Publications

Main contributions in Blades & Rotor Systems, Wind Flow & Power Modeling & Measurements, Control Systems, Energy Storage & Grid Integration, Foundations & Towers and Future Concepts, are as follows:

Journal Publications 

Year-to-date 

  1. Ahmed, W. U., & Iungo, G. V. (2024). Effects of wind shear and thrust coefficient on the induction zone of a porous disk: A wind tunnel study. Wind Energy. https://doi.org/10.1002/we.2910
  2. Aju, E. J., Gong, P., Kumar, D., Rotea, M. A., & Jin, Y. (2024). Power output fluctuations and unsteady aerodynamic loads of a scaled wind turbine subjected to periodically oscillating wind environments. Journal of Renewable and Sustainable Energy, 16(5). https://doi.org/10.1063/5.0219853
  3. Bernardoni, F., Rotea, M. A., & Leonardi, S. (2024). Impact of yaw misalignment on turbine loads in the presence of wind farm blockage. Wind Energy. https://doi.org/10.1002/we.2899
  4. Bian, N., Ren, Y., Shrivastava, A., Wang, Z., Yang, D. J., Roy, S., Baughman, R., & Lu, H. (2024). Enhancing the interlaminar adhesion of carbon fiber composites via carbon nanotube sheets. Academia Materials Science. https://doi.org/10.20935/acadmatsci6206
  5. Cao, D., Bouzolin, D., Paniagua, C., Lu, H., & Griffith, D. T. (2024). Effect of process parameters on the mechanical performance of fusion-joined additively manufactured segments. Rapid Prototyping Journal. https://doi.org/10.1108/rpj-09-2023-0319
  6. Cao, D., Lu, H., & Griffith, D. T. (2024). Cohesive zone modeling of the buckling behavior of a fusion-joined, additive-manufactured wind blade. Academia Materials Science. https://doi.org/10.20935/acadmatsci7281
  7. Cao, D., Bouzolin, D., Lu, H., & Griffith, D. T. (2024). Enhanced joining strength in additive-manufactured polylactic-acid structures fused by embedded heated metallic meshes. Journal of Manufacturing Processes, 121, 100–120. https://doi.org/10.1016/j.jmapro.2024.04.089
  8. Cao, D., Xu, T., Zhang, M., Wang, Z., Griffith, D. T., Roy, S., Baughman, R. H., & Lu, H. (2024). Strengthening sandwich composites by laminating ultra-thin oriented carbon nanotube sheets at the skin/core interface. Composites Part B: Engineering, 111496. https://doi.org/10.1016/j.compositesb.2024.111496
  9. Chen, Y., & Griffith, D. T. (2024). Model-free dynamic response prediction at unmeasured locations for three-dimensional structures based on polynomial shape functions. Journal of Vibration Engineering & Technologies. https://doi.org/10.1007/s42417-024-01311-5
  10. Iungo, G. V., Guala, M., Hong, J., Bristow, N., Puccioni, M., Hartford, P., Ehsani, R., Letizia, S., Li, J., & Moss, C. (2024). Grand-scale atmospheric imaging apparatus (gaia) and wind lidar multiscale measurements in the atmospheric surface layer. Bulletin of the American Meteorological Society, 105(1). https://doi.org/10.1175/bams-d-23-0066.1
  11. Moss, C., Maulik, R., & Iungo, G. V. (2024). Augmenting insights from wind turbine data through data-driven approaches. Applied Energy, 376, 124116. https://doi.org/10.1016/j.apenergy.2024.124116
  12. Moss, C., Maulik, R., & Iungo, G. V. (2024). A call for enhanced data-driven insights into wind energy flow physics. Theoretical and Applied Mechanics Letters, 14(1), 100488. https://doi.org/10.1016/j.taml.2023.100488
  13. Puccioni, M., Moss, C. F., Solari, M. S., Roy, S., Iungo, G. V., Wharton, S., & Moriarty, P. (2024). Quantification and assessment of the atmospheric boundary layer height measured during the awaken experiment by a scanning lidar. Journal of Renewable and Sustainable Energy, 16(5). https://doi.org/10.1063/5.0211259
  14. Yousefi, K., Hora, G. S., Yang, H., Veron, F., & Giometto, M. G. (2024). A machine learning model for reconstructing skin-friction drag over ocean surface waves. Journal of Fluid Mechanics, 983. https://doi.org/10.1017/jfm.2024.81
  15. Zhang, Y., Kehtarnavaz, N., Rotea, M., & Dasari, T. (2024a). Prediction of icing on wind turbines based on SCADA data via temporal convolutional network. Energies, 17(9), 2175. https://doi.org/10.3390/en17092175

Referred Conference Publications 

Year-to-date 

  1. Abootorabi, S., Leonardi, S., Rotea, M., & Zare, A. (2024). Short-term wind forecasting using surface pressure measurements. 2024 American Control Conference (ACC), 1024–1029. https://doi.org/10.23919/acc60939.2024.10644987
  2. Ahmed, W. U., Moss, C., Roy, S., Shams Solari, M., Puccioni, M., Panthi, K., Moriarty, P., & Iungo, G. V. (2024). Wind Farm wakes and farm-to-farm interactions: Lidar and wind tunnel tests. Journal of Physics: Conference Series, 2767(9), 092105. https://doi.org/10.1088/1742-6596/2767/9/092105
  3. Bernardi, C., Manganelli, F., Leonardi, S., Cherubini, S., & De Palma, P. (2024). Assessing the effect of turbine size on the coherent structures in the wake using DMD. Journal of Physics: Conference Series, 2767(9), 092019. https://doi.org/10.1088/1742-6596/2767/9/092019
  4. Bodini, N., Abraham, A., Doubrawa, P., Letizia, S., Thedin, R., Agarwal, N., Carmo, B., Cheung, L., Corrêa Radünz, W., Gupta, A., Goldberger, L., Hamilton, N., Herges, T., Hirth, B., Iungo, G. V., Jordan, A., Kaul, C., Klein, P., Krishnamurthy, R., … Wharton, S. (2024). An international benchmark for wind plant wakes from the American Wake Experiment (awaken). Journal of Physics: Conference Series, 2767(9), 092034. https://doi.org/10.1088/1742-6596/2767/9/092034
  5. Bouzolin, D., Settelmaier, K., & Todd Griffith, D. (2024). Design for repowering of wind farms: An initial framework. Journal of Physics: Conference Series, 2767(8), 082009. https://doi.org/10.1088/1742-6596/2767/8/082009
  6. Dai, J., Zhang, Y., Rotea, M., & Kehtarnavaz, N., A review of machine learning approaches for prediction of icing on wind turbines, Proceedings of IEEE Conference on Industrial Electronics and Applications, Norway, August 2024.
