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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 1,711 records · Page 95
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Subfemtosecond Wakefield Injector and Accelerator Based on an Undulating Plasma Bubble Controlled by a Laser Phase

Here, we demonstrate that a long-propagating plasma bubble executing undulatory motion can be produced in the wake of two copropagating laser pulses: a near-single-cycle injector and a multicycle driver. When the undulation amplitude exceeds the analytically derived threshold, highly localized injections of plasma electrons into the bubble are followed by their long-distance acceleration. While the locations of the injection regions are controlled by the carrier-envelope phase (CEP) of the injector pulse, the monoenergetic spectrum of the accelerated subfemtosecond high-charge electron bunches is shown to be nearly CEP independent.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Dynamic Cooling on Contemporary Quantum Computers

We study the problem of dynamic cooling whereby a target qubit is cooled at the expense of heating up N − 1 further identical qubits by means of a global unitary operation. A standard back-of-the-envelope high-temperature estimate establishes that the target qubit temperature can be dynamically cooled by at most a factor of 1 / N . Here we provide the exact expression for the minimum temperature to which the target qubit can be cooled and reveal that there is a crossover from the high initial temperature regime, where the scaling is 1 / N , to a low initial temperature regime, where a much faster scaling of 1 / N occurs. This slow, 1 / N scaling, which was relevant for early high-temperature NMR quantum computers, is the reason dynamic cooling was dismissed as ineffectual around 20 years ago; the fact that current low-temperature quantum computers fall in the fast, 1 / N scaling regime, reinstates the appeal of dynamic cooling today. We further show that the associated work cost of cooling is exponentially more advantageous in the low-temperature regime. We discuss the implementation of dynamic cooling in terms of quantum circuits and examine the effects of hardware noise. We successfully demonstrate dynamic cooling in a three-qubit system on a real quantum processor. Since the circuit size grows quickly with N , scaling dynamic cooling to larger systems on noisy devices poses a challenge. We therefore propose a suboptimal cooling algorithm, whereby relinquishing a small amount of cooling capability results in a drastically reduced circuit complexity, greatly facilitating the implementation of dynamic cooling on near-future quantum computers. Published by the American Physical Society2024

Physics↗
DOE OSTI2026

Coherence-mediated quantum thermometry in a hybrid circuit quantum electrodynamics architecture

Quantum thermometry plays a critical role in the development of low-temperature sensors and quantum information platforms. Here, in this work, we propose and analyze a hybrid circuit quantum electrodynamics architecture in which a superconducting qubit is dispersively coupled to two distinct bosonic modes: one initialized in a weak coherent state as a phase reference and information buffer and the other coupled to a thermal environment. We show that the qubit serves as a sensitive readout of the probe mode, mapping the interplay between thermal and coherent photon-number fluctuations onto measurable dephasing. This coherence-mediated mechanism enables improved sensitivity to thermal energy fluctuations in the sub-millikelvin regime through Ramsey interferometry. We derive analytic expressions for the probe coherence envelope, compute the quantum Fisher information for temperature estimation, and demonstrate numerically that the presence of a coherent reference enhances the qubit's sensitivity to small changes in thermal photon occupancy. Our results establish a coherence-enabled approach to thermometry and provide a scalable platform for future calorimetric sensing in high-energy physics and quantum metrology.

Zhu, Shaojiang [Fermi National Accelerator Laborat↗
DOE OSTI2020

A high-power, high-repetition-rate THz source for pump–probe experiments at Linac Coherent Light Source II

Experiments using a THz pump and an X-ray probe at an X-ray free-electron laser (XFEL) facility like the Linac Coherent Light Source II (LCLS II) require frequency-tunable (3 to 20 THz), narrow bandwidth (~10%), carrier-envelope-phase-stable THz pulses that produce high fields (>1 MV cm –1 ) at the repetition rate of the X-rays and are well synchronized with them. Herein, a two-bunch scheme to generate THz radiation at LCLS II is studied: the first bunch produces THz radiation in an electromagnet wiggler immediately following the LCLS II undulator that produces X-rays from the second bunch. The initial time delay between the two bunches is optimized to compensate for the path difference in THz transport. The two-bunch beam dynamics, the THz wiggler and radiation are described, as well as the transport system bringing the THz pulses from the wiggler to the experimental hall.

