Rapid detection and identification of bacteria and other pathogens is important for many civilian and military applications. The taxonomic significance, or the ability to differentiate one microorganism from another, using fatty acid content and distribution is well known. For analysis fatty acids are usually converted to fatty acid methyl esters (FAMEs). Bench-top methods are commercially available and recent publications have demonstrated that FAMEs can be obtained from whole bacterial cells in an in situ single-step pyrolysis/methylation analysis. This report documents the progress made during a three year Laboratory Directed Research and Development (LDRD) program funded to investigate the use of microfabricated components (developed for other sensing applications) for the rapid identification of bioorganisms based upon pyrolysis and FAME analysis. Components investigated include a micropyrolyzer, a microGC, and a surface acoustic wave (SAW) array detector. Results demonstrate that the micropyrolyzer can pyrolyze whole cell bacteria samples using only milliwatts of power to produce FAMEs from bacterial samples. The microGC is shown to separate FAMEs of biological interest, and the SAW array is shown to detect volatile FAMEs. Results for each component and their capabilities and limitations are presented and discussed. This project has produced the first published work showing successful pyrolysis/methylation of fatty acids and related analytes using a microfabricated pyrolysis device.
The Defense Advanced Research Projects Agency (DARPA) has recognized that biological and chemical toxins are a real and growing threat to troops, civilians, and the ecosystem. The Explosives Components Facility at Sandia National Laboratories (SNL) has been working with the University of Montana, the Southwest Research Institute, and other agencies to evaluate the feasibility of directing honeybees to specific targets, and for environmental sampling of biological and chemical ''agents of harm''. Recent work has focused on finding and locating buried landmines and unexploded ordnance (UXO). Tests have demonstrated that honeybees can be trained to efficiently and accurately locate explosive signatures in the environment. However, it is difficult to visually track the bees and determine precisely where the targets are located. Video equipment is not practical due to its limited resolution and range. In addition, it is often unsafe to install such equipment in a field. A technology is needed to provide investigators with the standoff capability to track bees and accurately map the location of the suspected targets. This report documents Light Detection and Ranging (LIDAR) tests that were performed by SNL. These tests have shown that a LIDAR system can be used to track honeybees. The LIDAR system can provide both the range and coordinates of the target so that the location of buried munitions can be accurately mapped for subsequent removal.
This paper presents a description of the SIERRA Framework Version 3 parallel transfer operators. The high-level design including object interrelationships, as well as requirements for their use, is discussed. Transfer operators are used for moving field data from one computational mesh to another. The need for this service spans many different applications. The most common application is to enable loose coupling of multiple physics modules, such as for the coupling of a quasi-statics analysis with a thermal analysis. The SIERRA transfer operators support the transfer of nodal and element fields between meshes of different, arbitrary parallel decompositions. Also supplied are ''copy'' transfer operators for efficient transfer of fields between identical meshes. A ''copy'' transfer operator is also implemented for constraint objects. Each of these transfer operators is described. Also, two different parallel algorithms are presented for handling the geometric misalignment between different parallel-distributed meshes.
In a Synthetic Aperture Radar (SAR) system, the purpose of the receiver is to process incoming radar signals in order to obtain target information and ultimately construct an image of the target area. Incoming raw signals are usually in the microwave frequency range and are typically processed with analog circuitry, requiring hardware designed specifically for the desired signal processing operations. A more flexible approach is to process the signals in the digital domain. Recent advances in analog-to-digital converter (ADC) and Field Programmable Gate Array (FPGA) technology allow direct digital processing of wideband intermediate frequency (IF) signals. Modern ADCs can achieve sampling rates in excess of 1GS/s, and modern FPGAs can contain millions of logic gates operating at frequencies over 100 MHz. The combination of these technologies is necessary to implement a digital radar receiver capable of performing high speed, sophisticated and scalable DSP designs that are not possible with analog systems. Additionally, FPGA technology allows designs to be modified as the design parameters change without the need for redesigning circuit boards, potentially saving both time and money. For typical radars receivers, there is a need for operation at multiple ranges, which requires filters with multiple decimation rates, i.e., multiple bandwidths. In previous radar receivers, variable decimation was implemented by switching between SAW filters to achieve an acceptable filter configuration. While this method works, it is rather ''brute force'' because it duplicates a large amount of hardware and requires a new filter to be added for each IF bandwidth. By implementing the filter digitally in FPGAs, a larger number of decimation values (and consequently a larger number of bandwidths) can be implemented with no need for extra components. High performance, wide bandwidth radar systems also place high demands on the DSP throughput of a given digital receiver. In such applications, the maximum clock frequency of a given FPGA is not adequate to support the required data throughput. This problem can be overcome by employing a parallel implementation of the pane filter. The parallel pane filter uses a polyphase parallelization technique to achieve an aggregate data rate which is twice that of the FPGA clock frequency. This is achieved at the expense of roughly doubling the FPGA resource usage.
The Nuclear Regulatory Commission (NRC) approves new package designs for shipping fissile quantities of UF{sub 6}. Currently there are three packages approved by the NRC for domestic shipments of fissile quantities of UF{sub 6}: NCI-21PF-1; UX-30; and ESP30X. For approval by the NRC, packages must be subjected to a sequence of physical tests to simulate transportation accident conditions as described in 10 CFR Part 71. The primary objective of this project was to relate the conditions experienced by these packages in the tests described in 10 CFR Part 71 to conditions potentially encountered in actual accidents and to estimate the probabilities of such accidents. Comparison of the effects of actual accident conditions to 10 CFR Part 71 tests was achieved by means of computer modeling of structural effects on the packages due to impacts with actual surfaces, and thermal effects resulting from test and other fire scenarios. In addition, the likelihood of encountering bodies of water or sufficient rainfall to cause complete or partial immersion during transport over representative truck routes was assessed. Modeled effects, and their associated probabilities, were combined with existing event-tree data, plus accident rates and other characteristics gathered from representative routes, to derive generalized probabilities of encountering accident conditions comparable to the 10 CFR Part 71 conditions. This analysis suggests that the regulatory conditions are unlikely to be exceeded in real accidents, i.e. the likelihood of UF{sub 6} being dispersed as a result of accident impact or fire is small. Moreover, given that an accident has occurred, exposure to water by fire-fighting, heavy rain or submersion in a body of water is even less probable by factors ranging from 0.5 to 8E-6.