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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Numerical simulations of space processing - The reality, the myth, and the future

The present capabilities offered by the combination of advanced special and general purpose numerical codes and supercomputers are discussed. Emphasis is placed on unique demands placed by the space environment on the computation of realistic space experiments and recommendations for improving the scientific return on the space investment. It is concluded that numerical simulations can provide timely and relevant answers which can guide and improve future space experiments.

Chait, A.↗
NASA NTRS1994

A distributed parallel storage architecture and its potential application within EOSDIS

We describe the architecture, implementation, use of a scalable, high performance, distributed-parallel data storage system developed in the ARPA funded MAGIC gigabit testbed. A collection of wide area distributed disk servers operate in parallel to provide logical block level access to large data sets. Operated primarily as a network-based cache, the architecture supports cooperation among independently owned resources to provide fast, large-scale, on-demand storage to support data handling, simulation, and computation.

Johnston, William E.↗
NASA NTRS1994

Radiative force model performance for TOPEX/Poseidon precision orbit determination

The TOPEX/Poseidon spacecraft was launched to study the Earth's oceans. To maximize the benefit from the alimetric data collected, mission requirements dictate that TOPEX/Poseidon's orbit must be computed at extremely high accuracy. To meet these demands, a nonconservative force model which accounts for the satellite's complex geometry, attitude and surface properties has been developed. The 'box-wing' representation treats the spacecraft as the combination of flat plates arranged in the shape of a box and a connected solar array. Model performance and parameter sensitivities are discussed.

TRT-THEORETICAL↗
NASA NTRS2006

Platform for Testing Robotic Vehicles on Simulated Terrain

The variable terrain tilt platform (VTTP) is a means of providing simulated terrain for mobility testing of engineering models of the Mars Exploration Rovers. The VTTP could also be used for testing the ability of other robotic land vehicles (and small vehicles in general) to move across terrain under diverse conditions of slope and surface texture, and in the presence of obstacles of various sizes and shapes. The VTTP consists mostly of a 16-ft-(4.88-m)-square tilt table. The tilt can be adjusted to any angle between 0 (horizontal) and 25 . The test surface of the table can be left bare; can be covered with hard, high-friction material; or can be covered with sand, gravel, and/or other ground-simulating material or combination of materials to a thickness of as much as 6 in. (approx. 15 cm). Models of rocks, trenches, and other obstacles can be placed on the simulated terrain. For example, for one of the Mars- Rover tests, a high-friction mat was attached to the platform, then a 6-in.- ( 15 cm) deep layer of dry, loose beach sand was deposited on the mat. The choice of these two driving surface materials was meant to bound the range of variability of terrain that the rover was expected to encounter on the Martian surface. At each of the different angles at which tests were performed, for some of the tests, rocklike concrete obstacles ranging in height from 10 to 25 cm were placed in the path of the rover (see figure). The development of the VTTP was accompanied by development of a methodology of testing to characterize the performance and modes of failure of a vehicle under test. In addition to variations in slope, ground material, and obstacles, testing typically includes driving up-slope, down-slope, cross-slope, and at intermediate angles relative to slope. Testing includes recording of drive-motor currents, wheel speeds, articulation of suspension mechanisms, and the actual path of the vehicle over the simulated terrain. The collected data can be used to compute curves that summarize torque, speed, power-demand, and slip characteristics of wheels during the traverse.

Lindemann, Randel↗
NASA NTRS2015

Space Biotech: Hindsight, Insight, Foresight

Over the past forty years, microgravity has inspired and enabled applications in a wide range of sectors including medicine, materials, computers, communications, and national defense. Trends show that demand for high-tech solutions is increasing in these sectors, solutions that require higher resolution, greater precision, novel materials, innovative processes, and more sophisticated tools. These are areas where microgravity can offer unique capabilities for innovation. The Emerging Space Office (ESO) has engaged in multiple studies over the past year that have found that microgravity RD is one of the most promising technology areas for contributing to economic growth and to NASAs mission. The focus of these studies was on terrestrial markets rather than NASA applications, applied research rather than basic research, and commercial rather than academic investigators. There have been more success stories than are generally appreciated and there are significant areas of promising future potential. Many of the problems that have limited commercial microgravity development in the past are being solved. Microgravity research and development (RD) requires iteration and learning, as rapidly as possible. New technologies enable high throughput and rapid data collection in increasingly small payloads. The International Space Station is in orbit and provides a laboratory that is available 247 at least until 2024. Frequent flights by commercial space providers to and from the ISS now enable the fast learning cycles needed by high-tech industries. Launch costs are decreasing and the ability to return payloads to Earth is increasing. New commercial space laboratories, such as those being developed by SpaceX and Bigelow Aerospace, are in the final stages of development and testing. This ecosystem for microgravity RD has never been available before. These are game-changer conditions for attracting high-tech industries to space for terrestrial, as well as NASA, applications. However, few know that these capabilities are available or how to use them. In aggregate, the potential value for new applications from microgravity RD over the next ten years could add billions of dollars per year in terrestrial applications to the future economy, create new jobs, and generate a wide range of public benefits in medical advances, while broadening the customer base for the emerging space industry.

