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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Reproduction of the Yucca Mountain Project TSPA-LA Uncertainty and Sensitivity Analyses and Preliminary Upgrade of Models

Sandia National Laboratories (SNL) continued evaluation of total system performance assessment (TSPA) computing systems for the previously considered Yucca Mountain Project (YMP). This was done to maintain the operational readiness of the computing infrastructure (computer hardware and software) and knowledge capability for total system performance assessment (TSPA) type analysis, as directed by the National Nuclear Security Administration (NNSA), DOE 2010. This work is a continuation of the ongoing readiness evaluation reported in Lee and Hadgu (2014) and Hadgu et al. (2015). The TSPA computing hardware (CL2014) and storage system described in Hadgu et al. (2015) were used for the current analysis. One floating license of GoldSim with Versions 9.60.300, 10.5 and 11.1.6 was installed on the cluster head node, and its distributed processing capability was mapped on the cluster processors. Other supporting software were tested and installed to support the TSPA-type analysis on the server cluster. The current tasks included verification of the TSPA-LA uncertainty and sensitivity analyses, and preliminary upgrade of the TSPA-LA from Version 9.60.300 to the latest version 11.1. All the TSPA-LA uncertainty and sensitivity analyses modeling cases were successfully tested and verified for the model reproducibility on the upgraded 2014 server cluster (CL2014). The uncertainty and sensitivity analyses used TSPA-LA modeling cases output generated in FY15 based on GoldSim Version 9.60.300 documented in Hadgu et al. (2015). The model upgrade task successfully converted the Nominal Modeling case to GoldSim Version 11.1. Upgrade of the remaining of the modeling cases and distributed processing tasks will continue. The 2014 server cluster and supporting software systems are fully operational to support TSPA-LA type analysis.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

PNNL OS3300 Alpha/Beta Monitoring System Software and Hardware Operations Manual (Rev.2)

The Pacific Northwest National Laboratory (PNNL) OS3300 Alpha/Beta Monitoring System Software and Hardware Operations Manual describes how to operate the software and hardware on a personal computer in conjunction with the Berthold Technologies LB150D continuous air monitor. Included are operational details for the software functions and information about how to read and use the dropdown menus and how to understand readings and calculations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2002

Latest Sensors and Data Acquisition Development Efforts at KSC

This viewgraph presentation summarizes the characteristics required on sensors by consumers desiring access to space, a long term plan developed at KSC (Kennedy Space Center) to identify promising technologies for NASA's own future sensor needs, and the characteristics of several smart sensors already developed. Also addressed are the computer hardware and architecture used to operate sensors, and generic testing capabilities. Consumers desire sensors which are lightweight, inexpensive, intelligent, and easy to use.

Perotti, Jose M.↗
NASA NTRS2007

Computational Support for Technology- Investment Decisions

Strategic Assessment of Risk and Technology (START) is a user-friendly computer program that assists human managers in making decisions regarding research-and-development investment portfolios in the presence of uncertainties and of non-technological constraints that include budgetary and time limits, restrictions related to infrastructure, and programmatic and institutional priorities. START facilitates quantitative analysis of technologies, capabilities, missions, scenarios and programs, and thereby enables the selection and scheduling of value-optimal development efforts. START incorporates features that, variously, perform or support a unique combination of functions, most of which are not systematically performed or supported by prior decision- support software. These functions include the following: Optimal portfolio selection using an expected-utility-based assessment of capabilities and technologies; Temporal investment recommendations; Distinctions between enhancing and enabling capabilities; Analysis of partial funding for enhancing capabilities; and Sensitivity and uncertainty analysis. START can run on almost any computing hardware, within Linux and related operating systems that include Mac OS X versions 10.3 and later, and can run in Windows under the Cygwin environment. START can be distributed in binary code form. START calls, as external libraries, several open-source software packages. Output is in Excel (.xls) file format.

