About the DOE NETL Program:
The National Energy Technology Laboratory (NETL), part of DOE’s national laboratory system, is owned and operated by the U.S. Department of Energy (DOE). NETL supports DOE’s mission to advance the national, economic, and energy security of the United States.
NETL implements a broad spectrum of energy and environmental research and development (R&D) programs that will return benefits for generations to come:
- Enabling domestic coal, natural gas, and oil to economically power our Nation’s homes, industries, businesses, and transportation …
- While protecting our environment and enhancing our energy independence.
NETL has expertise in coal, natural gas, and oil technologies, contract and project management, analysis of energy systems, and international energy issues.
In addition to research conducted onsite, NETL’s project portfolio includes R&D conducted through partnerships, cooperative research and development agreements, financial assistance, and contractual arrangements with universities and the private sector. Together, these efforts focus a wealth of scientific and engineering talent on creating commercially viable solutions to national energy and environmental problems.
Unconventional and Renewable Energy Research Utilizing Advanced Computer Simulations at Utah
The ability to develop science-based and validated computational tools to simulate and facilitate the development of clean, highly efficient energy systems of the future requires innovation in several key computational science technologies, including scientific data management, scientific visualization, scientific software environments, and scientific computing. The overall objective of this work is to leverage our expertise and experience in both scientific visualization and complex science-based simulations toward the accurate and robust simulation of science-based phenomena in the area of unconventional and renewable energy research. This work is aimed at garnering a better understanding of science-based phenomena in energy research and also the advancement of the Uintah software system. The Uintah software system accommodates the massive amounts of data and advanced algorithmic, software, and hardware technologies required to deal with the enormity and complexity of the simulation data in this area of research. To accomplish these goals, we are creating new numerical and visualization techniques needed to assess the uncertainty of the simulation, extend the Uintah scientific problem-solving environment for large-scale simulation of science-based systems, and integrate and extend the data provenance infrastructure of Uintah to systematically capture provenance information and track simulation parameter studies.
Science-based development of clean and efficient energy systems often involves modeling and simulations of fluid flows, chemical reactions and mechanical properties within heterogeneous media. As part of our DOE-funded (1997-2009) Center for Simulation of Accidental Fires and Explosions (C-SAFE), we created the Uintah scientific problem-solving environment. Uintah is a parallel software environment for solving large-scale computational mechanics and fluid dynamics systems, and has particular strengths when dealing with systems that require large deformations, fire simulation, and fluid-structure interactions. Uintah, general-purpose, fluid-structure interaction code has been used to characterize a wide array of physical systems and processes encompassing a wide range of time and length scales - from microseconds and microns to minutes and meters. Complex simulations require both immense computational power and complex software. Typical simulations include solvers for structural mechanics, fluids, chemical reactions, and material models, which are efficiently integrated to achieve the scalability required to perform the simulations. Uintah scales to cores by using a novel asynchronous task-based approach for challenging AMR applications. Novel parallel computing algorithms, on both CPUs and GPUs, are needed when simulating large-scale complex science-based energy systems. In moving beyond petascale, it will be necessary to make use of GPU-like architectures as the ongoing convergence between GPUs and multi-core CPUs continues. Task-based codes like Uintah are very well placed to exploit such architectures.
The challenge for finite element type simulations is that the memory access patterns are not well suited for the cache coherency required for efficient operations on streaming architectures. The problem becomes worse for the sparse systems associated with large simulations, and performance improvements over CPU implementations have been limited. An alternative is to take advantage of the geometric configuration of unstructured meshes, and to invent compact, efficient data structures that allow SIMD processing of individual cells and subsequent SIMD assembly of cell computations and mapping onto global degrees of freedom in the solution. The problem becomes more challenging for algorithms that are effective on multi-GPU clusters, such as the NVIDIA cluster at the SCI Institute. We anticipate the need for hierarchical domain decompositions that provide sufficient computational density and efficient communication. This work will pursue GPU and GPU-cluster based algorithms for numerical simulations of combustion using both generic linear solvers and specialized solutions that directly map unstructured and structured domains onto streaming architectures.
The system must also provide data visualization capabilities that allow interaction and analysis of the simulated data. The SCI Institute is an international leader in scientific visualization research. The PI, Chris Johnson, co-leads the DOE SciDAC Scalable Data Management, Analysis, and Visualization (SDAV). In this work we are leveraging our expertise in large-scale visualization research and development toward the seamless integration of high-end visualization techniques with simulation results of science-based energy systems. Additionally we are exploring the use of higher fidelity visualization with methods based on the use of high-order mesh elements.
With large computational simulations there is substantial uncertainty inherent in any prediction of science-based systems. A number of factors contribute to uncertainty, including experimental measurements, mathematical formulation, and the way different processes are coupled together in the numerical approach for simulation. Tracking of and analysis of this uncertainly is critical to any work that will truly impact the creation of future energy systems.
Exploration of large-scale scientific systems using computational simulations produces massive amounts of data that must be managed and analyzed. Because of the volume of data manipulated, and the complexity of the simulations and analysis workflows, which are iteratively adjusted as users generate and evaluate hypotheses, it is crucial to maintain detailed provenance (i.e., audit trails or histories) of the derived results. Provenance is necessary to ensure reproducibility as well as enable verification and validation of the simulation codes and results. In order to manage large-scale simulations and the analysis of their results, we will use systems such as the VisTrails software (http://www.vistrails.org), an open-source provenance management and scientific workflow system that was designed to support the scientific discovery process, to guide us in building ”hooks” into Uintah for provenance systems.