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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Design of an optical amplifier for amplified OSC in IOTA facility at Fermilab

Optical stochastic cooling (OSC) is a cutting-edge beam cooling technology to reduce, control the 3 dimensional spread and the motion of particle beams. It has recently been successfully, experimentally, demonstrated in Fermilab's IOTA storage ring, marking a major step forward in beam cooling. OSC has the potential to significantly improve both the performance and flexibility as a beam cooling system. One promising way to boost OSC performance is by adding a high-gain optical amplifier. However, this amplifier must be carefully designed to meet the specific constraints of the OSC system. A major challenge lies in the limited optical delay, which is just 6 mm for the case of IOTA, set by the beam bypass, restricts us to use a short-length gain medium. This, along with IOTA’s high repetition rate and the relatively long duration of the optical pulses, limits the peak power available for the pump laser without damaging the crystal, which is crucial for achieving strong nonlinear gain. Additionally, it's essential to preserve the phase coherence of the undulator radiation during amplification, which further complicates the amplifier design. This report details a specialized amplifier setup that addresses these challenges, includes simulations of the integrated system, and summarizes the latest experimental progress and results.

Mondal, Abhishek [Fermilab]↗
NASA NTRS2008

Water-Based Coating Simplifies Circuit Board Manufacturing

The Structures and Materials Division at Glenn Research Center is devoted to developing advanced, high-temperature materials and processes for future aerospace propulsion and power generation systems. The Polymers Branch falls under this division, and it is involved in the development of high-performance materials, including polymers for high-temperature polymer matrix composites; nanocomposites for both high- and low-temperature applications; durable aerogels; purification and functionalization of carbon nanotubes and their use in composites; computational modeling of materials and biological systems and processes; and developing polymer-derived molecular sensors. Essentially, this branch creates high-performance materials to reduce the weight and boost performance of components for space missions and aircraft engine components. Under the leadership of chemical engineer, Dr. Michael Meador, the Polymers Branch boasts world-class laboratories, composite manufacturing facilities, testing stations, and some of the best scientists in the field.

Source record↗
DOE OSTI2022

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

42 ENGINEERING↗
DOE OSTI2024

High-gradient performance of a prototype accelerator cavity for a 3 GeV proton radiography booster

This paper reports the design, fabrication, and results of the high-gradient conditioning and testing for a two-cell, π -mode, standing wave normal-conducting prototype booster cavity for the proposed 3 GeV proton linac upgrade at Los Alamos Neutron Science Center. Increasing the energy of proton beam from the existing 800 MeV to 3 GeV will improve resolution of the proton radiography by up to 10 times. The proposed energy boost can be achieved with a compact normal-conducting high-gradient radio-frequency (rf) linac section. The C-band section of the booster linac was designed with optimized-shaped copper accelerator cavities with distributed rf coupling. A short two-cell test prototype structure was designed for the frequency of 5.712 GHz, fabricated, and tested at the C-band Engineering Research Test Facility in New Mexico (CERF-NM) at Los Alamos National Laboratory. The maximum klystron power coupled into the test structure was 8.3 MW with 1 μ s pulse length and 100 Hz repetition rate. The breakdown probabilities were recorded as functions of the accelerating gradient and peak surface fields. Operation of the test cavity at accelerating gradients of up to 100 MV / m was demonstrated. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
NASA NTRS2022

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

Online anomaly detection↗
NASA NTRS2007

Cosmological Effects of Powerful AGN Outbursts in Galaxy Clusters: Insights from an XMM-Newton Observation of MS 0735+7421

We report on the results of an analysis o f XMM-Newton observations o f MS 0735+7421, the galaxy cluster that hosts the most energetic AGN outburst currently known. .The previous chandra image shows twin giant x-ray cavities (approx.200 kpc diameter) filled with radio emission and surrounded by a weak shock front. XMM data are consistent with these findings. The total energy in cavities and shock (1E62 erg(approx.100 kpc), to heat the gas within 1 Mpc by approx.1/4 kev per particle. The cluster exhibits an upward departure (factor approx.2) from the mean L-T relation. The boost in emissivity produced by the ICM compression in the bright shells due to the cavity expansion may contribute to explain the high luminosity and high central gas mass fraction that we measure. The scaled temperature and metallicity profiles are in general agreement with those observed in re1axed clusters. Also, the quantities we measure are consistent with the observed M-T relation. We conclude that violent outbursts such as the one in MS 0735+7421 do not cause dramatic scaling relations (other than the L-T relation). However, if they are relatively common they may play a role in creating the global cluster properties.

