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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Improved Characterization and Modeling of Tight Oil Formations for CO 2 Enhanced Oil Recovery Potential and Storage Capacity Estimation

Tight oil formations such as those in the Bakken petroleum system are known to hold hundreds of billions of barrels of oil in place; however, the primary recovery factor for these plays is typically less than 10%. Tight oil formations, including the Bakken Formation, therefore, may be attractive candidates for enhanced oil recovery (EOR) using CO 2 . Multiphase fluid behavior and flow in fluid-rich shales can vary substantially depending on the size of pore throats, and properties such as fluid viscosity and density are much different in nanoscale pores than in macroscale pores. Thus it is critical to understand the nature and distribution of nano-, micro-, and macroscale pores and fracture networks. To address these issues, the Energy & Environmental Research Center (EERC) has been conducting a research program entitled “Improved Characterization and Modeling of Tight Oil Formations for CO 2 Enhanced Oil Recovery Potential and Storage Capacity Estimation.” The objectives of the project are 1) the use of advanced characterization methods to better understand and quantify the petrophysical and geomechanical factors that control CO 2 and oil mobility within tight oil formation samples, 2) the determination of CO 2 permeation and oil extraction rates in tight reservoir rocks and organic-rich shales of the Bakken, and 3) the integration of the laboratory-based CO 2 permeation and oil extraction data and the characterization data into geologic models and dynamic simulations to develop predictions of CO 2 storage resource and EOR in the Bakken tight oil formation. A combination of standard and advanced petrophysical characterization techniques were applied to characterize samples of Bakken Formation tight reservoir rock and shales from multiple wells. Techniques included advanced computer tomography (CT) imaging, scanning electron microscopy (SEM) techniques, whole-core and micro x-ray CT imaging, field emission (FE) SEM, and focused ion beam (FIB) SEM. Selected samples were also analyzed for geomechanical properties. X-ray CT imaging yielded information on the occurrence of fractures, bedding planes, fossils, and bioturbation in core, as well as data on bulk density and photoelectric factor logs, which were used to interpret porosity, organic content, and mineralogy. FESEM was used for characterization of nano- and microscale features, including nanoscale pore visualization and micropore and pore throat mineralogy. FIBSEM yielded micro- to nanoscale visualization of fracture networks, porosity and pore-size distribution, connected versus isolated porosity, and distribution of organics. Results from the characterization activities provide insight on nanoscale fracture properties, pore throat mineralogy and connectivity, rock matrix characteristics, mineralogy, and organic content. Laboratory experiments demonstrated that CO 2 can permeate the tight matrix of Bakken shale and nonshale reservoir samples and mobilize oil from those samples. Geologic models were created at scales ranging from the core plug to the reservoir, and dynamic simulations were conducted. The data from the characterization and laboratory-based activities were integrated into modeling research activities to determine the fundamental mechanisms controlling fluid transport in the Bakken, which support EOR scheme design and estimation of CO 2 storage potential in tight oil formations. Simulation results suggest a CO 2 storage resource estimate range of 169 million to 1.5 billion tonnes for the Bakken in North Dakota, possibly resulting in 1.8 billion to 16 billion barrels of incremental oil.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2018

Low Loss Graded Index Polymer Optical Fiber for Local Networking

Here, the objective of this Department of Energy SBIR program has been to develop technology for the advancement of advanced computing systems. NanoSonic worked with two subcontractors, the Polymicro Division of Molex, a U.S.-based manufacturer of specialized optical fiber and fiber components, and Virginia Tech, a research university involved through the Global Environment for Network Innovations (GENI) program in high-speed computer networking research. NanoSonic developed a patented molecular-level self-assembly process to manufacture polymer-based optical fibers in a way similar to the modified chemical vapor deposition (MCVD) approach typically used to make glass optical fibers. Although polymer fiber has a higher attenuation per unit length than glass fiber, short connectorized polymer fiber jumpers offer significant cost savings over their glass counterparts, particularly due to the potential use of low-cost plastic fiber connectors. As part of the SBIR commercialization process, NanoSonic exclusively licensed this technology to a large ($100B+ market cap) U.S.-based manufacturing conglomerate near the end of the first year of the Phase II program. With this base technology developed and licensed, NanoSonic then worked with Polymicro to address secondary program goals of using related but not conflicting production methods to enhance the performance of other specialty optical fiber products and components, and Virginia Tech continued its evaluation of developed polymer fibers in its network infrastructure system on the university campus. We also report our current understanding of the observation during the Phase I program of quantum conductance and partial quantum conductance in metal-insulator-metal (MIM) devices. Such conductance behavior may be modeled as singlemode behavior in one-dimensional electrically conducting waveguides, similar in principle to singlemode optical propagation in dielectric fiber waveguides. Although NanoSonic has not licensed any of the additional technology developed during the second year of the program, several proprietary discussions with major materials companies are underway as of the conclusion of Phase II.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Abstract - Cooperative Research and Development Agreement between Ames National Laboratory and National Energy Technology Laboratory AGMT-0609

