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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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DOE OSTI2017

Twofold symmetries of the pure gravity action

Here, we recast the action of pure gravity into a form that is invariant under a twofold Lorentz symmetry. To derive this representation, we construct a general parameterization of all theories equivalent to the Einstein-Hilbert action up to a local field redefinition and gauge fixing. We then exploit this freedom to eliminate all interactions except those exhibiting two sets of independently contracted Lorentz indices. The resulting action is local, remarkably simple, and naturally expressed in a field basis analogous to the exponential parameterization of the nonlinear sigma model. The space of twofold Lorentz invariant field redefinitions then generates an infinite class of equivalent representations. By construction, all off-shell Feynman diagrams are twofold Lorentz invariant while all on-shell tree amplitudes are automatically twofold gauge invariant. We extend our results to curved spacetime and calculate the analogue of the Einstein equations. Finally, while these twofold invariances are hidden in the canonical approach of graviton perturbation theory, they are naturally expected given the double copy relations for scattering amplitudes in gauge theory and gravity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Matminer: An open source toolkit for materials data mining

As materials data sets grow in size and scope, the role of data mining and statistical learning methods to analyze these materials data sets and build predictive models is becoming more important. This manuscript introduces matminer, an open-source, Python-based software platform to facilitate data-driven methods of analyzing and predicting materials properties. Matminer provides modules for retrieving large data sets from external databases such as the Materials Project, Citrination, Materials Data Facility, and Materials Platform for Data Science. It also provides implementations for an extensive library of feature extraction routines developed by the materials community, with 47 featurization classes that can generate thousands of individual descriptors and combine them into mathematical functions. Finally, matminer provides a visualization module for producing interactive, shareable plots. These functions are designed in a way that integrates closely with machine learning and data analysis packages already developed and in use by the Python data science community. We explain the structure and logic of matminer, provide a description of its various modules, and showcase several examples of how matminer can be used to collect data, reproduce data mining studies reported in the literature, and test new methodologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

Recent advances in thermoelectric nanocomposites

Thermoelectric power generation represents a class of energy conversion technology, which has been used in power supply of aeronautic and astronautic exploring missions, now showing notable advantages to harvest the widely distributed waste heat and convert the abundant solar energy into electricity at lower cost than Si-based photovoltaic technology. Thermoelectric dimensionless figure of merit (ZT) plays a key role in the conversion efficiency from thermal to electrical energy. Most state-of-the-art thermoelectric materials are characterized with various nanostructures from fine grains, dispersed particles, nano-inclusions to atomic defects, and therefore are referenced as nanocomposite. Recently, remarkable advances have been achieved in various material systems through the introduction of different nanostructures to tune the transport of phonons and electrons. In this review, we firstly clarify some fundamental tradeoffs dictating the ZT value through the relationship ZT=(S 2 σ/κ)T. We also pay special attentions to the recent advances in some selected materials, including half-Heuslers, PbTe, CoSb 3 , Bi 2 Te 3 , etc. We provide an outlook of new directions in this filed, especially a proposal on a new concept of ordered nanocomposite.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Molecular Driving Force for Facet Selectivity of Sequence-Defined Amphiphilic Peptoids at Au–Water Interfaces

Shape-controlled colloidal nanocrystal syntheses often require facet-selective solution-phase chemical additives to regulate surface free energy, atom addition/migration fluxes or particle attachment rates. Because of their highly tunable properties and robustness to a wide range of experimental conditions, peptoids represent a very promising class of next generation functional additives for control over nanocrystal growth. However, understanding the origin of facet selectivity at the molecular level is critical to generalizing their design. In this work, we employ molecular dynamics simulations and biased sampling methods and report stronger selectivity to Au(111) than to Au(100) for Nce3Ncp6, a peptoid that has been shown to assist the formation of five-fold twinned Au nanostars. We find that facet-selectivity is achieved through synergistic effects of both peptoid-surface and solvent-surface interactions. Moreover, the amphiphilic nature of Nce3Ncp6 together with the order of peptoid-peptoid and peptoid-surface binding energies, i.e., peptoid-Au(100) < peptoid-peptoid < peptoid-Au(111), further amplifies its distinct collective behavior on different Au surfaces. Our studies provide a fundamental understanding of the molecular origin of facet-selective adsorption and highlight the possibility of future designs and uses of sequence-defined peptoids for predictive syntheses of nanocrystals with designed shapes and properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

B - L violating nucleon decay and GUT scale baryogenesis in SO(10)

