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EFRE Research Projects

Specific projects include studies of how canopy and topographic induced wakes affect momentum, evaporation, and trace gas fluxes at the air-land and air-water interfaces for ecosystem, lake and wetland modeling; investigation on how deforestation patterns in the Amazon rainforest may be parameterized in weather and climate models; and studies of wind farm-atmosphere interactions to understand the impact of atmospheric stability on wind turbine wakes and wind farm configuration effects on ABL dynamics and surface fluxes. Understanding how landscape complexity affects momentum flux and transport of heat, moisture, and trace gases is important for developing physics-based parameterizations for large-scale weather, climate, hydrologic, and wind resource assessment models, as well as for interpreting measurements, which may be spatially or temporally limited. We are also actively conducting research to improve understanding and modeling of particle transport related to blowing snow, avian wildlife behavior in wind farms, and harmful algal blooms.

Advancing Wind and Hydrokinetic Energy Prediction

White wind turbines against a brilliant blue skyWind turbines extract kinetic energy from the atmospheric boundary layer (ABL), the lowest region of the atmosphere, which is characterized by a wide range of variability due to turbulence, stratification, complex terrain, and wakes from nearby wind turbines. However, current prediction models do not account for the effects of this variability resulting in significant errors in wind power forecast. Our research involves developing models to predict energy generation from wind farms for the range of atmospheric conditions. Wind plant operators and balancing authorities use forecasts to optimize integration of renewable wind power and delivery to the electric grid. Analogously, we investigate turbulent flow and turbine configurations for river and tidal turbines. We can use our improved understanding of feedback between turbines and flow in turbine arrays under variable environmental conditions to develop models to optimize renewable power generation.

Team members and collaborators: Mohsen Vahidzadeh, Jian Teng, Payham Aghsaee, Peter Brugger (EPFL), Fernando Carbajo Fuertes (EPFL), and Fernando Porté-Agel (EPFL)

Development of improved wind power forecast models for wind farms in complex terrain

Diagram illustrating how information flows from a model to products and consumers

Schematic showing the flow of information from the environment to consumers.

We are developing improved models for energy generation assimilating turbulence measurements from tall towers and nacelle-mounted wind LiDARs under realistic field conditions to evaluate the performance of the models compared with SCADA data from utility-scale wind turbines. Measurements of wind speed and direction, turbulent fluxes, wind shear, wind veer, yaw error, temperature, pressure, and relative humidity are used to improve power predictions for grid integration. Results from our research are reported in Carbajo Fuertes, Markfort, and Porté-Agel (2018) and Vahidzadeh and Markfort (2019).

  • Complex terrain and density stratification affect wind power plant performance and make prediction of wind energy particularly challenging
  • Turbulence enhances supply of energy to wind turbines, but also leads to fatigue and premature failure of turbine components
  • We utilize measurements using Doppler wind LiDAR and meteorological towers, as well as wind tunnel studies, to improve models of environmental flows and wind turbine wakes
  • Simulations of the interactions between wind turbines and the atmospheric boundary layer can be used to optimize wind power plant design and control to maximize power production based on local conditions

Wind farm optimization using an analytical wake model with performance data from wind turbines

Diagram in red, yellow, green, and blue showing Output from analytical wake model for wind speed for a wind turbine array

Output from analytical wake model for wind speed for a wind turbine array.

Wind turbine wakes significantly affect wind farm power generation. Reducing the wake effect can significantly increase the power generation and reduce turbine fatigue. Our goal is to develop a procedure that wind farm operator can easily utilize with existing SCADA system to reduce wake effect.  The procedure employs wind turbine power curve modeling and incoming wind speed correction to account for high turbulence intensity. The analytical wake model is augmented with on-line SCADA data. The model can help to optimize wind farm performance as well as improve understanding and mitigate impacts of low wind regions on risks to wildlife such as bats.