The goal of this project is to develop a merged environment for simulation and analysis (MESA) at the National Energy Technology Laboratory’s (NETL) Hybrid Performance (Hyper) project laboratory. The MESA sensor lab developed as a component of this research will provide a development platform for investigating: 1) advanced control strategies, 2) testing and development of sensor hardware, 3) various modeling in-the-loop algorithms and 4) other advanced computational algorithms for improved plant performance using sensors, real-time models, and complex systems tools.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Radiation Effects Science: Advancing the Frontiers of Science and Engineering

Recent improvements in the radiation effects science capability at Sandia have achieved the following: Record gamma-ray outputs at HERMES enable qualification for Life Extension Programs (LEPs) and evaluation of electromagnetic pulse (EMP) effects on electric grid elements; Completed blind comparison for model validation of cavity system generated EMP in advanced computer simulations; For the first time, new diagnostics provide a high resolution photon energy spectrum on Z (Grand Challenge LDRD); ACRR completed record number of operations, two years in a row, to support design and qualification; Installed the nation’s largest GHz electromagnetic test chamber for design and qualification.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Asynchronous Graphs (Final Report)

The most performant massive-scale distributed graph analytic engines were essentially unusable for anything but the analytic they were tuned to solve. The project team had previously developed HavoqGT and used this project to demonstrate it as a flexible graph framework, aiming to extend algorithmic capabilities, improve underlying data structures, and develop novel communication paradigms to enable new data analysis missions. Highlights of the technology developed include (a) a new distributed communication paradigm dubbed You’ve Got Mail (YGM), (b) a performant exact and fuzzy HPC graph pattern matching approach, and (c) a distributed biased random walk simulation capability. The project was successful on several measurable fronts, including advancing computer science (reflected by publishing 14 peer-reviewed research papers yielding 4 publication awards and over 16 conference presentations), contributing tangibly to LLNL recruiting rare talent, and obtaining follow-on funding. These technologies were transferred to several FY19-20 programmatic projects and will continue to be leveraged by new research projects for several years.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

High Performance Perovskite-Based Solar Cells (Final Technical Report)

Hybrid lead-halide perovskite semiconductors have become one of the most heavily studied photovoltaic (PV) materials within the past ten years. However, these materials suffer from two key limitations that have ultimately limited the progression towards commercialization: (i) the inclusion of toxic lead (Pb) and (ii) instability towards moisture, oxygen, and temperature. This project has targeted the development of perovskite absorber materials that replace Pb with a benign alternative and that are stable under atmospheric conditions. Under this broad goal, the project had four major objectives: 1) Develop high-performance Pb-based PV devices and study alternative inorganic contact materials to improve the device stability; 2) Advance computational techniques to understand the electronic and optical properties of perovskite semiconductors from a theoretical perspective; 3) Explore various hybrid and inorganic halide, chalcohalide and chalcogenide perovskite absorbers as highly stable, Pb-free absorber alternatives; 4) Develop thin-film deposition and device fabrication methodologies to deposit high-quality Pb-free perovskite absorbers and high-performance devices. Within the first objective, the researchers fabricated high-performing methylammonium lead iodide solar cells based on both traditional architectures (using a TiO 2 contact) and inverted architectures using NiOx and novel CuCrO 2 as hole transport layers (HTL). The work with CuCrO 2 is the first work demonstrating CuCrO 2 as a promising contact material for future, stable perovskite solar cells. Towards the second objective researchers have calculated the intrinsic stability and optoelectronic properties of multiple Pb-free systems including chalcohalide (e.g. CsBiOF 2 ), halide (e.g. Cs 3 Sb 2 I 9 ), and chalcogenide perovskites (e.g. BaZr 1-x TixS 3 ). Additionally, the researchers have furthered the basic understanding of perovskite materials by introducing the concept of electronic dimensionality and its impact on optoelectronic properties. This understanding has had implications in PV applications and beyond. In the third and fourth objectives, the investigators have exhausted a myriad of Pb-free materials in search of a stable, promising Pb-free perovskite absorber, including chalochalides based on CsBiOF 2 , Pb-free halides (such as Cs 2 AgBiBr 6 , Cs 2 TiBr 6 , and Cs(Ge/Sn)I 3 ) , chalcogenide perovskites (e.g. BaZrS 3 ), and perovskite-related halides InI and silver bismuth iodide. While the researchers have studied many promising perovskites and perovskite-alternatives, a stable, high-performing Pb-free absorber has proven to be elusive.

