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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 OSTI2023

Distribution Grid Impacts of Community Solar [Slides]

Community solar (CS) projects often face uncertain interconnection costs and fees associated with distribution grid infrastructure upgrades required to connect the project. These costs can determine the economic viability of a CS project, but they are difficult to assess. Cost uncertainty can discourage new projects and prevent communities from accessing the benefits of community solar projects. At the same time, CS deployment strategies hold potential to defer or avoid some distribution costs due to new loads. To mitigate CS interconnection costs, it is important to find least-cost combinations of distribution system infrastructure solutions (transformer upgrades, reconductoring, voltage regulators, storage), and to understand how location of CS projects within a feeder impact distribution grid upgrade costs. This study aims to quantify CS impacts on the distribution grid and provide policy and regulatory insights and CS deployment strategies to address them. It is the first analysis that has systematically studied the technical impacts of community solar projects on a wide range of distribution feeders using state-of-the-art optimization and power flow tools. The analysis employs Berkeley Lab’s novel Least-cost Optimal Distribution Grid Expansion (LODGE) model, a deterministic version of the REPAIR model, that optimally upgrades hundreds or even thousands of distribution circuits or feeders. This is the first application of the LODGE model. LODGE finds the least-cost portfolio of traditional distribution system upgrades to integrate CS in combination with alternative solutions, such as utility-owned storage and downsizing CS capacity. Working with a set of least-cost solutions per feeder allows us to benchmark, compare and find techno-economic trends in CS interconnection.

14 SOLAR ENERGY↗
DOE OSTI2023

A Theoretical Open Architecture Framework and Technology Stack for Digital Twins in Energy Sector Applications

Digital twin is often viewed as a technology that can assist engineers and researchers make data-driven system and network-level decisions. Across the scientific literature, digital twins have been consistently theorized as a strong solution to facilitate proactive discovery of system failures, system and network efficiency improvement, system and network operation optimization, among others. With their strong affinity to the industrial metaverse concept, digital twins have the potential to offer high-value propositions that are unique to the energy sector stakeholders to realize the true potential of physical and digital convergence and pertinent sustainability goals. Although the technology has been known for a long time in theory, its practical real-world applications have been so far limited, nevertheless with tremendous growth projections. In the energy sector, there have been theoretical and lab-level experimental analysis of digital twins but few of those experiments resulted in real-world deployments. There may be many contributing factors to any friction associated with real-world scalable deployment in the energy sector such as cost, regulatory, and compliance requirements, and measurable and comparable methods to evaluate performance and return on investment. Those factors can be potentially addressed if the digital twin applications are built on the foundations of a scalable and interoperable framework that can drive a digital twin application across the project lifecycle: from ideation to theoretical deep dive to proof of concept to large-scale experiment to real-world deployment at scale. This paper is an attempt to define a digital twin open architecture framework that comprises a digital twin technology stack (D-Arc) coupled with information flow, sequence, and object diagrams. Those artifacts can be used by energy sector engineers and researchers to use any digital twin platform to drive research and engineering. This paper also provides critical details related to cybersecurity aspects, data management processes, and relevant energy sector use cases.

Gourisetti, Sri, Nikhil Gupta (ORCID:0000000188778↗
DOE OSTI2021

SO3/H2SO4 continuous real-time sensor demonstration at a power plant

We present results from field testing of a new, innovative sensor developed to measure SO3/H2SO4 continuously and in real time at a coal-fired power plant. The system utilizes the sensitivity, specificity, and real-time capabilities of mid-infrared (Mid-IR) laser-based sensor technology, along with a heated, close-coupled cell mounted directly to a power plant duct. Measurements were made by the laser sensor over a 2-day period and compared to results from the accepted method, EPA 8A, which requires a 30-minute collection, labor intensive processing, and off-site analysis at a lab. In contrast to the condensation method, the laser sensor continuously samples flue gas and reports measurements of the concentration of SO3/H2SO4, SO2 ever second with unattended operation. This initial demonstration proved the sensor concept and paves the way for its use for optimizing sorbent injection used to neutralize SO3/H2SO4. Optimized sorbent injection will enable significant cost savings associated with efforts to mitigate the presence and effects of SO3/H2SO4 such as “Blue Plume”, air heater fouling, and duct corrosion. In particular, the real-time, actionable information will enable better control of additive injection in flex conditions and variable fuels.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Conquering Data Chaos: Research Data Management with Kubernetes

