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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 OSTI2022

NREL Fleet Analysis Support Through Technology Integration Collaboration

This study leveraged the partnership between the United States Department of Energy's (DOE) Clean Cities Coalition Network and the Association for the Work Truck Industry (NTEA) to launch a vehicle and fleet analysis project that assisted fleets in identifying opportunities to save energy, improve efficiency, reduce costs, and meet environmental goals via short term data logging and analysis. The National Renewable Energy Laboratory (NREL) sought to establish a process that included initial data acquisition, provided data storage, and developed analytic methods to inform fleets of areas of opportunity based on approximately 30 days of in use vehicle performance data. However, long-term the project will require ongoing funding to fully develop and maintain the data sharing platform and to produce more complex analysis.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Empirical Estimation of the Energy Impacts of Projects Installed through Residential Property Assessed Clean Energy Financing Programs in California

We examine the energy use impacts of energy efficiency and solar PV projects financed by residential property assessed clean energy (R-PACE) programs in California. We leverage household-level interval meter data to apply normalized metered energy consumption (NMEC) methods at significant scale—more than 25,000 electric meters and more than 15,000 gas meters. We develop a comparison group to account for non-project-related changes in usage. The projects include homes that replaced existing HVAC equipment with higher-efficiency units and homes that installed central heating or air conditioning equipment for the first time. We have limited information on pre-project household equipment stock so we develop a method to infer new installations. We find that projects that installed energy efficiency technologies reduce electricity consumption by approximately 3% and gas consumption by approximately 3.5% on average. When we remove homes that installed new cooling and heating equipment for the first time, savings rise to approximately 5% for electricity and approximately 6% for gas. Given the California climate and the results of an existing study of similar California projects, these results are in line with expectations. Solar PV projects produce electricity that offsets approximately 69% of household electricity consumption on average. We estimate that California R-PACE projects installed through the end of 2019 produce annual reductions in grid-tied electricity consumption of 506 GWh (equivalent to the electricity consumption of approximately 74,000 California households) and gas consumption reductions of 2 million therms (equivalent to the gas consumption of approximately 4700 California households) in a typical weather year.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Data-driven projection pursuit adaptation of polynomial chaos expansions for dependent high-dimensional parameters

Uncertainty quantification (UQ) and inference involving a large number of parameters are valuable tools for problems associated with heterogeneous and non-stationary behaviors. The difficulty with these problems is exacerbated when these parameters are statistically dependent requiring statistical characterization over joint measures. Probabilistic modeling methodologies stand as effective tools in the realms of UQ and inference. Among these, polynomial chaos expansions (PCE), when adapted to low-dimensional quantities of interest (QoI), provide effective yet accurate approximations for these QoI in terms of an adapted orthogonal basis. These adaptation techniques have been cast as projection pursuits in Gaussian Hilbert space in what has been referred to as a projection pursuit adaptation (PPA) by Xiaoshu Zeng and Roger Ghanem (2023). The PPA method efficiently identifies an optimal low-dimensional space for representing the QoI and simultaneously evaluates an optimal PCE within that space. The quality of this approximation clearly depends on the size of the training dataset, which is typically a function of the adapted reduced dimension. Here, the complexity of the problem is thus mediated by the complexity of the low-dimensional quantity of interest and not the complexity of the high-dimensional parameter space.

Data-driven↗
DOE OSTI2023

A Fourth-Order Embedded Boundary Finite Volume Method for the Unsteady Stokes Equations with Complex Geometries

A fourth-order finite volume embedded boundary (EB) method is presented for the unsteady Stokes equations. The algorithm represents complex geometries on a Cartesian grid using EB, employing a technique to mitigate the ``small cut-cell"" problem without mesh modifications, cell merging, or state redistribution. Spatial discretizations are based on a weighted least-squares technique that has been extended to fourth-order operators and boundary conditions, including an approximate projection to enforce the divergence-free constraint. Solutions are advanced in time using a fourth-order additive implicit-explicit Runge-Kutta method, with the viscous and source terms treated implicitly and explicitly, respectively. Formal accuracy of the method is demonstrated with several grid convergence studies, and results are shown for an application with a complex bio-inspired material. In conclusion, the developed method achieves fourth-order accuracy and is stable despite the pervasive small cells arising from complex geometries.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Mathematical methods for optimal polynomial recovery of high-dimensional systems from noisy data