Minimal SO(10) models which utilize a 126-plet of Higgs for breaking B - L symmetry are predictive schemes for neutrino masses and mixings. These models have been known to predict sin{sup 2}2{theta}{sub 13} Asymptotically-Equal-To 0.09, which is consistent with recent measurements. It is shown that the same class of models generates naturally baryon number violating dimension seven operators leading to novel nucleon decay modes such as n{yields}e{sup -}K{sup +}, e{sup -}{pi}{sup +} and p{yields}{nu}{pi}{sup +} which violate B - L by two units. We find that in two-step breaking schemes of non-supersymmetric SO(10), the partial lifetimes for these modes can be within reach of experiments. The interactions responsible for these decay modes also provide a new way to understand the origin of matter in the universe via the decays of GUT scale colored scalar bosons. Their (B - L)-violating nature guarantees that the GUT scale induced baryon asymmetry is not washed out by the electroweak sphaleron interactions. In minimal SO(10) models this asymmetry is closely tied to the masses of quarks, leptons and the neutrinos.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2013

Constraint analysis for variational discrete systems

A canonical formalism and constraint analysis for discrete systems subject to a variational action principle are devised. The formalism is equivalent to the covariant formulation, encompasses global and local discrete time evolution moves and naturally incorporates both constant and evolving phase spaces, the latter of which is necessary for a time varying discretization. The different roles of constraints in the discrete and the conditions under which they are first or second class and/or symmetry generators are clarified. The (non-) preservation of constraints and the symplectic structure is discussed; on evolving phase spaces the number of constraints at a fixed time step depends on the initial and final time step of evolution. Moreover, the definition of observables and a reduced phase space is provided; again, on evolving phase spaces the notion of an observable as a propagating degree of freedom requires specification of an initial and final step and crucially depends on this choice, in contrast to the continuum. However, upon restriction to translation invariant systems, one regains the usual time step independence of canonical concepts. This analysis applies, e.g., to discrete mechanics, lattice field theory, quantum gravity models, and numerical analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Unsteady magnetohydrodynamics mixed convection flow in a rotating medium with double diffusion

Exact solutions of an unsteady Magnetohydrodynamics (MHD) flow over an impulsively started vertical plate in a rotating medium are presented. The effects of thermal radiative and thermal diffusion on the fluid flow are also considered. The governing equations are modelled and solved for velocity, temperature and concentration using Laplace transforms technique. Expressions of velocity, temperature and concentration profiles are obtained and their numerical results are presented graphically. Skin friction, Sherwood number and Nusselt number are also computed and presented in tabular forms. The determined solutions can generate a large class of solutions as special cases corresponding to different motions with technical relevance. The results obtained herein may be used to verify the validation of obtained numerical solutions for more complicated fluid flow problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Synthesis and characterization of polyaniline coated gold nanocomposites

Considerable attention has been drawn during the last two decades to prepare nanocomposites consists of conducting polymer and noble metal due to their potential ability to generate a new class of material with novel optical, chemical, electronic or mechanical properties for various applications. In this work, an attempt has been made to synthesize nanocomposite of polyaniline (PANI) coated with gold nanoparticles (AuNPs) chemically with various types of surfactants such as polyvinylpyrrolidone (PVP), and sodium dodecyl sulphate (SDS) which act as stabilizing agents to help in stabilization of the PANI/Gold nanocomposites system. The synthesized nanocomposites were characterized by UV-Visible, field emission scanning electron microscope (FESEM) and particle size analyzer (PSA). The formation of finger like structure can be seen in the FESEM images when the AuNPs were incorporated into the polymer matrix. The EDX data showed that 18.66% and 12.67% of AuNPs atoms were present in the composite system thus proved the incorporation of AuNPs into the polymer matrix. A small red shift of the absorption peak in the UV-Vis of both PANI/AuNPs composites system may be due to the incorporation of AuNPs in the PANI matrix.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Some constructions on total labelling of m triangles

Let mK{sub 3} = (V{sub m}, E{sub m}) be a finite disconnected graph consisting of m disjoint triangles K{sub 3}, where V{sub m} is the set of vertices, E{sub m} is the set of edges and both V{sub m} and E{sub m} are of the same size 3m. A total labelling of mK{sub 3} is a function f which maps the elements in V{sub m} and E{sub m} to positive integer values, i.e. f : V{sub m} ∪ E{sub m} → {1, 2, 3,···}. Let c be a positive integer. A triangle is said have a c-Erdősian triangle labelling if it is a total labelling f : V{sub m} ∪ E{sub m} → {c, c + 1, ···, c + 6m − 1} such that f (x) + f (y) = f (xy) for any x, y ∈ V{sub m} and an edge xy ∈ E{sub m} joining them. In order to find all the c-Erdősian triangle labelling, a straightforward is to use the exhaustive search. However, the exhaustive search is only able to find c-Erdősian triangle labelling for m ≤ 5 due to combinatorial explosion. By studying the constant sum of vertex labels, we propose a strong permutation approach, which allows us to generate a certain classes of c-Erdősian triangle labelling up until m = 8.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Oxygen migration during resistance switching and failure of hafnium oxide memristors

