Sulfur in petroleum diesel is typically detected by wavelength dispersive X-ray fluorescence (XRF) spectrometry by comparing the response of the unknown to a linear calibration curve composed of a series of matrix-identical standards. Because biodiesel contains about 11% oxygen by mass and diesel is oxygen-free, the determination of sulfur in biodiesel using petroleum diesel calibrants is predicted to be biased ∼ -16% due to oxygen absorptive attenuation of the X-ray signal. A gravimetric standard addition method (SAM) was hypothesized to overcome this bias because it should be matrix-independent. Samples of both petroleum diesel (SRM 2723a and European Reference Material EF674a) and biodiesel (candidate SRM 2773, NREL 52537, and NREL 52533) were analyzed, comparing the traditional calibration curve method to the gravimetric SAM approach. As expected, no significant difference was found between the two methods when measuring sulfur in petroleum diesel. Sulfur determinations in biodiesel with petroleum diesel calibrants were lower by ∼19% relative to the gravimetric SAM at the 3, 7, and 12 µg/g levels. It is concluded that XRF using gravimetric SAM yields accurate sulfur measurements in biodiesel samples. In addition, the gravimetric SAM approach is insensitive to differences in the C/H ratio.

As a pollution free source of energy, wind is among the most popular and fastest growing forms of electricity generation in the world. Compared to their horizontal axis counterparts, vertical axis wind turbines have lagged considerably in development and implementation. The University of Virginia Rotating Machinery and Controls laboratory has undertaken a systematic review of vertical axis wind turbine design in order to address this research gap, starting with establishment of a methodology for vertical axis wind turbine simulation using ANSYS CFX. A 2D model of a recently published Durham University vertical axis wind turbine was generated. Full transient CFD simulations using the moving mesh capability available in ANSYS-CFX were run from turbine start-up to operating speed and compared with the experimental data in order to validate the technique. A scalable k-ε turbulence model transient CFD simulation has been demonstrated to accurately predict vertical axis wind turbine operating speed within 12% error using a two-dimensional structured mesh in conjunction with a carefully specified series of boundary conditions.