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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

A proposed Applications Information System - Concept, implementation, and growth

This paper describes a newly developed concept within NASA for an Applications Information System (AIS). The AIS would provide the opportunity to the public and private sectors of shared participation in a remote sensing research program directed to a particular set of land-use or environmental problems. Towards this end, the AIS would offer the technological framework and information system resources to overcome many of the deficiencies that end-users have faced over the years such as limited access to data, delay in data delivery, and a limited access to data reduction algorithms and models to convert data to geophysical measurements. In addition, the AIS will take advantage of NASA developments in networking among information systems and use of state of the art technology, such as CD Roms and optical disks for the purpose of increasing the scientific benefits of applied environmental research. The rationale for the establishment of an AIS, a methodology for a step-wise, modular implementation, and the relationship of the AIS to other NASA information systems are discussed.

Mcconnell, Dudley G.↗
NASA NTRSDate not supplied

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become imperative across various sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot is constructed, utilizing natural language processing (NLP) techniques, to facilitate the deployment of these ML models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

Development↗
NASA NTRSDate not supplied

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become imperative across various sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot is constructed, utilizing natural language processing (NLP) techniques, to facilitate the deployment of these ML models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

Development↗
NASA NTRS2022

An Earth System Digital Twin for Flood Prediction and Analysis

An Earth System Digital Twin (ESDT) is a dynamic, interactive, digital replica of the state and temporal evolution of Earth systems. It integrates multiple models along with observation data, and connecting them with analysis, AI, and visualization tools. Together, these enable users to explore the current state of the Earth system, predict future conditions, and run hypothetical scenarios to understand how the system would evolve under various assumptions. The NASA’s Advanced Information Systems Technology (AIST)’s Integrated Digital Earth Analysis System (IDEAS) project is to establish an extensible architectural solution to develop digital twins of our physical environment for Earth Science. IDEAS delivers a formal system architecture with mechanisms for the outputs of one model to feed into others; for driving models with observation data; and for harmonizing observation data and model outputs for analysis. To validate and demonstrate the IDEAS architecture, this project collaborates with the Space Climate Observatory (SCO)’s FloodDAM project and the Centre National d’Etudes Spatiales (CNES) to focus on floods detection, prediction and their impacts.

Kettig, Peter↗
NASA NTRS2024

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become crucial across diverse economic sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot that specifically focuses on propulsion has been developed. Leveraging natural language processing (NLP) techniques, this chatbot simplifies the deployment of machine learning (ML) models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

AI Chatbot↗
NASA NTRS1991

Numerical simulation of extended corona

A three-dimensional, time-dependent MHD model is presented for the study of coronal dynamics. The model, written in spherical coordinates, extends from the solar surface and was developed with two major issues in mind, namely for interpretation of various steady state and evolutionary dynamical structures in the corona. In order to achieve these objectives, two different numerical techniques are used to seek solutions for these two different, but related, problems; steady state structures and evolutionary structures. These two numerical techniques are: (1) relaxation technique for steady state structures; and (2) FICE (Full-Implicit-Continuous-Eulerian) technique for evolutionary structures. To illustrate this model, numerical results are given for examples of both the steady state and evolutionary structure of the corona which show the additional physical features which cannot be shown by a two-dimensional model.

Wu, S. T.↗
NASA NTRS2024

Artificial Intelligence Workshop Report

The 4th NASA Science Mission Directorate (SMD) Artificial Intelligence (AI) Workshop, held during March 25-27, 2024, in Huntsville, AL, highlighted the significant potential of AI and machine learning (ML) in scientific research and processes. The workshop, supported by the NASA Office of Chief Science Data Officer (OCSDO), emphasized the critical role of foundation models (FMs) and large language models (LLMs) in advancing scientific disciplines. The event brought together domain scientists, computer scientists, AI experts, program managers, program scientists, and industry partners to address key challenges and explore opportunities in applying these advanced technologies.

Manil Maskey↗
NASA NTRS1988

AI tools in computer based problem solving

The use of computers to solve value oriented, deterministic, algorithmic problems, has evolved a structured life cycle model of the software process. The symbolic processing techniques used, primarily in research, for solving nondeterministic problems, and those for which an algorithmic solution is unknown, have evolved a different model, much less structured. Traditionally, the two approaches have been used completely independently. With the advent of low cost, high performance 32 bit workstations executing identical software with large minicomputers and mainframes, it became possible to begin to merge both models into a single extended model of computer problem solving. The implementation of such an extended model on a VAX family of micro/mini/mainframe systems is described. Examples in both development and deployment of applications involving a blending of AI and traditional techniques are given.