Adumitroaie, Virgil↗
NASA NTRS2003

CLARAty Functional-Layer Software

Functional-layer software for the Coupled Layer Architecture for Robotics Autonomy (CLARAty) is being developed. [CLARAty was described in Coupled-Layer Architecture for Advanced Software for Robots (NPO-21218), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48. To recapitulate: CLARAty was proposed to improve the modularity of robotic software while tightening the coupling between planning/execution and control subsystems. Whereas prior robotic software architectures have typically contained three levels, the CLARAty architecture contains two layers: a decision layer and a functional layer.] Just as an operating system provides abstraction from computational hardware, the CLARAty functional-layer software provides for abstraction for the different robotic systems. The functional-layer software establishes interrelated, object-oriented hierarchies that contain active and passive objects that represent the different levels of system abstrations and components. The functional-layer software is decomposed into a set of reusable core components and a set of extended components that adapt the reusable set to specific hardware implementations. The reusable components (a) provide behavior and interface definitions and implementations of basic functionality, (b) provide local executive capabilities, (c) manage local resources, and (d) support state and resource queries by the decision layer. Software for robotic systems can be built by use of these components.

Nesnas, Issa↗
NASA NTRS1994

Implementing Software Safety in the NASA Environment

Until recently, NASA did not consider allowing computers total control of flight systems. Human operators, via hardware, have constituted the ultimate safety control. In an attempt to reduce costs, NASA has come to rely more and more heavily on computers and software to control space missions. (For example. software is now planned to control most of the operational functions of the International Space Station.) Thus the need for systematic software safety programs has become crucial for mission success. Concurrent engineering principles dictate that safety should be designed into software up front, not tested into the software after the fact. 'Cost of Quality' studies have statistics and metrics to prove the value of building quality and safety into the development cycle. Unfortunately, most software engineers are not familiar with designing for safety, and most safety engineers are not software experts. Software written to specifications which have not been safety analyzed is a major source of computer related accidents. Safer software is achieved step by step throughout the system and software life cycle. It is a process that includes requirements definition, hazard analyses, formal software inspections, safety analyses, testing, and maintenance. The greatest emphasis is placed on clearly and completely defining system and software requirements, including safety and reliability requirements. Unfortunately, development and review of requirements are the weakest link in the process. While some of the more academic methods, e.g. mathematical models, may help bring about safer software, this paper proposes the use of currently approved software methodologies, and sound software and assurance practices to show how, to a large degree, safety can be designed into software from the start. NASA's approach today is to first conduct a preliminary system hazard analysis (PHA) during the concept and planning phase of a project. This determines the overall hazard potential of the system to be built. Shortly thereafter, as the system requirements are being defined, the second iteration of hazard analyses takes place, the systems hazard analysis (SHA). During the systems requirements phase, decisions are made as to what functions of the system will be the responsibility of software. This is the most critical time to affect the safety of the software. From this point, software safety analyses as well as software engineering practices are the main focus for assuring safe software. While many of the steps proposed in this paper seem like just sound engineering practices, they are the best technical and most cost effective means to assure safe software within a safe system.

Wetherholt, Martha S.↗
DOE OSTI2025

Synthetic-domain computing and neural networks using lithium niobate integrated nonlinear phononics

Analogue computing uses the physical behaviours of devices to provide energy-efficient arithmetic operations. However, scaling up analogue computing platforms by simply increasing the number of devices leads to challenges such as device-to-device variation. Here, in this study, we report scalable analogue computing and neural networks in the synthetic frequency domain using an integrated nonlinear phononic platform on lithium niobate. This synthetic-domain computing is robust to device variations, as vectors and matrices are concurrently encoded at different frequencies within a single device, achieving a high throughput per area. Leveraging inherent nonlinearities, our device-aware neural network can perform a four-class classification task with an accuracy of 98.2%. The nonlinear phononic computing hardware also maintains consistent performance over a wide operational temperature range (characterized up to 192 °C). Our synthetic-domain computing combines single-device parallelism, inherent nonlinearity and environmental stability, and could be of use in edge computing applications in which power efficiency and environmental resilience are crucial.

