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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

WE-E-BRF-01: The ESTRO-AAPM Joint Symposium On Imaging for Proton Treatment Planning and Guidance

In this first inaugural joint ESTRO-AAPM session we will attempt to provide some answers to the problems encountered in the clinical application of particle therapy. Indeed the main advantage is that the physical properties of ion beams offer high ballistic accuracy for tightly conformal irradiation of the tumour volume, with excellent sparing of surrounding healthy tissue and critical organs, This also its Achilles' heel calling for an increasing role of imaging to ensure safe application of the intended dose to the targeted area during the entire course of fractionated therapy. We have three distinguished speakers addressing possible solutions. Katia Parodi (Ludwig Maximilians University, Munich, Germany) To date, Positron Emission Tomography (PET) is the only technique which has been already clinically investigated for in-vivo visualization of the beam range during or shortly after ion beam delivery. The method exploits the transient amount of β{sup 2}-activity induced in nuclear interactions between the primary beam and the irradiated tissue, depending on the ion beam species, the tissue elemental composition and physiological properties (in terms of biological clearance), as well as the time course of irradiation and imaging. This contribution will review initial results, ongoing methodological developments and remaining challenges related to the clinical usage of viable but often suboptimal instrumentation and workflows of PET-based treatment verification. Moreover, it will present and discuss promising new detector developments towards next-generation dedicated PET scanners relying on full-ring or dual-head designs for in-beam quasi real-time imaging. Denis Dauvergne (Institut de Physique Nucleaire de Lyon, Lyon, France) Prompt gamma radiation monitoring of hadron therapy presents the advantage of real time capability to measure the ion range. Both simulations and experiments show that millimetric verification of the range can be achieved at the pencil beam scale for active proton beam delivery in homogenous targets. The development of gamma cameras, that has been studied by several groups worldwide over the last years, now reaches - for some of them - the stage of being applicable in clinical conditions, with real size prototypes and count rate capability matching the therapeutic beam intensities. We will review the different concepts of gamma cameras, the advantages and limitations of this method, and the main challenges that should still be overcome before the widespread of prompt gamma quality assurance for proton and hadrontherapy. Jon Kruse (Mayo Clinic, Rochester, MN, USA) Treatment simulation images for proton therapy are used to determine proton stopping power and range in the patient. This talk will discuss the careful control of CT numbers and conversion of CT number to stopping power required in proton therapy. Imaging for treatment guidance of proton therapy also presents unique challenges which will be addressed. Among them are the enhanced relationship between internal anatomy changes and dosimetry, the need for imaging to support adaptive planning protocols, and high operational efficiency. Learning Objectives: To learn about the possibilities of using activation products to determine the range of particle beams in a patient treatment setting To be informed on an alternative methodology using prompt gamma detectors To understand the impact of the accuracy of the knowledge of the patient information with respect to the delivered treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Multiscale Neural Networks for Approximating Green’s Functions

Neural networks (NNs) have been widely used to solve partial differential equations (PDEs) in the applications of physics, biology, and engineering. One effective approach for solving PDEs with a fixed differential operator is learning Green’s functions. However, Green’s functions are notoriously difficult to learn due to their poor regularity, which typically requires larger NNs and longer training times. In this work, we address these challenges by leveraging multiscale NNs to learn Green’s functions. Through theoretical analysis using multiscale Barron space methods and experimental validation, we show that the multiscale approach significantly reduces the necessary NN size and accelerates training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Conflation of Geospatial POI Data and Ground-level Imagery via Link Prediction on Joint Semantic Graph

With the proliferation of smartphone cameras and social networks, we have rich, multi-modal data about points of interest (POIs) - like cultural landmarks, institutions, businesses, etc. - within a given areas of interest (AOI) (e.g., a county, city or a neighborhood) available to us. Data conflation across multiple modalities of data sources is one of the key challenges in maintaining a geographical information system (GIS) which accumulate data about POIs. Given POI data from nine different sources, and ground-level geo-tagged and scene-captioned images from two different image hosting platforms, in this work we explore the application of graph neural networks (GNNs) to perform data conflation, while leveraging a natural graph structure evident in geospatial data. The preliminary results demonstrate the capacity of a GNN operation to learn distributions of entity (POIs and images) features, coupled with topological structure of entity's local neighborhood in a semantic nearest neighbor graph, in order to predict links between a pair of entities.

