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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Reconfigurable Hardware Adapts to Changing Mission Demands

A new class of computing architectures and processing systems, which use reconfigurable hardware, is creating a revolutionary approach to implementing future spacecraft systems. With the increasing complexity of electronic components, engineers must design next-generation spacecraft systems with new technologies in both hardware and software. Derivation Systems, Inc., of Carlsbad, California, has been working through NASA s Small Business Innovation Research (SBIR) program to develop key technologies in reconfigurable computing and Intellectual Property (IP) soft cores. Founded in 1993, Derivation Systems has received several SBIR contracts from NASA s Langley Research Center and the U.S. Department of Defense Air Force Research Laboratories in support of its mission to develop hardware and software for high-assurance systems. Through these contracts, Derivation Systems began developing leading-edge technology in formal verification, embedded Java, and reconfigurable computing for its PF3100, Derivational Reasoning System (DRS ), FormalCORE IP, FormalCORE PCI/32, FormalCORE DES, and LavaCORE Configurable Java Processor, which are designed for greater flexibility and security on all space missions.

Source record↗
NASA NTRS1996

Storage and network bandwidth requirements through the year 2000 for the NASA Center for Computational Sciences

The data storage and retrieval demands of space and Earth sciences researchers have made the NASA Center for Computational Sciences (NCCS) Mass Data Storage and Delivery System (MDSDS) one of the world's most active Convex UniTree systems. Science researchers formed the NCCS's Computer Environments and Research Requirements Committee (CERRC) to relate their projected supercomputing and mass storage requirements through the year 2000. Using the CERRC guidelines and observations of current usage, some detailed projections of requirements for MDSDS network bandwidth and mass storage capacity and performance are presented.

Salmon, Ellen↗
DOE OSTI2020

The Locus Algorithm III: A Grid Computing system to generate catalogues of optimised pointings for Differential Photometry

This paper discusses the hardware and software components of the Grid Computing system used to implement the Locus Algorithm to identify optimum pointings for differential photometry of 61,662,376 stars and 23,799 quasars. The scale of the data, together with initial operational assessments demanded a High Performance Computing (HPC) system to complete the data analysis. Grid computing was chosen as the HPC solution as the optimum choice available within this project. The physical and logical structure of the National Grid computing Infrastructure informed the approach that was taken. That approach was one of layered separation of the different project components to enable maximum flexibility and extensibility.

Creaner, Oisń↗
NASA NTRS1979

Adaptive allocation of decisionmaking responsibility between human and computer in multitask situations

As human and computer come to have overlapping decisionmaking abilities, a dynamic or adaptive allocation of responsibilities may be the best mode of human-computer interaction. It is suggested that the computer serve as a backup decisionmaker, accepting responsibility when human workload becomes excessive and relinquishing responsibility when workload becomes acceptable. A queueing theory formulation of multitask decisionmaking is used and a threshold policy for turning the computer on/off is proposed. This policy minimizes event-waiting cost subject to human workload constraints. An experiment was conducted with a balanced design of several subject runs within a computer-aided multitask flight management situation with different task demand levels. It was found that computer aiding enhanced subsystem performance as well as subjective ratings. The queueing model appears to be an adequate representation of the multitask decisionmaking situation, and to be capable of predicting system performance in terms of average waiting time and server occupancy. Server occupancy was further found to correlate highly with the subjective effort ratings.

Chu, Y.-Y.↗
NASA NTRS1984

Functional integration of vertical flight path and speed control using energy principles

A generalized automatic flight control system was developed which integrates all longitudinal flight path and speed control functions previously provided by a pitch autopilot and autothrottle. In this design, a net thrust command is computed based on total energy demand arising from both flight path and speed targets. The elevator command is computed based on the energy distribution error between flight path and speed. The engine control is configured to produce the commanded net thrust. The design incorporates control strategies and hierarchy to deal systematically and effectively with all aircraft operational requirements, control nonlinearities, and performance limits. Consistent decoupled maneuver control is achieved for all modes and flight conditions without outer loop gain schedules, control law submodes, or control function duplication.

