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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Strategies for concurrent processing of complex algorithms in data driven architectures

The results of ongoing research directed at developing a graph theoretical model for describing data and control flow associated with the execution of large grained algorithms in a spatial distributed computer environment is presented. This model is identified by the acronym ATAMM (Algorithm/Architecture Mapping Model). The purpose of such a model is to provide a basis for establishing rules for relating an algorithm to its execution in a multiprocessor environment. Specifications derived from the model lead directly to the description of a data flow architecture which is a consequence of the inherent behavior of the data and control flow described by the model. The purpose of the ATAMM based architecture is to optimize computational concurrency in the multiprocessor environment and to provide an analytical basis for performance evaluation. The ATAMM model and architecture specifications are demonstrated on a prototype system for concept validation.

Stoughton, John W.↗
NASA NTRSDate not supplied

Testing a Run-Time Assurance Framework Coupled with Integrated Risk Mitigation Capabilities for Autonomous Urban UAS Flights

The In-Time Aviation Safety Management System (IASMS) Concept of Operations (ConOps) envisions new capabilities to monitor, assess, and mitigate flight safety risks. Systems will be tailored to mission type, vehicle/equipage type, operational environment, and safety risk tolerance. Within an IASMS framework, several capabilities may be implemented spanning three operational phases (pre-flight, in-flight, and post-flight/off-line); and consisting of lower level functions and information services which may reside onboard the aircraft, on third-party server(s), and/or on ground/operator station(s). Each capability will be designed to produce and disseminate safety-relevant information; perform detection, diagnosis, and prediction of unsafe situations; and/or execute mitigation actions when hazardous events warrant such changes. This paper focuses on recent testing of airborne capabilities that demonstrate inflight aspects of the overarching concept for autonomous unmanned aircraft systems (UAS) operations in urban environments. A flight test architecture is described that applies run-time assurance principles (e.g., executes independent of the unassured autopilot), real-time risk assessment, and a technique to execute contingencies if necessary either automatically or via pilot intervention. Several tests using small UAS were conducted to verify the assured in-flight risk mitigation capability. The paper draws significantly from a larger NASA technical report and recent prior conference papers, providing additional details. Data is analyzed for two representative flights to illustrate the performance for various sequential and simultaneous hazards used during testing. During each automated flight, several hazards are encountered at various points along the flight path. At each point, the hazard is mitigated by the system, with the vehicle then continuing to subsequent points. The paper concludes with lessons-learned regarding relevant aspects of the overarching IASMS concept and how it may be updated and further advanced in the future.

population activity↗
NASA NTRS2016

On the Maneuvers Operational Response for NASA’s Soil Moisture Active-Passive (SMAP) Mission

The Soil-Moisture Active-Passive (SMAP) spacecraft requires various kinds of in-orbit maneuvers over the course of its three-year mission. The types of maneuvers include pre-planned commissioning maneuvers to reach its science orbit, regularly executed orbit maintenance maneuvers to overcome drag and other nominally occurring phenomena, as well as (the possibility of) collision avoidance maneuvers. The architecture of the spacecraft – in terms of availability of commanding via ground assets, the inherited avionics' ability to sequence and execute commands, and the capability of available subsystems able to carry out maneuvers – was well defined early in the development of the spacecraft and mission, well before the operational plan for responding to maneuver requests was cemented. The systems engineering challenge became: how to accommodate all three types of maneuvers in the confines of this well-defined architecture. This paper will describe how the operations team on SMAP successfully met this challenge. Specifically, it will dive into the three pronged approach that SMAP developed to handle each type of maneuver described above – to meet the timeliness requirements leveraged on the operations team to execute said maneuvers, while continuing to fit within the allotted staffing profile during nominal operations. Defining this paradigm to fit the mission’s architecture meant re-defining the original paradigm, (planned maneuvers being thought of separately than collision avoidance maneuvers), and re-classifying all responses to maneuver requests as variations and permutations of a singular operational response to a maneuver request. This paper will also describe the tools that were created to simplify the human interface and automate as much of the response as was possible. Finally, this paper will describe, at a very high level, some of the problems encountered and lessons learned by the operations team when this process was executed the first four times during the first ninety days of operations. Though the architecture of the operations team's response to maneuver requests will never be repeated exactly, the flexibility that was inserted via redefining the scope of the problem and by redefining the human interfaces should influence future projects' architectures earlier in their development – in the hopes that said influence will save time and money in the future.