biotech↗
DOE OSTI2022

Grid-Interactive Building Control Via Reinforcement Learning

In this work, we present the proposed two-stage reinforcement learning approach for training building controllers to help buildings to participate in DR events. The original conference paper for this research idea can be found at https://www.nrel.gov/docs/fy21osti/78000.pdf.

building control↗
DOE OSTI2023

US ATLAS and US CMS HPC and Cloud Blueprint

The Large Hadron Collider (LHC) at CERN houses two general purpose detectors - ATLAS and CMS - which conduct physics programs over multi-year runs to generate increasingly precise and extensive datasets. The efforts of the CMS and ATLAS collaborations lead to the discovery of the Higgs boson, a fundamental particle that gives mass to other particles, representing a monumental achievement in the field of particle physics that was recognized with the awarding of the Nobel Prize in Physics in 2013 to François Englert and Peter Higgs. These collaborations continue to analyze data from the LHC and are preparing for the high luminosity data taking phase at the end of the decade. The computing models of these detectors rely on a distributed processing grid hosted by more than 150 associated universities and laboratories worldwide. However, such new data will require a significant expansion of the existing computing infrastructure. To address this, both collaborations have been working for years on integrating High Performance Computers (HPC) and commercial cloud resources into their infrastructure and continue to assess the potential role of such resources in order to cope with the demands of the new high luminosity era. US ATLAS and US CMS computing management have charged the authors to provide a blueprint document looking at current and possibly future use of HPC and Cloud resources, outlining integration models, possibilities, challenges and costs. The document will address key questions such as the optimal use of resources for the experiments and funding agencies, the main obstacles that need to be overcome for resource adoption, and areas that require more attention.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2003

The Ames Power Monitoring System

The Ames Power Monitoring System (APMS) is a centralized system of power meters, computer hardware, and specialpurpose software that collects and stores electrical power data by various facilities at Ames Research Center (ARC). This system is needed because of the large and varying nature of the overall ARC power demand, which has been observed to range from 20 to 200 MW. Large portions of peak demand can be attributed to only three wind tunnels (60, 180, and 100 MW, respectively). The APMS helps ARC avoid or minimize costly demand charges by enabling wind-tunnel operators, test engineers, and the power manager to monitor total demand for center in real time. These persons receive the information they need to manage and schedule energy-intensive research in advance and to adjust loads in real time to ensure that the overall maximum allowable demand is not exceeded. The APMS (see figure) includes a server computer running the Windows NT operating system and can, in principle, include an unlimited number of power meters and client computers. As configured at the time of reporting the information for this article, the APMS includes more than 40 power meters monitoring all the major research facilities, plus 15 Windows-based client personal computers that display real-time and historical data to users via graphical user interfaces (GUIs). The power meters and client computers communicate with the server using Transmission Control Protocol/Internet Protocol (TCP/IP) on Ethernet networks, variously, through dedicated fiber-optic cables or through the pre-existing ARC local-area network (ARCLAN). The APMS has enabled ARC to achieve significant savings ($1.2 million in 2001) in the cost of power and electric energy by helping personnel to maintain total demand below monthly allowable levels, to manage the overall power factor to avoid low power factor penalties, and to use historical system data to identify opportunities for additional energy savings. The APMS also provides power engineers and electricians with the information they need to plan modifications in advance and perform day-to-day maintenance of the ARC electric-power distribution system.

Osetinsky, Leonid↗
DOE OSTI2022

Partner with a Third-Party Delivery Service or Not? A Prediction-and-Decision Tool for Restaurants Facing Takeout Demand Surges During a Pandemic

