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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Stability and Phase Noise Tests of Two Cryo-Cooled Sapphire Oscillators

A cryocooled Compensated Sapphire Oscillator (CSO), developed for the Cassini Ka-band Radio Science experiment, and operating in the 8K - 10K temperature range was previously demonstrated to show ultra-high stability of sigma(sub y) = 2.5 x 10 (exp -15) for measuring times 200 seconds less than or equal to tau less than or equal to 600 seconds using a hydrogen maser as reference. We present here test results for a second unit which allows CSO short-term stability and phase noise to be measured for the first time. Also included are design details of a new RF receiver and an intercomparison with the first CSO unit. Cryogenic oscillators operating below about 10K offer the highest possible short term stability of any frequency sources. However, their use has so far been restricted to research environments due to the limited operating periods associated with liquid helium consumption. The cryocooled CSO is being built in support of the Cassini Ka-band Radio Science experiment and is designed to operate continuously for periods of a year or more. Performance targets are a stability of 3-4 x 10 (exp -15) (1 second less than or equal to tau less than or equal to 100 seconds) and phase noise of -73dB/Hz @ 1Hz measured at 34 GHz. Installation in 5 stations of NASA's deep space network (DSN) is planned in the years 2000 - 2002. In the previous tests, actual stability of the CSO for measuring times tau less than or equal to 200 seconds could not be directly measured, being masked by short-term fluctuations of the H-maser reference. Excellent short-term performance, however, could be inferred by the success of an application of the CSO as local oscillator (L.O.) to the JPL LITS passive atomic standard, where medium-term stability showed no degradation due to L.O. instabilities at a level of (sigma)y = 3 x 10 (exp -14)/square root of tau. A second CSO has now been constructed, and all cryogenic aspects have been verified, including a resonator turn-over temperature of 7.907 K, and Q of 7.4 x 10 (exp 8). These values compare to a turn-over of 8.821 K and Q of 1.0 x 10 (exp 9) for the first resonator. Operation of this second unit provides a capability to directly verify for the first time the short-term (1 second less than or equal to tau less than or equal to 200 seconds) stability and the phase noise of the CSO units. The RF receiver used in earlier tests was sufficient to meet Cassini requirements for tau greater than or equal to 10 seconds but had short-term stability limited to 2-4 x 10 (exp -14) at tau = 1 second, a value 10 times too high to meet our requirements. A new low-noise receiver has been designed to provide approximately equal to 10-15 performance at 1 second, and one receiver is now operational, demonstrating again short-term CSO performance with H maser-limited stability. Short-term performance was degraded in the old receiver due to insufficient tuning bandwidth in a 100MHZ quartz VCO that was frequency-locked to the cryogenic sapphire resonator. The new receivers are designed for sufficient bandwidth, loop gain and low noise to achieve the required performance.

Dick, G. John↗
DOE OSTI2024

Validation of the NOνA experiment 2023-tuning on simulated neutrino-matter interactions

NOνA is a long-baseline neutrino oscillation experiment that utilizes a two-detector design to study the oscillations of muon neutrinos into electron neutrinos over a baseline of 810 km. The Near Detector (ND) measures the neutrino beam spectrum and composition before oscillation, which is then compared to the oscillated neutrino energy spectrum observed in the Far Detector (FD). In the ND, the neutrinos are detected through their interactions with the heavy target nuclei within the detector. NOνA employs the GENIE neutrino event generator for simulating these neutrino-nucleus interactions. However, the default GENIE prediction does not adequately reproduce the ND data. To address this, NOνA developed a tune of the neutrino interaction models within GENIE version 3:0:6 to minimize discrepancies between the simulated predictions and the observed data in the ND. This dissertation tests the NOνA’s 2023 tune of the GENIE neutrino cross-section simulations by performing a data/simulations comparison for the ND. The analysis employed datasets comprising $2.55\times10^{21}$ protons-on-target (POT) in neutrino beam mode and $1.14\times10^{21}$ POT in antineutrino beam mode. The NOνA tuning of neutrino-matter interaction simulations matches with ND data within the $1\sigma$ error band, overestimating muon neutrino and antineutrino charged current interactions by approximately 6 % and 9 %, respectively. Discrepancies were observed in the energy region dominated by Quasi-Elastic-like interactions. Systematic uncertainties associated with the modeling of the neutrino cross-section, particularly those pertaining Quasi-Elastic like interactions, contributed considerably to the overall error in the simulations. Furthermore, the reconstruction algorithm used in NOνA for particle classification demonstrated significant misidentifications between charged pions and protons, as well as a tendency to overlook additional pions or protons in multi-particle simulated events.

