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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 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Wide-Field Astronomical Surveys in the Next Decade

Wide-angle surveys have been an engine for new discoveries throughout the modern history of astronomy, and have been among the most highly cited and scientifically productive observing facilities in recent years. This trend is likely to continue over the next decade, as many of the most important questions in astrophysics are best tackled with massive surveys, often in synergy with each other and in tandem with the more traditional observatories. We argue that these surveys are most productive and have the greatest impact when the data from the surveys are made public in a timely manner. The rise of the 'survey astronomer' is a substantial change in the demographics of our field; one of the most important challenges of the next decade is to find ways to recognize the intellectual contributions of those who work on the infrastructure of surveys (hardware, software, survey planning and operations, and databases/data distribution), and to make career paths to allow them to thrive.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

PLEXUS: A Pattern-Oriented Runtime System Architecture for Resilient Extreme-Scale High-Performance Computing Systems

For high-performance computing (HPC) system designers and users, meeting the myriad challenges of next-generation exascale supercomputing systems requires rethinking their approach to application and system software design. Among these challenges, providing resiliency and stability to the scientific applications in the presence of high fault rates requires new approaches to software architecture and design. As HPC systems become increasingly complex, they require intricate solutions for detection and mitigation for various modes of faults and errors that occur in these large-scale systems, as well as solutions for failure recovery. These resiliency solutions often interact with and affect other system properties, including application scalability, power and energy efficiency. Therefore, resilience solutions for HPC systems must be thoughtfully engineered and deployed.In previous work, we developed the concept of resilience design patterns, which consist of templated solutions based on well-established techniques for detection, mitigation and recovery. In this paper, we use these patterns as the foundation to propose new approaches to designing runtime systems for HPC systems. The instantiation of these patterns within a runtime system enables flexible and adaptable end-to-end resiliency solutions for HPC environments. The paper describes the architecture of the runtime system, named Plexus, and the strategies for dynamically composing and adapting pattern instances under runtime control. This runtime-based approach enables actively balancing the cost-benefit trade-off between performance overhead and protection coverage of the resilience solutions. Based on a prototype implementation of PLEXUS, we demonstrate the resiliency and performance gains achieved by the pattern-based runtime system for a parallel linear solver application.

Hukerikar, Saurabh↗
DOE OSTI2023

Igor

SAND2023-05551O Igor, an open-source software, manages large clusters of users in a high-performance computing community. The systems allows users to choose an operating system and reserve and request hosts. The administrative side sets up and executes requests from users while simultaneously giving them tools to manage and enforce reservation policies and user access to the hosts. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Bagwell, Allen↗
DOE OSTI2024

Maximum Switching Throughput Density Estimator

SAND2024-11125O The Maximum Switching Throughput Density Estimator software performs a simple analysis that estimates the maximum logic switching throughput density that’s achieved in various CMOS technology nodes on the International Roadmap for Devices and Systems. This software utilizes simple device models and optimization techniques, performing a simple sweep over a range of possible logic supply voltages, and analytically calculating the maximum switching frequency for the given logic voltage that meets the power density constraint. It does this by using simple models of power dissipation in conventional and fully adiabatic switching. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Frank, Michael↗
DOE OSTI2019

Addendum to the REBUS Users Guide: REBUS-PC (V.1.5)

The Department of Energy (DOE) National Nuclear Security Administration (NNSA) Material Management and Minimization (M3) Reactor Conversion Program uses the REBUS-PC computer code to provide reactor physics and core design information such as neutron flux distributions in space, energy, and time, and to track isotopic changes in fuel and neutron absorbers with burnup. REBUS-PC has evolved from the original REBUS code, which was created starting in the 1960’s to study large liquid metal cooled fast reactors. In particular, additional outputs were added to improve user convenience and some inputs were modified to improve the functionality of the software. REBUS and REBUS-PC both model the external cycle, and are very general codes with 1D, 2D, and 3D neutronics capabilities, and with complete fuel shuffling capabilities. REBUS remains an extremely efficient reactor analysis tool for use in fast reactor systems, and so development has continued at Argonne National Laboratory (ANL) to this day for non-thermal reactor applications. While retaining the efficient computational engine of REBUS, REBUS-PC has evolved to its present status with continuous development since 1999. REBUS-PC has numerous changes and enhancements directed toward the needs of the thermal reactor analyst using Windows or Linux-based computational platforms.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Novel Low-Cost Approach for Acquiring High Resolution High-Speed Data

