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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Producing 236 U reference standards for Accelerator Mass Spectrometry at the University of Notre Dame

36 U is a rare isotope of uranium, naturally occurring in ores with an abundance of 236 U/ 238 U$<$ 1 x 10 -9 . The ability to detect it and make isotopic ratio measurements has applications ranging from nuclear forensics and nonproliferation to energy production and environmental protection. Currently, Accelerator Mass Spectrometry (AMS) is the only technique sensitive enough to accurately measure 236 U/ 238 U isotopic ratios as they exist in naturally occurring ores in the range of 236 U/ 238 U = 10 -12 $-$ 10 -9 . Some AMS facilities have demonstrated their capabilities to make these measurements. Historically, the lack of commercially available reference standards covering the range of naturally occurring 236 U/ 238 U abundances has necessitated the use of absolute measurements, notoriously difficult to do using AMS, resulting in increased uncertainties in measurements and a reliance on knowledge of systematic effects. To mitigate these issues, various AMS facilities have sought to develop their own reference standards. Using a reference standard prepared for other forms of mass spectrometry, a series of AMS suitable standards was created through dilution with low-background natural uranium. The techniques used to produce and characterize these materials as well as analysis of them using AMS will be discussed.

236U↗
DOE OSTI2020

High-Brightness Continuous-Wave Electron Beams from Superconducting Radio-Frequency Photoemission Gun

Continuous-wave photoinjectors operating at high accelerating gradients promise to revolutionize many areas of science and applications. They can establish the basis for a new generation of monochromatic x-ray free electron lasers, high-brightness hadron beams, or a new generation of microchip production. In this Letter we report on the record-performing superconducting rf electron gun with CsK 2 Sb photocathode. The gun is generating high charge electron bunches (up to 10 nC / bunch ) and low transverse emittances, while operating for months with a single photocathode. As such, this achievement opens a new era in generating high-power beams with a very high average brightness.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Rapid assessment of interfacial stabilization mechanisms of metastable precipitates to accelerate high-temperature Al-alloy development

Precipitate strengthened high-temperature alloys are currently used in safety-critical applications. Understanding precipitate stability and solute segregation mechanisms at high temperatures is key to designing high-strength alloys. Rapid in-situ approaches, therefore, are pivotal in accelerating the alloy design process. Hereby using the test case of a promising high-temperature Al-Cu-Mn-Zr alloy, we demonstrate the value of in-situ atom probe tomography coupled with in-situ transmission electron microscopy to reveal atomic-scale mechanisms that lead to the emergence of non-equilibrium solute segregation. Mn and Zr segregation at strengthening precipitate(θ’)-matrix interface increases the kinetic barrier for phase transformation thus retaining high-temperature strength.

36 MATERIALS SCIENCE↗
NASA NTRS1996

Particle Acceleration in Active Galactic Nuclei

The investigation of stochastic particle acceleration through resonant interactions with plasma waves that populate the magnetosphere surrounding an accreting black hole is presented. Stochastic acceleration has been successfully applied to the problem of ion and electron energization in solar flares, and is capable of accounting for a wide range of both neutral and charged particle emissions. It is also a component in diffusive shock acceleration, since pitch-angle scattering (which is necessary for multiple shock crossings) is accompanied by diffusion in momentum space, which in turn yields a net systematic energy gain; however, stochastic energization will dominate the first-order shock process only in certain parameter regimes. Although stochastic acceleration has been applied to particle energization in the lobes of radio galaxies, its application to the central regions of AGNs (active galactic nuclei) has only recently been considered, but not in detail. We proposed to systematically investigate the plasma processes responsible for stochastic particle acceleration in black hole magnetospheres along with the energy-loss processes which impede particle energization. To this end, we calculated acceleration rates and escape time scales for protons and electrons resonating with Alfven waves, and for electrons resonating with whistlers. We also considered the "hot" topic of gamma-ray line emission from the Orion complex. We proposed that the observed gamma-ray lines are produced by energetic ions that are stochastically accelerated by cascading Alfven waves in the accretion plasma near a black hole. Related research papers that were published in journals are listed.