Gitti, M.↗
DOE OSTI2025

Optimizing superconducting Nb film cavities by mitigating medium-field Q -slope through annealing

Niobium films are of interest in applications in various superconducting devices, such as superconducting radiofrequency cavities for particle accelerators and superconducting qubits for quantum computing. In this study, we address the persistent medium-field Q-slope issue in Nb film cavities, which, despite their high-quality factor at low RF fields, exhibit a significant Q-slope at medium RF fields compared to bulk Nb cavities. Traditional heat treatments, effective in reducing surface resistance and mitigating the Q-slope in bulk Nb cavities, are challenging for Nb-coated copper cavities. To overcome this challenge, we employed DC bias high-power impulse magnetron sputtering to deposit Nb film onto a 1.3 GHz single-cell elliptical bulk Nb cavity, followed by annealing treatments aimed at modifying the properties of the Nb film. In-situ annealing at 340 °C increased the quench field from 10.0 to 12.5 MV m −1 . Vacuum furnace annealing at 600 °C and 800 °C for 3 h resulted in a quench field increase of 13.5 and 15.3 MV m −1 , respectively. Further annealing at 800 °C for 6 h boosted the quench field to 17.5 MV m −1 . Additionally, the annealing treatments significantly reduced the field dependence of the surface resistance. However, increasing the annealing temperature to 900 °C induced a Q-switch phenomenon in the cavity. The analysis of RF performance and material characterization before and after annealing has provided critical insights into how the microstructure and impurity levels in Nb films influence the evolution of the Q-slope in Nb film cavities. Our findings highlight the significant roles of hydrides, high local misorientation, and lattice and surface defects in driving field-dependent losses. By strategically optimizing film properties and controlling impurity levels, we demonstrate a promising pathway to mitigate the medium-field Q-slope, paving the way for more efficient superconducting RF technologies.

Nb film↗
DOE OSTI2022

Random Forest Regressor-Based Approach for Detecting Fault Location and Duration in Power Systems

Power system failures or outages due to short-circuits or “faults” can result in long service interruptions leading to significant socio-economic consequences. It is critical for electrical utilities to quickly ascertain fault characteristics, including location, type, and duration, to reduce the service time of an outage. Existing fault detection mechanisms (relays and digital fault recorders) are slow to communicate the fault characteristics upstream to the substations and control centers for action to be taken quickly. Fortunately, due to availability of high-resolution phasor measurement units (PMUs), more event-driven solutions can be captured in real time. In this paper, we propose a data-driven approach for determining fault characteristics using samples of fault trajectories. A random forest regressor (RFR)-based model is used to detect real-time fault location and its duration simultaneously. This model is based on combining multiple uncorrelated trees with state-of-the-art boosting and aggregating techniques in order to obtain robust generalizations and greater accuracy without overfitting or underfitting. Four cases were studied to evaluate the performance of RFR: 1. Detecting fault location (case 1), 2. Predicting fault duration (case 2), 3. Handling missing data (case 3), and 4. Identifying fault location and length in a real-time streaming environment (case 4). A comparative analysis was conducted between the RFR algorithm and state-of-the-art models, including deep neural network, Hoeffding tree, neural network, support vector machine, decision tree, naive Bayesian, and K-nearest neighborhood. Experiments revealed that RFR consistently outperformed the other models in detection accuracy, prediction error, and processing time.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

System-Level Efficiency Study of Modular DC/DC and Fuel Cell Stack Series–Parallel Configurations