The Oak Ridge National Laboratory (ORNL) has developed and tested a novel system architecture for acquiring high fidelity high-speed data. The approach uses a low-cost consumer grade audio recording device coupled with computing technology running open-source software. The main advantage of this approach is perchannel cost; an instrument grade data acquisition system typically costs between $800 to $2,000 per channel compared to less than $50 per channel for these consumer grade components. Three systems, each featuring four channels, have been deployed for acquiring data from geophones and the electrical supply system that supports the High Flux Isotope Reactor (HFIR) and the Radiochemical Engineering Development Center (REDC) at ORNL. Each channel samples at 96 kHz at 24-bit resolution. The deployed systems operate continuously 24/7 and produce about 4 terabytes of data per month per system. This paper provides a technical overview of this approach, its implementation, and some preliminary results from qualification testing. This work was conducted in support of the Multi-Informatics for Nuclear Operations Scenarios (MINOS).

Wetherington Jr, G↗
DOE OSTI2016

Crash simulation of UNS electric vehicle under frontal front impact

Sebelas Maret University has been developing an Electric Vehicle namely SmarT-EV UNS. The main structure of the car are chasis and body. The chasis is made from steel and the body is made from fiberglass composite. To ensure the safety of the car, both static and dynamic tests were carried out to these structures, including their materials, like: tensile test, bending test, and impact test. Another test needed by this vehicle is crashworthiness test. To perform the test, it is needed complex equipments and it is quite expensive. Another way to obtain vehicle crashworthiness behaviour is by simulate it. The purpose of this study was to simulate the response of the Smart-EV UNS electric vehicle main structure when crashing rigid barrier from the front. The crash simulation was done in according to the NHTSA (National Highway Traffic Safety Administration) within the speed of the vehicle of 35 mph. The UNS Electric Vehicle was modelled using SolidWorks software, and the simulation process was done by finite element method using ANSYS software. The simulation result showed that the most internal impact energy was absorbed by chassis part. It absorbed 76.2% of impact energy, then the base absorbed 11.3 %, while the front body absorbed 2.5 %, and the rest was absorbed by fender, hood, and other parts.

42 ENGINEERING↗
DOE OSTI2024

Hazards and Probabilistic Risk Assessments of Advanced Nuclear Reactors Coupled with Industrial Facilities

This report provides a roadmap and tool kit for site specific risk assessments across a broad range of industrial customers co-located with advanced nuclear power plants (ANPP) that are not currently built and operating in the U.S. This report builds upon the body of work sponsored by the Department of Energy (DOE) Integrated Energy Systems Pathway that has produced industrial requirements studies and techno-economic assessments on the topics of feasibility of ANPP supported industrial processes. This report also leverages the DOE Light Water Reactor Sustainability (LWRS) program that has presented hazards assessment and generic probabilistic risk assessments (PRAs) for the addition of a heat extraction system (HES) to light-water reactors (LWRs) co-located with hydrogen production facilities. Many of the hazard assessments and risk assessments performed for the LWRS report are agnostic to whether the nuclear reactor is an ANPP or were adapted to the ANPP focus. The report performs hazards assessments to include industrial facilities: an oil refinery, a methanol plant, a synthetic fuel (synfuel) plant, the production of synthetic gas (syngas) as part of the methanol and synfuel plants, wood pulp and paper mills, and hydrogen production. Hydrogen production facilities are assessed in depth through prior reports in the LWRS program and the results are leveraged in this report. All these facilities are specified through industrial process and requirements research performed by national laboratories, universities, and interaction with industry. Many of the processes used in this report are pre-conceptual designs to use for decarbonization of the current technology facilities. A process of failure modes and effects analysis (what can go wrong) and accidentology (what has historically gone wrong) was used to determine the hazards presented to the nuclear power plant by the addition of the HES and the industrial customer. Chemical properties of feedstocks and products are summarized as part of the hazards assessment. Example analysis procedures are provided for each of the hazard types identified. These deterministic analyses can be used to assess adherence to licensing criteria. They can also be used to meet other safety goals like protection of the public, workers, or industrial facility equipment. A modular high temperature gas-cooled reactor (MHTGR) PRA only existing on paper was modeled and verified in modern PRA software. This will provide a tool for representative ANPP probabilistic analyses for future research.

