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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Contribution of IAEA coordinated research projects to light water reactors advanced technology fuel testing and simulation

The Fukushima-Daiichi accident in 2011 highlighted the pressing need for enhanced design and safety analysis of nuclear fuels, especially under accident conditions in nuclear power plants (NPPs). To address this need, the international nuclear fuel community has developed several concepts of Accident Tolerant and Advanced Technology Fuels (ATFs) for light water reactors. The International Atomic Energy Agency (IAEA), at the request of its Member States, has initiated a series of three Coordinated Research Projects (CRPs) to aid in the development and testing of these ATFs. Further, these projects, known as FUMAC, ACTOF, and ATF-TS, focus on the experimentation and simulation of ATFs under various accident conditions, including design basis accidents and design extension conditions. This paper provides a comprehensive overview of the main activities, as well as the achieved or anticipated results of these three CRPs. The insights and advancements gained from these projects will enable IAEA Member States to further refine nuclear fission technology, thereby contributing to efforts aimed at mitigating climate change.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Crustal Deformation and Gravitational Effects From Dynamic Ocean Mass Redistribution Impact Projected Sea‐Level Change

As the climate warms, associated changes in ocean dynamics will redistribute sea‐water mass within the ocean, contributing to relative sea‐level change. This mass redistribution will cause additional sea‐level changes due to gravitational self‐attraction, deformation of the solid Earth, and shifts in the Earth's rotation axis (GRD), which are not incorporated in sea‐level projections. Using CMIP6 climate model output, we quantify relative sea‐level changes induced by GRD from ocean‐dynamic mass loading through 2100. These effects act to amplify projected ocean‐dynamic sea‐level patterns, causing sea‐level rise in coastal regions, particularly along wide continental shelves and at high latitudes. On average, the magnitude of such GRD‐induced sea‐level change is equivalent to ∼15% of the signal due to dynamic ocean mass redistribution. Although our results show substantial inter‐model spread, they reveal that GRD‐induced relative sea‐level changes from ocean mass redistribution represent a non‐negligible component of regional sea‐level change and should be considered in projections.

58 GEOSCIENCES↗
DOE OSTI2022

Machine learning-based observation-constrained projections reveal elevated global socioeconomic risks from wildfire

Reliable projections of wildfire and associated socioeconomic risks are crucial for the development of efficient and effective adaptation and mitigation strategies. The lack of or limited observational constraints for modeling outputs impairs the credibility of wildfire projections. Here, we present a machine learning framework to constrain the future fire carbon emissions simulated by 13 Earth system models from the Coupled Model Intercomparison Project phase 6 (CMIP6), using historical, observed joint states of fire-relevant variables. During the twenty-first century, the observation-constrained ensemble indicates a weaker increase in global fire carbon emissions but higher increase in global wildfire exposure in population, gross domestic production, and agricultural area, compared with the default ensemble. Such elevated socioeconomic risks are primarily caused by the compound regional enhancement of future wildfire activity and socioeconomic development in the western and central African countries, necessitating an emergent strategic preparedness to wildfires in these countries.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Robust projection of East Asian summer monsoon rainfall based on dynamical modes of variability

Abstract The Asian monsoon provides the freshwater that a large population in Asia depends on, but how anthropogenic climate warming may alter this key water source remains unclear. This is partly due to the prevailing point-wise assessment of climate projections, even though climate change patterns are inherently organized by dynamics intrinsic to the climate system. Here, we assess the future changes in the East Asian summer monsoon precipitation by projecting the precipitation from several large ensemble simulations and CMIP6 simulations onto the two leading dynamical modes of internal variability. The result shows a remarkable agreement among the ensembles on the increasing trends and the increasing daily variability in both dynamical modes, with the projection pattern emerging as early as the late 2030 s. The increase of the daily variability of the modes heralds more monsoon-related hydrological extremes over some identifiable East Asian regions in the coming decades.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Robustness and uncertainties in global multivariate wind-wave climate projections

Understanding climate-driven impacts on the multivariate global wind-wave climate is paramount to effective offshore/coastal climate adaptation planning. However, the use of single-method ensembles and variations arising from different methodologies has resulted in unquantified uncertainty amongst existing global wave climate projections. Here, assessing the first coherent, community-driven, multi-method ensemble of global wave climate projections, we demonstrate widespread ocean regions with robust changes in annual mean significant wave height and mean wave period of 5–15% and shifts in mean wave direction of 5–15°, under a high-emission scenario. Approximately 50% of the world’s coastline is at risk from wave climate change, with ~40% revealing robust changes in at least two variables. Finally, we find that uncertainty in current projections is dominated by climate model-driven uncertainty, and that single-method modelling studies are unable to capture up to ~50% of the total associated uncertainty.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Capturing convection essential for projections of climate change in African dust emission

