SEARCH · Engineering Papers

Results for “future accelerators”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,549 records · Page 86

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Co-design of Advanced Architectures for Graph Analytics using Machine Learning

A graph is an excellent way of representing relationships among entities. We can use graph analytics to synthesize and analyze such relational data, and extract relevant features that are useful for various tasks such as machine learning. Considering the crucial role of graph analytics in various domains, it is important and timely to investigate the right hardware configurations that can achieve optimal performance for graph workloads on future high-performance computing systems. Design space exploration studies facilitate the selection of appropriate configurations (e.g. memory) to achieve a desired system performance. Recently, the approach of accelerating graph analytics using persistent non-volatile memory has gained a lot of attention. Traditional system simulators such as Gem5 and NVMain can be used to explore the design space of these advanced memory architectures for graph workloads. However, these simulators are slow in execution thus limiting the efficiency of design space exploration studies. To overcome this challenge, we proposed a machine learning based approach to co-design advanced memory architectures for graph workloads. We tested our approach with DRAM, non-volatile memory, and hybrid memory (DRAM+NVM) using a breadth first search benchmark algorithm. Our results showed the applicability of the proposed machine learning based approach to the co-design of the advanced memory architectures. In this paper, we provide recommendations on selecting advanced memory architectures to achieve desired performance for graph workloads. We also discuss the performances of different machine learning models that were considered in this study.

Kurte, Kuldeep↗

DOE OSTI2026

Preliminary Introduction of the IOTA Bake System

The Integrable Optics Test Accelerator (IOTA) ring is vital to the advancement of accelerator sciences, and a large part of its attractiveness to accelerator physicists is its modularity and the versatility that this function provides. Up until this point, the FAST accelerator has provided electron beam for the studies in IOTA. With the soon to be commissioned IOTA Proton Injector in lieu, the requirement for better vacuum to support future proton studies in the ring have arrived. Our solution is the IOTA Bake System which has the goal of facilitating this requirement.

Thompson, T. K. [Fermilab]↗

DOE OSTI2017

Analyzing The Impacts of the Biogas-to-Electricity Purchase Incentives on Electric Vehicle Deployment with the MA3T Vehicle Choice Model

This analysis represents the biogas-to-electricity pathway under the Renewable Fuel Standard (RFS) as a point of purchase incentive and tests the impact of this incentive on EV deployment using a vehicle consumer choice model. The credit value generated under this policy was calculated in a number of scenarios based on electricity use of each power train choice on a yearly basis over the 15 year vehicle lifetime, accounting for the average electric vehicle miles travelled and vehicle efficiency, competition for biogas-derived electricity among electric vehicles (EVs), the RIN equivalence value and the time value of money. The credit value calculation in each of these scenarios is offered upfront as a point of purchase incentive for EVs using the Market Acceptance of Advanced Automotive Technologies (MA3T) vehicle choice model, which tracks sales, fleet size and energy use over time. The majority of the scenarios use a proposed RIN equivalence value, which increases the credit value as a way to explore the analysis space. Additional model runs show the relative impact of the equivalence value on EV deployment. The MA3T model output shows that a consumer incentive accelerates the deployment of EVs for all scenarios relative to the baseline (no policy) case. In the scenario modeled to represent the current biogas-to-electricity generation capacity (15 TWh/year) with a 5.24kWh/RIN equivalence value, the policy leads to an additional 1.4 million plug-in hybrid electric vehicles (PHEVs) and 3.5 million battery electric vehicles (BEVs) in 2025 beyond the no-policy case of 1.3 million PHEVs and 2.1 million BEVs when the full value of the credit is passed on to the consumer. In 2030, this increases to 2.4 million PHEVs and 7.3 million BEVs beyond the baseline. This larger impact on BEVs relative to PHEVs is due in part to the larger credit that BEVs receive in the model based on the greater percentage of electric vehicle miles traveled by BEVs relative to PHEVs. In this scenario 2025 also represents the last year in which biogas-derived electricity is able to fully supply the transportation electricity demand in the model. After 2025, the credit value declines on a per vehicle basis. At the same time a larger fraction of the credit may shift towards biogas producers in order to incent additional biogas production. The expanded 41 TWh/year biogas availability scenarios represent an increase beyond today s generation capacity and allow greater eRIN generation. With a 5.24kWh/RIN equivalence value, when all of the credit is directed towards reducing vehicle purchase prices, the 41 TWh/year biogas scenario results in 4.1 million additional PHEVs and 12.2 million additional BEVs on the road in 2030 beyond the baseline of 2.5 million PHEVs and 6.1 million BEVs. Under this expanded biogas capacity, biogas-derived electricity generation is able to fully supply electricity for a fleet of over 21 million EVs (15.6 million BEVs and 5.8 million PHEVs) on a yearly basis. In addition to assessing the full value credit scenarios described above, multiple scenarios were analyzed to determine the impact if only a fraction of the credit value was passed on to the consumer. In all of these cases, the EV deployment was scaled back as the fraction of the credit that was passed on to the consumer was reduced. These scenarios can be used to estimate the impact if the credit value is reduced in other ways as well, as demonstrated by the scenarios where the current (22.6 kWh/RIN) equivalence value was used. The EV deployment that results from an equivalence value of 22.6 kWh/RIN equivalence value is roughly equivalent to the EV deployment observed in the 25% case using the 5.24 kWh/RIN equivalence value. A higher equivalence value means that a smaller number of credits, and therefore value, is created for each kWh, and therefore the impact on EV deployment is reduced. This analysis shows several of the drivers that will impact eRIN generation and credit value, and tests the impact of an eRIN point of purchase incentive on EV deployment. This additional incentive can accelerate the deployment of EVs when it is used to reduce vehicle purchase prices. However, the ultimate impact of this policy, as modeled here, will be determined by future RIN prices, the extent to which eRIN credit value can be passed on to the consumer as a point of purchase incentive and the equivalence value.

