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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Identifying attributes of CO 2 leakage zones in shallow aquifers using a parametric level set method: Original Research Article: Identifying attributes of CO 2 leakage zones in shallow aquifers using a parametric level set method

Leakage through abandoned wells and geologic faults poses the greatest risk to CO 2 storage permanence. For shallow aquifers, secondary CO 2 plumes emanating from the leak zones may go undetected for a sustained period of time and has the greatest potential to cause large-scale and long-term environmental impacts. Identification of the attributes of leak zones, including their shape, location, and strength, is required for environmental risk assessment. This study applies a parametric level set (PaLS) method to characterize leak zones. Level set methods are appealing for tracking topological changes and recovering unknown shapes of objects. However, level set evolution using the conventional level set methods is computationally challenging. In PaLS, the level set function is approximated by using a weighted sum of basis functions and the level set evolution problem is replaced by an optimization problem. The efficacy of PaLS is demonstrated by reconstructing attributes of a CO 2 leak zone in a carbonate aquifer. Our results show that PaLS is a robust source identification method that can recover the approximate leak zone locations in the presence of measurement errors, model parameter uncertainty, and inaccurate guesses of source flux strengths. Accurate delineation of source geometry is achievable when a relatively dense observation network is used. The PaLS-based, source recovery framework introduced in this work is generic and can be adapted for any reactive transport model by simply switching the pre- and post-processing routines.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

A novel approach to estimate emissions from large transportation networks: Hierarchical clustering-based link-driving-schedules for EPA-MOVES using dynamic time warping measures

We present that EPA-MOVES (Motor Vehicle Emission Simulator) is often integrated with traffic simulators to assess emission levels of large-scale urban networks with signalized intersections. High variations in speed profiles exist in the context of congested urban networks with signalized intersections. The traditional average-speed-based emission estimation technique with EPA-MOVES provides faster execution while underestimates the emissions in most cases because of ignoring the speed variation at congested networks with signalized intersections. In contrast, the atomic second-by-second speed profile (i.e., the trajectory of each vehicle)-based technique provides accurate emissions at the cost of excessive computational power and time. We addressed this issue by developing a novel method to determine the link-driving-schedules (LDSs) for the EPA-MOVES tool. Our research developed a hierarchical clustering technique with dynamic time warping similarity measures (HC-DTW) to find the LDS for EPA-MOVES that is capable of producing emission estimates better than the average-speed-based technique with execution time faster than the atomic speed profile approach. We applied the HC-DTW on a sample data from a signalized corridor and found that HC-DTW can significantly reduce computational time without compromising the accuracy. The developed technique in this research can substantially contribute to the EPA-MOVES-based emission estimation process for large-scale urban transportation network by reducing the computational time with reasonably accurate estimates. This method is highly appropriate for transportation networks with higher variation in speed such as signalized intersections. Lastly, experimental results show error difference ranging from 2% to 8% for most pollutants except PM 10 .

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

AI-Enhanced Co-Design for Next-Generation Microelectronics: Innovating Innovation [Workshop Report]

In April 5-7, 2022, Sandia National Laboratories hosted a second virtual workshop to further explore the potential for developing AI-enhanced co-design for microelectronics (AICoM). This second piece in an ongoing workshop series again brought together two themes. The first theme, co-design for next generation microelectronics, was drawn from the 2018 Department of Energy Office of Science (DOE SC) “Basic Research Needs for Microelectronics” (BRN) report (DOE/SC, 2018, 2021), which called for a “fundamental rethinking” of the traditional design approach to microelectronics, in which subject matter experts (SMEs) in each microelectronics discipline (materials, devices, circuits, algorithms, etc.) work near-independently. Instead, the BRN called for a non-hierarchical, egalitarian vision of co-design, wherein “each scientific discipline informs and engages the others” in “parallel but intimately networked efforts to create radically new capabilities.” The second theme, exploiting and advancing artificial intelligence (AI) to support co-design for microelectronics, acknowledges the continuing breakthroughs in AI that are currently enhancing and accelerating solutions to traditional design problems in materials synthesis and processing, circuit design, and electronic design automation (EDA).