  7. Escalera Mendoza, A.S., Griffith, D.T., dos Santos, C., Abdelmoteleb, S., Bachynski-Polić, E., and Oggiano, L., Definition of a Baseline Rotor Design for a 25MW Floating Offshore Wind Turbine, DeepWind 2024 Conference, Trondheim, Norway, January 2024.
  8. Hossain, M. S., Escalera Mendoza, A. S., Ahsan, F., Todd Griffith, D., Brownstein, I., Strom, B., & Frye, A. (2024). Design of a floating vertical axis wind turbine for wind-Wave Basin experiments. Journal of Physics: Conference Series, 2767(6), 062007. https://doi.org/10.1088/1742-6596/2767/6/062007
  9. Lee, Y.H., Bayat, S., Allison, J.T., Hossain, M.S., and Griffith, D.T., “Modeling and Control Co-Design of a Floating Offshore Vertical-axis Wind Turbine System,” Proceedings of the ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC2024), August 25–28, 2024, Washington, DC Paper: DETC2024-143495.
  10. Li, H., Li, M., Carroll, J., & Zhang, J. (2024). Techno-economic analysis incorporating intelligent operation and Maintenance Management: A case study of an integrated offshore wind and Hydrogen Energy System. Journal of Physics: Conference Series, 2767(6), 062016. https://doi.org/10.1088/1742-6596/2767/6/062016
  11. Lingad, M. V., Rodrigues, M., Leonardi, S., & Zare, A. (2024). Three-dimensional stochastic dynamical modeling for wind farm flow estimation. Journal of Physics: Conference Series, 2767(5), 052065. https://doi.org/10.1088/1742-6596/2767/5/052065
  12. Naseri, M., and Zare, A., Model-based analysis of turbulent channel flow over riblets via change of coordinates, In Proceedings of the 2024 AIAA Aviation Forum, Las Vegas, NV, p. 3715 (13 pages), 2024.
  13. Phadnis, M., Escalera Mendoza, A. S., Jeong, M., Loth, E., Todd Griffith, D., Pusch, M., & Pao, L. (2024). Comparison of 25 MW downwind and upwind turbine designs with individual pitch control. Journal of Physics: Conference Series, 2767(3), 032039. https://doi.org/10.1088/1742-6596/2767/3/032039
  14. Rotea, M. A., Kumar, D., Aju, E. J., & Jin, Y. (2024). Multi-row extremum seeking for Wind Farm Power Maximization. Journal of Physics: Conference Series, 2767(3), 032043. https://doi.org/10.1088/1742-6596/2767/3/032043
  15. Tubije, J. M., Jin, Y., & Leonardi, S. (2024). Numerical investigation of effects of Riblets on wind turbine performance. Journal of Physics: Conference Series, 2767(2), 022023. https://doi.org/10.1088/1742-6596/2767/2/022023
  16. Yan, J., Senemmar, S., & Zhang, J. (2024). Inter-turn Short Circuit Fault Diagnosis and severity estimation for wind turbine generators. Journal of Physics: Conference Series, 2767(3), 032021. https://doi.org/10.1088/1742-6596/2767/3/032021
  17. Yousefi, K., Hora, G. S., Yang, H., & Giometto, M. (2024). Data-driven MET-ocean model for offshore wind energy applications. Journal of Physics: Conference Series, 2767(5), 052005. https://doi.org/10.1088/1742-6596/2767/5/052005

Archival Journal Publications

2023
  1. Abootorabi, S., & Zare, A. (2023). Model-based spectral coherence analysis. Journal of Fluid Mechanics, 958. https://doi.org/10.1017/jfm.2023.82
  2. Aju, E. J., Kumar, D., Leffingwell, M., Rotea, M. A., & Jin, Y. (2023). The influence of yaw misalignment on turbine power output fluctuations and unsteady aerodynamic loads within wind farms. Renewable Energy, 215, 118894. https://doi.org/10.1016/j.renene.2023.06.015
  3. Ahsan, F., & Todd Griffith, D. (2023). Impact of aerodynamic modeling assumptions on flutter speeds of vertical-axis wind turbines. AIAA Journal, 1–13. https://doi.org/10.2514/1.j062912
  4. Bernardi, C., Porcacchia, F., Testa, C., De Palma, P., Leonardi, S., & Cherubini, S. (2023). NREL-5MW wind turbine noise prediction by FWH-Les. International Journal of Turbomachinery, Propulsion and Power, 8(4), 54. https://doi.org/10.3390/ijtpp8040054
  5. Bhatt, A. H., Rodrigues, M., Bernardoni, F., Leonardi, S., & Zare, A. (2023). Stochastic dynamical modeling of Wind Farm Turbulence. Energies, 16(19), 6908. https://doi.org/10.3390/en16196908
  6. Boo, S. Y., Shelley, S. A., Griffith, D. T., & Escalera Mendoza, A. S. (2023). Responses of a modular floating wind TLP of MARSVAWT supporting a 10 MW vertical axis wind turbine. Wind, 3(4), 513–544. https://doi.org/10.3390/wind3040029
  7. Cao, D., Bouzolin, D., Lu, H., & Griffith, D. T. (2023). Bending and shear improvements in 3D-printed core sandwich composites through modification of resin uptake in the skin/core interphase region. Composites Part B: Engineering, 264, 110912. https://doi.org/10.1016/j.compositesb.2023.110912
  8. Chen, Y., Jacob, R. A., Gel, Y. R., Zhang, J., & Poor, H. V. (2023). Learning Power Grid outages with higher-order topological neural networks. IEEE Transactions on Power Systems, 1–13. https://doi.org/10.1109/tpwrs.2023.3266956
  9. Chen, Y., & Griffith, D. T. (2023). Blade mass imbalance identification and estimation for three-bladed wind turbine rotor based on modal analysis. Mechanical Systems and Signal Processing, 197, 110341. https://doi.org/10.1016/j.ymssp.2023.11034
  10. Chen, Y., & Griffith, D. T. (2023). Experimental and numerical full-field displacement and strain characterization of wind turbine blade using a 3D scanning laser Doppler vibrometer. Optics & Laser Technology, 158, 108869.
