Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “User Facility”

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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

LAMP Low-Energy Region Options: Workshop Report and Ranking Assessment

The LANSCE accelerator complex at Los Alamos National Laboratory provides beam to five user facilities: IPF, pRad, UCN, WNR and the Lujan Center. Each user facility receives a beam tailored to its specific requirements, including species (H+ or H- ) and beam pulse format. The capabilities and beam requirements of the LANSCE user facilities are documented elsewhere. The core components of the LANSCE accelerator complex – the beam source area, drift-tube and cavity-coupled linear accelerators – are more than 50 years old; a critical subsystem for beam delivery to the Lujan Center, the proton storage ring (PSR), is approximately 40 years old, with its last major refresh being completed in the late 1990s. The LAMP project is intended to begin a revitalization and update of the LANSCE accelerator complex, starting with the beam source region, drift-tube linac, and PSR.

43 PARTICLE ACCELERATORS↗
NASA NTRS1995

SSBRP User Operations Facility (UOF) Overview and Development Strategy

This paper will present the Space Station Biological Research Project (SSBRP) User Operations Facility (UOF) architecture and development strategy. A major element of the UOF at NASA Ames Research Center, the Communication and Data System (CDS) will be the primary focus of the discussions. CDS operational, telescience, security, and development objectives will be discussed along with CDS implementation strategy. The implementation strategy discussions will include: Object Oriented Analysis & Design, System & Software Prototyping, and Technology Utilization. A CDS design overview that includes: CDS Context Diagram, CDS Architecture, Object Models, Use Cases, and User Interfaces will also be presented. CDS development brings together "cutting edge" technologies and techniques such as: object oriented development, network security, multimedia networking, web-based data distribution, JAVA, and graphical user interfaces. Use of these "cutting edge" technologies and techniques translates directly to lower development and operations costs.

Picinich, Lou↗
DOE OSTI2021

Convolutional neural network based non-iterative reconstruction for accelerating neutron tomography *

Abstract Neutron computed tomography (NCT), a 3D non-destructive characterization technique, is carried out at nuclear reactor or spallation neutron source-based user facilities. Because neutrons are not severely attenuated by heavy elements and are sensitive to light elements like hydrogen, neutron radiography and computed tomography offer a complementary contrast to x-ray CT conducted at a synchrotron user facility. However, compared to synchrotron x-ray CT, the acquisition time for an NCT scan can be orders of magnitude higher due to lower source flux, low detector efficiency and the need to collect a large number of projection images for a high-quality reconstruction when using conventional algorithms. As a result of the long scan times for NCT, the number and type of experiments that can be conducted at a user facility is severely restricted. Recently, several deep convolutional neural network (DCNN) based algorithms have been introduced in the context of accelerating CT scans that can enable high quality reconstructions from sparse-view data. In this paper, we introduce DCNN algorithms to obtain high-quality reconstructions from sparse-view and low signal-to-noise ratio NCT data-sets thereby enabling accelerated scans. Our method is based on the supervised learning strategy of training a DCNN to map a low-quality reconstruction from sparse-view data to a higher quality reconstruction. Specifically, we evaluate the performance of two popular DCNN architectures—one based on using patches for training and the other on using the full images for training. We observe that both the DCNN architectures offer improvements in performance over classical multi-layer perceptron as well as conventional CT reconstruction algorithms. Our results illustrate that the DCNN can be a powerful tool to obtain high-quality NCT reconstructions from sparse-view data thereby enabling accelerated NCT scans for increasing user-facility throughput or enabling high-resolution time-resolved NCT scans.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Opportunities for retrieval and tool augmented large language models in scientific facilities

Upgrades to advanced scientific user facilities such as next-generation x-ray light sources, nanoscience centers, and neutron facilities are revolutionizing our understanding of materials across the spectrum of the physical sciences, from life sciences to microelectronics. However, these facility and instrument upgrades come with a significant increase in complexity. Driven by more exacting scientific needs, instruments and experiments become more intricate each year. This increased operational complexity makes it ever more challenging for domain scientists to design experiments that effectively leverage the capabilities of and operate on these advanced instruments. Large language models (LLMs) can perform complex information retrieval, assist in knowledge-intensive tasks across applications, and provide guidance on tool usage. Using x-ray light sources, leadership computing, and nanoscience centers as representative examples, we describe preliminary experiments with a Context-Aware Language Model for Science (CALMS) to assist scientists with instrument operations and complex experimentation. With the ability to retrieve relevant information from facility documentation, CALMS can answer simple questions on scientific capabilities and other operational procedures. With the ability to interface with software tools and experimental hardware, CALMS can conversationally operate scientific instruments. By making information more accessible and acting on user needs, LLMs could expand and diversify scientific facilities’ users and accelerate scientific output.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Building partnerships for development of sustainable energy systems with atmospheric measurements

