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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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At least 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A New Miniaturized Acoustic Transmitter for Marine Animal Tracking

Most marine renewable energy (MRE) technologies are still in their infancy. Many uncertainties remain regarding MRE systems’ impacts on the marine environment and species where they are deployed, which is one of the major hurdles for these systems to be widely adopted at a commercial scale. Currently, the primary technologies to monitor marine animals’ behavior around existing or potential MRE deployment sites are optical and acoustic imaging. However, these methods cannot identify individual animals and thus are less useful in identifying protected species and studying their behavior. Implantable acoustic transmitters with unique identification codes could serve as a complementary technology. To minimize potential bias introduced to the study results, the implanted transmitters must be sufficiently small and light to not affect the animals’ behavior while still having acceptable signal strengths and service life. Recently, we developed a new miniaturized acoustic transmitter that offers a significantly improved signal strength and service life (140 days [projected value] at a 5-s transmission interval) in a smaller and lighter package (0.45 g in the air), compared to the existing technologies. The detection range and detection efficiency of the new transmitter were tested in an actual marine environment and demonstrated a detection range of up to 330 m, a 65% improvement over the existing commercial counterparts operating at similar frequencies. Here, we report the design principles, performance, and manufacturing procedure of the transmitter. Finally, theoretical estimates of the detection range, the actual range, and detection efficiency results from the field testing are presented.

acoustic telemetry↗
DOE OSTI2022

Worldwide Physics-Based Lifetime Prediction of c-Si Modules Due to Solder-Bond Failure

Lifetime prediction of the fielded c-Si solar modules due to location-specific weather conditions has been an important topic of photovoltaic research and the economic viability of solar energy. Data analytic techniques such as the performance ratio method, Statistical clear sky model, and Suns-Vmp methods quantify the degradation from measured data of a solar farm, however, the nonlinear time-dependence and correlated degradations make it difficult to use the empirical degradation rates for ultimate lifetime projection. In this article, we propose a complementary physics-based model to predict the solder bond failure caused by mechanical stress associated with the variations of the temperature. Integrating the worldwide weather information from NASA/NSRDB databases, the model predicts the location-specific output-power degradation and the lifetime of a module due to solder bond failure. The model parameters are calibrated against qualification tests involving thermal cycling of specific batches of modules from a specific technology/manufacturer. The results may be summarized as: 1) Modules installed at higher latitudes show a longer lifetime due to reduced damage accumulation. 2) The reduction of temperature fluctuation close to large bodies of water, such as seashores, increases solder bond lifetime significantly. 3) Relatively speaking, modules installed close to the Tropic of Cancer/Capricorn (23.5 degrees North/South) suffer from a higher solder bond damage and have a shorter lifetime, suggesting a conservative design. This model should serve as a building block of a comprehensive reliability framework that can predict the lifetime of a module that experiences simultaneous and correlated degradation mechanisms involving yellowing, corrosion, and potential-induced degradation.

14 SOLAR ENERGY↗
DOE OSTI2025

Design, Preparation, and Execution of the 100-AV Field Test for the CIRCLES Consortium: Methodology and Implementation of the Largest Mobile Traffic Control Experiment to Date

This article presents the comprehensive design, setup, execution, and evaluation of the MegaVanderTest (MVT) experiment conducted by the Congestion Impacts Reduction via CAV-in-the-Loop Lagrangian Energy Smoothing (CIRCLES) Consortium, which aimed to mitigate traffic congestion using partially autonomous vehicles (AVs) (see “Summary”). The experiment involved 100 vehicles on Nashville’s Interstate 24 (I-24) highway, utilizing various control algorithms to smooth stop-and-go traffic waves. The execution of the MVT experiment required a coordinated effort from multiple teams. This article details the meticulous planning process, the coordinated efforts of multiple teams, and the innovative use of a dynamic agent-based simulation framework for traffic evaluation. Here, the contributions of this work include demonstrating and providing a detailed roadmap for large-scale live traffic experiments, illustrating the lessons learned from the MVT experiment, and introducing the other articles in this issue and their complementary relationship in the MVT experiment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Geodesic-based relaxation for deep canonical correlation analysis

Deep canonical correlation analysis (DCCA) is often applied to paired data samples from diverse sources to extract meaningful common information. However, when the data sources are heterogeneous, some of the useful information may be complementary but not exactly common. In spite of this fact, existing techniques learn maximally correlated representations from multiple views and are formulated so that they aim to yield identical latent subspaces for each view. This approach is sub-optimal in estimating the true signal subspaces for heterogeneous data sources. We propose a residual relaxation for deep canonical correlation analysis (RDCCA) based on a subspace distance metric, which generalizes the existing problem formulation and extracts representations that are better estimates of the actual, non-identical subspaces. We demonstrate that when using such a relaxation, the learned representations are closer to the true ones and that RDCCA outperforms CCA and DCCA in scenarios with heterogeneous data.