14 SOLAR ENERGY↗
DOE OSTI2020

Evaporation Sub-model Development for Volume of Fluid (eVOF) Method Applicable to Spray-Wall Interaction Including Film Characteristics with Validation at High Pressure and Temperature Conditions (Final Report)

Internal combustion engines have seen a great evolution over the last several decades through application of high pressure direct injection, multiple injections, and other technologies to reduced fuel consumption, NOx, and PM. Although combustion systems with advanced injection strategies have been studied extensively, there exists a significant fundamental knowledge gap on the fuel-spray interactions with the piston surface and chamber walls. Advanced computational codes validated with experimental techniques have to be developed for accurate representation of the drop impingement, fuel film formation, and vaporization. Current engine CFD spray models utilize a Lagrangian framework for modeling which lacks critical considerations of the physics pertaining to these interactions and thus requiring extensive parameterization, tuning and validation. The team from Michigan Technological University, University of Massachusetts Dartmouth, and Argonne National Laboratory is composed of experts in sprays, combustion, engines and CFD with a wide spectrum of knowledge including specific expertise in the area under consideration. In the proposed work, a VOF modeling approach has been adopted for the spray-wall interaction, film formation and spreading, and vaporization. With the inclusion of a vaporization submodel, a more predictive and accurate simulation of the spray-film was performed without extensive need of parameterization and tuning. Extensive experimentation of the spray-wall interaction under the range of conditions matching the thermodynamic and surface temperatures that occur in diesel and gasoline engines were conducted to validate the SWI submodels and for development of the evaporation sub-model, which has been implemented in the Converge software.

42 ENGINEERING↗
DOE OSTI2020

Evaporation Submodel Development for Volume of Fluid (eVOF) Method Applicable to Spray-Wall Interaction Including Film Characteristics with Validation at High Pressure and Temperature Conditions

Internal combustion engines have seen a great evolution over the last several decades through application of high pressure direct injection, multiple injections, and other technologies to reduced fuel consumption, NOx, and PM. Although combustion systems with advanced injection strategies have been studied extensively, there exists a significant fundamental knowledge gap on the fuel-spray interactions with the piston surface and chamber walls. Advanced computational codes validated with experimental techniques have to be developed for accurate representation of the drop impingement, fuel film formation, and vaporization. Current engine CFD (Computational Fluid Dynamics) spray models utilize a Lagrangian framework for modeling which lacks critical considerations of the physics pertaining to these interactions and thus requiring extensive parameterization, tuning and validation. The team from Michigan Technological University, University of Massachusetts Dartmouth, and Argonne National Laboratory is composed of experts in sprays, combustion, engines and CFD with a wide spectrum of knowledge including specific expertise in the area under consideration. In the proposed work, a VOF (Volume of Fluid) modeling approach has been adopted for the spray-wall interaction, film formation and spreading, and vaporization. With the inclusion of a vaporization submodel, a more predictive and accurate simulation of the spray-film was performed without extensive need of parameterization and tuning. Extensive experimentation of the spray-wall interaction under the range of conditions matching the thermodynamic and surface temperatures that occur in diesel and gasoline engines were conducted to validate the SWI submodels and for development of the evaporation submodel, which has been implemented in the flow solver.