  11. Ciri, U., Tubije, J. M., Guzmán-Hernandez, M. A., Rodríguez-Abudo, S., & Leonardi, S. (2023). Direct numerical simulations of oscillatory boundary layers over rough walls. International Journal of Heat and Fluid Flow, 103, 109170 https://doi.org/10.1016/j.ijheatfluidflow.2023.109170
  12. Debnath, M., Moriarty, P., Krishnamurthy, R., Bodini, N., Newsom, R., Quon, E., Lundquist, J. K., Letizia, S., Iungo, G. V., & Klein, P. (2023). Characterization of wind speed and directional shear at the Awaken Field Campaign Site. Journal of Renewable and Sustainable Energy, 15(3). https://doi.org/10.1063/5.0139737
  13. Escalera Mendoza, A. S., Griffith, D. T., Jeong, M., Qin, C., Loth, E., Phadnis, M., Pao, L., & Selig, M. S. (2023). Aero-structural rapid screening of new design concepts for offshore wind turbines. Renewable Energy, 219, 119519. https://doi.org/10.1016/j.renene.2023.119519
  14. Iungo, G. V., Guala, M., Hong, J., Bristow, N., Puccioni, M., Hartford, P., Ehsani, R., Letizia, S., Li, J., & Moss, C. (2023). Grand-scale atmospheric imaging apparatus (gaia) and wind lidar multi-scale measurements in the atmospheric surface layer. Bulletin of the American Meteorological Society. https://doi.org/10.1175/bams-d-23-0066.1
  15. Jacob, R. A., & Zhang, J. (2023). Modeling and control of nuclear–renewable integrated energy systems: Dynamic system model for green electricity and hydrogen production. Journal of Renewable and Sustainable Energy, 15(4). https://doi.org/10.1063/5.0139875
  16. Johnson, S. B., Chetan, M., Griffith, D. T., & Sherwood, J. (2023). A design-driven wind blade manufacturing model to identify opportunities to reduce wind blade costs. Renewable Energy, 215, 118945. https://doi.org/10.1016/j.renene.2023.118945
  17. Kaminski, M., Simpson, J., Loth, E., Fingersh, L. J., Scholbrock, A., Johnson, N., Johnson, K., Pao, L., & Griffith, T. (2023). Gravo-aeroelastically-scaled demonstrator field tests to represent blade response of a flexible extreme-scale downwind turbine. Renewable Energy, 218, 119217. https://doi.org/10.1016/j.renene.2023.119217
  18. Kumar, D., Rotea, M. A., Aju, E. J., & Jin, Y. (2023). Wind plant power maximization via extremum seeking yaw control: A wind tunnel experiment. Wind Energy, 26(3), 283–309. https://doi.org/10.1002/we.2799
  19. Li, H., & Zhang, J. (2023). Towards sustainable integration: Techno-economic analysis and future perspectives of co-located wind and Hydrogen Energy Systems. Journal of Mechanical Design, 1–22. https://doi.org/10.1115/1.4063971
  20. Moss, C., Puccioni, M., Maulik, R., Jacquet, C., Apgar, D., & Valerio Iungo, G. (2023). Profiling wind lidar measurements to quantify blockage for onshore wind turbines. Wind Energy. https://doi.org/10.1002/we.2877
  21. Moss, C., Maulik, R., Moriarty, P., & Iungo, G. V. (2023). Predicting Wind Farm operations with Machine Learning and the p2d‐rans model: A case study for an awaken site. Wind Energy. https://doi.org/10.1002/we.2874
  22. Moss, C., Maulik, R., & Iungo, G. V. (2023). A call for enhanced data-driven insights into wind energy flow physics. Theoretical and Applied Mechanics Letters, 100488. https://doi.org/10.1016/j.taml.2023.100488
  23. Puccioni, M., Moss, C., & Iungo, G. V. (2023). Coupling wind lidar fixed and volumetric scans for enhanced characterization of wind turbulence and flow three‐dimensionality. Wind Energy. https://doi.org/10.1002/we.2865
  24. Puccioni, M., Calaf, M., Pardyjak, E. R., Hoch, S., Morrison, T. J., Perelet, A., & Iungo, G. V. (2023). Identification of the energy contributions associated with wall-attached eddies and very-large-scale motions in the near-neutral atmospheric surface layer through wind lidar measurements. Journal of Fluid Mechanics, 955. https://doi.org/10.1017/jfm.2022.1080
  25. Puccioni, M., Moss, C. F., Jacquet, C., & Iungo, G. V. (2023). Blockage and speedup in the proximity of an onshore wind farm: A scanning wind lidar experiment. Journal of Renewable and Sustainable Energy, 15(5). https://doi.org/10.1063/5.0157937
  26. Rahman, J., & Zhang, J. (2023). Multi-timescale operations of nuclear-renewable hybrid energy systems for reserve and thermal product provision. Journal of Renewable and Sustainable Energy, 15(2), 025901. https://doi.org/10.1063/5.0138648
  27. Sadman Sakib, M., Todd Griffith, D., Hossain, S., Bayat, S., & Allison, J. T. (2023). Intracycle rpm control for vertical axis wind turbines. Wind Energy. https://doi.org/10.1002/we.2885
  28. Wu, J., Chen, X., Badakhshan, S., Zhang, J., & Wang, P. (2023). Spectral graph clustering for intentional islanding operations in Resilient Hybrid Energy Systems. IEEE Transactions on Industrial Informatics, 19(4), 5956–5964. https://doi.org/10.1109/tii.2022.3199240
  29. Wu, Z., & Li, Y. (2023). Optimal control of wind farm power output with delay compensated nested-loop extreme seeking control. Journal of Renewable and Sustainable Energy, 15(4). https://doi.org/10.1063/5.0134878
  30. Zhang, Junqiang, Chowdhury, S., Zhang, J., Tong, W. and Messac, A., “Optimal Selection of Time Windows for Preventive Maintenance of Offshore Wind Farms Subject to Wake Losses,” Wind Energy, 2023. (in press)