42 ENGINEERING↗
DOE OSTI2016

Anti-reflective articles with nanosilica-based coatings and barrier layer

An article including a transparent substrate having an anti-reflective, structured surface and a sintered coating including a porous network of silica nanoparticles thereon, wherein the silica nanoparticles are bonded to adjacent silica nanoparticles, wherein the structured substrate further includes a backing face, and a moisture barrier layer bonded to the backing face of the structured substrate.

14 SOLAR ENERGY↗
DOE OSTI2023

Predictive scale-bridging simulations through active learning

Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number of mesh elements. For instance, quantitative predictions of transport in nanoporous media, critical to hydrocarbon extraction from tight shale formations, are impossible without accounting for molecular-level interactions. Similarly, inertial confinement fusion simulations rely on numerical diffusion to simulate molecular effects such as non-local transport and mixing without truly accounting for molecular interactions. With these two disparate applications in mind, we develop a novel capability which uses an active learning approach to optimize the use of local fine-scale simulations for informing coarse-scale hydrodynamics. Our approach addresses three challenges: forecasting continuum coarse-scale trajectory to speculatively execute new fine-scale molecular dynamics calculations, dynamically updating coarse-scale from fine-scale calculations, and quantifying uncertainty in neural network models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Solving key challenges in collider physics with foundation models

Foundation models are neural networks that are capable of simultaneously solving many problems. Large language foundation models like ChatGPT have revolutionized many aspects of daily life, but their impact for science is not yet clear. In this paper, we use a new foundation model for hadronic jets to solve three key challenges in collider physics. In particular, we show how experiments can (1) save significant computing power when developing reconstruction algorithms, (2) perform a complete uncertainty quantification for high-dimensional measurements, and (3) search for new physics with model agnostic methods using low-level inputs. In each case, there are significant computational or methodological challenges with current methods that limit the science potential of deep learning algorithms. By solving each problem, we take jet foundation models beyond proof-of-principle studies and into the toolkit of practitioners.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Microstructural evolution of ion-irradiated sol–gel-derived thin films

In this paper, the effects of ion irradiation on the microstructural evolution of sol–gel-derived silica-based thin films were examined by combining the results from Fourier transform infrared, Raman, and X-ray photoelectron spectroscopy, Rutherford backscattering spectrometry, and elastic recoil detection. Variations in the chemical composition, density, and structure of the constituent phases and interfaces were studied, and the results were used to propose a microstructural model for the irradiated films. It was discovered that the microstructure of the films after ion irradiation and decomposition of the starting organic materials consisted of isolated hydrogenated amorphous carbon clusters within an amorphous and carbon-incorporated silica network. A decrease in the bond angle of Si–O–Si bonds in amorphous silica network along with an increase in the concentration of carbon-rich SiO x C y tetrahedra were the major structural changes caused by ion irradiation. Finally, in addition, hydrogen release from free carbon clusters was observed with increasing ion energy and fluence.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Invariant Molecular Representations for Heterogeneous Catalysis

Catalyst screening is a critical step in the discovery and development of heterogeneous catalysts, which are vital for a wide range of chemical processes. In recent years, computational catalyst screening, primarily through density functional theory (DFT), has gained significant attention as a method for identifying promising catalysts. However, the computation of adsorption energies for all likely chemical intermediates present in complex surface chemistries is computationally intensive and costly due to the expensive nature of these calculations and the intrinsic idiosyncrasies of the methods or data sets used. This study introduces a novel machine learning (ML) method to learn adsorption energies from multiple DFT functionals by using invariant molecular representations (IMRs). To do this, we first extract molecular fingerprints for the reaction intermediates and later use a Siamese-neural-network-based training strategy to learn invariant molecular representations or the IMR across all available functionals. Our Siamese network-based representations demonstrate superior performance in predicting adsorption energies compared with other molecular representations. Notably, when considering mean absolute values of adsorption energies as 0.43 eV (PBE-D3), 0.46 eV (BEEF-vdW), 0.81 eV (RPBE), and 0.37 eV (scan+rVV10), our IMR method has achieved the lowest mean absolute errors (MAEs) of 0.18 0.10, 0.16, and 0.18 eV, respectively. These results emphasize the superior predictive capacity of our Siamese network-based representations. The empirical findings in this study illuminate the efficacy, robustness, and dependability of our proposed ML paradigm in predicting adsorption energies, specifically for propane dehydrogenation on a platinum catalyst surface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A high spatial resolution land surface phenology dataset for AmeriFlux and NEON sites