Beane, Arthur J.↗
NASA NTRS1988

Graph-based real-time fault diagnostics

A real-time fault detection and diagnosis capability is absolutely crucial in the design of large-scale space systems. Some of the existing AI-based fault diagnostic techniques like expert systems and qualitative modelling are frequently ill-suited for this purpose. Expert systems are often inadequately structured, difficult to validate and suffer from knowledge acquisition bottlenecks. Qualitative modelling techniques sometimes generate a large number of failure source alternatives, thus hampering speedy diagnosis. In this paper we present a graph-based technique which is well suited for real-time fault diagnosis, structured knowledge representation and acquisition and testing and validation. A Hierarchical Fault Model of the system to be diagnosed is developed. At each level of hierarchy, there exist fault propagation digraphs denoting causal relations between failure modes of subsystems. The edges of such a digraph are weighted with fault propagation time intervals. Efficient and restartable graph algorithms are used for on-line speedy identification of failure source components.

Padalkar, S.↗
NASA NTRS1989

Formal verification of AI software

The application of formal verification techniques to Artificial Intelligence (AI) software, particularly expert systems, is investigated. Constraint satisfaction and model inversion are identified as two formal specification paradigms for different classes of expert systems. A formal definition of consistency is developed, and the notion of approximate semantics is introduced. Examples are given of how these ideas can be applied in both declarative and imperative forms.

Rushby, John↗
NASA NTRS2021

Future Sea Level Change Under CMIP5 and CMIP6 Scenarios From the Greenland and Antarctic Ice Sheets

Projections of the sea level contribution from the Greenland and Antarctic ice sheets (GrIS and AIS) rely on atmospheric and oceanic drivers obtained from climate models. The Earth System Models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) generally project greater future warming compared with the previous Coupled Model Intercomparison Project phase 5 (CMIP5) effort. Here we use four CMIP6 models and a selection of CMIP5 models to force multiple ice sheet models as part of the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). We find that the projected sea level contribution at 2100 from the ice sheet model ensemble under the CMIP6 scenarios falls within the CMIP5 range for the Antarctic ice sheet but is significantly increased for Greenland. Warmer atmosphere in CMIP6 models results in higher Greenland mass loss due to surface melt. For Antarctica, CMIP6 forcing is similar to CMIP5 and mass gain from increased snowfall counteracts increased loss due to ocean warming.

Antarctica↗
NASA NTRSDate not supplied

LSKnowledge: Nexus for Transformative Scientific Discoveries and Enhanced Information Retrieval in NASA Life Sciences Portal

We stand at the brink of an extraordinary transformation in the field of AI, driven by the convergence of generative AI and semantic technologies (e.g., knowledge graphs). This fusion holds immense potential and could redefine the future of scientific exploration, particularly in the realm of life sciences research. In this context, we shed light on the pivotal roles that Large Language Models (LLMs) and semantic technologies will play in advancing research, unearthing and comprehending life sciences information through innovative approaches, and empowering researchers to extract insights from NASA's extensive Life Sciences Data Archive. Within the NASA Life Sciences Portal (NLSP), the integration of LLMs and semantic technologies unlocks several advanced capabilities. First and foremost, it equips scientists with sophisticated tools to manage the ever-expanding wealth of scientific literature and data. Furthermore, it facilitates the creation of knowledge graphs that visually represent intricate relationships among biological entities, enabling comprehensive systems-level analysis. Additionally, the fusion of generative AI (including LLMs) and semantic technology can significantly benefit NASA's life sciences research by enhancing information retrieval and hypothesis generation. These tools enhance natural language understanding, facilitating knowledge discovery within NLSP. The overarching vision is to establish a cohesive knowledge ecosystem within NLSP, harnessing the power of LLMs and semantic technologies to synthesize and cross-reference data from diverse missions, disciplines, and research domains. This holistic approach ultimately deepens our understanding of how space environments impact life sciences data. To advance this initiative, we have launched LSKnowledge, aimed at enhancing the information retrieval capabilities of NLSP. In the short term, our primary goal is to develop a robust semantic search system. This system will empower HRP (Human Research Program) researchers to navigate NLSP data repositories more efficiently and precisely, catalyzing the process of hypothesis formation and scientific breakthroughs. To achieve this, we have employed pre-trained LLMs as part of a semantic search tool that can rank and highlight the most relevant records for user queries. To assess the tool's performance, we have curated a set of approximately 200 queries from subject matter experts (SMEs) and manually ranked the top records retrieved by both the current search system and the new semantic search, using SME judgments as the gold standard for relevancy. Herein, we present the results of our comparative analysis and illustrate how these findings have informed the fine-tuning of the system for enhanced performance. In the long term, our objectives include 1) retrieving publicly available information and integrating it with NLSP data to provide more precise answers to user queries, and 2) incorporating non-textual information from the NLSP database into our approach. In conclusion, the fusion of LLMs and semantic technologies within NLSP represents a pioneering stride towards reshaping the landscape of scientific discovery. This synergy not only equips researchers with powerful tools to navigate the burgeoning sea of information but also facilitates a deeper understanding of complex biological relationships, all while accelerating hypothesis generation and knowledge discovery. Through our initiative, LSKnowledge, we are committed to continually refining and expanding these capabilities, with the aim of not only enhancing information retrieval but also integrating diverse data sources to provide more precise insights. In the grand vision, NLSP strives to become the cornerstone of a comprehensive knowledge ecosystem, unraveling the enigmatic intricacies of life sciences phenomena in the context of space environments.