  31. Zhang, R., Liu, Y., Zheng, T., Eddin, S., Nolet, S., Liang, Y.-L., Rezazadeh, S., Wilson, J., Lu, H., & Qian, D. (2023). A fast spatio-temporal temperature predictor for vacuum assisted resin infusion molding process based on Deep Machine Learning Modeling. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-023-02113-4
  32. Zani, Md. R., Maor, N. S., Bhamitipadi Suresh, D., & Jin, Y. (2024). Turbulent boundary layer control with multi-scale Riblet design. Energies, 17(15), 3827. https://doi.org/10.3390/en17153827

2022
  1. Kumar, D., Rotea, M. A., Aju, E. J., & Jin, Y. (2022). Wind plant power maximization via extremum seeking yaw control: A wind tunnel experiment. Wind Energy, 26(3), 283–309. doi:10.1002/we.2799
  2. Kianbakht, S., Martin, D., Johnson, K., Zalkind, D., Pao, L., Loth, E., Simpson, J., Yao, S., Chetan, M., & Griffith, D. T. (2022). Design space exploration and decision‐making for a segmented ultralight morphing 50‐MW Wind turbine. Wind Energy, 25(12), 2016–2035. doi:0.1002/we.2781
  3. Panthi, K., & Iungo, G. V. (2022). Quantification of wind turbine energy loss due to leading‐edge erosion through infrared‐camera imaging, Numerical Simulations, and assessment against SCADA and Meteorological Data. Wind Energy, 26(3), 266–282. doi:10.1002/we.2798
  4. Della Posta, G., Leonardi, S., & Bernardini, M. (2022). Large eddy simulations of a utility‐scale horizontal axis wind turbine including unsteady aerodynamics and fluid‐structure interaction modelling. Wind Energy, 26(1), 98–125. doi:10.1002/we.2789
  5. Liu, C., Gupta, A., & Rotea, M. A. (2022). Wind turbine gust load alleviation with active flow control. Energies, 15(17), 6474. doi:10.3390/en15176474
  6. De Cillis, G., Semeraro, O., Leonardi, S., De Palma, P., & Cherubini, S. (2022). Dynamic-mode-decomposition of the wake of the NREL-5MW wind turbine impinged by a laminar inflow. Renewable Energy, 199, 1–10. doi:10.1016/j.renene.2022.08.113
  7. Chen, Y., Escalera Mendoza, A. S., & Griffith, D. T. (2022). Experimental dynamic characterization of both surfaces of structures using 3D scanning laser Doppler vibrometer. Experimental Techniques. doi:10.1007/s40799-022-00604-2
  8. Chetan, M., Yao, S., & Griffith, D. T. (2022). Flutter behavior of highly flexible blades for two- and three-bladed wind turbines. Wind Energy Science, 7(4), 1731–1751. doi:10.5194/wes-7-1731-2022
  9. Nanos, E. M., Bottasso, C. L., Campagnolo, F., Mühle, F., Letizia, S., Iungo, G. V., & Rotea, M. A. (2022). Design, steady performance and wake characterization of a scaled wind turbine with pitch, torque and Yaw Actuation. Wind Energy Science, 7(3), 1263–1287. doi:10.5194/wes-7-1263-2022
  10. Gong, P., Aju, E. J., & Jin, Y. (2022). On the aerodynamic loads and flow statistics of airfoil with deformable vortex generators. Physics of Fluids, 34(6), 067106. doi:10.1063/5.0092187
  11. Chen, Y., & Griffith, D. T. (2022). Experimental and numerical investigation of the structural dynamic characteristics for both surfaces of a wind turbine blade. Journal of Vibration and Control, 107754632210974. doi:10.1177/10775463221097470
  12. Johnson, S. B., Chetan, M., Griffith, D. T., & Sherwood, J. A. (2022). Development of high‐fidelity design‐driven wind blade manufacturing process models to investigate labor predictions in wind blade manufacture. Wind Energy, 25(8), 131-1331. doi:10.1002/we.2731
  13. Iungo, G. V., Maulik, R., Renganathan, S. A., & Letizia, S. (2022). Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster Analysis of Wind Lidar Measurements. Journal of Renewable and Sustainable Energy, 14(2), 023307. doi:10.1063/5.0070094
  14. Della Posta, G., Leonardi, S., & Bernardini, M. (2022). A two-way coupling method for the study of aeroelastic effects in large wind turbines. Renewable Energy, 190, 971-992. doi:10.1016/j.renene.2022.03.158
  15. Sakib, M. S., & Griffith, D. T. (2022). Parked and operating load analysis in the aerodynamic design of multi-megawatt-scale floating vertical-axis wind turbines. Wind Energy Science, 7(2), 677–696. doi:10.5194/wes-7-677-2022
  16. Letizia, S., & Iungo, G. V. (2022). Pseudo-2D Rans: A LIDAR-driven mid-fidelity model for simulations of wind farm flows. Journal of Renewable and Sustainable Energy, 14(2), 023301. doi:10.1063/5.0076739
  17. Zhang, B., Jin, Y., Cheng, S., Zheng, Y., & Chamorro, L. P. (2022). On the dynamics of a model wind turbine under passive tower oscillations. Applied Energy, 311, 118608. doi:10.1016/j.apenergy.2022.118608
  18. Kumar, D., & Rotea, M. A. (2022). Wind turbine power maximization using log-power proportional-integral extremum seeking. Energies, 15(3), 1004. doi:10.3390/en15031004
  19. Ahsan, F., Griffith, D. T., & Gao, J. (2022). Modal Dynamics and flutter analysis of floating offshore vertical axis wind turbines. Renewable Energy, 185, 1284–1300. doi:10.1016/j.renene.2021.12.041
  20. Haus, L. C., Griffith, D. T., Coe, R. G., & Bacelli, G. (2022). Development and characterization of a coupled structural dynamics model for the Sandia Wave Energy Converter Testbed. Journal of Ocean Engineering and Marine Energy, 8(2), 117-135. doi:10.1007/s40722-021-00220-z
  21. Aju, E. J., Gong, P., Pham, D. T., Kaushik, K., & Jin, Y. (2022). On the wake dynamics and thrust generation of a foil flapping over solid and sedimentary beds. Experiments in Fluids, 63(1). doi:10.1007/s00348-022-03386-w
