Innovation Partners proposed and successfully demonstrated in this SBIR Phase I grant a software/hardware co-design approach to reduce both the difficulty and time to implement Field Programmable Gate Array (FPGA) solutions to data acquisition and specialized computational applications. FPGAs can require excessive time for programming and require specialized knowledge that will be greatly reduced by the company's solution. Not only are FPGAs ideal for DAQ and embedded solutions, they can also be the best solution to specialized signal processing to replace Digital Signal Processors (DSPs). By allowing FPGA programming to be done in C with the equivalent of a simple compilation, algorithm changes and improvements can be implemented decreasing the life-cycle costs and allow subsitution of new FPGA designs staying above the technological details.

The DOE supported research is a theoretical statistical-mechanical based study of the thermophysical properties of fluids and fluid mixtures. It focuses upon thermodynamic and transport properties in particular. In addition the study covers the development of new ways for predicting the microscopic structure of fluids in a wide range of thermodynamic state parameters, including the critical point.

On December 17, 2008, the reference-case projections from Annual Energy Outlook 2009 (AEO 2009) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference-case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables can play in mitigating such risk. As such, we were curious to see how the latest AEO reference-case gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. Note that this memo pertains only to natural gas fuel price risk (i.e., the risk that natural gas prices might differ over the life of a gas-fired generation asset from what was expected when the decision to build the gas-fired unit was made). We do not take into consideration any of the other distinct attributes of gas-fired and renewable generation, such as dispatchability (or lack thereof), differences in capital costs and O&M expenses, or environmental externalities. A comprehensive comparison of different resource types--which is well beyond the scope of this memo--would need to …

A good method for solving the nonlinear Vlasov equation is the semi-Lagrangian algorithm, in which the phase space density is represented by its values on a fixed Cartesian grid with interpolation to off-grid points. At each time step, orbits are followed backward from grid points. Since this method is expensive with phase space dimension D > 2, we seek a more efficient discretization of the density. Taking a cue from the theory of numerical quadrature in high dimensions, we explore the idea of replacing the grid by scattered data sites from a low-discrepancy (quasirandom) sequence. We hope to see a reduction in the required number of sites, especially for D > 2. In our first implementation we follow forward orbits rather than backward, and work only with D = 2. We are able to reduce the number of sites by a factor of 8, at least for a limited time of integration. A much bigger reduction is expected in higher dimensions.

Many of the important challenges facing humanity, including developing alternative sources of energy and improving health, are being addressed by advances that demand the improved understanding and control of matter. While the visualization, exploration, and manipulation of macroscopic matter have long been technological goals, scientific developments in the twentieth century have focused attention on understanding matter on the atomic scale through the underlying framework of quantum mechanics. Of special interest is matter that consists of natural or artificial nanoscale building blocks defined either by atomic structural arrangements or by electron or spin formations created by collective correlation effects The essence of the challenge to the scientific community has been expressed in five grand challenges for directing matter and energy recently formulated by the Basic Energy Sciences Advisory Committee [1]. These challenges focus on increasing our understanding of, and ultimately control of, matter at the level of atoms, electrons. and spins, as illustrated in Figure 1.1, and serve the entire range of science from advanced materials to life sciences. Meeting these challenges will require new tools that extend our reach into regions of higher spatial, temporal, and energy resolution. X-rays with energies above 10 keV offer capabilities extending beyond the nanoworld …

Neutronics analysis to find nuclear heating rates and personnel dose rates were conducted in support of the integration of diagnostics in to the ITER Upper Port Plugs. Simplified shielding models of the Visible-Infrared diagnostic and of a large aperture diagnostic were incorporated in to the ITER global CAD model. Results for these systems are representative of typical designs with maximum shielding and a small aperture (Vis-IR) and minimal shielding with a large aperture. The neutronics discrete-ordinates code ATTILA® and SEVERIAN® (the ATTILA parallel processing version) was used. Material properties and the 500 MW D-T volume source were taken from the ITER “Brand Model” MCNP benchmark model. A biased quadrature set equivelant to Sn=32 and a scattering degree of Pn=3 were used along with a 46-neutron and 21-gamma FENDL energy subgrouping. Total nuclear heating (neutron plug gamma heating) in the upper port plugs ranged between 380 and 350 kW for the Vis-IR and Large Aperture cases. The Large Aperture model exhibited lower total heating but much higher peak volumetric heating on the upper port plug structure. Personnel dose rates are calculated in a three step process involving a neutron-only transport calculation, the generation of activation volume sources at pre-defined time steps …

Mass-analyzed threshold ionization (MATI) spectroscopy using synchrotron radiation (Advanced Light Source, Lawrence Berkeley National Laboratory) has been performed for Ar, N2, O2, N2O, H2O, C2H2, and C6H6. MATI allows for a better determination of ionization energies compared to those derived from photoionization efficiency curves traditionally used in synchrotron photoionization mass spectrometry. The separation of the long-lived Rydberg state from the directly-formed prompt ion, essential for a meaningful MATI spectrum, has been accomplished by employing an arrangement of ion optics coupled to unique electric-field pulsing schemes. For Ar, a number of resolved bands below the ionization energy are observed, and these are ascribed to high-n,l Rydberg states prepared in the MATI scheme. The first vibrational stateresolved MATI spectra of N2 and O2 are reported and spectral characteristics are discussed in comparison with previously-reported threshold photoelectron spectroscopic studies. While MATI performed with synchrotron radiation is intrinsically less sensitive compared to laser based sources, this work demonstrates that MATI spectroscopy performed with widely tunable VUV radiation is a complementary technique for studying the ionization spectroscopy of polyatomic molecules.

The quantification of carbon fluxes between the terrestrial biosphere and the atmosphere is of scientific importance and also relevant to climate-policy making. Eddy covariance flux towers provide continuous measurements of ecosystem-level exchange of carbon dioxide spanning diurnal, synoptic, seasonal, and interannual time scales. However, these measurements only represent the fluxes at the scale of the tower footprint. Here we used remotely-sensed data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to upscale gross primary productivity (GPP) data from eddy covariance flux towers to the continental scale. We first combined GPP and MODIS data for 42 AmeriFlux towers encompassing a wide range of ecosystem and climate types to develop a predictive GPP model using a regression tree approach. The predictive model was trained using observed GPP over the period 2000-2004, and was validated using observed GPP over the period 2005-2006 and leave-one-out cross-validation. Our model predicted GPP fairly well at the site level. We then used the model to estimate GPP for each 1 km x 1 km cell across the U.S. for each 8-day interval over the period from February 2000 to December 2006 using MODIS data. Our GPP estimates provide a spatially and temporally continuous measure of gross primary production …