Atmospheric dynamics often play a critical role in the sustainability and reliability of diverse forms of energy production. This is especially true for the growing number of renewable energy deployments that harness aspects of the environment for power production. While the University of Memphis has a strong research background in energy systems, we have little experience working with the Earth and Environmental Systems Science Division (EESSD) and their associated User Facilities. Of particular interest to us is the Atmospheric Science Research and the Atmospheric Radiation Measurement (ARM) user facility to address surface-boundary layer interactions and physical phenomena. One of the major challenges for understanding and developing energy systems and management platforms is accurate modeling/forecasting of atmospheric conditions across disparate spatial and temporal scales. These conditions are often required to understand the lowest levels of the atmospheric boundary layer, but are also important to understand higher atmospheric conditions where aerosols affect cloud development. The objective of this work was to develop partnerships with national laboratories for collaboration on environmental science and its intersection with sustainable energy systems, as well as to leverage the ARM user facility data repositories to enhance our research capabilities in energy systems and their inter-dependence on environmental systems for future engagement with EESSD. Specifically, we accomplished these objectives by (1) developing collaborations with Oakridge National Laboratory ARM Data Science and Integration Group which resulted in student internships, (2) employed ARM data to develope modeling of the atmospheric boundary layer optical turbulence, and (3) optimally-sized large-scale renewable energy systems and their associated energy storage systems with ARM repository data.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Post-DTL Beam Delivery

Ensuring that the beam delivered from the upgraded Front-End (FE) meets the Key Performance Parameters (KPPs) at each user facility is critical to the success of the LANSCE Accelerator Modernization Project (LAMP). For a high-intensity, multi-user facility like LANSCE, compliance with beam loss and radiation thresholds is as important as the charge delivered to each target. While early LAMPF/LANSCE operations relied on iterative tuning to minimize losses from beam halo and tail particles, the new FE may introduce different beam distributions and loss modes—making predictive modeling essential. To manage this, the F2E (Front-End to End) effort is developing detailed particle-tracking models that reflect realistic beamline conditions, including halo formation and expected diagnostic readings. These "snapshot" simulations aim to benchmark live machine performance at a given moment. This will help quantify how beam quality from the new FE will propagate downstream through the facility. Only by validating these models can we confidently assess and mitigate the potential impacts of the LAMP FE on beam delivery. Post-DTL, the beam splits to serve five major user facilities. Historically, low-energy beam transport has been modeled using TRACE, and higher-energy sections with TRANSPORT. These have now been unified into MAD-X format and validated with codes such as Elegant, pyOrbit, XSuite, Impact-Z, and HPSim. The primary focus now is on accurate modeling of full particle distributions (including beam halo) as they traverse the accelerator and beamlines to each experimental station. All models are at various stages of validation with empirical data.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Overview of the Neutron Radiography Reactor (NRAD) for Neutron Imaging and In-Core Experiment Capabilities at Idaho National Laboratory

NRAD is a 250-kilowatt TRIGA research reactor that first went online at INL in 1977. (TRIGA stands for Training, Research, Isotopes, General Atomics.) Historically, NRAD was utilized as a neutron radiography reactor that provides comprehensive, non-destructive information about the internal condition of irradiated nuclear fuel. Idaho National Laboratory (INL) has multiple nuclear fuels research and development programs that routinely evaluate irradiated fuels using neutron radiography at NRAD. In recent years, NRAD has gone through a transformation from the single purpose radiography reactor for which it was designed into a multipurpose research reactor, and expanding its in-core irradiation capabilities to support a broader mission for the US Department of Energy (DOE) Nuclear Energy (NE) programs, Basic Energy Science (BES) Programs, as well as Fusion Energy programs. NRAD is a designated user facility under the DOE Nuclear Science User Facility (NSUF) program, and is available for access for general public via a competitive proposal process. More information about NSUF and NRAD are available from the website: https://nsuf.inl.gov/Home/Facility/654.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