  22. Escalera Mendoza, A. S., Yao, S., Chetan, M., & Griffith, D. T. (2022). Design and analysis of a segmented blade for a 50 MW wind turbine rotor. Wind Engineering, 46(4), 1146-1172. doi:10.1177/0309524×211069393
  23. Ashwin Renganathan, S., Maulik, R., Letizia, S., & Iungo, G. V. (2022). Data-driven wind turbine wake modeling via Probabilistic Machine Learning. Neural Computing and Applications, 34(8), 6171-6186. doi:10.1007/s00521-021-06799-6
  24. De Cillis, G., Cherubini, S., Semeraro, O., Leonardi, S., & De Palma, P. (2022). The influence of incoming turbulence on the dynamic modes of an NREL-5MW wind turbine wake. Renewable Energy, 183, 601-616. doi:10.1016/j.renene.2021.11.037
  25. Gao, J., Griffith, D. T., Sakib, M. S., & Boo, S. Y. (2022). A semi-coupled aero-servo-hydro numerical model for floating vertical axis wind turbines operating on TLPS. Renewable Energy, 181, 692–713. doi:10.1016/j.renene.2021.09.076
  26. De Cillis, G., Cherubini, S., Semeraro, O., Leonardi, S., & De Palma, P. (2022). Stability and optimal forcing analysis of a wind turbine wake: Comparison with pod. Renewable Energy, 181, 765–785. doi:10.1016/j.renene.2021.09.025

2021
  1. Chen, Y., & Griffith, D. T. (2021). Mode shape recognition of complicated spatial beam-type structures via polynomial shape function correlation. Experimental Techniques, 46(6), 905–917. doi:10.1007/s40799-021-00505-w
  2. Chetan, M., Sakib, M. S., Griffith, D. T., Gupta, A., & Rotea, M. A. (2021). Design of a 3.4‐MW wind turbine with integrated plasma actuator‐based Load Control. Wind Energy, 25(3), 517–536. doi:10.1002/we.2684
  3. Bernardoni, F., Ciri, U., Rotea, M. A., & Leonardi, S. (2021). Identification of wind turbine clusters for effective real time yaw control optimization. Journal of Renewable and Sustainable Energy, 13(4), 043301. doi:10.1063/5.0036640
  4. Cao, D., Malakooti, S., Kulkarni, V. N., Ren, Y., Liu, Y., Nie, X., Qian, D., Griffith, D. T., & Lu, H. (2021). The effect of resin uptake on the flexural properties of compression molded sandwich composites. Wind Energy, 25(1), 71–93. doi:10.1002/we.2661
  5. Chen, Y., Escalera Mendoza, A. S., & Griffith, D. T. (2021). Experimental and numerical study of high-order complex curvature mode shape and mode coupling on a three-bladed wind turbine assembly. Mechanical Systems and Signal Processing, 160, 107873. doi:10.1016/j.ymssp.2021.107873 
  6. Chetan, M., Yao, S., & Griffith, D. T. (2021). Multi‐Fidelity Digital Twin Structural Model for a sub‐scale downwind wind turbine rotor blade. Wind Energy, 24(12), 1368–1387. doi:10.1002/we.2636 
  7. Gupta, A., Rotea, M. A., Chetan, M., Sakib, M. S., & Griffith, D. T. (2021). A methodology for robust load reduction in wind turbine blades using flow control devices. Energies, 14(12), 3500. doi:10.3390/en14123500 
  8. Kaminski, M., Noyes, C., Loth, E., Damiani, R., Hughes, S., Bay, C., Chetan, M., Griffith, D. T Martin, D. (2021). Gravo-aeroelastic scaling of a 13-MW downwind rotor for 20% scale blades. Wind Energy, 24(3), 229-245. doi:10.1002/we.2569 
  9. Letizia, S., Zhan, L., & Iungo, G. V. (2021). LiSBOA (LiDAR statistical Barnes objective analysis) for optimal design of lidar scans and retrieval of wind statistics-part 1: Theoretical framework. Atmospheric Measurement Techniques, 14(3), 2065-2093. doi:10.5194/amt-14-2065-2021  
  10. Letizia, S., Zhan, L., & Iungo, G. V. (2021). LiSBOA (LiDAR statistical Barnes objective analysis) for optimal design of lidar scans and retrieval of wind statistics-part 2: Applications to lidar measurements of wind turbine wakes. Atmospheric Measurement Techniques, 14(3), 2095-2113. doi:10.5194/amt-14-2095-2021 
  11. Pao, L. Y., Zalkind, D. S., Griffith, D. T., Chetan, M., Selig, M. S., Ananda, G. K. & Loth, E. (2021). Control co-design of 13 MW downwind two-bladed rotors to achieve 25% reduction in levelized cost of wind energy. Annual Reviews in Control, 51, 331-343. doi:10.1016/j.arcontrol.2021.02.001
  12. Yao, S., Chetan, M., & Griffith, D. T. (2021). Structural design and optimization of a series of 13.2 MW downwind rotors. Wind Engineering, 45(6), 1459-1478. doi:10.1177/0309524X20984164 
  13. Yao, S., Chetan, M., Griffith, D. T., Escalera Mendoza, A. S., Selig, M. S., Martin, D., Kianbakht, S., Johnson, K., & Loth, E. (2021). Aero-structural design and optimization of 50 MW wind turbine with over 250-M blades. Wind Engineering, 46(1), 273–295. doi:10.1177/0309524×211027355

2020
  1. Aju, E. J., Suresh, D. B., & Jin, Y. (2020). The influence of winglet pitching on the performance of a model wind turbine: Aerodynamic loads, rotating speed, and wake statistics. Energies, 13(19), 5199. doi:10.3390/en13195199
  2. Ashuri, T., Li, Y. & Hosseini S. E. (2020). Recovery of energy losses using an online data-driven optimization technique. Energy Conversion and Management, Vol. 225, 113339, doi:10.1016/j.enconman.2020.113339 
  3. Cao D., Malakooti S., Kulkarni V. N., Ren Y & Lu H. (2020). Nanoindentation measurement of core-skin interphase viscoelastic properties in a sandwich glass composite. Mechanics of Time-Dependent Materials, 25(3),353-363. doi: 10.1007/s11043-020-09448-y
  4. Li, B., Sedzro, K., Fang, X., Hodge, B.-M., & Zhang, J. (2020). A clustering-based scenario generation framework for power market simulation with Wind Integration. Journal of Renewable and Sustainable Energy, 12(3), 036301. doi:10.1063/5.0006480
