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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Study of Regional Climate Change Final Technical Report

Anthropogenic activity of fossil fuel and biomass burning emissions increase tropospheric aerosol concentrations causing a direct cooling effect by scattering solar radiation and thus increasing the Earth’s albedo. In addition, the aerosols can act as cloud condensation nuclei to regulate cloud droplet number and size, thereby changing both the cloud albedo, and the cloud lifetime and precipitation. Thus, the aerosol climate effect concerns intricate aerosol-cloud-radiation-precipitation interactions which are recognized to introduce the largest uncertainties in using GCMs to project future regional climate changes. The climate effects of aerosols are significant in the land regions of the Northern Hemisphere, in particular the sulfate aerosols over East Asia where significant anthropogenic SO 2 emissions have been observed and concentrations of other aerosol species are also high. The objective of the present project is to advance “modeling aerosol-cloud-climate interactions” in two areas: the aerosol-cloud microphysics interactions and model simulations of aerosol effects on regional climate with focus on East Asia. To achieve the objective, in addition to analyzing relevant observations, the project conducts and diagnoses regional and global climate model simulations. Specifically, two collaborative tasks were carried out: Task-1: Incorporating a physics-based, two-moments cloud scheme developed by J. P. Chen/National Taiwan University (NTU) into regional models (WRF and WRF-Chem) to study “aerosol-cloud microphysics interactions over eastern China and the southeast Pacific” ; for latter region, CMIP5 models exhibit distinct biases in (weaker) cloud radiative effects and (warmer) sea surface temperature. The task is conducted in collaboration with J.-P. Chen, H.-W. Xue/Peking University, and H. Liao/Institute of Atmospheric Physics; and Task-2: Diagnosis of NCAR-CESM and WRF model simulations of “aerosol climate effects” focusing on changes of circulation, precipitation and clouds over East Asia. The task is conducted in collaboration with J.-P. Chen, and I-Chun Tsai & H.-H. Hsu/Academia Sinica. For each task, several topical studies were carried out and documented in eleven (11) articles (five for Task-1 and six for Task-2), published in: J. Atmospheric Sciences (2); J. Climate (1); Climate Dynamics (3); J. Geophysical Research (2); Geophysical Research Letter (1); Int. J. Climatology (1); and Advances in Atmospheric Sciences (1). In addition, Wei-Chyung Wang (the PI) continues serving as the U. S. Chief Scientist for the joint agreement, “Climate Sciences”, between the U. S. DOE and China Ministry of Sciences and Technology (Riches et al., 2010). In this capacity, he works closely with Michael Riches/DOE (the U. S. Manager) to coordinate the scientific exchanges (visiting scientists) between the two countries. Educations (graduate students) and trainings (postdoctoral research associates) are key elements of the research project.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Strategy for NACS investment in Machine Learning

The Nuclear and Chemical Sciences (NACS) Division furnishes the expertise in the scientific areas of chemical, nuclear and isotopic sciences that are foundational in the Laboratory’s national security missions. This expertise is maintained and advanced through identification, development and application of state-of-the-art theoretical, computational and experimental methods and tools. Recent developments in artificial intelligence and machine learning (AI/ML) techniques enabled by advances in computing capabilities and widespread availability of powerful software implementations have made use of these techniques ubiquitous across both science and industry. While the scope of AI/ML applications is incredibly large and evolves very rapidly, the topics most relevant to NACS missions fall into the general category of detecting, categorizing or identifying features in large, complex datasets using either supervised or unsupervised learning. This covers both basic scientific data analysis and the development of efficient surrogate models of real-life technological systems, experimental detectors, or theoretical models. To remain at the forefront of its core scientific disciplines, NACS must both cultivate ML expertise as well as continuously explore applying this expertise to new problems or utilizing new methods. This document identifies the key areas where this support is critical and provides a strategy for investing in them.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2026

Wind Plant Flow Physics and Power Performance in Complex Environments: Cooperative Research and Development (Final Report)

Cornell University will partner with NLR on the topic of wind farm wake effects to improve understanding of interactions between complex atmospheric flows, terrain, and wind turbine wakes and plant efficiency. Wind plant flow simulation tools will also be validated. The work performed will help improve wind farm modeling by analyzing data, applying models, designing and performing experiments to acquire additional wind farm data, and develop better models.