42 ENGINEERING↗
DOE OSTI2021

Macromolecular phasing using diffraction from multiple crystal forms

A phasing algorithm for macromolecular crystallography is proposed that utilizes diffraction data from multiple crystal forms – crystals of the same molecule with different unit-cell packings (different unit-cell parameters or space-group symmetries). The approach is based on the method of iterated projections, starting with no initial phase information. The practicality of the method is demonstrated by simulation using known structures that exist in multiple crystal forms, assuming some information on the molecular envelope and positional relationships between the molecules in the different unit cells. With incorporation of new or existing methods for determination of these parameters, the approach has potential as a method for ab initio phasing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Data collection from crystals grown in microfluidic droplets

Protein crystals grown in microfluidic droplets have been shown to be an effective and robust platform for storage, transport and serial crystallography data collection with a minimal impact on diffraction quality. Single macromolecular microcrystals grown in nanolitre-sized droplets allow the very efficient use of protein samples and can produce large quantities of high-quality samples for data collection. However, there are challenges not only in growing crystals in microfluidic droplets, but also in delivering the droplets into X-ray beams, including the physical arrangement, beamline and timing constraints and ease of use. Here, the crystallization of two human gut microbial hydrolases in microfluidic droplets is described: a sample-transport and data-collection approach that is inexpensive, is convenient, requires small amounts of protein and is forgiving. It is shown that crystals can be grown in 50–500 pl droplets when the crystallization conditions are compatible with the droplet environment. Local and remote data-collection methods are described and it is shown that crystals grown in microfluidics droplets and housed as an emulsion in an Eppendorf tube can be shipped from the US to the UK using a FedEx envelope, and data can be collected successfully. Details of how crystals were delivered to the X-ray beam by depositing an emulsion of droplets onto a silicon fixed-target serial device are provided. After three months of storage at 4°C, the crystals endured and diffracted well, showing only a slight decrease in diffracting power, demonstrating a suitable way to grow crystals, and to store and collect the droplets with crystals for data collection. This sample-delivery and data-collection strategy allows crystal droplets to be shipped and set aside until beamtime is available.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A machine learning degradation model for electrochemical capacitors operated at high temperature

Electrochemical capacitors (ECs) have only recently been considered as an alternative power source for telemetry sensors of drilling equipment for geothermal or oil and gas exploration. The lifecycle analysis and modelling of ECs is underrepresented in literature in comparison to other storage devices e.g. Li-ion batteries. This paper investigates the degradation of ECs when cycled outside the manufacturer-specified operating temperature envelope and proposes a machine learning-based approach for modelling the degradation. Experimental results show that end of life, defined as a 30% decrease in capacitance, occurs at 1,000 cycles when the environmental temperature exceeds the maximum operating temperature by 30%. The life-cycle test data is then used as an input to a Gaussian process regression (GPR) algorithm to predict the capacitance fade trend. The GPR is validated on a total of nine commercial cells from two different manufacturers, achieving an average root mean squared percent error of less than 2% and a mean calibration score of 93% when referenced to a 95% confidence interval. The model can be utilized to determine the EC degradation rate at a range of operating temperature values.