LDRD 2022 Annual Report: Laboratory Directed Research and Development Program Activities

Each year, Brookhaven National Laboratory (BNL) is required to provide a report of its completed Laboratory Directed Research and Development Program (LDRD) projects to the Department of Energy (DOE) Office of Scientific and Technical Information in accordance with DOE Order 413.2C Chg1 (MinChg) dated August 2, 2018. This report provides a detailed look at the scientific and technical activities for each of the LDRD projects funded by BNL in FY 2022, in fulfillment of that requirement. In FY 2022, the BNL LDRD Program funded 70 projects, 30 of which were new starts, at a total cost of $17.2M. The investments that BNL makes in its LDRD program support the Laboratory’s strategic goals. BNL has identified seven scientific initiatives that define the Laboratory’s scientific future and that will enable it to realize its overall vision. This requires simultaneous excellence in all aspects of BNL’s work – from science and operations, to external partnerships with the local, state, and national communities, and beyond. This is enabled by safe, efficient, and secure operations; by an unwavering commitment to a diverse, equitable, and inclusive environment, including workforce development, both with staff and reaching out to the community; and by a strong focus on renewed infrastructure. The seven scientific initiatives are: 1) Nuclear Physics: uncover the structure of visible matter by constructing and operating the Electron-Ion Collider at BNL to maintain international leadership in nuclear physics for decades; 2) Clean Energy and Climate: support a net-zero U.S. economy through fundamental research in basic energy and climate sciences to revolutionize grid-scale storage, renewable integration, and the study of atmospheric processes with a new facility to improve climate predictability; 3) Quantum Information Science and Technology: discover new quantum materials to enhance quantum computers and develop an entanglement sharing quantum network as a prototype for the first quantum internet; 4) Discovery Science Driven by the Human-AI Facility Integration: revolutionize the operation of experiments across the sciences at user facilities and in core programs; 5) High Energy Physics: understand the origin of space and time with the ATLAS high luminosity upgrade at CERN and the future Long Baseline Neutrino Facility/Deep Underground Neutrino Experiment; 6) Isotope Production: accelerate and expand isotope production to ensure the security of the Nation’s supply; 7) Accelerator Science and Technology: harness the cross-cutting accelerator science expertise at BNL to develop new facilities, improve and expand its user facilities, and promote the use of accelerators in industry. The funded projects support BNL’s seven scientific initiatives and priority programs as well as new areas of research and competencies at the Laboratory that are consistent with the Laboratory’s vision and mission. In total, these LDRD investments supported 43 postdoctoral researchers in whole or in part and resulted in 138 publications and 7 awards. This Program Activities Report represents the future of BNL science; it is an impressive body of exploratory work that investigates many scientific and technical directions in support of the DOE and BNL missions.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Nuclear Physics Network Requirements Review Report

The Energy Sciences Network (ESnet) is the Office of Science’s high-performance network user facility, delivering highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the U.S. Department of Energy (DOE) science mission by connecting each and every DOE lab and its user facilities. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) Program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. ESnet connects DOE national laboratories, user facilities, and major experiments so scientists can use remote instruments and computing resources as well as share data with collaborators, transfer large data sets, and access distributed data repositories. While ESnet provides network connectivity, it cannot be characterized as an internet service provider as it is specifically built to provide a range of network services that are tailored to meet the unique requirements of DOE’s data-intensive science.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

FY23 Status Report of the Activated Materials Laboratory at the Advanced Photon Source

The Activated Materials Laboratory (AML) is a new radiological facility located at the Advanced Photon Source (APS) in Argonne National Laboratory (ANL), adjacent to the high-energy x-ray microscopy (HEXM) beamline in the long beamline building (LBB) constructed under the APS-upgrade (APS-U) project. The AML is a centralized facility to facilitate the safe conduct of experiments on activated materials at the APS. This report provides an overview of the status of the AML as a Nuclear Science User Facilities (NSUF) partner user facility in preparation for the general user access in 2024 upon the commissioning of the APS-U beamlines. Specifically, details are provided regarding the laboratory's scope, functionality, components, operational blueprint, and data management approach. The plans on augmenting the instrumentation, refining operational procedures and developing a robust data management strategy for FY24 and beyond are also discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

FY24 accomplishments in preparation for startup of Activated Materials Laboratory (AML)