While the recent establishment of the role of thermophoresis/diffusion-driven oxygen migration during resistance switching in metal oxide memristors provided critical insights required for memristor modeling, extended investigations of the role of oxygen migration during ageing and failure remain to be detailed. Such detailing will enable failure-tolerant design, which can lead to enhanced performance of memristor-based next-generation storage-class memory. Furthermore, we directly observed lateral oxygen migration using in-situ synchrotron x-ray absorption spectromicroscopy of HfO x memristors during initial resistance switching, wear over millions of switching cycles, and eventual failure, through which we determined potential physical causes of failure. Using this information, we reengineered devices to mitigate three failure mechanisms and demonstrated an improvement in endurance of about three orders of magnitude.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Flow-based likelihoods for non-Gaussian inference

We investigate the use of data-driven likelihoods to bypass a key assumption made in many scientific analyses, which is that the true likelihood of the data is Gaussian. In particular, we suggest using the optimization targets of flow-based generative models, a class of models that can capture complex distributions by transforming a simple base distribution through layers of nonlinearities. We call these flow-based likelihoods (FBL). We analyze the accuracy and precision of the reconstructed likelihoods on mock Gaussian data, and show that simply gauging the quality of samples drawn from the trained model is not a sufficient indicator that the true likelihood has been learned. We nevertheless demonstrate that the likelihood can be reconstructed to a precision equal to that of sampling error due to a finite sample size. We then apply FBLs to mock weak lensing convergence power spectra, a cosmological observable that is significantly non-Gaussian (NG). We find that the FBL captures the NG signatures in the data extremely well, while other commonly used data-driven likelihoods, such as Gaussian mixture models and independent component analysis, fail to do so. This suggests that works that have found small posterior shifts in NG data with data-driven likelihoods such as these could be underestimating the impact of non-Gaussianity in parameter constraints. By introducing a suite of tests that can capture different levels of NG in the data, we show that the success or failure of traditional data-driven likelihoods can be tied back to the structure of the NG in the data. Here, unlike other methods, the flexibility of the FBL makes it successful at tackling different types of NG simultaneously. Because of this, and consequently their likely applicability across datasets and domains, we encourage their use for inference when sufficient mock data are available for training.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Few-shot Learning for Post-disaster Structure Damage Assessment

Automating post-disaster damage assessment with remote sensing data is critical for faster surveys of structures impacted by natural disasters. One significant obstacle to training state-of-the-art deep neural networks to support this automation is that large quantities of labelled data are often required. However, obtaining those labels is particularly unrealistic to support post-disaster damage assessment in a timely manner. Few-shot learning methods could help to mitigate this by reducing the amount of labelled data required to successfully train a model while achieving satisfactory results. To this end, we explore a feature reweighting method to the YOLOv3 object detection architecture to achieve few-shot learning of damage assessment models on the xBD dataset. Our results show that the feature reweighting approach yield improved mAP over the baseline with significantly fewer labelled samples. In addition, we use t-SNE to analyze the class-specific reweighting vectors generated by the reweighting module in order to evaluate their inter-class and intra-class similarity. We find that the vectors form clusters based on class, and that these clusters overlap with visually similar classes. Those results show the potential to employ this few-shot learning strategy for rapid damage assessment with post-event remote sensing images.

Bowman, Jordan↗
DOE OSTI2023

DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction

Limited-Angle Computed Tomography (LACT) is a non-destructive evaluation technique used in a variety of applications ranging from security to medicine. The limited angle coverage in LACT is often a dominant source of severe artifacts in the reconstructed images, making it a challenging inverse problem. We present DOLCE, a new deep model-based framework for LACT that uses a conditional diffusion model as an image prior. Diffusion models are a recent class of deep generative models that are relatively easy to train due to their implementation as image denoisers. DOLCE can form high-quality images from severely under-sampled data by integrating data-consistency updates with the sampling updates of a diffusion model, which is conditioned on the transformed limited-angle data. We show through extensive experimentation on several challenging real LACT datasets that, the same pre-trained DOLCE model achieves the SOTA performance on drastically different types of images. Additionally, we show that, unlike standard LACT reconstruction methods, DOLCE naturally enables the quantification of the reconstruction uncertainty by generating multiple samples consistent with the measured data.