Miller, James A.↗
DOE OSTI2019

Development of High-Q SRF Structures by Nitrogen Doping for Superconducting Electron Linacs

One of the most exciting recent developments in superconducting RF technology has been the discovery of a nitrogen-doping process which can reliably increase the superconducting quality factor of niobium resonators well above 10 10 at high frequency (>1 GHz). This process is now well demonstrated in particular for TESLA-style 9-cell cavities at 1.3 GHz. Nitrogen doping can significantly increase efficiency of the cavities as well as the cryogenic efficiency accelerators, and allowing significant capital cost reductions. This project will expand the application of nitrogen doping into the low-frequency regime and investigate and quantify effects on the quality factor in low frequency superconducting structures from the nitrogen doping process.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Radioisotope production using lasers: From basic science to applications

The discovery of chirped pulse amplification has led to great improvements in laser technology, enabling energetic laser beams to be compressed to pulse durations of tens of femtoseconds and focused to a few micrometers. Protons with energies of tens of MeV can be accelerated using, for instance, target normal sheath acceleration and focused on secondary targets. Under such conditions, nuclear reactions can occur, with the production of radioisotopes suitable for medical application. The use of high-repetition lasers to produce such isotopes is competitive with conventional methods mostly based on accelerators. In this paper, we study the production of 67Cu, 63Zn, 18F, and 11C, which are currently used in positron emission tomography and other applications. At the same time, we study the reactions 10B(p,α)7Be and 70Zn(p,4n)67Ga to put further constraints on the proton distributions at different angles, as well as the reaction 11B(p,α)8Be relevant for energy production. The experiment was performed at the 1 PW laser facility at Vega III in Salamanca, Spain. Angular distributions of radioisotopes in the forward (with respect to the laser direction) and backward directions were measured using a high purity germanium detector. Our results are in reasonable agreement with numerical estimates obtained following the approach of Kimura and Bonasera [Nucl. Instrum. Methods Phys. Res., Sect. A 637, 164–170 (2011)].

11C↗
DOE OSTI2023

Sustainable materials acceleration platform reveals stable and efficient wide-bandgap metal halide perovskite alloys

The vast chemical space of emerging semiconductors, like metal halide perovskites, and their varied requirements for semiconductor applications have rendered trial-and-error environmentally unsustainable. Here, in this work, we demonstrate RoboMapper, a materials acceleration platform (MAP), that achieves 10-fold research acceleration by formulating and palletizing semiconductors on a chip, thereby allowing high-throughput (HT) measurements to generate quantitative structure-property relationships (QSPRs) considerably more efficiently and sustainably. We leverage the RoboMapper to construct QSPR maps for the mixed ion FA 1-y Cs y Pb(I 1-x Br x ) 3 halide perovskite in terms of structure, bandgap, and photostability with respect to its composition. We identify wide-bandgap alloys suitable for perovskite-Si hybrid tandem solar cells exhibiting a pure cubic perovskite phase with favorable defect chemistry while achieving superior stability at the target bandgap of ~1.7 eV. RoboMapper’s palletization strategy reduces environmental impacts of data generation in materials research by more than an order of magnitude, paving the way for sustainable data-driven materials research.

36 MATERIALS SCIENCE↗
NASA NTRS1992

Improved HIRAP flight calibration technique

A method of removing non-aerodynamic acceleration signals and calibrating the High Resolution Accelerometer Package (HiRAP) has been developed and improved. Twelve HiRAP mission data sets have been analyzed applying the improved in-flight calibration technique. The application of flight calibration factors to the data sets from these missions produced calibrated acceleration levels within +/- 5.7 micro-g of zero during a time in-flight when the acceleration level was known to be less than 1 g. To validate the current in-flight calibration technique, the atmospheric density results, specifically the normal-to-axial density ratios, have been compared with the analysis results obtained with the previous in-flight calibration technique. This comparison shows an improvement (up to 12.4 percent per flight) in the density ratio when the updated in-flight calibration technique is used.

