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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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Wind tunnel study of drag characteristics of boattailed afterbodies and afterbody terminal fairings for single engine jet aircraft
Computer program for optimizing design of ring-stiffened sheet metal nose fairings
Computer program for near optimum structural designs of ring stiffened honeycomb sandwich nose fairings
Evaluation of sandwich shaped nose fairings for Saturn launch vehicle
Centaur AC-6 nose fairing separation tests
Rigid body ejection dynamics of split-fairing system for four-stage Javelin sounding rocket under various parameter changes
Efficient resource allocation is crucial in many domains, particularly in senior care, where assigning resources to older adults must consider uncertainties associated with vulnerable populations. In collaboration with Senior Health Facilities (SHFs) and domain experts, this paper presents iFair, a novel framework designed to assist decision-makers in equitably allocating scarce resources to older adults. iFair was prototyped in the context of ongoing work on a data exchange platform, CAREDEX, used for enhancing older adults' resilience during disasters. A key novelty of iFair focuses on aligning resident preferences with resources in urgent situations, expediting care, and enhancing task efficiency. We integrate static and dynamic environmental data, including facility layouts and sensor data, with detailed resident profiles to cater to the individual needs and preferences of residents. While our framework primarily focuses on allocation within facilities, it also extends to a regional scale to support the planning and transfer of seniors to mutual aid facilities. Our experiments adapt data from a real SHF to emulate resource allocation in an emergency fire evacuation setting and highlight the delicate balance that decision-makers can achieve between efficiency and fairness.
The massive data generated by scientists daily serve as both a major catalyst for new discoveries and innovations, as well as a significant roadblock that restricts access to the data. Here, our paper introduces a new approach to removing Big Data barriers and democratizing access to petascale data for the broader scientific community. Our novel data fabric abstraction layer allows user-friendly querying of scientific information while hiding the complexities of dealing with file systems or cloud services. We enable FAIR (Findable, Accessible, Interoperable, and Reusable) access to datasets such as NASA’s petascale climate datasets. Our paper presents an approach to managing, visualizing, and analyzing petabytes of data within a browser on equipment ranging from the top NASA supercomputer to commodity hardware like a laptop. Our novel data fabric abstraction utilizes state-of-the art progressive compression algorithms and machine-learning insights to power scalable visualization dashboards for petascale data. The result provides users with the ability to identify extreme events or trends dynamically, expanding access to scientific data and further enabling discoveries. We validate our approach by improving the ability of climate scientists to visually explore their data via three fully interactive dashboards. We further validate our approach by deploying the dashboards and simplified training materials in the classroom at a minority-serving institution. These dashboards, released in simplified form to the general public, contribute significantly to a broader push to democratize the access and use of climate data.
FAIR to WISE (F2W) is an iterative, large-language model (LLM) driven pipeline that turns unstructured research PDFs into structured, queryable knowledge graphs (KGs). Core features include schema-driven extraction to a LinkML model; full provenance capture; ontology-grounded enrichment (e.g., chemical validation and ChEBI lookup); graph construction to JSON-LD with stable IDs; and KG-RAG question answering with evidence-aware retrieval. The system is engineered for reproducibility and accessibility (open-source Ollama models, temperature=0, NVTX/Nsight profiling) with robust QA (relation verification, deduplication, and deterministic outputs). Primary uses are literature-to-KG automation, knowledge-grounded Q&A, and experimental steering support. We demonstrate the approach in organic photovoltaics, where the pipeline ingests papers, builds a domain KG, and evaluates answers against expert competency questions to guide experimental planning and interpretation. Compared with off-the-shelf LLMs and ad-hoc NLP tools, F2W addresses ontology gaps and reduces hallucination risk by grounding responses in extracted evidence and enforcing schema constraints; it also offers deterministic, provenance-linked outputs and open, cost-aware deployment. Evidence-aware ranking further improves answer quality over pure vector search.
Abstract Laser-powder bed fusion (L-PBF) is a popular additive manufacturing (AM) process with rich data sets coming from both in situ and ex situ sources. Data derived from multiple measurement modalities in an AM process capture unique features but often have different encoding methods; the challenge of data registration is not directly intuitive. In this work, we address the challenge of data registration between multiple modalities. Large data spaces must be organized in a machine-compatible method to maximize scientific output. FAIR (findable, accessible, interoperable, and reusable) principles are required to overcome challenges associated with data at various scales. FAIRified data enables a standardized format allowing for opportunities to generate automated extraction methods and scalability. We establish a framework that captures and integrates data from a L-PBF study such as radiography and high-speed camera video, linking these data sets cohesively allowing for future exploration. Graphical abstract
This report provides an overview of the collaboration between the U.S. Department of Energy's Communities LEAP Pilot and the community-led Project EMPOWER (Energy Modernization for People, Opportunity, Work, Equity, and Renewables) in Alachua County, FL. Through Communities LEAP, the National Renewable Energy Laboratory and others provided technical assistance to Project EMPOWER in the areas of community engagement, solar power, weatherization and energy efficiency, green jobs, and fund development. This final report summarizes work accomplished during the engagement and provides potential next steps for the EMPOWER team to consider as it continues pursuing its goal of a fair and equitable clean energy transition for Alachua County.