42 ENGINEERING↗
DOE OSTI2020

Comprehensive Efficiency Analysis of Current Source Inverter Based SPM Machine Drive System for Traction Applications

A current-source inverter (CSI) has the natural capability of boosting the output voltage which is a notable advantage over voltage source inverter (VSI) in traction drive applications. This paper investigates the voltage boosting feature of the CSI to improve the overall efficiency of the CSI-based surface permanent magnet (SPM) machine drive system in the constant power region. The effects of the boost function on the total system losses, including the machine copper loss, core loss, and magnet loss, as well as the device conduction and switching loss in the CSI and dc/dc converter, are described using analytical models. The operating characteristics of the machine and CSI are validated by 2-D finite-element analysis (FEA) and simulations. Based on the loss model, the effects of the modulation index on the overall drive system losses and power factor have been analyzed. A genetic algorithm has been used to optimize the CSI's boost ratio, demonstrating that the drive system efficiency can be increased by 1% to 2.5% along the constant-power regime envelope by using the boost function.

current source inverter, genetic algorithm, SPM ma↗
DOE OSTI2023

Design and Comparison of Surface Inset Permanent Magnet Machine and Surface Permanent Magnet Machine without Heavy Rare Earth Magnets for Traction Applications

This paper presents two machine designs: a surface inset permanent magnet (SIPM) machine and a surface permanent magnet (SPM) machine, both without heavy rare-earth magnets, that achieve 50 kW/L active power density and a constant-power speed ratio of 3 for EV traction applications. Compared to surface PM machines, SIPM machines do not require a special containment sleeve to hold the magnets in place at high speeds, provided that the rotor laminations are carefully designed together with the magnet mounting configuration to ensure the rotor’s structural integrity. Comparisons between the SPM and SIPM machines sharing the same stator, same current level, and same power speed envelope are presented. Both deliver very similar performance characteristics, but the SIPM machine’s simpler magnet containment configuration may give it an advantage in terms of reduced material and manufacturing costs.

42 ENGINEERING↗
DOE OSTI2026

Artificial Intelligence-Driven Management of Sustainable Energy Resources: Visibility, Operation, and Control

The rapid global transition toward sustainable energy resources (SERs) is reshaping how modern power systems are observed, optimized, and controlled. While SERs have significantly advanced decarbonization, their weather dependence, variability, and inverter-dominated characteristics challenge traditional, centralized, and deterministic grid operation. At the same time, the proliferation of high-resolution data from inverters, smart meters, and sensors offers unprecedented visibility into system dynamics. Yet, it also exceeds the analytical capability of conventional model-based approaches. Artificial intelligence (AI) provides a new foundation for addressing these challenges by bridging physical laws with data-driven learning, enabling accurate state awareness, adaptive operation, and coordinated control across distributed assets. This article examines how AI transforms the management of SER-rich power systems along three critical dimensions: 1) enhancing visibility by inferring behind-the-meter (BTM) activities, assessing SER flexibility, and reconstructing system states from sparse or noisy measurements; 2) improving operation through AI-enhanced SER service provision, volt/var control (VVC), and dynamic operating envelopes (DOE) for efficiency and security; and 3) advancing control by embedding learning-based intelligence into inverter coordination, voltage and frequency regulation, and long-term dispatch. Together, these developments reveal how AI can convert the variability of SERs from an operational challenge into a source of flexibility, resilience, and intelligence, paving the way toward sustainable, adaptive, and self-optimizing power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Compact, All-PM Fiber Integrated and Alignment-Free Ultrafast Yb:Fiber NALM Laser With Sub-Femtosecond Timing Jitter

We report a simple and compact design of a dispersion compensated mode-locked Yb:fiber oscillator based on a nonlinear amplifying loop mirror (NALM). The fully polarization maintaining (PM) fiber integrated laser features a chirped fiber Bragg grating (CFBG) for dispersion compensation and a fiber integrated compact non-reciprocal phase bias device, which is alignment-free. The main design parameters were determined by numerically simulating the pulse evolution in the oscillator and by analyzing their impact on the laser performance. Experimentally, we achieved an 88 fs compressed pulse duration with sub-fs timing jitter at 54 MHz repetition rate and 51 mW of output power with 5.5×10 -5 [20 Hz, 1 MHz] integrated relative intensity noise (RIN). Furthermore, we demonstrate tight phase-locking of the laser's carrier-envelope offset frequency ( fceo ) to a stable radio frequency (RF) reference and of one frequency comb tooth to a stable optical reference at 291 THz.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function

