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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 361 records · Page 20

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NASA NTRS1990

Compiling knowledge-based systems from KEE to Ada

The dominant technology for developing AI applications is to work in a multi-mechanism, integrated, knowledge-based system (KBS) development environment. Unfortunately, systems developed in such environments are inappropriate for delivering many applications - most importantly, they carry the baggage of the entire Lisp environment and are not written in conventional languages. One resolution of this problem would be to compile applications from complex environments to conventional languages. Here the first efforts to develop a system for compiling KBS developed in KEE to Ada (trademark). This system is called KATYDID, for KEE/Ada Translation Yields Development Into Delivery. KATYDID includes early prototypes of a run-time KEE core (object-structure) library module for Ada, and translation mechanisms for knowledge structures, rules, and Lisp code to Ada. Using these tools, part of a simple expert system was compiled (not quite automatically) to run in a purely Ada environment. This experience has given us various insights on Ada as an artificial intelligence programming language, potential solutions of some of the engineering difficulties encountered in early work, and inspiration on future system development.

Filman, Robert E.↗

NASA NTRSDate not supplied

Design of an AI Trash Sorting Machine for Use on the Moon and Mars

As NASA prepares for Mars colonization, resource conservation will be critical for survival. Artificial Intelligence (AI) powered waste sorting technologies, already emerging on Earth, offer promising solutions for recycling and material recovery. These systems use advanced sensors and machine learning algorithms to identify and separate materials with remarkable accuracy. On Mars, where every item has significant value, efficient recycling will be essential to reduce resupply needs and support closed-loop life support systems. This paper explores how terrestrial AI-based trash sorting technologies can be adapted for Martian conditions, focusing on challenges such as the harsh surface environment, minimizing system mass, power, volume, and estimating waste composition. Addressing these issues will be key to enabling sustainable operations on the Red Planet.

AI↗

DOE OSTI2025

I/O in Machine Learning Applications on HPC Systems: A 360-degree Survey

Growing interest in Artificial Intelligence (AI) has resulted in a surge in demand for faster methods of Machine Learning (ML) model training and inference. This demand for speed has prompted the use of high performance computing (HPC) systems that excel in managing distributed workloads. Because data is the main fuel for AI applications, the performance of the storage and I/O subsystem of HPC systems is critical. In the past, HPC applications accessed large portions of data written by simulations or experiments or ingested data for visualizations or analysis tasks. ML workloads perform small reads spread across a large number of random files. This shift of I/O access patterns poses several challenges to modern parallel storage systems. In this paper, we survey I/O in ML applications on HPC systems, and target literature within a 6-year time window from 2019 to 2024. We define the scope of the survey, provide an overview of the common phases of ML, review available profilers and benchmarks, examine the I/O patterns encountered during offline data preparation, training, and inference, and explore I/O optimizations utilized in modern ML frameworks and proposed in recent literature. Lastly, we seek to expose research gaps that could spawn further R&D.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

A Framework for Addressing Hydropower Modeling Gaps in Electric Grid Planning and Operational Studies [Slides]

Realistic representation of hydropower in power system planning and operation studies is extremely important as it helps in avoiding under/over-estimation of the services the hydro power units can provide. A framework and tools have been developed to account for hydrological conditions and modify power system models accordingly. For example, HyDat – data and AI driven platform, that provides that data required for editing the power system model files Collaborative effort with V&R Energy. Also, development of POM based tools have been performed, a) Tool1: editing .raw files with realistic current and maximum hydrogeneration and redispatch of other units, and b) Tool2: Editing .dyr files to represent water head, current and maximum generation. Finally the framework, HASP framework combines HyDat and V&R energy tools to produce modified power system model files. Key Take-away points from contingency analysis studies include a) the number of critical contingencies increases as the water levels are reduced (70%>75%>80%), and b) both the extent and the quality of dynamic response of the Hydropower unit is different with varying water head levels.

13 HYDRO ENERGY↗

NASA NTRS1991

Issues on combining human and non-human intelligence

The purpose here is to call attention to some of the issues confronting the designer of a system that combines human and non-human intelligence. We do not know how to design a non-human intelligence in such a way that it will fit naturally into a human organization. The author's concern is that, without adequate understanding and consideration of the behavioral and psychological limitations and requirements of the human member(s) of the system, the introduction of artificial intelligence (AI) subsystems can exacerbate operational problems. We have seen that, when these technologies are not properly applied, an overall degradation of performance at the system level can occur. Only by understanding how human and automated systems work together can we be sure that the problems introduced by automation are not more serious than the problems solved.