Abstract Vegetation phenology is a key control on water, energy, and carbon fluxes in terrestrial ecosystems. Because vegetation canopies are heterogeneous, spatially explicit information related to seasonality in vegetation activity provides valuable information for studies that use eddy covariance measurements to study ecosystem function and land-atmosphere interactions. Here we present a land surface phenology (LSP) dataset derived at 3 m spatial resolution from PlanetScope imagery across a range of plant functional types and climates in North America. The dataset provides spatially explicit information related to the timing of phenophase changes such as the start, peak, and end of vegetation activity, along with vegetation index metrics and associated quality assurance flags for the growing seasons of 2017–2021 for 10 × 10 km windows centred over 104 eddy covariance towers at AmeriFlux and National Ecological Observatory Network (NEON) sites. These LSP data can be used to analyse processes controlling the seasonality of ecosystem-scale carbon, water, and energy fluxes, to evaluate predictions from land surface models, and to assess satellite-based LSP products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Networks in ATLAS

Networks have played a critical role in high-energy physics (HEP), enabling us to access and effectively utilize globally distributed resources to meet the needs of our physicists. Because of their importance in enabling our grid computing infrastructure many physicists have taken leading roles in research and education (R&E) networking, participating in, and even convening, network related meetings and research programs with the broader networking community worldwide. This has led to HEP benefiting from excellent global networking capabilities for little to no direct cost. However, as other science domains ramp-up their need for similar networking it becomes less clear that this situation will continue unchanged. What this means for ATLAS in particular needs to be understood. ATLAS has evolved its computing model since the LHC started based upon its experience with using globally distributed resources. The most significant theme of those changes has been increased reliance upon, and use of, its networks. We will report on a number of networking initiatives in ATLAS including participation in the global perfSONAR network monitoring and measuring efforts of WLCG and OSG, the collaboration with the LHCOPN/LHCONE effort, the integration of network awareness into PanDA, the use of the evolving ATLAS analytics framework to better understand our networks and the changes in our DDM system to allow remote access to data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

A Systematic Analysis of a Deep Mouse Epididymal Sperm Proteome

Spermatozoa are highly specialized cells that, when mature, are capable of navigating the female reproductive tract and fertilizing an oocyte. The sperm cell is thought to be largely quiescent in terms of transcriptional and translational activity. As a result, once it has left the male reproductive tract, the sperm cell is essentially operating with a static population of proteins. It is therefore theoretically possible to understand the protein networks contained in a sperm cell and to deduce its cellular function capabilities. To this end we have performed a proteomic analysis of mouse sperm isolated from the cauda epididymis and have confidently identified 2,850 proteins, which is the most comprehensive sperm proteome for any species reported to date. These proteins comprise many complete cellular pathways, including those for energy production via glycolysis, β-oxidation and oxidative phosphorylation, protein folding and transport, and cell signaling systems. This proteome should prove a useful tool for assembly and testing of protein networks important for sperm function.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2025

Ground-state-based model reduction with unitary circuits

Here, we present a method to numerically obtain low-energy effective models based on a unitary transformation of the ground state. The algorithm finds a unitary circuit that transforms the ground state of the original model to a projected wavefunction with only the low-energy degrees of freedom. The effective model can then be derived using the unitary transformation encoded in the circuit. We test our method on the one-dimensional and two-dimensional square-lattice Hubbard model at half-filling, and obtain more accurate effective spin models than the standard perturbative approach.

