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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 OSTI2014

Generic inference of inflation models by non-Gaussianity and primordial power spectrum reconstruction

We present a generic inference method for inflation models from observational data by the usage of higher-order statistics of the curvature perturbation on uniform density hypersurfaces. This method is based on the calculation of the posterior for the primordial non-Gaussianity parameters f{sub NL} and g{sub NL}, which in general depend on specific parameters of inflation and reheating models, and enables to discriminate among the still viable inflation models. To keep analyticity as far as possible to dispense with numerically expensive sampling techniques a saddle-point approximation is introduced, whose precision is validated for a numerical toy example. The mathematical formulation is done in a generic way so that the approach remains applicable to cosmic microwave background data as well as to large scale structure data. Additionally, we review a few currently interesting inflation models and present numerical toy examples thereof in two and three dimensions to demonstrate the efficiency of the higher-order statistics method. A second quantity of interest is the primordial power spectrum. Here, we present two Bayesian methods to infer it from observational data, the so called critical filter and an extension thereof with smoothness prior, both allowing for a non-parametric spectrum reconstruction. These methods are able to reconstruct the spectra of the observed perturbations and the primordial ones of curvature perturbation even in case of non-Gaussianity and partial sky coverage. We argue that observables like T- and B-modes permit to measure both spectra. This also allows to infer the level of non-Gaussianity generated since inflation.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Optimal hybrid power plants for electric vehicle charging demand

Transmission constraints, increasing motivations to decarbonize, and concerns over peak electric vehicle (EV) load impacts on local grids have driven electric customers to consider behind-the-meter, hybrid power plant generation and storage at the distributed-grid level for EV charging. In this study, we develop capabilities to optimize hybrid power plant component capacities for EV charging. We then demonstrate these capabilities in a case study for Boulder, Colorado, using public EV charging data as well as wind and solar resource data. Our results show system designs that balance the cost of energy with load-meeting and peak shaving performance. Within the case study, systems designed for wind, solar photovoltaic (PV), and storage resulted in lower cost of energy than those optimized for PV and storage only. This indicates that in areas where wind resource exists, hybrid power plants that include wind, PV, and battery assets can better meet EV charging loads (including peak loads that are prone to overloading local grids) than PV and battery assets alone. Future work to address limitations in this paper include extending cost modeling to include performance losses (e.g., based on operations or weather) and charging station costs to estimate levelized cost of charging, and quantifying uncertainty and error in our aggregation methods for estimating EV charging loads at the hourly timescale.

14 SOLAR ENERGY↗

DOE OSTI2022

Canted antiferromagnetic phases in the candidate layered Weyl material EuMnSb 2

EuMnSb 2 is a candidate topological material which can be tuned towards a Weyl semimetal, but there are differing reports for its antiferromagnetic (AFM) phases. The coupling of bands dominated by pure Sb layers hosting topological fermions to Mn and Eu magnetic states provides a potential path to tune the topological properties. In this study we present single-crystal neutron diffraction, magnetization, and heat-capacity data as well as polycrystalline 151 Eu Mössbauer data which show that three AFM phases exist as a function of temperature, and we present a detailed analysis of the magnetic structure in each phase. The Mn magnetic sublattice orders into a C-type AFM structure below T N Mn = 323 (1) K with the ordered Mn magnetic moment μ Mn lying perpendicular to the layers. AFM ordering of the Eu sublattice occurs below T N Eu1 = 23 (1) K with the ordered Eu magnetic moment μ Eu canted away from the layer normal and μ Mn retaining its higher temperature order. μ Eu is ferromagnetically aligned within each Eu layer but exhibits a complicated AFM layer stacking. Both of these higher-temperature phases are described by magnetic space group (MSG) $\mathcal{Pn'm'a'}$ with the chemical and magnetic unit cells having the same dimensions. Cooling below T N Eu2 =9 (1) K reveals a third AFM phase where μ Mn remains unchanged but μ Eu develops an additional substantial in-plane canting. This phase has MSG $\mathcal{P11}$ $\frac{2_1}{a′}$. We also find some evidence of short-range magnetic correlations associated with the Eu between 12 K≲ T≲ 30 K . Using the determined magnetic structures, we postulate the signs of nearest-neighbor intralayer and interlayer exchange constants and the magnetic anisotropy within a general Heisenberg model. We then discuss implications of the various AFM states in EuMnSb 2 and their potential for tuning topological properties.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