Managing massive volumes of data and effectively making it accessible to researchers poses significant challenges and is a barrier to scientific discovery. In many cases, critical data is locked up in unwieldy file formats or one-off databases and is too large to effectively process on a single machine. This talk explores the role of Kubernetes, an open-source container orchestration platform, in addressing research data management challenges. I will discuss how we are using a set of publicly available open-source and home-grown tools in the National Renewable Energy Lab (NREL) Data, Analysis, and Visualization (DAV) group to help researchers overcome data-related bottlenecks. The talk will begin by providing an overview of the data challenges faced in research data management, including data storage, processing, and analysis. I will highlight Kubernetes' ability to handle large-scale data by leveraging containerization and distributed computing, including distributed storage. Kubernetes allows researchers to encapsulate data processing infrastructure and workflows into portable containers, enabling reproducibility and ease of deployment. Kubernetes can then schedule and manage the resource allocation of these containers to enable efficient utilization of limited computing resources, leading to more efficient data processing and analysis. I will discuss some limitations of traditional, siloed approaches to dealing with data and emphasize the need for solutions which foster collaboration. I will highlight how we are using Kubernetes at NREL to facilitate data sharing and cooperation among research teams. Kubernetes' flexible architecture enables the deployment of shared computing environments, such as Apache Superset, where researchers can seamlessly access and analyze shared datasets. Providing the ability to have one research team easily consume data generated by another, utilizing Kubernetes' as a central data platform, is one of the major wins we've encountered by adopting the platform. Finally, I will showcase real-world use cases from NREL where we have used Kubernetes to solve some persistent data challenges involving large volumes of sensor and monitoring data. I will discuss the challenges we encountered when creating our cluster and making it available as a production-ready resource. I will also discuss the specific suite of tools, including Postgres and Apache Druid for columnar and timeseries data, and Redpanda Kafka for streaming data we have deployed in our infrastructure, and the process that went into the selection of these tools.

collaborative environment↗
DOE OSTI2025

Bayesian analysis of nucleon-nucleon scattering data in pionless effective field theory

We perform Bayesian model calibration of two-nucleon (NN) low-energy constants (LECs) appearing in an NN interaction based on pionless effective field theory (πEFT). The calibration is carried out for potentials constructed using naive dimensional analysis in NN relative momenta (p) up to next-to-leading order [NLO, O(p 2 )] and next-to-next-to-next-to-leading order [N3LO, O(p 4 )]. We consider two classes of pionless πEFT potential: one that acts in all partial waves and another that is dominated by s-wave physics. The two classes produce broadly similar results for calibrations to NN data up to E lab = 5 MeV. Our analysis accounts for the correlated uncertainties that arise from the truncation of the pionless πEFT. We simultaneously estimate both the πEFT LECs and the parameters that quantify the truncation error. This permits the first quantitative estimates of the pionless πEFT breakdown scale, Λ b : the 95% intervals are Λ b ∈[50.11,63.03] MeV at NLO and Λ b ∈[72.27,88.54] MeV at N3LO. Furthermore, invoking naive dimensional analysis for the NN potential, therefore, does not lead to consistent results across orders in pionless πEFT. This exemplifies the possible use of Bayesian tools to identify inconsistencies in a proposed EFT power counting.