Hubbard model↗
DOE OSTI2011

Unique Thylakoid Membrane Architecture of a Unicellular N2-Fixing Cyanobacterium Revealed by Electron Tomography

Cyanobacteria, descendants of the endosymbiont that gave rise to modern-day chloroplasts, are vital contributors to global biological energy conversion processes. A thorough understanding of the physiology of cyanobacteria requires detailed knowledge of these organisms at the level of cellular architecture and organization. In these prokaryotes, the large membrane protein complexes of the photosynthetic and respiratory electron transport chains function in the intracellular thylakoid membranes. Like plants, the architecture of the thylakoid membranes in cyanobacteria has direct impact on cellular bioenergetics, protein transport, and molecular trafficking. However, whole-cell thylakoid organization in cyanobacteria is not well understood. Here we present, by using electron tomography, an in-depth analysis of the architecture of the thylakoid membranes in a unicellular cyanobacterium, Cyanothece sp. ATCC 51142. Based on the results of three-dimensional tomographic reconstructions of near-entire cells, we determined that the thylakoids in Cyanothece 51142 form a dense and complex network that extends throughout the entire cell. This thylakoid membrane network is formed from the branching and splitting of membranes and encloses a single lumenal space. The entire thylakoid network spirals as a peripheral ring of membranes around the cell, an organization that has not previously been described in a cyanobacterium. Within the thylakoid membrane network are areas of quasi-helical arrangement with similarities to the thylakoid membrane system in chloroplasts. This cyanobacterial thylakoid arrangement is an efficient means of packing a large volume of membranes in the cell while optimizing intracellular transport and trafficking.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Water-anion hydrogen bonding dynamics: Ultrafast IR experiments and simulations

Many of water’s remarkable properties arise from its tendency to form an intricate and robust hydrogen bond network. Understanding the dynamics that govern this network is fundamental to elucidating the behavior of pure water and water in biological and physical systems. In ultrafast nonlinear infrared experiments, the accessible time scales are limited by water’s rapid vibrational relaxation (1.8 ps for dilute HOD in H 2 O), precluding interrogation of slow hydrogen bond evolution in non-bulk systems. Here in this paper, hydrogen bonding dynamics in bulk D 2 O were studied from the perspective of the much longer lived (36.2 ps) CN stretch mode of selenocyanate (SeCN-) using polarization selective pump-probe (PSPP) experiments, two-dimensional infrared (2D IR) vibrational echo spectroscopy, and molecular dynamics simulations. The simulations make use of the empirical frequency mapping approach, applied to SeCN - for the first time. The PSPP experiments and simulations show that the orientational correlation function decays via fast (2.0 ps) restricted angular diffusion (wobbling-in-a-cone) and complete orientational diffusive randomization (4.5 ps). Spectral diffusion, quantified in terms of the frequency-frequency correlation function, occurs on two time scales. The initial 0.6 ps time scale is attributed to small length and angle fluctuations of the hydrogen bonds between water and SeCN - . The second 1.4 ps measured time scale, identical to that for HOD in bulk D 2 O, reports on the collective reorganization of the water hydrogen bond network around the anion. Lastly, the experiments and simulations provide details of the anion-water hydrogen bonding and demonstrate that SeCN- is a reliable vibrational probe of the ultrafast spectroscopy of water.

14 SOLAR ENERGY↗
DOE OSTI2012

National Clean Fleets Partnership [Fact Sheet]

Provides an overview of Clean Cities National Clean Fleets Partnership (NCFP). The NCFP is open to large private-sector companies that have fleet operations in multiple states. Companies that join the partnership receive customized assistance to reduce petroleum use through increased efficiency and use of alternative fuels. This initiative provides fleets with specialized resources, expertise, and support to successfully incorporate alternative fuels and fuel-saving measures into their operations. The National Clean Fleets Partnership builds on the established success of DOE's Clean Cities program, which reduces petroleum consumption at the community level through a nationwide network of coalitions that work with local stakeholders. Developed with input from fleet managers, industry representatives, and Clean Cities coordinators, the National Clean Fleets Partnership goes one step further by working with large private-sector fleets.

