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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 OSTI2016

Chattanooga Electric Power Board Case Study Distribution Automation

In 2009, the U.S. Department of Energy under the American Recovery and Reinvestment Act (ARRA) awarded a grant to the Chattanooga, Tennessee, Electric Power Board (EPB) as part of the Smart Grid Investment Grant Program. The grant had the objective “to accelerate the transformation of the nation’s electric grid by deploying smart grid technologies.” This funding award enabled EPB to expedite the original smart grid implementation schedule from an estimated 10-12 years to 2.5 years. With this funding, EPB invested heavily in distribution automation technologies including installing over 1,200 automated circuit switches and sensors on 171 circuits. For utilities considering a commitment to distribution automation, there are underlying questions such as the following: “What is the value?” and “What are the costs?” This case study attempts to answer these questions. The primary benefit of distribution automation is increased reliability or reduced power outage duration and frequency. Power outages directly impact customer economics by interfering with business functions. In the past, this economic driver has been difficult to effectively evaluate. However, as this case study demonstrates, tools and analysis techniques are now available. In this case study, the impact on customer costs associated with power outages before and after the implementation of distribution automation are compared. Two example evaluations are performed to demonstrate the benefits: 1) a savings baseline for customers under normal operations1 and 2) customer savings for a single severe weather event. Cost calculations for customer power outages are performed using the US Department of Energy (DOE) Interruption Cost Estimate (ICE) calculator2. This tool uses standard metrics associated with outages and the customers to calculate cost impact. The analysis shows that EPB customers have seen significant reliability improvements from the implementation of distribution automation. Under normal operations, the investment in distribution automation has enabled a 43.5% reduction in annual outage minutes since 2012. This has led to an estimated total savings of $26.8 million per year. Examining a single severe weather event3, the distribution automation was able to restore power to 40,579 (nearly 56%) customers within 1–2 seconds and reduce outage minutes by 29.0%. This saved customers an estimated $23.2 million over the course of the storm.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Predictive coarse-graining

We propose a data-driven, coarse-graining formulation in the context of equilibrium statistical mechanics. In contrast to existing techniques which are based on a fine-to-coarse map, we adopt the opposite strategy by prescribing a probabilistic coarse-to-fine map. This corresponds to a directed probabilistic model where the coarse variables play the role of latent generators of the fine scale (all-atom) data. From an information-theoretic perspective, the framework proposed provides an improvement upon the relative entropy method and is capable of quantifying the uncertainty due to the information loss that unavoidably takes place during the coarse-graining process. Furthermore, it can be readily extended to a fully Bayesian model where various sources of uncertainties are reflected in the posterior of the model parameters. The latter can be used to produce not only point estimates of fine-scale reconstructions or macroscopic observables, but more importantly, predictive posterior distributions on these quantities. Predictive posterior distributions reflect the confidence of the model as a function of the amount of data and the level of coarse-graining. The issues of model complexity and model selection are seamlessly addressed by employing a hierarchical prior that favors the discovery of sparse solutions, revealing the most prominent features in the coarse-grained model. A flexible and parallelizable Monte Carlo – Expectation–Maximization (MC-EM) scheme is proposed for carrying out inference and learning tasks. A comparative assessment of the proposed methodology is presented for a lattice spin system and the SPC/E water model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Distributed output-feedback model predictive control for multi-agent consensus

We introduce a distributed output-feedback model predictive control approach for achieving consensus among multiple agents. Each agent computes a distributed control action based on an output-feedback measurement of a local neighborhood tracking error and communicates information only to its neighbors, according to a communication network modeled as a directed graph. Each agent computes its distributed control action by solving a local min–max optimization problem that simultaneously computes a local state estimate and control input under worst-case assumptions on unmeasured input disturbances and measurement noise. Under easily verified controllability and observability assumptions, this distributed output-feedback model predictive control approach provides an upper bound on the group consensus error, thereby ensuring practical consensus in the presence of unmeasured disturbances and noise. A numerical example with four agents connected in a directed graph is demonstrated to illustrate the results.