Kuschel, Maurice↗
DOE OSTI2025

Optimal Complementarity Analysis of Potential Floating Solar Co-Located With Existing Hydropower Assets Across the Contiguous United States

The U.S. is expected to double its rate of renewable capacity from 2024 to 2030. However, the stochastic nature of renewable energy poses challenges to the operation and reliability of our power grid. The combined generation from renewable energy sources, with dispatchable sources (such as hydropower) operating as a hybrid energy plant, could mitigate this variability. In this paper, the complementarity analysis of selected U.S. reservoirs with existing hydropower assets (EHAs) and potential floating photovoltaics (FPVs) is conducted for the continuous U.S. (CONUS). The optimal FPV capacity for each site is determined by minimizing the variability of the combined output, while adhering to the FPV potential. Our results indicate that over 50% of the analyzed reservoirs achieve a stability coefficient exceeding 0.5, leading to a less-variable output after optimization. Finally, we analyze the complementary hydro-FPV hybrid reservoirs by considering both the Pearson correlation coefficient and the stability coefficient on daily, monthly, and yearly scales. Summaries are included of locations of theoretical FPVs co-located with hydropower plants that exhibit high complementarity based on the selected metrics.

13 - HYDRO ENERGY↗
DOE OSTI2025

Link Scheduling in Satellite Networks via Machine Learning Over Riemannian Manifolds

Low Earth Orbit (LEO) satellites play a crucial role in enhancing global connectivity, serving a complementary solution to existing terrestrial systems. In wireless networks, scheduling is a vital process that allocates time-frequency resources to users for interference management. However, LEO satellite networks face significant challenges in scheduling their links towards ground users due to the satellites’ mobility and overlapping coverage. This paper addresses the dynamic link scheduling problem in LEO satellite networks by considering spatio-temporal correlations introduced by the satellites’ movements. The first step in the proposed solution involves modeling the network over Riemannian manifolds, thanks to their representation as symmetric positive definite matrices. We introduce two machine learning (ML)-based link scheduling techniques that model the dynamic evolution of satellite positions and link conditions over time and space. To accurately predict satellite link states, we present a recurrent neural network (RNN) over Riemannian manifolds, which captures spatio-temporal characteristics over time. Furthermore, we introduce a separate model, the convolutional neural network (CNN) over Riemannian manifolds, which captures geometric relationships between satellites and users by extracting spatial features from the network topology across all links. Simulation results demonstrate that both RNN and CNN over Riemannian manifolds deliver comparable performance to the fractional programming-based link scheduling (FPLinQ) benchmark. Remarkably, unlike other ML-based models that require extensive training data, both models only need 30 training samples to achieve over 99% of the sum rate while maintaining similar computational complexity relative to the benchmark.

42 ENGINEERING↗
DOE OSTI2023

Testing GFM and GFL Inverters Operating With Synchronous Condensers

Inverter-based resources (IBRs) are quickly evolving into a source of reliability services to the electric grid. Penetration levels of variable generation are rapidly increasing, ranging from 60% -70% in some systems. To go further, a fundamental shift is required to address challenges associated with grid stability. Synchronous condensers (SCs) are considered an important complementary technology in such transitions. This paper provides one-of-a-kind insights into real experimental results from operating a real MW-scale system consisting of a 2.2-MVA grid-forming (GFM) and grid-following (GFL) battery energy storage system, 2.5-MVA SCs, and a 3-MVA load bank operating as a small, islanded grid. Challenges that are expected in larger systems were mimicked in this small but real system to demonstrate seamless transitions from one mode to another as well as stable operation with different combinations of GFM and GFM resources, including a "loss of last synchronous generator" case. The experimental results presented in this paper are a valuable resource to everyone involved in developing and validating models or developing controls of power systems consisting of inverters and synchronous condensers.

