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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Nye County Nevada Perspectives on the State of the Yucca Mountain Project - 12388

Responding to the Department of Energy decision to try to withdraw the Yucca Mountain license application and the Administration actions to close down the Yucca Mountain project, Nye County undertook a number of activities to articulate its support for continuing the Yucca Mountain project. The activities included responding to inquiries from federal agencies, including investigations undertaken by the Government Accountability Office addressing other potential uses for the Yucca Mountain site, responding to a Draft Environmental Impact Statement on the possible use of Yucca Mountain for disposal of Greater than Class C wastes, testifying in hearings, and interacting with the President's Blue Ribbon Commission on America's Nuclear Future. The paper summarizes Nye County's position on the Yucca Mountain repository, Nye County's perspectives on the various activities that were developed and considered by the Government Accountability Office, Nye County's concerns with the use of the Nevada National Security Site for Disposal of Greater than Class C Low-Level Radioactive Wastes, testimony of Nye County officials expressing local community support for the Yucca Mountain project, and Nye County's perspectives on recommendations provided by the Blue Ribbon Commission to move the nation's high-level radioactive waste disposal programs forward without consideration of the role Yucca Mountain could have served in those recommendations. Nye County believes that every effort should be made to, at a minimum, fund the Nuclear Regulatory Commission to complete the license application review. Then, if Congress does decide to change the Nuclear Waste Policy Act, there will be valuable information available to support new policy development. This administration contends that Congressional language associated with the FY2010 and FY2011 appropriations and authorization process is sufficient evidence of its intent to terminate the Yucca Mountain repository program. The appropriation process needs to be explicit that, absent explicit language to the contrary, the Nuclear Waste Policy Act stands. It also should include language that requires the Department of Energy to preserve all necessary records until the Nuclear Waste Policy Act is amended or rescinded by specific Congressional action. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Andra's Memory Program - Linguistics and Semiology - 15587

In the framework of its Memory Program, Andra has launched various studies related to semiotics, for the medium and long term. Part of this work, dealing with linguistics and semiotics, is performed at the Centre de Recherches Semiotiques (CeReS) of the University of Limoges. Preliminary results highlight the fact that language durability may be reasonably considered for the medium term, but cannot be expected beyond a few thousands of years, and that languages cannot guarantee message appropriation. The role of Man and of the disposal site itself in message transmission is emphasized. In this context, art may play an important role in this transmission, by transmitting the meaning without necessarily using language. By the way, Andra has been exploring for several years the role of art for memory transmission, by supporting the work of artists interested in this field. In future research, Andra will promote more cross fertilization of artistic and academic reflections. (authors)

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Building Sustainable and Efficient Markers to Bridge Ten Millennia: The Understanding Issues-18058

Markers of nuclear waste deposits that can be understood through ten millennia have been proposed for preventing from inadvertent human intrusion. It is postulated that currently designed waste repository markers are likely to fail, and an alternative system of markers is proposed based on a praxeological device, i.e., a language-learning by- action device. The key challenge is how should we represent radioactivity in waste repositories in such a way that it is understood by anyone who encounters such markers? Namely, for an adequate interpretation, two issues have to be addressed: to accommodate differing world views, and inevitable changes in languages. What is required is an integrated system that allows us to communicate a complex and abstract concept to all people at all times. That is the goal of our praxeological device, a sign that links a person's actions (praxis) in a particular context to a communication system about those actions and that particular context. That means anyone who experiences such a praxeological device will understand the actions that s/he is about to perform will help her/him to learn the iconic language s/he is reading. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Nuclear, Land marker for a Waste Isolation Site: International Architecture Competition - 18240