  5. De Cillis G., Cherubini S., Semeraro O., Leonardi S. & De Palma P. (2020). The role of coherent structures in wind turbine wakes, Wind Energy, 24(6), 609-633. doi:10.1002/we.2592 
  6. Kaminski, M., Loth, E., Griffith, D. T. & Qin, C. C. (2020). Ground testing of a 1% gravo-aeroelastically scaled additively-manufactured wind turbine blade with bio-inspired structural design. Renewable Energy, 148, 639-650. doi:10.1016/j.renene.2019.10.152 
  7. Kumar, D., Li, Y. & Wu, Z. (2020). Power-setpoint Extremum Seeking Control for Maximizing Wind Power Capture of Turbine and Farm Operation. Wind Engineering, 45(5), 1340-1360. doi:10.1177/0309524X20979914
  8. Li, B., Ofori-Boateng, D., Gel, Y. R., & Zhang, J. (2020). A hybrid approach for transmission grid resilience assessment using reliability metrics and power system local network topology. Sustainable and Resilient Infrastructure, 6(1-2), 26–41. doi:10.1080/23789689.2019.1708182
  9. Qin, C., Loth, E., Zalkind, D., Pao, L., Yao, S., Griffith, D.T., Selig, M., & Damiani, R. (2020). Downwind coning concept rotor for a 25 MW offshore wind turbine. Renewable Energy, 156, 314-327. doi:10.1016/j.renene.2020.04.039
  10. Rocchio B., Ciri U., Salvetti M. V.& Leonardi S. (2020). Appraisal and calibration of the Actuator Line Model for the prediction of turbulent separated wakes. Wind Energy, 23(5), 1231-1248,  doi:10.1002/we.2483 
  11. Santoni C., García-Cartagena E.J., Ciri U., Zhan L., Iungo G.V. & Leonardi S. (2020). One-way mesoscale-microscale coupling for simulating a wind farm in North Texas: Assessment against SCADA and LiDAR data. Wind Energy, 23(3), 691 – 710. doi:10.1002/we.2452 
  12. Wu, Z. & Li, Y. (2020). Nacelle Anemometer Measurement based Extremum-Seeking Wind Turbine Region-2 Control for Improved Convergence in Fluctuating Wind. Wind Energy, 23(4), 1118-1134. doi:10.1002/we.2477
  13. Wu, Z. & Li, Y. (2020). Platform Stabilization and Load Reduction of Floating Offshore Wind Turbines with Tension-leg Platform using Dynamic Vibration Absorbers. Wind Energy, 23(3), 711-730. doi:10.1002/we.2453
  14. Wu, Z. & Li, Y. (2020). Platform Stabilization of Floating Offshore Wind Turbines by Artificial Muscle based Active Mooring Line Force Control. IEEE/ASME Transactions on Mechatronics, 25(6), 2765-2776. doi:10.1109/TMECH.2020.2992711
  15. Xiao, Y., Fahimi, B., Rotea, M. A. & Li, Y. (2020). Multiple Reference Frame Based Torque Ripple Reduction in DFIG-DC System. IEEE Transactions on Power Electronics, 35(5), 4971-4983. doi:10.1109/tpel.2019.2941957
  16. Yao, S., Griffith, D. T., Chetan, M., Bay, C. J., Damiani, R., Kaminski, M., & Loth, E. (2020). A gravo-aeroelastically scaled wind turbine rotor at field-prototype scale with strict structural requirements. Renewable Energy, 156, 535-547. doi:10.1016/j.renene.2020.03.157
  17. Zhan, L., Letizia, S., & Iungo G. V. (2020). Optimal tuning of engineering wake models through lidar measurements. Wind Energy Science, 5(4), 1601-1622. doi:10.5194/wes-5-1601-2020
  18. Zhan L., Letizia S., & Iungo G.V. (2020). LiDAR measurements for an onshore wind farm: Wake variability for different incoming wind speeds and atmospheric stability regimes. Wind Energy, 23(3), 501 – 527. doi.org/10.1002/we.2430

Archival Referred Conference Publications 

2023
  1. Ahsan, F., & Griffith, D. T. (2023). Impact of relaxing assumptions of Theodorsen’s unsteady aerodynamic theory and edgewise aerodynamics on flutter prediction of floating vertical axis wind turbines. AIAA SCITECH 2023 Forum. https://doi.org/10.2514/6.2023-0590
  2. Bernardi, C., Della Posta, G., De Palma, P., Leonardi, S., Bernardoni, F., Bernardini, M., & Cherubini, S. (2023). The effect of the Tower’s modeling on the Aero-elastic response of the NREL 5 MW wind turbine. Journal of Physics: Conference Series, 2505(1), 012037. https://doi.org/10.1088/1742-6596/2505/1/012037
  3. Bernardoni, F., Guzman, M., & Leonardi, S. (2023). Implications of complex terrain topography on the performance of a Real Wind Farm. Journal of Physics: Conference Series, 2505(1), 012052. https://doi.org/10.1088/1742-6596/2505/1/012052
  4. Chen, Yuanchang, & Griffith, D. T. (2023). Blade mass imbalance identification and estimation for three-bladed wind turbine rotor based on modal analysis. Mechanical Systems and Signal Processing, 197, 110341. https://doi.org/10.1016/j.ymssp.2023.110341
  5. Escalera Mendoza, A. S., Mishra, I., & Griffith, D. T. (2023). An open-source NuMAD model for the IEA 15 MW blade with baseline structural analysis. AIAA SCITECH 2023 Forum. https://doi.org/10.2514/6.2023-2093
  6. Griffith, D. T., Cao, D., Lu, H., & Qian, D. (2023). Composite materials in wind energy: Design, manufacturing, operation, and end-of-life. IOP Conference Series: Materials Science and Engineering, 1293(1), 012002. https://doi.org/10.1088/1757-899x/1293/1/012002
  7. Harmjanz, J. F., Westergaard, T., & Griffith, D. T. (2023). Assessment of control methods for vertical axis wind turbines: Start-up, active flow control, and overspeed control. AIAA SCITECH 2023 Forum. https://doi.org/10.2514/6.2023-0612
  8. He, L., Zhang, J. and Hobbs, B., (2023). Estimation of Regulation Reserve Requirements in California ISO: A Data-driven Method, IEEE Power & Energy Society General Meeting.