Statler, Irving C.↗

NASA NTRS1994

Performance results of cooperating expert systems in a distributed real-time monitoring system

There are numerous definitions for real-time systems, the most stringent of which involve guaranteeing correct system response within a domain-dependent or situationally defined period of time. For applications such as diagnosis, in which the time required to produce a solution can be non-deterministic, this requirement poses a unique set of challenges in dynamic modification of solution strategy that conforms with maximum possible latencies. However, another definition of real time is relevant in the case of monitoring systems where failure to supply a response in the proper (and often infinitesimal) amount of time allowed does not make the solution less useful (or, in the extreme example of a monitoring system responsible for detecting and deflecting enemy missiles, completely irrelevant). This more casual definition involves responding to data at the same rate at which it is produced, and is more appropriate for monitoring applications with softer real-time constraints, such as interplanetary exploration, which results in massive quantities of data transmitted at the speed of light for a number of hours before it even reaches the monitoring system. The latter definition of real time has been applied to the MARVEL system for automated monitoring and diagnosis of spacecraft telemetry. An early version of this system has been in continuous operational use since it was first deployed in 1989 for the Voyager encounter with Neptune. This system remained under incremental development until 1991 and has been under routine maintenance in operations since then, while continuing to serve as an artificial intelligence (AI) testbed in the laboratory. The system architecture has been designed to facilitate concurrent and cooperative processing by multiple diagnostic expert systems in a hierarchical organization. The diagnostic modules adhere to concepts of data-driven reasoning, constrained but complete nonoverlapping domains, metaknowledge of global consequences of anomalous data, hierarchical reporting of problems that extend beyond a single domain, and shared responsibility for problems that overlap domains. The system enables efficient diagnosis of complex system failures in real-time environments with high data volumes and moderate failure rates, as indicated by extensive performance measurements.

Schwuttke, U. M.↗

NASA NTRSDate not supplied

Arm DevSummit Keynote: Environment for Data Engineering in Virtual Reality: Ethical Considerations

Currently, the US Government is going through a large-scale data Transformation effort, where the GSA playbook is guiding all agencies to make their processes data-driven with the help of emerging technologies. To address concerns about algorithm sharing, AI adoption, and vendor lock-in, the NASA Langley Research Center Digital Transformation Group has developed the Environment for Data Engineering in Virtual Reality (EnDEVR), a data science ecosystem that allows users to command and investigate customizable data analyses from a VR environment. The system has been evaluated in the Oculus Rift S and Quest environments, two popular VR systems powered by the ARM architecture. Current and future development will employ several AI capabilities to guide research and automation within the environment and to facilitate algorithm sharing. In this talk, we will discuss the results of an initial ethical investigation and recommended considerations for the use of AI within the system.

Artificial Intelligence↗

NASA NTRSDate not supplied

Responsible AI Framework for Air Traffic Management

Future system will require increased levels of automation to address increased diversity, density, environmental considerations resulting in higher complexity. Will automation be able to manage off-nominal, non-normal, unexpected, contingency situations?

artificial intelligence↗

NASA NTRSDate not supplied

Responsible AI Framework for Air Traffic Management

artificial intelligence↗

DOE OSTI2025

Gaia: An AI-enabled genomic context–aware platform for protein sequence annotation

Protein sequence similarity search is fundamental to biology research, but current methods are typically not able to consider crucial genomic context information indicative of protein function, especially in microbial systems. Here, we present Gaia (Genomic AI Annotator), a sequence annotation platform that enables rapid, context-aware protein sequence search across genomic datasets. Gaia leverages gLM2, a mixed-modality genomic language model trained on both amino acid sequences and their genomic neighborhoods to generate embeddings that integrate sequence-structure-context information. This approach allows for the identification of functionally and/or evolutionarily related genes that are found in conserved genomic contexts, which may be missed by traditional sequence- or structure-based search alone. Gaia enables real-time search of a curated database comprising more than 85 million protein clusters from 131,744 microbial genomes. We compare the homolog retrieval performance of Gaia search against other embedding and alignment-based approaches. We provide Gaia as a web-based, freely available tool.

Jha, Nishant↗

DOE OSTI2024

Developing an Energy-Conscious Traffic Signal Control System for Optimized Fuel Consumption in Connected Vehicle Environments

The project titled “Developing an Energy-Conscious Traffic Signal Control System for Optimized Fuel Consumption in Connected Vehicle Environments” addresses energy-related challenges associated with adaptive traffic control systems by integrating connected vehicles (CV) and connected infrastructure (CI). The system developed in this project, a CV-based adaptive traffic control system, aims to improve fuel consumption in mixed traffic environments by capitalizing on emerging CV and CI communication technologies, as well as leveraging recent advances in Artificial Intelligence (AI), optimization, and edge computing. The system was tested at the MLK Smart Corridor, an urban testbed managed by the University of Tennessee at Chattanooga (UTC) and the City of Chattanooga. The system was validated through extensive simulations, both Software-in-the-Loop (SILS) and Hardware-in-the-Loop (HILS), and was further implemented and tested in real-world conditions at several intersections along the corridor. The Fuel Consumption Performance Index (FC-PI) and the Ecological Performance Index (Eco-PI) were developed as the key components for evaluating the system’s impact on fuel consumption and emissions. These metrics provided a comprehensive means of understanding the impact of traffic signal control optimization in mixed traffic environments. The report presents an in-depth analysis of the Eco-PI, FC-PI, adaptive traffic control system integration, and the testing and field implementation of the system. The results demonstrate significant reductions in fuel consumption and emissions, showcasing the system’s capability to contribute to more sustainable urban traffic management. The report also documents the challenges encountered and recommendations for scaling and further improving the system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1989

