Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “accelerator modeling”

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.

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

At least 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Power now, pay later: the evolution of U.S. residential solar financing

Most U.S. residential rooftop solar customers finance their solar purchases through loans or by buying power from third-party owned systems. Prior research demonstrates how third-party ownership (TPO) models such as leases emerged in the early 2010s and accelerated solar adoption by low- and moderate-income households while driving market concentration in the installation industry. Since 2015, loans have emerged as a prevalent financing alternative, but the potential effects of loans on the customer base and industry remain understudied. Here, we fill that research gap by developing a methodology to identify loan-financed and third-party owned systems in a household-level solar adopter data set. The data suggest that loans accounted for increasing solar market shares from 2017 until reaching as high as 70% in 2022, but that the market has since shifted back to TPO. The data show that TPO adopters in our sample earned about 16%–18% less and loan recipients earned 3%–7% less, at the median, than customers who self-financed systems. These results reaffirm prior research showing that TPO has accelerated low- and moderate-income adoption and that loans have likewise expanded the customer base to a lesser extent. The results suggest that loan-financed systems entail around a 16%–26% price premium that is only partly explained by loan fees. Finally, the data suggest that the emergence of loans has likely reduced market concentration in the rooftop solar industry.

financing↗
DOE OSTI2020

Constraints on millicharged particles from cosmic-ray production

We study cosmic-ray-atmosphere collisions as a permanent production source of exotic millicharged particles (MCPs) for all terrestrial experiments. [MCPs are also known as charged massive particles (CHAMPs).] Based on data from Super-K, this allows us to derive new limits on MCPs that are competitive with, or improve, the currently leading bounds from accelerator-based searches for masses up to 1.5 GeV. In models where a subdominant component of dark matter (DM) is fractionally charged, these constraints probe parts of the parameter space that is inaccessible for conventional direct-detection DM experiments, independently of assumptions about the DM abundance.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Pseudospectral particle-in-cell formulation with arbitrary charge and current-density time dependencies for the modeling of relativistic plasmas

This paper introduces a formulation of the particle-in-cell (PIC) method for the modeling of relativistic plasmas, that leverages the ability of the pseudospectral analytical time-domain solver (PSATD) to handle arbitrary time dependencies of the charge and current densities during one PIC cycle (applied to second-order polynomial dependencies here). Here, the formulation is applied to a modified set of Maxwell's equations that was proposed earlier in the context of divergence cleaning, and to recently proposed extensions of the PSATD-PIC algorithm. Detailed analysis and testings revealed that, under some condition, the formulation can expand the range of numerical parameters under which PIC simulations are stable and accurate when modeling relativistic plasmas such as, e.g., plasma-based particle accelerators.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Analysis of GPU Data Access Patterns on Complex Geometries for the D3Q19 Lattice Boltzmann Algorithm

GPU performance of the lattice Boltzmann method (LBM) depends heavily on memory access patterns. When implemented with GPUs on complex domains, typically, geometric data is accessed indirectly and lattice data is accessed lexicographically. Although there are a variety of other options, no study has examined the relative efficacy between them. Here, we examine a suite of memory access schemes via empirical testing and performance modeling. We find strong evidence that semi-direct is often better suited than the more common indirect addressing, providing increased computational speed and reducing memory consumption. For the layout, we find that the Collected Structure of Arrays (CSoA) and bundling layouts outperform the common Structure of Array layout; on V100 and P100 devices, CSoA consistently outperforms bundling, however the relationship is more complicated on K40 devices. When compared to state-of-the-art practices, our recommendations lead to speedups of 10–40 percent and reduce memory consumption up to 17 percent. Using performance modeling and computational experimentation, we determine the mechanisms behind the accelerations. We demonstrate that our results hold across multiple GPUs on two leadership class systems, and present the first near-optimal strong results for LBM with arterial geometries run on GPUs.

42 ENGINEERING↗
DOE OSTI2022

A noncoding single-nucleotide polymorphism at 8q24 drives IDH1 -mutant glioma formation

Establishing causal links between inherited polymorphisms and cancer risk is challenging. Here, we focus on the single-nucleotide polymorphism rs55705857, which confers a sixfold greater risk of isocitrate dehydrogenase (IDH)–mutant low-grade glioma (LGG). Here, we reveal that rs55705857 itself is the causal variant and is associated with molecular pathways that drive LGG. Mechanistically, we show that rs55705857 resides within a brain-specific enhancer, where the risk allele disrupts OCT2/4 binding, allowing increased interaction with the Myc promoter and increased Myc expression. Mutating the orthologous mouse rs55705857 locus accelerated tumor development in an Idh1 R132H -driven LGG mouse model from 472 to 172 days and increased penetrance from 30% to 75%. Our work reveals mechanisms of the heritable predisposition to lethal glioma in ~40% of LGG patients.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Neural Phase Simulation