Computational biology is one of many scientific disciplines ripe for innovation and acceleration with the advent of high-performance computing (HPC). In recent years, the field of machine learning has also seen significant benefits from adopting HPC practices. In this work, we present a novel HPC pipeline that incorporates various machine-learning approaches for structure-based functional annotation of proteins on the scale of whole genomes. Our pipeline makes extensive use of deep learning and provides computational insights into best practices for training advanced deep-learning models for high-throughput data such as proteomics data. We showcase methodologies our pipeline currently supports and detail future tasks for our pipeline to envelop, including large-scale sequence comparison using SAdLSA and prediction of protein tertiary structures using AlphaFold2.

Gao, Mu↗
DOE OSTI2020

Electricity Produced from Photovoltaic Systems in Apartment Buildings and Self-Consumption: Comparison of the Situation in Various IEA PVPS Countries

Over the last two decades, grid-connected solar photovoltaic systems have increased from a niche market to one of the leading power generation capacity additions annually. In 2018 the total installed photovoltaic electricity generation capacity exceeded 500 GW. Another doubling is forecasted until the early 2020s. Therefore, the further development is coupled with the question at what prices solar photovoltaic electricity can be provided and delivered to the customers. The installation of PV systems for self-consumption is already now an interesting option for many people but in general limited to those who have access to a rooftop they own or can use. A new envelopment to enable residents of multi apartment buildings to commonly use electricity generated by a PV system (collective self-consumption) is a relatively new development and is still facing a lot of administrative and regulatory challenges.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Measuring the Moisture Content of Wood Sheathing with Continuous Wave Radars

Radars were studied for measuring the moisture content of wood-based materials because of their non-destructive nature and their ability to measure the moisture content of several layers at once. These convenient characteristics enable a quick and non-invasive method to find areas of high moisture content in building envelopes so that problems can be fixed before the structure degrades. The need to operate the radar at millimeter ranges required the development of new techniques and methods for analyzing and calculating the moisture content of individual layers of building material. The techniques are providing accurate results for oriented strand board, and efforts are continuing for investigating other construction materials, meeting FCC requirements, and building a system that will be easy to deploy.

Killough, Stephen M↗
DOE OSTI2024

Magnets for a Muon Collider—Needs and Plans

We describe the magnet challenges for a Muon Collider, an exciting option considered for the future of particle physics at the energy frontier. Starting from the comprehensive work performed by the US Muon Accelerator Program, we have reviewed the performance specifications dictated by beam physics and the operating conditions to satisfy the accelerator needs. Among the many magnets that make up a muon collider, we have identified four systems that represent well the envelope of challenges: the target and capture solenoid, the final cooling solenoid, the accelerator dipoles and the collider dipoles. These systems provide focus for the development of novel concepts, largely based on HTS for reasons of performance, cost and sustainability. After giving a consolidated overview of the needs for the magnet systems, we describe here the basic technology options considered, and the plan for design and development activities.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Comparative Analysis of Pendulum and Flywheel Power Take-Off Mechanisms for Wave Energy Conversion

Here, this study compares three power-take-off (PTO) mechanisms for wave energy converters—a flywheel -type pitch resonator and two pendulum variants (normal and inverted)—all designed to fit within a fixed envelope. Performance is assessed via linear frequency-domain models and bi-conjugate impedance matching under a representative sea state. The inverted-pendulum PTO achieves 27.9% of the incident wave energy, versus 16.5% for the normal pendulum and 4.2% for the flywheel—that is, 6.6 × and 3.9 × improvements, respectively. These gains arise because pendulum-based systems generate higher torque for a given buoy motion and maintain impedance matching across a wider frequency band. Parametric studies of mass, pendulum arm length, spring stiffness, generator friction, and motor constant quantify the impact of key design choices on power output. By locating the pendulum pivot near the buoy's center of gravity and positioning the center of rotation below the COG, surge–pitch coupling is improved and energy transfer is further boosted.