Cortés Parra, Camilo Andrés↗
DOE OSTI2020

Multi-objective Optimization Paradigm for Toroidal Inductors with Spatially Tuned Permeability

Spatially tuning core permeability of an electromagnetic device enables superior performance. A permeability profile can be heuristically selected to improve the flux distribution in a device with a given geometry, but in order to fully leverage the capacity of spatial dependent permeability engineering, the geometry and the permeability should be optimized simultaneously. The work herein presented sets forth a multi-physics design optimization paradigm that includes the permeability profile tuning in the context of an inductor design. This approach enables the determination of Pareto optimal fronts consisting of a set of optimal solutions against competing objectives (e.g. mass and loss) under imposed constraints. To this end, analytical solutions of the heat transfer and electromagnetic formulations are derived for toroidal inductors. The software implemented in Matlab 2018b is available online as an attachment to this paper.

42 ENGINEERING↗
NASA NTRS1977

Strapdown gyro test program

The power spectral noise characteristic performance of the Teledyne two-degree-of-freedom dry tuned gimbal gyroscope was determined. Tests were conducted using a current configuration SDG-5 gyro in conjunction with test equipment with minor modification. Long term bias stability tests were conducted as well as some first difference performance tests. The gyro, test equipment, and the tests performed are described. Results are presented.

Irvine, R. B.↗
DOE OSTI2024

Predictive analytics of selections of russet potatoes

We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato (Solanum tuberosum L.) clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties that meet processing industry standards. Leveraging manually collected data from trials in the state of Oregon, we investigate the potential of a wide variety of state-of-the-art binary classification models. The dataset includes 1086 clones, with data on 38 attributes recorded for each clone, focusing on yield, size, appearance, and frying characteristics, with several control varieties planted consistently across four Oregon regions from 2013 to 2021. We conduct a comprehensive analysis of the dataset that includes preprocessing, feature engineering, and imputation to address missing values. We focus on several key metrics such as accuracy, F1-score, and Matthews correlation coefficient (MCC) for model evaluation. The top-performing models, namely a feedforward neural network classifier (Neural Net), a histogram-based gradient boosting classifier (HGBC), and a support vector machine classifier (SVM), demonstrate consistent and significant results. To further validate our findings, we conducted a simulation study using the aims, data-generating mechanisms, estimands, methods, and performance measures (ADEMP) framework, simulating different data-generating scenarios to assess model robustness and performance through true positive, true negative, false positive, and false negative distributions, area under the receiver operating characteristic curve (AUC-ROC) and MCC. The simulation results highlight that non-linear models like SVM and HGBC consistently show higher AUC-ROC and MCC than logistic regression, thus outperforming the traditional linear model across various distributions, and emphasizing the importance of model selection and tuning in agricultural trials. Variable selection further enhances model performance and identifies influential features in predicting trial outcomes. The findings emphasize the potential of machine learning in streamlining the selection process for potato varieties, offering benefits such as increased efficiency, substantial cost savings, and judicious resource utilization. Our study contributes insights into precision agriculture and showcases the relevance of advanced technologies for informed decision-making in breeding programs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Advanced PGM-free Cathode Engineering for High Power Density and Durability