Kim, Hyojin↗
DOE OSTI2016

Searching for Physics Beyond the Standard Model: Strongly-Coupled Field Theories at the Intensity and Energy Frontiers

This proposal is to develop the software and algorithmic infrastructure needed for the numerical study of quantum chromodynamics (QCD), and of theories that have been proposed to describe physics beyond the Standard Model (BSM) of high energy physics, on current and future computers. This infrastructure will enable users (1) to improve the accuracy of QCD calculations to the point where they no longer limit what can be learned from high-precision experiments that seek to test the Standard Model, and (2) to determine the predictions of BSM theories in order to understand which of them are consistent with the data that will soon be available from the LHC. Work will include the extension and optimizations of community codes for the next generation of leadership class computers, the IBM Blue Gene/Q and the Cray XE/XK, and for the dedicated hardware funded for our field by the Department of Energy. Members of our collaboration at Brookhaven National Laboratory and Columbia University worked on the design of the Blue Gene/Q, and have begun to develop software for it. Under this grant we will build upon their experience to produce high-efficiency production codes for this machine. Cray XE/XK computers with many thousands of GPU accelerators will soon be available, and the dedicated commodity clusters we obtain with DOE funding include growing numbers of GPUs. We will work with our partners in NVIDIA's Emerging Technology group to scale our existing software to thousands of GPUs, and to produce highly efficient production codes for these machines. Work under this grant will also include the development of new algorithms for the effective use of heterogeneous computers, and their integration into our codes. It will include improvements of Krylov solvers and the development of new multigrid methods in collaboration with members of the FASTMath SciDAC Institute, using their HYPRE framework, as well as work on improved symplectic integrators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

A Non-hydrostatic Variable Resolution Atmospheric Model in ACME

The Accelerated Climate Modeling for Energy (ACME) Project is designed to perform coupled climate simulations at high resolution on current and next generation DOE leadership class supercomputers. Prior to this project, the ACME atmospheric model employed the primitive equations of motion, which constrained its maximum resolution and limited the physical phenomena that it could resolve. Our team removed this constraint by implementing a non-hydrostatic (NH) atmospheric model in the ACME Framework. Collaboration with SciDAC FASTMath institutes facilitated improvements to key technologies needed for the non-hydrostatic model. This model was tested in a variable resolution framework, which enables high resolution simulation regions at reasonable computational costs. The correct performance of the NH model was verified with standard dynamical-core tests, and by demonstrating that it reproduced expected climate statistics in the hydrostatic regime. The completed model was applied to evaluate the limitations of existing turbulent mountain stress and gravity wave drag parameterizations, in order to demonstrate the power of the new nonhydrostatic model to model new phenomena and enhance the quality of present day 1/4 degree simulations.