Amidst the COVID-19 pandemic, restaurants become more reliant on no-contact pick-up or delivery ways for serving customers. As a result, they need to make tactical planning decisions such as whether to partner with online platforms, to form their own delivery team, or both. In this paper, we develop an integrated prediction-decision model to analyze the profit of combining the two approaches and to decide the needed number of drivers under stochastic demand. We first use the susceptible-infected-recovered (SIR) model to forecast future infected cases in a given region and then construct an autoregressive-moving-average (ARMA) regression model to predict food-ordering demand. Using predicted demand samples, we formulate a stochastic integer program to optimize food delivery plans. We conduct numerical studies using COVID-19 data and food-ordering demand data collected from local restaurants in Nuevo Leon, Mexico, from April to October 2020, to show results for helping restaurants build contingency plans under rapid market changes. Our method can be used under unexpected demand surges, various infection/vaccination status, and demand patterns. Here, our results show that a restaurant can benefit from partnering with third-party delivery platforms when (i) the subscription fee is low, (ii) customers can flexibly decide whether to order from platforms or from restaurants directly, (iii) customers require more efficient delivery, (iv) average delivery distance is long, or (v) demand variance is high.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2000

Scheduling and Estimating the Cost of Crew Time

In a previous paper, Theory and Application of the Equivalent System Mass Metric, Julie Levri, David Vaccari, and Alan Drysdale developed a method for computing the Equivalent System Mass (ESM) of crew time. ESM is an analog of cost. The suggested approach has been applied but seems to impose too high a cost for small additional requirements for crew time. The proposed method is based on the minimum average cost of crew time. In this work, the scheduling of crew time is examined in more detail, using suggested crew time allocations and daily work schedules. Crew tasks are typically assigned using priorities, which can also be used to construct a crew time demand curve mapping the value or cost per hour versus the total number of hours worked. The cost of additional crew time can be estimated by considering the intersection and shapes of the demand and supply curves. If e assume a mathematical form for the demand curve, a revised method can be developed for computing the cost or ESM of crew time. This method indicates a low cost per hour for small additional requirements for crew time and an increasing cost per hour for larger requirements.

Jones, Harry↗
DOE OSTI2025

Atomate2: modular workflows for materials science

High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2's improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Visualization of Multi-Fidelity Approximations of Stochastic Economic Dispatch

As renewable energy generation deployment increases, the operation of electrical grids becomes more complex. Economic dispatch is part of a grid operator's regular decision process where the amount of energy to generate is determined based on the number of available generators and the actual level of energy demand. Renewable generators are inherently stochastic due to the chaotic nature of weather patterns, and thus, real-time decisions of economic dispatch become increasingly complex. Modeling efforts to assist in these decisions in the highest fidelity typically take hours to days to solve on leadership-class computers; too long for the 5-minute operational time-frame demanded of operators. Alternatively, multi-fidelity approximations can be used to predict generation levels quickly and with sufficient accuracy to be used for real-time operations. We have developed a visualization tool to demonstrate the utility of multi-fidelity approximations by displaying contextual results of economic dispatch approximations, comparisons across fidelity levels of generation levels and possible failures to meet demand, and meta-data on the modeling setup.

interactive visualization↗
DOE OSTI2020

FPDeep: Scalable Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters

In this paper, we propose a framework called FPDeep, which uses a hybrid of model and layer paral- lelism to configure distributed reconfigurable clusters to train DNNs. This approach has numerous benefits. First, the design does not suffer from batch size growth. Second, novel workload and weight partitioning leads to balanced loads of both among nodes. And third, the entire system is fine-grained pipeline. This leads to high parallelism and utilization and also minimizes the time features need to be cached while waiting for back-propagation.

Wang, Tianqi↗
NASA NTRS1973

Optimum spaceborne computer system design by simulation

A deterministic simulator is described which models the Automatically Reconfigurable Modular Multiprocessor System (ARMMS), a candidate computer system for future manned and unmanned space missions. Its use as a tool to study and determine the minimum computer system configuration necessary to satisfy the on-board computational requirements of a typical mission is presented. The paper describes how the computer system configuration is determined in order to satisfy the data processing demand of the various shuttle booster subsytems. The configuration which is developed as a result of studies with the simulator is optimal with respect to the efficient use of computer system resources.

Williams, T.↗
NASA NTRS2001

Accurate Time-Dependent Traveling-Wave Tube Model Developed for Computational Bit-Error-Rate Testing

