and Bob Kufeld helped us understand the challenges of converting our field measurement turbine to a wind tunnel model. Thanks also to Bob Kufeld for rotating through our data collection stations to give us a break. Thanks to Janet Beegle and Joe Sacco for coordinating NASA resources. Thanks to David Nishikawa for incorporating last-minute changes to the NASA file format. Art Silva and Ben Bailey kept all the NASA instrumentation operating smoothly. Rob Fong continued to troubleshoot the balance scales until they worked. Thanks also to the crew on the first shift that initiated the walk-through process so that we could maximize our time in the tunnel. We appreciate Romy Montano for resetting the wind tunnel breakers when needed and Rusty Hunt for getting the tunnel ready to run after the motor rewind procedure.Without our colleagues at the National Renewable Energy Laboratory (NREL), we would never have managed to prepare for the test and transport all the equipment. Jim Adams machined parts as they were designed. Scott Wilde, Andy Meiser, and Garth Johnson were instrumental in preparing the cargo container to hold all of the equipment and the turbine. Thanks also for bringing the turbine home.Lastly, we are grateful to the visionaries of the wind industry, particularly Sandy Butterfield, whose perseverance and persistence over the past decade resulted in a coveted time slot in this busy wind tunnel. Management support from Mike Robinson was also crucial in the success of this test.

No abstract

This article describes the application of an incompressible Reynolds-averaged Navier-Stokes solver to several upwind cases from the NREL/NASA Ames wind tunnel tests. In connection with the NREL blind code comparison the present results showed the overall best agreement with experimental measurements. Based on this, it is of great interest to demonstrate the quality that can be obtained in 3D CFD rotor computations. All six cases we present have 0°yaw angle and 3°tip pitch angle. All computations are performed as rotor-only computations, excluding the tower and nacelle. In this article we compare computed results and measurements in the form of shaft torque, flap and edge moments, aerodynamic coefficients and pressure distributions as a function of wind speed. The spanwise force distributions are compared with measurements for all wind speeds, along with the pressure distributions at five spanwise positions. Finally, we show how 3D CFD computations can be used to extract information about three-dimensional aerodynamic effects.

Endothelial cells (EC) appear to adapt their morphology and function to the in vivo hemodynamic environment in which they reside. In vitro experiments indicate that similar alterations occur for cultured EC exposed to a laminar steady-state flow-induced shear stress. However, in vivo EC are exposed to a pulsatile flow environment; thus, in this investigation, the influence of pulsatile flow on cell shape and orientation and on actin microfilament localization in confluent bovine aortic endothelial cell (BAEC) monolayers was studied using a 1-Hz nonreversing sinusoidal shear stress of 40 +/- 20 dynes/cm2 (type I), 1-Hz reversing sinusoidal shear stresses of 20 +/- 40 and 10 +/- 15 dynes/cm2 (type II), and 1-Hz oscillatory shear stresses of 0 +/- 20 and 0 +/- 40 dynes/cm2 (type III). The results show that in a type I nonreversing flow, cell shape changed less rapidly, but cells took on a more elongated shape than their steady flow controls long-term. For low-amplitude type II reversing flow, BAECs changed less rapidly in shape and were always less elongated than their steady controls; however, for high amplitude reversal, BAECs did not stay attached for more than 24 hours. For type III oscillatory flows, BAEC cell shape remained polygonal as in static culture and did not exhibit actin stress fibers, such as occurred in all other flows. These results demonstrate that EC can discriminate between different types of pulsatile flow environments.(ABSTRACT TRUNCATED AT 250 WORDS)

Abstract. Using detailed upwind and nacelle-based measurements from a General Electric (GE) 1.5sle model with a 77 m rotor diameter, we calculate power curves and annual energy production (AEP) and explore their sensitivity to different atmospheric parameters to provide guidelines for the use of stability and turbulence filters in segregating power curves. The wind measurements upwind of the turbine include anemometers mounted on a 135 m meteorological tower as well as profiles from a lidar. We calculate power curves for different regimes based on turbulence parameters such as turbulence intensity (TI) as well as atmospheric stability parameters such as the bulk Richardson number (R B ). We also calculate AEP with and without these atmospheric filters and highlight differences between the results of these calculations. The power curves for different TI regimes reveal that increased TI undermines power production at wind speeds near rated, but TI increases power production at lower wind speeds at this site, the US Department of Energy (DOE) National Wind Technology Center (NWTC). Similarly, power curves for different R B regimes reveal that periods of stable conditions produce more power at wind speeds near rated and periods of unstable conditions produce more power at lower wind speeds. AEP results suggest that calculations without filtering for these atmospheric regimes may overestimate the AEP. Because of statistically significant differences between power curves and AEP calculated with these turbulence and stability filters for this turbine at this site, we suggest implementing an additional step in analyzing power performance data to incorporate effects of atmospheric stability and turbulence across the rotor disk.