High-throughput computational modeling of multiphase flows—NETL is a leader in applying high-performance computing to computationally demanding multiphase flow problems, and in tackling challenging industrial-scale unit flow characterization and troubleshooting.
Development, validation, and application of multiphase flow tools (MFiX Suite)—a part of the Computational Device Engineering Team, the Multiphase Flow Science group has produced a software portfolio of physics-based modeling codes to guide the design, operation and troubleshooting of multiphase flow devices, with an emphasis on fossil fuel technologies (e.g., coal gasifiers, CO2 capture devices and chemical looping).
NETL, Lawrence Berkley National Laboratory and the University of Colorado Boulder are conducting a multi-year effort to enable NETL’s open-source code MFiX to run on exascale computers. The project is supported by the Exascale Computing Project, a collaborative effort of DOE’s Office of Science and National Nuclear Security Administration. This effort will increase the scale and speed capabilities of MFiX, enabling it to simulate with higher fidelity reactors used in fossil energy technologies. The simulations will help reduce the risks, costs and time required for scaling up laboratory designs to industrial sizes, maximizing the benefits of high-performance computing for U.S. economic competitiveness.
Experimental activities—experimental investigation of fluidization behavior and technology applied to coal combustion, gasification, and emissions clean-up. The MFS Group’s Multiphase Flow Analysis Laboratory includes reacting experimental units, enabling generation of well-characterized multiphase flow data at different length and time scales to aid in understanding complex fluidization behavior in reactors, thereby underpinning the development of mathematical models and validating software code. Experiments result in comprehensive data sets for validation, and the experimental units provide platforms for development and validation of novel measurement techniques.
Computational modeling of materials—first principles quantum mechanics calculations, classical and quantum molecular dynamics, Monte Carlo simulations, microkinetic modeling, and mesoscale modeling are used by the Computational Materials Team to characterize materials properties involved in diverse applications of interest such as catalysts and electrocatalysts, solid and liquid membranes for gas capture and separation, oxygen carrier materials, materials for solid-oxide fuel cell applications, and novel nanostructured materials for energy conversion and the development of gas sensors. High-throughput computational screening—for designing materials or novel alloy compositions with controlled properties, assessing the materials’ thermomechanical and microstructure evolution under heat or electromagnetic treatments, optimization of their performances under diverse process conditions, and assessment of their performance-to-cost ratio.