The design and construction of a nuclear waste marker must include many architectural considerations. Very little research has been conducted by architects to address this issue to date. Historically, the task of designating a nuclear waste site has been a discussion predominantly contained within the scientific and engineering communities. In an effort to engage architects within this dialogue, arch out loud hosted the NUCLEAR competition as an international, architectural investigation into the design of a nuclear waste site marker. The NUCLEAR competition asks participants to design a marker to designate the site of the Waste Isolation Pilot Plant (WIPP) located near Carlsbad, New Mexico. The marker will be meant to be a means of passive institutional controls (PICs) to deter inadvertent human intrusion upon the site and should last 10,000 years. The NUCLEAR competition defined the purpose of the nuclear waste site marker as a means to communicate the danger of inadvertent intrusion from future generations. The competition requires that entries must be both legible in communication and durable in their construction. The competition brief challenged participants to consider how customs and languages will evolve over time and to predict what motivations future generations might have for digging at the site. Other background information was provided about the site such as how waste was stored and how the unique geology of the site seals the waste. The 1993 Sandia National Laboratories report titled Expert Judgment on Markers to Deter Inadvertent Human Intrusion into the Waste Isolation Pilot Plant [1] was referenced along with the 1992 WIPP Land Withdrawal Act [2], interviews with WIPP scientists [3] involved with the WIPP PICs program and the Nuclear Energy Agency Records Knowledge and Memory working group. Competition participants were given digital 3-D Computer-Aided Design models for reference that included the underground storage portion of the site, existing buildings and the 16-square-mile site boundary identified in the 1992 WIPP Land Withdrawal Act [2]. This paper will: - Provide a detailed description of the submitted proposals including graphic analysis of the visualizations, diagrams, technical drawings, jury comments and statements. - Catalogue and identify the consistent themes within all submissions with possible categories including: 1) overall architectural form and language, 2) integration of written language and symbols within architecture, 3) use of technology and robotics, 4) construction methods and materials. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Automation of Vulnerability and Patch Management: Information Extraction, Association, and Optimization

Vulnerability and patch management is an integral part of a robust cybersecurity program, yet it grows increasingly complex due to the sheer amount of data that must be analyzed. Particularly in Operational Technology (OT) environments, analysis must be done manually because of the lack of automated solutions. Additionally, there are many steps in this process, from the initial discovery of the vulnerability to the implementation of its remediation, and each step in the process requires different data in order to be performed effectively. In this work, we provide approaches and strategies to assist operators in industrial or OT environments throughout the vulnerability management cycle. Security advisories provide key information about mitigation strategies, or actions that can be taken when a patch is unavailable or cannot be installed. Details of these strategies are not shared in public vulnerability databases and must be found manually. We approach this problem by designing a solution to automatically identify that information within vendor security advisories and retrieve it for operator use. We start with an approach that requires domain-specific knowledge of certain frequently-seen reference websites. Next, an approach that can work on an arbitrary website but relies on certain keywords. Finally, an approach that uses Natural Language Processing (NLP) methods and does not require specific knowledge or keywords. Each of these approaches is more general than its predecessor; we demonstrate high accuracy for all approaches Advisories also often contain details of affected products in non-standard or natural language formats. While this information can be easily understood when read by an operator, the non-standard format acts as a barrier to effective automation. We provide an approach for the first step in this process: identifying vendors in security advisories and mapping them to a standard framework for representing digital assets and software products. We evaluate five established string similarity algorithms, plus one of our own design that combines string similarity and information theory, on the task of mapping vendors to their corresponding entries in the Common Platform Enumeration (CPE) repository. Our results show that our proposed metric outperforms all others. Due to the constraints on time, finances, and personnel for organizations, Large Language Models (LLMs) may seem like attractive opportunities for security operators to speed up information gathering; however, it is still not clear whether LLMs can handle vulnerability management tasks well. To answer this question, we perform an empirical study of LLMs’ ability to provide consistent, accurate information about vulnerabilities in order to guide organizations in their adoption of LLMs. We observe poor performance for all models tested, suggesting that these models are not well-suited to the consistent retrieval of accurate vulnerability information. Finally, once vulnerabilities have been identified and any additional information has been obtained, operators must decide which remediation actions to implement based on their available resources. This already-complex problem becomes even more so when we consider that a vulnerability may have multiple avenues for remediation. We formulate this scenario as two knapsack problems and provide solutions, which we then compare against several existing strategies for vulnerability prioritization seen in real operational environments.

McClanahan, Kylie↗
DOE OSTI2022

Fault Graph (fg)

This software package offers a novel fault graph modeling language and a simulation tool for executing models specified in the language. The modeling language has three primary features that distinguish it from similar reliability analysis tools. These are (1) a random variable modeling several distinct outcomes of a single fault; (2) chains of faults in which one fault triggers another; and (3) time to fail sampled from probability distributions including positive normal, exponential, Weibull with a minimum, or immediate.