The summertime Sahara and Sahel are the world’s largest source of airborne mineral dust. Cold-pool outflows from moist convection (‘haboobs’) are a dominant source of summertime uplift but are essentially missing in global models, raising major questions on the reliability of climate projections of dust and dust impacts. Here we use convection-permitting simulations of pan-African climate change, which explicitly capture haboobs, to investigate whether this key limitation of global models affects projections. We show that explicit convection is key to capturing the observed summertime maximum of dust-generating winds, which is missed with parameterised convection. Despite this, future climate changes in dust-generating winds are more sensitive to the effects of explicit convection on the wider meteorology than they are to the haboobs themselves, with model differences in the change in dust-generating winds reaching 60% of current values. The results therefore show the importance of improving convection in climate models for dust projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Neutronics Calculation Advances at Los Alamos: Manhattan Project to Monte Carlo

The history and advances of neutronics calculations at Los Alamos during the Manhattan Project through the present are reviewed. Substantial improvements to neutron diffusion methods and the invention of both the Monte Carlo neutron transport methods in 1947 and deterministic discrete ordinates Sn in 1953 were all made at Los Alamos just after the Manhattan Project. We briefly summarize early simpler and more approximate neutronics methods and then describe the need to better predict neutronics behavior through consideration of theoretical equations, models and algorithms, experimental measurements, and available computing capabilities and their limitations. This paper briefly covers key advances in deterministic methods during the Manhattan Project. These capabilities, coupled with increasing postwar defense needs and the invention of electronic computing with the Electronic Numeric Integrator and Computer, known as ENIAC, and the Mathematical Analyzer Numerical Integrator and Automatic Computer Model, known as MANIAC, led to the creation of Monte Carlo and deterministic discrete ordinates neutronics transport methods. We note the important role that the scientific comradery between the Los Alamos scientists played in the process. This paper briefly covers the early methods, algorithms, computers, and electronic and women pioneers that enabled Monte Carlo to spread to all areas of science. We focus heavily on these early developments and the subsequent creation of the MCNP® code, advances in its associated nuclear data, and its applications to problems of national defense at Los Alamos.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Nuclear states projected from a pair condensate

Atomic nuclei exhibit deformation, pairing correlations, and rotational symmetries. To meet these competing demands in a computationally tractable formalism, we revisit the use of general pair condensates with good particle number as a trial wave function for even-even nuclei. After minimizing the energy of the condensate, allowing for general triaxial deformations, we project out states with good angular momentum with a fast projection technique. To show applicability, we present example calculations from pair condensates in several model spaces and compare against angular-momentum projected Hartree-Fock and full configuration-interaction shell-model calculations. This approach successfully generates spherical, vibrational, and rotational spectra, demonstrating potential for modeling medium- to heavy-mass nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Remark on Algorithm 1012: Computing Projections with Large Datasets

In ACM TOMS Algorithm 1012, the DELAUNAYSPARSE software is given for performing Delaunay interpolation in medium to high dimensions. When extrapolating outside the convex hull of the training set, DELAUNAYSPARSE calls the nonnegative least squares solver DWNNLS to compute projections onto the convex hull. However, DWNNLS and many other available sum-of-squares optimization solvers were not intended for usage with many variable problems, which result from the large training sets that are typical in machine learning applications. Thus, a new PROJECT subroutine is given, based on the highly customizable quadratic program solver BQPD. This solution is shown to be as robust as DELAUNAYSPARSE for projection onto both synthetic and real-world datasets, where other available solvers frequently fail. Although it is intended as an update for DELAUNAYSPARSE, due to the difficulty and prevalence of the problem, this solution is likely to be of external interest as well.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A survey of software implementations used by application codes in the Exascale Computing Project

The US Department of Energy Office of Science and the National Nuclear Security Administration initiated the Exascale Computing Project (ECP) in 2016 to prepare mission-relevant applications and scientific software for the delivery of the exascale computers starting in 2023. The ECP currently supports 24 efforts directed at specific applications and six supporting co-design projects. These 24 application projects contain 62 application codes that are implemented in three high-level languages—C, C++, and Fortran—and use 22 combinations of graphical processing unit programming models. The most common implementation language is C++, which is used in 53 different application codes. The most common programming models across ECP applications are CUDA and Kokkos, which are employed in 15 and 14 applications, respectively. This article provides a survey of the programming languages and models used in the ECP applications codebase that will be used to achieve performance on the future exascale hardware platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Community Engagement and Equity in Renewable Energy Projects: A Literature Review

Projects to transition away from fossil fuels toward renewables can prompt opposition even if their proponents engage with the public and affected communities. Therefore, many scholars and practitioners call for a nuanced understanding of the challenges and opportunities of participation and engagement in decision-making processes. This literature review examines: (a) the nature of and links between engagement and equity in energy transition projects, and (b) the lessons learned, best practices and options in this emerging field. The goal of this report is to inform the U.S. Department of Energy (DOE) and proponents of renewable energy projects, such as the siting of bioenergy infrastructure, with knowledge on what engagement is, why and how it works on the ground, and how it relates to crucial societal elements of energy equity such as social acceptance, energy democracy, and equity outcomes.