10 SYNTHETIC FUELS↗
DOE OSTI2025

Vibrational Signatures of Electronic Properties in Renewable-Energy Catalysis

The objective of this research program is to discover and develop new approaches for ab initio computational simulations of molecular motion. Specifically, this program aims to decipher the connection between the underlying electronic structure and the accordant molecular vibrations in reactive ions and radicals for renewable-energy purposes. Recent experimental progress in ion sources and optical spectroscopies has unearthed considerable new inner-sphere detail for these complexes, but the connection between these spectral signatures and mechanistic information often remains elusive, to the continued frustration of experimentalists. For this purpose, new anharmonic vibrational frequency methods, along with a publicly deployed software package, will be developed. Working closely with committed experimental collaborators, this conceptual and computational framework will be used to explain the results of new spectroscopy experiments, focusing specifically on the inner-shell mechanisms of renewable-energy catalysis. The oxidation half of catalytic water-splitting chemistry will be a central focus, along with fundamental studies of the manner in which strong ions and radicals activate solvent as a chemical species. The resulting products of the research program will include openly available software and algorithms for the ab initio simulation of challenging vibrational spectra, as well as critical mechanistic insight into energy-focused catalytic processes that are opaque to other existing analytical techniques.

42 ENGINEERING↗
DOE OSTI2026

A Simulator for Neyer Tests of Explosives

Explosives and explosive devices such as detonators are typically tested by applying a range of stimuli such as voltage or mechanical shock, and recording binary “detonated/did not detonate” responses. These are analyzed using maximum likelihood or generalized linear models to provide estimates of quantities such as the all-fire and no-fire points. Given that the true threshold for detonation is unknown a priori , sequential design methods are typically used to optimize the set of test points. One popular method, implemented in commercial software, is Neyer’s algorithm. To support simulation and experimental design, we have developed code in the R programming language to duplicate the functions of the Neyer software. We provide code for the simulator along with a description and examples of usage.

42 ENGINEERING↗
DOE OSTI2020

RouteE: A Vehicle Energy Consumption Prediction Engine

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL’s Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

RouteE: A Vehicle Energy Consumption Prediction Engine

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL's Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Using Low-Cost “Garage Band” Recording Technology for Acquiring High Resolution High-Speed Data

The Oak Ridge National Laboratory (ORNL) has developed and tested a novel system architecture for acquiring high fidelity high-speed data. The approach uses a consumer grade audio recording device that is normally associated with “garage band” recording of music. ORNL has coupled this low-cost data acquisition hardware with computing technology running open-source software. The main advantage of this approach is per-channel cost; an instrument grade data acquisition system typically costs between $800 to $2000 per channel compared to less than $50 per channel for these consumer grade components. Three systems, each featuring four channels, have been deployed for acquiring data from geophones and the electrical supply system that supports the High Flux Isotope Reactor (HFIR) and the Radiochemical Engineering Development Center (REDC) at ORNL. Each channel samples at 96 kHz at 24-bit resolution. The deployed systems operate continuously 24/7 and produce about 4 terabytes of data per month per system. This paper provides a technical overview of this approach, its implementation, and some preliminary results from qualification testing. This work was conducted in support of the Multi-Informatics for Nuclear Operations Scenarios (MINOS).Keywords: Data acquisition Measurement Informatics

Wetherington Jr, G↗
DOE OSTI2024

Directing Nanoparticle Organization in Response to Diverse Chemical Inputs

Signaling cascades are crucial for transducing stimuli in biological systems, enabling multiple stimuli to regulate a downstream target with precisely controlled timing and amplifying signals through a series of intermediary reactions. Developing a robust signaling system with such capabilities would be pivotal for programming complex behaviors in synthetic DNA-based molecular devices. However, although “software” such as nucleic acid circuits could potentially be harnessed to relay signals to DNA-based nanostructure hardware, such explorations have been limited. Here, in this study, we develop a platform for transducing a variety of stimuli via messenger-mediated reactions to regulate the release and reloading of gold nanoparticles (AuNPs) in a 3D DNA framework. In the first step, an in vitro transcription circuit is engineered to sense and amplify chemical stimuli, including arbitrary DNA sequences and proteins, producing RNA. In the second step, the RNA releases the DNA-coated AuNPs from the DNA framework via a strand displacement reaction. AuNP reloading is controlled by a separate step driven by degradation of the RNA. Our platform holds promise for applications requiring dynamic multiagent control over DNA-based devices, offering a versatile tool for advanced molecular device engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2016