17 WIND ENERGY↗
DOE OSTI2022

Report of the Snowmass 2021 Topical Group on Lattice Gauge Theory

Lattice gauge theory continues to be a powerful theoretical and computational approach to simulating strongly interacting quantum field theories, whose applications permeate almost all disciplines of modern-day research in High-Energy Physics. Whether it is to enable precision quark- and lepton-flavor physics, to uncover signals of new physics in nucleons and nuclei, to elucidate hadron structure and spectrum, to serve as a numerical laboratory to reach beyond the Standard Model, or to invent and improve state-of-the-art computational paradigms, the lattice-gauge-theory program is in a prime position to impact the course of developments and enhance discovery potential of a vibrant experimental program in High-Energy Physics over the coming decade. This projection is based on abundant successful results that have emerged using lattice gauge theory over the years: on continued improvement in theoretical frameworks and algorithmic suits; on the forthcoming transition into the exascale era of high-performance computing; and on a skillful, dedicated, and organized community of lattice gauge theorists in the U.S. and worldwide. The prospects of this effort in pushing the frontiers of research in High-Energy Physics have recently been studied within the U.S. decadal Particle Physics Planning Exercise (Snowmass 2021), and the conclusions are summarized in this Topical Report.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

A First–Principles–Based Sub–Lattice Formalism for Predicting Off–Stoichiometry in Materials for Solar Thermochemical Applications: The Example of Ceria

Theoretical models that reliably can predict off-stoichiometry in materials via accurate descriptions of underlying thermodynamics are crucial for energy applications. For example, transition-metal and rare-earth oxides that can tolerate a large number of oxygen vacancies, such as CeO 2 and doped CeO 2 , can split water and carbon dioxide via a two-step, oxide-based solar thermochemical (STC) cycle. The search for new STC materials with a performance superior to that of state-of-the-art CeO 2 can benefit from predictions accurately describing the thermodynamics of oxygen vacancies. The sub-lattice formalism, a common tool used to fit experimental data and build temperature-composition phase diagrams, can be useful in this context. Here, sub-lattice models are derived solely from zero-temperature quantum mechanics calculations to estimate fairly accurate temperature- and oxygen-partial-pressure-dependent off-stoichiometries in CeO 2 and Zr-doped CeO 2 . Physical motivations for deriving some of the “excess” sub-lattice model parameters directly from quantum mechanical calculations, instead of fitting to minimize deviations from experimental and/or theoretical data, are identified. As a result, important limitations and approximations of the approach used are specified and extensions to multi-cation oxides are also suggested to help identify novel candidates for water and carbon dioxide splitting and related applications.

08 HYDROGEN↗
DOE OSTI2018

Analysis of the Co-existence of Long-range Transport Biomass Burning and Dust in the Subtropical West Pacific Region

Biomass burning and wind-blown dust has been well investigated during the past decade regarding their impacts on environment, but their co-existence hasn’t been recognized because they usually occur in different locations and episodes. In this study we reveal the unique co-existence condition that dust from the Taklamakan and Gobi Desert (TGD) and biomass burning from Peninsular Southeast Asia (PSEA) can reach to the west Pacific region simultaneously in boreal spring (March and April). The upper level trough at 700hPa along east coast of China favors the large scale subsidence of TGD dust while it travels southeastwards, and drives the PSEA biomass burning plume carried by the westerlies at 3–5 km to descend rapidly to around 1.5 km and mix with dust around southeast China and Taiwan. As compared to the monthly averages in March and April, surface observations suggested that concentrations of PM 10 , PM 2.5 , O 3 , and CO were 69%, 37%, 20%, and 18% higher respectively during the 10 identified co-existence events which usually lasted for 2–3 days. Co-existence also lowers the surface O 3 , NOx, and SO 2 by 4–5% due to the heterogeneous chemistry between biomass burning and mineral dust as indicated by model simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Bayesian differential programming for robust systems identification under uncertainty

This paper presents a machine learning framework for Bayesian systems identification from noisy, sparse and irregular observations of nonlinear dynamical systems. The proposed method takes advantage of recent developments in differentiable programming to propagate gradient information through ordinary differential equation solvers and perform Bayesian inference with respect to unknown model parameters using Hamiltonian Monte Carlo sampling. This allows an efficient inference of the posterior distributions over plausible models with quantified uncertainty, while the use of sparsity-promoting priors enables the discovery of interpretable and parsimonious representations for the underlying latent dynamics. A series of numerical studies is presented to demonstrate the effectiveness of the proposed methods, including nonlinear oscillators, predator–prey systems and examples from systems biology. Taken together, our findings put forth a flexible and robust workflow for data-driven model discovery under uncertainty. All codes and data accompanying this article are available at https://bit.ly/34FOJMj .