The goal of our Early Career Research Project (ECRP) is to establish a modern mathematical foundation that will enable next-generation computational methods for polynomial approximation of high-dimensional systems, having a certain set of constraints, from a limited amount of noisy data. Such a foundation is critical to realizing the future potential of the DOE user facilities, and will ultimately empower scientists to address a fundamental question, namely, “how many realizations of a nonlinear manifold are required to recover the entire high-dimensional solution map, with optimal approximation guarantees and minimal computational cost?” The central theme of this effort aims to conquer this challenge by pioneering the development of extraordinarily innovative theoretical analysis and transformational non-intrusive computational methodologies. Such approaches will enable the reconstruction of the entire high-dimensional solution map, with accuracy comparable to the best approximation, while utilizing an optimal number of samples. During this reporting period we have made significant progress on four thrusts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Implications of End Points on Remediation - 20476

The Port Hope Area Initiative (PHAI) is a federal environmental clean-up program. Its mandate is the remediation and local, long-term, safe management of approximately 1.7 million cubic metres of historic low-level radioactive waste in the adjacent communities of Port Hope and Port Granby in Southern Ontario, Canada. The PHAI is being undertaken by Canadian Nuclear Laboratories (CNL), on behalf of Atomic Energy of Canada Limited, a federal Crown corporation. It is currently one of the largest environmental remediation projects in Canada. The safety of workers, the public and the environment while delivering the PHAI compliantly, on time and within budget is CNL's number one priority. The PHAI is being implemented as two projects - the Port Hope Project and the Port Granby Project. The Port Hope Project is the larger of the two and includes the cleanup of approximately 1.2 million cubic metres of historic waste. The Port Hope Project includes a unique and important component - the remediation of approximately 1,200 private residences and businesses to include both interior and exterior verification of soil and surface-contaminated objects. Planning and consultation for the PHAI clean-up criteria and remediation verification standard operating procedures (RVSOP) were initiated in the early 2000's. The criteria were developed through a cooperative effort involving the PHAI, scientific specialists, federal and provincial government agencies, peer reviewers, the municipalities and members of the public. Subsequent verification procedures which detail the unique systematic methods to verify compliance with the clean-up criteria were developed by CNL in consultation with federal regulators and relevant stakeholders. Now, specifically with the Port Hope Small-Scale Sites project having been underway for a year and approximately 38 properties in some stage of remediation, it has become increasingly evident that the practical application of the clean-up criteria and RVSOP methodology is posing unique challenges that are having direct impact on project execution. The current RVSOP approach is based on discrete sample verification not allowing failure of any one sample. When implemented in the field, particularly at the Small-Scale Sites, it leads to a much more extensive excavation than what was accounted for in the original design, the implications of which are substantial delays in project progress and much more significant impact on the individual properties than originally anticipated. This paper will examine the evolution of PHAI clean-up criteria, subsequent RVSOP methodology and its impact on current project execution and the justification for exploring potential revisions to methods of remediation verification. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Uncertainty Quantification of Geophysical Inversion Using Stochastic Partial Differential Equations (LDRD #218329)

This report summarizes work completed under the Laboratory Directed Research and Development (LDRD) project "Uncertainty Quantification of Geophysical Inversion Using Stochastic Differential Equations." Geophysical inversions often require computationally expensive algorithms to find even one solution, let alone propagating uncertainties through to the solution domain. The primary purpose of this project was to find more computationally efficient means to approximate solution uncertainty in geophysical inversions. We found multiple computationally efficient methods of propagating Earth model uncertainty into uncertainties in solutions of full waveform seismic moment tensor inversions. However, the optimum method of approximating the uncertainty in these seismic source solutions was to use the Karhunen-Love theorem with data misfit residuals. This method was orders of magnitude more computationally efficient than traditional Monte Carlo methods and yielded estimates of uncertainty that closely approximated those of Monte Carlo. We will summarize the various methods we evaluated for estimating uncertainty in seismic source inversions as well as work toward this goal in the realm of 3-D seismic tomographic inversion uncertainty.