This paper presents a system-level efficiency study of modular DC/DC converter and fuel cell stack configurations under series, parallel, and series–parallel connections. The investigation considers selected DC/DC converter topologies, including isolated and non-isolated architectures, boost, non-inverting buck-boost, and resonant converters, integrated with commercially available fuel cell stacks, including the Accelera FCE150, Ballard FCgen-HPS, and Toyota TFCM2. DC/DC converter modules are evaluated in modular configurations rated at 60 kW and 90 kW and two distinct output voltage ranges, specifically 580–730V and 780–930V, examining how different interconnection schemes impact overall system efficiency. The converters are evaluated under full load (100%), partial load (66%), and light load (33%) conditions, providing a comprehensive assessment of efficiency and operational characteristics across varying power demands. Approximately two-thousand efficiency data points are obtained from laboratory prototype–level component measurements and validated design evaluations across multiple converter topologies, modular configurations, voltage ranges, and load conditions, providing a robust dataset for comparative system-level efficiency analysis. The results highlight the effects of modularity and topology selection on system-level efficiency, offering a “playbook” framework for designers to select appropriate DC/DC converter arrangements and fuel cell stack connections for series, parallel, or hybrid configurations based on efficiency considerations.

DC/DC↗
DOE OSTI2024

Transverse Momentum Distributions from Lattice QCD without Wilson Lines

The transverse-momentum-dependent distributions (TMDs), which are defined by gauge-invariant 3D parton correlators with staple-shaped lightlike Wilson lines, can be calculated from quark and gluon correlators fixed in the Coulomb gauge on a Euclidean lattice. These quantities can be expressed gauge invariantly as the correlators of Coulomb-gauge-dressed fields, which reduce to the standard TMD correlators under principal-value prescription in the infinite boost limit. In the framework of large-momentum effective theory, a quasi-TMD defined from such correlators in a large-momentum hadron state can be matched to the TMD via a factorization formula, whose exact form is derived using soft collinear effective theory and verified at one-loop order. Compared to the currently used gauge-invariant correlators, this new method can substantially improve statistical precision and simplify renormalization for the time-reversal-even TMDs, which will greatly enhance the predicative power of lattice QCD in the nonperturbative region.

Effective field theory↗
NASA NTRS1986

Future applications of tethers in space

Various experiments with tethered satellite systems (TSS) have reached the stage of being considered for demonstration programs as follow-ups to the planned electrodynamic power system and atmospheric missions at the close of the 1980s. All TSS operations involve an initial separation thrust to a tethered object and then begin transferring angular momentum from one object to another while preserving the total angular momentum. The demonstrations include tethered launch of an Orbiter payload to a higher orbit, deployment of the Orbiter from the Space Station (SS), and launch of an Orbital Transfer Vehicle from the SS. The operations can be sequential, with the Orbiter providing the SS with an upward boost while being lowered to an easier deorbit position, followed by the SS releasing on OTV (or a repaired Space Telescope) to a higher orbit while the SS returns to a more favorable lower orbit.

Von Tiesenhausen, G.↗
DOE OSTI2026

A Machine Learning based Approach of Estimating Equivalent Circuit Model Parameters at Different SoCs of Li-ion Batteries from Voltage Relaxation

Abstract: In this study, an approach of estimating the equivalent circuit model (ECM) parameters for Li-ion batteries (LIBs) is proposed based on the voltage value at different intervals while relaxing the LIB after discharge. The typical approach for estimating ECM parameters of a LIB is to conduct electrochemical impedance spectroscopy (EIS) measurements at different frequencies and fit them to a predefined circuit model, which requires additional measuring arrangements and specialized devices. The proposed methodology utilizes four different voltages at 0s, 60s, 360s, and 1800s alongside the specific state of charge (SoC) value for a specific constant discharge current value of ~1C until the relaxation stage to train and evaluate three regression-based machine learning models— Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Gaussian Process Regression (GPR)—for estimating the ECM parameters of the selected model. Bayesian optimization is employed for hyperparameter tuning to achieve optimal performance for all the regressor models, among which, the GPR provided the best performance with the root-mean-squared error (RMSE) of less than 4x10-4 on average for the resistive components and less than 0.27 for capacitive components with excellent R2 scores. The simplicity of the approach enables it to eliminate the need for sophisticated measuring equipment and computation power.