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 OSTI2017

Exacution: Enhancing Scientific Data Management for Exascale

As we continue toward exascale, scientific data volume is continuing to scale and becoming more burdensome to manage. In this paper, we lay out opportunities to enhance state of the art data management techniques. We emphasize well-principled data compression, and using it to achieve progressive refinement. This can both accelerate I/O and afford the user increased flexibility when she interacts with the data. The formulation naturally maps onto enabling partitioning of the progressively improving-quality representations of a data quantity into different media-type destinations, to keep the highest priority information as close as possible to the computation, and take advantage of deepening memory/storage hierarchies in ways not previously possible. Careful monitoring is requisite to our vision, not only to verify that compression has not eliminated salient features in the data, but also to better understand the performance of massively parallel scientific applications. Increased mathematical rigor would be ideal,to help bring compression on a better-understood theoretical footing, closer to the relevant scientific theory, more aware of constraints imposed by the science, and more tightly error-controlled. Throughout, we highlight pathfinding research we have begun exploring related these topics, and comment toward future work that will be needed.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Computational Fluid Dynamics Modeling of the Fuel Reactor in NETL's 50 kW th Chemical Looping Facility

The National Energy Technology Laboratory (NETL) has explored chemical looping in its 50 kW th facility using a number of oxygen carriers. In this work, the results for methane conversion in the fuel reactor with a hematite iron ore as the oxygen carrier are analyzed. The experimental results are compared to predictions using CPFD's barracuda computational fluid dynamics (CFD) code with kinetics derived from the analysis of fixed bed data. It has been found through analytical techniques from thermal gravimetric analysis data as well as the same fixed bed data that the kinetics for the methane–hematite reaction follows a nucleation and growth or Johnson–Mehl–Avrami (JMA) reaction mechanism. barracuda does not accept nucleation and growth kinetics; however, there is enough sufficient variability of the solids dependence within the software such that the nucleation and growth behavior can be mimicked. This paper presents the method to develop the pseudo-JMA kinetics for barracuda extracted from the fixed bed data and then applies these values to the fuel reactor data to compare the computational results to experimental data obtained from 50 kW th unit for validation. Lastly, a fuel reactor design for near complete conversion is proposed.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2015

Sparsity-based acoustic inversion in cross-sectional multiscale optoacoustic imaging

Purpose: With recent advancement in hardware of optoacoustic imaging systems, highly detailed cross-sectional images may be acquired at a single laser shot, thus eliminating motion artifacts. Nonetheless, other sources of artifacts remain due to signal distortion or out-of-plane signals. The purpose of image reconstruction algorithms is to obtain the most accurate images from noisy, distorted projection data. Methods: In this paper, the authors use the model-based approach for acoustic inversion, combined with a sparsity-based inversion procedure. Specifically, a cost function is used that includes the L1 norm of the image in sparse representation and a total variation (TV) term. The optimization problem is solved by a numerically efficient implementation of a nonlinear gradient descent algorithm. TV–L1 model-based inversion is tested in the cross section geometry for numerically generated data as well as for in vivo experimental data from an adult mouse. Results: In all cases, model-based TV–L1 inversion showed a better performance over the conventional Tikhonov regularization, TV inversion, and L1 inversion. In the numerical examples, the images reconstructed with TV–L1 inversion were quantitatively more similar to the originating images. In the experimental examples, TV–L1 inversion yielded sharper images and weaker streak artifact. Conclusions: The results herein show that TV–L1 inversion is capable of improving the quality of highly detailed, multiscale optoacoustic images obtained in vivo using cross-sectional imaging systems. As a result of its high fidelity, model-based TV–L1 inversion may be considered as the new standard for image reconstruction in cross-sectional imaging.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