Bayesian methods↗
DOE OSTI2023

Single-cell quantification of ribosome occupancy in early mouse development

Translation regulation is critical for early mammalian embryonic development. However, previous studies had been restricted to bulk measurements, precluding precise determination of translation regulation including allele-specific analyses. Here, to address this challenge, we developed a novel microfluidic isotachophoresis (ITP) approach, named RIBOsome profiling via ITP (Ribo-ITP), and characterized translation in single oocytes and embryos during early mouse development. We identified differential translation efficiency as a key mechanism regulating genes involved in centrosome organization and N 6 -methyladenosine modification of RNAs. Our high-coverage measurements enabled, to our knowledge, the first analysis of allele-specific ribosome engagement in early development. These led to the discovery of stage-specific differential engagement of zygotic RNAs with ribosomes and reduced translation efficiency of transcripts exhibiting allele-biased expression. By integrating our measurements with proteomics data, we discovered that ribosome occupancy in germinal vesicle-stage oocytes is the predominant determinant of protein abundance in the zygote. The Ribo-ITP approach will enable numerous applications by providing high-coverage and high-resolution ribosome occupancy measurements from ultra-low input samples including single cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Multidimensional measurements of beam single-spin asymmetries in semi-inclusive deep-inelastic charged-kaon electroproduction off protons in the valence region

Measurements of beam single-spin asymmetries in semi-inclusive deep-inelastic electron scattering (SIDIS) with positively charged kaons off protons have been performed with 10.6 and 10.2 GeV incident electron beams using the CLAS12 spectrometer at Jefferson Lab. We report an analysis of the electroproduction of positively charged kaons over a large kinematic range of fractional energy, Bjorken 𝑥, transverse momentum, and photon virtualities 𝑄 2 ranging from 1 GeV 2 up to 6 GeV 2 . This is the first published multidimensionally binned CLAS12 measurement of a kaon SIDIS single-spin asymmetry in the valence quark regime. The data provide constraints on the structure function ratio $𝐹^{sin \space𝜙}_{𝐿⁢𝑈}$⁡/$𝐹_{𝑈⁢𝑈}$, where $𝐹^{sin \space𝜙}_{𝐿⁢𝑈}$ is a quantity with a leading twist of twist-3 that can reveal novel aspects of the quark-gluon correlations within the nucleon. The impact of the data on understanding the underlying reaction mechanisms and their kinematic variation is explored using theoretical models for the different contributing twist-3 parton-distribution functions (PDFs) and fragmentation functions (FFs).

form factors↗
DOE OSTI2023

Factors Influencing Building Demand Flexibility

The U.S. Department of Energy’s National Roadmap for Grid-interactive Efficient Buildings (GEB) acknowledged that building demand flexibility (DF) is both an important strategy to decarbonizing the buildings sector and an important resource for meeting the changing needs of the electrical grid such as improving grid reliability. However, understanding the complexity and uncertainties in real building field performance of DF strategies is a large gap hindering stakeholders on both grid and buildings side to make investments on deploying such strategies. The research work in this report intended to advance understanding of the variability and influential factors in building demand flexibility. Adding such knowledge based on lab testing results and measured performance data from real buildings is an important contribution. The report uses standardized metrics and methods to quantify DF performance from field-measured DF datasets of two significant building groups of big-box retail and medium office buildings to present the challenge of building DF variability in multiple dimensions. The report presents findings related to how several key factors influence building demand flexibility from implementing a common, cost-effective DF control strategy (i.e., adjusting zone temperatures). The findings are supported by full-scale lab testing, field data analysis and simulation research. The authors also provided application-oriented recommendations to stakeholders such as building aggregators, utility program design professionals, sophisticated building portfolio owners, and more.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Leveraging Cloud Platforms for Grid Modernization

Overview of the DOE funded Grid Operator Analytics and Assessment tools for Inverter-Based Resources Dominated Grid (GOAAT-IBR) Project, including insight to the lab setup and event analysis and dashboards that are being developed for this project.

Aminifar, Ph.D., Farrokh↗
DOE OSTI2021

Behind the Meter Solar+Storage: Market data and trends [Slides]

As the distributed solar market evolves toward more dynamic forms of deployment, interest in paired solar-plus-storage applications continues to gain steam, but details on the current state of the market are relatively sparse. To fill that void, Berkeley Lab has released an in-depth analysis of this budding market segment. This report draws on the Lab’s Tracking the Sun dataset to characterize trends in deployment, system sizing and equipment selection, installer-market development, and system pricing. The report also provides indicative analyses of the financial and resilience value that host customers in several key markets presently receive by pairing storage with solar.