02 PETROLEUM↗
DOE OSTI2021

High Energy Physics Network Requirements Review (Final Report, July-October 2020)

Throughout 2020, ESnet and the Office of High Energy Physics (HEP) of the DOE SC organized an ESnet requirements review of HEP-supported activities. Preparation for this event included identification of key stakeholders: program and facility management, research groups, technology providers, and a number of external observers. These individuals were asked to prepare formal case study documents about their relationship to the HEP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward. A series of pre-planning meetings better prepared case study authors for this task, along with guidance on how the review would proceed in a virtual fashion.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Growth and opportunities in networked synthesis through AmeriFlux

Since its inception in 1996 with just 15 sites, the AmeriFlux network has grown to include over 300 sites, representing every major ecosystem type across the Americas. This grassroots coalition of the willing has for the past two decades continuously measured the exchange of carbon, water and energy between ecosystems and the atmosphere. Recent years in particular have seen remarkable growth in both the degree of coordination of activities within the AmeriFlux network, and the number of researchers involved. Through the AmeriFlux Management Project, the network has made public over 1500 site-years of observations. This project now represents over 5000 registered members, a quarter of whom have signed up in the past year, who use the observations for a range of applications, including ecosystem science, modeling, and remote sensing, but also education and outreach.

photosynthesis↗
DOE OSTI2021

The Evolving Solar Energy Innovation Ecosystem in Puerto Rico

This report presents initial findings from the research project, “Developing socially and economically generative, resilient PV-energy systems for low- and moderate-income communities: Applications for Puerto Rico.” This material is based upon work supported primarily by the US Department of Energy’s Office of Energy Efficiency and Renewable Energy under the Solar Energy Technology Office Fiscal Year 2018 Funding Program award number DE-EE0008570 and in part by the National Science Foundation (NSF) and the Department of Energy (DOE) under NSF CA No. EEC-1041895. This project is a joint effort of Arizona State University’s Center for Energy and Society; the University of Puerto Rico-Mayaguez’s Center for Hemispheric Cooperation in Research and Education in Engineering and Applied Science, Sustainable Energy Center, and National Institute for Energy and Island Sustainability; and the National Renewable Energy Laboratory. The project seeks to understand the place of energy in low-income communities in Puerto Rico, the opportunities for solar energy to provide solutions to the energy challenges facing these communities, and the capacity of the solar energy innovation ecosystem in Puerto Rico to deliver relevant solar solutions for these communities. We define the solar energy innovation ecosystem as the diverse actors and stakeholders whose collective networks and actions—and the flows among them of ideas, knowledge, data, influence, money, and equipment, as well as the rules that govern those flows—are essential to the success of renewable energy markets and projects. This report presents results from the first year of the project’s research, which has concentrated on several key goals: (1) characterizing the trajectory of solar energy development in Puerto Rico in the three years since Hurricane María; (2) mapping and characterizing the current solar energy innovation ecosystem in Puerto Rico, including the technical potential for solar energy in Puerto Rico, and the business and policy landscape shaping solar developments; and (3) beginning to characterize the broad landscape of community energy initiatives underway in Puerto Rico. To pursue these goals, we engaged in a systematic review of solar energy developments in Puerto Rico and conducted interviews with over 100 members of the solar energy innovation, including policy, business, finance, research, and civil society leaders, as well as solar users. Solar technical potential data for Puerto Rico was developed by the National Renewable Energy Laboratory (NREL) and is publicly available at: https://data.nrel.gov/submissions/144. The documentation for the NREL dataset is included as Appendix A.

14 SOLAR ENERGY↗