Lambregts, A. A.↗
DOE OSTI2024

Data Center Immersion Cooling: A Case Study and Summary of High-Performance Computing Cooling Technologies

The future of High Performance Computing (HPC) is carved out for all high-performance facilities. As computer processing increases exponentially, especially with the demand for AI training, more speed will equate to more computing density and ultimately more power draw and resource demand. Sandia National Laboratories has not seen or known of anything that would lead us to think this might change in the next 5 to 10 years.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2022

NASA's Next Generation of Atmospheric Data Science

The Multi-Angle Imager for Aerosols (MAIA) and the Tropospheric Emission: Monitoring of Pollution(TEMPO) are NASA’s next-generation satellite missions for air quality monitoring. These missions will produce high-quality, high-resolution air quality data to support cross-displinary research. The MAIA mission is collaborating with health science researchers and epidemiologists to study the impacts of air quality on health outcomes. TEMPO aims to improve our understanding of tropospheric air pollution chemistry and our ability to make predictions about air quality and climate forcing. TEMPO will offer hourly measurements of tropospheric ozone, aerosols, and clouds focused on North America at high-spatial resolution, while MAIA will produce high-resolution measurements of speciated particulate matter targeting densely populated cities around the globe. Data from these missions will help improve our understanding of the sources, types, and interactions among the aerosols and trace gases that are polluting Earth’s atmosphere, as well as our understanding of the impact of air pollution on pollution on a wide range of important areas including human health, agriculture, weather, and climate change. The challenges of cross-disciplinary research, computationally expensive multi-variate analyses, and high-resolution data at both local and global scales are driving substantial changes across all of NASA’s Distributed Active Archive Centers (DAACs). High resolution data at scales such these requires a new approach to data ingest, archive, and publication. Like other NASA DAACs, the Atmospheric Science Data Center (ASDC), the DAAC that will be responsible for publishing MAIA and TEMPO data products has historically archived and distributed data on premise. DAACs of the future will archive and distribute data in the cloud, enabling them to remake themselves as research-focused data centers that will support on-demand, data-intensive computations for highly accurate retrospective analyses and predictions. Under the new paradigm, data formats and metadata must support on-demand spatial and temporal sub-setting, as well as other data transformation services such as re-gridding and re-sampling. This presentation will discuss work being done to address data formatting and metadata requirements in this dynamic new environment. In addition to the changes in data stewardship practices at the ASDC, the increased focus on supporting scientific research is driving changes in the relationship between DAACs and researchers. While the ASDC will continue to provide first rate data management and stewardship, it is increasingly focused on serving as a partner not only to the science teams that gather and produce the data it publishes, but to the researchers that use that data.

Beth Huffer↗
NASA NTRS1997

Computational Considerations for the Simulation of Discontinuous Flows

The numerical study of aeroacoustic problems places stringent demands on the choice of a computational algorithm, because it requires the ability to propagate disturbances of small amplitude and short wavelength. The demands are particularly high when shock waves are involved, because the chosen algorithm must also resolve discontinuities in the solution. In a previous work the capabilities and deficiencies of shock-capturing methods for aeroacoustic problems were demonstrated using a high-order essentially nonoscillatory (ENO) numerical method. It was shown that first-order results are obtained when simulating time-dependent flows with discontinuities. The present study reaffirms this conclusion by comparing the ENO results with those obtained using a conventional linear scheme. A sixth-order-accurate compact implicit finite difference scheme is used to investigate various discontinuous flows. The design order of accuracy is achieved in the smooth regions of a steady-state, quasi-one-dimensional Euler test case, as well as in the time-dependent Burgers' equation. However, in the unsteady Euler sound-shock interaction, first-order results are obtained downstream of the shock. A comparison is made between the linear and nonlinear results, noting the advantages of each method. A discontinuous linear model problem is then used to identify the cause of the first-order results. Here, the nature of the solution error is quantified as being predominantly a numerical phase shift, and a post-processing procedure is demonstrated which increases the solution accuracy downstream of the discontinuity to second-order.

Carpenter Mark H.↗
DOE OSTI2024

Accelerating science: The usage of commercial clouds in ATLAS Distributed Computing

The ATLAS experiment at CERN is one of the largest scientific machines built to date and will have ever growing computing needs as the Large Hadron Collider collects an increasingly larger volume of data over the next 20 years. ATLAS is conducting R&D projects on Amazon Web Services and Google Cloud as complementary resources for distributed computing, focusing on some of the key features of commercial clouds: lightweight operation, elasticity and availability of multiple chip architectures. The proof of concept phases have concluded with the cloud-native, vendoragnostic integration with the experiment’s data and workload management frameworks. Google Cloud has been used to evaluate elastic batch computing, ramping up ephemeral clusters of up to O(100k) cores to process tasks requiring quick turnaround. Amazon Web Services has been exploited for the successful physics validation of the Athena simulation software on ARM processors. We have also set up an interactive facility for physics analysis allowing endusers to spin up private, on-demand clusters for parallel computing with up to 4 000 cores, or run GPU enabled notebooks and jobs for machine learning applications. The success of the proof of concept phases has led to the extension of the Google Cloud project, where ATLAS will study the total cost of ownership of a production cloud site during 15 months with 10k cores on average, fully integrated with distributed grid computing resources and continue the R&D projects.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