Keow, Alicia Li Jen [Sandia National Laboratories ↗
DOE OSTI2020

Toroidal Field Reversing Switch

A motor-driven, high-current switch is being designed by Stäubli Electrical Connectors in collaboration with Princeton Plasma Physics Laboratory to reverse the DIII-D Toroidal field coil current. The existing field reversing procedure requires approximately 4 h of labor performed between run days to reconfigure the bus work, thus requiring a separate run day to complete experimental scans with both field directions. This typically results in different wall conditions which complicate the results or make them invalid. The proposed switch will complete the reversal process in approximately 4-5 min, between shots, so that wall conditions are not a factor, and easily fits within the 10-12 min DIII-D shot cycle. The switch is a compact design fitting into a 157.5 × 41 cm envelope. The switch uses two copper pins that slide between Stäubli MULTILAM contacts. The switch is rated for total current of 180-kA dc for 10 s with a 12-min repetition rate; the maximum operating coil current is 124-kA dc. The switch diagnostics include: voltage drop, current monitor, thermal monitors, position sensors, and motor torque. Here, the switch is rated for 300 cycles per year, and 3000 cycles overall before contact replacement, with contact inspection yearly. The design details and operation of the switch are covered in this article.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

3D Deep Learning Joint Inversion of Active Seismic Full Waveform and Passive Seismic Traveltime Data for Reservoir Imaging and Uncertainty Quantification

Here, we present deep learning (DL) networks for three-dimensional (3D) joint inversion of active seismic full waveform and passive seismic traveltime data to image reservoirs and their properties and quantify imaging uncertainties. Active seismic full-waveform data can provide high-resolution monitoring images but are collected only intermittently because of their high acquisition cost. In contrast, passive seismic data can be gathered at relatively low cost between regular active surveys, although their imaging quality can be compromised by factors such as low signal-to-noise ratios and limited ray coverage of the target. Although these datasets are routinely acquired together at CO 2 storage sites, their combined inversion within a 3D DL framework has not been previously demonstrated. To our knowledge, this is the first study to address this gap, combining the strength of both data types. For efficient data storage and DL training with large 3D seismic datasets, we use a 3D data matrix in which a random number of passive seismic traveltime data are stored as parabolic envelopes using one-hot encoding and a 3D full-waveform data matrix in which multiple shot gathers are summed. Two network architectures are evaluated: a single-encoder U-Net for single-data type inversion and a dual-encoder U-Net for joint inversion of active and passive seismic data. We also evaluate the single-encoder U-Net for joint inversion by concatenating full-waveform data and traveltime data. We propose a systematic approach for selecting an optimal dropout rate that balances regularization during training and Monte Carlo dropout-based uncertainty quantification during prediction by examining the correlation coefficient between standard deviation and prediction error, along with the training misfit, across a range of dropout rates. 3D DL inversion experiments include five different network configurations, with evaluations under ideal, noisy and dropout-enabled conditions. Both model and data uncertainties are assessed, as well as their combined effects. Across all conditions, the networks consistently predict accurate CO 2 saturation models with low prediction errors, such as a structural similarity index of 0.993 and CO 2 difference of 1.1%. Uncertainty estimates show strong spatial correlation with prediction errors, confirming the effectiveness of the proposed dropout selection approach. The results demonstrate that our DL approach, utilizing compact data representations and appropriate uncertainty quantification, yields accurate subsurface images under various inversion conditions and provides valuable insights into the reliability of predictions.

Um, Evan Schankee [Lawrence Berkeley National Labo↗