The Activated Materials Laboratory (AML) will be a new radiological facility at the Advanced Photon Source (APS) in Argonne National Laboratory (ANL), adjacent to the high-energy x-ray microscopy (HEXM) beamline in the long beamline building (LBB) constructed under the APS-upgrade (APS-U) project. The AML is a centralized facility to facilitate the safe conduct of experiments on activated materials at the APS. This report provides an overview of the status of the AML as a Nuclear Science User Facilities (NSUF) partner user facility in preparation for the general user access in 2025 upon the commissioning of the APS-U beamlines. The AML's scope, functionality and components are detailed. The NSUF partner beamlines’ commissioning status in the post-APS-U era is provided, along with the AML’s operational updates and operational plan.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

NSUF FY24 Program Overview and Updates

The Nuclear Science User Facilities (NSUF) is one of a diverse number of U.S. Department of Energy (DOE) user facilities established to provide researchers with the most advanced tools of modern science. The NSUF was established to provide access to unique capabilities to a broad range of researchers to address the important issues relevant to irradiation effects in nuclear fuels and materials. The NSUF represents a consortium of capabilities distributed across the U.S. at twenty institutions. The NSUF is centered at the Idaho National Laboratory, but it coordinates activities at nineteen “partner” institutions. These institutions have capabilities that include neutron, ion, and gamma irradiation, hot cells, advanced material characterization, and high-performance computing. The NSUF goal is to provide access these capabilities at no cost to nuclear energy researchers to produce the highest quality research results to increase understanding of advanced nuclear energy technologies important to DOE-NE.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

AI for Science: Report on the Department of Energy (DOE) Town Halls on Artificial Intelligence (AI) for Science

The report documents the DOE Town Halls held during 2019 at Argonne National Laboratory, Oak Ridge National Laboratory, Lawrence Berkeley National Laboratory, and in Washington, DC. From July to October 2019, the Argonne, Oak Ridge, and Berkeley National Laboratories hosted a series of four town hall meetings attended by more than 1,000 U.S. scientists and engineers. The goal of the town hall series was to examine scientific opportunities in the areas of artificial intelligence (AI), Big Data, and high-performance computing (HPC) in the next decade, and to capture the big ideas, grand challenges, and next steps to realizing these opportunities. In this report and in the Department of Energy (DOE) laboratory community, we use the term “AI for Science” to broadly represent the next generation of methods and scientific opportunities in computing, including the development and application of AI methods (e.g., machine learning, deep learning, statistical methods, data analytics, automated control, and related areas) to build models from data and to use these models alone or in conjunction with simulation and scalable computing to advance scientific research. The AI for Science town hall discussions focused on capturing the transformational uses of AI that employ HPC and/or data analysis, leveraging data sets from HPC simulations or instruments and user facilities, and addressing scientific challenges unique to DOE user facilities and the agency’s wide-ranging fundamental and applied science enterprise.

36 MATERIALS SCIENCE↗
NASA NTRS1975

Manned orbital facility: A user's guide

The salient conceptual features and expected evolution of the facility are discussed; the baseline design is offered as a model against which the reader can compare his needs. The overall program is discussed, supporting services and resources are described, and examples of typical payload applications are given. The general design features and configurations representing the baseline MOF developed and derived with due consideration given to applicable designs and subsystems such as those available in the Skylab, orbiter, and space lab vehicles.

Source record↗
DOE OSTI2021

Enabling modern data discovery for atmospheric measurements

The Atmospheric Radiation Measurement (ARM) user facility is a US Department of Energy Office of Science user facility that is managed and operated through a collaborative effort led by nine US Department of Energy national laboratories. The ARM Data Center, located at Oak Ridge National Laboratory, is responsible for the timely collection, processing, and delivery of data products to the scientific community. The ARM Data Center holds more than 11,000 data products, including metadata collected from field campaigns, instruments, value-added products, and principal investigator–contributed data. These data sets are checked for successful transfer (for most data, this transfer is carried out automatically via the network; however, some of the largest data sets and some of the most remote sites require manual shipping of hard disks) and both the data and metadata are processed to a standard format, which is an ARM-standardized structure, via the Network Common Data Form. The Network Common Data Form is a self-describing binary format with many compatible software tools. Once processed, the data are cataloged, stored in the ARM Data Archive, and made discoverable through association with an array of metadata-characterizing information, such as location and measurement classification. These metadata enable powerful search capabilities through the ARM Data Center Data Discovery interface. This paper discusses the workflow of how the new discovery system has been redesigned from user requirements and how the data are distributed to the scientific community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Application of the FaMUS Methodology to the NSUF Research Outputs Through End of 2019