Life Sciences↗
NASA NTRS2005

An Interoperable, Agricultural Information System Based on Satellite Remote Sensing Data

Monitoring global agricultural crop conditions during the growing season and estimating potential seasonal production are critically important for market development of US. agricultural products and for global food security. The Goddard Space Flight Center Earth Sciences Data and Information Services Center Distributed Active Archive Center (GES DISC DAAC) is developing an Agricultural Information System (AIS), evolved from an existing TRMM Online Visualization and Analysis System (TOVAS), which will operationally provide satellite remote sensing data products (e.g., rainfall) and services. The data products will include crop condition and yield prediction maps, generated from a crop growth model with satellite data inputs, in collaboration with the USDA Agricultural Research Service. The AIS will enable the remote, interoperable access to distributed data, by using the GrADS-DODS Server (GDS) and by being compliant with Open GIS Consortium standards. Users will be able to download individual files, perform interactive online analysis, as well as receive operational data flows. AIS outputs will be integrated into existing operational decision support systems for global crop monitoring, such as those of the USDA Foreign Agricultural Service and the U.N. World Food Program.

Teng, William↗
NASA NTRS1992

A multilayer perceptron solution to the match phase problem in rule-based artificial intelligence systems

In rule-based AI planning, expert, and learning systems, it is often the case that the left-hand-sides of the rules must be repeatedly compared to the contents of some 'working memory'. The traditional approach to solve such a 'match phase problem' for production systems is to use the Rete Match Algorithm. Here, a new technique using a multilayer perceptron, a particular artificial neural network model, is presented to solve the match phase problem for rule-based AI systems. A syntax for premise formulas (i.e., the left-hand-sides of the rules) is defined, and working memory is specified. From this, it is shown how to construct a multilayer perceptron that finds all of the rules which can be executed for the current situation in working memory. The complexity of the constructed multilayer perceptron is derived in terms of the maximum number of nodes and the required number of layers. A method for reducing the number of layers to at most three is also presented.

Sartori, Michael A.↗
NASA NTRS1988

AI, automation and the Flight Telerobotic Servicer

A NASA study for the preliminary definition of a teleoperated robotic device has been recently completed. The Fligt Telerobotic Servicer (FTS) will be used to assist astronauts in many of the on-board tasks of assembly, maintenance, servicing, and inspection of the Space Station. The role of artificial intelligence (AI) in furthering the FTS automation capabilities and, hence, extending its capacity for growth and evolution is discussed. Relevant system engineering issues are identified, and an approach for insertion of AI technology is presented in terms of the NASA/NBS Standard Reference Model control architecture NASREM.

Goforth, Andre↗
NASA NTRS1991

Approaches to the study of intelligence

A survey and an evaluation are conducted for the Rosenbloom et al. (1991) 'SOAR' model of intelligence, both as found in humans and in prospective AI systems, which views it as a representational system for goal-oriented symbolic activity based on a physical symbol system. Attention is given to SOAR's implications for semantic and episodic memory, symbol processing, and search within a uniform problem space; also noted are the relationships of SOAR to competing AI schemes, and its potential usefulness as a theoretical tool for cognitive psychology.

Norman, Donald A.↗