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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A framework for multirate time integration of interface-coupled problems

The research described here was performed as part of the DOE SciDAC project Coupling Approaches for Next Generation Architectures (CANGA). A framework was developed for the derivation of novel algorithms for the multirate time integration of two-component systems coupled across an interface between spatial domains. The multirate aspect means that different time steps are allowed by each component integrator. The framework provides a way to construct multirate integrators with desirable properties related to stability, accuracy and preservation of system invariants. This report describes the framework and summarizes the major results, examples and research products.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The Computing Architectures Donnybrook

Explains the context and framework I'm creating as the Computer Architectures associate editor for IEEE Computer Magazine. The article is largely a historical recitation of computer architectures.

Williams, Joseph A.↗
NASA NTRS1990

A control architecture for a Mars walking vehicle

A design framework for a Mars Rover, intended for missions where a high degree of autonomy is dictated, is presented. The framework provides a logical computing architecture for rover mobility and local navigation subsystem design by defining a set of functional modules and interfaces to facilitate software and hardware specification. The similarities and dissimilarities between the present approach and related architectures for autonomous navigation and robotic control are discussed. An approach is presented for specifying actions to be taken by the mobility and local navigation subsystems of the rover.

Spiessbach, Andrew J.↗
NASA NTRSDate not supplied

Integrated Framework to Enable Design Space Exploration of In-Space Transportation Architectures

Even with the recent shift in focus of NASA’s human spaceflight program towards the Moon, the long-term goal continues to be crewed missions to Mars. Regardless of the accepted architecture, in-space transportation systems are a critical portion to achieving this goal. An emphasis is placed on presenting decision makers with many options early on to make an informed down-selection. Although current processes are able to address any aspects of the challenging underlying multi-disciplinary analysis and optimization problem of these advanced concepts, they are unable to comprehensively perform design space exploration inexpensively,potentially leaving attractive alternatives on the table. A design framework is proposed that is capable of enabling the integrated mission analysis, vehicle sizing and synthesis problem for in-space transportation architectures. This framework is enabled through the use of the vehicle synthesis tool, Dynamic Rocket Equation Tool, various subsystem sizing models, and low-thrust trajectory optimization codes. As a case study to demonstrate the framework, two design points of a hybrid propulsion stage concept, studied by Georgia Tech’s 2018 Revolutionary Aerospace Systems Concepts-Academic Linkage team, is explored.

Akshay Prasad↗
NASA NTRSDate not supplied

Integrated Framework to Enable Design Space Exploration of In-Space Transportation Architectures

Even with the recent shift in focus of NASA’s human spaceflight program towards the Moon, the long-term goal continues to be crewed missions to Mars. Regardless of the accepted architecture, in-space transportation systems are a critical portion to achieving this goal. An emphasis is placed on presenting decision makers with many options early on to make an informed down-selection. Although current processes are able to address any aspects of the challenging underlying multi-disciplinary analysis and optimization problem of these advanced concepts, they are unable to comprehensively perform design space exploration inexpensively, potentially leaving attractive alternatives on the table. A design framework is proposed that is capable of enabling the integrated mission analysis, vehicle sizing and synthesis problem for in-space transportation architectures. This framework is enabled through the use of the vehicle synthesis tool, Dynamic Rocket Equation Tool, various subsystem sizing models, and low-thrust trajectory optimization codes. As a case study to demonstrate the framework, two design points of a hybrid propulsion stage concept, studied by Georgia Tech’s 2018 Revolutionary Aerospace Systems Concepts-Academic Linkage team, is explored.

Akshay Prasad↗
NASA NTRS2006

Trade Studies of Space Launch Architectures using Modular Probabilistic Risk Analysis