Evaluating the effect of flow depth on wake recovery of hydrokinetic turbines

Model wind turbinesIn-stream hydrokinetic turbines have the potential to produce a significant amount of clean energy from river and tidal currents. We are investigating the effects of flow depth and turbine spacing on the performance and wake behavior downstream of hydrokinetic turbines. Using Laser Doppler Velocimetry, we measured the mean velocity and turbulence in the wake under varying flow depth conditions and found that for equivalent inflow conditions, a deeper flow resulted in more rapid wake recovery (Aghsaee and Markfort, 2018).

 


Evaluating Environmental Impacts of Wind Energy

Wind farms extract momentum from the atmospheric boundary layer while also generating turbulence and enhancing mixing. These affects can affect fluxes of energy, heat, and scalars (e.g. pollutants) at the land or ocean surface. Wildlife may also be affected by interacting with turbines and impact by wind turbine blades.

With a team from the Dept. of Electrical and Computer Engineering led by Professor Anton Kruger, we are developing artificial intelligence/machine learning tools that advance monitoring systems that identify species flying in the vicinity or colliding with wind turbines to help predict species behavior and develop strategies to avoid wildlife impacts.

Team members and collaborators: Shivendra Prakash, Jian Teng, Bingchun Huo, David Wu, Jim Niemeier, and Anton Kruger

Developing a ballistic model framework for prediction of bat fate after impact with wind turbine blades

A large number of bat fatalities have been reported in wind energy facilities in different parts of the world. The wind farm operators are required to monitor bat fatalities by conducting carcass surveys at wind farms. Our research proposes a framework of mechanics-based ballistic model to quantify the carcass search radius around wind turbines incorporating turbine operational and meteorological variables and validating an improved ballistic model with the carcass survey data. Due to lack of available data on the aerodynamic properties of bat carcasses, we conducted a study to measure bat carcass drag coefficients for various species after impact with turbine blades (Prakash and Markfort (in review)).

Graphs denoting wind turbine bat fatalities

Experimental investigation of aerodynamic characteristics of bat carcasses after collision with a wind turbine

Developing strategies for monitoring bat activities in wind farms and interactions with turbine rotors

To reduce wind farm impacts on bats, we are exploring methods for monitoring how bat behavior is affected by environmental variables, including temperature, rainfall, atmospheric pressure, and wind. Low wind speed regions within wind turbine arrays may pose particular risk as bats often are observed approaching turbines from within the wake region where windspeeds may be more favorable for bats. Using analytical wake models, X-band Doppler radar, and thermal cameras, we track the behavior of birds and bats at landscape and individual turbine scales, improving our understanding of bat behaviors near turbines and guiding new smart curtailment strategies. By understanding how the behavior of birds and bats relates to local atmospheric conditions in and around wind farms, we are developing improved prediction and mitigation measures to minimize risk to wildlife while maximizing wind power generation through enhanced wind plat control.

X-Band doppler radar

Images of X-Band doppler radar and thermal cameras deployed in an Iowa wind farm

 


Wake Effects on Surface-Atmosphere Interactions and Aquatic Ecosystem Processes

Team members and collaborators: Stephen Cropper (Grinnell), Wei Zhang (CSU)

Influence of boundary-layer wakes on measurements and modeling of surface fluxes

Wakes are generated by complex terrain, tall vegetation, and structures such as buildings and wind turbines, leading to flow separation, reduced wind speeds and increased turbulence in the atmospheric boundary layer (ABL). Due to the extent of wake-generating features on Earth’s surface, wakes play an important role in turbulent transport and surface fluxes, affecting measurements of ecosystem-atmosphere exchange and numerical models for weather, hydrology, and wind forecasting. Large wind turbine arrays are becoming a significant feature with the rapid development of onshore and offshore wind farms that must be accounted for in surface-atmosphere models (Markfort et al., 2017; Zhang et al., 2013). Our research strives to better understand the effects of wakes on the coupling between the ABL and the surface boundary layer (SBL) of coastal oceans, estuaries, and inland waters to improve descriptions of air-water interaction in next generation land-water-atmosphere models (Markfort et al., 2010). Scanning Doppler wind LiDARs, laser-based, time-resolved, 3D flow-field measurements, and Large-Eddy Simulation (LES) are used to investigate the effects of wakes on surface-atmosphere interactions and the effect on surface fluxes and ecosystem processes.