grid-following converter↗
DOE OSTI2020

Stability of Tin-Lead Halide Perovskite Solar Cells

Tin-lead perovskites have low band gaps (1.2 eV - 1.4 eV) that offer complementary absorption spectra to mixed halide perovskites with band gaps in the range of 1.6 eV - 1.85 eV. In combination, tin-lead and mixed halide perovskites can be used to make efficient all-perovskite tandem solar cells. While these can achieve high efficiency, they are hampered by the unproven longterm stability of tin-containing perovskites. We make the first demonstration of tin-lead perovskite solar cells that pass benchmark 1000-hour tests of stability under stressors of heat, light, and atmospheric exposure. We identify that mixed tin-lead perovskites oxidise by a different chemical pathway, one that is much less favourable, than the oxidation pathway followed by pure tin perovskites, making them significantly more stable. Fortuitously, mixed tin-lead perovskites also have band gaps that are close to ideal for the rear cell of an all-perovskite monolithic two-junction tandem. Beyond selecting the composition of the perovskite to be one that has inherently suppressed tendency to oxidise, we also fabricate a novel heterojunction-based architecture that eliminates the commonly used acidic PEDOT:PSS layer, which leads to enhanced stability. By packaging the resulting solar cell in a glass-on-glass package with a pliable low-elastic-modulus polyolefin encapsulant and a butyl rubber edge seal, we also demonstrate that the low band gap perovskite solar cell that passes the IEC damp heat test for thin film solar cells - a milestone that demonstrates the removal of a significant impediment to the commercialization of high efficiency perovskite tandem solar cells.

14 SOLAR ENERGY↗
DOE OSTI2021

Numerical Analysis of Time Resolved Photoluminescence for Alumina/Cd(Se,Te) Double Heterostructures

Al2O3 has demonstrated excellent Cd(Se,Te) surface passivation. In this work, recombination mechanisms were investigated by comparing simulations of time resolved photoluminescence (TRPL) to PL decay data (320 ns effective lifetime) for Al2O3 /Cd(Se,Te)/Al2O3 double heterostructures. Models were employed to investigate the effects of bulk and surface recombination, electric fields, mobility, and doping by simulating intensity-dependent single- (1PE) and two-photon excitation (2PE) TRPL. 2PE simulations replicate decay behavior while 1PE simulations require attention to charge redistribution. Various coupled mechanisms cannot be distinguished from PL decay data alone. Complementary measurements are required to constrain the model parameters and isolate mechanisms.

CdTe↗
DOE OSTI2022

Quantifying Energy Flows in PV Circularity Processes

As sustainable deployment and end-of-life management become a hot topic to timely address in the PV community, a dynamic comparative evaluation of the benefits of circular pathways such as reuse, and remanufacturing, recycling has not been performed holistically beyond material flows or LCA analysis. Energy flows are critical for evaluating energy generation technologies. Previously they have been used to compare renewables to fossil generation and then between PV technologies. This paper quantifies energy flows to evaluate circular pathways for PV. The energy flows tracking manufacturing, generation, and losses complementary to the mass flows of silicon are quantified leveraging the PV ICE framework.

circular economy↗
DOE OSTI2021

Hyperspectral X-ray Imaging: Progress Towards Chemical Analysis in the SEM

The Hyperspectral X-ray Imaging (HXI) project will enable non-destructive chemical-state determination of nano-scale samples in the electron microscope for nuclear safeguards applications. To efficiently measure chemical state through electron-excited X-ray emission requires a combination of wide spectral bandwidth, high resolution, and high count rate capability. We are building a next-generation X-ray detector based on an array of transition edge sensors (TESs) to make these measurements possible and routine in the scanning electron microscope (SEM). Leveraging the large pixel densities afforded by microwave multiplexing readout and continuous, uninterrupted operation of a cryogen-free dilution refrigerator, this instrument will have efficiency to allow chemical species identification of nano-scale samples in hours instead of days to weeks. We describe prototype pixel designs for this HXI instrument, comprising three pixel types that will make up the hybrid TES array. Engineering design of the integrated HXI cryostat and SEM system is in progress, with full detector commissioning expected in Spring 2021 followed closely by full-scale integration with the SEM. Furthermore, we also report on the commissioning of a complementary TES-based X-ray emission spectroscopy platform for bulk samples to build a spectral library for HXI sample identification.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