Sagar, Md. Samiul [The University of Alabama (UA)]↗
NASA NTRS1976

Scramjet integration on hypersonic research airplane concepts

Several rocket-boosted research airplane concepts were evaluated with a research scramjet engine to determine their potential to provide research on critical aspects of airframe-integrated hypersonic systems. Extensive calculations to determine the force and moment contributions of the scramjet inlet, combustor, nozzle, and airframe were conducted to evaluate the overall performance of the combined engine/airframe system at hypersonic speeds. Results of both wind-tunnel tests and analysis indicate that it is possible to develop a research airplane configuration that will cruise at hypersonic speed on scramjet power alone, and will also have acceptable low-speed aerodynamic characteristics for landing.

Weidner, J. P.↗
DOE OSTI2024

"Forward" Projects Boost U.S. Leadership in Advanced Computing and Artificial Intelligence

High-performance computing (HPC) has been an indispensable research tool for accessing physical realms difficult, or impossible, achieve with experiment alone. For several decades, the Department of Energy’s (DOE’s) Office of Science has deployed sophisticated HPC systems for solving the nation’s most pressing grand challenge problems in energy, climate change, and human health. In addition, DOE’s National Nuclear Security Administration (NNSA) has adeptly applied HPC in support of key national security objectives, such as nuclear science and stockpile modernization and stewardship. Over time, HPC systems have become increasingly more complex and capable, and as each new machine has come online, scientists and engineers have taken advantage of vast increases in compute power to accelerate scientific discoveries and engineering innovation.

42 ENGINEERING↗
NASA NTRS1990

Optimal aeroassisted orbital transfer with plane change using collocation and nonlinear programming

The fuel optimal control problem arising in the non-planar orbital transfer employing aeroassisted technology is addressed. The mission involves the transfer from high energy orbit (HEO) to low energy orbit (LEO) with orbital plane change. The basic strategy here is to employ a combination of propulsive maneuvers in space and aerodynamic maneuvers in the atmosphere. The basic sequence of events for the aeroassisted HEO to LEO transfer consists of three phases. In the first phase, the orbital transfer begins with a deorbit impulse at HEO which injects the vehicle into an elliptic transfer orbit with perigee inside the atmosphere. In the second phase, the vehicle is optimally controlled by lift and bank angle modulations to perform the desired orbital plane change and to satisfy heating constraints. Because of the energy loss during the turn, an impulse is required to initiate the third phase to boost the vehicle back to the desired LEO orbital altitude. The third impulse is then used to circularize the orbit at LEO. The problem is solved by a direct optimization technique which uses piecewise polynomial representation for the state and control variables and collocation to satisfy the differential equations. This technique converts the optimal control problem into a nonlinear programming problem which is solved numerically. Solutions were obtained for cases with and without heat constraints and for cases of different orbital inclination changes. The method appears to be more powerful and robust than other optimization methods. In addition, the method can handle complex dynamical constraints.

Shi, Yun. Y.↗
DOE OSTI2024

Halogenated Dibenzo[ f , h ]quinoxaline Units Constructed 2D‐Conjugated Guest Acceptors for 19% Efficiency Organic Solar Cells