High Energy Physics Network Requirements Review: Final Report, July 2024–December 2024

The world-class research infrastructure at the US Department of Energy (DOE) Office of Science (SC) provides the research community with premier observational, experimental, computational, and network capabilities. Each user facility is designed to provide unique capabilities to advance the core DOE mission in science and technology for its SC program to stimulate rich scientific discoveries and enhance its innovation ecosystem. Research communities gather and flourish around each user facility, bringing together new and enhanced perspectives. The continual reinvention of the practice of science — as users and staff forge novel approaches expressed in research workflows — unlocks new discoveries and propels scientific progress. Within this research ecosystem, the high-performance computing (HPC) and networking user facilities stewarded by the SC’s Advanced Scientific Computing Research (ASCR) program play a dynamic cross-cutting role, enabling complex workflows demanding high-performance data, networking, and computing solutions. The ASCR facilities enterprise seeks to understand and meet the needs and requirements across SC and DOE domain science programs and priority efforts, highlighted by the formal requirements review methodology. Between July and December 2024, the Energy Sciences Network (ESnet) and the Office of High Energy Physics (HEP) of the DOE-SC organized an ESnet requirements review of HEP-supported program activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about its relationship to the HEP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Solving Unit Commitment Problems with Demand Responsive Loads: Preprint

This paper focuses on using variations of the Frank-Wolfe algorithm for solving unit commitment problems with high volumes of demand responsive loads on the power grid. We present a formulation of the unit commitment problem with demand responsive loads. We then show through reformulation and relaxations of the problem that variations of the Frank-Wolfe algorithm can be used to determine the time series decisions for the demand responsive loads. We show through computational experiments on the RTS-GMLC test system that the time series of demand responsive load decisions obtained through our approach are near optimal and provide details regarding how large scale parallel implementations of our approach can be highly computationally efficient.

demand response↗
NASA NTRS1996

Computational considerations for the simulation of shock-induced sound

The numerical study of aeroacoustic problems places stringent demands on the choice of a computational algorithm, because it requires the ability to propagate disturbances of small amplitude and short wavelength. The demands are particularly high when shock waves are involved, because the chosen algorithm must also resolve discontinuities in the solution. The extent to which a high-order-accurate shock-capturing method can be relied upon for aeroacoustics applications that involve the interaction of shocks with other waves has not been previously quantified. Such a study is initiated in this work. A fourth-order-accurate essentially nonoscillatory (ENO) method is used to investigate the solutions of inviscid, compressible flows with shocks in a quasi-one-dimensional nozzle flow. The design order of accuracy is achieved in the smooth regions of a steady-state test case. However, in an unsteady test case, only first-order results are obtained downstream of a sound-shock interaction. The difficulty in obtaining a globally high-order-accurate solution in such a case with a shock-capturing method is demonstrated through the study of a simplified, linear model problem. Some of the difficult issues and ramifications for aeroacoustics simulations of flows with shocks that are raised by these results are discussed.

Casper, Jay↗
DOE OSTI2023

National Transmission Planning Study - Long-Term Modeling Results

The National Transmission Planning Study (NTP Study) analyzed the transformation needed to ensure the U.S. transmission system continues to reliably meet electricity demand as the power sector evolves. The study linked several long- and short-term power systems models to test numerous interregional and regional transmission buildout scenarios. The results included in this dataset are from the long-term capacity expansion modeling, completed with NREL's ReEDS model. This includes nearly 100 future transmission scenarios with a wide range of economic, reliability, and resilience conditions. The dataset includes all the results visualized in the Tableau Public site, as linked below.