MDPCT1 Quench Data and Performance Analysis

MDPCT1 is a four-layer cos-theta Nb3Sn dipole demonstrator developed and tested at FNAL in the framework of the U.S. Magnet Development Program. The magnet reached record fields for accelerator magnets of 14.1 T at 4.5 K in the first test and 14.5 T at 1.9 K in the second test and then showed large degradation. While its inner coils performed exceptionally well with only two quenches up to 14.5 T and no evidence of degradation, the outer coils degraded over the course of testing. By adopting new measurement and analysis techniques at FNAL we are discussing in detail what happened. Both success and failure in our diagnostics are discussed. The evolution of techniques over the course of two tests (and three thermal cycles) shows the path to address challenges brought by the first four-layer magnet tested at FNAL. This paper presents the analysis of quench data along with diagnostic features and complementary measurements taken in support of the magnet performance analysis.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

R&D Needs for a US Fusion Magnet Base Program

Significant technology maturation efforts are underway by privately funded fusion startups with the goal to demonstrate mature HTS magnet technology. To support the private sector development effort and the DOE milestone based program, a U.S. Fusion Magnet Community Workshop was held on March 14-15, 2023 in Princeton, NJ. This was the first U.S. community workshop focused on fusion magnet technologies aimed at determining the structure and technical direction for a public program designed to complement the private fusion industry landscape. Based on the wide range of different contributions, a set of general themes and fusion magnet R&D needs were identified and discussed. Feedback received to the workshop charge questions highlighted critical magnet R&D gaps such as availability of existing large cable and coil test facilities, a magnet education program that can generate a trained and essential workforce by leveraging R&D capabilities of universities, U.S. national labs, and fusion industry. Other opportunities synergistic and complementary with high energy physics, high field magnets that are open for a broad range of science drivers. The defined R&D gaps underpin the need for a mid-term and long-term public program in fusion magnet development, which reflects the purpose of the workshop in developing the rationale and consent for such a base program. A self-consistent, fusion specific U.S. fusion magnet program will complement and de-risk fusion pilot plants (FPPs) of promising magnetic configurations developed by private companies on a timeline consistent with the NASEM report on bringing fusion to the U.S. grid. We describe the magnet challenges presented and R&D needs discussed in the workshop. In conclusion, these challenges and R&D needs provide focus for the development of U.S. mid-term and long term roadmaps on enabling HTS for high field fusion.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Development of Real-Time High-Density Pulsar Data Transmission and Processing for Grid Synchronization

Taking advantage of the extreme stability of the pulsar period, it can serve as the timing source for grid synchronization to compensate for the timing drift instigated by the loss of GPS signal. Nevertheless, the real-time transmission and processing of the pulsar data suffer from its high-frequency data rate, varying from megahertz to gigahertz, resulting in reduced computing speed and increased time delay. To mitigate this issue, the hardware and software frameworks are implemented for the high-density pulsar data transmission and processing for grid synchronization in this research. Initially, the high-density pulsar data is transferred using open-source software. The complementary duty cycle timing module is designed to coordinate the operation of the dual-channel high-speed interface and software. Subsequently, the multiple-threading is applied to the receiving, parsing, and splicing pulsar data. Next, the pulsar signal extraction method is implemented based on the polyphase filterbank and time of arrival estimation. Ultimately, real-time performance verification experiments are carried out for different components under two hardware platforms. Finally, the results demonstrate that only 0.482 s is required for processing 4 Gigabyte data through multiple-threading, which is 3.8 times faster than the single thread. The pulsar signal extraction can also be executed within 707 ms for 4.8 seconds of data, thereby indicating that real-time requirements can be met.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

INSURE: An Information Theory iNspired diSentanglement and pURification modEl for Domain Generalization