Abstract Halogenation of Y‐series small‐molecule acceptors (Y‐SMAs) is identified as an effective strategy to optimize photoelectric properties for achieving improved power‐conversion‐efficiencies (PCEs) in binary organic solar cells (OSCs). However, the effect of different halogenation in the 2D‐structured large π‐fused core of guest Y‐SMAs on ternary OSCs has not yet been systematically studied. Herein, four 2D‐conjugated Y‐SMAs (X‐QTP‐4F, including halogen‐free H‐QTP‐4F, chlorinated Cl‐QTP‐4F, brominated Br‐QTP‐4F, and iodinated I‐QTP‐4F) by attaching different halogens into 2D‐conjugation extended dibenzo[ f , h ]quinoxaline core are developed. Among these X‐QTP‐4F, Cl‐QTP‐4F has a higher absorption coefficient, optimized molecular crystallinity and packing, suitable cascade energy levels, and complementary absorption with PM6:L8‐BO host. Moreover, among ternary PM6:L8‐BO:X‐QTP‐4F blends, PM6:L8‐BO:Cl‐QTP‐4F obtains a more uniform and size‐suitable fibrillary network morphology, improved molecular crystallinity and packing, as well as optimized vertical phase distribution, thus boosting charge generation, transport, extraction, and suppressing energy loss of OSCs. Consequently, the PM6:L8‐BO:Cl‐QTP‐4F‐based OSCs achieve a 19.0% efficiency, which is among the state‐of‐the‐art OSCs based on 2D‐conjugated Y‐SMAs and superior to these devices based on PM6:L8‐BO host (17.70%) and with guests of H‐QTP‐4F (18.23%), Br‐QTP‐4F (18.39%), and I‐QTP‐4F (17.62%). The work indicates that halogenation in 2D‐structured dibenzo[ f , h ]quinoxaline core of Y‐SMAs guests is a promising strategy to gain efficient ternary OSCs.

14 SOLAR ENERGY↗
DOE OSTI2025

Day-Ahead Probabilistic Forecasting of Net-Load and Demand Response Potentials with High Penetration of Behind-the-Meter Solar-plus-Storage

The goal of this project is to develop advanced methods for day-ahead net-load forecasting, by leveraging the state-of-the-art machine learning techniques. The developed models produce both point and probabilistic forecasts for a variety of use cases, and are versatile to work with different types of data sets. The innovation lies in the novel design of the architectures, leveraging the most recent advances in machine learning that have not been explored in power systems, accompanied by techniques in the broader artificial intelligence fields such as fuzzy systems. This project has achieved the following accomplishments: (1) preprocessing of over 10 data sets covering varying geographical regions, time horizons, and system levels, which form a robust foundation for training and evaluating forecasting models across a wide range of realistic grid scenarios; (2) development of an interactive web app that enables exploratory analysis of load and generation data, and supports better understanding of data trends, anomalies, and correlations, facilitating model development and stakeholder engagement; (3) implementation of over 10 benchmark models for point and probabilistic forecasting, which include a mix of conventional machine learning methods and state-of-the-art deep learning approaches, providing a comprehensive baseline for performance comparison and validation of the proposed models; (4) development of a fuzzy system based gradient boosting model, tailored for small (less than 3 years) data sets, which achieves a mean absolute percentage error (MAPE) of 4% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (5) development of a Transformer (a state-of-the-art deep learning architecture) based neural network model, tailored for large (3 years or more) data sets, which achieves a MAPE of 2% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (6) development of a methodology for quantifying DR potential, and extensions of the previous models for multi-target forecasting of net load and DR potential, which achieve a MAPE of 10% for DR potential.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Evaporated CdSe for Efficient Polycrystalline CdSeTe Thin-Film Solar Cells

Recent progress has shown that alloying cadmium telluride (CdTe) with cadmium selenide (CdSe) to create a CdSexTe1-x (CdSeTe) gradient region can significantly boost the performance of polycrystalline CdSeTe thin-film solar cells. However, improper CdSeTe alloying might introduce problematic band alignment and deleterious voids at the front interface, limiting the benefit maximization of this technique. Here, we show that the CdSe layers deposited by thermal evaporation result in CdSeTe cells with a higher performance than the sputtered CdSe. This is because evaporated CdSe can avoid the formation of voids at the front interface, producing improved front junction quality with suppressed front junction nonradiative recombination. The champion cell using evaporated CdSe demonstrated a power conversion efficiency (PCE) of 19.7%, much higher than 18.1% in the cell using sputtered CdSe.

cadmium selenide↗