This is a two hour version of the FAIR(ER) tutorial we released at Barcelona 9/24. SAND2024-12152C. The only modifications were largely deletions, which don't require additional review. The one key difference that actually has changed material is in the Language section for Equitable Accessibility, which is almost word for word the same as previously approved SAND2025-04087W which is the website version of the presentation.
This report focuses on current perceptions, challenges, and processes related to interconnection of LELs to the grid; while these loads include several types of entities, the primary focus of this work is on data centers, the largest current driver of this growth. It begins by summarizing relevant context related to load growth in the United States, including key stakeholder perceptions as data center deployment drive historic load growth nationwide. It then reviews the current landscape of large load interconnection processes and challenges and then proposes a framework of potential strategies intended to support a more consistent, streamlined, and fair approach to load interconnection. This framework is adapted from solutions proposed or adopted to address similar challenges faced in generator interconnection queues as well as notable reforms currently being proposed or enacted for large loads that could be more widely adopted.
Introduction The agriculture genomics community has numerous data submission standards available, but the standards for describing and storing single-cell (SC, e.g., scRNA- seq) data are comparatively underdeveloped. Methods To bridge this gap, we leveraged recent advancements in human genomics infrastructure, such as the integration of the Human Cell Atlas Data Portal with Terra, a secure, scalable, open-source platform for biomedical researchers to access data, run analysis tools, and collaborate. In parallel, the Single Cell Expression Atlas at EMBL-EBI offers a comprehensive data ingestion portal for high-throughput sequencing datasets, including plants, protists, and animals (including humans). Developing data tools connecting these resources would offer significant advantages to the agricultural genomics community. The FAANG data portal at EMBL-EBI emphasizes delivering rich metadata and highly accurate and reliable annotation of farmed animals but is not computationally linked to either of these resources. Results Herein, we describe a pilot-scale project that determines whether the current FAANG metadata standards for livestock can be used to ingest scRNA-seq datasets into Terra in a manner consistent with HCA Data Portal standards. Importantly, rich scRNA-seq metadata can now be brokered through the FAANG data portal using a semi-automated process, thereby avoiding the need for substantial expert curation. We have further extended the functionality of this tool so that validated and ingested SC files within the HCA Data Portal are transferred to Terra for further analysis. In addition, we verified data ingestion into Terra, hosted on Azure, and demonstrated the use of a workflow to analyze the first ingested porcine scRNA-seq dataset. Additionally, we have also developed prototype tools to visualize the output of scRNA-seq analyses on genome browsers to compare gene expression patterns across tissues and cell populations. This JBrowse tool now features distinct tracks, showcasing PBMC scRNA-seq alongside two bulk RNA-seq experiments. Discussion We intend to further build upon these existing tools to construct a scientist-friendly data resource and analytical ecosystem based on Findable, Accessible, Interoperable, and Reusable (FAIR) SC principles to facilitate SC-level genomic analysis through data ingestion, storage, retrieval, re-use, visualization, and comparative annotation across agricultural species.
As large language models (LLMs) continue to evolve, their capacity to replace humans as their surrogates is also improving. As increasing numbers of intelligent tutoring systems (ITSs) are embracing the integration of LLMs for digital tutoring, questions are arising as to how effective they are and if their hallucinatory behaviors diminish their perceived advantages. One critical question that is seldom asked if the availability, plurality, and relative weaknesses in the reasoning process of LLMs are contributing to the much discussed digital divide and equity and fairness in online learning. In this paper, we present an experiment with database design theory assignments and demonstrate that while their capacity to reason logically is improving, LLMs are still prone to serious errors. We demonstrate that in online learning and in the absence of a human instructor, LLMs could introduce inequity in the form of “wrongful” tutoring that could be devastatingly harmful for learners, which we call ignorant bias, in increasingly popular digital learning. We also show that significant challenges remain for STEM subjects, especially for subjects for which sound and free online tutoring systems exist. Based on the set of use cases, we formulate a possible direction for an effective ITS for online database learning classes of the future.
To measure fluid pressure with minimum disturbance to fluid flow, a miniature pressure transducer can be imbedded and faired into the test surface. Incorporated in the design are piezoresistive elements mounted on a diaphragm, which transform pressure strains into an electrical signal.
Installation and fairing of flush, static pressure orifices on wing surfaces of supersonic aircraft