Tirona, Joseph↗
DOE OSTI2021

IRIS: A Portable Runtime System Exploiting Multiple Heterogeneous Programming Systems

Across embedded, mobile, enterprise, and HPC systems, computer architectures are becoming more heterogeneous and complex. This complexity is causing a crisis in programming systems and performance portability. Several programming systems are working to address these challenges, but the increasing architectural diversity is forcing software stacks and applications to specialize for each architecture. As we show, all of these approaches critically depend on their runtime system for discovery, execution, scheduling, and data orchestration. To address this challenge, we believe that a more agile and proactive runtime system is essential to increase performance portability and improve user productivity. In this regard, we have designed and implemented IRIS: a portable runtime system exploiting multiple heterogeneous programming systems. IRIS can discover available resources, manage multiple diverse programming systems (e.g., CUDA, Hexagon, HIP, Level Zero, OpenCL, OpenMP) simultaneously in the same execution, respect data dependencies, orchestrate data movement proactively, and provide for user-configurable scheduling. Our evaluation on three architectures, ranging from Qualcomm Snapdragon to a Summit supercomputer node, shows that IRIS improves portability across a wide range of diverse heterogeneous architectures with negligible overhead.

Kim, Jungwon↗
NASA NTRS2004

The Robotic Lunar Exploration Program (RLEP): An Introduction to the Goals, Approach, and Architecture

The Vision for Space Exploration calls for undertaking lunar exploration activities to enable sustained human and robotic exploration of Mars and beyond, including more distant destinations in the solar system. In support of this vision, the Robotic Lunar Exploration Program (RLEP) is expected to execute a series of robotic missions to the Moon, starting in 2008, in order to pave the way for further human space exploration. This paper will give an introduction to the RLEP program office, its role and its goals, and the approach it is taking to executing the charter of the program. The paper will also discuss candidate architectures that are being studied as a framework for defining the RLEP missions and the context in which they will evolve.

Watzin, James G.↗
NASA NTRS2012

Virtual Machine Language 2.1

VML (Virtual Machine Language) is an advanced computing environment that allows spacecraft to operate using mechanisms ranging from simple, time-oriented sequencing to advanced, multicomponent reactive systems. VML has developed in four evolutionary stages. VML 0 is a core execution capability providing multi-threaded command execution, integer data types, and rudimentary branching. VML 1 added named parameterized procedures, extensive polymorphism, data typing, branching, looping issuance of commands using run-time parameters, and named global variables. VML 2 added for loops, data verification, telemetry reaction, and an open flight adaptation architecture. VML 2.1 contains major advances in control flow capabilities for executable state machines. On the resource requirements front, VML 2.1 features a reduced memory footprint in order to fit more capability into modestly sized flight processors, and endian-neutral data access for compatibility with Intel little-endian processors. Sequence packaging has been improved with object-oriented programming constructs and the use of implicit (rather than explicit) time tags on statements. Sequence event detection has been significantly enhanced with multi-variable waiting, which allows a sequence to detect and react to conditions defined by complex expressions with multiple global variables. This multi-variable waiting serves as the basis for implementing parallel rule checking, which in turn, makes possible executable state machines. The new state machine feature in VML 2.1 allows the creation of sophisticated autonomous reactive systems without the need to develop expensive flight software. Users specify named states and transitions, along with the truth conditions required, before taking transitions. Transitions with the same signal name allow separate state machines to coordinate actions: the conditions distributed across all state machines necessary to arm a particular signal are evaluated, and once found true, that signal is raised. The selected signal then causes all identically named transitions in all present state machines to be taken simultaneously. VML 2.1 has relevance to all potential space missions, both manned and unmanned. It was under consideration for use on Orion.

Riedel, Joseph E.↗
DOE OSTI2026

AutoLabs: cognitive multi-agent systems with self-correction for autonomous chemical experimentation