Nutaro, James↗
DOE OSTI2017

pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations

We describe pyomo.dae, an open source Python-based modeling framework that enables high-level abstract specification of optimization problems with differential and algebraic equations. The pyomo.dae framework is integrated with the Pyomo open source algebraic modeling language, and is available at http://www.pyomo.org. One key feature of pyomo.dae is that it does not restrict users to standard, predefined forms of differential equations, providing a high degree of modeling flexibility and the ability to express constraints that cannot be easily specified in other modeling frameworks. Other key features of pyomo.dae are the ability to specify optimization problems with high-order differential equations and partial differential equations, defined on restricted domain types, and the ability to automatically transform high-level abstract models into finite-dimensional algebraic problems that can be solved with off-the-shelf solvers. Moreover, pyomo.dae users can leverage existing capabilities of Pyomo to embed differential equation models within stochastic and integer programming models and mathematical programs with equilibrium constraint formulations. Collectively, these features enable the exploration of new modeling concepts, discretization schemes, and the benchmarking of state-of-the-art optimization solvers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

VISION: a modular AI assistant for natural human-instrument interaction at scientific user facilities

Scientific user facilities, such as synchrotron beamlines, are equipped with a wide array of hardware and software tools that require a codebase for human-computer-interaction. This often necessitates developers to be involved to establish connection between users/researchers and the complex instrumentation. The advent of generative AI presents an opportunity to bridge this knowledge gap, enabling seamless communication and efficient experimental workflows. Here we present a modular architecture for the Virtual Scientific Companion by assembling multiple AI-enabled cognitive blocks that each scaffolds large language models (LLMs) for a specialized task. With VISION, we performed LLM-based operation on the beamline workstation with low latency and demonstrated the first voice-controlled experiment at an x-ray scattering beamline. The modular and scalable architecture allows for easy adaptation to new instruments and capabilities. Development on natural language-based scientific experimentation is a building block for an impending future where a science exocortex—a synthetic extension to the cognition of scientists—may radically transform scientific practice and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Performance on HPC Platforms Is Possible Without C++

Computing at large scales has become extremely challenging due to increasing heterogeneity in both hardware and software. More and more scientific workflows must tackle a range of scales and use machine learning and AI intertwined with more traditional numerical modeling methods, placing more demands on computational platforms. These constraints indicate a need to fundamentally rethink the way computational science is done and the tools that are needed to enable these complex workflows. The current set of C++-based solutions may not suffice, and relying exclusively upon C++ may not be the best option, especially because several newer languages and boutique solutions offer more robust design features to tackle the challenges of heterogeneity. In June 2023, we held a mini symposium that explored the use of newer languages and heterogeneity solutions that are not tied to C++ and that offer options beyond template metaprogramming and Parallel. For for performance and portability. In conclusion, we describe some of the presentations and discussion from the mini symposium in this article.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

What Is the Agent Doing? Visualizing Agentic AI Querying Workflows

We explore how visualizations can help users understand what an AI agent is doing as it builds and runs queries over data. As part of the LinkQ system, a natural language interface for querying knowledge graphs with a large language model (LLM), we designed two complementary views: A State Diagram that shows where the agent is within a larger workflow, and a Live Action Display that gives real-time updates about the agent's current task. In a study with 14 practitioners, we found that these visuals helped participants build stronger mental models of the agent's behavior while also increasing their confidence in the system. However, we also observed that users sometimes trusted incorrect outputs simply because the agent appeared to be doing the "right" thing. Our findings point to both the value and risk of visualizing agent behavior in interactive AI systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Influence of Karen Immigrant Students on Teachers’ Instructional Decisions in the Rural United States

Across the United States, the number of classrooms welcoming young students who are new English language learners (ELL) is rapidly growing. Motivated by a dramatic increase in the number of Myanmar- and Burma-originating immigrants now attending English-speaking schools in the rural, upper midwestern United States, this study systematically considers how the presence of Karen immigrant students impacts the instructional decisions that K-12 classroom teachers make using an exploratory case study method. Several recurrent themes emerged, and findings can be used to help better prepare both pre-service and in-service teachers to meet the needs of their ELL students, as well as policy makers. These emergent themes include the following: (a) authentic teacher–family relationships are key; (b) visual/hands-on learning and the use of technology enhance student achievement; (c) student work must be completed during the school day in classrooms; (d) teachers’ purposeful use of parallel language is critical for learning; (e) rigor can be maintained, although quantity is often adjusted; and (f) targeted ESL/cultural training for pre-service and newly hired teachers is positively influential. Such informed training often seems to help teachers learn key cultural distinctions and become better able to identify the most effective ways to meet the needs of students.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Teaching AI when to care about gender

