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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Offshore Advanced Infrastructure Integrity Model (AIIM) Dashboard

The Advanced Infrastructure Integrity Model (AIIM) is a multivariate, multi-machine learning modeling technology applied to evaluate the integrity of offshore energy infrastructure (e.g., pipelines, platforms) in the U.S Gulf Region. Offshore energy infrastructure plays an essential role in ensuring access to safe and secure energy for the United States. According to the U.S. Energy Information Administration (EIA), production in the U.S. Gulf Region accounts for 15% of total crude and 5% of total natural gas from the United States. Many of these structures have been operating for close to or past their design life, while others have the chance of attrition before return on investment. To better understand the potential for reuse or life extension opportunities, an assessment of the infrastructure integrity is critical to inform safe decision making. Assessing structural integrity, AIIM provides key insights that inform infrastructure use and reuse, as well as hazard prevention planning, in support of stakeholders including researchers and industry.

Advanced Infrastructure Integrity Model↗
DOE OSTI2016

Addendum to the 2015 Eastern Interconnect Baselining and Analysis Report

This report serves as an addendum to the report 2015 Eastern Interconnect Baselining and Analysis Report (Amidan, Follum, and Freeman, 2015). This addendum report investigates the following: the impact of shorter record lengths and of adding a daily regularization term to the date/time models for angle pair measurements, additional development of a method to monitor the trend in phase angle pairs, the effect of changing the length of time to determine a baseline, when calculating atypical events, and a comparison between quantitatively discovered atypical events and actual events.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

FIU Project 3: Waste and D&D Engineering and Technology Development [Slides]

This project focuses on delivering solutions under deactivation and decommissioning (D&D) in support of DOE EM-4.11 as well as IT development for environmental applications (KM-IT for EM-4.11) and waste & material management (WIMS for EM-4.22). All technology development related activities will also engage the Office of Technology Development (EM-3.2). This work is also relevant to infrastructure management activities being carried out at DOE sites such as Oak Ridge, Savannah River, Hanford, Idaho and Portsmouth. As appropriate and within the parameters of the DOE-FIU Cooperative Agreement (CA), coordination at the proper level will occur with the sites and national laboratories involved in the project research efforts as well as with the points-of-contact at DOE HQ (e.g., HQ Project Leads, HQ Field Liaisons, Office of Technology Development, CA Technical Monitor, COR, etc.).

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

7th World Congress on Integrated Computational Materials Engineering (ICME 2023) (Final Technical Report)

Integrated Computational Materials Engineering (ICME) has received international attention due to its potential to shorten product development time, while lowering cost and improving design and manufacturing outcomes. ICME is an approach to designing materials solutions for specific applications that use computer modeling programs to predict the behavior of materials and integrate this information into the overall materials, processing, and manufacturing design cycle. The 7th World Congress on Integrated Computational Materials Engineering (ICME 2023) was held in Orlando, Florida from May 21–25, 2023 with the goal to convene stakeholders from across all areas of modeling and simulation, experimental specialization, and design, as well as from across academia, government, and industry, to address ICME tools and techniques and their integration, as well as to examine their application in engineering. This atmosphere facilitated rich interactions between the experimentalists, modelers, and computational and design, from academia, government, and industry, to discuss ICME tools and techniques and their application in engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Water narratives in local newspapers within the United States

Sustainable use of water resources continues to be a challenge across the globe. This is in part due to the complex set of physical and social behaviors that interact to influence water management from local to global scales. Analyses of water resources have been conducted using a variety of techniques, including qualitative evaluations of media narratives. This study aims to augment these methods by leveraging computational and quantitative techniques from the social sciences focused on text analyses. Specifically, we use natural language processing methods to investigate a large corpus (approx. 1.8M) of newspaper articles spanning approximately 35 years (1982–2017) for insights into human-nature interactions with water. Focusing on local and regional United States publications, our analysis demonstrates important dynamics in water-related dialogue about drinking water and pollution to other critical infrastructures, such as energy, across different parts of the country. Our assessment, which looks at water as a system, also highlights key actors and sentiments surrounding water. Extending these analytical methods could help us further improve our understanding of the complex roles of water in current society that should be considered in emerging activities to mitigate and respond to resource conflicts and climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Editorial: Transplant Rejection and Tolerance—Advancing the Field through Integration of Computational and Experimental Investigation

Seventy years after the first proof of concept that the immune system can be trained to accept transplanted tissues via induction of immune tolerance, we are still waiting for a clinical approach that could be used routinely in transplant patients. Transplantation is a life-saving surgical procedure that is still only successful when paired with life-long administration of immunosuppressive drugs. However, the debilitating side effects of the long-term use of these drugs, together with their incomplete control of the immune system, compromise the quality of life and survival of transplant recipients. Thus, there is a strong push to find new therapeutic strategies that promote indefinite acceptance of a transplanted tissue without compromising the effectiveness of the patient’s immune system. Although many exciting ideas have been explored, none of the resulting strategies have been successfully converted into a widely applicable therapeutic approach.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Secondary Ion Mass Spectral Imaging of Metals and Alloys

Secondary Ion Mass Spectrometry (SIMS) is an outstanding technique for Mass Spectral Imaging (MSI) due to its notable advantages, including high sensitivity, selectivity, and high dynamic range. As a result, SIMS has been employed across many domains of science. In this review, we provide an in-depth overview of the fundamental principles underlying SIMS, followed by an account of the recent development of SIMS instruments. The review encompasses various applications of specific SIMS instruments, notably static SIMS with time-of-flight SIMS (ToF-SIMS) as a widely used platform and dynamic SIMS with Nano SIMS and large geometry SIMS as successful instruments. We particularly focus on SIMS utility in microanalysis and imaging of metals and alloys as materials of interest. Additionally, we discuss the challenges in big SIMS data analysis and give examples of machine leaning (ML) and Artificial Intelligence (AI) for effective MSI data analysis. Finally, we recommend the outlook of SIMS development. It is anticipated that in situ and operando SIMS has the potential to significantly enhance the investigation of metals and alloys by enabling real-time examinations of material surfaces and interfaces during dynamic transformations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management.

42 ENGINEERING↗
NASA NTRS2019

Machine Learning Lifecycle for Earth Science Application: A Practical Insight into Production Deployment

Earth science domain presents unique sets of problems that are increasingly being solved using data driven approaches. The availability of big Earth science data offers immense potential for Machine learning (ML) as evident from numerous research publications lately. However, many of these publications are not ending up as production applications mainly because the data scientists who develop the ML models are now expected to complete the ML lifecycle by deploying and scaling the models in production. We introduce ML lifecycle to the Earth science community including the opportunities and challenges that lie ahead in each phase of the lifecycle. We demonstrate the lifecycle using an Earth science problem that we used ML to address and transitioned to production.

Maskey, Manil↗
DOE OSTI2021

Accelerating Clean Energy @ Scale

NREL helps translate community energy ambitions into actions. Rooted in inclusive community engagement, Accelerating Clean Energy @ Scale (ACES) brings together NREL’s experience, expertise, and capabilities to illuminate pathways for clean, affordable, equitable, secure, and resilient energy systems. Through customized, holistic modeling and analysis, we uncover insights and collaboratively develop strategies that address communities’ unique energy- system goals and diverse stakeholder priorities. NREL provides support in evaluating and implementing community-selected options, as well as assistance for workforce development.

accelerating clean energy scale analysis↗
DOE OSTI2019

Enhancing Lifeline Infrastructure Resilience

Establish operational critical infrastructure research environment with focus on control systems security Continue enhancement of critical infrastructure cyber-physical interdependency analysis and knowledge management capabilities

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