The Neural Phase Simulation (NPS) is a package of codes for simulating microstructure evolution and accelerated molecular dynamics with deep neural-networks based surrogate models. NPS is designed to offer quantitatively accurate and computationally efficient simulation capabilities by leveraging modern machine-learning techniques. The primary intended use cases of NPS are training neural network surrogate models, though performing simulations on a single node is also supported. The NPS surrogate models can be trained from ground truth simulation methods, which are supposed to be accurate but expensive, such as molecular dynamics, phase field methods, kinetic Monte Carlo and discrete dislocation dynamics.

Zhou, Fei↗
DOE OSTI2024

Measurement of the anomalous spin precession frequency $\omega_a$ in the Muon g - 2 experiment at Fermilab

The muon magnetic anomaly aµ = (gµ - 2)/2 is a low-energy observable,which can be both measured and computed to high precision, making ita sensitive test of the Standard Model (SM). In April 2021, the E989 Collaborationat Fermilab National Accelerator Laboratory (FNAL) published the first resultbased on the first year of data taking (Run-1), and in August 2023 a new resultwas published based on two more years of data taking (Run-2 and Run-3). Thenew result was in agreement with the first one and with the previous experimentat Brookhaven National Laboratory (BNL), and the combination of these resultsbrought the uncertainty on the experimental measurement of aµ to the unprecedentedvalue of 0.19 parts per million (ppm). This paper will present details aboutthe improvements and upgrades since the 2021 result, and it will describe the finalstatistical and systematic sources of uncertainty on .a in the 2023 result.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

An introduction to neuromorphic computing and its potential impact for unattended ground sensors

Neuromorphic computers are hardware systems that mimic the brain’s computational process phenomenology. This is in contrast to neural network accelerators, such as the Google TPU or the Intel Neural Compute Stick, which seek to accelerate the fundamental computation and data flows of neural network models used in the field of machine learning. Neuromorphic computers emulate the integrate and fire neuron dynamics of the brain to achieve a spiking communication architecture for computation. While neural networks are brain-inspired, they drastically oversimplify the brain’s computation model. Neuromorphic architectures are closer to the true computation model of the brain (albeit, still simplified). Neuromorphic computing models herald a 1000x power improvement over conventional CPU architectures. Sandia National Labs is a major contributor to the research community on neuromorphic systems by performing design analysis, evaluation, and algorithm development for neuromorphic computers. Space-based remote sensing development has been a focused target of funding for exploratory research into neuromorphic systems for their potential advantage in that program area; SNL has led some of these efforts. Recently, neuromorphic application evaluation has reached the NA-22 program area. This same exploratory research and algorithm development should penetrate the unattended ground sensor space for SNL’s mission partners and program areas. Neuromorphic computing paradigms offer a distinct advantage for the SWaP-constrained embedded systems of our diverse sponsor-driven program areas.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

H2@Scale Program Multi-Party Cooperative Research and Development Agreement: California Hydrogen Infrastructure Research Consortium Task (Cooperative Research and Development Final Report, CRADA Number CRD-18-00754)

Many stakeholders are working on hydrogen and fuel cell products, markets, requirements, mandates, and policies. California has been leading the way for hydrogen infrastructure and fuel cell electric vehicle (FCEV) deployment. This leadership has advanced a hydrogen network that is not duplicated anywhere in the United States and is unique in the world for its focus on providing a retail fueling experience. In addition, the advancements have identified many lessons learned for hydrogen infrastructure development, deployment, and operation. Other interested states and countries are using California's experience as a model case, making success in California paramount to enabling market acceleration and uptake in the United States. The technical research capability of the national laboratories can be used to assist California in decisions and evaluations, as well as to verify solutions to problems impacting the industry. Because these challenges cannot be addressed by one agency or one laboratory, a hydrogen research consortium has been organized to combine and collaborate. The collaboration aims to ensure that data are available to evaluate projects and inform decision makers, independently verify and validate component solutions, provide experimental results for future hydrogen infrastructure, increase the availability of technical experts for quick-need issues for California hydrogen infrastructure development, deployment, operation, and technology advances. The proposed tasks include data collection from operational stations, component failure fix verification (i.e. nozzle freeze lock), new fueling methods for medium and heavy duty applications, and ensuring hydrogen quality is maintained. U.S. leadership for hydrogen technologies is rooted in California, a location for implementing many H2@Scale pathways such as reducing curtailment and stranded resources, reducing petroleum use and emissions, and developing and creating jobs.