42 ENGINEERING↗
DOE OSTI2019

Basic Research Needs Workshop on Compact Accelerators for Security and Medicine: Tools for the 21st Century, May 6-8, 2019

Accelerator-based radiation sources are ubiquitous tools for imaging and treatment in the fields of medicine and security that save millions of lives, impact billions of dollars of commercial goods annually, and fulfill a critical role in US national security. Today’s commercially available accelerator technology has fallen significantly behind the state-of-the-art. Advancing and transferring state-of-the-art compact accelerator technology into broader use holds the promise of achieving greater control, power, and automation, which can significantly enhance society’s ability to sense and control the world around us. The Workshop identified a wealth of opportunities to advance these technologies from today’s commercial baselines, largely based on half-century old developments, by employing emerging concepts in charged particle acceleration and radiation generation and detection, along with modern ways of thinking about and utilizing developments in systems engineering, advanced materials, supply chain management, manufacturing, advanced computation, energy storage, and artificial intelligence.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

2019 LDRD Annual Report

This summary report provides an overview of all LDRD projects at Argonne that concluded in Fiscal Year 2019. Many projects are funded for multiple years, the initial fiscal year for each project is indicated by the first four digits of the LDRD project number. Focus areas were: Advanced Computing; Biological and Environmental Science Capability Development; Hard X-ray Sciences; Manufacturing Science and Engineering; Materials and Chemistry; National and Global Security; Photon Sciences; Securing Energy and Critical Resources; and, The Universe as Our Laboratory (ULab).

36 MATERIALS SCIENCE↗
DOE OSTI2019

Accelerator Physics at NSLS-II: Research Accomplishments in 2019

NSLS-II Accelerator Physics group provides scientific support for user operations, as well as for improvements and developments of the accelerator systems. In parallel to the facility support, the accelerator physicists are working on studies of topical scientific problems: development of novel lattices; studies of collective effects and beaminduced heating; advanced computations including machine learning; ultrafast electron diffraction and microscopy (LDRD). The group also made important contributions to the electron-ion collider project at BNL and to the APS upgrade project (MOUs). Research accomplishments of the NSLS-II Accelerator Physics group achieved in 2019, are summarized in this report.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Laboratory Directed Research and Development Program (FY 2019 Annual Summary of Completed Projects)

Oak Ridge National Laboratory (ORNL) is the US Department of Energy’s (DOE’s) largest multiprogram science, technology, and energy laboratory with distinctive capabilities in neutron science, computing, advanced materials, and nuclear science and technology. Using these capabilities, ORNL conducts basic and applied research and development (R&D) to support DOE’s overarching mission to “ensure America’s security and prosperity by addressing its energy, environmental and nuclear challenges through transformative science and technology solutions.” As a national resource, ORNL also applies its capabilities and skills to specific needs of other federal agencies and customers through the DOE Strategic Partnership Projects (SPP) program. Information about the laboratory and its programs is available at the ORNL website. The Laboratory Directed Research and Development (LDRD) program at ORNL operates under the authority of the DOE Order 413.2C, “Laboratory Directed Research and Development,” which establishes DOE’s requirements for the program while providing the laboratory director broad flexibility for program implementation. The LDRD program funds are obtained through a charge to all laboratory programs. ORNL reports on the program status to DOE each year.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Development of Explainable, Knowledge-Guided AI Models to Enhance the E3SM Land Model Development and Uncertainty Quantification