Hall, David Matthew↗
DOE OSTI2014

Draft Electric Rule 21 Test Protocols for Advanced Inverter Functions

Disclaimer: The following document includes draft certification protocols and should not be viewed as a consensus-based performance standard. Distributed energy resources (DERs), such as photovoltaic (PV) systems, when deployed in a large scale, are capable of significantly influencing the operation of bulk and local power systems. Looking to the future, European and American stakeholders are working on standards to make it possible to manage the potentially complex interactions between DER and the power system. One of the jurisdictions considering modifications to the DER interconnection requirements is California. To determine what changes could improve electric grid reliability and allow greater penetrations of renewable energy, the California Public Utilities Commission (CPUC) and the California Energy Commission (CEC), in conjunction with consultant Frances Cleveland, convened the Smart Inverter Working Group (SIWG) in January 2013. The SIWG--composed of state agencies, utility engineers, national laboratories, manufacturers, trade associations, and advocacy groups--provided the CPUC a set of recommendations in early 2014 1 . The recommendations included specific advanced DER functions and interoperability requirements, along with a proposed timeline, for California to add new capabilities to grid-interconnected DER. On August 18, 2014 the three California Investor-Owned Utilities (IOUs)--Pacific Gas and Electric Company (PG&E), Southern California Edison Company (SCE) and San Diego Gas & Electric Company (SDG&E)--drafted a Advice Letter filing to the CPUC setting forth revisions to Electric Tariff Rule 21 to conform to the seven recommendations made by the Working Group 2 . After a comment period, the CPUC issued an update to the IOU recommendations 3 . At the time of this publication, there was no final legal ruling on the CPUC Rulemaking (R.) 11-09-011 ("Order Instituting Rulemaking on the Commission's Own Motion to improve distribution level interconnection rules and regulations for certain classes of electric generators and electric storage resources"). In the U.S., Nationally Recognized Test Laboratories (NRTLs) independently verify products to safety and functional standards. PV inverters are certified to Underwriters Laboratories (UL) Standard 1741 4 . However, new advanced inverter functions described in the SIWG and IOU proposals are not included in this standard, so there is a critical need to develop test protocols for these functions in preparation of a positive ruling by the CPUC. Through a California Solar Initiative Grant, Sandia National Laboratories (Sandia or SNL), Underwriters Laboratories (UL), Electric Power Research Institute, Inc. (EPRI), Xanthus Consulting, SunSpec Alliance, Loggerware, and utility and PV inverter manufacturers have collaborated to draft this certification protocol for the Rule 21 SIWG Phase 1 advanced inverter functions. This report also includes test procedures for Phase 2 and Phase 3 functions that will be demonstrated later in the CSI4 project. This collaborative effort was performed in close junction with the UL 1741 Standards Technical Panel (STP) working group. This document represents a snapshot of the draft testing protocols at the time of publication and not a consensus certification protocol for advanced inverters. 1 California Public Utilities Commission, "Recommendations for Updating the Technical Requirements for Inverters in Distributed Energy Resources, Smart Inverter Working Group Recommendations," Filed 7 Feb 2014. 2 J.J. Newlander, R.G. Litteneker, S.W. Walter, M. Dwyer, Joint motion of Pacific Gas and Electric Company (U 39 E), Southern California Edison Company (U 338 E) and San Diego Gas & Electric Company (U 902 E) regarding implementation of smart inverter functionalities, Rulemaking 11-09-011 Advice Letter, 18 July, 2014. 3 J.T. Sullivan, CPUC Rulemaking 11-09-011 Agenda ID #13460, 13 Nov, 2014. 4 Underwriters Laboratories Std. 1741 Ed. 2, "Inverters, Converters, Controllers and Interconnection System Equipment for use with Distributed Energy Resources," 2010.

42 ENGINEERING↗
DOE OSTI2016

SciDAC-Data, A Project to Enabling Data Driven Modeling of Exascale Computing

The SciDAC-Data project is a DOE funded initiative to analyze and exploit two decades of information and analytics that have been collected by the Fermilab Data Center on the organization, movement, and consumption of High Energy Physics data. The project will analyze the analysis patterns and data organization that have been used by the NOvA, MicroBooNE, MINERvA and other experiments, to develop realistic models of HEP analysis workflows and data processing. The SciDAC-Data project aims to provide both realistic input vectors and corresponding output data that can be used to optimize and validate simulations of HEP analysis. These simulations are designed to address questions of data handling, cache optimization and workflow structures that are the prerequisites for modern HEP analysis chains to be mapped and optimized to run on the next generation of leadership class exascale computing facilities. We will address the use of the SciDAC-Data distributions acquired from Fermilab Data Center’s analysis workflows and corresponding to around 71,000 HEP jobs, as the input to detailed queuing simulations that model the expected data consumption and caching behaviors of the work running in HPC environments. In particular we describe in detail how the Sequential Access via Metadata (SAM) data handling system in combination with the dCache/Enstore based data archive facilities have been analyzed to develop the radically different models of the analysis of HEP data. We present how the simulation may be used to analyze the impact of design choices in archive facilities.

Mubarak, M.↗
DOE OSTI2021

ExaWind: Predictive Wind Energy Simulations

This presentation describes the ExaWind project and the team's progress in creating a suite of performance-portable codes designed for predictive simulations of wind farms on next-generation exascale-class supercomputers. Such simulations will require the resolution of scales spanning many orders of magnitude, from blade boundary layers to wind farm flow structures. In the U.S., the first exascale systems will be GPU accelerated, and different GPU manufacturers have been chosen for the different systems. At the heart of the ExaWind software is a hybrid-solver approach based on the codes Nalu-Wind and AMR-Wind, which are computational fluid dynamics solvers for the incompressible Navier-Stokes equations. Nalu-Wind is an unstructured-grid code used to resolve wind turbine geometry and blade boundary layers, whereas AMR-Wind is a structured-grid background solver for atmospheric turbulent flow and turbine wake propagation. The models are coupled with overset meshes and global linear systems are approximated through a loose-coupling algorithm. Results will include validation-quality high-fidelity simulations and strong/weak scaling results from the Summit supercomputer.

49 EE - Wind and Water Power Program - Wind (EE-4W↗