Gurav, Rutuja↗
DOE OSTI2023

Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill

Physics-based simulations of Arctic sea ice are highly complex, involving transport between different phases, length scales, and time scales. Resultantly, numerical simulations of sea ice dynamics have a high computational cost and model uncertainty. We employ data-driven machine learning (ML) to make predictions of sea ice motion. The ML models are built to predict present-day sea ice velocity given present-day wind velocity and previous-day sea ice concentration and velocity. Models are trained using reanalysis winds and satellite-derived sea ice properties. We compare the predictions of three different models: persistence (PS), linear regression (LR), and a convolutional neural network (CNN). We quantify the spatiotemporal variability of the correlation between observations and the statistical model predictions. Additionally, we analyze model performance in comparison to variability in properties related to ice motion (wind velocity, ice velocity, ice concentration, distance from coast, bathymetric depth) to understand the processes related to decreases in model performance. Results indicate that a CNN makes skillful predictions of daily sea ice velocity with a correlation up to 0.81 between predicted and observed sea ice velocity, while the LR and PS implementations exhibit correlations of 0.78 and 0.69, respectively. The correlation varies spatially and seasonally: lower values occur in shallow coastal regions and during times of minimum sea ice extent. LR parameter analysis indicates that wind velocity plays the largest role in predicting sea ice velocity on 1-day time scales, particularly in the central Arctic. Regions where wind velocity has the largest LR parameter are regions where the CNN has higher predictive skill than the LR.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Abisko: Deep codesign of an architecture for spiking neural networks using novel neuromorphic materials

The Abisko project aims to develop an energy-efficient spiking neural network (SNN) computing architecture and software system capable of autonomous learning and operation. The SNN architecture explores novel neuromorphic devices that are based on resistive-switching materials, such as memristors and electrochemical RAM. Equally important, Abisko uses a deep codesign approach to pursue this goal by engaging experts from across the entire range of disciplines: materials, devices and circuits, architectures and integration, software, and algorithms. Here, the key objectives of our Abisko project are threefold. First, we are designing an energy-optimized high-performance neuromorphic accelerator based on SNNs. This architecture is being designed as a chiplet that can be deployed in contemporary computer architectures and we are investigating novel neuromorphic materials to improve its design. Second, we are concurrently developing a productive software stack for the neuromorphic accelerator that will also be portable to other architectures, such as field-programmable gate arrays and GPUs. Third, we are creating a new deep codesign methodology and framework for developing clear interfaces, requirements, and metrics between each level of abstraction to enable the system design to be explored and implemented interchangeably with execution, measurement, a model, or simulation. As a motivating application for this codesign effort, we target the use of SNNs for an analog event detector for a high-energy physics sensor.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

A post-occupancy monitored evaluation of the dimmable lighting, automated shading, and underfloor air distribution system in The New York Times Building

With aggressive goals to reduce national energy use and carbon emissions, the US Department of Energy will be looking to exemplary buildings that have already invested in new approaches to achieving the energy performance goals now needed at a national level. The New York Times Building, in New York, New York, incorporates a number of innovative technologies, systems and processes and could become a model for widespread replication in new and existing buildings. Post-occupancy data are invaluable in establishing confidence in innovation. A year-long monitored study was conducted to verify energy performance, assess occupant comfort and satisfaction with the indoor environment, and evaluate impacts on maintenance and operations. Lessons learned were derived from the analysis; these lessons could help identify and shape policy, financial, or supporting strategies to accelerate diffusion in the commercial building market.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2013

A Post-Occupancy Monitored Evaluation of the Dimmable Lighting, Automated Shading, and Underfloor Air Distribution System in The New York Times Building

With aggressive goals to reduce national energy use and carbon emissions, the U.S. Department of Energy (DOE) will be looking to exemplary buildings that have already invested in new approaches to achieving the energy performance goals now needed at a national level. The New York Times Building, in New York, New York, incorporates a number of innovative technologies, systems and processes and could become model for widespread replication in new and existing buildings. A year-long monitored study was conducted to verify energy performance, assess occupant comfort and satisfaction with the indoor environment, and evaluate impact on maintenance and operations. Lessons learned were derived from the analysis; these lessons could help identify and shape policy, financial, or supporting strategies to accelerate diffusion in the commercial building market.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Conceptual Design of an Improved Sodium Plugging Loop