09 BIOMASS FUELS↗

DOE OSTI2018

Coupled Electron-Photon Monte Carlo Radiation Transport for Next-Generation Computing Systems

Traditional Monte Carlo particle transport codes are expected to run inefficiently on next-generation architectures as they are memory-intensive and highly divergent. Since electrons and photons also behave differently, the future for coupled electron-photon radiation transport looks even worse. This project describes preliminary efforts to improve the performance of Monte Carlo particle transport codes when using accelerators like the graphics processing unit (GPU). Two key issues are addressed: how to handle memory-intensive tallies, and how to reduce divergence. Tallying on the GPU can be done efficiently by post-processing particle data, or by using a feature called warp shuffle for summing scores in parallel during the simulation. Reducing divergence is possible by using an event-based algorithm for particle tracking instead of the traditional history-based one. Although performance tests presented in this work show that the history-based algorithm generally outperformed the event-based one for simple problems, this outcome will likely change as the complexity of the code increases.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

IOTA Bake System

The IOTA ring is vital to the advancement of accelerator sciences, and a large part of its attractiveness to accelerator physicists is its modularity and the versatility that this function provides. Up until this point, the FAST accelerator has provided electron beam for the studies in IOTA. With the soon to be commissioned IOTA Proton Injector in lieu, the requirement for better vacuum to support future proton studies in the ring have arrived. Our solution is the IOTA Bake System which has the goal of facilitating this requirement.

Thompson, Travante [Fermilab]↗

DOE OSTI2022

Developments in Performance and Portability for MadGraph5_aMC@NLO

Event generators simulate particle interactions using Monte Carlo techniques, providing the primary connection between experiment and theory in experimental high energy physics. These software packages, which are the first step in the simulation worflow of collider experiments, represent approximately 5 to 20% of the annual WLCG usage for the ATLAS and CMS experiments. With computing architectures becoming more heterogeneous, it is important to ensure that these key software frameworks can be run on future systems, large and small. In this contribution, recent progress on porting and speeding up the Madgraph5_aMC@NLO event generator on hybrid architectures, i.e. CPU with GPU accelerators, is discussed. The main focus of this work has been in the calculation of scattering amplitudes and "matrix elements", which is the computational bottleneck of an event generation application. For physics processes limited to QCD leading order, the code generation toolkit has been expanded to produce matrix element calculations using C++ vector instructions on CPUs and using CUDA for NVidia GPUs, as well as using Alpaka, Kokkos and SYCL for multiple CPU and GPU architectures. Performance is reported in terms of matrix element calculations per time on NVidia, Intel, and AMD devices. The status and outlook for the integration of this work into a production release usable by the LHC experiments, with the same functionalities and very similar user interfaces as the current Fortran version, is also described.

Valassi, Andrea↗

DOE OSTI2022

Defining the S-Glutathionylation Proteome by Biochemical and Mass Spectrometric Approaches

Protein S-glutathionylation (SSG) is a reversible post-translational modification (PTM) featuring the conjugation of glutathione to a protein cysteine thiol. SSG can alter protein structure, activity, subcellular localization, and interaction with small molecules and other proteins. Thus, it plays a critical role in redox signaling and regulation in various physiological activities and pathological events. In this review, we summarize current biochemical and analytical approaches for characterizing SSG at both the proteome level and at individual protein levels. To illustrate the mechanism underlying SSG-mediated redox regulation, we highlight recent examples of functional and structural consequences of SSG modifications. Finally, we discuss the analytical challenges in characterizing SSG and the thiol PTM landscape, future directions for understanding of the role of SSG in redox signaling and regulation and its interplay with other PTMs, and the potential role of computational approaches to accelerate functional discovery.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Ion Acceleration in Driven Magnetic Reconnection during High-energy–Density Plasma Interaction

