People

Education

• Ph.D., Mechanical Engineering, Stanford University | 2020
• M.S., Mechanical Engineering, Stanford University | 2018
• B.S., Mechanical Engineering, Johns Hopkins University | 2016

Research Interests

Our group studies the flow physics of Earth’s atmosphere and the modeling, optimization, and control of renewable energy generation systems. This work is focused at the intersection of fluid mechanics, weather and climate modeling, uncertainty quantification, and optimization and control with an emphasis on renewable energy systems. We use synergistic approaches including simulations, laboratory and field experiments, and modeling to understand the operation of renewable energy systems, with the goal of improving the efficiency, predictability, and reliability of low-carbon energy generation. We are pursuing two coupled research themes:

1. Modeling complex environment-energy system interactions through the use of simulations and experiments
2. Leveraging the developed models in full-scale field experiments or case studies to improve the operation and integration of low-carbon generation

Teaching Interests

1. Seminar on the development of low-carbon energy resources
2. Atmospheric boundary layer flow and wind energy
3. Fluid mechanics and turbulence

Selected Publications

1. Howland, M. F., et al. “Influence of atmospheric conditions on the power production of utility-scale wind turbines in yaw misalignment.” arXiv preprint arXiv:2008.00873 (2020).
2. Howland, M. F., Ghate, A. S., Lele, S. K. & Dabiri, J. O. “Optimal closed-loop wake steering, Part 1: Conventionally neutral atmospheric boundary layer conditions.” Wind Energy Science, to appear,  (2020).
3. Wei, N. J., Brownstein, I. D., Cardona, J. L., Howland, M. F. & Dabiri, J. O. “Near-wake structure of full-scale vertical-axis wind turbines.” Journal of Fluid Mechanics, to appear (2020).
4. Howland, M. F., Ghate, A. S. & Lele, S. K. “Influence of the geostrophic wind direction on the atmospheric boundary layer flow.” Journal of Fluid Mechanics 883 (2020).
5. Cardona, J. L., Howland M. F., Dabiri J. O. “Seeing the wind: Visual wind speed prediction with a coupled convolutional and recurrent neural network,” NeurIPS 2019.
6. Howland, M. F., Lele, S. K. & Dabiri, J. O. “Wind farm power optimization through wake steering.” Proceedings of the National Academy of Sciences 116.29 (2019): 14495-14500. (cover article).
7. Howland, M. F. & Dabiri, J. O. “Wind Farm Modeling with Interpretable Physics-Informed Machine Learning.” Energies 12.14 (2019): 2716.
8. Howland, M. F. & Yang, X. I. A. “Dependence of small-scale energetics on large scales in turbulent flows.” Journal of Fluid Mechanics 852, 641-662, 2018.
9. Yang, X. I. A. & Howland, M. F. “Implication of Taylor’s hypothesis on measuring flow modulation.” Journal of Fluid Mechanics 836, 222-237, 2018.
10. Bossuyt, J., Howland, M. F., Meneveau, C. & Meyers, J. “Measurement of unsteady loading and power output variability in a micro wind farm model in a wind tunnel.” Experiments in Fluids 58.1 (2017):
11. Howland, M. F., Bossuyt, J., Martinez, L. A., Meyers, J. & Meneveau, C. “Wake structure in actuator disk models of wind turbines in yaw under uniform inflow conditions.” Journal of Renewable and Sustainable Energy 8.4 (2016): 043301.