Based upon lessons learned in operating the initial sodium portion of the Sodium Plugging Phenomena Loop that itself became plugged and could not be unplugged, a set of improved design features have been identified for a new sodium loop that is less prone to inadvertent plugging. The new design features include a new sodium vessel that combines the functionalities of an expansion tank and a cold trap, increasing the loop tubing diameter/size, improving the thermal designs of the pump and flow meter, improved heater zoning, and an improved temperature distribution strategy along the loop. A new plugging loop conceptual design implementing these features has been developed.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Cyber Resilience and Social Equity: Twin Pillars of a Sustainable Energy Future

This paper examines the intersection of security and accessibility within energy systems amidst the rise of grid modernization and digitization, especially considering the regulatory changes and the imperatives of inclusive energy strategies. It addresses the dual need for secure, resilient infrastructure and a commitment to mitigate energy poverty while maintaining equitable access to energy. Amid escalating cybersecurity and physical threats, the paper advocates for sustainable energy delivery systems that ensure robust defenses without compromising the goals of reducing energy poverty and ensuring energy security. This paper identifies the pressing need for Cyber-Informed Engineering (CIE) and Secure-by-Design (SbD) principles, highlighting how these strategies can protect critical infrastructure and democratize access to secure energy, particularly for disadvantaged communities. The analysis underscores the challenges presented by the expansion of attack surfaces, interoperability requirements, and grid-edge analytics, offering innovative solutions that leverage advanced technologies and data-driven insights. Furthermore, this paper addresses the workforce development gap, emphasizing the necessity for public-private partnerships and vendor engagement in creating a skilled cybersecurity workforce. This paper has a dual focus on both the technological aspect of cybersecurity and the social dimension of equity within the context of sustainable energy development. It suggests a comprehensive examination of how these two critical elements interact and support the overarching goal of a sustainable energy future.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

Energy Transition Initiative: Islands Playbook (Book)

The Island Energy Playbook (the Playbook) provides an action-oriented guide to successfully initiating, planning, and completing a transition to an energy system that primarily relies on local resources to eliminate a dependence on one or two imported fuels. It is intended to serve as a readily available framework that any community can adapt to organize its own energy transition effort.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

SUPERCONDUCTING LINAC UPGRADE PLAN FOR THE SECOND TARGET STATION PROJECT AT SNS

The beam power of the Linac for the Second Target Station (STS) at the Spallation Neutron Source (SNS) will be doubled to 2.8 MW. For the energy upgrade, seven additional cryomodules will be installed in the reserved space at the end of the linac tunnel to produce linac output energy of 1.3 GeV. The cryomodules for STS will have the same physical length but will incorporate some design changes based on the lessons learned from operational experience over the last 10 years and from the high beta spare cryomodule developed in house. The average macro-pulse beam current for the STS will be 38 mA which is about a 40 % increase from the present beam current for 1.4 MW operation. Plans for the new cryomodules and for the existing cryomodules to support higher beam current for the STS are presented in this paper.

Kim, Sang-Ho↗
DOE OSTI2021

BETO 2021 Peer Review - Biomethanation to Upgrade Biogas to Pipeline Grade Methane WBS 5.1.3.102

We are developing, innovating and de-risking a biomethanation process capable of megawatt-scale deployment that upgrades biogas waste streams to produce pipeline quality renewable natural gas (RNG). Biomethanation is a two-step process using a methanogenic microorganism to convert renewable hydrogen (H2) and waste carbon dioxide (CO2) to renewable methane (CH4) - the primary component in natural gas. Using biogenic CO2 from biogas sources like dairies, wastewater treatment plants, and landfills allows production of this drop-in direct replacement fuel to participate in the growing number of carbon markets; like California's Low Carbon Fuel Standard and the Federal Renewable Fuel Standard. The end-of-project goal is to demonstrate pipeline quality RNG production (> 95% CH4, < 4% H2, <1% CO2, < 0.2% O2 and < 4 parts per million hydrogen sulfide) using real biogas feedstocks. We will accomplish this goal by designing and building a pressurized (18 bar) mobile lab-scale (20L) bioreactor research platform, including integrated electrolyzer system, based on lessons learned from operating the 700L pilot system from Southern California Gas Company. In collaboration with Electrochaea, natural gas utilities and Argonne National lab, will provide the data to establish a preliminary range of carbon intensity to help accelerate the deployment of utility-scale H2 production and qualify the biomethanation pathway process for RNG production.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