Publications

Mariner, Paul M.; Debusschere, Bert D.; Hammond, Glenn E.; Seidl, Daniel T.; laura, swiler l.; Vo , Jonathon V.

Abstract not provided.

Mariner, Paul M.; Connolly, Laura A.; Cunningham, Leigh C.; Debusschere, Bert D.; Dobson, David C.; Frederick, Jennifer M.; Hammond, Glenn E.; Jordan, Spencer H.; LaForce, Tara; Nole, Michael A.; Park, Heeho D.; Perry, Frank V.; Rogers, Ralph D.; Seidl, Daniel T.; Sevougian, Stephen D.; Stein, Emily S.; Swift, Peter N.; Swiler, Laura P.; Vo, Jonathan V.; Wallace, Michael G.

Abstract not provided.

Supercomputing Frontiers and Innovations

Bartlett, Roscoe B.; Demeshko, Irina; Gamblin, Todd; Hammond, Glenn E.; Heroux, Michael A.; Johnson, Jeffrey; Klinvex, Alicia M.; Li, Xiaoye; McInnes, Lois C.; Moulton, J.D.; Osei-Kuffuor, Daniel; Sarich, Jason; Smith, Barry; Willenbring, James M.; Yang, Ulrike M.

Extreme-scale computational science increasingly demands multiscale and multiphysics formulations. Combining software developed by independent groups is imperative: no single team has resources for all predictive science and decision support capabilities. Scientific libraries provide high-quality, reusable software components for constructing applications with improved robustness and portability. However, without coordination, many libraries cannot be easily composed. Namespace collisions, inconsistent arguments, lack of third-party software versioning, and additional difficulties make composition costly. The Extreme-scale Scientific Software Development Kit (xSDK) defines community policies to improve code quality and compatibility across independently developed packages (hypre, PETSc, SuperLU, Trilinos, and Alquimia) and provides a foundation for addressing broader issues in software interoperability, performance portability, and sustainability. The xSDK provides turnkey installation of member software and seamless combination of aggregate capabilities, and it marks first steps toward extreme-scale scientific software ecosystems from which future applications can be composed rapidly with assured quality and scalability.

O'Hern, Timothy J.; Hammond, Glenn E.; Orear, Leslie O.; van Bloemen Waanders, Bart G.

Threats to water distribution systems include release of contaminants and Denial of Service (DoS) attacks. A better understanding, and validated computational models, of the flow in water distribution systems would enable determination of sensor placement in real water distribution networks, allow source identification, and guide mitigation/minimization efforts. Validation data are needed to evaluate numerical models of network operations. Some data can be acquired in real-world tests, but these are limited by 1) unknown demand, 2) lack of repeatability, 3) too many sources of uncertainty (demand, friction factors, etc.), and 4) expense. In addition, real-world tests have limited numbers of network access points. A scale-model water distribution system was fabricated, and validation data were acquired over a range of flow (demand) conditions. Standard operating variables included system layout, demand at various nodes in the system, and pressure drop across various pipe sections. In addition, the location of contaminant (salt or dye) introduction was varied. Measurements of pressure, flowrate, and concentration at a large number of points, and overall visualization of dye transport through the flow network were completed. Scale-up issues that that were incorporated in the experiment design include Reynolds number, pressure drop across nodes, and pipe friction and roughness. The scale was chosen to be 20:1, so the 10 inch main was modeled with a 0.5 inch pipe in the physical model. Controlled validation tracer tests were run to provide validation to flow and transport models, especially of the degree of mixing at pipe junctions. Results of the pipe mixing experiments showed large deviations from predicted behavior and these have a large impact on standard network operations models.3