About

Dennice Gayme is a professor of mechanical engineering with secondary appointments in the departments of Electrical and Computer Engineering and Applied Mathematics and Statistics at Johns Hopkins University. Her research focuses on modeling, analysis, and control of spatially distributed and large-scale networked systems in applications such as wall-bounded turbulent and transitional flows, wind farms, and power grids. Her lab utilizes computational and theoretical methods from applied mathematics, dynamics, controls, optimization, and fluid mechanics. She has received numerous awards including the IEEE Control Systems Society Distinguished Member Award in 2025, the Turbulence and Shear Flow Phenomena (TSFP12) Nobuhide Kasagi Award in 2022, the JHU Discovery Awards in 2019, 2022, and 2024, a WSE Excellence in Teaching Award in 2020, a CAREER Award from the National Science Foundation in 2017, the Office of Naval Research’s Young Investigator Program award in 2017, and a JHU Catalyst Award in 2015. Gayme serves on editorial boards for PRX Energy, Physical Review Fluids, the Annual Review of Fluid Mechanics, and is an associate editor of the Journal of Renewable and Sustainable Energy. She is a senior member of IEEE and a fellow of the American Physical Society. Her educational background includes a bachelor’s degree in mechanical engineering and society from McMaster University in 1997, an MS in mechanical engineering from the University of California at Berkeley in 1998, and a PhD in control and dynamical systems from the California Institute of Technology in 2010. She was a postdoctoral fellow at Caltech until 2011 and previously worked as a senior research scientist for Honeywell Laboratories. She joined Johns Hopkins’ Department of Mechanical Engineering in 2012 and was the Carol Croft Linde Faculty Scholar from 2017 to 2024.

Research topics

• Computer Science
• Physics
• Environmental science
• Meteorology
• Engineering
• Classical mechanics
• Systems engineering
• Electrical engineering
• Process engineering
• Marine engineering
• Geography
• Architectural engineering
• Mechanics

Selected publications

• Cross-Atlantic research agenda for scalable grid architectures and distributed flexibility

  Smart Energy · 2026-03-31

  articleOpen access

  Electric power systems are rapidly evolving into deeply digital, cyber–physical infrastructures in which large fleets of distributed energy resources must be coordinated as system-level flexibility across multiple spatial and temporal scales. Despite growing distributed energy resource deployment, existing grid and market architectures lack scalable, interoperable mechanisms to reliably translate device-level flexibility into grid-aware services, creating risks to reliability, affordability, and resilience at high penetration. We propose that scalable and reliable coordination of distributed energy resource-based flexibility in future power systems is fundamentally an architectural problem that can be addressed through laminar cyber–physical design using minimal, standardized interoperability interfaces that link device autonomy with system-level objectives. To assess this claim, we present and discuss a layered cyber–physical systems architecture and explicate its implementation through standards-based interfaces, Flexibility Functions, hierarchical control, and case studies spanning U.S. and Danish regulatory, market, and operational contexts. Empirical evidence from New York’s Grid of the Future proceedings, Danish Smart Energy Operating System pilots, and operational aggregator deployments demonstrates that such architecture enables predictable, grid-aware flexibility while preserving device autonomy, interoperability, reliability, and quality of service. These results support a cross-Atlantic research agenda centered on joint testbeds, harmonized interoperability mechanisms, and coordinated policy experiments to accelerate the deployment of resilient, scalable, and flexible clean energy systems. • Layered cyber–physical architecture enables scalable flexibility coordination. • Minimal interoperability interfaces link devices, aggregators, and operators. • Pilots in Denmark and the United States demonstrate flexibility architectures. • Hierarchical control and digital twins improve coordination reliability. • Transatlantic research agenda for shared testbeds and harmonized interfaces.

