The Computational Science Center's expertise includes the following five areas:
High-Performance Computing
High-performance computing experts in the Computational Science Center collaborate with NREL researchers to take full advantage of advanced computing hardware—such as RedMesa, RedRock, and WinHPC—and software resources necessary to advance renewable energy and energy efficiency technologies. We use our expertise in the following activities:
Applications: Implementations and strategies of using large, highly parallel computer resources—from interacting with users on system use, to improving the mechanisms for using high-performance computing (HPC) systems.
Parallel Performance: Improvements to computational science and engineering research by using 100s to 1,000s to 10,000s of processing cores all applied to solving a single problem.
Software Architecture: Design and optimization of software to take advantage of large hardware resources to address research and analysis goals.
Computational Science
Computational scientists are domain scientists using computers as their primary method of investigating and researching scientific, engineering, and analysis problems. Activities within the Center focus on the following:
Atomic, Molecular, Nano, and Biological Systems: Systems where quantum mechanical, molecular chemistry, nanoscience, molecular biology, and biochemical phenomena are the primary concern.
Reacting Flows: Macroscopic and mesoscopic phenomena where systems can be described as one or more interacting fluids and include phenomena in bioreactors, batteries, fuel cells, gas turbines, power electronics, and wind and ocean turbines.
Informatics: Extraction of non-obvious relationships in simulation and experimental data by applying a variety of techniques from statistics-based analysis of variance (ANOVA) and dimension reduction to clustering, classification, and association.
Applied Mathematics
Applied mathematicians improve NREL's ability to address research challenges by bringing new and advanced mathematical techniques to scientific, technical, and analysis problems. Activities within the Center focus on the following:
Discrete Systems: Mathematics required to describe, model, simulate, solve, explore, and optimize discrete systems, whether those systems are interacting atoms, systems of chemical reactions, or engineering systems describing buildings and wind turbines.
Continuous Systems: Mathematics required to describe, model, simulate, solve, explore, and optimize continuous systems, whether those systems are interacting fluids, fluid-structure interactions, or electromagnetic fields.
Computational Statistics: Quantification and understanding of the uncertainty in simulations and experiments to improve understanding and guide new simulations and experiments.
Data and Visualization
Increasingly, the process of moving from research to deployment is becoming a data-intensive enterprise. Methods at NREL based on experimentation, simulation, and observation are producing tens of terabytes of data and millions of related records because of the continuing evolution of digital technology. Scientific visualization and data management researchers improve our ability to capture, mine, analyze, and visualize data to address scientific and technical goals. Activities within the Center focus on the following:
Scientific Visualization: Advanced visualization techniques on large, complex data sets to improve understanding and enable interpretation of scientific data.
Data Management: Scientific data management strategies to improve the integrity and accessibility of scientific and technical data, including data modeling, aggregation, inline analysis, integration, and search.
Collaboration: Interaction on research problems as a group, including the inclusion and expression of research information and interactivity.
Systems
Activities within the Center focus on the following:
High-Performance Computing Systems: Systems with thousands of processing cores, very high-performance networks, and hundreds of thousands of gigabytes of disk space for hosting the models and simulations of many researchers and engineers.
Storage Systems: Requirements of the HPC systems to enable the integration of complex and large-volume experiments with HPC resources.
Knowledge Platforms and Science Portals: Systems constructed to enable scientists and engineers to correlate and annotate data from HPC and storage systems to better express and access knowledge.