09 BIOMASS FUELS↗
DOE OSTI2023

Graton Rancheria Solar G.R.E.E.N. Project (Final Report)

Final Technical Report Graton Rancheria Solar GREEN Project to install approximately 1.7 megawatts (MW) of rooftop solar to provide electricity to four Tribally owned buildings on Tribal trust land located in Sonoma County. The project is the first solar photovoltaic (PV) installation by the Tribe on Tribal lands and complement previously installed clean energy projects including multiple electric vehicle chargers located at the Tribe’s economic enterprise.

14 SOLAR ENERGY↗
DOE OSTI2025

Pipeline for Integrated Projects in Energy Systems (PIPES): A Tool for Integrated System Planning [Slides]

The Pipeline for Integrated Projects in Energy Systems (PIPES) is a comprehensive project, data, and workflow management tool designed for integrated modeling teams. PIPES facilitates the management of data requirements, tasks, and progress tracking, serving as a higher-level integration layer that works across various data and modeling software. This tool integrates models, data, and tools to perform large-scale, integrated analysis work at scale. PIPES is designed to streamline integrated modeling projects, enhance collaboration, and ensure the quality and efficiency of data management and workflow processes. This presentation introduces PIPES a multi-model tool for integrated system planning; it describes the underlying architecture, deep dives into common user workflows, and outlines the upcoming development roadmap beyond its current alpha state.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

BUQEYE guide to projection-based emulators in nuclear physics

The BUQEYE collaboration (Bayesian Uncertainty Quantification: Errors in Your effective field theory) presents a pedagogical introduction to projection-based, reduced-order emulators for applications in low-energy nuclear physics. The term emulator refers here to a fast surrogate model capable of reliably approximating high-fidelity models. As the general tools employed by these emulators are not yet well-known in the nuclear physics community, we discuss variational and Galerkin projection methods, emphasize the benefits of offline-online decompositions, and explore how these concepts lead to emulators for bound and scattering systems that enable fast and accurate calculations using many different model parameter sets. We also point to future extensions and applications of these emulators for nuclear physics, guided by the mature field of model (order) reduction. All examples discussed here and more are available as interactive, open-source Python code so that practitioners can readily adapt projection-based emulators for their own work.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Predicting beam transmission using 2-dimensional phase space projections of hadron accelerators

We present a method to compress the 2D transverse phase space projections from a hadron accelerator and use that information to predict the beam transmission. This method assumes that obtaining at least three projections of the 4D transverse phase space is possible and that an accurate simulation model is available for the beamline. Using a simulated model, we show that—a computer can train a convolutional autoencoder to reduce phase-space information which can later be used to predict the beam transmission. Finally, we argue that although using projections from a realistic nonlinear distribution produces less accurate results, the method still generalizes well.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Distributed Computing for the Project 8 Experiment

The Project 8 collaboration aims to measure the absolute neutrino mass or improve on the current limit by measuring the tritium beta decay electron spectrum. We present the current distributed computing model for the Project 8 experiment. Project 8 is in its second phase of data taking with a near continuous data rate of 1Gbps. The current computing model uses DIRAC (Distributed Infrastructure with Remote Agent Control) for its workflow and data management. A detailed meta-data assignment using the DIRAC File Catalog is used to automate raw data transfers and subsequent stages of data processing. The DIRAC system is deployed on containers managed using a Kubernetes cluster to provide a scalable infrastructure. A modified DIRAC Site Director provides the ability to submit jobs using Singularity on opportunistic High-Performance Computing (HPC) sites.

Distributed Computing, Kubernetes, DIRAC, Project ↗
DOE OSTI2021

Using projection operators with maximum overlap methods to simplify challenging self-consistent field optimization

Maximum overlap methods are effective tools for optimizing challenging ground- and excited-state wave functions using self-consistent field models such as Hartree-Fock and Kohn-Sham density functional theory. Nevertheless, such models have shown significant sensitivity to the user-defined initial guess of the target wave function. Here in this work, a projection operator framework is defined and used to provide a metric for non-aufbau orbital selection in maximum-overlap-methods. The resulting algorithms, termed the Projection-based Maximum Overlap Method (PMOM) and Projection-based Initial Maximum Overlap Method (PIMOM), are shown to perform exceptionally well when using simple user-defined target solutions based on occupied/virtual molecular orbital permutations. This work also presents a new metric that provides a simple and conceptually convenient measure of agreement between the desired target and the current or final SCF results during a calculation employing a maximum-overlap method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