The automation of chemical research through self-driving laboratories (SDLs) promises to accelerate scientific discovery, yet the reliability and granular performance of the underlying AI agents remain critical, under-examined challenges. In this work, we introduce AutoLabs, a self-correcting, multi-agent architecture designed to autonomously translate natural-language instructions into executable protocols for a high-throughput liquid handler. The system engages users in dialogue, decomposes experimental goals into discrete tasks for specialized agents, performs tool-assisted stoichiometric calculations, and iteratively self-corrects its output before generating a hardware-ready file. We present a comprehensive evaluation framework featuring five benchmark experiments of increasing complexity, from simple sample preparation to multi-plate timed syntheses. Through a systematic ablation study of 20 agent configurations, we assess the impact of reasoning capacity, architectural design (single- vs. multi-agent), tool use, and self-correction mechanisms. Our results demonstrate that agent reasoning capacity is the most critical factor for success, reducing quantitative errors in chemical amounts (nRMSE) by over 85% in complex tasks. When combined with a multi-agent architecture and iterative self-correction, AutoLabs approaches expert-authored reference procedures on the benchmark (F1-score > 0.89) on challenging multi-plate syntheses. These findings establish a clear blueprint for developing robust and trustworthy AI partners for autonomous laboratories, highlighting the synergistic effects of modular design, advanced reasoning, and self-correction to ensure both performance and reliability in high-stakes scientific applications. Code: https://github.com/pnnl/autolabs

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Design and Performance Characterization of RADICAL-Pilot on Leadership-Class Platforms

Many extreme scale scientific applications have workloads comprised of a large number of individual highperformance tasks. The Pilot abstraction decouples workload specification, resource management, and task execution via job placeholders and late-binding. As such, suitable implementations of the Pilot abstraction can support the collective execution of large number of tasks on supercomputers. We introduce RADICAL-Pilot (RP) as a portable, modular and extensible Pilot enabled runtime system. We describe RP's design, architecture and implementation. We characterize its performance and show its ability to scalably execute workloads comprised of tens of thousands heterogeneous tasks on DOE and NSF leadership-class HPC platforms. Specifically, we investigate RP's weak/strong scaling with CPU/GPU, single/multi core, (non)MPI tasks and python functions when using most of ORNL Summit and TACC Frontera. RADICAL-Pilot can be used stand-alone, as well as the runtime for third-party workflow systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

DUNE Software and High Performance Computing

DUNE, like other HEP experiments, faces a challenge related to matching execution patterns of our production simulation and data processing software to the limitations imposed by modern high-performance computing facilities. In order to efficiently exploit these new architectures, particularly those with high CPU core counts and GPU accelerators, our existing software execution models require adaptation. In addition, the large size of individual units of raw data from the far detector modules pose an additional challenge somewhat unique to DUNE. Here we describe some of these problems and how we begin to solve them today with existing software frameworks and toolkits. We also describe ways we may leverage these existing software architectures to attack remaining problems going forward. This whitepaper is a contribution to the Computational Frontier of Snowmass21.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1997

A Multi-Level Parallelization Concept for High-Fidelity Multi-Block Solvers

The integration of high-fidelity Computational Fluid Dynamics (CFD) analysis tools with the industrial design process benefits greatly from the robust implementations that are transportable across a wide range of computer architectures. In the present work, a hybrid domain-decomposition and parallelization concept was developed and implemented into the widely-used NASA multi-block Computational Fluid Dynamics (CFD) packages implemented in ENSAERO and OVERFLOW. The new parallel solver concept, PENS (Parallel Euler Navier-Stokes Solver), employs both fine and coarse granularity in data partitioning as well as data coalescing to obtain the desired load-balance characteristics on the available computer platforms. This multi-level parallelism implementation itself introduces no changes to the numerical results, hence the original fidelity of the packages are identically preserved. The present implementation uses the Message Passing Interface (MPI) library for interprocessor message passing and memory accessing. By choosing an appropriate combination of the available partitioning and coalescing capabilities only during the execution stage, the PENS solver becomes adaptable to different computer architectures from shared-memory to distributed-memory platforms with varying degrees of parallelism. The PENS implementation on the IBM SP2 distributed memory environment at the NASA Ames Research Center obtains 85 percent scalable parallel performance using fine-grain partitioning of single-block CFD domains using up to 128 wide computational nodes. Multi-block CFD simulations of complete aircraft simulations achieve 75 percent perfect load-balanced executions using data coalescing and the two levels of parallelism. SGI PowerChallenge, SGI Origin 2000, and a cluster of workstations are the other platforms where the robustness of the implementation is tested. The performance behavior on the other computer platforms with a variety of realistic problems will be included as this on-going study progresses.