14 SOLAR ENERGY↗
DOE OSTI2023

Utility-Scale Solar, 2023 Edition: Analysis of Empirical Plant-level Data from U.S. Ground-mounted PV, PV+battery, and CSP Plants (exceeding 5 MWAC)

Berkeley Labs "Utility-Scale Solar", 2023 Edition presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC. While focused on key developments in 2022, this report explores trends in deployment, technology, capital and operating costs, capacity factors, the levelized cost of solar energy (LCOE), power purchase agreement (PPA) prices, wholesale market value, and interconnection queue data.

analysis↗
DOE OSTI2022

Utility-Scale Solar, 2022 Edition: Analysis of Empirical Plant-level Data from U.S. Ground-mounted PV, PV+battery, and CSP Plants (exceeding 5 MWAC)

Berkeley Labs "Utility-Scale Solar", 2022 Edition presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC. While focused on key developments in 2021, this report explores trends in deployment, technology, capital and operating costs, capacity factors, the levelized cost of solar energy (LCOE), power purchase agreement (PPA) prices, wholesale market value, and interconnection queue data.

2022↗
DOE OSTI2024

Utility-Scale Solar, 2024 Edition: Analysis of Empirical Plant-level Data from U.S. Ground-mounted PV, PV+battery, and CSP Plants (exceeding 5 MWAC)

Berkeley Labs "Utility-Scale Solar", 2024 Edition presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC. While focused on key developments in 2023, this report explores trends in deployment, technology, capital and operating costs, capacity factors, the levelized cost of solar energy (LCOE), power purchase agreement (PPA) prices, wholesale market value, net value, and interconnection queue data.

analysis↗
DOE OSTI2025

Taking three-dimensional x-ray diffraction (3DXRD) from the synchrotron to the laboratory scale

Three-dimensional x-ray diffraction (3DXRD), a rotating x-ray diffraction technique, is a powerful tool for studying the micromechanical behavior of polycrystalline materials, capable of measuring the volume, position, orientation, and strain of thousands of grains simultaneously. However, its application has been historically limited to synchrotron facilities. Here, we present the first demonstration of laboratory-scale 3DXRD (Lab-3DXRD) using a liquid-metal-jet source. Lab-3DXRD achieves accuracy comparable to synchrotron-based 3DXRD, as validated against laboratory diffraction contrast tomography (LabDCT) and synchrotron-3DXRD. Over 96% of the grains detected with Lab-3DXRD are cross-validated, particularly for coarse grains (> ~60 μm), while the results suggest that finer grains should be accessible by taking advantage of high-efficiency detectors. We further demonstrate that its sensitivity to finer grains is enhanced by incorporating pre-characterization into the analysis. This study establishes Lab-3DXRD as a practical alternative to synchrotron techniques, making 3DXRD accessible to a wider range of academic and industrial researchers.