The MG-RAST API explorer: an on-ramp for RESTful query composition

Background The MG-RAST API provides search capabilities and delivers organism and function data as well as raw or annotated sequence data via the web interface and its RESTful API. For casual users, however, RESTful APIs are hard to learn and work with. Results We created the graphical MG-RAST API explorer to help researchers more easily build and export API queries; understand the data abstractions and indices available in MG-RAST; and use the results presented in-browser for exploration, development, and debugging. Conclusions The API explorer lowers the barrier to entry for occasional or first-time MG-RAST API users.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

February 2014 Dixie Valley Binary Cycle Production Data

Dixie Valley Binary Plant data points for January - March 6 2014. Used to demonstrate the techno-economic feasibility of utilizing the available unused heat to generate additional electric power from a binary power plant. Data includes Brine Flow, Brine Return Temperature, Brine Supply Temperature, Generation Output, Net Generation, Parasitic Loss, and Dry Bulb Temperature.

15 GEOTHERMAL ENERGY↗

DOE OSTI2026

2014-2015 Puget Sound Regional Travel Study

The 2014-2015 Puget Sound Regional Travel Study collected information about household and individual travel patterns for residents throughout a four-county region in Washington State. Study results were used to update the region's travel and land-use models and to calibrate local traffic and travel models. The study also helped the Puget Sound Regional Council (PSRC) and its regional partners develop plans that accommodate the diverse travel needs and preferences of residents. The Resource Systems Group administered the study on behalf of PSRC. Global positioning system (GPS)-equipped smartphones were used to provide data pertaining to the daily travel of 547 individual participants. Because the region's university students may have been underrepresented in the initial 2014 household travel study, the PSRC added a college-population travel survey in fall 2014. In spring 2015, a second household data collection effort was conducted to increase the frequency of data collection and to collect GPS data as well as a sample of longitudinal data from households that completed the 2014 survey.

1Hz data↗

DOE OSTI2023

AmeriFlux FLUXNET-1F US-xCP NEON Central Plains Experimental Range (CPER)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xCP NEON Central Plains Experimental Range (CPER). This is the FLUXNET version of the carbon flux data for the site US-xCP NEON Central Plains Experimental Range (CPER) produced by applying the standard ONEFlux (1F) software. Site Description - Central Plains Experimental Range (CPER) site in north central Colorado. CPER served as part of the Shortgrass Steppe LTER from 1982-2014 and is now home to a fully instrumented NEON site and a wealth of relevant historical data as well as ongoing collections of complementary data(i.e. airborne remote sensing, soil temperature and moisture, phenology measurements, plant biomass and more)

Network), NEON (National Ecological Observatory↗

DOE OSTI2014

Closing the data life cycle: using information management in macrosystems ecology research

An important goal of macrosystems ecology research is to advance understanding of ecological systems at both fine and broad temporal and spatial scales. Our premise in this paper is that such projects require information management that is integrated into projects from their inception. Such efforts will lead to improved communication and sharing of knowledge among diverse project participants, better science outcomes, and more open science. We promote "closing the data life cycle" by publishing well-documented data sets, which allows for re-use of data to answer new and different questions from the ones conceived by the original projects. The practice of documenting and submitting data sets to publicly accessible data repositories ensures that research results and data are accessible to and useable by other researchers, thus fostering open science. Ecologists are often not familiar with the information management tools and requirements to effectively preserve data, however, and receive little institutional or professional incentive to do so. This paper describes recommended steps to these ends, and gives examples from current macrosystem ecology projects of why information management is so critical to ensuring that scientific results can be both reproduced and data shared for future use.

Ruegg, Janine↗

DOE OSTI2013

Data mining the EXFOR database

The EXFOR database contains the largest collection of experimental nuclear reaction data available as well as this data's bibliographic information and experimental details. We created an undirected graph from the EXFOR datasets with graph nodes representing single observables and graph links representing the connections of various types between these observables. This graph is an abstract representation of the connections in EXFOR, similar to graphs of social networks, authorship networks, etc. Analysing this abstract graph, we are able to address very specific questions such as 1) what observables are being used as reference measurements by the experimental community? 2) are these observables given the attention needed by various standards organisations? 3) are there classes of observables that are not connected to these reference measurements? In addressing these questions, we propose several (mostly cross section) observables that should be evaluated and made into reaction reference standards.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2016