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) concerned with solving language tasks by modeling large amounts of textual data. Some NLP techniques use word embeddings which are semantic models where machine learning (ML) is used to learn to cluster semantically related words by learning about word co-occurrences in the original training text. Unfortunately, these models tend to reflect or even exaggerate biases that are present in the training corpus. Here we describe the Word Embedding Navigator (WEN), which is a tool for exploring word embedding models. We examine a specific potential use case for this tool: interactive discovery and neutralization of gender bias in word embedding models, and compare this human-in-the-loop approach to reducing bias in word embeddings with a debiasing post-processing technique.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Artificial intelligence to unlock real-world evidence in clinical oncology: A primer on recent advances

Purpose: Real world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time-consuming manual case-finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology. Methods: We performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology. Results: Artificial intelligence (AI) approaches are increasingly used to efficiently phenotype patients and tumors at large scale. These tools also may provide novel biological insights and improve risk prediction through multimodal integration of radiographic, pathological, and genomic datasets. Custom language processing pipelines and large language models hold great promise for clinical prediction and phenotyping. Conclusions: Despite rapid advances, continued progress in computation, generalizability, interpretability, and reliability as well as prospective validation are needed to integrate AI approaches into routine clinical care and real-time monitoring of novel therapies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Altered Anterior-Posterior Connectivity Through the Arcuate Fasciculus in Temporal Lobe Epilepsy

How the interactions between cortices through a specific white matter pathway change during cognitive processing in patients with epilepsy remains unclear. Here, we used surface-based structural connectivity analysis to examine the change in structural connectivity with Broca’s area/the right Broca’s homologue in the lateral temporal and inferior parietal cortices through the arcuate fasciculus (AF) in 17 patients with left temporal lobe epilepsy (TLE) compared with 17 healthy controls. Then, we investigated its functional relevance to the changes in task-related responses and task-modulated functional connectivity with Broca’s area/the right Broca’s homologue during a semantic classification task of a single word. The structural connectivity through the AF pathway and task-modulated functional connectivity with Broca’s area decreased in the left midtemporal cortex. Furthermore, task-related response decreased in the left mid temporal cortex that overlapped with the region showing a decrease in the structural connectivity. In contrast, the region showing an increase in the structural connectivity through the AF overlapped with the regions showing an increase in task-modulated functional connectivity in the left inferior parietal cortex. These structural and functional changes in the overlapping regions were correlated. The results suggest that the change in the structural connectivity through the left frontal–temporal AF pathway underlies the altered functional networks between the frontal and temporal cortices during the language-related processing in patients with left TLE. The left frontal–parietal AF pathway might be employed to connect anterior and posterior brain regions during language processing and compensate for the compromised left frontal–temporal AF pathway.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

PyFly: A fast, portable aerodynamics simulator

Here, we present a fast, user-friendly implementation of a potential flow solver based on the unsteady vortex lattice method (UVLM), namely PyFly. UVLM computes the aerodynamic loads applied on lifting surfaces while capturing the unsteady effects such as the added mass forces, the growth of bound circulation, and the wake while assuming that the flow separation location is known a priori. This method is based on discretizing the body surface into a lattice of vortex rings and relies on the Biot–Savart law to construct the velocity field at every point in the simulated domain. We introduce the pointwise approximation approach to simulate the interactions of the far-field vortices to overcome the computational burden associated with the classical implementation of UVLM. The computational framework uses the Python programming language to provide an easy to handle user interface while the computational kernels are written in Fortran. The mixed language approach enables high performance regarding solution time and great flexibility concerning easiness of code adaptation to different system configurations and applications. The computational tool predicts the unsteady aerodynamic behavior of multiple moving bodies (e.g., flapping wings, rotating blades, suspension bridges) subject to incoming air. The aerodynamic simulator can also deal with enclosure effects, multi-body interactions, and B-spline representation of body shapes. Finally, we simulate different aerodynamic problems to illustrate the usefulness and effectiveness of PyFly.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Parallel ICA of FDG-PET and PiB-PET in three conditions with underlying Alzheimer's pathology

