National Energy Technology Laboratory

Coordinator: Madhava (Syam) Syamlal

Review abstracts for current and past practicum experiences at NETL >>

As the lead laboratory for DOE’s Office of Fossil Energy, the National Energy Technology Laboratory (NETL) relies on a strong onsite research program conducted by federal scientists and engineers working in partnership with academia, other research institutions, and the private sector. NETL’s state-of-the-art capabilities and facilities are located in Morgantown, West Virginia; Pittsburgh, Pennsylvania; and Albany, Oregon.

NETL’s computational science and engineering capability utilizes multi-scale computational methods to develop and deliver energy technologies at a faster pace, lower cost, and reduced risk in support of DOE’s mission. The capability integrates physical and chemical experimental research with computational research to generate insights beyond the reach of experiments alone. Three research teams — Computational Materials Engineering, Computational Device Engineering and the Science-based Artificial Intelligence/Machine Learning (AI/ML) Institute (SAMI) — leverage world-class expertise and facilities to accelerate the development and deployment of novel fossil energy materials, processes, and device designs. Applications of the computational science and engineering capabilities increases oil and gas recovery rates, develops cybersecurity for energy infrastructure, optimizes energy system performance, reduces the time required to develop new materials, develops modular fossil energy plants, and reduces the time for decision making.

NETL maintains two cutting-edge computational facilities. The lab’s Center for Computational Science and Engineering maintains NETL’s high-performance supercomputer, Joule 2.0. This facility ranks 10th fastest in the DOE National Laboratories with a 3.6 petaflop system. NETL’s Center for Artificial Intelligence and Machine Learning, WATT, links 104 GPUs with 16 petabytes of storage to provide unparalleled opportunities for the use of AI/ML to enable scientific discovery and R&D acceleration. DOE CSGF students will use these key facilities during the course of their research. Opportunities exist in the following areas for fellowship students:

High-Throughput Screening for Materials

NETL maintains expertise in the modeling of materials at the atomic, molecular, and meso scales, which enables a fundamental understanding of materials behavior and provides insight into subsequent materials development opportunities and optimization strategies. Scientists and engineers use a combination of physics-based models and AI/ML approaches, in conjunction with experimental data, to identify new materials and assess their performance in the context of new energy technologies that achieve increased efficiency and reliability at reduced cost. Opportunities in materials engineering include:

Carbon Capture: Ionic liquids (ILs) exhibit many desirable properties for deployment as part of a carbon capture process, including thermal stability, low vapor pressure, negligible flammability and, depending on the functionalization, good selectivity for CO2. Information regarding the energetics of the reaction of CO2 with ILs, obtained via ab initio calculations, can help shed light on the suitability of candidate ILs for use in real-world carbon capture operations. Another area of interest is in the screening of mixed matrix membranes consisting of metalorganic framework (MOF) materials embedded in polymeric materials. NETL is interested in accurately modeling such reactions using ab initio methods such as coupled cluster, resolution of the identity second-order Møller–Plesset perturbation theory, Density Functional Theory, symmetry adapted perturbation theory, and quantum mechanics/molecule mechanics methods.

Metal Alloy Performance: Another area of interest is multiscale modeling for fundamental understanding of metal oxidation at different length and time scales. The goal is to develop an integrated computational framework based on a mesoscale phase-field methodology with fundamental inputs from kinetic Monte Carlo and Density Functional Theory to gain insight into the fundamental material properties/processes that influence and control macroscopic oxidation. eXtremeMAT (XMAT) is an Office of Fossil Energy program that works to design, develop and qualify improved heat resistant alloys for existing and future fossil energy power cycles. XMAT will result in tool sets that address the gaps in current physics-based materials modeling, data analytics and machine learning to enable reliable prediction of materials performance over long service lifetimes in fossil energy power plant environments and improved alloy design capability to increase high temperature capability of steels and or reduce the cost of Ni alloys.

Design and Optimization of Chemical Reactors and Devices

Understanding the performance of energy, environmental, and chemical process devices based on multiphase flow physics is extremely challenging. Having the means to impact their design early in the developmental process is critically important to control costs and reduce the risk of not meeting performance standards. There is a critical need for science-based models with quantified uncertainty for reducing the cost and time required for the development of multiphase flow devices. NETL’s Multiphase Flow Science research program is a strategic combination of computational and physical models of reacting multiphase flows whose purpose is to provide these validated science-based modeling tools.

Central to NETL’s multiphase flow reactor modeling effort is the laboratory’s suite of multiphase computational fluid dynamics (CFD) code, called MFIX. This open-source suite of software tools has over three decades of development history and more than 5,000+ registered users worldwide and cited over 400 times per year in research publications. This software has become the standard test bed for comparing, implementing, and evaluating multiphase flow constitutive models and has been applied to an extremely diverse range of applications involving multiphase flows.