58 GEOSCIENCES↗
DOE OSTI2024

2023 Central Hanford Ecological Integrity Assessments Monitoring Report

The Hanford Site is comprised of an expanse of shrub-steppe habitats that provides exceptional ecological value to plants and animals located on the site and in the surrounding greater Columbia Basin. The U.S. Department of Energy (DOE)-managed portion of the Hanford Site, referred to from here on as Central Hanford, has been the focus of various ecological monitoring efforts, including vegetation monitoring. The scope and goals of vegetation surveys have varied greatly since the Hanford Site was established, but previous studies have documented a rapidly changing landscape, making it clear that continued vegetation monitoring is integral to preserving the ecological value of Central Hanford. A new vegetation monitoring effort was initiated in 2023 using methods based on ecological integrity assessments (EIA) (Natural Heritage Report [NHR] 2020-05) developed by the Washington Natural Heritage Program (WNHP), a division of the Washington State Department of Natural Resources (DNR) and NatureServe®. The methods were modified and supplemented to meet monitoring goals at the Hanford Site. The 2023 EIA monitoring effort consisted of field surveys to evaluate vegetation and soil condition across Central Hanford. Surveys were focused on upland habitats, and areas under consideration for upcoming projects were prioritized. Vegetation cover estimates were used to score a variety of metrics for vegetation conditions in each area. In 2023, approximately half of the site was surveyed. Monitoring methods and results for the 2023 field season are summarized in this report, and management recommendations are also provided.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Discrete Empirical Interpolation Method Based Dynamic Load Model Reduction

Dynamic load models add significant complexity to bulk power system time-domain simulations. The complexity is due to the large number of ordinary differential equations (ODEs) introduced by the dynamic load components such as induction motors. It is challenging to derive reduced-order models (ROMs) for dynamic loads due to the nonlinear functions in their governing equations. This paper applies the discrete empirical interpolation method enhanced proper orthogonal decomposition (DEIM-POD) to approximate the full dynamic load model with the ROM that minimizes the projection error of the nonlinear functions in dynamic load ODEs onto their dominant modes. This approach only requires evaluation of nonlinear functions at selected observation points. The observation points selected by DEIM also provide information for screening critical load buses where dynamic load model parameters contribute the most to the accuracy of ROM across multiple contingencies. The proposed approach is validated on IEEE 9-bus, WECC 179-bus and 2384-bus Polish systems.

bulk power system↗
DOE OSTI2024

RandONets: Shallow networks with random projections for learning linear and nonlinear operators

Deep neural networks have been extensively used for the solution of both the forward and the inverse problem for dynamical systems. However, their implementation necessitates optimizing a high-dimensional space of parameters and hyperparameters. This fact, along with the requirement of substantial computational resources, pose a barrier to achieving high numerical accuracy, but also interpretability. Here, to address the above challenges, we present Random Projection-based Operator Networks (RandONets): shallow networks with random projections and tailor-made numerical analysis methods that learn accurately and fast linear and nonlinear operators. Building on previous works, we prove that RandOnets are universal approximators of linear and nonlinear operators. Due to their simplicity, RandONets provide a one-step transformation of the input space, facilitating interpretability. For the evaluation of their performance, we focus on operators of PDEs. We show, that RandONets outperform by several orders of magnitude, both in terms of numerical approximation accuracy and computational cost, the “vanilla” DeepONets. Hence, we believe that our method will trigger further developments in the field of scientific machine learning, for the development of new ‘’light”schemes that will provide high accuracy while reducing dramatically the computational cost. A MATLAB toolbox for RandONets, including demos, is available on GitHub at https://github.com/GianlucaFabiani/RandONets.

Interpretable machine learning↗
DOE OSTI2020

Near-Single-Cell Proteomics Profiling of the Proximal Tubular and Glomerulus of the Normal Human Kidney

Molecular assessments at the single-cell level can accelerate biological research by providing detailed assessments of cellular organization and tissue heterogeneity in both disease and health. The human kidney has complex multi-cellular states with varying functionality, much of which can now be completely harnessed with recent technological advances in tissue proteomics at a near single-cell level. We discuss the foundational steps in the first application of this mass spectrometry (MS) based proteomics method for analysis of sub-sections of the normal human kidney, as part of the Kidney Precision Medicine Project (KPMP). Using approximately 30-40 laser captured micro-dissected kidney cells, we identified more than 2500 human proteins, with specificity to the proximal tubular (PT; n=25 proteins) and glomerular (Glom; n=67 proteins) regions of the kidney and their unique metabolic functions. This pilot study provides the roadmap for application of our near-single-cell proteomics workflow for the analysis of other renal micro-compartments, on a larger scale, to unravel perturbations of renal sub-cellular function in the normal kidney as well as different etiologies of acute and chronic kidney disease.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Distribution of centrality measures on undirected random networks via the cavity method