  PublisherDOI

• From Cut-In to Rated: Multi-Region Floating Offshore Wind Farm Control for Secondary Frequency Regulation

  arXiv (Cornell University) · 2026-04-09

  articleOpen accessSenior author

  This paper describes a multi-region control framework for floating offshore wind farms. Specifically, we propose a novel generator torque controller that regulates rotor speed in Region 2, corresponding to wind speeds between the cut-in and rated values. In Region 3 (wind speeds at or above rated but below cut-out speed) we employ a PI-LQR for collective blade pitch. Control blending across the transitional wind speeds (Region 2.5) employs a sigmoid weighting function applied to the control variables. Two modeling paradigms are proposed for farm-level power tracking with rotor speed regularization: a nonlinear model predictive controller (NL-MPC) with a dynamic wake model, and a reduced order model predictive controller based on linear parameter varying turbine models with a time delay representation of wake advection (LPVTD-MPC). These approaches are evaluated over three wind inlet conditions using the PJM ancillary service certification criteria for participation in a secondary frequency regulation market. Results show that both approaches achieve scores of at least 89.9\% for the three different testing scenarios, which are well above the qualification threshold of 75\%. However, the LPVTD-MPC approach solves the problem in under half the time versus NL-MPC but with slightly larger fluctuations in farm-level power output, highlighting the trade-off between performance and computational tractability. The control framework is among the first to address multi-region wind turbine dynamics together with market driven power tracking objectives for floating offshore wind farms. Such multi-region control becomes increasingly necessary in the floating turbine setting where large (region spanning) wind speed variations are common due to wave induced platform pitching.

  PublisherOA PDF

• From Cut-In to Rated: Multi-Region Floating Offshore Wind Farm Control for Secondary Frequency Regulation

  arXiv (Cornell University) · 2026-04-09

  preprintOpen accessSenior author

  This paper describes a multi-region control framework for floating offshore wind farms. Specifically, we propose a novel generator torque controller that regulates rotor speed in Region 2, corresponding to wind speeds between the cut-in and rated values. In Region 3 (wind speeds at or above rated but below cut-out speed) we employ a PI-LQR for collective blade pitch. Control blending across the transitional wind speeds (Region 2.5) employs a sigmoid weighting function applied to the control variables. Two modeling paradigms are proposed for farm-level power tracking with rotor speed regularization: a nonlinear model predictive controller (NL-MPC) with a dynamic wake model, and a reduced order model predictive controller based on linear parameter varying turbine models with a time delay representation of wake advection (LPVTD-MPC). These approaches are evaluated over three wind inlet conditions using the PJM ancillary service certification criteria for participation in a secondary frequency regulation market. Results show that both approaches achieve scores of at least 89.9\% for the three different testing scenarios, which are well above the qualification threshold of 75\%. However, the LPVTD-MPC approach solves the problem in under half the time versus NL-MPC but with slightly larger fluctuations in farm-level power output, highlighting the trade-off between performance and computational tractability. The control framework is among the first to address multi-region wind turbine dynamics together with market driven power tracking objectives for floating offshore wind farms. Such multi-region control becomes increasingly necessary in the floating turbine setting where large (region spanning) wind speed variations are common due to wave induced platform pitching.

  PublisherDOI

• Modal triadic interaction informed restricted nonlinear models

  Journal of Physics Conference Series · 2026-05-01

  articleOpen accessSenior author

  Abstract Turbulent flows involve high-dimensional nonlinear interactions across a range of spatial and temporal scales, and the need to capture such interactions makes them challenging to model and simulate. We present a resolvent-based framework to guide scale selection in reduced-order models of turbulence. Resolvent analysis offers a physics-based framework to decompose the Navier-Stokes equation into linear and nonlinear components and to identify the most amplified flow structures. Building on this framework, we introduce an energy exchange coefficient that directly quantifies nonlinear energy transfer between resolvent modes, enabling an energy-based ordering of triadic interactions in turbulent channel flow without relying on data-driven closures. Triads with large interaction coefficients are incorporated into the augmented restricted nonlinear (ARNL) model, which is a physics-based reduced-order representation that requires specification of streamwise flow scales permitted to interact nonlinearly (large-scales) and small-scale streamwise perturbations whose linear evolution is a function of these large scales. Resolvent-informed ARNL simulations are shown to produce normal Reynolds stresses, energy spectra, and energy transport consistent with DNS data. These results demonstrate the potential of the resolvent-based approach for identifying important nonlinear interactions and as a tool for improving physics-grounded turbulence modeling.