42 ENGINEERING↗
DOE OSTI2019

Optical-property coefficient estimation of bulky medium in experiments with a succinctly analytical calculation

Diffuse optical imaging enables to reconstruct distribution of optical properties, absorption and scattering coefficients, in tissue for breast cancer detection based on diffusion equation with the help of the initial guess obtained from measured data. To estimate the initial guess of the optical-property coefficients, an analytical solution of diffusion equation can be used and compared with the measured data. The analytical solution for a homogeneous infinite medium can be obtained in the frequency domain, expressing that the photon intensity and the phase lag relative to the distance between source and detector in a linear relationship. In this study, a succinct calculation using the trigonometric relation is proposed to estimate the optical-property coefficients. A tank-type and a cylinder Lipovenoes phantoms with two concentrations of 1.25% and 2.5% are employed and measured for verification. It is found that the method proposed here shows better results and results in estimation errors of 0–14.81% for μ{sub a} and 20–42% for μ{sub s}′.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Sky subtraction in an era of low surface brightness astronomy

ABSTRACT The Vera C. Rubin Observatory Wide-Fast Deep sky survey will reach unprecedented surface brightness depths over tens of thousands of square degrees. Surface brightness photometry has traditionally been a challenge. Current algorithms which combine object detection with sky estimation systematically oversubtract the sky, biasing surface brightness measurements at the faint end and destroying or severely compromising low surface brightness light. While it has recently been shown that properly accounting for undetected faint galaxies and the wings of brighter objects can in principle recover a more accurate sky estimate, this has not yet been demonstrated in practice. Obtaining a consistent spatially smooth underlying sky estimate is particularly challenging in the presence of representative distributions of bright and faint objects. In this paper, we use simulations of crowded and uncrowded fields designed to mimic Hyper Suprime-Cam data to perform a series of tests on the accuracy of the recovered sky. Dependence on field density, galaxy type, and limiting flux for detection are all considered. Several photometry packages are utilized: source extractor, gnuastro, and the LSST science pipelines. Each is configured in various modes, and their performance at extreme low surface brightness analysed. We find that the combination of the source extractor software package with novel source model masking techniques consistently produce extremely faint output sky estimates, by up to an order of magnitude, as well as returning high fidelity output science catalogues.

Astronomy & Astrophysics↗
DOE OSTI2017

Modeling residence-time distribution in horizontal screw hydrolysis reactors

The dilute-acid thermochemical hydrolysis step used in the production of liquid fuels from lignocellulosic biomass requires precise residence-time control to achieve high monomeric sugar yields. Difficulty has been encountered reproducing residence times and yields when small batch reaction conditions are scaled up to larger pilot-scale horizontal auger-tube type continuous reactors. A commonly used naive model estimated residence times of 6.2-16.7 min, but measured mean times were actually 1.4-2.2 the estimates. Here, this study investigated how reactor residence-time distribution (RTD) is affected by reactor characteristics and operational conditions, and developed a method to accurately predict the RTD based on key parameters. Screw speed, reactor physical dimensions, throughput rate, and process material density were identified as major factors affecting both the mean and standard deviation of RTDs. The general shape of RTDs was consistent with a constant value determined for skewness. The Peclet number quantified reactor plug-flow performance, which ranged between 20 and 357.

09 BIOMASS FUELS↗
DOE OSTI2020

The Impact of Behind-the-Meter Heterogeneous Distributed Energy Resources on Distribution Grids: Preprint