Strongly driven magnetic reconnection occurs in astrophysical events and also in laboratory experiments with laser-produced plasma. We have performed 2.5D particle-in-cell simulations of collisions of two high-energy–density plasmas resulting in strongly driven magnetic reconnection that demonstrates significant non-thermal ion acceleration. Such acceleration is significant only when the plasma beta is sufficiently low that the Alfvén speed at the reconnection inflow exceeds the thermal speed. Under these conditions, the most energetic ions are primarily accelerated by the Hall electric field in the reconnection outflow, especially at the trailing edge of an emerging plasmoid in the outflow. Here, laboratory experiments in the near future should be able to confirm these predictions and their applicability to astrophysical situations.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1981

Combustion characteristics of hydrogen. Carbon monoxide based gaseous fuels

An experimental rig program was conducted with the objective of evaluating the combuston performance of a family of fuel gases based on a mixture of hydrogen and carbon monoxide. These gases, in addition to being members of a family, were also representative of those secondary fuels that could be produced from coal by various gasification schemes. In particular, simulated Winkler, Lurgi, and Blue-water low and medium energy content gases were used as fuels in the experimental combustor rig. The combustor used was originally designed as a low NOx rich-lean system for burning liquid fuels with high bound nitrogen levels. When used with the above gaseous fuels this combustor was operated in a lean-lean mode with ultra long residence times. The Blue-water gas was also operated in a rich-lean mode. The results of these tests indicate the possibility of the existence of an 'optimum' gas turbine hydrogen - carbon monoxide based secondary fuel. Such a fuel would exhibit NOx and high efficiency over the entire engine operating range. It would also have sufficient stability range to allow normal light-off and engine acceleration. Solar Turbines Incorporated would like to emphasize that the results presented here have been obtained with experimental rig combustors. The technologies generated could, however, be utilized in future commercial gas turbines.

Notardonato, J. J.↗

NASA NTRS1992

Unstructured mesh algorithms for aerodynamic calculations

The use of unstructured mesh techniques for solving complex aerodynamic flows is discussed. The principle advantages of unstructured mesh strategies, as they relate to complex geometries, adaptive meshing capabilities, and parallel processing are emphasized. The various aspects required for the efficient and accurate solution of aerodynamic flows are addressed. These include mesh generation, mesh adaptivity, solution algorithms, convergence acceleration, and turbulence modeling. Computations of viscous turbulent two-dimensional flows and inviscid three-dimensional flows about complex configurations are demonstrated. Remaining obstacles and directions for future research are also outlined.

Mavriplis, D. J.↗

NASA NTRS1992

Unstructured mesh algorithms for aerodynamic calculations

Mavriplis, D. J.↗

NASA NTRS1991

Motion requirements for antennas in geostationary earth orbit

Design dependencies of antennas in geostationary earth orbits are evaluated, and the tradeoffs between the optical geoscientific goals and achievable architecture capabilities are surveyed. The radial and tangential resolutions are investigated to ascertain whether mission objectives of the antennas may be satisfied. The effects of off-nadir scanning are examined to determine the limits of scan based on realizable resolution. Formulas for raster and spiral scanning time are reviewed and examined to find necessary accelerations. The resulting knowledge is used to evaluate two possible layouts. Torques necessary for slewing these configurations are calculated. A number of rotatory actuators are reviewed to evaluate future design goals for on-board momentum compensation.

Tentor, Lawrence B.↗

NASA NTRS2008

Detection and Attribution of the Recovery of Polar Ozone

The Antarctic ozone hole develops each year and culminates by early spring (late September - early October). The severity of the hole has been assessed from satellites using the minimum total ozone value from the October monthly mean (depth of the hole), calculating the average area coverage during this September-October period, and by estimating ozone mass deficit. Profile information shows that ozone is completely destroyed in the 14-2 1 km layer by early October. Ozone is mainly destroyed by halogen (chlorine and bromine) catalytic cycles, and these losses are modulated by temperature variations. Because atmospheric halogen levels are responding to international agreements that limit or phase out production, the amount of halogens in the stratosphere should decrease over the next few decades. Both models and projections of ozone depleting substances (ODSs) into the 21St century reveal that polar ozone levels should recover in the 2060- 2070 period. In this talk, we will review current projections of polar ozone recovery. Using models and ODs projections, we explore both the past, near future (2008-2025), and far future (> 2025) levels of polar ozone. Finally, we will discuss various factors that complicate recovery such as greenhouse gas changes (e.g., cooling in the upper stratosphere) and the acceleration of the Brewer-Dobson circulation.

Newman, Paul A.↗

NASA NTRS2020

Building the RS-25 Engine for NASA’s Next Generation of Exploration.