  9. Kumar, D., & Rotea, M. (2023). Brief Communication: Real-Time Estimation of Optimal Tip-Speed Ratio for Controlling Wind Turbines with Degraded Blades. https://doi.org/10.5194/wes-2023-144
  10. Li, H., Feng, C. and Zhang, J., (2023). A Multi-Fidelity Gaussian Process Regression Method For Probabilistic Wind Farm Power Curve Estimation, ASME 2023 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Paper No. IDETC2023-114762.
  11. Li, H., Kiviluoma, J. and Zhang, J., (2023). Techno-Economic Analysis for Co-located Solar and Hydrogen Plants, 19th International Conference on the European Energy Market.
  12. Puccioni, M., Iungo, G. V., Moss, C., Solari, M. S., Letizia, S., Bodini, N., & Moriarty, P. (2023). Lidar measurements to investigate farm-to-farm interactions at the awaken experiment. Journal of Physics: Conference Series, 2505(1), 012045. https://doi.org/10.1088/1742-6596/2505/1/012045
  13. Rodrigues, M., Burgess, N. A., Bhatt, A. H., Leonardi, S., & Zare, A. (2023). Robustness of two-dimensional stochastic dynamical wake models for yawed wind turbines. 2023 American Control Conference (ACC). https://doi.org/10.23919/acc55779.2023.10156101
  14. Shelley, S.A., Boo, S.Y., Luyties, W.H., Griffith, D.T. (2023). How Texas Can Become the Leader in Offshore Wind in North and South America by Using New Technology and Leveraging Existing Infrastructure and Expertise, 2023 Offshore Technology Conference, Houston, Texas, USA, May 2023, Paper Number: OTC-32282-MS, DOI: https://doi.org/10.4043/32282-MS.
2022
  1. dos Santos, C. R., Abdelmoteleb, S.-E., Mendoza, A. S., Bachynski-Polic, E. E., Griffith, D. T., & Oggiano, L. (2022). Application of a PI-controller to a 25 MW floating wind turbine. 2022 IEEE 61st Conference on Decision and Control (CDC). doi:10.1109/cdc51059.2022.9992349
  2. Gao, J., Griffith, D. T., Jafari, M., Yao, S., & Ahsan, F. (2022). Impact of rotor solidity on the design optimization of floating vertical axis wind turbines. Volume 8: Ocean Renewable Energy. doi:10.1115/omae2022-78715
  3. Cherubini, S., Cillis, G. D., Semeraro, O., Leonardi, S., & Palma, P. D. (2022). How incoming turbulence affects wake recovery of an NREL-5MW wind turbine. Journal of Physics: Conference Series, 2385(1), 012139. doi:10.1088/1742-6596/2385/1/012139
  4. Abdelmoteleb, S.-E., Mendoza, A. S., Santos, C. R., Bachynski-Polić, E. E., Griffith, D. T., & Oggiano, L. (2022). Preliminary sizing and optimization of semisubmersible substructures for future generation offshore wind turbines. Journal of Physics: Conference Series, 2362(1), 012001. doi:10.1088/1742-6596/2362/1/012001
  5. Ciri, U., Rodriguez-Abudo, S., & Leonardi, S. (2022). Direct numerical simulations of oscillatory boundary layers over rough walls. Proceeding of Twelfth International Symposium on Turbulence and Shear Flow Phenomena. http://www.tsfp-conference.org/proceedings/2022/373.pdf
  6. Escalera Mendoza, A. S., Todd Grifth, D., Qin, C., Loth, E., & Johnson, N. (2022). Rapid approach for structural design of the Tower and monopile for a series of 25 MW offshore turbines. Journal of Physics: Conference Series, 2265(3), 032030. doi:10.1088/1742-6596/2265/3/032030
  7. Gupta, A., Rotea, M. A., Chetan, M., Sadman Sakib, M., & Todd Griffith, D. (2022). Effect of wind turbine size on load reduction with active flow control. Journal of Physics: Conference Series, 2265(3), 032093. doi:10.1088/1742-6596/2265/3/032093
  8. Jafari, M., Sakib, M. S., Griffith, D. T., Brownstein, I., Strom, B., & Cooney, J. (2022). Wind tunnel experiment to study aerodynamics and control of H-rotor vertical axis wind turbine. Journal of Physics: Conference Series, 2265(2), 022084. doi:10.1088/1742-6596/2265/2/022084
  9. Letizia, S., Moss, C., Puccioni, M., Jacquet, C., Apgar, D., & Valerio Iungo, G. (2022). Effects of the thrust force induced by wind turbine rotors on the incoming wind field: A wind lidar experiment. Journal of Physics: Conference Series, 2265(2), 022033. doi:10.1088/1742-6596/2265/2/022033
  10. Loth, E., Ananda, G., Chetan, M., Damiani, R., Todd Griffith, D., Johnson, K., Kianbakht, S., Kaminski, M., Pao, L., Phadnis, M., Qin, C. (C., Scholbrock, A., Selig, M., Simpson, J., & Yao, S. (2022). Field tests of a highly flexible downwind ultralight rotor to mimic a 13-MW turbine rotor. Journal of Physics: Conference Series, 2265(3), 032031. doi:10.1088/1742-6596/2265/3/032031
  11. Qin, C. (C., Loth, E., Zalkind, D. S., Pao, L. Y., Yao, S., Todd Griffith, D., Selig, M. S., & Damiani, R. (2022). Active rotor coning for a 25 MW downwind offshore wind turbine. Journal of Physics: Conference Series, 2265(3), 032022. doi:10.1088/1742-6596/2265/3/032022
  12. Li, H., Rahman, J., & Zhang, J. (2022). Optimal planning of co-located wind energy and hydrogen plants: A techno-economic analysis. Journal of Physics: Conference Series, 2265(4), 042063. doi:10.1088/1742-6596/2265/4/042063
  13. Ahmed, W. U., Panthi, K., Iungo, G. V., Griffith, D. T., Rotea, M., Cooney, J., Szlatenyi, C., Janik, A., Mickelson, P., & Fine, N. (2022). Wind-Tunnel Force-measurements of gurney flaps for active control of wind turbine blades. AIAA SCITECH 2022 Forum. doi:10.2514/6.2022-2294
2021
  1. Bush, B., Chen, Y., Ofori-Boateng, D. & Gel, Y.R. (2021). Topological Machine Learning Methods for Power System Responses to Contingencies. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15278-15285. doi:10.1609/aaai.v35i17.17791
  2. Chen Y., Escalera Mendoza, A., & Griffith D. T. (2021). Experimental and Analytical Study of High-order Complex Curvature Mode Shape and Mode Coupling on a Three-Bladed Wind Turbine Assembly. Proceedings of the 39th International Modal Analysis Conference. 
