Gas-particle flows in fluidized beds and riser reactors are inherently unstable, and they manifest fluctuations over a wide range of length and time scales. Two-fluid models for such flows reveal unstable modes whose length scale is as small as ten particle diameters. Yet, because of limited computational resources, gas-particle flows in large fluidized beds are invariably simulated by solving discretized versions of the two-fluid model equations over a coarse spatial grid. Such coarse-grid simulations do not resolve the small-scale spatial structures which are known to affect the macroscale flow structures both qualitatively and quantitatively. Thus there is a need to develop filtered two-fluid models which are suitable for coarse-grid simulations and capturing the effect of the small-scale structures through closures in terms of the filtered variables. The overall objective of the project is to develop validated closures for filtered two-fluid models for gas-particle flows, with the transport gasifier as a primary, motivating example. In this project, highly resolved three-dimensional simulations of a kinetic theory based two-fluid model for gas-particle flows have been performed and the statistical information on structures in the 100-1000 particle diameters length scale has been extracted. Based on these results, closures for filtered two-fluid models have been …

The Petascale Computing Enabling Technologies (PCET) project addressed challenges arising from current trends in computer architecture that will lead to large-scale systems with many more nodes, each of which uses multicore chips. These factors will soon lead to systems that have over one million processors. Also, the use of multicore chips will lead to less memory and less memory bandwidth per core. We need fundamentally new algorithmic approaches to cope with these memory constraints and the huge number of processors. Further, correct, efficient code development is difficult even with the number of processors in current systems; more processors will only make it harder. The goal of PCET was to overcome these challenges by developing the computer science and mathematical underpinnings needed to realize the full potential of our future large-scale systems. Our research results will significantly increase the scientific output obtained from LLNL large-scale computing resources by improving application scientist productivity and system utilization. Our successes include scalable mathematical algorithms that adapt to these emerging architecture trends and through code correctness and performance methodologies that automate critical aspects of application development as well as the foundations for application-level fault tolerance techniques. PCET's scope encompassed several research thrusts in computer science …