Domain Generalization (DG) aims to learn a generalizable model on the unseen target domain by only training on the multiple observed source domains. Although a variety of DG methods have focused on extracting domain-invariant features, the domain-specific class-relevant features have attracted attention and been argued to benefit generalization to the unseen target domain. To take into account the class-relevant domain-specific information, in this paper we propose an Information theory iNspired diSentanglement and pURification modEl (INSURE) to explicitly disentangle the latent features to obtain sufficient and compact (necessary) class-relevant feature for generalization to the unseen domain. Specifically, we first propose an information theory inspired loss function to ensure the disentangled class-relevant features contain sufficient class label information and the other disentangled auxiliary feature has sufficient domain information. Additionally, we further propose a paired purification loss function to let the auxiliary feature discard all the class-relevant information and thus the class-relevant feature will contain sufficient and compact (necessary) class-relevant information. Moreover, instead of using multiple encoders, we propose to use a learnable binary mask as our disentangler to make the disentanglement more efficient and make the disentangled features complementary to each other. We conduct extensive experiments on five widely used DG benchmark datasets including PACS, VLCS, OfficeHome, TerraIncognita, and DomainNet. The proposed INSURE achieves state-of-the-art performance. We also empirically show that domain-specific class-relevant features are beneficial for domain generalization. The code is available at https://github.com/yuxi120407/INSURE .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Tackling the Challenges in Scene Graph Generation With Local-to-Global Interactions

In this work, we seek new insights into the underlying challenges of the scene graph generation (SGG) task. Quantitative and qualitative analysis of the visual genome (VG) dataset implies: 1) ambiguity: even if interobject relationship contains the same object (or predicate), they may not be visually or semantically similar; 2) asymmetry: despite the nature of the relationship that embodied the direction, it was not well addressed in previous studies; and 3) higher-order contexts: leveraging the identities of certain graph elements can help generate accurate scene graphs. Motivated by the analysis, we design a novel SGG framework, Local-to-global interaction networks (LOGINs). Locally, interactions extract the essence between three instances of subject, object, and background, while baking direction awareness into the network by explicitly constraining the input order of subject and object. Globally, interactions encode the contexts between every graph component (i.e., nodes and edges). Finally, Attract and Repel loss is utilized to fine-tune the distribution of predicate embeddings. By design, our framework enables predicting the scene graph in a bottom-up manner, leveraging the possible complementariness. To quantify how much LOGIN is aware of relational direction, a new diagnostic task called Bidirectional Relationship Classification (BRC) is also proposed. Overall, experimental results demonstrate that LOGIN can successfully distinguish relational direction than existing methods (in BRC task), while showing state-of-the-art results on the VG benchmark (in SGG task).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Low-Frequency Noise and Deep Level Transient Spectroscopy in n-p-n Si Bipolar Junction Transistors Irradiated with Si Ions

The properties of defects in n-p-n Si bipolar junction transistors (BJTs) caused by 17 MeV Si ions are investigated via current-voltage, low-frequency noise, and deep-level transient spectroscopy (DLTS) measurements. Four prominent radiation-induced defects in the base-collector junction of these transistors are identified via DLTS. At least two defect levels are observed in temperature-dependent low-frequency 1/ f noise measurements, one that is similar to a prominent defect in DLTS and another that is not. Defect microstructures are discussed. Here our results show that DLTS and 1/ f noise measurements can provide complementary information about defects in linear bipolar devices.

42 ENGINEERING↗
DOE OSTI2026

RADAI: A Large-Scale Realistic Dataset for Radiation Detection Algorithm Development

Open, realistic datasets are essential for developing and benchmarking radiation detection algorithms, yet they remain scarce. The Radiological Anomaly Detection and Identification (RADAI) project was develop to create datasets that meet the training and testing needs for sophisticated radiation detection algorithms. The RADAI dataset is a large-scale synthetic resource that integrates high-fidelity Monte Carlo simulations with realistic urban scenarios to capture both background variability and source signatures. RADAI models construction-material NORM, people and vehicles, urban clutter, and dynamic environmental effects such as cosmic-ray and rain-induced transients, and they provide list-mode detector data with motion and response modeling suitable for algorithm training and evaluation. The RADAI project resulted in three publicly-released complementary datasets together with an online scoring portal for standardized performance assessment and an open software toolkit that supports data access, augmentation, model development, and evaluation. These resources enable reproducible comparisons across methods and promote rigorous studies at the scale required by contemporary machine learning. By grounding algorithm development in realistic, well-documented conditions, RADAI supports progress toward more robust detection, identification, and localization in complex urban environments.

Ghawaly, James M. [Division of Computer Science an↗