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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Knowledge Graph for End-to-End Traceability of an Integrated Human-Earth System Model

Integrated human-Earth system models inform energy-water-land system dynamics and policies, yet their results are difficult to trace through input-data, model structure, scenario configurations, and solved outputs. Because this information is siloed across disconnected artifacts, process-based IAMs have historically lacked a unified, queryable representation. Such lack of traceability prevents researchers from systematically isolating the multi-sector drivers of complex outcomes (such as tracing water-scarcity results back to distant energy-system dynamics) or conducting holistic uncertainty attribution across hundreds of interacting parameters. To address this concern, our work documents the software engineering process of a knowledge graph that unifies these four layers for the Global Change Analysis Model (GCAM-USA_Reference scenario, GCAM v9.1). The graph was built as a relational property graph in DuckDB from the run’s own artifacts: the input-preparation dependency map (gcamdata chunk map), the model’s XML input files, the run configuration, and the results database (BaseX), successfully mapping the model’s declared structure. The resulting graph comprises 204,321 nodes and 1,687,814 edges across 16 node types and 15 edge types, with approximately 16.3 million time-series values stored separately to maintain structural efficiency. To ensure representation fidelity, every edge carries an epistemic-status annotation recording the warrant for the relationship (structural, provenance, dependency, or model-derived), and a machine-readable provenance ledger classifying the origin of every schema element. Evaluation against a fixed five-benchmark suite with locked baselines reports zero structural orphans, zero dangling edge endpoints, and 100% of output-producing technologies traceable to raw input files. Two interactive interfaces present the graph, including a serverless browser application built on DuckDB-Wasm. By establishing the first end-to-end provenance framework for an IAM, this work enables researchers and scientists to systematically audit complex policy scenarios, debug model structures, and trace policy-relevant outputs to their data origins in real time.

Artifical Intelligence↗
NASA NTRS2002

Architecting an approach to knowledge management

This paper sets forth a method to develop an architectural approach to the design, development, creation, and implementation of knowledge management at a specific organization.

knowledge management information technology organi↗
NASA NTRS1997

Knowledge Acquisition for the Onboard Planner of an Autonomous Spacecraft

This paper discusses the knowledge acquisition issues involved in transitioning their novel technology in to space flight software, developing the planer in the context of a large software projet and completing the work under a compressed development schedule.

Knowledge Aquisition Deep Space↗
NASA NTRS1995

Knowledge Discovery and Data Mining: An Overview

The process of knowledge discovery and data mining is the process of information extraction from very large databases. Its importance is described along with several techniques and considerations for selecting the most appropriate technique for extracting information from a particular data set.

data mining knowledge discovery data search↗
NASA NTRS1994

Towards an Intelligent Planning Knowledge Base Development Environment

ract describes work in developing knowledge base editing and debugging tools for the Multimission VICAR Planner (MVP) system. MVP uses artificial intelligence planning techniques to automatically construct executable complex image processing procedures (using models of the smaller constituent image processing requests made to the JPL Multimission Image Processing Laboratory.

image processing knowledge bases artificial intell↗
NASA NTRS2003

Draft position paper on knowledge management in space activities

As other fields of industry, space activities are facing the challenge of Knowledge Management and the International Academy of Astronautics decided to settle in 2002 a Study Group to analyse the problem and issue general guidelines. This communication presents the draft position paper of this group in view to be discussed during the 2003 IAF Congress.

NASA↗
NASA NTRS2013

Preparing for Humans at Mars, MPPG Updates to Strategic Knowledge Gaps and Collaboration with Science Missions

The Mars Program Planning Group (MPPG) was an agency wide effort, chartered in March 2012 by the NASA Associate Administrator for Science, in collaboration with NASA's Associate Administrator for Human Exploration and Operations, the Chief Scientist, and the Chief Technologist. NASA tasked the MPPG to develop foundations for a program-level architecture for robotic exploration of Mars that is consistent with the President's challenge of sending humans to the Mars system in the decade of the 2030s and responsive to the primary scientific goals of the 2011 NRC Decadal Survey for Planetary Science. The Mars Exploration Program Analysis Group (MEPAG) also sponsored a Precursor measurement Strategy Analysis Group (P-SAG) to revisit prior assessments of required precursor measurements for the human exploration of Mars. This paper will discuss the key results of the MPPG and P-SAG efforts to update and refine our understanding of the Strategic Knowledge Gaps (SKGs) required to successfully conduct human Mars missions.

