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Savannah River Remediation's (SRR's) Defense Waste Processing Facility (DWPF) Laboratory currently tests for sludge carry-over into the Recycle Collection Tank (RCT) by evaluating the iron concentration in the Slurry Mix Evaporator Condensate Tank (SMECT) and relating this iron concentration to the amount of sludge solids present. A new method was proposed for detecting the amount of sludge in the SMECT that involves the use of an Optek turbidity sensor. Waste Services Laboratory (WSL) personnel conducted testing on two of these units following a test plan developed by Waste Solidification Engineering (WSE). Both Optek units (SN64217 and SN65164) use sensor model AF16-N and signal converter model series C4000. The sensor body of each unit was modified to hold a standard DWPF 12 cc sample vial, also known as a ''peanut'' vial. The purpose of this testing was to evaluate the use of this model of turbidity sensor, or meter, to provide a measurement of the sludge solids present in the SMECT based upon samples from that tank. During discussions of the results from this study by WSE, WSL, and Savannah River National Laboratory (SRNL) personnel, an upper limit on the acceptable level of solids in SMECT samples was set at 0.14 …

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The phenomena related to pore-water extraction from unsaturated sediments have been previously examined with limited laboratory experiments and numerical modeling. However, key scale-up issues have not yet been addressed. Laboratory experiments and numerical modeling were conducted to specifically examine pore-water extraction for sediment conditions relevant to the vadose zone beneath the SX Tank Farm at Hanford Site in southeastern Washington State. Available SX Tank Farm data were evaluated to generate a conceptual model of the subsurface for a targeted pore-water extraction application in areas with elevated moisture and Tc-99 concentration. The hydraulic properties of the types of porous media representative of the SX Tank Farm target application were determined using sediment mixtures prepared in the laboratory based on available borehole sediment particle size data. Numerical modeling was used as an evaluation tool for scale-up of pore-water extraction for targeted field applications.

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One of the most fundamental properties of ideal magnetohydrodynamics is the condition that plasma motion cannot change magnetic topology. The conventional representation of ideal magnetohydrodynamic modes by perturbing a toroidal equilibrium field through δ Β = ∇ X (xi X B) ensures that δ B • ∇ ψ = 0 at a resonance, with ψ labelling an equilibrium flux surface. Also useful for the analysis of guiding center orbits in a perturbed field is the representation δ Β = ∇ X αB. These two representations are equivalent, but the vanishing of δ B • ∇ψ at a resonance is necessary but not sufficient for the preservation of field line topology, and a indiscriminate use of either perturbation in fact destroys the original equilibrium flux topology. It is necessary to find the perturbed field to all orders in xi to conserve the original topology. The effect of using linearized perturbations on stability and growth rate calculations is discussed

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Managing scientific data has been identified by the scientific community as one of the most important emerging needs because of the sheer volume and increasing complexity of data being collected. Effectively generating, managing, and analyzing this information requires a comprehensive, end-to-end approach to data management that encompasses all of the stages from the initial data acquisition to the final analysis of the data. Fortunately, the data management problems encountered by most scientific domains are common enough to be addressed through shared technology solutions. Based on community input, we have identified three significant requirements. First, more efficient access to storage systems is needed. In particular, parallel file system and I/O system improvements are needed to write and read large volumes of data without slowing a simulation, analysis, or visualization engine. These processes are complicated by the fact that scientific data are structured differently for specific application domains, and are stored in specialized file formats. Second, scientists require technologies to facilitate better understanding of their data, in particular the ability to effectively perform complex data analysis and searches over extremely large data sets. Specialized feature discovery and statistical analysis techniques are needed before the data can be understood or visualized. Furthermore, interactive …

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