Innovative computing for diagnoses from medical, magnetic-resonance imaging

The author presents a final report on a Laboratory-Directed Research and Development (LDRD) project, Innovative Computing for Diagnoses from Medical, Magnetic-Resonance Imaging, performed during fiscal years 1992 and 1993. The project defined a role for high-performance computing in surgery: the supercomputer can automatically summarize the three-dimensional extents of lesions and other clinically-relevant structures, and can deliver these summaries to workstation-based, augmented-reality environments at the clinical site. The author developed methods and software to make these summaries from the digital data already acquired using clinical, magnetic-resonance machines. In joint work with Albuquerque`s Department of Veterans Affairs Hospital, the author applied this work, and obtained a basis for planning, for rehearsal, and for guidance during surgery

Model-based statistical estimation of Sandia RF ohmic switch dynamic operation form stroboscopic, x-ray imaging.

We define a new diagnostic method where computationally-intensive numerical solutions are used as an integral part of making difficult, non-contact, nanometer-scale measurements. The limited scope of this report comprises most of a due diligence investigation into implementing the new diagnostic for measuring dynamic operation of Sandia's RF Ohmic Switch. Our results are all positive, providing insight into how this switch deforms during normal operation. Future work should contribute important measurements on a variety of operating MEMS devices, with insights that are complimentary to those from measurements made using interferometry and laser Doppler methods. More generally, the work opens up a broad front of possibility where exploiting massive high-performance computers enable new measurements

Improving analytical understanding through the addition of information: Bayesian and hybrid mathematics approaches

Safety analysts frequently must provide results that are based on sparse (or even no) data. When data (or more data) become available, it is important to utilize the new information optimally in improving the analysis results. Two methods for accomplishing this purpose are Bayesian analysis, where "prior" probability distributions are modified to become "posterior" distributions based on the new data, and hybrid (possibilistic/probabilistic analysis) where possibilistic "membership" portrays the subjectivity involved and the probabilistic analysis is "frequentist." Each of these approaches has interesting features, and it is advantageous to compare and contrast the two. In addition to describing and contrasting these two approaches, we will discuss how features of each can be combined to give new advantages neither offers by itself

Evaluation of West Virginia University`s iron catalyst impregnated on coal

The objectives to evaluate and compare the activities/selectivities of fine-particle size catalysts being developed in the DOE/PETC Advanced Research (AR) Coal Liquefaction program by using standard coal liquefaction activity test procedures. Previously reported results have described the standard test procedure developed at Sandia to evaluate fine-particle size iron catalysts being developed in DOE/PETC`s AR Coal Liquefaction Program and described the evaluation of several catalysts (commercially available pyrite, University of Pittsburgh`s catalyst, Pacific Northwest Laboratories` catalyst) using these procedures. The test uses DECS-17 Blind Canyon Coal, phenanthrene as the reaction solvent, and a factorial experimental design that enables evaluation of a catalyst over ranges of temperature (350 to 400{degree}C), time (20 to 60 minutes), and catalyst loading (0 to 1 wt % on an as-received coal basis). Recent work has focused on the evaluation of West Virginia University`s iron catalyst that WVU impregnated on DECS-17 Blind Canyon coal. Results showed good activity for this catalyst including the highest amount of 9,10-dihydrophenanthrene (13.2%) observed in a reaction product and a small but significant catalytic effect for heptane conversion (0.5%). Additional experiments are being performed to enable comparison with previously tested catalysts. Tetrahydrofuran insolubles from selected reactions have been sent to the University of Kentucky for Mossbauer characterization of the iron phases present

Evaluation of fine-particle size catalysts using bituminous and subbituminous coals

The objectives of Sandia`s fine-particle size catalyst testing project are to evaluate and compare the activities of fine-particle size catalysts being developed in DOE/PETC`s Advanced Research Coal Liquefaction Program by using Sandia`s standard coal liquefaction test procedures. The first test procedure uses bituminous coal (DECS-17 Blind Canyon coal), phenanthrene as the reaction solvent, and a factorial experimental design that is used to evaluate catalysts over ranges of temperature, time, and catalyst loading. The best catalyst evaluated to date is West Virginia University`s iron catalyst that was impregnated onto the coal. Current work is aimed at developing a standard test procedure using subbituminous Wyodak coal. Ibis test is being developed using Pacific Northwest Laboratories` 6-line ferrihydrite catalyst and coal samples impregnated with either molybdenum or iron at Argonne National Laboratories. Results of testing catalysts with bituminous coal will be summarized and the development of the subbituminous coal test procedure will be presented