Hatay, Ferhat F.↗
DOE OSTI2022

MAPredict: Static Analysis Driven Memory Access Prediction Framework for Modern CPUs

Application memory access patterns are crucial in deciding how much traffic is served by the cache and forwarded to the dynamic random-access memory (DRAM). However, predicting such memory traffic is difficult because of the interplay of prefetchers, compilers, parallel execution, and innovations in manufacturer-specific micro-architectures. This research introduced MAPredict, a static analysis-driven framework that addresses these challenges to predict last-level cache (LLC)-DRAM traffic. By exploring and analyzing the behavior of modern Intel processors, MAPredict formulates cache-aware analytical models. MAPredict invokes these models to predict LLC-DRAM traffic by combining the application model, machine model, and user-provided hints to capture dynamic information. MAPredict successfully predicts LLC-DRAM traffic for different regular access patterns and provides the means to combine static and empirical observations for irregular access patterns. Evaluating 130 workloads from six applications on recent Intel micro-architectures, MAPredict yielded an average accuracy of 99% for streaming, 91% for strided, and 92% for stencil patterns. By coupling static and empirical methods, up to 97% average accuracy was obtained for random access patterns on different micro-architectures.

Monil, M. A. H.↗
DOE OSTI2025

HPDR: High-Performance Portable Scientific Data Reduction Framework

The rapid growth in scientific data generation is outpacing advancements in computing systems necessary for efficient storage, transfer, and analysis, particularly in the context of exascale computing. With the deployment of first-generation exascale computing systems and next-generation experimental facilities, this gap is widening and necessitates effective data reduction techniques to manage enormous data volumes. Over the past decade, various data reduction methods, including lossless compression, error-controlled lossy compression, and data refactoring, have been developed to accelerate I/O in scientific workflows. Despite significant reductions in data volume, these methods introduce considerable computational overhead, which can become the new bottleneck in data processing. To mitigate this, GPU-accelerated data reduction algorithms have been introduced. However, challenges remain in their integration into exascale workflows, including limited portability across different GPU architectures, substantial memory transfer overhead, and reduced scalability on dense multi-GPU systems. To address these challenges, we propose HPDR, a high-performance and portable data reduction framework. HPDR is designed to enable the execution of state-of-the-art reduction algorithms across diverse processor architectures while reducing memory transfer overhead to 2.3 % of the original, resulting in up to 3.5× faster throughput compared to existing solutions. It also achieves up to 96% of the theoretical speedup in multi-GPU settings. In addition, evaluations on accelerating I/O operations at scale up to 1,024 nodes of the Frontier supercomputer demonstrate that HPDR can achieve up to 103 TB/s reduction throughput, providing up to 4× acceleration in parallel I/O performance compared to existing data reduction routines. This work highlights the potential of HPDR to significantly enhance data reduction efficiency in exascale computing environments.

Chen, Jieyang [University of Oregon]↗
NASA NTRS1989

Space applications of artificial intelligence; Proceedings of the Annual Goddard Conference, Greenbelt, MD, May 16, 17, 1989

Theoretical and implementation aspects of AI systems for space applications are discussed in reviews and reports. Sections are devoted to planning and scheduling, fault isolation and diagnosis, data management, modeling and simulation, and development tools and methods. Particular attention is given to a situated reasoning architecture for space repair and replace tasks, parallel plan execution with self-processing networks, the electrical diagnostics expert system for Spacelab life-sciences experiments, diagnostic tolerance for missing sensor data, the integration of perception and reasoning in fast neural modules, a connectionist model for dynamic control, and applications of fuzzy sets to the development of rule-based expert systems.

Rash, James L.↗
NASA NTRS2001

General Temporal Knowledge for Planning and Data Mining

We consider the architecture of systems that combine temporal planning and plan execution and introduce a layer of temporal reasoning that potential1y improves both the communication between humans and such systems, and the performance of the temporal planner itself. In particular, this additional layer simultaneously supports more flexibility in specifying and maintaining temporal constraints on plans within an uncertain and changing execution environment, and the ability to understand and trace the progress of plan execution. It is shown how a representation based on single set of abstractions of temporal information can be used to characterize the reasoning underlying plan generation and execution interpretation. The complexity of such reasoning is discussed.

Morris, Robert↗
NASA NTRS2005

NASA Capability Roadmaps Executive Summary

This document is the result of eight months of hard work and dedication from NASA, industry, other government agencies, and academic experts from across the nation. It provides a summary of the capabilities necessary to execute the Vision for Space Exploration and the key architecture decisions that drive the direction for those capabilities. This report is being provided to the Exploration Systems Architecture Study (ESAS) team for consideration in development of an architecture approach and investment strategy to support NASA future mission, programs and budget requests. In addition, it will be an excellent reference for NASA's strategic planning. A more detailed set of roadmaps at the technology and sub-capability levels are available on CD. These detailed products include key driving assumptions, capability maturation assessments, and technology and capability development roadmaps.