The increasing integration of distributed energy resources (DERs) on the electric grid brings new challenges and opportunities for utility grid operations. With the rapid deployment of DERs, there is emerging interest in integrating these controllable devices with utility operations at all levels for monitoring and management. Different types of DERs such as Photovoltaics (PV), energy storage, electric vehicles (EVs), etc. have varying effects on the grid based on how and where they are deployed. Again, the DER deployment and its impact are network-dependent, while the traditional electric grids were not designed to host DERs. To understand the challenges with increasing behind-the-meter (BTM) DERs and to identify the needs in deploying advanced controls, a comprehensive grid impact study is indispensable. This paper investigates the impacts of integrating a mix of DERs in the utility distribution network in Colorado, U.S. Firstly, BTM DERs at the residential scale including distributed PV; battery energy storage systems (BESS); heating, ventilation, and air-conditioning (HVAC) load; electric water heater (EWH) load; and EVs are modeled. The impacts of integrating these resources on the distribution network are evaluated by conducting time-series simulations for different scenarios considering different days to capture the worst-case conditions. Monte Carlo simulations are conducted to generate the realistic EV charging profile. The voltage issues, substation transformer loadings, and critical nodes in the network are identified with the incorporation of uncoordinated EV charging loads and other DER in the network. This analysis helps visualize the DER impact on the grid, identify the challenges, and provides an insight into the new distribution management and control needs to enable reliable and resilient distribution grid operations. Additionally, further analysis is performed to estimate the daily residential electricity cost with the inclusion of DERs under time-of-use tariffs. The result shows the daily residential electricity cost reduced by 20:7% on average with DERs compared to the case without DERs.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2024

Method for Spatiotemporal Solar Power Profile Estimation for a Proposed U.S.–Caribbean–South America Super Grid under Hurricanes

Solar photovoltaic (PV) generation technology stands out as a scalable and cost-effective solution to enable the transition toward decarbonization. However, PV solar output, beyond the daily solar irradiance variability and unavailability during nights, is very sensitive to weather events like hurricanes. Hurricanes nucleate massive amounts of clouds around their centers, shading hundreds of kilometers in their path, reducing PV power output. This research proposes a spatiotemporal method, implemented in MATLAB R2023b coding, to estimate the shading effect of hurricanes over a wide distribution of PV solar plants connected to a high-voltage power infrastructure called the U.S.–Caribbean–South America super grid. The complete interconnection of the U.S., the Caribbean, and South America results in the lowest power valley levels, i.e., an overall percentual reduction in PV power output caused by hurricane shading. The simulations assess the impact of hurricanes in 10 synthetic trajectories spanning from Texas to Florida. The Caribbean would also experience lower power valleys with expanded interconnectivity schemes. The U.S.–Caribbean–South America super grid reduces Caribbean variability from 37.8% to 8.9% in the case study. The proposed spatiotemporal method for PV power profile estimation is a valuable tool for future solar power generation expansion, transmission planning, and system design considering the impact of hurricanes.

14 SOLAR ENERGY↗
DOE OSTI2019

Setting a baseline for Integrated Landscape Design: Cost and risk assessment in herbaceous feedstock supply chains

The literature on bioenergy has pointed out that financial risks and limited performance benchmarks are leading barriers to wide-spread industry expansion. The purpose of this paper is to document a baseline cost and risk assessment from the perspective of biorefinery management for two herbaceous feedstocks, switchgrass and corn stover, in an application of Integrated Landscape Design (ILD). Integrating energy crops into row crops has attracted considerable attention leading to a variety of ways to implement ILD. However, variation in production conditions, different methods of implementing unit operations, and technology performance variability are among many factors leading to financial risk facing biorefineries from feedstock supply based on ILD. Utilizing data from field-based trials on switchgrass and corn stover in Virginia and Iowa in the United States, we estimate feedstock logistics cost and characterize it by a distribution of potential outcomes. The results show that, across fields included in the study, logistics cost for switchgrass vary from 73 per Mg to 171 per Mg. For corn stover, the inter-field variation in cost ranges from 83 per Mg to 105 per Mg. Results for intra-field uncertainty show that the cost for switchgrass ranges from 58 per Mg to 74 per Mg, and for corn stover they range from 50 per Mg to 65 per Mg. Furthermore, these results inform a baseline from which improvements in ILD can be compared, and provide biorefinery management with a starting point for cost and risk estimates of feedstock from ILD.