08 HYDROGEN↗
DOE OSTI2023

Utilizing data-based modeling with low life cycle GHG emissions algae biofuels for engine optimization

Aquatic microalgae are a highly promising feedstock for the production of biocrude and tailored biofuels, with distinct advantages over traditional terrestrial crops, such as reduced land use and avoidance of food production competition. However, unlocking their full potential requires the development of biofuels with low life cycle greenhouse emissions biofuels, such as algae biofuels, which can significantly reduce the environmental impact of the transportation systems without requiring a complete overhaul of existing engine technology. In this study, we employ cutting-edge data-based AI modeling techniques to optimize the performance of heavy-duty engines, with a focus on transitioning towards biofuels with low life cycle greenhouse emissions biofuels. Our methodology offers significant advantages over traditional sweep testing, enabling efficient and accurate optimization of engine performance with minimal time and resources consumption. Our findings demonstrate the potential of utilizing this approach, with up to 55% NOx emissions reductions and up to 2% reduction in fuel consumption compared to the baseline optimized point. Moving forward, we plan to utilize a 30% blend of algae biofuels with diesel fuel, with the ultimate goal of achieving up to 60% lifecycle GHG emissions. Lastly, we plan to compare the results with 100% renewable biodiesel to add an additional dimension of investigating the impact of fuel chemistry on engine optimization. Overall, this study underscores the vital importance of biofuels for reducing the carbon footprint of the transportation sector and supporting a sustainable future. By harnessing the power of data-based AI modeling with low life cycle greenhouse emissions biofuels, we can accelerate the adoption of more environmentally friendly transportation systems and reduce their impact on the planet. Our findings contribute to this transition and offer insights for developing efficient and effective strategies for addressing global climate change.

09 BIOMASS FUELS↗
DOE OSTI2025

Extending SST vanadis to Add SIMT Functional Units

Sandia National Laboratories is currently investigating scalable architectural simulation capabilities, with a focus on simulating and evaluating highly scalable supercomputers for high-performance computing applications. This exploration is driven by the shift toward more specialized forms of compute and the need for a more diverse set of accurate models. This project will explore the use of General-Purpose Graphical Processing Units (GPGPUs) in high-performance computing using both physical systems and new simulator models – traditional GPUs as well as tightly-coupled SIMT accelerators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Helium bubble formation in aged plutonium metal at ambient conditions

A model of helium bubble formation is developed that assumes the initial nascent bubbles are generated when two diffusing helium atoms come within a distance of about the interatomic spacing of the plutonium atoms. These nascent bubbles are assumed to be locked in place and grow as additional helium atoms diffuse across their surfaces. Calculations of the helium-bubble number density and sizes as a function of age are compared to experiment. This leads to an inferred helium diffusion coefficient of-the-order of 10 -20 m 2 /s, supports the idea that most of the helium from alpha-decay end up in bubbles, and that previous transmission electron microscopy size estimates are biased low. The energy that helium can transfer to 40-year-old plutonium, upon expansion, is determined to be <0.1 Joules per gram of metal. The model predicts differences between the bubble properties in naturally and accelerated aged plutonium.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Highlight of IC project: w25_dreamxd

(left) The Earth’s radiation belts are donut-shaped regions containing MeV electrons (color contour for density) trapped by the magnetic field (white curves). We use DREAMxD code to model their dynamics. (right) We develop a new method to accelerate a key piece of the code – diffusion coefficient calculation. Comparing the compute time in node hours required by the standard approach to the compute time required for the fast method, it shows that the new method can be 100x faster for a large problem size.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Status of the Measurement of Neutrino-Electron Elastic Scattering in the NOvA Near Detector

Neutrino–electron elastic scattering is a purely leptonic process with a precisely calculable Standard Model cross section, providing an \textit{in situ} constraint on accelerator neutrino flux predictions. Using data collected with the NOvA Near Detector in forward horn current mode, we perform a measurement of neutrino–electron elastic scattering and apply a flux-constraint technique based on the reconstructed electron-energy spectrum. This approach improves the precision of the predicted $\nu_\mu$ flux and enhances the accuracy of near-detector cross-section measurements. The analysis also demonstrates a practical framework for flux constraints in current and future accelerator neutrino experiments.

