This paper explores the possibility of separating and classifying remotely-sensed multispectral data from rocks and minerals onto seven geological rock-type groups. These groups are extracted from the general categories of metamorphic, igneous and sedimentary rocks. The study is performed under ideal conditions for which the data is generated according to laboratory hyperspectral data for the members, which are, in turn, passed through the Multi-spectral Thermal Imager (MTI) filters yielding 15 bands. The main challenge in separability is the small size of the training data sets, which initially did not permit direct application of Bayesian decision theory. To enable Bayseian classification, the original training data is linearly perturbed with the addition minerals, vegetation, soil, water and other valid impurities. As a result, the size of the training data is significantly increased and accurate estimates of the covariance matrices are achieved. In addition, a set of reduced (five) linearly-extracted canonical features that are optimal in providing the most important information about the data is determined. An alternative nonlinear feature-selection method is also employed based on spectral indices comprising a small subset of all possible ratios between bands. By applying three optimization strategies, combinations of two and three ratios are found that provide reliable separability and classification between all seven groups according to the Bhattacharyya distance. To set a benchmark to which the MTI capability in rock classification can be compared, an optimization strategy is performed for the selection of optimal multispectral filters, other than the MTI filters, and an improvement in classification is predicted.
Twinning is ubiquitous in electroplated metals. Here, we identify and discuss unique aspects of twinning found in electrodeposited Ni-Mn alloys. Previous reports concluded that the twin boundaries effectively refine the grain size, which enhances mechanical strength. Quantitative measurements from transmission electron microscopy (TEM) images show that the relative boundary length in the as-plated microstructure primarily comprises twin interfaces. Detailed TEM characterization reveals a range of length scales associated with twinning beginning with colonies ({approx}1000 nm) down to the width of individual twins, which is typically <50 nm. We also consider the connection between the crystallographic texture of the electrodeposit and the orientation of the twin planes with respect to the plating direction. The Ni-Mn alloy deposits in this work possess a 110-fiber texture. While twinning can occur on {l_brace}111{r_brace} planes either perpendicular or oblique to the plating direction in {l_brace}110{r_brace}-oriented grains, plan-view TEM images show that twins form primarily on those planes parallel to the plating direction. Therefore, grains enclosed by twins and multiply twinned particles are produced. Another important consequence of a high twin density is the formation of large numbers of twin-related junctions. We measure an area density of twin junctions that is comparable to the density of dislocations in a heavily cold-worked metal.
The radiographic integrated test stand (RITS-3) is a 5-MV, 160-kA, 70-ns inductive voltage adder accelerator at Sandia National Laboratories used to develop critical understanding of x-ray sources and flash radiographic drivers. On RITS-3 three pulse forming lines (PFLs) are used to drive three inductive voltage adder cavities. Each PFL contains a fast-pulse-charged, self-breakdown annular water switch that is used for initial pulse shaping and timing. Low loss in the switches combined with good synchronization is required for efficient operation of the accelerator. Switch maintenance is closely monitored over time to determine the effects of wear on switch breakdown performance.
An incident location must be translated into an address that responders can find on the ground. In populated areas it's street name and address number. For sparsely populated areas or highways it's typically road name and nearest milepost number. This is paired with road intersection information to help responders approach the incident as quickly and safely as possible. If responders are new to the area, or for cross-country response, more assistance is needed. If dispatchers had mileposts as points on their maps they could provide this assistance as well as vital information to public safety authorities as the incident unfolds. Mileposts are already universally understood and used. The missing rural response piece is to get milepost locations onto dispatch and control center screens.
This paper describes the integration of lookmarks into the ParaView visualization tool. Lookmarks are pointers to views of specific parts of a dataset. They were so named because lookmarks are to a visualization tool and dataset as bookmarks are to a browser and the World Wide Web. A lookmark can be saved and organized among other lookmarks within the context of ParaView. Then at a later time, either in the same ParaView session or in a different one, it can be regenerated, displaying the exact view of the data that had previously been saved. This allows the user to pick up where they left off, to continue to adjust the view or otherwise manipulate the data. Lookmarks facilitate collaboration between users who wish to share views of a dataset. They enable more effective data comparison because they can be applied to other datasets. They also serve as a way of organizing a user's data. Ultimately, a lookmark is a time-saving tool that automates the recreation of a complex view of the data.
Brief disclosures may often be sufficient for the filing of a Technical Advance with Sandia's Intellectual Property Center, but still be inadequate to facilitate an optimum patent application where more detail and explanation are required. Consequently, the crafting of a patent application may require considerably more additional interaction between the application preparer and the inventors. This inefficiency can be considerably mitigated if the inventors address some critical aspects of a patent application when they write a technical report.
At 70 miles per hour, overcoming aerodynamic drag represents about 65% of the total energy expenditure for a typical heavy truck vehicle. The goal of this US Department of Energy supported consortium is to establish a clear understanding of the drag producing flow phenomena. This is being accomplished through joint experiments and computations, leading to the smart design of drag reducing devices. This paper will describe our objective and approach, provide an overview of our efforts and accomplishments, and discuss our future direction.
Sandia National Laboratories has been conducting studies on performance of laboratory and commercial lithium-ion and other types of electrochemical cells using inductive models [1]. The objectives of these investigations are: (1) To develop procedures and techniques to rapidly determine performance degradation rates while these cells undergo life tests; (2) To model cell voltage and capacity in order to simulate cell performance characteristics under variable load and temperature conditions; (3) To model rechargeable battery degradation under charge/discharge cycles and many other conditions. The inductive model and methodology are particularly useful when complicated cell performance behaviors are involved, which are often difficult to be interpreted from simple empirical approaches. We find that the inductive model can be used effectively: (1) To enable efficient predictions of battery life; (2) To characterize system behavior. Inductive models provide convenient tools to characterize system behavior using experimentally or analytically derived data in an efficient and robust framework. The approach does not require detailed phenomenological development. There are certain advantages unique to this approach. Among these advantages is the ability to avoid making measurements of hard to determine physical parameters or having to understand cell processes sufficiently to write mathematical functions describing their behavior. We used artificial neural network for inductive modeling, along with ancillary mathematical tools to improve their accuracy. This paper summarizes efforts to use inductive tools for cell and battery modeling. Examples of numerical results will be presented. One of them is related to high power lithium-ion batteries tested under the U.S. Department of Energy Advanced Technology Development Program for hybrid vehicle applications. Sandia National Laboratories is involved in the development of accelerated life testing and thermal abuse tests to enhance the understanding of power and capacity fade issues and predict life of the battery under a nominal use condition. This paper will use power and capacity fade behaviors of a Ni-oxide-based lithium-ion battery system to illustrate how effective the inductive model can interpret the cell behavior and provide predictions of life. We will discuss the analysis of the fading behavior associated with the cell performance and explain how the model can predict cell performance.
Significantly improving the scalability of large structured adaptive mesh refinement (SAMR) applications is challenging. It requires sophisticated capabilities for using the underlying parallel computer's resources in the most efficient way. This is non-trivial, since the basic conditions for how to allocate the resources change dramatically during run-time due to the dynamics inherent in these applications. This paper presents a first characterization of a hybrid and dynamic partitioner for parallel SAMR applications. Specifically, we investigate parameter settings for trade-offs like communication vs. load balance and speed vs. quality. The key contribution is that the characterization shows that the partitioner is able to respond accurately to stimuli from system and application state, and hence adapt to various SAMR scenarios. This potentially reduces the run-time for large SAMR applications.