  3. Escalera Mendoza, A., Chetan, M., & Griffith, D. T. (2021). Quantification of Extreme-scale Wind Turbine Performance Parameters due to Variations in Beam Properties. AIAA Scitech 2021 Forum. doi:10.2514/6.2021-1603
  4. Gupta, A., & Rotea, M. A. (2021). Higher-harmonic load control of wind turbine blades with actuator saturation. 2021 American Control Conference (ACC). doi:10.23919/acc50511.2021.9483294
  5. Kumar, D., Gans, N., & Rotea, M. A. (2021). Multi-objective logarithmic extremum seeking for wind turbine power capture with load reduction. 2021 American Control Conference (ACC). doi:10.23919/acc50511.2021.9483187
  6. Kumar, D. & Rotea, M.A. (2021). Log-power PIESC for wind turbine power maximization below-rated wind conditions. AIAA SciTech 2021 Forum, doi:10.2514/6.2021-1288
  7. Maulik, R., Rao, V., Renganathan, S.A., Letizia, S. & Iungo, G.V. (2021). Cluster analysis of wind turbine wakes measured through a scanning doppler wind lidar. AIAA SciTech 2021 Forum, doi:10.2514/6.2021-1181
  8. Pu S., Yang F. & Akin B. (2021). Active Channel Impact on SiC MOSFET Gate Oxide Reliability.2021 IEEE Applied Power Electronics Conference and Exposition (APEC). doi:10.1109/apec42165.2021.9487362
  9. Rahman, J. & Zhang, J. (2021). Optimization of Nuclear-Renewable Hybrid Energy System Operation in Forward Electricity Market. 2021 IEEE Green Technologies Conference (GreenTech). doi:10.1109/greentech48523.2021.00078
  10. Yang F., Pu S., Wang G., Butler S. W., & Akin B. (2021). Package Degradation’s Impact on SiC MOSFETs Loss: A Comparison of Kelvin and Non-Kelvin Designs. 2021 IEEE Applied Power Electronics Conference and Exposition (APEC). doi:10.1109/apec42165.2021.9487466
  11. Zhang, N., Pu, S., & Akin, B. (2021). An automated multi-device characterization system for Reliability Assessment of Power Semiconductors. 2021 IEEE 13th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED). doi:10.1109/sdemped51010.2021.9605489
2020
  1. Bernardoni, F., Ciri, U., Rotea, M., & Leonardi, S. (2020). Real-time identification of clusters of turbines. Journal of Physics: Conference Series, 1618(2), 022032. doi:10.1088/1742-6596/1618/2/022032
  2. Chen, Y., Avrachenkov, K., Gel, Y.R. (2020). LFGCN: Levitating over Graphs with Levy Flights. 2020 IEEE International Conference on Data Mining (ICDM). doi:10.1109/icdm50108.2020.00109
  3. Ciri, U., Rotea, M. A., & Leonardi, S. (2020). Increasing wind farm efficiency by yaw control: Beyond ideal studies towards a realistic assessment. Journal of Physics: Conference Series, 1618(2), 022029. doi:10.1088/1742-6596/1618/2/022029
  4. De Cillis, G., Cherubini, S., Semeraro, O., Leonardi, S. & De Palma, P. (2020). POD analysis of the recovery process in wind turbine wakes. Journal of Physics: Conference Series, 1618(6), 062016 doi:10.1088/1742-6596/1618/6/062016
  5. De Tavernier, D., Sakib, M., Griffith, D.T., Pirrung, G., Paulsen, U., Madsen, H., Keijer, W. & Ferreira , C. (2020). Comparison of 3D Aerodynamic Models for Vertical-axis Wind Turbines: H-rotor and Φ-rotor. Journal of Physics: Conference Series 1618 (2020) 052041, doi:10.1088/1742-6596/1618/5/052041
  6. Escalera-Mendoza, A.S. & Griffith D.T. (2020). Determination of Aero-elastic Properties of Swept and Pre-bend Wind Turbine Blades. Proc. of the International Modal Analysis Conference, February 10-13, 2020, Houston, TX, US, Paper #8284 
  7. Griffith, D. T., Fine, N. E., Cooney, J. A., Rotea, M. A. & Iungo, V. G. (2020). Active aerodynamic load control for improved wind turbine design. Journal of Physics: Conference Series, 1618(5), 052079. doi:10.1088/1742-6596/1618/5/052079
  8. Guo, Y., Rotea, M. & Summers, T. (2020). Stochastic dynamic programming for wind farm power maximization. 2020 American Control Conference (ACC). doi:10.23919/ACC45564.2020.9148006
  9. Liu, C., Gupta, A. & Rotea, M. (2020). Multi-sectional lift actuation for wind turbine load alleviation. Journal of Physics: Conference Series, 1618(2), 022018. doi:10.1088/1742-6596/1618/2/022018
  10. Nanos, E. M., Letizia, S., Clemente, D. J. B., Wang, C., Rotea, M., Iungo, V. G. & Bottasso, C. L. (2020). Vertical wake deflection for offshore floating wind turbines by differential ballast control. Journal of Physics: Conference Series, 1618(2), 022047. doi:10.1088/1742-6596/1618/2/022047
  11. Sakib, M.S., Chetan, M., & Griffith, D. T. (2020). Aero-Structural Design Optimization of a 3.4 MW Wind Turbine Using Plasma Actuator Based Load Control. AIAA Aviation 2020 Forum.  doi:10.2514/6.2020-3148
  12. Zhan, L., Letizia, S. & Iungo, G. V. (2020). Wind LiDAR measurements of wind turbine wakes evolving over flat and complex terrains: Ensemble statistics of the velocity field. Journal of Physics: Conference Series, 1452(1), 012077. doi:10.1088/1742-6596/1452/1/012077