The Katz centrality of a node in a complex network is a measure of the node’s importance as far as the flow of information across the network is concerned. For ensembles of locally tree-like undirected random graphs, this observable is a random variable. Its full probability distribution is of interest but difficult to handle analytically because of its “global” character and its definition in terms of a matrix inverse. Leveraging a fast Gaussian Belief Propagation-Cavity algorithm to solve linear systems on tree-like structures, we show that i) the Katz centrality of a single instance can be computed recursively in a very fast way, and ii) the probability P ( K ) that a random node in the ensemble of undirected random graphs has centrality K satisfies a set of recursive distributional equations, which can be analytically characterized and efficiently solved using a population dynamics algorithm. We test our solution on ensembles of Erdős-Rényi and Scale Free networks in the locally tree-like regime, with excellent agreement. The analytical distribution of centrality for the configuration model conditioned on the degree of each node can be employed as a benchmark to identify nodes of empirical networks with over- and underexpressed centrality relative to a null baseline. We also provide an approximate formula based on a rank- 1 projection that works well if the network is not too sparse, and we argue that an extension of our method could be efficiently extended to tackle analytical distributions of other centrality measures such as PageRank for directed networks in a transparent and user-friendly way.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

R-matrix calculations for opacities: I. Methodology and computations

Abstract An extended version of the R -matrix methodology is presented for calculation of radiative parameters for improved plasma opacities. Contrast and comparisons with existing methods primarily relying on the distorted wave approximation are discussed to verify accuracy and resolve outstanding issues, particularly with reference to the opacity project (OP). Among the improvements incorporated are: (i) large-scale Breit–Pauli R -matrix calculations for complex atomic systems including fine structure, (ii) convergent close coupling wave function expansions for the ( e + ion) system to compute oscillator strengths and photoionization cross sections, (iii) open and closed shell iron ions of interest in astrophysics and experiments, (iv) a treatment for plasma broadening of autoionizing resonances as function of energy-temperature-density dependent cross sections, (v) a ‘top-up’ procedure to compare convergence with R -matrix calculations for highly excited levels, and (vi) spectroscopic identification of resonances and bound ( e + ion) levels. The present R -matrix monochromatic opacity spectra are fundamentally different from OP and lead to enhanced Rosseland and Planck mean opacities. An outline of the work reported in other papers in this series and those in progress is presented. Based on the present re-examination of the OP work, opacities of heavy elements might require revisions in high temperature-density plasma sources.

Pradhan, A. K. (ORCID:0000000187753643)↗
DOE OSTI2025

Sensitivity of magnetic islands in permanent magnet stellarators using the gradient and Hessian methods

Stellarator plasmas are known to be very sensitive to perturbations in the magnetic field. The permanent magnet stellarator was in part developed as a solution to high machining tolerances placed on the shape properties of electromagnetic coils in traditional stellarators. However, as a consequence of this high sensitivity to the field structure, sensitivities of permanent magnet stellarator plasmas to perturbations of permanent magnet properties must necessarily be well-understood. The gradient and Hessian matrix methods have been previously demonstrated to be useful sensitivity analysis methods for modular coils. We apply these two methods to the study of island width sensitivities in both the MUSE and PM4STELL permanent magnet stellarator projects. These sensitivity methods were used to determine the relative impacts of permanent magnet parameter perturbations on island widths in the vacuum field approximation of both stellarator equilibria. The square of resonant magnetic field perturbation is used here as a proxy for island width. In particular, gradients of magnetizations of individual magnets were examined in MUSE, as well as gradients of magnet group displacements informed by device design. Three different forms of permanent magnet magnetization perturbations are investigated for MUSE, and the flux surface response to perturbations is demonstrated. The Hessian matrix method is applied to PM4STELL, illustrating the sensitivity of dominant island widths to displacements of toroidal wedge structures. These methods allow for selective direction of experimental resources toward regions of heightened sensitivity, while constraints on less impactful permanent magnet parameters can be relaxed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