Ji, Jun [Virginia Polytechnic Inst. and State Univ↗
DOE OSTI2025

Even Higher-Level Synthesis: An Exploration of AI Hardware Accelerators using HLS4ML

With the rise of artificial intelligence, the popularization of deep learning, and a constantly evolving industry, the demand for flexible and efficient tools has never been greater. As algorithms grow more complex, their runtime and energy consumption increase exponentially. Customized hardware accelerators, long used for specific mathematical operations, remain essential for managing modern applications' computational and power demands. Hardware accelerators can speed up complex computations by orders of magnitude, but their manual design and verification processes are often challenging and time-consuming. High-Level Synthesis (HLS) provides a solution by transforming high-level algorithm descriptions, typically written in C++ or SystemC, into synthesizable RTL suitable for hardware implementation. This approach reduces development time for RTL engineers while offering flexibility beyond what traditional handwritten RTL can provide. We extended this capability to the machine-learning domain with the open-source framework hls4ml, which allows neural networks trained in Python frameworks like Tensorflow or PyTorch to be synthesized into efficient hardware representations for the traditional FPGA and ASIC flows. This breakthrough addresses the growing need for reduced design turnaround and easy verification of ML hardware accelerators with low latency and power efficiency constraints. During this tutorial, we will demonstrate how Python complements HLS by simplifying the ML design process, bridging the gap between software and hardware development. Attendees will explore how we translate neural networks modeled in Python into fixed-point C++ models suitable for HLS workflows. We will dive into strategies like Value-Range Analysis and Quantization-Aware Training, which optimize these designs for deployment and evaluate their accuracy, power consumption, and energy efficiency. To exemplify these concepts, experts from Fermilab will share their experiences applying this technology to high-energy physics experiments, where real-time, low-latency processing is critical. Over the years, Fermilab engineers have demonstrated how deep neural networks, optimized for hardware using hls4ml, can meet the stringent requirements of trigger systems at the CERN Large Hadron Collider. These systems rely on rapid decision-making to process immense data volumes while retaining only the most relevant events for further analysis. The application of hls4ml has also been extended to innovative technologies like smart pixel arrays. These smart pixels integrate ML inference capabilities directly into sensor devices, enabling localized data processing at the pixel level. This approach drastically reduces the need to transmit raw data to external processing units, significantly decreasing power consumption and latency. By embedding neural networks within the pixel architecture, the smart pixels can identify and prioritize relevant data in real time, providing a highly efficient solution for edge computing in scenarios such as particle detectors and imaging systems. Fermilab's work highlights the potential of hardware-accelerated ML in scenarios where both speed and power efficiency are mission-critical. Through this tutorial, attendees will gain valuable insights into the challenges and solutions of deploying ML in hardware. Understanding how HLS and hls4ml streamline the development of neural network-based hardware accelerators is fundamental for the industry's future. Participants will learn how these technologies are shaping the future of AI and scientific computing.

Di Guglielmo, Giuseppe [Fermilab]↗
DOE OSTI2017

Characteristics and Energy Use of Volume Servers in the United States

Servers’ field energy use remains poorly understood, given heterogeneous computing loads, configurable hardware and software, and operation over a wide range of management practices. This paper explores various characteristics of 1- and 2-socket volume servers that affect energy consumption, and quantifies the difference in power demand between higher-performing SPEC and ENERGY STAR servers and our best understanding of a typical server operating today. We first establish general characteristics of the U.S. installed base of volume servers from existing IDC data and the literature, before presenting information on server hardware configurations from data collection events at a major online retail website. We then compare cumulative distribution functions of server idle power across three separate datasets and explain the differences between them via examination of the hardware characteristics to which power draw is most sensitive. We find that idle server power demand is significantly higher than ENERGY STAR benchmarks and the industry-released energy use documented in SPEC, and that SPEC server configurations—and likely the associated power-scaling trends—are atypical of volume servers. Next, we examine recent trends in server power draw among high-performing servers across their full load range to consider how representative these trends are of all volume servers before inputting weighted average idle power load values into a recently published model of national server energy use. Finally, we present results from two surveys of IT managers (n=216) and IT vendors (n=178) that illustrate the prevalence of more-efficient equipment and operational practices in server rooms and closets; these findings highlight opportunities to improve the energy efficiency of the U.S. server stock.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1974

MIDAS, prototype Multivariate Interactive Digital Analysis System, phase 1. Volume 1: System description

