Two LANL “intelligent wind turbine” projects aim to assess operations under varying weather conditions

LANL models will provide guidance on the optimal operational status expected from each turbine under various weather conditions

An atmospheric simulation of wind loading used WindBlade computational fluid dynamics.

The Department of Energy has a goal of generating 20 percent of the U.S. energy using wind resources by 2030. However, there are technical challenges to overcome before this goal can be reached. Although wind turbines can last for 20 years, the high failure rates of turbine blades, gearboxes, and electronic components and the resulting unscheduled maintenance diminishes the return on investment for wind-farm operators. These high failure rates may be caused by unsteady loading on the turbines from atmospheric turbulence and shear layers.

LANL scientists and engineers are addressing these technical challenges through two complementary projects to study the aero-structural dynamics of wind turbines. The objective is to predict, mitigate, and control these failure rates through a combination of computational, experimental, and modeling tools developed at the Laboratory. The overall goal is to develop predictive models, tuned to the particular environment in which the wind farm resides. These models will help manage individual turbines by providing guidance on the optimal operational status expected from each turbine under various weather conditions. The projects are collaborations from organizations within the Science, Technology, and Engineering Directorate (PADSTE), the Global Security Directorate (PADGS), and the Weapons Program Directorate (PADWP) to support fundamental capabilities needed for LANL’s mission as the premier national security science laboratory. The individual projects are described in more detail in the following paragraphs.

Continuous animations in a diagnostic first
The DOE Office of Energy Efficiency and Renewable Energy is funding LANL to develop a new in-situ rotating Particle-Image Velocimetry diagnostic to measure air flows around the blades of wind turbines operating under field conditions. This diagnostic, which measures two-dimensional velocity fields, is the first diagnostic to provide continuous animations of the velocity field around the turbine. The previous single-point measurement techniques provide limited spatial and temporal resolution. The new diagnostic is a valuable tool to understand the wake structure of wind turbines in large farms, which directly affects the health and output power of the turbines.

In the project “Full-scale Field Measurements of the Turbulent Velocity Field around Wind Turbines using in-situ Particle-Image Velocimetry,” the scientists will implement and test this diagnostic on a 4.5m diameter turbine at Technical Area 49 (near Bandelier National Monument).  Balakumar Balasubramaniam of the Neutron Science and Technology group (P-23) is the principal investigator. Other researchers include Chris Tomkins of Applied Modern Physics, Kathy Prestridge of P-23, and collaborators Dale Berg and Matt Barone of Sandia National Laboratories.  Karl Jonietz is the LANL program manager.

Damage-aware/damage-mitigating system maximizes performance, extends life
The multi-disciplinary “Intelligent Wind Turbines” Laboratory Directed Research and Development (LDRD) project transforms current engineering practice by advancing and integrating innovative techniques to understand, identify, and manage turbine rotor damage and enhance wind turbine/plant design criteria. The researchers are developing multiphysics modeling capabilities to assess and control the effect of coupled aerodynamic and structural conditions on power output and turbine health. 

They also are creating sensing technologies to measure wind turbine response on multiple time- and length-scales for state awareness, control, and damage detection. These components are coupled with novel data interrogation and uncertainty quantification techniques for damage-aware/damage-mitigating system control to maximize performance and extend service life.  Tests validate the modeling and sensing work through controlled laboratory and field experiments on small- and mid-scale turbines.  Turbines from scales as small as 0.2 m to 19.3 m are tested at LANL, New Mexico State University, and the U.S. Department of Agriculture’s Bushland test facility. Curtt Ammerman of Applied Engineering Technology 1 (AET-1) is the principal investigator.  Other researchers include Gretchen Ellis and Kevin Farinholt of AET-1, Thomas Claytor of Applied Engineering Technology 6, James Ahrens of Computer Science for HPC, Eunmo Koo and Rodman Linn of Computational Earth Science, Gyuhae Park and Stuart Taylor of the Institutes Office, Donald Hush of Space and Remote Sensing, Eric Raby of Space Data Systems), Balakumar Balasubramaniam Neutron Science and Technology, D.J. Luscher of Advanced Engineering Analysis, Matthew Bement and Francois Hemez of Lagrangian Codes.