Neural networks in support of manned space

Many lobbyists in Washington have argued that artificial intelligence (AI) is an alternative to manned space activity. In actuality, this is the opposite of the truth, especially as regards artificial neural networks (ANNs), that form of AI which has the greatest hope of mimicking human abilities in learning, ability to interface with sensors and actuators, flexibility and balanced judgement. ANNs and their relation to expert systems (the more traditional form of AI), and the limitations of both technologies are briefly reviewed. A Few highlights of recent work on ANNs, including an NSF-sponsored workshop on ANNs for control applications are given. Current thinking on ANNs for use in certain key areas (the National Aerospace Plane, teleoperation, the control of large structures, fault diagnostics, and docking) which may be crucial to the long term future of man in space is discussed.

Werbos, Paul J.↗

DOE OSTI2026

MCP-eGridGPT (MCP-Enabled Chatbot with Electrical Power System Analysis and Interactive Visualization Tool) [SWR-25-126]

This software is an advanced chatbot system that integrates the Model Context Protocol (MCP) to provide intelligent electrical power system analysis and automated visualization generation. The system enables users to interact with complex electrical engineering tools through natural language, automatically analyzes power system data for voltage violations and grid health assessment, and generates professional interactive HTML dashboards and reports. Key features include dynamic tool discovery from MCP servers, multi-LLM provider support, intelligent data interpretation using large language models, automated chart generation, and a web-based interface for real-time analysis. The software bridges sophisticated electrical engineering analysis with user-friendly interfaces, making power system diagnostics accessible through conversational AI.

Choi, Seong [National Laboratory of the Rockies (N↗

NASA NTRS1988

Machine vision for real time orbital operations

Machine vision for automation and robotic operation of Space Station era systems has the potential for increasing the efficiency of orbital servicing, repair, assembly and docking tasks. A machine vision research project is described in which a TV camera is used for inputing visual data to a computer so that image processing may be achieved for real time control of these orbital operations. A technique has resulted from this research which reduces computer memory requirements and greatly increases typical computational speed such that it has the potential for development into a real time orbital machine vision system. This technique is called AI BOSS (Analysis of Images by Box Scan and Syntax).

Vinz, Frank L.↗

NASA NTRS1990

An integrated environment for tactical guidance research and evaluation

NASA-Langley's Tactical Guidance Research and Evaluation System (TGRES) constitutes an integrated environment for the development of tactical guidance algorithms and evaluating the effects of novel technologies; the modularity of the system allows easy modification or replacement of system elements in order to conduct evaluations of alternative technologies. TGRES differs from existing systems in its capitalization on AI programming techniques for guidance-logic implementation. Its ability to encompass high-fidelity, six-DOF simulation models will facilitate the analysis of complete aircraft dynamics.

Goodrich, Kenneth H.↗

NASA NTRS2001

Autonomously generating operations sequences for a Mars Rover using AI-based planning

This paper discusses a proof-of-concept prototype for ground-based automatic generation of validated rover command sequences from highlevel science and engineering activities. This prototype is based on ASPEN, the Automated Scheduling and Planning Environment. This Artificial Intelligence (AI) based planning and scheduling system will automatically generate a command sequence that will execute within resource constraints and satisfy flight rules.

Mars Rover↗

NASA NTRS1993

Operations Automation Using the Link Monitor and Control Operator Assistant

The Link Monitor & Control Operator Assistant (LMC OA) is a knowledge-based prototype system which uses Artificial Intelligence (AI) techniques to provide semi-automated monitor and control functions to support operations of the Deep Space Network (DSN) 70-Meter antenna at the Goldstone Deep Space Communications Camplex (DSCC).

Goldstone↗

DOE OSTI2026

Artificial Intelligence-Driven Management of Sustainable Energy Resources: Visibility, Operation, and Control

The rapid global transition toward sustainable energy resources (SERs) is reshaping how modern power systems are observed, optimized, and controlled. While SERs have significantly advanced decarbonization, their weather dependence, variability, and inverter-dominated characteristics challenge traditional, centralized, and deterministic grid operation. At the same time, the proliferation of high-resolution data from inverters, smart meters, and sensors offers unprecedented visibility into system dynamics. Yet, it also exceeds the analytical capability of conventional model-based approaches. Artificial intelligence (AI) provides a new foundation for addressing these challenges by bridging physical laws with data-driven learning, enabling accurate state awareness, adaptive operation, and coordinated control across distributed assets. This article examines how AI transforms the management of SER-rich power systems along three critical dimensions: 1) enhancing visibility by inferring behind-the-meter (BTM) activities, assessing SER flexibility, and reconstructing system states from sparse or noisy measurements; 2) improving operation through AI-enhanced SER service provision, volt/var control (VVC), and dynamic operating envelopes (DOE) for efficiency and security; and 3) advancing control by embedding learning-based intelligence into inverter coordination, voltage and frequency regulation, and long-term dispatch. Together, these developments reveal how AI can convert the variability of SERs from an operational challenge into a source of flexibility, resilience, and intelligence, paving the way toward sustainable, adaptive, and self-optimizing power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