Science & Technology - Other Topics↗
DOE OSTI2016

FleCSI: Connection to Legion

These are slides which are part of the ASC L2 Milestone Review. The following topics are covered: Legion Backend, Distributed-Memory Partitioning, Sparse Data Representations, and MPI-Legion Interoperability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

QuGAN: A Quantum State Fidelity based Generative Adversarial Network

In the recent years, Generative Adversarial Networks (GANs) have been arguably one of the largest strides forward in Deep Learning. Many papers illustrate the use of GANs to accomplish extremely impressive goals, such as text-to-image or image augmentation. Specifically, GANs have seen this success in the computer vision domain. However, GANs are not without their own set of problems. GANs are computationally expensive, sometimes computationally prohibitive, and can suffer a multitude of convergence problems. As research on classical GANs continues to push the topic further, a branch of GANs, namely Quantum GANs, has seen research interest in the past years. In this work, we intend to push this research further with an illustration of a Quantum GAN architecture that provide stable convergence of the model and is extended onto real data sets. Furthermore, unlike many other Quantum GANs out there, our model's GAN architecture runs the discriminator and the generator primarily on Quantum hardware through the use of a Quantum-based similarity metric. When compared to the very few other Quantum GAN papers, our architecture leads to significantly better results in almost all aspects.

Stein, Samuel A.↗
DOE OSTI2023

New Developments and Capabilities Within WEC-Sim

WEC-Sim is an open-source software for simulating wave energy converters and has been actively developed and applied since its initial release in 2014 to simulate a wide variety of device archetypes. WEC-Sim is developed jointly by the National Renewable Energy Laboratory and Sandia National Laboratories within the MATLAB/SIMULINK environment. A general wave-to-wire model begins with a deployment site resource characterization, which is used to complete the hydrodynamic simulation of wave energy converters (WEC), with the power generation profile imported to a grid simulator to understand the influence on the local electrical network. While modeling the entire wave-to-wire is difficult and encompasses multiple time scales and physics, WEC-Sim is focused on the hydrodynamics simulation to predict, analyze, and optimize WEC dynamics and power performance. WEC-Sim simulations are performed in the time domain based on the radiation and diffraction method using hydrodynamics coefficients derived from boundary element method (BEM)-based frequency-domain potential flow solvers (e.g., WAMIT, NEMOH, Capytaine, or ANSYS-AQWA). With this level of modeling fidelity, WEC-Sim can handle floating body hydrodynamics, mechanical and electrical power generation methods, advanced control implementation, mooring systems, and other unique applications such as desalination. Additional WEC-Sim functionalities include pre-built Simulink blocks and MATLAB scripts that can simulate a wide range of floating systems and the corresponding auxiliary subsystems. The developers of WEC-Sim continue to release new versions of the software, at least annually, with the latest release in September 2022. These releases include bug fixes, updates to software documentation, as well as new features to expand WEC-Sim's capabilities to model a wide range of WEC concepts. This publication will highlight the new features added to WEC-Sim between versions 4.1.0 to 5.0.1, which spans a 2-year period from June 2020 to September 2022. New features described here include topics such as continuous integration checks, revised Morison Element and nonlinear hydro implementations, run directly from Simulink (required for hardware-in-the-loop execution), BEMIO updates to import Capytaine BEM hydrodynamics, addition of cable blocks, and new wave visualization features.

TIDAL AND WAVE POWER↗
DOE OSTI2015

Search for Low-Mass Dark Matter wtih SuperCDMS Soudan and Study of Shorted Electric Field Configurations in CDMS Detectors