The MIDAS System is described as a third-generation fast multispectral recognition system able to keep pace with the large quantity and high rates of data acquisition from present and projected sensors. A principal objective of the MIDAS program is to provide a system well interfaced with the human operator and thus to obtain large overall reductions in turnaround time and significant gains in throughput. The hardware and software are described. The system contains a mini-computer to control the various high-speed processing elements in the data path, and a classifier which implements an all-digital prototype multivariate-Gaussian maximum likelihood decision algorithm operating at 200,000 pixels/sec. Sufficient hardware was developed to perform signature extraction from computer-compatible tapes, compute classifier coefficients, control the classifier operation, and diagnose operation.

Kriegler, F. J.↗
NASA NTRS1993

Upgrading NASA/DOSE laser ranging system control computers

Laser ranging systems now managed by the NASA Dynamics of the Solid Earth (DOSE) and operated by the Bendix Field Engineering Corporation, the University of Hawaii, and the University of Texas have produced a wealth on interdisciplinary scientific data over the last three decades. Despite upgrades to the most of the ranging station subsystems, the control computers remain a mix of 1970's vintage minicomputers. These encompass a wide range of vendors, operating systems, and languages, making hardware and software support increasingly difficult. Current technology allows replacement of controller computers at a relatively low cost while maintaining excellent processing power and a friendly operating environment. The new controller systems are now being designed using IBM-PC-compatible 80486-based microcomputers, a real-time Unix operating system (LynxOS), and X-windows/Motif IB, and serial interfaces have been chosen. This design supports minimizing short and long term costs by relying on proven standards for both hardware and software components. Currently, the project is in the design and prototyping stage with the first systems targeted for production in mid-1993.

Ricklefs, Randall L.↗
NASA NTRS1989

Animation graphic interface for the space shuttle onboard computer

Graphics interfaces designed to operate on space qualified hardware challenge software designers to display complex information under processing power and physical size constraints. Under contract to Johnson Space Center, MICROEXPERT Systems is currently constructing an intelligent interface for the LASER DOCKING SENSOR (LDS) flight experiment. Part of this interface is a graphic animation display for Rendezvous and Proximity Operations. The displays have been designed in consultation with Shuttle astronauts. The displays show multiple views of a satellite relative to the shuttle, coupled with numeric attitude information. The graphics are generated using position data received by the Shuttle Payload and General Support Computer (PGSC) from the Laser Docking Sensor. Some of the design considerations include crew member preferences in graphic data representation, single versus multiple window displays, mission tailoring of graphic displays, realistic 3D images versus generic icon representations of real objects, the physical relationship of the observers to the graphic display, how numeric or textual information should interface with graphic data, in what frame of reference objects should be portrayed, recognizing conditions of display information-overload, and screen format and placement consistency.

Wike, Jeffrey↗
NASA NTRS1984

Study of fault-tolerant software technology

Presented is an overview of the current state of the art of fault-tolerant software and an analysis of quantitative techniques and models developed to assess its impact. It examines research efforts as well as experience gained from commercial application of these techniques. The paper also addresses the computer architecture and design implications on hardware, operating systems and programming languages (including Ada) of using fault-tolerant software in real-time aerospace applications. It concludes that fault-tolerant software has progressed beyond the pure research state. The paper also finds that, although not perfectly matched, newer architectural and language capabilities provide many of the notations and functions needed to effectively and efficiently implement software fault-tolerance.

Slivinski, T.↗
NASA NTRS1994

Baseline tests of an autonomous telerobotic system for assembly of space truss structures

Several proposed space missions include precision reflectors that are larger in diameter than any current or proposed launch vehicle. Most of these reflectors will require a truss structure to accurately position the reflector panels and these reflectors will likely require assembly in orbit. A research program has been conducted at the NASA Langley Research Center to develop the technology required for the robotic assembly of truss structures. The focus of this research has been on hardware concepts, computer software control systems, and operator interfaces necessary to perform supervised autonomous assembly. A special facility was developed and four assembly and disassembly tests of a 102-strut tetrahedral truss have been conducted. The test procedures were developed around traditional 'pick-and-place' robotic techniques that rely on positioning repeatability for successful operation. The data from two of the four tests were evaluated and are presented in this report. All operations in the tests were controlled by predefined sequences stored in a command file, and the operator intervened only when the system paused because of the failure of an actuator command. The tests were successful in identifying potential pitfalls in a telerobotic system, many of which would not have been readily anticipated or incurred through simulation studies. Addressing the total integrated task, instead of bench testing the component parts, forced all aspects of the task to be evaluated. Although the test results indicate that additional developments should be pursued, no problems were encountered that would preclude automated assembly in space as a viable construction method.