Friction Velocity, Wind Speed, and drag coefficient graphs and tables

(a) Measurements of friction velocity versus wind speed; (b) drag coefficient versus wind speed, both from a flux station on a lake surrounded by forest. The red shading indicates the expected range over open water; and (c) distribution of vertical turbulent momentum flux downwind of a model canopy measured in a wind tunnel (Markfort et. al., 2014).

Measurements of coupled atmospheric boundary layer, surface waves, and surface boundary layer turbulence

Turbulence coupling across the air-water interface of the ocean, estuaries, and inland waters drives water temperatures, stratification, and mixing, which affects ecosystem productivity and water quality. Coupling of high and low momentum may explain the presence of large-scale flow patterns, e.g. Langmuir Circulations. We are developing measurement techniques using duel time-resolved, stereo-PIV to quantify the dynamics coupling of the two-boundary layer system.  The air-side boundary layer is developed over a free water surface to a boundary layer depth of approximately 50 cm. This allows for investigation of turbulence with a wide range of scales. Flow field measurements collected at 750Hz and millimeter resolution provide detailed 3D velocity information. Image processing techniques applied to the combined air and water images allow for automated surface tracking. Flow over breaking waves, spray generation, and interactions with structures such as model offshore wind turbines are investigated (Markfort and Stegmeir, APS DFD 2018).

Dual time-resulved and wind-wave Develppment section

Boundary-layer wind-wave tunnel and dual time-resolved stereo PIV system

 


Detection and Modeling of Hazardous Environmental Conditions

Team members and collaborators: Sarah Douglas, Hao Chen, Mohsen Vahidzadeh, Marian Muste, Mary Skopec, Greg LeFevre

Determining spatial distribution of microcystin levels in HABs using UAV-based multispectral imagery and pigment/toxin correlations

Harmful algal blooms (HABs) contain cyanobacteria which can release cyanotoxins into the aquatic environment. Microcystin are the most abundant cyanotoxin found in Iowa lakes. Grab and composite samples are collected during the summer to test for microcystin levels, but these sampling techniques cannot capture spatial variability. However, multispectral imagery detects the chlorophyll-a pigment in these cyanobacteria at high spatial resolution. A pigment/toxin correlation can then estimate the microcystin concentrations throughout the study area, resulting in a visual depiction of microcystin variability. This technique has the potential to be used as a risk-assessment tool for lake managers to understand the development and movement of HABs.

UAV detection graph and diagram

UAV detection of harmful algal blooms and model correlating multispectral data to Chl-a and microcystin

Determining snow transport by atmospheric boundary layer winds to improve snow relocation models

Blowing and drifting snow causes significant problems for maintaining safe driving conditions, transportation efficiency, and highway maintenance. Snow fence is designed to trap snow and prevent it from drifting onto roadways. However, the distance a snow fence must be set back from the roadway depends on how much snow is transported to the fence and how efficiently the fence traps the snow. The snow relocation coefficient (SRC) is the ratio of drifted snow to snowfall in terms of equivalent water. It is a key parameter for snow fence design. To improve estimates of snow transport by wind, we are conducting field experiments during winter to measure snow trapped by snow fence and environmental variables that contribute to snow transport using flux towers. We are developing automated image-based methods to quantify snow trapped by structural and living snow fence. Data collected in the field using survey equipment and drone imagery, as well as continuous web camera recordings are used for mapping the snow and quantify the volume of snow trapped by snow fence.

Images and example data from a field campaign including images and models

Images and example data from a field campaign to qantify snow trapped by a snow fence. (a) Webcamera used to track snow deposit, (b) Field survey of snow deposit, (c) Snow profile measured using webcamera and field survey, (d) Drone used to survey of snow deposit volume, (e) 3D map of snow deposite using photogrametry technique.

 

 

Last modified on April 11th, 2020
Posted on September 19th, 2019