Willcoxon, Rita↗
NASA NTRS2010

A High-Fidelity Simulation of a Generic Commercial Aircraft Engine and Controller

A new high-fidelity simulation of a generic 40,000 lb thrust class commercial turbofan engine with a representative controller, known as CMAPSS40k, has been developed. Based on dynamic flight test data of a highly instrumented engine and previous engine simulations developed at NASA Glenn Research Center, this non-proprietary simulation was created especially for use in the development of new engine control strategies. C-MAPSS40k is a highly detailed, component-level engine model written in MATLAB/Simulink (The MathWorks, Inc.). Because the model is built in Simulink, users have the ability to use any of the MATLAB tools for analysis and control system design. The engine components are modeled in C-code, which is then compiled to allow faster-than-real-time execution. The engine controller is based on common industry architecture and techniques to produce realistic closed-loop transient responses while ensuring that no safety or operability limits are violated. A significant feature not found in other non-proprietary models is the inclusion of transient stall margin debits. These debits provide an accurate accounting of the compressor surge margin, which is critical in the design of an engine controller. This paper discusses the development, characteristics, and capabilities of the C-MAPSS40k simulation

May, Ryan D.↗
NASA NTRS2016

Future Standardization of Space Telecommunications Radio System with Core Flight System

NASA Glenn Research Center (GRC) is integrating the NASA Space Telecommunications Radio System (STRS) Standard with the Core Flight System (cFS). The STRS standard provides a common, consistent framework to develop, qualify, operate and maintain complex, reconfigurable and reprogrammable radio systems. The cFS is a flexible, open architecture that features a plug-and-play software executive called the Core Flight Executive (cFE), a reusable library of software components for flight and space missions and an integrated tool suite. Together, STRS and cFS create a development environment that allows for STRS compliant applications to reference the STRS APIs through the cFS infrastructure. These APis are used to standardize the communication protocols on NASAs space SDRs. The cFE-STRS Operating Environment (OE) is a portable cFS library, which adds the ability to run STRS applications on existing cFS platforms. The purpose of this paper is to discuss the cFE-STRS OE prototype, preliminary experimental results performed using the Advanced Space Radio Platform (ASRP), the GRC Sband Ground Station and the SCaN (Space Communication and Navigation) Testbed currently flying onboard the International Space Station. Additionally, this paper presents a demonstration of the Consultative Committee for Space Data Systems (CCSDS) Spacecraft Onboard Interface Services (SOIS) using electronic data sheets inside cFE. This configuration allows for the data sheets to specify binary formats for data exchange between STRS applications. The integration of STRS with cFS leverages mission-proven platform functions and mitigates barriers to integration with future missions. This reduces flight software development time and the costs of software-defined radio (SDR) platforms. Furthermore, the combined benefits of STRS standardization with the flexibility of cFS provide an effective, reliable and modular framework to minimize software development efforts for spaceflight missions.

space communications↗
DOE OSTI2026

Evaluating Application Characteristics for GPU Portability Layer Selection

GPUs have become the dominant source of computing power for high performance computing and are increasingly being used across the High Energy Physics computing landscape for a wide variety of tasks. Though NVIDIA is currently the main provider of GPUs, AMD and Intel are rapidly increasing their market share. As a result, programming using a vendor-specific language such as CUDA can significantly reduce deployment choices. There are a number of portability layers such as Kokkos, Alpaka, SYCL, OpenMP and std::par that permit execution on a broad range of GPU and CPU architectures, significantly increasing the flexibility of application programmers. However, each of these portability layers has its own characteristics, performing better at some tasks and worse at others, or placing limitations on aspects of the application. In this presentation, we report on a study of application and kernel characteristics that can influence the choice of a portability layer and show how each layer handles these characteristics. We have analyzed representative heterogeneous applications from CMS (patatrack and p2r), DUNE (Wire-Cell Toolkit), and ATLAS (FastCaloSim) to identify key application characteristics that have different behaviors for the various portability technologies. Using these results, developers can make more informed decisions on which GPU portability technology is best suited to their application.

Atif, Mohammad [Brookhaven]↗