The goal of this project was to make a first assessment of the major carbon stocks and fluxes and their climatic determinants in a lowland neotropical rain forest, the La Selva Biological Station, Costa Rica. Our research design was based on the concurrent use of several of the best available approaches, so that data could be cross-validated. A major focus of our effort was to combine meteorological studies of whole-forest carbon exchange (eddy flux), with parallel independent measurements of key components of the forest carbon budget. The eddy flux system operated from February 1998 to February 2001. To obtain field data that could be scaled up to the landscape level, we monitored carbon stocks, net primary productivity components including tree growth and mortality, litterfall, woody debris production, root biomass, and soil respiration in a series of replicated plots stratified across the major environmental gradients of the forest. A second major focus of this project was on the stocks and changes of carbon in the soil. We used isotope studies and intensive monitoring to investigate soil organic stocks and the climate-driven variation of soil respiration down the soil profile, in a set of six 4m deep soil shafts stratified across the …

Two-dimensional full-wave time-dependent simulations in full plasma geometry are presented which show that conventional reflectometry (without a lens) can be used to synthetically image density fluctuations in fusion plasmas under conditions where the parallel correlation length greatly exceeds the poloidal correlation length of the turbulence. The advantage of synthetic imaging is that the image can be produced without the need for a large lens of high optical quality, and each frequency that is launched can be independently imaged. A particularly simple arrangement, consisting of a single receiver located at the midpoint of a microwave beam propagating along the plasma midplane is shown to suffice for imaging purposes. However, as the ratio of the parallel to poloidal correlation length decreases, a poloidal array of receivers needs to be used to synthesize the image with high accuracy. Simulations using DIII-D relevant parameters show the similarity of synthetic and optical imaging in present-day experiments.

(B204)The fundamental motivation for our work is that supernovae are not well understood. Recent observations have clarified the depth of our ignorance, by producing observed phenomena that current theory and computer simulations cannot reproduce. Such theories and simulations involve, however, a number of physical mechanisms that have never been studied in isolation. We perform experiments, in compressible hydrodynamics and radiation hydrodynamics, relevant to supernovae and supernova remnants. These experiments produce phenomena in the laboratory that are believed, based on simulations, to be important to astrophysics but that have not been directly observed in either the laboratory or in an astrophysical system. During the period of this grant, we have focused on the scaling of an astrophysically relevant, radiative-precursor shock, on preliminary studies of collapsing radiative shocks, and on the multimode behavior and the three-dimensional, deeply nonlinear evolution of the Rayleigh-Taylor (RT) instability at a decelerating, embedded interface. These experiments required strong compression and decompression, strong shocks (Mach {approx}10 or greater), flexible geometries, and very smooth laser beams, which means that the 60-beam Omega laser is the only facility capable of carrying out this program.

This report describes how Learning-by-Doing (LBD) is implemented endogenously in the National Energy Modeling System (NEMS) for generating plants. LBD is experiential learning that correlates to a generating technology's capacity growth. The annual amount of Learning-by-Doing affects the annual overnight cost reduction. Currently, there is no straightforward way to integrate and make sense of all the diffuse information related to the endogenous learning calculation in NEMS. This paper organizes the relevant information from the NEMS documentation, source code, input files, and output files, in order to make the model's logic more accessible. The end results are shown in three ways: in a simple spreadsheet containing all the parameters related to endogenous learning; by an algorithm that traces how the parameters lead to cost reductions; and by examples showing how AEO 2004 forecasts the reduction of overnight costs for generating technologies over time.

Resonant magnetic x-ray scattering from dense self-assemblies of 9-nm diameter epsilon-Co and hcp-Co particles is reported. For lower anisotropy epsilon-Co we find remanent magnetic scattering that is significantly enhanced, indicating preferred inter-particle moment orientations of both antiferromagnetic and ferromagnetic character. This interaction-mediated collective behavior is consistent with dipolar fields and exists well above the isolated particles blocking temperature where thermal activation is operative, suggesting that magnetostatic super-spin waves exist in such systems.

We present a summary of recent progress on the development and application of adaptive mesh refinement algorithms for low Mach number reacting flows. Our approach uses a form of the low Mach number equations based on a general equation of state that discretely conserves both mass and energy. The discretization methodology is based on a robust projection formulation that accommodates large density contrasts. The algorithm supports modeling of multicomponent systems and incorporates an operator-split treatment of stiff reaction terms. The basic computational approach is embedded in an adaptive projection framework that uses structured hierarchical grids with subcycling in time that preserves the discrete conservation properties of the underlying single-grid algorithm. We present numerical examples illustrating the application of the methodology to turbulent premixed combustion and nuclear flames in type Ia supernovae.