A top-down risk assessment in the early phases of space exploration architecture development can provide understanding and intuition of the potential risks associated with new designs and technologies. In this approach, risk analysts draw from their past experience and the heritage of similar existing systems as a source for reliability data. This top-down approach captures the complex interactions of the risk driving parts of the integrated system without requiring detailed knowledge of the parts themselves, which is often unavailable in the early design stages. Traditional probabilistic risk analysis (PRA) technologies, however, suffer several drawbacks that limit their timely application to complex technology development programs. The most restrictive of these is a dependence on static planning scenarios, expressed through fault and event trees. Fault trees incorporating comprehensive mission scenarios are routinely constructed for complex space systems, and several commercial software products are available for evaluating fault statistics. These static representations cannot capture the dynamic behavior of system failures without substantial modification of the initial tree. Consequently, the development of dynamic models using fault tree analysis has been an active area of research in recent years. This paper discusses the implementation and demonstration of dynamic, modular scenario modeling for integration of subsystem fault evaluation modules using the Space Architecture Failure Evaluation (SAFE) tool. SAFE is a C++ code that was originally developed to support NASA s Space Launch Initiative. It provides a flexible framework for system architecture definition and trade studies. SAFE supports extensible modeling of dynamic, time-dependent risk drivers of the system and functions at the level of fidelity for which design and failure data exists. The approach is scalable, allowing inclusion of additional information as detailed data becomes available. The tool performs a Monte Carlo analysis to provide statistical estimates. Example results of an architecture system reliability study are summarized for an exploration system concept using heritage data from liquid-fueled expendable Saturn V/Apollo launch vehicles.

Mathias, Donovan L.↗
NASA NTRS2008

Service-Oriented Architecture for NVO and TeraGrid Computing

The National Virtual Observatory (NVO) Extensible Secure Scalable Service Infrastructure (NESSSI) is a Web service architecture and software framework that enables Web-based astronomical data publishing and processing on grid computers such as the National Science Foundation's TeraGrid. Characteristics of this architecture include the following: (1) Services are created, managed, and upgraded by their developers, who are trusted users of computing platforms on which the services are deployed. (2) Service jobs can be initiated by means of Java or Python client programs run on a command line or with Web portals. (3) Access is granted within a graduated security scheme in which the size of a job that can be initiated depends on the level of authentication of the user.

Jacob, Joseph↗
NASA NTRSDate not supplied

A Distributed Simulation-to-Flight Framework to Support Investigating Trust/Trustworthiness in Multi-Agent Systems

As autonomous systems continue to grow both in use and complexity, the necessity for robust and extensible simulation-to-flight methods is paramount for establishing an effective architecture for autonomous systems. A fundamental objective of the ATTRACTOR (Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability) project was to design and develop a distributed mixed-reality simulation environment to begin establishing a basis for certification of autonomous systems via research into trust and trustworthiness. In this paper, we present an autonomous systems architecture and development framework paired with a persistent distributed modeling and simulation environment for test and evaluation of autonomous systems. The Autonomous Entity Operations Network (AEON) framework enables autonomous system development with an easily extensible collection of libraries and plug-n-play nodes facilitated by the Data Distribution Service (DDS) communication protocol standard. The Baseline Environment for Autonomous Modeling (BEAM) simulation environment is a distributed mixed-reality Unity™-based environment built around the same DDS communication paradigm allowing for easy integration with AEON-based autonomous applications. They were designed under ATTRACTOR in order to measure and establish trustworthiness and trust in single- and multi-agent human-machine systems whether these machines are fixed-wing general aviation, rotary-wing Unmanned Aerial Vehicles (UAVs), ground rovers, or even spacecraft. Together AEON and BEAM enable sim-to-flight with minimal configuration changes. By using AEON and BEAM, source code that runs in simulation ports directly to hardware and has successfully flown in the lab and in the National Airspace System (NAS) at NASA LaRC many times over the lifetime of ATTRACTOR.

Benjamin N Kelley↗
NASA NTRSDate not supplied

A Distributed Simulation-to-Flight Framework to Support Investigating Trust/Trustworthiness in Multi-Agent Systems

