Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “ENERGY SCIENCES NETWORK”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

BNF Radar b1 Data Processing Report: Spring 2025

The U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) User Facility supports atmospheric science through an integrated network of fixed and mobile observatories. These facilities collect continuous and campaign-based observations of atmospheric properties, with the goal of improving the representation of clouds, aerosols, precipitation, and radiation in Earth system models. The Bankhead National Forest (BNF) site, established as an ARM Mobile Facility (AMF) on 1 October 2024, is situated in a forested region of northern Alabama. Its strategic location in a southeastern U.S. environment characterized by complex terrain, diverse land cover, and frequent convective storms provides a valuable opportunity to examine coupled land-atmosphere processes under natural variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

IDEAS-Watersheds FY21 Annual Report for July 1, 2020-June 30, 2021

Watersheds play a critical role in our water supply infrastructure and require sustainable management in a changing environment. Sustainable management of watershed systems and their interaction with the built environment rely on understanding the hydrologic and biogeochemical processes that control watershed system dynamics and water availability and quality. The overarching objective of the U.S. Department of Energy’s (DOE’s) Environmental System Science (ESS) program is to advance a robust, predictive understanding of how watersheds function and respond to perturbations as integrated hydrobiogeochemical systems. ESS supports a network of watershed testbeds within the United States where national laboratories and university partners work in interdisciplinary teams to advance watershed system science for energy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Many Body Methods from Chemistry to Physics: Novel Computational Techniques for Materials-Specific Modelling: A Computational Materials Science and Chemistry Network

Understanding the behavior of interacting electrons in molecules and solids so that one can predict new superconductors, catalysts, light harvesters, energy and battery materials and optimize existing ones is the ``quantum many-body problem’’. This is one of the scientific grand challenges of the 21 st century. A complete solution to the problem has been proven to be exponentially hard, meaning that straightforward numerical approaches fail. New insights and new methods are needed to provide accurate yet feasible approximate solutions. This CMSCN project brought together chemists and physicists to combine insights from the two disciplines to develop innovative new approaches. Outcomes included the Density Matrix Embedding method, a new, computationally inexpensive and extremely accurate approach that may enable first principles treatment of superconducting and magnetic properties of strongly correlated materials, new techniques for existing methods including an Adaptively Truncated Hilbert Space approach that will vastly expand the capabilities of the dynamical mean field method, a self-energy embedding theory and a new memory-function based approach to the calculations of the behavior of driven systems. The methods developed under this project are now being applied to improve our understanding of superconductivity, to calculate novel topological properties of materials and to characterize and improve the properties of nanoscale devices.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Transferring predictions of formation energy across lattices of increasing size*

In this study, we show the transferability of graph convolutional neural network (GCNN) predictions of the formation energy of the nickel-platinum solid solution alloy across atomic structures of increasing sizes. The original dataset was generated with the large-scale atomic/molecular massively parallel simulator using the second nearest-neighbor modified embedded-atom method empirical interatomic potential. Geometry optimization was performed on the initially randomly generated face centered cubic crystal structures and the formation energy has been calculated at each step of the geometry optimization, with configurations spanning the whole compositional range. Using data from various steps of the geometry optimization, we first trained our open-source, scalable implementation of GCNN called HydraGNN on a lattice of 256 atoms, which accounts well for the short-range interactions. Using this data, we predicted the formation energy for lattices of 864 atoms and 2048 atoms, which resulted in lower-than-expected accuracy due to the long-range interactions present in these larger lattices. We accounted for the long-range interactions by including a small amount of training data representative for those two larger sizes, whereupon the predictions of HydraGNN scaled linearly with the size of the lattice. Therefore, our strategy ensured scalability while reducing significantly the computational cost of training on larger lattice sizes.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A comparison of Gaussian processes and neural networks for computer model emulation and calibration

The Department of Energy relies on complex physics simulations for prediction in domains like cosmology, nuclear theory, and materials science. These simulations are often extremely computationally intensive, with some requiring days or weeks for a single simulation. In order to assure their accuracy, these models are calibrated against observational data in order to estimate inputs and systematic biases. Because of their great computational complexity, this process typically requires the construction of an emulator, a fast approximation to the simulation. In this paper, two emulator approaches are compared: Gaussian process regression and neural networks. Their emulation accuracy and calibration performance on three real problems of Department of Energy interest is considered. On these problems, the Gaussian process emulator tends to be more accurate with narrower, but still well-calibrated uncertainty estimates. The neural network emulator is accurate, but tends to have large uncertainty on its predictions. Finally, as a result, calibration with the Gaussian process emulator produces more constrained posteriors that still perform well in prediction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Recent Progress in Transparent Conductive Materials for Photovoltaics

Transparent conducting materials (TCMs) are essential components for a variety of optoelectronic devices, such as photovoltaics, displays and touch screens. In recent years, extensive efforts have been made to develop TCMs with both high electrical conductivity and optical transmittance. Based on material types, they can be mainly categorized into the following classes: metal oxides, metal nanowire networks, carbon-material-based TCMs (graphene and carbon nanotube networks) and conjugated conductive polymers (PEDOT:PSS). This review will discuss the fundamental electrical and optical properties, typical fabrication methods and the applications in solar cells for each class of TCMs and highlight the current challenges and potential future research directions.