Array↗
DOE OSTI2024

Learning Constrained Parametric Differentiable Predictive Control Policies With Guarantees

We present differentiable predictive control (DPC), a method for offline learning of constrained neural control policies for nonlinear dynamical systems with performance guarantees. We show that the sensitivities of the parametric optimal control problem can be used to obtain direct policy gradients. Specifically, we employ automatic differentiation (AD) to efficiently compute the sensitivities of the model predictive control (MPC) objective function and constraints penalties. To guarantee safety upon deployment, we derive probabilistic guarantees on closed-loop stability and constraint satisfaction based on indicator functions and Hoeffding’s inequality. We empirically demonstrate that the proposed method can learn neural control policies for various parametric optimal control tasks. In particular, we show that the proposed DPC method can stabilize systems with unstable dynamics, track time-varying references, and satisfy nonlinear state and input constraints. Our DPC method has practical time savings compared to alternative approaches for fast and memory-efficient controller design. Specifically, DPC does not depend on a supervisory controller as opposed to approximate MPC based on imitation learning. We demonstrate that, without losing performance, DPC is scalable with greatly reduced demands on memory and computation compared to implicit and explicit MPC while being more sample efficient than model-free reinforcement learning (RL) algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Powered by dsgrid [Slides]

NREL's demand-side grid (dsgrid) toolkit harnesses decades of sector-specific energy modeling expertise to understand current and future U.S. electricity load for power systems analyses. The primary purpose of dsgrid is to create comprehensive electricity load data sets at high temporal, geographic, sectoral, and end-use resolution. These data sets enable detailed analyses of current patterns and future projections of end-use loads. This presentation will include NREL power grid researcher Elaine Hale.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1997

Out-of-Core Streamline Visualization on Large Unstructured Meshes

It's advantageous for computational scientists to have the capability to perform interactive visualization on their desktop workstations. For data on large unstructured meshes, this capability is not generally available. In particular, particle tracing on unstructured grids can result in a high percentage of non-contiguous memory accesses and therefore may perform very poorly with virtual memory paging schemes. The alternative of visualizing a lower resolution of the data degrades the original high-resolution calculations. This paper presents an out-of-core approach for interactive streamline construction on large unstructured tetrahedral meshes containing millions of elements. The out-of-core algorithm uses an octree to partition and restructure the raw data into subsets stored into disk files for fast data retrieval. A memory management policy tailored to the streamline calculations is used such that during the streamline construction only a very small amount of data are brought into the main memory on demand. By carefully scheduling computation and data fetching, the overhead of reading data from the disk is significantly reduced and good memory performance results. This out-of-core algorithm makes possible interactive streamline visualization of large unstructured-grid data sets on a single mid-range workstation with relatively low main-memory capacity: 5-20 megabytes. Our test results also show that this approach is much more efficient than relying on virtual memory and operating system's paging algorithms.

Ueng, Shyh-Kuang↗
DOE OSTI2023

Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022

Simulated hourly electric vehicle charging profiles for light-duty household passenger vehicles in the contiguous United States, 2018-2050. Profiles are differentiated by scenario, county, household and vehicle types, and charging type. Data was produced in 2022 using the Transportation Energy & Mobility Pathway Options (TEMPO) model and published in demand-side grid (dsgrid) toolkit format. Data are available for three adoption scenarios: "AEO Reference Case", which is aligned with the U.S. EIA Annual Energy Outlook 2018 (linked below), "EFS High Electrification", which is aligned with the High Electrification scenario of the Electrification Futures Study (linked below), and "All EV Sales by 2035", which assumes that average passenger light-duty EV sales reach 50% in 2030 and 100% in 2035. The charging shapes are derived from two key assumptions of which data users should be aware: "ubiquitous charger access", meaning that drivers of vehicles are assumed to have access to a charger whenever a trip is not in progress, and "immediate charging", meaning that immediately after trip completion, vehicles are plugged in and charge until they are either fully recharged or taken on another trip. These assumptions result in a bounding case in which vehicles' state of charge is maximized at all times. This bounding case would minimize range anxiety, but is unrealistic from the point of view of both electric vehicle service equipment (EVSE) (i.e., charger) access, and plug-in behavior as it can result in dozens of charging sessions per week for battery electric vehicles (BEVs) that in reality are often only plugged in a few times per week.

Array↗
DOE OSTI2021

Solving inverse problems in stochastic models using deep neural networks and adversarial training

Inverse problems associated with stochastic models constitute a significant portion of scientific and engineering applications. In such cases the unknown quantities are distributions. The applicability of traditional methods is limited because of their demanding assumptions or prohibitive computational consumption; for example, maximum likelihood methods require closed-form density functions, and Markov Chain Monte Carlo needs a large number of simulations. We propose a new method that estimates the unknown distribution by matching the statistical properties between observed and simulated random processes. We leverage the expressive power of neural networks to approximate the unknown distribution and use a discriminative neural network for computing the statistical discrepancies between the observed and simulated random processes. Here we demonstrated numerically that the proposed methods can estimate both the model parameters and learn complicated unknown distributions.

42 ENGINEERING↗