As autonomous systems continue to grow both in use and complexity, the necessity for robust and extensible simulation-to-flight methods is paramount for establishing an effective architecture for autonomous systems. A fundamental objective of the ATTRACTOR (Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability) project was to design and develop a distributed mixed-reality simulation environment to begin establishing a basis for certification of autonomous systems via research into trust and trustworthiness. In this paper, we present an autonomous systems architecture and development framework paired with a persistent distributed modeling and simulation environment for test and evaluation of autonomous systems. The Autonomous Entity Operations Network (AEON) framework enables autonomous system development with an easily extensible collection of libraries and plug-n-play nodes facilitated by the Data Distribution Service (DDS) communication protocol standard. The Baseline Environment for Autonomous Modeling (BEAM) simulation environment is a distributed mixed-reality Unity™-based environment built around the same DDS communication paradigm allowing for easy integration with AEON-based autonomous applications. They were designed under ATTRACTOR in order to measure and establish trustworthiness and trust in single- and multi-agent human-machine systems whether these machines are fixed-wing general aviation, rotary-wing Unmanned Aerial Vehicles (UAVs), ground rovers, or even spacecraft. Together AEON and BEAM enable sim-to-flight with minimal configuration changes. By using AEON and BEAM, source code that runs in simulation ports directly to hardware and has successfully flown in the lab and in the National Airspace System (NAS) at NASA LaRC many times over the lifetime of ATTRACTOR.

Benjamin N Kelley↗
NASA NTRS2005

A CSP-Based Agent Modeling Framework for the Cougaar Agent-Based Architecture

Cognitive Agent Architecture (Cougaar) is a Java-based architecture for large-scale distributed agent-based applications. A Cougaar agent is an autonomous software entity with behaviors that represent a real-world entity (e.g., a business process). A Cougaar-based Model Driven Architecture approach, currently under development, uses a description of system's functionality (requirements) to automatically implement the system in Cougaar. The Communicating Sequential Processes (CSP) formalism is used for the formal validation of the generated system. Two main agent components, a blackboard and a plugin, are modeled as CSP processes. A set of channels represents communications between the blackboard and individual plugins. The blackboard is represented as a CSP process that communicates with every agent in the collection. The developed CSP-based Cougaar modeling framework provides a starting point for a more complete formal verification of the automatically generated Cougaar code. Currently it is used to verify the behavior of an individual agent in terms of CSP properties and to analyze the corresponding Cougaar society.

Gracanin, Denis↗
DOE OSTI2025

ADEPT: A Pedagogical Framework for Integrating Agentic AI with Deterministic Scientific Workflows

The integration of Large Language Models (LLMs) into scientific research promises to accelerate discovery, yet a significant gap remains between the dynamic reasoning of Artificial Intelligence (AI) agents and the static, deterministic nature of canonical scientific workflows. This paper introduces ADEPT (Agentic Discovery and Exploration Platform for Tools), a reference architecture and pedagogical framework explicitly designed to bridge this gap. ADEPT's primary mission is to provide a transparent, "glass-box" environment where researchers and engineers can learn to effectively wrap established scientific software (e.g., BLAST, Nextflow pipelines) and compose it into reliable, agent-driven workflows. We describe its modular, multi-server architecture, which leverages the Model Context Protocol (MCP) for tool serving, LangGraph for robust agentic orchestration, and a secure nsjail-based sandbox for safe code execution. By prioritizing architectural clarity, safety, and modularity, ADEPT serves as an extensible blueprint for building trustworthy AI-augmented systems and fosters the collaborative development necessary to responsibly employ agentic AI for science. We provide practical examples of how to adapt and extend this framework, highlighting its utility in workforce development and AI-readiness capabilities across research and development projects.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2006

Instrument Remote Control Application Framework

The Instrument Remote Control (IRC) architecture is a flexible, platform-independent application framework that is well suited for the control and monitoring of remote devices and sensors. IRC enables significant savings in development costs by utilizing extensible Markup Language (XML) descriptions to configure the framework for a specific application. The Instrument Markup Language (IML) is used to describe the commands used by an instrument, the data streams produced, the rules for formatting commands and parsing the data, and the method of communication. Often no custom code is needed to communicate with a new instrument or device. An IRC instance can advertise and publish a description about a device or subscribe to another device's description on a network. This simple capability of dynamically publishing and subscribing to interfaces enables a very flexible, self-adapting architecture for monitoring and control of complex instruments in diverse environments.

Ames, Troy↗
NASA NTRS2006

Multidisciplinary Environments: A History of Engineering Framework Development

This paper traces the history of engineering frameworks and their use by Multidisciplinary Design Optimization (MDO) practitioners. The approach is to reference papers that have been presented at one of the ten previous Multidisciplinary Analysis and Optimization (MA&O) conferences. By limiting the search to MA&O papers, the authors can (1) identify the key ideas that led to general purpose MDO frameworks and (2) uncover roadblocks that delayed the development of these ideas. The authors make no attempt to assign credit for revolutionary ideas or to assign blame for missed opportunities. Rather, the goal is to trace the various threads of computer architecture and software framework research and to observe how these threads contributed to the commercial framework products available today.