14 SOLAR ENERGY↗
DOE OSTI2018

Substrate Engineered Interconnected Graphene Electrodes with Ultrahigh Energy and Power Densities for Energy Storage Applications

Supercapacitors combine the advantages of electrochemical storage technologies like high energy density batteries and high power density capacitors. At 5-10 W h Kg -1 , the energy densities of current supercapacitors are still significantly lower than energy densities of lead acid (20-35 W h Kg -1 ), Ni-metal hydride (40-100 W h Kg -1 ) and Li-ion (120-170 W h Kg -1 ) batteries. Recently, graphene based supercapacitors have shown energy density of 40-80 W h kg -1 . However, their performance is mainly limited due to the reversible agglomeration and restacking of individual graphene layers caused by π-π interactions. The restacking of graphene layers leads to significant decrease of ion-accessible surface area and the low capacitance of graphene based supercapacitors. Here, we introduce a microstructure substrate based method to produce fully delaminated and stable interconnected graphene structure using flash reduction of GO in a few seconds. With this structure, here we achieve the highest amount of volumetric capacitance obtained so far by any type of pure carbon based material. The affordable and scalable production method is capable of producing electrodes with energy density of 0.37 Wh cm -3 and power density of 416.6 W cm -3 . This electrode maintained more than 91 % of its initial capacitance after 5000 cycles. Moreover, combining with ionic liquid, this solvent free graphene electrode material is highly promising for on-chip electronics, micro supercapacitors as well as high power applications.

25 ENERGY STORAGE↗
NASA NTRS1996

Computational Intelligence and Its Impact on Future High-Performance Engineering Systems

This document contains presentations from the joint UVA/NASA Workshop on Computational Intelligence held at the Virginia Consortium of Engineering and Science Universities, Hampton, Virginia, June 27-28, 1995. The presentations addressed activities in the areas of fuzzy logic, neural networks, and evolutionary computations. Workshop attendees represented NASA, the National Science Foundation, the Department of Energy, National Institute of Standards and Technology (NIST), the Jet Propulsion Laboratory, industry, and academia. The workshop objectives were to assess the state of technology in the Computational intelligence area and to provide guidelines for future research.

Noor, Ahmed K.↗
NASA NTRS2013

The Astrophysical Multimessenger Observatory Network (AMON)

We summarize the science opportunity, design elements, current and projected partner observatories, and anticipated science returns of the Astrophysical Multimessenger Observatory Network (AMON). AMON will link multiple current and future high-energy, multimessenger, and follow-up observatories together into a single network, enabling near real-time coincidence searches for multimessenger astrophysical transients and their electromagnetic counterparts. Candidate and high-confidence multimessenger transient events will be identified, characterized, and distributed as AMON alerts within the network and to interested external observers, leading to follow-up observations across the electromagnetic spectrum. In this way, AMON aims to evoke the discovery of multimessenger transients from within observatory subthreshold data streams and facilitate the exploitation of these transients for purposes of astronomy and fundamental physics. As a central hub of global multimessenger science, AMON will also enable cross-collaboration analyses of archival datasets in search of rare or exotic astrophysical phenomena.

comparison↗
DOE OSTI2020

ASCR@40: Highlights and Impacts of ASCR’s Programs

A report compiled by the ASCAC Subcommittee on the 40-year history of ASCR for the U.S. Department of Energy’s Office of Advanced Scientific Computing Research. The Office of Advanced Scientific Computing Research (ASCR) sits within the Office of Science in the Department of Energy (DOE). Per their web pages, “the mission of the ASCR program is to discover, develop, and deploy computational and networking capabilities to analyze, model, simulate, and predict complex phenomena important to the DOE.” This succinct statement encompasses a wide range of responsibilities for computing and networking facilities; for procuring, deploying, and operating high performance computing, networking, and storage resources; for basic research in mathematics and computer science; for developing and sustaining a large body of software; and for partnering with organizations across the Office of Science and beyond. While its mission statement may seem very contemporary, the roots of ASCR are quite deep—long predating the creation of DOE. Applied mathematics and advanced computing were both elements of the Theoretical Division of the Manhattan Project. In the early 1950s, the Manhattan Project scientist and mathematician John von Neumann, then a commissioner for the AEC (Atomic Energy Commission), advocated for the creation of a Mathematics program to support the continued development and applications of digital computing. Los Alamos National Laboratory (LANL) scientist John Pasta created such a program to fund researchers at universities and AEC laboratories. Under several organizational name changes, this program has persisted ever since, and would eventually grow to become ASCR.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Lessons from the COVID Era and Visions for the Future