Rhodes, Marvin D.↗
NASA NTRS1995

Hardware And Software For Development Of Robot Arms

System of modular, reusable hardware and software assembled for use in developing remotely controlled robotic arms. Includes (1) central computer and peripheral equipment at control and monitoring station and (2) remote mechanical platform that supports robotic arm. Central computer controls motor drives of robotic arm, but optically, platform holds on-board computer for autonomous operation. Consists mostly of commercial hardware and software. Simulated results of commands viewed in three dimensions.

Usikov, Daniel↗
NASA NTRS2002

Implementation of Autonomous Control Technology for Plant Growth Chambers

The Kennedy Space Center has significant infrastructure for research using controlled environment plant growth chambers. Such research supports development of bioregenerative life support technology for long-term space missions. Most of the existing chambers in Hangar L and Little L will be moved to the new Space Experiment Research and Processing Laboratory (SERPL) in the summer of 2003. The impending move has created an opportunity to update the control system technologies to allow for greater flexibility, less labor for set-up and maintenance, better diagnostics, better reliability and easier data retrieval. Part of these improvements can be realized using hardware which communicates through an ethernet connection to a central computer for supervisory control but can be operated independently of the computer during routine run-time. Both the hardware and software functionality of an envisioned system were tested on a prototype plant growth chamber (CEC-4) in Hangar L. Based upon these tests, recommendations for hardware and software selection and system design for implementation in SERPL are included.

Costello, Thomas A.↗
NASA NTRS1974

MIDAS, prototype Multivariate Interactive Digital Analysis System, Phase 1. Volume 2: Diagnostic system

The MIDAS System is a third-generation, fast, multispectral recognition system able to keep pace with the large quantity and high rates of data acquisition from present and projected sensors. A principal objective of the MIDAS Program is to provide a system well interfaced with the human operator and thus to obtain large overall reductions in turn-around time and significant gains in throughout. The hardware and software generated in Phase I of the over-all program are described. The system contains a mini-computer to control the various high-speed processing elements in the data path and a classifier which implements an all-digital prototype multivariate-Gaussian maximum likelihood decision algorithm operating 2 x 105 pixels/sec. Sufficient hardware was developed to perform signature extraction from computer-compatible tapes, compute classifier coefficients, control the classifier operation, and diagnose operation. Diagnostic programs used to test MIDAS' operations are presented.

Kriegler, F. J.↗
DOE OSTI2022

Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration

Abstract Materials acceleration platforms (MAPs) operate on the paradigm of integrating combinatorial synthesis, high‐throughput characterization, automatic analysis, and machine learning. Within a MAP, one or multiple autonomous feedback loops may aim to optimize materials for certain functional properties or to generate new insights. The scope of a given experiment campaign is defined by the range of experiment and analysis actions that are integrated into the experiment framework. Herein, the authors present a method for integrating many actions within a hierarchical experimental laboratory automation and orchestration (HELAO) framework. They demonstrate the capability of orchestrating distributed research instruments that can incorporate data from experiments, simulations, and databases. HELAO interfaces laboratory hardware and software distributed across several computers and operating systems for executing experiments, data analysis, provenance tracking, and autonomous planning. Parallelization is an effective approach for accelerating knowledge generation provided that multiple instruments can be effectively coordinated, which the authors demonstrate with parallel electrochemistry experiments orchestrated by HELAO. Efficient implementation of autonomous research strategies requires device sharing, asynchronous multithreading, and full integration of data management in experimental orchestration, which to the best of the authors’ knowledge, is demonstrated for the first time herein.

36 MATERIALS SCIENCE↗