  PublisherDOI

• Flow, turbulence, and wind energy

  Journal of Renewable and Sustainable Energy · 2025-11-01

  articleOpen access

  This special issue is dedicated to celebrating the scientific career and ongoing influence of Charles Meneveau on the fields of turbulence, fluid mechanics, and wind energy. Mirroring the breadth of Meneveau's wide-ranging contributions, the issue brings together a diverse collection of articles describing advances in large-eddy simulations, wake modeling, wind farm optimization, atmospheric boundary layer characterization, and data-informed modeling frameworks. These works span theoretical, computational, and experimental approaches that collectively highlight not only foundational developments in turbulence and renewable energy but also the vibrant and evolving directions of a field shaped by his leadership.

  PublisherOA PDFDOI

• Wind Farm Dynamics over a Diurnal Cycle: Analysis of a Comprehensive Large Eddy Simulation, Web-Services Accessible Dataset

  ArXiv.org · 2025-10-06

  preprintOpen access

  The atmospheric boundary layer undergoes significant changes throughout a diurnal cycle, affecting wind turbine performance and wakes in wind farms. Wind farm Large Eddy Simulations (LES) under such conditions provide rich datasets to study the underlying dynamics and identify important trends. Here, we describe a comprehensive open dataset generated using LES of an 8-turbine wind farm consisting of four rows of two turbines. To avoid specifying either prescribed surface temperature or heat flux, a local 1D soil heat conduction model is used with time-periodic solar surface heating, coupled to LES. After several days of low-resolution LES, an approximately time periodic behavior is achieved, after which high-resolution LES is continued during a 24-hour period. Analysis of the LES data reveals that wind turbine wakes have a significant impact on the temperature field and spatial surface heat flux patterns and exhibiting increased surface temperature behind the wind farm at night under the specific conditions of the simulation (dry unvegetated soil, clear sky). We observe that for a few morning hours the first row of wind turbines generates less power compared to the last row. Detailed analyses of the data using innovative web-services facilitated data access tools reveal that during the morning transition, the presence of a low-level jet and the wind farm blockage effect combine to cause cooling and a reduction in wind speed at hub height upstream of the wind farm. In addition, larger turbulence levels exist downstream in the wind farm, explaining the larger power production of downstream turbines.

  PublisherOA PDFDOI

• JHTDB-wind: a web-accessible large-eddy simulation database of a wind farm with virtual sensor querying

  Wind energy science · 2025-12-01 · 2 citations

  articleOpen accessCorresponding

  Abstract. This paper introduces JHTDB-wind (https://turbulence.idies.jhu.edu/datasets/windfarms, last access: 11 November 2025), a publicly accessible database containing large-eddy simulation (LES) data from wind farms. Building on the framework of the Johns Hopkins Turbulence Database (JHTDB), which hosts direct numerical simulation (DNS) and some LES datasets of canonical turbulent flows, JHTDB-wind stores the 4D space–time history of the flow and provides users the ability to access and query the data via a web-based virtual sensor interface. The initial dataset comprises LES results from a large wind farm with 10×6 turbines, modeled using a filtered actuator line method, under conventionally neutral atmospheric conditions. These data comprise 1 h (hour) of flow field data (velocity, pressure, potential temperature deviation, subgrid-scale (SGS) eddy viscosity, and turbine forces, approximately 15 TB (terabytes) and wind turbine data – including both turbine-level operational quantities and blade-level aerodynamic quantities (approximately 1.3 TB) – stored in Zarr and Parquet formats, respectively. Data retrieval is facilitated by the giverny Python package, allowing remote users to query the database in Python or MATLAB (C and Fortran support are available for flow field data). This paper details the simulation setup and demonstrates data access through examples that analyze wind farm flow structures and turbine performance. The framework is extensible to future datasets, including the JHTDB-wind diurnal cycle simulation analyzed in Xiao et al. (2025).