Polymer electrolyte fuel cells (PEFCs) are among the most promising technologies for future electric vehicles by using clean H2 with much-improved energy conversion efficiency, longer range, and rapid refueling. However, due to a large amount of platinum group metal (PGM) catalyst used in their electrodes, their prohibitively high cost hinders broad commercialization of PEFCs for transportation. Therefore, there is a critical need to develop low-cost, high-performance PGM-free cathode catalysts that have the potential to dramatically transform the economics of PEFC commercialization by reducing catalyst costs by one to two orders of magnitude. However, before PGM-free cathodes become viable, several technical challenges associated with PGM-free cathodes must be addressed, including insufficient activity and stability of the catalysts, as well as severe water flooding and large transport losses in the electrodes. Overcoming those barriers and ultimately meeting the challenging automotive PEFC performance targets was the focus of this comprehensive research and development effort on new PGM-free cathodes. To this end, we assembled a team including leading researchers from universities and industry with different but complementary expertise and capabilities. The project combined three novel and promising approaches: Advanced metal-organic framework (MOF)-derived M-N-C catalysts with a high activity and impressive durability, Novel PGM-free specific cathode architectures and fabrication strategies capable of addressing the substantial flooding and transport resistances in thicker cathodes by introducing engineered hydrophobicity through additives and support layers, and Advanced electrode ionomers with high proton conductivity for low ohmic losses across the electrode and more uniform catalyst utilization. The implementation of these new materials and electrode designs was supported by a suite of advanced experimental and simulation tools that allows us to identify performance and durability bottlenecks, devise solutions, and establish rational material design and synthesis targets. These methods include advanced electrochemical characterization, high-resolution imaging, and multi-scale modeling. In addition, the project team leveraged a broad cross-section of the ElectroCat consortium’s national laboratory facilities and expertise in advancing these materials and design strategies. Finally, the industry partners on the project facilitated the evaluation of scaled-up synthesis and manufacturing in the United States. Over its four-year period, the project made significant year-over-year advances in PGM-free cathode performance and viability. A combination of high activity and highly durable catalysts were developed through novel catalyst synthesis strategies, which met several performance and durability targets. More specifically, a catalyst prepared from MOFs and Fe2O3 nanoparticles with ammonium chloride and chemical vapor deposition treatments yielded a significant advancement in PGM-free cathode durability. Several novel strategies for fabricating cathodes were demonstrated, including those designed to reduce flooding and thickness of the cells for significantly increased volumetric power density. An optimized cathode with high conductivity ionomer and tuned ink processing for hydrophobicity yielded high fuel cell performance with new levels power density and maximum current. The scientific studies and modeling assessment also provided an outlook for future efforts, including a focus on catalysts with an increased density of the highly stable active sites developed in this project.

08 HYDROGEN↗
DOE OSTI2021

Managing Sn-supply to tune surface characteristics of vapor-diffusion coating of Nb3Sn

Nb₃Sn promises better RF performance (Q and E_{acc}) than niobium at any given temperature because of superior superconducting properties. Nb₃Sn-coated SRF cavities are now produced routinely by growing a few microns thick Nb₃Sn films inside Nb cavities via the tin vapor diffusion technique. Sn evaporation and consumption during the growth process notably affect the quality of the coating. Aiming at favorable surface characteristics that could enhance the RF performance, many coatings were produced by varying Sn sources and temperature profiles. Coupon samples were examined using different material characterization techniques, and a selected few sets of coating parameters were used to coat 1.3 GHz single-cell cavities for RF testing. The Sn supply’s careful tuning is essential to manage the microstructure, roughness, and overall surface characteristics of the coating. We summarize the material analysis of witness samples and discuss the performance of several Nb₃Sn-coated single-cell cavities linked to Sn-source characteristics and observed Sn consumption during the film growth process.

Pudasaini, U.↗
DOE OSTI2025

Organic Electrochemical Transistor Channel Materials: Copolymerization Versus Physical Mixing of Glycolated and Alkoxylated Polymers

Organic electrochemical transistors (OECTs) feature a polymer channel capable of conducting both ions and electronic charges. The choice of the channel material is critical for OECT performance. Many efforts have focused on improving performance via the chemical tunability of conjugated polymers – through backbone, side chain, and molar mass engineering – leading to useful design principles for accumulation-mode OECT materials. However, tuning the chemical structure of conjugated polymers often requires time-consuming optimization of the synthesis route. Meanwhile, variations in molar mass, dispersity, structural defects, and metal content present challenges when attempting to analyze the detailed effects of structural modifications, as multiple performance-determining factors are often (unintentionally) changed at the same time. Therefore, this study explores blended channel materials obtained by physically mixing glycolated and alkoxylated polymers in different ratios, and compares their OECT performance with the corresponding statistical copolymers. It is shown that mixing two well-performing materials creates blends that enable rational tuning of the transistor properties without compromising on performance. Thus, channels based on blends of alkoxylated and glycolated polymers hold promise for OECT technology with tailored response, as only two materials are needed to achieve any desired side chain ratio, simplifying the optimization of OECT characteristics.

copolymerization↗
DOE OSTI2020

Tuning of Nacelle Feedback Gains for Floating Wind Turbine Controllers Using a Two-DOF Model

Designing a collective blade pitch controller for floating offshore wind turbines (FOWTs) poses unique challenges due to the interaction of the controller with the dynamics of the platform. The controller must also handle the competing objectives of power production performance and fatigue load management. Existing solutions either detune the controller with the result of slowed response, make use of complicated tuning methods, or incorporate a nacelle velocity feedback gain. With the goal of developing a simple control tuning method for the general FOWT researcher that is easily extensible to a wide array of turbine and hull configurations, this last idea is built upon by proposing a simple tuning strategy for the feedback gain. This strategy uses a two degree-of-freedom (DoF) turbine model that considers tower-top fore-aft and rotor angular displacements. For evaluation, the nacelle velocity term is added to an existing gain scheduled proportional-integral controller as a proportional gain. The modified controller is then compared to baseline land-based and detuned controllers on an example system for several load cases. First-pass results are favorable, demonstrating how researchers can use the proposed tuning method to efficiently schedule gains for adequate controller performance as they investigate new FOWT configurations.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2025