SU-F-T-609: Impact of Dosimetric Variation for Prescription Dose Using Analytical Anisotropic Algorithm (AAA) in Lung SBRT

Purpose: Actual irradiated prescription dose to patients cannot be verified. Thus, independent dose verification and second treatment planning system are used as the secondary check. AAA dose calculation engine has contributed to lung SBRT. We conducted a multi-institutional study to assess variation of prescription dose for lung SBRT when using AAA in reference to using Acuros XB and Clarkson algorithm. Methods: Six institutes in Japan participated in this study. All SBRT treatments were planed using AAA in Eclipse and Adaptive Convolve (AC) in Pinnacle3. All of the institutes used a same independent dose verification software program (Simple MU Analysis: SMU, Triangle Product, Ishikawa, Japan), which implemented a Clarkson-based dose calculation algorithm using CT image dataset. A retrospective analysis for lung SBRT plans (73 patients) was performed to compute the confidence limit (CL, Average±2SD) in dose between the AAA and the SMU. In one of the institutes, a additional analysis was conducted to evaluate the variations between the AAA and the Acuros XB (AXB). Results: The CL for SMU shows larger systematic and random errors of 8.7±9.9 % for AAA than the errors of 5.7±4.2 % for AC. The variations of AAA correlated with the mean CT values in the voxels of PTV (a correlation coefficient : −0.7) . The comparison of AXB vs. AAA shows smaller systematic and random errors of −0.7±1.7%. The correlation between dose variations for AXB and the mean CT values in PTV was weak (0.4). However, there were several plans with more than 2% deviation of AAPM TG114 (Maximum: −3.3 %). Conclusion: In comparison for AC, prescription dose calculated by AAA may be more variable in lung SBRT patient. Even AXB comparison shows unexpected variation. Care should be taken for the use of AAA in lung SBRT. This research is partially supported by Japan Agency for Medical Research and Development (AMED)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Stabilizer Rank Strong Simulation v.0.1

This software improves upon the Clifford gate+T gate magic state strong simulation algorithm by Brayvi and Gosset. It provides a better stabilizer state decomposition that scales as 2^{0.463t}, an improvement over 2^{0.468t}, for t T gate magic states. This code enables simulations of universal quantum computing with that asymptotic scaling cost. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525 SAND2020-14293 O

SciDAC↗
DOE OSTI2023

DataShare Recon Assistant Tool (DSRAT) v.1.0.x

SAND2023-06798O DataShare Recon Assistant Tool (DSRAT) software is used for capturing recon data and information in an emergency response situation on an Android device. Users can capture images, video, item description and spectra information in DSRAT and upload it to DataShare, which is a multi-server application that shares data in real-time to all nodes on the network. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Gilbert, Christina↗
DOE OSTI2023

Deep Learning Model Segmentations on Computed Tomography 3D Reconstructions of Coffee Beans to Determine Void Ratio (U-Net) and Roast Level (LinkNet)

This project will evaluate the functionality of Object Research Dragonfly software’s user produced deep learning (DL) models on Computed Tomography (CT) scanned coffee beans from green (unroasted) through a dark roast. DL models will be expected to identify voids within the coffee beans, and identify the level of roast of the bean from the CT reconstruction. The scope of this project is intended to meet the Capstone Project requirements of University of California San Diego (UCSD) Structural Engineering master’s degree and offer useful insight on Dragonfly’s DL capability for LANL’s Non-Destructive Evaluation (NDE) CT team. The results of this project will be presented to the E-6 NDE group within LANL. Data acquisition was completed with a North Star Imaging (NSI) X-25 CT Cabinet.

97 MATHEMATICS AND COMPUTING↗