  PublisherDOI

• Celebrating the 30th Anniversary of WiC [Member Activities]

  IEEE Control Systems · 2025-08-01

  article

  Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

  PublisherDOI

• Towards Collective Control of Floating Offshore Wind Farms

  2025-07-08

  articleSenior author

  This paper describes a framework for collective control of dynamically coupled nonlinear control-oriented models of floating offshore wind turbines. A quadratic cost function is designed to produce turbine level commands that achieve farm level power tracking objectives while maintaining platform motion and turbine states within acceptable limits. An inner-outer loop control architecture is adopted due to the time scale separation between the farm and turbine level dynamics. In the inner loop, a linear quadratic regulator (LQR) is designed to regulate the individual turbine rotor speed using blade pitch, while maintaining stability of the floating turbine (i.e. both turbine and platform motion). The outer loop (farm level) control uses a model predictive approach with an embedded linear-parameter-varying (LPV) model of the turbines coupled with a time delay wake advection model. Closed loop simulations of an 8 turbine (4 row by 2 column) wind farm with wind inflow conditions based on high-fidelity simulations of the atmospheric boundary layer demonstrate good tracking performance of a time-varying power signal.

  PublisherDOI

• An extended analytical wake model and applications to yawed wind turbines in atmospheric boundary layers with different levels of stratification and veer

  Journal of Renewable and Sustainable Energy · 2025-05-01 · 7 citations

  article

  Analytical wake models provide a computationally efficient means to predict velocity distributions in wind turbine wakes in the atmospheric boundary layer (ABL). Most existing models are developed for neutral atmospheric conditions and correspondingly neglect the effects of buoyancy and Coriolis forces that lead to veer, i.e., changes in the wind direction with height. Both veer and changes in thermal stratification lead to lateral shearing of the wake behind a wind turbine, which affects the power output of downstream turbines. Here we develop an analytical engineering wake model for a wind turbine in yaw in ABL flows including Coriolis and thermal stratification effects. The model combines the new analytical representation of ABL vertical structure based on coupling Ekman and surface layer descriptions developed in Narasimhan et al. [Boundary Layer Meteorol. 190, 16 (2024)] with the vortex sheet-based wake model for yawed turbines proposed in Bastankhah et al. [J. Fluid Mech. 933, A2 (2022)], as well as a new method to predict the wake expansion rate based on the Townsend-Perry logarithmic scaling of streamwise velocity variance. The proposed wake model's predictions show good agreement with large-eddy simulation results, capturing the effects of wind veer and yawing, including the curled and sheared wake structures across various states of the ABL, ranging from neutrally to strongly stably stratified atmospheric conditions. The model significantly improves power loss predictions from wake interactions, especially in strongly stably stratified conditions where wind veer effects dominate.

  PublisherDOI

Recent grants

• CAREER: The restricted nonlinear framework: A new paradigm for modeling, analysis and control of wall-bounded turbulent flows

  NSF · $561k · 2017–2023

• Modeling, Analysis and Control Design for Spatially Distributed Systems with Application to Wind Farms

  NSF · $324k · 2016–2020

• CPS: Synergy: Collaborative Research: Beyond Stability: Performance, Efficiency and Disturbance Management for Smart Infrastructure Systems

  NSF · $284k · 2015–2020

• Collaborative Research: Empowering Next Generation Offshore Wind Farms Through Systematic Characterization of Floating Wind Turbine Array Dynamics

  NSF · $338k · 2021–2025

• SEP Collaborative: Integrating Heterogeneous Energy Resources for Sustainable Power Networks - A Systems Approach

  NSF · $1.2M · 2012–2017

Frequent coauthors

• F Lamnabhi-Lagarrigue

  Imperial College London

  208 shared

• Veronica Adetola

  208 shared

• Warren E. Dixon

  University of Florida

  208 shared

• Jacquelien M.A. Scherpen

  University of Groningen

  208 shared

• Francesco Bullo

  Dynamic Systems (United States)

  208 shared

• Okyay Kaynak

  Centre de Recherche sur les Risques et les Crises

  208 shared

• Thomas Parisini

  208 shared

• Dong‐il Cho

  208 shared

Education

• PhD, Control and Dynamical Systems

  California Institute of Technology

  2010

• MSc, Mechanical Engineering

  University of California Berkeley

  1998

Awards & honors

• IEEE Control Systems Society Distinguished Member Award (202…
• Turbulence and Shear Flow Phenomena (TSFP12) Nobuhide Kasagi…
• JHU Discovery Awards (2024)
• JHU Discovery Awards (2022)
• JHU Discovery Awards (2019)