09 BIOMASS FUELS↗
DOE OSTI2017

Crop yield response to climate change varies with crop spatial distribution pattern

The linkage between crop yield and climate variability has been confirmed in numerous studies using statistical approaches. A crucial assumption in these studies is that crop spatial distribution pattern is constant over time. Here, we explore how changes in county-level corn spatial distribution pattern modulate the response of its yields to climate change at the state level over the Contiguous United States. Our results show that corn yield response to climate change varies with crop spatial distribution pattern, with distinct impacts on the magnitude and even the direction at the state level. Corn yield is predicted to decrease by 20~40% by 2050s when considering crop spatial distribution pattern changes, which is 6~12% less than the estimates with fixed cropping pattern. The beneficial effects are mainly achieved by reducing the negative impacts of daily maximum temperature and strengthening the positive impacts of precipitation. Our results indicate that previous empirical studies could be biased in assessing climate change impacts by ignoring the changes in crop spatial distribution pattern. As a result, this has great implications for understanding the increasing debates on whether climate change will be a net gain or loss for regional agriculture.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

SU-F-J-59: Assessment of Dose Response Distribution in Individual Human Tumor

Purpose: To fulfill precision radiotherapy via adaptive dose painting by number, voxel-by-voxel dose response or radio-sensitivity in individual human tumor needs to be determined in early treatment to guide treatment adaptation. In this study, multiple FDG PET images obtained pre- and weekly during the treatment course were utilized to determine the distribution/spectrum of dose response parameters in individual human tumors. Methods: FDG PET/CT images of 18 HN cancer patients were used in the study. Spatial parametric image of tumor metabolic ratio (dSUV) was created following voxel by voxel deformable image registration. Each voxel value in dSUV was a function of pre-treatment baseline SUV and treatment delivered dose, and used as a surrogate of tumor survival fraction (SF). Regression fitting with break points was performed using the LQ-model with tumor proliferation for the control and failure group of tumors separately. The distribution and spectrum of radiation sensitivity and growth in individual tumors were determined and evaluated. Results: Spectrum of tumor dose-sensitivity and proliferation in the controlled group was broad with α in tumor survival LQ-model from 0.17 to 0.8. It was proportional to the baseline SUV. Tlag was about 21∼25 days, and Tpot about 0.56∼1.67 days respectively. Commonly tumor voxels with high radio-sensitivity or larger α had small Tlag and Tpot. For the failure group, the radio-sensitivity α was low within 0.05 to 0.3, but did not show clear Tlag. In addition, tumor voxel radio-sensitivity could be estimated during the early treatment weeks. Conclusion: Dose response distribution with respect to radio-sensitivity and growth in individual human tumor can be determined using FDG PET imaging based tumor metabolic ratio measured in early treatment course. The discover is critical and provides a potential quantitative objective to implement tumor specific precision radiotherapy via adaptive dose painting by number.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Stochastic Averaging of Radiative Transfer Coefficients for Relativistic Electrons

Abstract Synchrotron emissivities, absorptivities, and Faraday rotation and conversion coefficients are needed in modeling a variety of astrophysical sources, including Event Horizon Telescope (EHT) sources. We develop a method for estimating transfer coefficients that exploits their linear dependence on the electron distribution function, decomposing the distribution function into a sum of parts each of whose emissivity can be calculated easily. We refer to this procedure as stochastic averaging and apply it in two contexts. First, we use it to estimate the emissivity of an isotropicκdistribution function with a high-energy cutoff. The resulting coefficients can be evaluated efficiently enough to be used directly in ray-tracing calculations, and we provide an example calculation. Second, we use stochastic averaging to assess the effect of subgrid turbulence on the volume-averaged emissivity and along the way provide a prescription for a turbulent emissivity. We find that for parameters appropriate to EHT sources turbulence reduces the emissivity slightly. In the infrared, turbulence can dramatically increase the emissivity.