The phenomenal growth of the satellite communications industry has created a large demand for traveling-wave tubes (TWT's) operating with unprecedented specifications requiring the design and production of many novel devices in record time. To achieve this, the TWT industry heavily relies on computational modeling. However, the TWT industry's computational modeling capabilities need to be improved because there are often discrepancies between measured TWT data and that predicted by conventional two-dimensional helical TWT interaction codes. This limits the analysis and design of novel devices or TWT's with parameters differing from what is conventionally manufactured. In addition, the inaccuracy of current computational tools limits achievable TWT performance because optimized designs require highly accurate models. To address these concerns, a fully three-dimensional, time-dependent, helical TWT interaction model was developed using the electromagnetic particle-in-cell code MAFIA (Solution of MAxwell's equations by the Finite-Integration-Algorithm). The model includes a short section of helical slow-wave circuit with excitation fed by radiofrequency input/output couplers, and an electron beam contained by periodic permanent magnet focusing. A cutaway view of several turns of the three-dimensional helical slow-wave circuit with input/output couplers is shown. This has been shown to be more accurate than conventionally used two-dimensional models. The growth of the communications industry has also imposed a demand for increased data rates for the transmission of large volumes of data. To achieve increased data rates, complex modulation and multiple access techniques are employed requiring minimum distortion of the signal as it is passed through the TWT. Thus, intersymbol interference (ISI) becomes a major consideration, as well as suspected causes such as reflections within the TWT. To experimentally investigate effects of the physical TWT on ISI would be prohibitively expensive, as it would require manufacturing numerous amplifiers, in addition to acquiring the required digital hardware. As an alternative, the time-domain TWT interaction model developed here provides the capability to establish a computational test bench where ISI or bit error rate can be simulated as a function of TWT operating parameters and component geometries. Intermodulation products, harmonic generation, and backward waves can also be monitored with the model for similar correlations. The advancements in computational capabilities and corresponding potential improvements in TWT performance may prove to be the enabling technologies for realizing unprecedented data rates for near real time transmission of the increasingly larger volumes of data demanded by planned commercial and Government satellite communications applications. This work is in support of the Cross Enterprise Technology Development Program in Headquarters' Advanced Technology & Mission Studies Division and the Air Force Office of Scientific Research Small Business Technology Transfer programs.

Kory, Carol L.↗
DOE OSTI2024

Computational Modeling, Simulation, and Potential Applications of Optical Stochastic Cooling

With the rising demand for intense particles beams, much research is being conducted in the area of particle beam cooling. One of these methods, called Stochastic Cooling (SC) (developed at CERN in the 1970's), delivered a feedback method to improve the quality and lifetime of circulating proton beams by reducing their 6D phase-space and has been widely implemented in a number of hadron machines. However, traditional stochastic cooling schemes are limited by the bandwidth of microwave frequency systems. Optical Stochastic Cooling (OSC) is a promising extension of the stochastic cooling beam cooling technique. OSC instead uses optical wavelengths which allows for improved control and increased cooling but creates its own technical challenges. This lays out work conducted toward the experimental demonstration of OSC at Fermilab's Integrable Optics Test Accelerator (IOTA) storage ring. This includes the design and characterization of parts of the optical delay system, the development and validation of a high-fidelity computational model of the OSC process, and the investigations into possible applications of the OSC mechanism to advanced beam manipulations.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

An HPC benchmark survey and taxonomy for characterization

The field of High-Performance Computing (HPC) is defined by providing computing devices with highest performance for a variety of demanding scientific users. The tight co-design relationship between HPC providers and users propels the field forward, paired with technological improvements, achieving continuously higher performance and resource utilization. A key device for system architects, architecture researchers, and scientific users are benchmarks, allowing for well-defined assessment of hardware, software, and algorithms. Many benchmarks exist in the community, from individual niche benchmarks testing specific features, to large-scale benchmark suites for whole procurements. We survey the available HPC benchmarks, summarizing them in table form with key details and concise categorization, also through an interactive website. For categorization, we present a benchmark taxonomy for well-defined characterization of benchmarks.

Benchmarking↗
DOE OSTI2025

ReSpike: A Co-Design Framework for Evaluating SNNs on ReRAM-Based Neuromorphic Processors

With Moore’s law approaching its end, traditional von Neumann architectures are struggling to keep up with the exceeding performance and memory requirements of artificial intelligence and machine learning algorithms. Unconventional computing approaches such as neuromorphic computing that leverage spiking neural networks (SNNs) to perform computation are gaining traction and seek the paradigm shift necessary to sustain the increasing demands of modern applications. Novel memory technologies, such as resistive RAM (ReRAM), employ a crossbar architecture that possesses the inherent capability of efficiently computing vector-matrix multiplication—a dominant operation in SNNs. The prospect of naturally mapping SNNs to the crossbar structures provides a unique opportunity for achieving a high-performance, power-efficient neuromorphic system. In this work, we present ReSpike, which is a new framework, behavioral simulator, and architectural design based on ReRAM crossbar architectures, enabling modeling and co-design to achieve efficient execution of SNNs. We drive this co-design forward by quantifying the impact that ReRAM cell nonidealities have on the corresponding accuracy of an SNN application.

Asifuzzaman, Kazi [ORNL] (ORCID:0000000240044791)↗