–164,
https://doi.org/10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2, 1963.
Sood, I., Simon, E., Vitsas, A., Blockmans, B., Larsen, G. C., and Meyers, J.: Comparison of large eddy simulations against measurements from the Lillgrund offshore wind farm, Wind Energ. Sci., 7, 2469–2489, https://doi.org/10.5194/wes-7-2469-2022, 2022. a
Spalart, P. and Allmaras, S.: A One-Equation Turbulence Model for Aerodynamic Flows, in: 30th Aerospace Sciences Meeting and Exhibit, 6–9 January, Reno, NV, p. 439, https://doi.org/10.2514/6.1992-439, 1992. a
Spalart, P. R.: Comments on the Feasibility of LES for Wings, and on a Hybrid RANS/LES Approach, in: Proceedings of first AFOSR international conference on DNS/LES, Greyden Press,
https://cir.nii.ac.jp/crid/1571698599231647232 (last access: November 2021), 1997.
Stanly, R., Martinez-Tossas, L. A., Frankel, S. H., and Delorme, Y.: Large-eddy simulation of a wind turbine using a filtered actuator line model, J. Wind Eng. Indust. Aerodynam., 222, 104868, https://doi.org/10.1016/j.jweia.2021.104868, 2022. a
Walatka, P. P.: PLOT3D User's Manual, vol. 101067, NASA, https://ntrs.nasa.gov/citations/19900013774 (last access: November 2021), 1990. a
Wang, H. and Zhai, Z. J.: Advances in building simulation and computational
techniques: A review between 1987 and 2014, Energ. Build., 128, 319–335, https://doi.org/10.1016/j.enbuild.2016.06.080, 2016. a
Wissink, A. M., Sitaraman, J., Jayaraman, B., Roget, B., Lakshminarayan, V. K., Potsdam, M. A., Jain, R., Bauer, A., and Strawn, R.: Recent Advancements in the Helios Rotorcraft Simulation Code, in: 54th AIAA Aerospace Sciences Meeting, 4–8 January, San Diego, CA, p. 0563, https://doi.org/10.2514/6.2016-0563, 2016.
a
Wissink, A. M., Jayaraman, B., Tran, S. A., Jain, R., Potsdam, M. A.,
Sitaraman, J., Roget, B., and Lakshminarayan, V. K.: Assessment of
Rotorcraft Download Using Helios v8, in: 2018 AIAA Aerospace Sciences
Meeting, 8–12 January, Kissimmee, FL, p. 0026, https://doi.org/10.2514/6.2018-0026, 2018. a
Yeo, H., Bosworth, J., Acree Jr., C., and Kreshock, A. R.: Comparison of CAMRAD II and RCAS Predictions of Tiltrotor Aeroelastic Stability, J. Am. Helicopt. Soc., 63, 1–13, https://doi.org/10.4050/JAHS.63.022001, 2018. a
Yonekura, K. and Suzuki, K.: Data-Driven Design Exploration Method using
Conditional Variational Autoencoder for Airfoil Design, Struct. Multidisciplin. Optimiz., 64, 613–624, https://doi.org/10.1007/s00158-021-02851-0, 2021. a
Zehtabiyan-Rezaie, N., Iosifidis, A., and Abkar, M.: Data-driven fluid
mechanics of wind farms: A review, J. Renew. Sustain. Energ., 14, 032703, https://doi.org/10.1063/5.0091980, 2022. a
Short summary
Wind turbines have increasingly been leveraged as a viable approach for obtaining renewable energy. As such, it is essential that engineers have a high-fidelity, low-cost approach to modeling rotor load distributions. In this study, such an approach is proposed. This modeling approach was shown to make high-fidelity predictions at a low computational cost for rotor distributed-pressure loads as rotor geometry varied, allowing for an optimization of the rotor to be completed.
Wind turbines have increasingly been leveraged as a viable approach for obtaining renewable...
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