Astronomy & Astrophysics↗
DOE OSTI2021

Matrix Completion for Improved Observability in Low-Voltage Distribution Grids

This paper considers the problem of recovering missing entries in a partially observed matrix from relatively few measurements (i.e., the so-called matrix completion problem) with the aim of increasing the presently limited observability of low-voltage distribution grids. To this end, the partially observed matrix is formed using scarce voltage magnitude measurements while accounting for their spatial information. Voltage readings are assumed to be collected from distribution utility sensors and/or geographically-distributed cable television network sensors located in immediate proximity to distribution grid nodes. A matrix completion approach built on the parameter-less singular value shrinkage technique is used to estimate voltage magnitudes at otherwise non-observable low-voltage nodes using a small number of single- or multiple-snapshot data. The effectiveness of the proposed approach is demonstrated using a U.S.-style distribution test system from the synthetic SMART- DS data set under very low- to moderate-observability conditions.

low-rank matrix completion↗
DOE OSTI2025

Personalized and uncertainty-aware coronary hemodynamics simulations: From Bayesian estimation to improved multi-fidelity uncertainty quantification

Non-invasive simulations of coronary hemodynamics have improved clinical risk stratification and treatment outcomes for coronary artery disease, compared to relying on anatomical imaging alone. However, simulations typically use empirical approaches to distribute total coronary flow amongst the arteries in the coronary tree, which ignores patient variability, the presence of disease, and other clinical factors. Further, uncertainty in the clinical data often remains unaccounted for in the modeling pipeline. We present an end-to-end uncertainty-aware pipeline to (1) personalize coronary flow simulations by incorporating vessel-specific coronary flows as well as cardiac function; and (2) predict clinical and biomechanical quantities of interest with improved precision, while accounting for uncertainty in the clinical data. We assimilate patient-specific measurements of myocardial blood flow from clinical CT myocardial perfusion imaging to estimate branch-specific coronary artery flows. Simulated noise in the clinical data is used to estimate the joint posterior distributions of the model parameters using adaptive Markov Chain Monte Carlo sampling. Additionally, the posterior predictive distribution for the relevant quantities of interest is determined using a new approach combining multi-fidelity Monte Carlo estimation with non-linear, data-driven dimensionality reduction. This leads to improved correlations between high- and low-fidelity model outputs. Our framework accurately recapitulates clinically measured cardiac function as well as branch-specific coronary flows under measurement noise uncertainty. We observe substantial reductions in confidence intervals for estimated quantities of interest compared to single-fidelity Monte Carlo estimation and state-of-the-art multi-fidelity Monte Carlo methods. This holds especially true for quantities of interest that showed limited correlation between the low- and high-fidelity model predictions. In addition, the proposed multi-fidelity Monte Carlo estimators are significantly cheaper to compute than traditional estimators, under a specified confidence level or variance. The proposed pipeline for personalized and uncertainty-aware predictions of coronary hemodynamics is based on routine clinical measurements and recently developed techniques for CT myocardial perfusion imaging. The proposed pipeline offers significant improvements in precision and reduction in computational cost.

Bayesian parameter estimation↗
DOE OSTI2024

Commercialization of Distribution System Load Modeling Tool for Improved DER Interconnection Studies

Residential and commercial buildings have huge potential to contribute value to improve grid resilience by participating grid services. To reveal the significant value, it is critical to estimate the grid service capability from these buildings. Unlike the large-scale distributed energy resources such as wind and solar farms, those buildings need to participate grid services in aggregation, not by individual. Therefore, it is important to appropriately group buildings for aggregation. The load profiles in the same group will have similar characteristics at the same time step, so grid operators can send the grid service signal to the customer group with a higher chance to respond at that time step. In this paper, we develop a load profile clustering method to classify the building-level load profiles for grid service capability estimation. In our two-step clustering approach, we first calculate the total load consumption for each building, clustering the load profiles based on energy consumption level. Then, we further cluster the load profiles in each energy cluster based on the load shape. The parameter selection for each clustering step is discussed. The proposed method is applied on actual building-level load profiles, and the results have proved the effectiveness of this method.