The area of dark matter is one of the most interesting and exciting topics in physics today. Existing at the intersection of particle physics and astrophysics, the existence of a new dark matter particle can be used to explain many astrophysical and cosmological observations, as well as to reconcile outstanding issues in the standard model of particle physics. Experiments such as SuperCDMS are built to detect dark matter in the lab by looking for low-energy nuclear recoils produced by collisions between dark matter particles and atoms in terrestrial detectors. SuperCDMS Soudan is particularly well-suited to follow up on possible hints of low-mass dark matter seen by other recent experiments because of its low thresholds and excellent background discrimination. Analyzing SuperCDMS Soudan data to look for low-mass dark matter comes with particular challenges because of the low signal-to-noise very near threshold. However, with a detailed background model developed by scaling high-energy events down into the low-energy signal region, SuperCDMS Soudan produced worldleading limits on the existence of low-mass dark matter. In addition, a few SuperCDMS Soudan detectors experienced cold hardware problems that can affect the data collected. Of particular interest is one detector considered for the low-mass WIMP search that has one of its charge electrodes shorted to chassis ground. Three events were observed in this detector upon unblinding the SuperCDMS Soudan low-energy data, even though <1 event was expected based on pre-unblinding calulations. However, the data collected by the shorted detector may have been compromised since an electrode shorted to ground will modify the electric field in the detector. The SuperCDMS Detector Monte Carlo (DMC) provides an excellent way to model the effects of the modified electric field, so a new model of the expected backgrounds in the low-mass WIMP search is developed using the DMC to try to explain how the short may have affected the data collected. This thesis is organized as follows: Chapter 1 gives a broad introduction to dark matter, discussing the astrophysical and cosmological evidence for its existence, listing several possible particle physics candidates, and outlining several experimental strategies to look for dark matter. Chapter 2 is an overview of CDMS detector technology and the experimental setup at the Soudan Underground Laboratory, with a focus on how data coming out of Soudan is analyzed. Chapter 3 presents results from a search for low-mass dark matter at SuperCDMS Soudan and discusses the interpretation of the results. Chapter 4 contains follow-up work that uses the CDMS Detector Monte Carlo (DMC) to understand the possible systematics associated with a detector that had one of its charge electrodes shorted to ground. This chapter represents the first time the DMC has been used to inform ongoing CDMS analysis. Chapter 5 takes a brief detour into the world of effective field theory (EFT), examining the consequences of an expanded set of possible WIMP-nucleon interactions in the EFT framework. Finally, Chapter 6 wraps up the material of the previous chapters and discusses how the research presented in this thesis can be applied as CDMS moves toward SuperCDMS SNOLAB.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Editorial for Special Issue “Environmentally Sound In Situ Recovery Mining of Uranium”

This Special Issue features seven articles that cover a range of topics pertaining to the environmentally sound in situ recovery mining of uranium (U ISR). The topics include: (1) methods for assessing the potential environmental impacts of future U ISR operations using historical operational records and available geohydrologic, geochemical and climate data in a specific regional setting; (2) laboratory column studies to parameterize a uranium surface complexation model that can be used to predict uranium fate and transport in groundwater; and (3) a field evaluation of both a chemical reductant and a biostimulant to promote reducing geochemical conditions after oxidative mining at a U ISR facility.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

PDB ‐101: Educational resources supporting molecular explorations through biology and medicine

Abstract The Protein Data Bank (PDB) archive is a rich source of information in the form of atomic‐level three‐dimensional (3D) structures of biomolecules experimentally determined using macromolecular crystallography, nuclear magnetic resonance (NMR) spectroscopy, and electron microscopy (3DEM). Originally established in 1971 as a resource for protein crystallographers to freely exchange data, today PDB data drive research and education across scientific disciplines. In 2011, the online portal PDB‐101 was launched to support teachers, students, and the general public in PDB archive exploration ( pdb101.rcsb.org ). Maintained by the Research Collaboratory for Structural Bioinformatics PDB, PDB‐101 aims to help train the next generation of PDB users and to promote the overall importance of structural biology and protein science to nonexperts. Regularly published features include the highly popular Molecule of the Month series, 3D model activities, molecular animation videos, and educational curricula. Materials are organized into various categories (Health and Disease, Molecules of Life, Biotech and Nanotech, and Structures and Structure Determination) and searchable by keyword. A biennial health focus frames new resource creation and provides topics for annual video challenges for high school students. Web analytics document that PDB‐101 materials relating to fundamental topics (e.g., hemoglobin, catalase) are highly accessed year‐on‐year. In addition, PDB‐101 materials created in response to topical health matters (e.g., Zika, measles, coronavirus) are well received. PDB‐101 shows how learning about the diverse shapes and functions of PDB structures promotes understanding of all aspects of biology, from the central dogma of biology to health and disease to biological energy.

Zardecki, Christine↗