Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Glasses filled with ceramic or metallic powders have been developed for use as seals for solid oxide fuel cells (SOFC's) as part of the U.S. Department of Energy's Solid State Energy Conversion Alliance (SECA) Program. The composites of glass (alkaline earth-alumina-borate) and powders ({approx}20 vol% of yttria-stabilized zirconia or silver) were shown to form seals with SOFC materials at or below 900 C. The type and amount of powder were adjusted to optimize thermal expansion to match the SOFC materials and viscosity. Wetting studies indicated good wetting was achieved on the micro-scale and reaction studies indicated that the degree of reaction between the filled glasses and SOFC materials, including spinel-coated 441 stainless steel, at 750 C is acceptable. A test rig was developed for measuring strengths of seals cycled between room temperature and typical SOFC operating temperatures. Our measurements showed that many of the 410 SS to 410 SS seals, made using silver-filled glass composites, were hermetic at 0.2 MPa (2 atm.) of pressure and that seals that leaked could be resealed by briefly heating them to 900 C. Seal strength measurements at elevated temperature (up to 950 C), measured using a second apparatus that we developed, indicated that seals maintained 0.02 MPa (0.2 atm.) overpressures for 30 min at 750 C with no leakage. Finally, the volatility of the borate component of sealing glasses under SOFC operational conditions was studied using weight loss measurements and found by extrapolation to be less than 5% for the projected SOFC lifetime.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

This paper presents a parallel programming model, Parallel Phase Model (PPM), for next-generation high-end parallel machines based on a distributed memory architecture consisting of a networked cluster of nodes with a large number of cores on each node. PPM has a unified high-level programming abstraction that facilitates the design and implementation of parallel algorithms to exploit both the parallelism of the many cores and the parallelism at the cluster level. The programming abstraction will be suitable for expressing both fine-grained and coarse-grained parallelism. It includes a few high-level parallel programming language constructs that can be added as an extension to an existing (sequential or parallel) programming language such as C; and the implementation of PPM also includes a light-weight runtime library that runs on top of an existing network communication software layer (e.g. MPI). Design philosophy of PPM and details of the programming abstraction are also presented. Several unstructured applications that inherently require high-volume random fine-grained data accesses have been implemented in PPM with very promising results.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

This document describes how to obtain, install, use, and enjoy a better life with OVIS version 2.0. The OVIS project targets scalable, real-time analysis of very large data sets. We characterize the behaviors of elements and aggregations of elements (e.g., across space and time) in data sets in order to detect anomalous behaviors. We are particularly interested in determining anomalous behaviors that can be used as advance indicators of significant events of which notification can be made or upon which action can be taken or invoked. The OVIS open source tool (BSD license) is available for download at ovis.ca.sandia.gov. While we intend for it to support a variety of application domains, the OVIS tool was initially developed for, and continues to be primarily tuned for, the investigation of High Performance Compute (HPC) cluster system health. In this application it is intended to be both a system administrator tool for monitoring and a system engineer tool for exploring the system state in depth. OVIS 2.0 provides a variety of statistical tools for examining the behavior of elements in a cluster (e.g., nodes, racks) and associated resources (e.g., storage appliances and network switches). It calculates and reports model values and outliers relative to those models. Additionally, it provides an interactive 3D physical view in which the cluster elements can be colored by raw element values (e.g., temperatures, memory errors) or by the comparison of those values to a given model. The analysis tools and the visual display allow the user to easily determine abnormal or outlier behaviors. The OVIS project envisions the OVIS tool, when applied to compute cluster monitoring, to be used in conjunction with the scheduler or resource manager in order to enable intelligent resource utilization. For example, nodes that are deemed less healthy, that is, nodes that exhibit outlier behavior in some variable, or set of variables, that has shown to be correlated with future failure, can be discovered and assigned to shorter duration or less important jobs. Further, applications with fault-tolerant capabilities can invoke those mechanisms on demand, based upon notification of a node exhibiting impending failure conditions, rather than performing such mechanisms (e.g. checkpointing) at regular intervals unnecessarily.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.