NASA is aggressively pursuing a human lunar return to the Moon with the Artemis Program. The Space Launch System (SLS) is critical to the transportation architecture, providing both crew and cargo capability. To accelerate SLS development, NASA settled on space shuttle heritage propulsion technologies. Early missions will use repurposed RS-25 engines from the Shuttle Program adapted to SLS requirements. For future missions, however, NASA and engine contractor Aerojet Rocketdyne are restarting RS-25 production with a goal of using the latest manufacturing technologies to produce an RS-25 variant that will cost at least 30 percent less than the shuttle-era engines developed and flown for almost 30 years. The prospect of restarting the production line on these engines after many years and modifying the engine to reduce cost and better match the needs of the SLS vehicle presented unique challenges and opportunities for NASA and Aerojet Rocketdyne. NASA and AR are now deep into the process of adapting 16 RS-25 engines for the first four SLS Artemis flights and building the first RS-25 “Restart” engines for future Artemis missions. In addition to navigating programmatic, technical, and logistical challenges with the current RS-25 production work, NASA and AR have partnered to pursue new methods of building this engine through advanced manufacturing techniques to further reduce the cost and schedule required to build each RS-25 engine to contribute to the long-term affordability of the SLS vehicle. This presentation will discuss the goals for the restart program, the challenges, and results to date.

Jessica Jean Wood↗

NASA NTRSDate not supplied

Evaluating Machine Learning Approaches to Plume Tracking

On July 15, 2022, the Hunga Tonga-Hunga Ha’apai (HTHH) submarine volcano erupted, propelling trace gasses and ash through the troposphere and up into the stratosphere. Previous studies manually tracked the aerosol and trace gas plumes over time across different positions in the southern hemisphere. Using imagery from NASA’s Earth Observing System, including MODIS aerosol products and OMI sulfur dioxide products, this research demonstrates how open-source machine learning (ML) models, like Meta’s Segment Anything Model (SAM), can perform automatic plume tracking following the Hunga Tonga eruption. This extensible methodology, and modular data processing and modeling pipeline, establishes a framework for systematically and rapidly studying natural disasters, including additional volcanic eruptions and large-scale wildfires. By combining advanced machine learning techniques, such as SAM’s zero-shot learning, with large volumes of NASA’s Earth Observation and remote sensing data, this work shows how AI and open science can accelerate research and generate actionable results, even for unprecedented events. The tools and technologies presented here can help translate earth science to action from NASA’s current and future Earth observing satellite missions, and assist researchers and stakeholders in understanding, mapping, and responding to natural disasters in a changing world.

machine learning↗

DOE OSTI2022

Transforming Energy Through Computational Excellence: Bringing Low Mach Number Reactive Flow Simulations at the Exascale

PeleLMeX's unique capabilities are allowing for reactive flow modeling at unprecedented scales and a reasonable time and cost. The code is currently being extended to tackle more practical, design-oriented simulations by implementing Large Eddy Simulation and data-driven chemical models, providing a fast but accurate tool for engineers considering the emergence of GPU-accelerated platforms. These extensions are critical for enabling the physical insight required to design the next generation of combustion devices as a key component of a renewable energy future.

MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Wind Energy Accomplishments and Year-End Performance Report: Fiscal Year 2024

As the largest source of clean, renewable power generation in the United States and one of the fastest growing sources of new electricity supply, wind energy will play a large role in the nation's energy future. In Fiscal Year (FY) 2024, scientists, engineers, analysts, and support professionals at the U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) worked to accelerate the pace of innovation in wind energy science and technology, advance grid systems integration, and develop sustainable solutions to deployment challenges. Much of NREL's research, development, and deployment work aligns with addressing the Grand Challenges of Wind Energy. Beginning in 2019, DOE's Wind Energy Technologies Office partnered with the International Energy Agency to identify the barriers to greater wind energy deployment and related research gaps. The world's leading wind energy scientists and engineers identified five research areas as critical to advancing wind energy deployment: wind atmospheric science, wind turbine systems, wind plants and grid, environmental co-design, and social science. In FY 2024, NREL's accomplishments helped narrow the research gaps in these critical areas. This report provides details on those accomplishments.

accomplishments↗

DOE OSTI2019

A Guide to Zero Energy and Zero Energy Ready K-12 Schools

This guide was developed as part of the U.S. Department of Energy's (DOE's) Zero Energy Schools Accelerator (ZESA), a collaborative effort between DOE; the National Renewable Energy Laboratory; and school districts, states, and educational support groups from around the country. Because education is managed locally in the United States, this research documents some of the ways U.S. school officials and educators configure school financing, design, construction, curriculum development, and other school-related matters in their areas. The steps outlined here document the process of creating a zero energy school, and can provide a strong foundation for future ZE school projects.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