A Neural Optimizer With Decision-Focused Learning for Optimal Energy Storage Operation

Here, this article introduces a neural optimizer-based framework for optimizing battery energy storage system (BESS) control for grid services, including demand charge and energy cost reduction. By leveraging decision-focused learning (DFL), the proposed framework ensures seamless integration and adaptation, significantly enhancing control performance. A patch time-series transformer is employed for peak load forecasting, incorporating aleatoric uncertainty quantification to account for forecasting uncertainties within the decision-making process. The framework utilizes a solver-in-the-loop approach to generate optimal BESS actions, which are then used to train the neural optimizer-based agent. By co-optimizing both BESS operational modes and output power within the NN, the system achieves improved performance and robustness. After initial training, the forecasting and control models are jointly fine-tuned to account for forecasting errors, further improving decision precision and efficiency through DFL. Case studies are performed to validate the performance of the framework using multiple real-world datasets, demonstrating superior performance in monthly peak load forecasting compared to state-of-the-art models. In addition, the results are compared against existing decision-making approaches. The results demonstrate a reduction in monthly peak forecasting error by approximately 15% across various performance measures and achieve an optimization gap for BESS operation that is about three times smaller compared to existing methods.

Kim, Hyeonjin [Pacific Northwest National Laborato↗
DOE OSTI2026

Tuning the Coordination Environment of Rh Single Atoms on Highly Dispersed Reducible Oxides for Enhanced Reverse Water-Gas Shift Performance

Controlling the dynamic mobility of catalyst surface active sites and their interactions with the surrounding environment is critical in generating active surfaces that directly influence the catalytic activity and selectivity. Here, we report a strategy for tailoring the dispersion and electronic environment of single atom Rh catalysts by decorating the alumina support with highly dispersed (HD) cerium and molybdenum oxides. The resulting catalysts exhibit markedly different behavior in the Reverse Water Gas Shift (RWGS) reaction. In particular, Rh/MoOx(HD)/Al2O3 maintains atomically dispersed Rh even at elevated temperatures (up to 400 °C), achieving CO selectivity of up to 100% and resists sintering via the formation of a newly developed structure featuring Rh single atoms embedded in MoOx clusters. In situ spectroscopy and microscopy analyses confirm the stabilization of Rh and the dynamic evolution of Rh–Mo coordination under reaction conditions. Our findings highlight the power of support modification in steering active site structure and activity, offering a pathway toward enhanced and tunable single atom catalysts for CO2 valorization.

CO selectivity↗
DOE OSTI2025

Developing low-cost rechargeable batteries: beyond traditional layered oxide cathodes for Li-ion and beyond Li-ion batteries

Here, the rising demand for energy storage systems, driven by the rapid adoption of electric vehicles and the global shift toward renewable energy, necessitates continuous efforts to lower the cost of current lithium-ion batteries (LIBs) and enhance the sustainability of existing battery chemistries. This feature article examines the key challenges associated with Ni- and Co-containing LIB cathodes and compares advancements in cathode development for non-traditional Li-ion and beyond Li-ion chemistries. First, a review of earth-abundant element containing disordered rock-salt cathodes is presented, with a discussion of key strategies such as compositional tuning and carbon coating to improve their electrochemical performance. Hurdles in developing oxide-based cathodes for Na- and K-ion batteries are also highlighted, followed by an in-depth overview of polyanion and Prussian blue cathodes for Na- and K-ion systems. Overall, this article provides a systematic perspective on the design of earth-abundant, low-cost, and sustainable cathode materials for both LIB and beyond LIB technologies.