14 SOLAR ENERGY↗
DOE OSTI2022

Automating Component-Level Stress Measurements for Inverter Reliability Estimation

In the near future, grid operators are expected to regularly use advanced distributed energy resource (DER) functions, defined in IEEE 1547-2018, to perform a range of grid-support operations. Many of these functions adjust the active and reactive power of the device through commanded or autonomous operating modes which induce new stresses on the power electronics components. In this work, an experimental and theoretical framework is introduced which couples laboratory-measured component stress with advanced inverter functionality and derives a reduction in useful lifetime based on an applicable reliability model. Multiple DER devices were instrumented to calculate the additional component stress under multiple reactive power setpoints to estimate associated DER lifetime reductions. A clear increase in switch loss was demonstrated as a function of irradiance level and power factor. This is replicated in the system-level efficiency measurements, although magnitudes were different—suggesting other loss mechanisms exist. Using an approximate Arrhenius thermal model for the switches, the experimental data indicate a lifetime reduction of 1.5% when operating the inverter at 0.85 PF—compared to unity PF—assuming the DER failure mechanism thermally driven within the H-bridge. If other failure mechanisms are discovered for a set of power electronics devices, this testing and calculation framework can easily be tailored to those failure mechanisms.

Flicker, Jack (ORCID:0000000212232154)↗
DOE OSTI2018

Effect of temporal averaging of meteorological data on predictions of groundwater recharge

Accurate estimates of infiltration and groundwater recharge are critical for many hydrologic, agricultural and environmental applications. Anticipated climate change in many regions of the world, especially in tropical areas, is expected to increase the frequency of high-intensity, short-duration precipitation events, which in turn will affect the groundwater recharge rate. Estimates of recharge are often obtained using monthly or even annually averaged meteorological time series data. In this study we employed the HYDRUS-1D software package to assess the sensitivity of groundwater recharge calculations to using meteorological time series of different temporal resolutions (i.e., hourly, daily, weekly, monthly and yearly averaged precipitation and potential evaporation rates). Calculations were applied to three sites in Brazil having different climatological conditions: a tropical savanna (the Cerrado), a humid subtropical area (the temperate southern part of Brazil), and a very wet tropical area (Amazonia). To simplify our current analysis, we did not consider any land use effects by ignoring root water uptake. Temporal averaging of meteorological data was found to lead to significant bias in predictions of groundwater recharge, with much greater estimated recharge rates in case of very uneven temporal rainfall distributions during the year involving distinct wet and dry seasons. For example, at the Cerrado site, using daily averaged data produced recharge rates of up to 9 times greater than using yearly averaged data. In all cases, an increase in the time of averaging of meteorological data led to lower estimates of groundwater recharge, especially at sites having coarse-textured soils. Lastly, our results show that temporal averaging limits the ability of simulations to predict deep penetration of moisture in response to precipitation, so that water remains in the upper part of the vadose zone subject to upward flow and evaporation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The efficacy of event isotropy as an event shape observable

Event isotropy $\mathcal{I}$ sph , an event shape observable that measures the distance of a final state from a spherically symmetric state, is designed for new physics signals that are far from QCD-like. Using a new technique for producing a wide variety of signals that can range from near-spherical to jetty, we compare event isotropy to other observables. We show that thrust T and the C parameter (and λ max , the largest eigenvalue of the sphericity matrix) are strongly correlated and thus redundant, to a good approximation. By contrast, event isotropy adds considerable information, often serving to break degeneracies between signals that would have almost identical T and C distributions. Signals with broad distributions in T (or λ max ) and in $\mathcal{I}$ sph separately often have much narrower distributions, and are more easily distinguished, in the ($\mathcal{I}$ sph , λ max ) plane. An intuitive, semi-analytic estimation technique clarifies why this is the case and assists with the interpretation of the distributions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