Neutron-induced fission cross sections on targets of {sup 236,236m,237,238}Np, {sup 237,237m}Pu, and {sup 240,241,242,242m,243,244,244m}Am have been estimated for incident neutron energies of up to 6 MeV, using the ''surrogate'' technique and the ({sup 3}He,df) and ({sup 3}He,tf) reactions on stable targets to measure fission probabilities. In isotopes where low-lying isomeric states are known to exist, the (n,f) cross section on the corresponding isomeric targets has been estimated, using the surrogate technique. For targets of {sup 237}Np, {sup 241}Am, {sup 242m}Am, {sup 243}Am, measurements of the (n,f) cross section exist, and comparison with the surrogate-method results suggests that the (n,f) cross sections estimated by the surrogate technique are reliable to within 10% for incident neutron energies E{sub n}{approx}>2 MeV. Tabulated values of the estimated (n,f) cross sections are given in an appendix.

Nitrate is the number one drinking water contaminant in the United States. It is pervasive in surface and groundwater systems, and its principal anthropogenic sources have increased dramatically in the last 50 years. In California alone, one third of the public drinking-water wells has been lost since 1988 and nitrate contamination is the most common reason for abandonment. Effective nitrate management in groundwater is complicated by uncertainties related to multiple point and non-point sources, hydrogeologic complexity, geochemical reactivity, and quantification of dentrification processes. In this paper, we review an integrated experimental and simulation-based framework being developed to study the fate of nitrate in a 25 km-long groundwater subbasin south of San Jose, California, a historically agricultural area now undergoing rapid urbanization with increasing demands for groundwater. The modeling approach is driven by a need to integrate new and archival data that support the hypothesis that nitrate fate and transport at the basin scale is intricately related to hydrostratigraphic complexity, variability of flow paths and groundwater residence times, microbial activity, and multiple geochemical reaction mechanisms. This study synthesizes these disparate and multi-scale data into a three-dimensional and highly resolved reactive transport modeling framework.

The microstructural evolution of high purity steel under irradiation is modeled including a dislocation density that evolves simultaneously with void nucleation and growth. The predicted void swelling trends versus temperature, flux, and time are compared to experiment and to earlier calculations with a fixed dislocation density. The behavior is further analyzed within a simplified picture of segregation of irradiation defects to microstructural sinks. Agreement with experimental swelling behavior improves when dislocations co-evolve with the void content versus simulations with a fixed dislocation density. The time-dependent dislocation content dictates the rate of void nucleation and shapes the overall void size distribution so as to give steady swelling behavior over long times.

Despite the rapid combustion typically experienced in Homogeneous Charge Compression Ignition (HCCI), components in fuel mixtures do not ignite in unison or burn equally. In our experiments and modeling of blends of diethyl ether (DEE) and ethanol (EtOH), the DEE led combustion and proceeded further toward completion, as indicated by {sup 14}C isotope tracing. A numerical model of HCCI combustion of DEE and EtOH mixtures supports the isotopic findings. Although both approaches lacked information on incompletely combusted intermediates plentiful in HCCI emissions, the numerical model and {sup 14}C tracing data agreed within the limitations of the single zone model. Despite the fact that DEE is more reactive than EtOH in HCCI engines, they are sufficiently similar that we did not observe a large elongation of energy release or significant reduction in inlet temperature required for light-off, both desired effects for the combustion event. This finding suggests that, in general, HCCI combustion of fuel blends may have preferential combustion of some of the blend components.

Networks have recently emerged as a unifying theme in complex systems research [1]. It is in fact no coincidence that networks and complexity are so heavily intertwined. Any future definition of a complex system should reflect the fact that such systems consist of many mutually interacting components. These components are far from being identical as say electrons in systems studied by condensed matter physics. In a truly complex system each of them has a unique identity allowing one to separate it from the others. The very first question one may ask about such a system is which other components a given component interacts with? This information system wide can be visualized as a graph, whose nodes correspond to individual components of the complex system in question and edges to their mutual interactions. Such a network can be thought of as a backbone of the complex system. Of course, system's dynamics depends not only on the topology of an underlying network but also on the exact form of interaction of components with each other, which can be very different in various complex systems. However, the underlying network may contain clues about the basic design principles and/or evolutionary history of the complex …

The problem of imaging of targets in random media or cluttered environments is found in a wide variety of different applications, including ocean acoustics, medical ultrasound, geophysics, and radar. The solution often requires separating targets of interest from other scatterers, and compensating for wave speed variations in the medium. The problem is not usually the lack of data, but too much data, specifically the lack of a useful organizing principle for the data. The difficult part is separating the meaningful data from the remainder. It would therefore be most helpful if there were some means for skipping over those parts of the data that we do not really want to image very much, and looking at those parts (targets) that do interest us. This sounds challenging (maybe even impossible), but recent developments in acoustics make it clear that certain very limited imaging goals are achievable with much smaller data sets than are traditionally needed in, for example, seismic array processing. Early versions of this new method have been given the names of ''time-reversal acoustics'' or ''time-reversal mirrors,'' and have been developed most extensively by the French ultrasonics group led by Fink.