Lohani, Harshita [Lawrence Berkeley National Labor↗
DOE OSTI2020

Fast strength model characterization using Bayesian statistics

A variety of flow stress models exist with new models constantly being developed. These models aim to approximate the strength of materials in a variety of regimes from quasistatic loading through shock scenarios. All models contain an array of parameters which need to be tuned to the material under study. Some models perform well under limited conditions, requiring adjustment of the parameters when venturing outside of those predefined ranges. Other models perform well over a wide range of conditions with a set of parameters, but may be outperformed by other models optimized on a tighter range of conditions. Recent research by Los Alamos demonstrated the ability to optimize the Johnson Cook (JC) model using a set of 3 plate-impact experiments on Aluminum. They utilized Bayesian statistics and emulation to determine optimal parameters for the model with a quantification of parameter uncertainty. We present an extension of this capability to incorporate velocimetry from plate-impact tests, stress-strain data from split Hopkinson pressure bar and quasistatic compression tests, plus profiles from Taylor cylinders in a unified fashion. Statistically robust comparisons of the performance and uncertainty of different realizations of the JC flow stress model were carried out based on calibration to several possible combinations of these three different experiment types.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Predicting the von Neumann entanglement entropy using a graph neural network

Calculating the von Neumann entanglement entropy from experimental data is challenging due to its dependence on the complete wavefunction, forcing reliance on approximations such as classical mutual information (MI). We propose a machine learning approach using a graph neural network to predict the von Neumann entropy directly from experimentally accessible bitstrings. We test this approach on a Rydberg ladder system and achieve a mean absolute error of $3.6\,\times 10^{-3}$ when evaluating within the training range on a dataset with entropy values ranging from 0 to 1.9. The model achieves a mean absolute percentage error of 1.44% and outperforms MI-based bounds. When tested beyond the training range, the model maintains reasonable accuracy. Furthermore, we demonstrate that fine-tuning the model with small datasets significantly improves performance on data outside the original training range.

graph neural networks↗
DOE OSTI2021

Quantum criticality in Ce 1 - x Sm x Co In 5

Motivated by the possibility of observing the coexistence between magnetism and unconventional superconductivity in heavy-fermion Ce 1-x Sm x CoIn 5 alloys, we studied how the samarium substitution on the cerium site affects the magnetic field-tuned-quantum criticality of stoichiometric CeCoIn 5 by performing specific heat and resistivity measurements. By applying an external magnetic field, we have observed Fermi-liquid to non-Fermi-liquid crossovers in the temperature dependence of the electronic specific heat normalized by temperature and of the resistivity. We obtained the magnetic-field-induced quantum critical point (QCP) by extrapolating to zero temperature the temperature-magnetic field dependence at which the crossovers take place. Furthermore, a scaling analysis of the electronic specific heat is used to confirm the existence of the QCP. We have found that the magnitude of the magnetic-field-induced QCP decreases with increasing samarium concentration. Our analysis of heat capacity and resistivity data reveals a zero-field QCP for x cr ≈0.15, which falls inside the region where Sm ions antiferromagnetism and superconductivity coexist.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Combinatorial Study of MZO Emitters for CdTe-Based Solar Cells

Precise band alignment at the front interface of CdTe-based solar cells is necessary to reduce photoexcited charge carrier recombination and to enable higher energy conversion efficiencies. Magnesium zinc oxide is not only transparent but also offers the ability to tune the conduction band offset with CdTe at the front of the device stack. In this work, we describe reactive sputtering of Mg 1-x Zn x O using metal targets and its application in CdTe photovoltaics. Combinatorial libraries of Mg 1-x Zn x O were created to tune the conduction band alignment for the optimal performance of CdTe solar cells. Band gap variation of more than 0.4 eV is achieved across a 3-inch substrate. Kelvin probe measurements demonstrate variance of work function across the substrate. Device performance is optimized at band gaps of 3.5 eV for the CdTe device processing conditions employed in this study.

14 SOLAR ENERGY↗
DOE OSTI2022

Exaflops Biomedical Knowledge Graph Analytics

We are motivated by newly proposed methods for mining large-scale corpora of scholarly publications (e.g., full biomedical literature), which consists of tens of millions of papers spanning decades of research. In this setting, analysts seek to discover relationships among concepts. They construct graph representations from annotated text databases and then formulate the relationship-mining problem as an all-pairs shortest paths (APSP) and validate connective paths against curated biomedical knowledge graphs (e.g., Spoke). In this context, we present Coast (Exascale Communication-Optimized All-Pairs Shortest Path) and demonstrate 1.004 EF/s on 9,200 Frontier nodes (73,600 GCDs). We develop hyperbolic performance models (HYPERMOD), which guide optimizations and parametric tuning. The proposed Coast algorithm achieved the memory constant parallel efficiency of 99% in the single-precision tropical semiring. Looking forward, Coast will enable the integration of scholarly corpora like PubMed into the Spoke biomedical knowledge graph.

Kannan, Ramakrishnan {ramki}↗