Viking↗
NASA NTRS1993

DataHub--Knowledge-Based Science Data Management for Exploratory Data Analysis

It is our belief that new modes of research and new tools will be required to handle the massive amount of diverse data that is to be stored, organized, accessed, distributed, visualized, and analyzed. The fundamental innovation required is the integration of three automation technologies, videlicet knowledge-based expert systems, science visualization and science data management. This integration is based on a concept caled the DataHub, which we describe here.

DataHub↗
NASA NTRSDate not supplied

Requirement Discovery Using Embedded Knowledge Graph with ChatGPT

- NASA’s Air Traffic Management-Exploration (ATM-X) Urban Air Mobility (UAM) Airspace Subproject is conducting research that evolves UAM airspace towards a highly automated and operationally flexible system of the future. - (see https://www.nasa.gov/uam-overview/ for more information) - The complexity of UAM airspace, and its evolution through a series of transformative epochs, requires a planning tool to effectively organize, integrate, and communicate the research that will guide the evolution of UAM operations in the National Airspace System (NAS). - The planning tool, called the UAM airspace research roadmap (or just roadmap), is being developed as a new system engineering methodology leveraging model based system engineering (MBSE) and artificial intelligence capabilities. This presentation gives an overview of the Knowledge Graph and ChatGPT applications within this system engineering methodology and will describe how it is being used to meet the ATM-X UAM Airspace Subproject’s overarching research goals.

systems engineering↗
NASA NTRSDate not supplied

Nasa Genelab - Knowledge Graph Fabric Enables Deep Biomedical Analysis of Multi-Omics Datasets

The limited number of astronauts and human samples from long-duration space missions pose significant challenges for studying the health risks associated with spaceflight and developing new treatments. As a result, much of our understanding of the biological impact of space travel relies on samples from model organisms. NASA GeneLab, integrated into Open Science Data Repository (OSDR) is a centralized multi-omics resource containing almost 1000 datasets from over 500 space-related studies from human and model organism samples. Previous studies have demonstrated that human phenotypes and physiological changes caused by spaceflight can be identified by connecting gene expression data from model organisms flown in space to a biomedical knowledge graph (SPOKE). In this work, we present a data fabric connecting OSDR datasets to SPOKE that empowers biomedical analyses through the GeneLab visualization portal. This collaboration is funded by NSF’s Proto-OKN program.

data fabric↗
DOE OSTI2026

Inverse Mapping of the Collision Kernel and Wall Flux Scaling in a Tall Convection‐Cloud Chamber Using Local Sensors and Knowledge‐Informed Deep Learning

Droplet collision–coalescence is a crucial process in cloud physics, but accurately representing this process under different dynamical conditions remains challenging. A proposed future convective‐cloud chamber aims to investigate this key process, but the method for observing it remains unclear, even though it is theoretically established that collision‐coalescence will occur. This study serves as a proof‐of‐concept demonstration of how knowledge‐informed deep learning, combined with measurement data from local sensors in the chamber, can be used to estimate the collision kernels, which determine how the droplet size distribution evolves during collision‐coalescence. In addition to estimating the collision kernel, we also address wall fluxes, another uncertain but important process that acts as a source of heat and moisture in the chamber. Ensemble runs of large‐eddy simulations are conducted by scaling the wall fluxes and the collision kernel, while the measured flow and cloud properties are used as inputs for a neural network. Results indicate that this approach successfully maps the scaling of wall fluxes and the collision kernel with biases of approximately 1% or less relative to the range of the target data. This proof‐of‐concept lays the groundwork for future applications; when the real measurements are available, real sensor data combined with the trained model presented in this work will enable estimation of the actual wall fluxes and collision kernel.

cloud chamber↗