A practical method for modeling temporally-averaged ocean wave frequency-directional spectra for characterizing wave energy climates

Wave energy resource characterization investigations have been hindered because frequency-directional wave spectra are not broadly available for many coastal regions due to the insufficient spatial coverage of buoy observation networks and spectral wave model hindcast outputs. We address this problem by developing and validating a practical method for approximating temporally-averaged frequency-directional wave spectra averaged over periods of months or years from bulk wave parameters, which are available at a much greater coverage and resolution than frequency-directional wave spectra. While the temporally averaged frequency-directional wave spectrum over these periods cannot be used for analyzing a single sea state, it aggregates multiple sea states, identifies dominant wave energy systems representing wave climate, and resolves their spectral characteristics. Therefore, modelling temporally averaged frequency-directional wave spectra is of great value for planning and designing wave energy projects, e.g., resource characterization, site assessment, and conceptual design of wave energy converters. Temporally-averaged frequency-directional wave spectra and related important wave energy parameters approximated using this method are found to be more accurate than commonly used parametric wave spectrum models. This method can be applied to a wide range of wave climates given its universality and high accuracy. Also, the availability of bulk wave parameters from multi-decade high-resolution wave model hindcasts is increasing.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Neutron diffusion calculation in heterogeneous geometry based on local/global iteration using proper orthogonal decomposition

This study newly proposes a heterogeneous core calculation method based on local/global iteration using proper orthogonal decomposition (POD). By using the singular value decomposition (SVD) and the low-rank approximation, appropriate POD bases for expanding the neutron flux can be obtained from snapshot data of the neutron flux obtained by fine mesh calculations. By projection using the POD bases, the dimension of the target equation (e.g., discretized neutron diffusion equation) can be dramatically reduced. In the proposed method, POD is effectively applied to each single assembly calculation (local calculation). Furthermore, using the local/global iteration, the effective neutron multiplication factor and the neutron flux distribution in the whole core geometry can be obtained by combining the numerical results of the local calculation for each fuel assembly and the global calculation for the whole core. As a feasibility study, the proposed method is applied to a one-dimensional heterogeneous core analysis, and the accuracy is investigated by changing the total number of POD bases. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Why is My Zero Energy Home Not a Zero Carbon Home?

For years, carbon calculations were done very simply. The method of calculation was to take annual totals of energy consumption and multiply by an average emission factor, either for the grid serving a project or for a larger region (e.g. an EPA eGRID sub region). The level of accuracy of this approximation was reasonably good, although the issue of accuracy was not, to our knowledge, tested. And the data required were minimal – just a year’s worth of bills for each fuel and one lookup factor. But this method assures that a net zero energy home is automatically a net zero carbon home because zero times any possible emission factor is still zero. Starting in the early 2010s, things changed – grids were starting to rely more and more heavily on renewables, and the difference was showing up on aggregate load curves. This was perhaps noticed first in California, where aggressive renewable policies led to significant renewable power generation large enough to affect the overall shape of the diurnal load curve for the Independent Systems Operator.

14 SOLAR ENERGY↗
DOE OSTI2024

Self-Organized Stress Distributions in Polycrystalline Materials [Dissertation]

Understanding stress distributions in solid materials is complicated by the fact that most materials are polycrystalline in nature, with each crystal having an elastically anisotropic reaction to force. This study is to gain a better understanding on how external forces placed on a polycrystal are related to internal reactions within and between the grains. The hypothesis is that the stress distribution in porous to fully dense materials are self-organized based on strong contacts between and within the individual grains created by force chains. Force chains, commonly known in loaded granular materials, and could be the phenomenon that connect micro to macro deformation. Scale bridging measurements conducted through Raman spectroscopy, Atomic Force Microscopy, and Digital Image Correlation will be used to create stress maps, modulus maps, and elastic strain maps across a variety of geological and pharmaceutical polycrystals. When possible, the resulting maps will be compared to current homogenization schemes and a full field models. Finite element modeling will be used to assess whether the patterning seen in the experimental map is a reasonable approximation based on the orientation data of the samples used. A minimum of three publications is projected to be accomplished focusing each on a different method to experimentally test and analyze stress distributions.

36 MATERIALS SCIENCE↗