Focal Area(s): (2)Predictive modeling using AI techniques and AI-derived model components; use of AI and other tools to design a prediction system comprising of a hierarchy of models. (3) Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge- guided AI. Science Challenge: The Energy Exascale Earth System Model (E3SM) is a fully coupled, state-of-the-science Earth system model that uses code optimized for DOE's advanced computers to address the most critical scientific questions facing our nation and society (Golaz et al., 2019). The E3SM Land model (ELM) is designed to understand how the changes in terrestrial land surfaces will interact with other Earth system components and has been used to understand hydrologic cycles, biogeophysics, and ecosystem dynamics. In spite of great successes, the ELM has several known issues that restrain rapid improvements. For example, the ELM uses equilibrium models to simulate dynamic land-climate interactions and it requires long model spin-up time to identify suitable initial conditions for transient simulations. The ELM lacks built-in uncertainty mechanisms that can improve the robustness of model predictions. The ELM is a holistic, deterministic model system with a rigid design, and in many situations, it is hard to modify the ELM system to incorporate new theory/hypothesis and new data across scales to address emerging science problems (such as predicting the impacts of water cycle extremes). In addition, The ELM is technically optimized for traditional CPU-centric computers and it cannot fully utilize the current and incoming leadership computers for model simulations and uncertainty quantification (UQ). The success of artificial intelligence (AI) has inspired scientists to use AI models to discover intrinsic features from simulation data (Chattopadhyay et al., 2020) and observational data (Reichstein et al., 2019) to gain further process understanding of Earth science problems. However, autonomous AI model training through deep learning usually requires a huge amount of annotated data. To overcome the limitations from the data and computing resources, knowledge-guided AI models are necessary where human-knowledge is ingested in model construction (Banino et al., 2018) and training process (Silver et al., 2016) for efficient learning. Herein, we present a new way that leverages the process understanding from the ELM to guide AI model development for the ELM enhancement and UQ. We hope this study can inspire further Earth and environmental system model developments and transformations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Integrating AI with physics-based hydrological models and observations for insightinto changing climate and anthropogenic impacts

Focal Areas: Advanced computational methods that integrate AI, physics, and observations to provide predictive landscape hydrological modeling over large areas (regional, continental, worldwide) while incorporating increasingly available high-resolution data from drones, lidar and satellite. Science Challenge: Landscape data is available at finer scales than can be used in physics-based hydrological (PBH) models for regional or continental terrestrial water modeling. Thus, we throw away observable detail to achieve computability. We argue that integration of AI with PBH models and observed data can be used to provide upscaling for predictive models that are computable, retain physical conservation properties, and represent the fine-scale features that affect complex flow physics through both natural and urban environments. Developing such next-generation capabilities requires outside-the-box thinking that melds the different approaches of AI modeling, PBH modeling, and observation across multiple scales from local drones to satellites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Science and Technology Review (May 2021)

Lawrence Livermore has been on the forefront of cancer research for over 60 years. Early interest in cancer statistics stemmed from the nature of work, particularly how radiation affects humans. The Department of Energy funded research to investigate the effects of radiation on workers with long term exposure. This research quickly morphed into a wider breadth of cancer research topics, including the use of advanced computational models to investigate mutations in genes. Livermore is regarded as a leader in cancer research, from the Human Genome Center to its participation in the National Cancer Institute’s “Moonshot” project. The highly interdisciplinary Laboratory unites research in one more example: bringing together cancer biology, 3D printing, high-performance computing, big data, and materials science to address this pressing medical challenge.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Sandia Academic Alliance Program Collaboration Report: 2020-2021 Accomplishments

University partnerships play an essential role in sustaining Sandia’s vitality as a national laboratory. The SAA is an element of Sandia’s broader University Partnerships program, which facilitates recruiting and research collaborations with dozens of universities annually. The SAA program has two three-year goals. SAA aims to realize a step increase in hiring results, by growing the total annual inexperienced hires from each out-of-state SAA university. SAA also strives to establish and sustain strategic research partnerships by establishing several federally sponsored collaborations and multi-institutional consortiums in science & technology (S&T) priorities such as autonomy, advanced computing, hypersonics, quantum information science, and data science. The SAA program facilitates access to talent, ideas, and Research & Development facilities through strong university partnerships. Earlier this year, the SAA program and campus executives hosted John Myers, Sandia’s former Senior Director of Human Resources (HR) and Communications, and senior-level staff at Georgia Tech, U of Illinois, Purdue, UNM, and UT Austin. These campus visits provided an opportunity to share the history of the partnerships from the university leadership, tours of research facilities, and discussions of ongoing technical work and potential recruiting opportunities. These visits also provided valuable feedback to HR management that will help Sandia realize a step increase in hiring from SAA schools. The 2020-2021 Collaboration Report is a compilation of accomplishments in 2020 and 2021 from SAA and Sandia’s valued SAA university partners.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