Padula, Sharon L.↗
DOE OSTI2023

Jay : A software framework for prototyping and evaluating offloading applications in hybrid edge clouds

Abstract We present Jay , a software framework for offloading applications in hybrid edge clouds. Jay provides an API, services, and tools that enable mobile application developers to implement, instrument, and evaluate offloading applications using configurable cloud topologies, offloading strategies, and job types. We start by presenting Jay 's job model and the concrete architecture of the framework. We then present the programming API with several examples of customization. Then, we turn to the description of the internal implementation of Jay instances and their components. Finally, we describe the Jay Workbench, a tool that allows the setup, execution, and reproduction of experiments with networks of hosts with different resource capabilities organized with specific topologies. The complete source code for the framework and workbench is provided in a GitHub repository.

Silva, Joaquim↗
NASA NTRSDate not supplied

A Systems Approach to AI Model Integration and Performance Evaluation for the Generic UAM Simulation Framework

This paper introduces py-guam, an open-source experimentation framework developed for the NASA Generic Urban Air Mobility simulation (GUAM) environment, facilitating the integration and evaluation of advanced artificial intelligence (AI) algorithms. We present a systems approach which enables the seamless incorporation of data-driven models, including off-nominal and failure state detection, into the GUAM’s Cognitive Architecture (CA). The framework supports customizable experimentation parameters, derives Safety Performance Indicators (SPIs) from UL 4600 safety case analyses, and employs rapid UAM simulations to assess AI impacts on flight performance across diverse scenarios. Through comprehensive testing and validation experiments, we demonstrate GUAM’s capability to enhance safety and efficiency in urban air mobility operations. Additionally, the open-source nature of py-guam fosters community collaboration, ensuring continuous improvement and adaptability to evolving technological advancements. This work establishes a robust tool for developing and testing AI-driven urban air mobility (UAM) systems, advancing the safety and reliability of autonomous urban air vehicles.

Artificial Intelligence↗
NASA NTRSDate not supplied

Spacecraft-Initiated Scheduling of Commercial Communications Services

We propose a software framework enabling a spacecraft in Earth orbit to schedule its own access to space communications services via ground stations and relay satellites. All operations are automated, without a human in the loop, allowing for highly responsive service to meet time-varying needs. We describe a modular software architecture allowing the framework to interface with the scheduling systems of several providers - both government and commercial. Specifically, we present the results of an experiment with Amazon Web Services (AWS) Ground Station, an operational commercial service provider. Proposed software executed scheduling of a ground station antenna with minimal lead time (less than 20 minutes before the start of a pass). During the contact AWS cloud infrastructure was used to demodulate, process, and distribute data products from an orbiting satellite. We believe this work demonstrates the framework's ability to interface with additional providers and potential to integrate more complex decision-making in the future to schedule optimal contacts.

Scheduling↗
DOE OSTI2020

Deep Reinforcement Learning-Based Edge Caching in Wireless Networks

With the purpose to offload data traffic in wireless networks, content caching techniques have recently been studied intensively. Using these techniques and caching a portion of the popular files at the local content servers, the users can be served with less delay. Most of the content replacement policies are based on the content popularity, that depends on the users' preferences. In practice, such information varies over time. Therefore, an approach to determine the file popularity patterns must be incorporated into caching policies. In this context, we study content caching at the wireless network edge using a deep reinforcement learning framework with Wolpertinger architecture. In particular, we propose deep actor-critic reinforcement learning based policies for both centralized and decentralized content caching. For centralized edge caching, we aim at maximizing the cache hit rate. In decentralized edge caching, we consider both the cache hit rate and transmission delay as performance metrics. These proposed frameworks are assumed to neither have any prior information on the file popularities nor know the potential variations in such information. Via simulation results, the superiority of the proposed frameworks is verified by comparing them with other policies, including least frequently used (LFU), least recently used (LRU), and first-in-first-out (FIFO) policies.

42 ENGINEERING↗