The Nuclear Science User Facilities (NSUF) is one of a diverse group of DOE user facilities. It is focused on advancing the understanding of radiation effects in nuclear fuels and materials in support of nuclear energy applications. The NSUF has been operating since 2007 and has developed a significant portfolio of supported research. Therefore, it is appropriate to consider its achievements and to determine its successes and shortfalls. As part of this analysis of the NSUF research program, the NSUF has developed a novel and elegant formalism for assessing the current level of understanding of nuclear fuels and materials for use in nuclear environments: the NSUF Fuels and Materials Understanding Scale (FaMUS). The FaMUS methodology is being applied to the NSUF portfolio on an ongoing basis to quantify the progress made. This report summarizes the status of the assessment exercise and highlights general lessons learned. This examination will facilitate capability balancing of future research by identifying gaps in knowledge, understanding, testing, and demonstration allowing the NSUF program office in conjunction with DOE-NE to direct resources to important aspects of the DOE-NE mission using “emphasize & enhance”, “maintain”, and “encourage excellence” classifications as well as providing increased transparency to researchers.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

LaserNetUS. Final report

In this proposal we have formed a consortium in response to a request from the DoE Office of Fusion Energy Sciences to establish a national network of Petawatt-class laser systems to act as domestic user facilities that can enable a broad range of frontier scientific research. This new entity is called LaserNet US. The network formed here is directly responsive to recommendations made in a recently released National Academy of Sciences Report with regard to US strategy for high intensity laser research, “Opportunities in Intense Ultrafast Lasers: Reaching for the Brightest Light”. As detailed in this report, the research in this area has the potential to transform science in a number of research fields and to open up new areas of fundamental research. High field science was initiated in the US in the 1990’s, and subsequently Europe and Asia have embraced this research field, investing more than a billion dollars in this area over the past several years. Our network includes five academic and two national lab-based high intensity laser facilities. These facilities are distributed geographically throughout the US and the network provides access to complementary facilities which have unique world-class laser and experimental capabilities. LaserNet US will make these existing Petawatt-class laser facilities available to users from around the country who until now have not had regular access to such machines. Consequently, the network, which will develop over time, will also become a key element in driving forward national research in high field and high energy density plasma science. In particular, the University of Michigan provides access to the Hercules laser facility and the T-cubed laser facility for collaborative experiments as part of the first year operation. In the second year - to begin in summer 2019 - the experiments will be chosen via proposal submission and review by an outside panel formed by DOE.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Performance and Reliability Assessment of the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Data Advisor (ADA)

The Atmospheric Radiation Measurement (ARM) User Facility provides one of the world's largest openly accessible repositories of atmospheric observations through the ARM Data Discovery platform. Although the repository contains more than three decades of measurements collected from permanent observatories, mobile facilities, aircraft campaigns, and field experiments, identifying appropriate datasets can be challenging, particularly for new users unfamiliar with ARM instrumentation and datastream organization. To improve data accessibility, the ARM Data Center developed the ARM Data Advisor (ADA), an artificial intelligence-powered assistant designed to facilitate scientific data discovery, dataset interpretation, and user guidance. This report evaluates ADA's performance as a domain-specific scientific assistant using realistic atmospheric science workflows. The evaluation examines five key capabilities: data retrieval and curation efficiency, hallucination resistance, scientific reasoning, response to ambiguous queries, and content retention and session continuity. Representative prompts were developed to simulate typical interactions between researchers and the ARM Data Discovery platform, and ADA's responses were assessed for retrieval completeness, scientific accuracy, consistency, and practical usefulness. In these representative tests, ADA reduced the complexity of discovering and accessing ARM datasets by recommending appropriate datastreams, explaining instrumentation, interpreting metadata, and assisting with data processing workflows. ADA also exhibits strong domain knowledge of atmospheric science terminology and generally resists hallucination by acknowledging unavailable datasets and requesting clarification when appropriate. Overall, the results indicate that ADA represents a promising advancement in scientific data discovery within the ARM User Facility and has considerable potential to improve researcher productivity, particularly for new users and interdisciplinary scientists seeking efficient access to ARM observations.

Salvador, Christian [ORNL] (ORCID:0000000283287777↗