The relationships between clinical phenotype, β-amyloid (Aβ) deposition and neurodegeneration in Alzheimer's disease (AD) are incompletely understood yet have important ramifications for future therapy. The goal of this study was to utilize multimodality positron emission tomography (PET) data from a clinically heterogeneous population of patients with probable AD in order to: (1) identify spatial patterns of Aβ deposition measured by ( 11 C)-labeled Pittsburgh Compound B (PiB-PET) and glucose metabolism measured by FDG-PET that correlate with specific clinical presentation and (2) explore associations between spatial patterns of Aβ deposition and glucose metabolism across the AD population. We included all patients meeting the criteria for probable AD (NIA–AA) who had undergone MRI, PiB and FDG-PET at our center (N = 46, mean age 63.0 ± 7.7, Mini-Mental State Examination 22.0 ± 4.8). Patients were subclassified based on their cognitive profiles into an amnestic/dysexecutive group (AD-memory; n = 27), a language-predominant group (AD-language; n = 10) and a visuospatial-predominant group (AD-visuospatial; n = 9). All patients were required to have evidence of amyloid deposition on PiB-PET. To capture the spatial distribution of Aβ deposition and glucose metabolism, we employed parallel independent component analysis (pICA), a method that enables joint analyses of multimodal imaging data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

An AI-driven framework for evaluating local and state authorities’ permitting processes

The demand for new energy infrastructure is increasing across the United States, but heterogenous permitting processes and embedded requirements across different local jurisdictions can cause project delays, increase “soft costs,” and hinder developer expansion. This study analyzes the variability in local permitting requirements across the U.S. and develops a quantitative approach to describe their clarity and effectiveness in enabling infrastructure project development. By using an Energy Language Model (ELM), a large language model (LLM) for energy technologies, we systematically gathered permitting information from nearly 300 state-, county-, and city-level documents, creating a structured dataset of requirements and procedures on an unprecedented scale and speed. Our analysis revealed that local (city and county) permitting requirement documents are underrepresented compared to state-level guidance documents, which can impede timely and cost-effective installation of new electric infrastructure. Our validation process showed that the final database has an accuracy of approximately 95%. We, further, created a new quantitative method to score permitting requirements for clarity and efficiency, with electric vehicle supply equipment as an initial use case. The average local permitting document scored a 1.8 out of 5, which we interpret as meaning that half of the requirements developers face when installing electric infrastructure are ambiguous, increasing both cost and time. We also created a “Generalized Permit Process”, highlighting common procedural steps and identifying specific opportunities for municipalities to improve their documentation. This research establishes a systematic and scalable framework for evaluating the complexities of local infrastructure permitting processes by combining LLM-powered data collection and quantitative scoring. The framework enables policymakers and developers to identify and mitigate procedural bottlenecks, with the expectation that these improvements can accelerate application review and approval, reduce project costs, and expedite connection to utility distribution grids. As a foundational approach for streamlining local project development processes, this study’s methods are intended to be extended to a wide range of energy applications.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

MCP-enabled agentic AI workflow for building energy modelling: framework and use cases

Traditional building energy modelling workflows remain labor-intensive and error-prone, requiring specialized expertise that limits broader adoption. This paper introduces a novel Model Context Protocol (MCP)-enabled framework that connects AI assistants to EnergyPlus through MCP, a standardized interface for tool invocation and context management. Two complementary integration paradigms are presented and compared: conversational integration, where users interact through natural language while an AI assistant orchestrates MCP tools on demand, and agentic workflow integration, where specialized agents coordinate autonomously to complete multi-step tasks. Using an experimental testbed for residential buildings, the end-to-end workflows are demonstrated. The conversational approach reduced typical inspection and modification tasks from 1-2 h to under 15 min, while maintaining full transparency through visible tool invocations. The agentic approach automated parametric analysis. These demonstrations establish MCP as a foundational layer for AI-assisted building energy modelling, enabling natural language interactions with simulation tools while preserving professional oversight and decision-making authority.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