Xiao, Yiwen [UC, Irvine (main)] (ORCID:00000001531↗
DOE OSTI2022

Enhancing CO 2 Mineralization Rate and Extent of Iron and Steel Slag via Grinding

Roughly 10% of the CO 2 emissions from iron and steel making are attributable to the direct release of CO 2 from the thermal decomposition of carbonates to produce flux, mainly CaO, used for impurity removal. Notably, these direct emissions remain even if carbon-based steelmaking is replaced by hydrogen-based steelmaking. After removing impurities from the molten metal, this flux becomes the solid waste product called ‘slag’, a primarily Ca-silicate material. The transformation of slag back into carbonates is thermodynamically spontaneous with negative ΔG in the ambient environment, meaning that ~10% of the CO 2 emissions from iron and steel making could be negated if equipment and methods were developed to support CO 2 mineralization. However, the rate of CO 2 mineralization using slag is slowed by several environmental, geometric, and processing factors. We leverage an experimentally verified model of CO 2 mineralization to determine how to efficiently accelerate the process. Increasing the crystallinity of slag, increasing the relative humidity, and reducing the grain size of slag particles provide the greatest increase in CO 2 mineralization rate at the lowest energy penalty. Increasing the concentration of CO 2 and the temperature provide only modest increases in the CO 2 mineralization rate while incurring a substantial energy penalty. For steelmaking slags, CO 2 mineralization represents low-hanging fruit as the current reuse pathways are low value. For ironmaking slag, replacing the production of amorphous slag for the cement industry with the production of crystalline slag for CO 2 mineralization becomes financially preferable when a carbon price/tax exceeds 67.40 USD/t-CO 2 .

42 ENGINEERING↗
DOE OSTI2025

Reduced Contracting Time To Accelerate Construction: A Streamlined Approach for Speed and Scale

In the fast-paced construction world, efficiency isn't just an advantage - it's a necessity. By implementing the Cooperative Construction Contracting Agreement (CCCA), we are transforming how we approach major projects, replacing traditional multistep contracting with a streamlined, one-step process. This innovative contracting approach saves valuable time and resources, enabling us to accelerate delivery across our portfolio of projects. Under the old model, each construction phase required separate competitive solicitations for design, build, and other services. With the CCCA, we've consolidated these steps, working with a trusted partner from start to finish. This results in significant time savings that translate into faster project execution without compromising quality or safety.

collaborative partnerships↗
DOE OSTI2023

Low-Temperature Plasma Assisted Kinetics Study of Ethanol

Abstract: Non- equilibrium plasma assisted kinetics of ethanol at 0.5 atm over a temperature range of 523-1203 K were studied at stoichiometric conditions by using a plasma flow reactor (PFR). The effects of plasma chemistry on fuel and its influence on neutral chemistry is decoupled with both experimental and modelling efforts. Enhanced reactivity was observed experimentally in both plasma-assisted oxidation and pyrolysis cases with fuel consumption beginning at temperatures as low as 523 K. Absolute fuel consumption was observed at temperatures nearly 200 K earlier than pure thermal reactions highlighting the effectiveness of non-equilibrium plasma to promote reactivity. Modelling results illustrated the effect of electron impact reactions towards accelerating fuel consumption and promoting low temperature chemistry by generating excited state N2 and reactive O and OH radicals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Electronic structure and oxophilicity optimization of mono-layer Pt for efficient electrocatalysis

The slow kinetics of the anodic hydrogen oxidation reaction (HOR) in alkaline medium is one of the limitations for anion exchange membrane fuel cells (AEMFCs). Hence, accurately regulate and understand catalytic interface structure are essential to solve this bottleneck and reveal the structure-activity relationship. In this work, a novel trimetal core-shell model with mono-layer Pt-shell is employed for hydrogen electrooxidation to avoid thermodynamic driven bulk/surface composition deviation. On account of the atomic radius discrepancies of 3d-transition metals M (M = Fe, Co and Cu), the electronic structure of mono-layer Pt is regulated by the strain-engineered Pd-M core and the electrocatalytic activity for HOR is tuned accordingly. Nevertheless, step-by-step electrochemical monitoring and surface-treatment experiments indicate that the oxophilicity optimization by low-valence M(OH) x species, rather than the hydrogen binding energy (HBE) optimization caused by strain engineering, influence the activity obviously. It is mainly derived from the enhanced adsorption of OH ad and accelerated desorption of the H ad . The trimetal core-shell model breathes new life into the low-Pt catalyst design for hydrogen electrooxidation reaction.

77 NANOSCIENCE AND NANOTECHNOLOGY↗