MFIX-Exa is a CFD discrete element model (CFD-DEM) code being developed to run efficiently on Exascale computers, which are expected in 2021. On those machines the MFIX-Exa project team will conduct a challenge problem simulation, achieving a 1000-fold increase in the number of DEM particles compared with the current state-of-the-art.

Opportunities exist to work with a crosscutting team of engineers and scientists skilled in development and application of multiphase CFD software and multiphase experimentation. Typical applications of multiphase computation will include fluidized bed combustion, gasification, carbon capture, chemical looping combustion and gasification. Activities can span from fundamental code development, validation with experimental data, and uncertainty quantification.

Innovative Energy Concepts (IEC)

Available projects in IEC focus on development and implementation of computational methodologies to produce dynamic, high-fidelity, multi-scale physics models that support applied technology development in advanced turbines, magneto-hydrodynamics, solid oxide fuel cells, and hybrid power systems. Codes will typically describe physical phenomena, transport, and reaction processes featuring coupled structure and physics, and will often require consideration of processes occurring across broad time scales. Typical problems include: combustion simulations including associated heat and mass transfer, often at elevated temperature and pressure conditions; super-sonic and high-temperature flows, including ionized fluid flow in the presence of strong magnetic fields; mass and thermal transport in homogeneous media and across heterogeneous phase interfaces; diffusive, atomic-scale transport processes across electrochemically or electrically active solid phase boundaries; and dynamic modeling and operational control development of integrated energy conversion and transport processes.

Process Systems Engineering (PSE)

At NETL PSE research is concerned with the discovery, design, operation and optimization of complex, interacting energy systems in the context of many conflicting goals. PSE combines mathematics, operations research, and computer science, with traditional chemical engineering expertise. At NETL, new PSE techniques are being developed and applied to a broad range of advanced fossil energy systems, including chemical looping, transformational CO2 capture technologies, and supercritical CO2 cycles.

NETL and its collaborators created a number of innovative new computational capabilities as part of the U.S. Department of Energy’s Carbon Capture Simulation Initiative. Among these is an approach for the automated learning of algebraic models for optimization (ALAMO) and a framework for optimization and quantification of uncertainty and sensitivity (FOQUS). Several members of the National Academy of Engineering are members of NETL’s core PSE Team.

Advancing new effective energy technologies and processes can be lengthy and costly because experimental scientists are often unable to observe and measure aspects of design research. NETL’s Institute for The Design of Advanced Energy Systems (IDEAS) develops accelerates development of a broad range of advanced fossil energy systems. The Institute is pioneering development of new computational tools that can be used to optimize the performance of power plants at multiple scales over a full range of operating conditions — both supporting the existing fleet and enabling the design and scale up of transformative advanced coal energy systems. IDAES develops a rigorous computational approach for creating new concepts in power systems, biofuels, green chemistry and environmental management.

Strategic Monitoring of Natural-System Behavior

The National Energy Technology Laboratory (NETL) tackles the challenge of clean energy production from fossil energy sources by focusing on the behavior of natural systems at both the earth’s surface and subsurface, including prediction, control, and monitoring of fluid flow in porous and fractured media. Efforts include the long-term storage of CO2, the environmentally sound production of our nation’s conventional and unconventional fossil fuel resources, and the science base needed to bring methane hydrates into the domestic natural gas resource base.

Many of the most important frontiers in subsurface energy production and in environmental protection are related to the behavior of fractured systems. Geothermal energy production, hydrocarbon recovery from shales, carbon storage security, and wastewater disposal all rely on an ability to understand and predict the behavior of fractures within subsurface rocks, hydrologically and/or geomechanically. At NETL, we have a world class subsurface computational research program focused on fractured media. Examples of the types of problems that you will be able to work on include the degradation of geothermal resources based on geochemical changes, the impact of geomechanics on leakage from a carbon storage reservoir, improved stimulation of shale gas reservoirs, and identifying causes of induced seismic events.

NETL’s Science-Informed Machine Learning for Accelerating Real Time Decisions in Subsurface Application (SMART) initiative aims to transform subsurface engineering operations for geologic carbon storage, thus reducing costs through faster, more efficient, analysis of information, and reduced uncertainty in operations decision-making. The initiative has five primary areas of technical focus:

  • Real-time visualization of subsurface features/properties exploiting machine learning (ML) to achieve speed and enhanced detail.
  • Real-time forecasting for reservoir management
  • Computer-based virtual learning for field development and monitoring strategies
  • Autonomous monitoring utilizing smart sensor systems coupled with ML-based algorithms
  • Big data management - protocols and tools to allow access, transfer, curation, quality control, and maintenance of public and private datasets.