Final technical report DOE award DE-SC0007206 Improving CESM Efficiency to Study Variable C:N:P Stoichiometry in the Oceans

This report lists the accomplishments of the project, which includes: (1) analysis of inorganic nutrient concentration data as well as suspended particulate organic matter data in the ocean to demonstrate that the carbon to nitrogen to phosphorus ratios (C:N:P) of biological uptake and export vary on large spatial scales, (2) the development of a new computationally efficient method for simulating biogeochemical tracers in earth system models, (3) the application of the method to help calibrate an improved representation of dissolved organic matter in the ocean that includes variable C:N:P stoichiometry. This research is important because biological uptake of carbon and nutrients in the upper ocean and export by sinking particles and downward mixing of dissolved organic matter helps maintain a vertical gradient in the carbon dioxide concentration in the ocean. This gradient is key to understanding the partitioning of CO2 between the ocean and the atmosphere. The final report lists seven peer reviewed scientific publications, one Ph.D. thesis, one technical report and two papers in preparation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Quantifying the Financial Benefits of Multifamily Retrofits

The U.S. Department of Energy’s Building America research team Partnership for Advanced Residential Retrofit analyzed building, energy, and financial program data as well as other public and private data to examine the relationship between energy-efficiency retrofits and financial performance on three levels: building, city, and community.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

NCSP IER 476 CED-4A Documentation

This document is a memo to the National Criticality Safety Program (NCSP) management that details the KRUSTY high temperature run for the KRUSTY experiment, which took place in March of 2018. The goal for writing this memo was to collect all of the available information into one document, so that researchers can use this document for modeling and analysis. One important part to this document is the data package, which is referenced in the document and contains all the available experimental data for the KRUSTY high temperature run. The data package itself is quite large (1.6 GB) and is not included in the document itself. However, sample files have been provided in the document, in addition to plots of the data as well as a description of each data set.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

In Situ Data Analysis Through Physics-informed Tensor Decompositions (LDRD Final Report)

We introduce a new low-dimensional model of high-dimensional numerical simulation data based on low-rank tensor decompositions. Our new model aims to minimize differences between the model data and simulation data as well as functions of the model data and functions of the simulation data. This novel approach to dimensionality reduction of simulation data provides a means of directly incorporating quantities of interests and invariants associated with conservation principles associated with the simulation data into the low-dimensional model, thus enabling more accurate analysis of the simulation without requiring access to the full set of high-dimensional data. Computational results of applying this approach to two standard low-rank tensor decompositions of data arising from simulation of combustion and plasma physics are presented.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Community Requirements Meta-Analysis: Characterizing Needs and Opportunities for HPDF

This High Performance Data Facility (HPDF) Project is creating a new scientific user facility to provide advanced infrastructure for data-intensive science, supporting the DOE’s Office of Science (SC) community. HPDF’s mission is to enable and accelerate scientific discovery by delivering state-of-the-art data management infrastructure, capabilities, and tools. This meta-analysis examines the needs of the breadth of the SC community, captured in publicly available community reports or mission documents. The meta-analysis identifies and provides initial characterization of fifteen core requirements for the HPDF Project team to consider during the conceptual design phase. The fifteen requirements illustrate how scientific work among SC communities requires modern, seamless user experiences across the ASCR Ecosystem to advance the use of large volumes of heterogeneous data. The scientific community requires support for the missing middle of compute between local and HPC to interactively and collaboratively use growing datasets. Data producers and end users will benefit from enhanced data catalogs and portals that improve data access through advanced search of well curated data. The fifteen requirements are examined here organized across five themes for discussion. Examples in each theme illustrate the array of scientific needs that convey the important role that the fully realized and operational High Performance Data Facility will be able to play as an integral part of the evolving ASCR Ecosystem. Our amalgamated data tables from ESnet reports demonstrate ranges to the volumes of data HPDF must be concerned with, but limitations are inherent to this meta-analysis (see Key Challenges & Limitations). Feedback and validation of these requirements along with additional details and emergent community requirements will be gathered through user research and design activities.

97 MATHEMATICS AND COMPUTING↗