In December 2020, the U.S. Department of Energy Office of Science convened a virtual Roundtable of its 27 operating scientific user facilities to discuss facility challenges and lessons learned during the COVID-19 pandemic as well as facility responses, best practices, and innovations that could be adopted going forward. Roundtable participants included facility staff, users, and user executive committee chairs. This report summarizes their discussions, which encompassed topics such as user research and facility operations in virtual and physically distanced contexts; user training and engagement; computation, data, and network resources; and crosscutting issues.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2011

K{sub 2}Mg{sub 5-x}Sn{sub 3} and K{sub 3}Mg{sub 18}Tt{sub 11} (Tt=Sn, pb) with two types of Mg-Sn/Pb frameworks

K{sub 2}Mg{sub 5-x}Sn{sub 3} (x=0.28) and K{sub 3}Mg{sub 18}Tt{sub 11} (Tt=Sn, Pb) have been synthesized by reacting the mixture of the corresponding pure elements at high temperature, and structurally characterized by single-crystal X-ray diffraction studies. K{sub 2}Mg{sub 5-x}Sn{sub 3} (x=0.28) is isostructural with Ni{sub 7-x}SbQ{sub 2} (Q=Se, Te) series and features 2D corrugated [Mg{sub 5-x}Sn{sub 3}] layers that are separated by K{sup +} cations. The structure of K{sub 3}Mg{sub 18}Tt{sub 11} (Tt=Sn, Pb) is closely related to the Ho{sub 2}Rh{sub 12}As{sub 7} structural type and features 3D [Mg{sub 18}Tt{sub 11}] framework composed of 1D [Mg{sub 18}Tt{sub 11}] columns that are interconnected via Mg-Tt bonds, forming 1D hexagonal tunnels occupied by the K+ cations. Electronic structure calculations indicate that Mg atoms can function as either electron donor or as a participator in the network along with Tt atoms. Magnetic property measurements and band structure calculations indicate that these compounds are metallic. -- Graphical abstract: Two new K-Mg-Tt phases, K{sub 2}Mg{sub 5-x}Sn{sub 3} (x=0.28) and K{sub 3}Mg{sub 18}Tt{sub 11} (Tt=Sn, Pb), have been synthesized and structurally characterized. K{sub 2}Mg{sub 5-x}Sn{sub 3} is isostructural with Ni{sub 7-x}SbQ{sub 2} (Q=Se, Te) and features a 2D corrugated [Mg{sub 5}Sn{sub 3}] layer that is separated by K{sup +} cations, and K{sub 3}Mg{sub 18}Tt{sub 11} (Tt=Sn, Pb) is closely related to the Ho{sub 2}Rh{sub 12}As{sub 7} structure type and features a 3D [Mg{sub 18}Tt{sub 11}] network with 1D hexagonal tunnels along the c-axis occupied by K{sup +} cations. Display Omitted Research highlights: Two new polar intermetallics, K{sub 2}Mg{sub 5-x}Sn{sub 3} (x=0.28) and K{sub 3}Mg{sub 18}Tt{sub 11} (Tt=Sn, Pb). Electronic structure calculations were performed by both TB-LMTO and EHTB methods. Mg atom can function as either electron donor or participator in the network.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Conference Grant Report Submitted to the Office of Fusion Energy Science by the University of California, San Diego

The High Energy Density Science Association (HEDSA) in collaboration with the University of California San Diego (UCSD), hosted the High Energy-Density Science Summer School from July 30 – August 11, 2017 on the UC San Diego campus. The goal of the Summer School series is to introduce new talent to the breadth of the U.S. High Energy Density Science (HEDS) community through lectures, engaging workshops, and discussion sessions with leaders in academia and the national laboratories. The objectives are to inspire young scientists to pursue graduate and professional careers in the fields of high energy density science, teach them fundamental HED science and critical skills, and grant them the opportunity to network with leading academic and national laboratory researchers. Our focus was to attract promising early-career students from across the country.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