characterization and analytical techniques↗
DOE OSTI2023

FY23 Update: Aerosol Sampling for the Canister Deposition Field Demonstration

This report describes the results of preliminary testing of aerosol monitoring equipment that will be used to continuously monitor the aerosol source term for the multi-year Canister Deposition Field Demonstration (CDFD). These data are required inputs for the development and validation of models for the deposition of dust and potentially corrosive salts on the surface of spent nuclear fuel (SNF) dry storage canisters. Surface salt loads correlate with the extent of corrosion damage on a metal surface, and potentially to the likelihood and timing of initiation of stress corrosion cracks. Aerosols will be monitored at the CDFD site using three instruments. A Dekati ® ELPI+ cascade impactor will be used for real-time monitoring of aerosol particle sizes. It will also collect dust in 14 size bins on impactor targets that can be chemically analyzed to determine the soluble salts present as a function of particle size. However, this instrument can only measure dried aerosols, with a diameter of <10 µm. The second instrument is a Topas laser particle size spectrometer, which provides real-time monitoring of aerosol particle sizes up to ~40 µm in size. It monitors both the ambient (potentially deliquesced) aerosol particle size distributions required for the dust deposition models and the distributions of the equivalent dried particles, allowing correlation with the Dekati ® data. However, it does not discriminate between inert dust particles and salt aerosols, and it does not retain samples of the different particle sizes for later analysis. The third instrument that will monitor aerosols at the CDFD site is a Clean Air Status and Trends Network (CASTNET) tower, which uses a multiple canister system to collect weekly samples for analysis to total suspended aerosol particle compositions and atmospheric gas concentrations. This status report describes work in FY23 to develop the capabilities for using these tools. In two training exercises, the cascade impactor and laser particle sizer were deployed in two different testing environments, one indoor and one outdoor. For the cascade impactor, the tests provided opportunities for the operators to familiarize themselves with impactor substrate preparation, and post-test sample removal and analysis. For the laser particle sizer, the tests were used to evaluate different instrument parameters, to determine the most appropriate settings for capturing transient events. Data and samples were collected for weeks to months for each test, and the results are presented here. In addition to the preliminary testing, contracts were developed with WSP Analytical Labs for sample preparation and analysis of the cascade impactor samples. The impactor tower from outdoor test was delivered to WSP and used to train the staff there in disassembly, sample extraction, sample analysis, and tower reassembly with new target substrates. These are tasks that WSP will be performing routinely for the CDFD project. The CASTNET system cannot be purchased or tested until an actual site has been selected for the CDFD test. Work for this FY has been restricted to preparation of contracts for purchasing the CASTNET tower, and for sample analysis, once the tower is in operation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Keyhole-mode Microscopy Dataset for Laser Powder-bed Fusion Modeling

Laser powder-bed fusion (LPBF) is an additive manufacturing (AM) technology that uses high-power sintering lasers to precisely construct metal designs. Material is accumulated by selectively sintering regions of a metal powder layer to a growing structure underneath forming a 3D geometry. Certain conditions make fusion in this process to operate in "keyhole-mode," characterized by targeted materials evaporating as plasma. While keyhole-mode operations can produce deeper molten pools than that observed in "conduction-mode", this mode of operation is often undesirable, as its molten regions can collapse on themselves, encapsulating metal vapors and forming cavities. The formation of such cavities negatively affects strength and consistency of the fused materials. To identify and avoid these detrimental effects of keyhole-mode operation, considerable data collection and analysis regarding this mode of operation is needed. Therefore, under the support of the "Open Data Initiative," Lawrence Livermore National Lab is releasing a dataset for analysis of this keyhole effect, and the conditions which transition operation from conduction-mode to keyhole-mode melting. This dataset consists of 600+ micrographs from laser powder-bed single track runs, with differing cross-section angles and input parameters, operating under both normal and keyhole mode operations. This journal documents the collection, organization, and usage of this dataset.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Proton Spin Structure from Simultaneous Monte Carlo Global QCD Analysis

Despite the great effort and achievements made towards understanding proton spin structure in the past few decades, a complete picture is still elusive. Parton distribution functions (PDFs), which in quantum chromodynamics (QCD) encode the momentum and helicity distributions of quarks and gluons inside a proton, provide the means by which to quantify the proton structure information. Being inherently nonperturbative, PDFs have to be extracted from unpolarized and polarized lepton-hadron and hadron-hadron scattering data. In particular, experiments that measure unpolarized and polarized jet observables can provide insight into the momentum and helicity distributions of gluons, which have generally been more difficult to determine reliably than those of quarks. In the past, extraction of the spin-averaged and spin-dependent (or helicity) PDFs has been performed in separate analyses. In this thesis, we perform the first simultaneous extraction of both types of quantities from deep-inelastic scattering (DIS), Drell-Yan and single jet observables, within the Monte Carlo global QCD analysis framework developed by the Jefferson Lab Angular Momentum (JAM) Collaboration. The results from this work indicate that the gluon helicity distributions depend rather strongly on the theory assumptions on which the global analysis is based, which calls for the need of measurements with higher precision. As an application of the new simultaneous JAM analysis, we perform an impact study for future Electron-Ion Collider (EIC) data with parity-conserving and parity-violating polarization asymmetries on quark and gluon helicity distributions in the proton. The extrapolation of structure functions from the current data is studied for the first time in the context of the impact study. Theory assumptions, such as SU(2) and SU(3) flavor symmetries, are also studied to give a more thorough understanding of the impact of EIC pseudodata on proton spin structure.

Zhou, Yiyu↗