Moats, Christina D.↗
DOE OSTI2025

Acceleration of Thermochemistry Solves in MOOSE and Pronghorn

This work focuses on the development and implementation of strategies to accelerate thermochemical calculations within MOOSE-based multiphysics simulations, particularly for applications in MSRs. We highlight the inherent complexity of nuclear materials, which require a multiscale approach to accurately model their behavior across various physical domains, including mechanical, chemical, and thermal phenomena. Thermochemical equilibrium calculations are crucial for predicting material properties and enhancing the fidelity of these simulations. The integration of Thermochimica, a Gibbs energy minimizer, into MOOSE allows for the direct minimization of Gibbs energy at every point on the mesh. However, the computational cost of such integration is significant. To address this, we explored acceleration strategies such as multi-threading support and the use of a thermodynamic ValueCache to reduce redundant calculations. Additionally, we investigated modifications to Thermochimica to enable phase constraints and improve its coupling with phase-field models, which are essential for simulating microstructural evolution and corrosion in MSR. These efforts aim to optimize the computational efficiency and accuracy of multiphysics simulations, thereby supporting the development of reliable and efficient nuclear materials for next-generation reactor technologies.

36 - MATERIALS SCIENCE↗
DOE OSTI2023

HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations

Publicly available collections of drug-like molecules have grown to comprise tens of billions of compounds due to advances in combinatorial chemistry. Traditional methods for identifying "hit" molecules from a large collection of potential drug-like candidates have relied on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have the major drawback that they require exceptional computing capabilities for even relatively small collections of molecules. Hyperdimensional Computing (HDC) is a recently-proposed learning paradigm that represents data with high-dimension binary vectors; this allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas. We consider existing HDC approaches for molecular property classification and introduce two novel encodings of a commonly-used molecular representation, the extended connectivity fingerprint (ECFP). We show that HDC-based inference methods are as much as 91 times more efficient than traditional machine learning methods, and achieve an acceleration of nearly nine orders of magnitude compared to molecular docking. Our results show that HDC accelerated methods retain competitive accuracy on a number of well-studied tasks such as molecular property predictions using the MoleculeNet dataset, and bind/no-bind activity classification using the DUD-E and LIT-PCBA datasets. Our work thus motivates further investigation into molecular representation learning to develop ultraefficient pre-screening tools.

Jones, William↗
DOE OSTI2020

Strong surface magnetic field generation in relativistic short pulse laser–plasma interaction with an applied seed magnetic field

Abstract While plasma often behaves diamagnetically, we demonstrate that the laser irradiation of a thin opaque target with an embedded target-transverse seed magnetic field B seed can trigger the generation of an order-of-magnitude stronger magnetic field with opposite sign at the target surface. Strong surface field generation occurs when the laser pulse is relativistically intense and results from the currents associated with the cyclotron rotation of laser-heated electrons transiting through the target and the compensating current of cold electrons. We derive a predictive scaling for this surface field generation, B gen ∼ −2 πB seed Δ x / λ 0 (in the large spot size limit), where Δ x is the target thickness and λ 0 is the laser wavelength, and conduct 1D and 2D particle-in-cell simulations to confirm its applicability over a wide range of conditions. We additionally demonstrate that both the seed and surface-generated magnetic fields can have a strong impact on application-relevant plasma dynamics, for example substantially altering the overall expansion and ion acceleration from a μ m-thick laser-irradiated target with a kilotesla-level seed magnetic field.

Weichman, K. (ORCID:0000000234877922)↗
NASA NTRS1984

Assessing photovoltaic module life from long-term environmental tests

An assessment is made of results obtained by an experimental program having as its aim the identification of critical temperature-humidity bias degradation mechanisms in solar cell modules intended to have an operating lifetime of the order of 20-30 years. These mechanisms are studied in the context of conditions encountered over the course of long term operation in various sites in the U.S. Accelerated tests for the assessment of product lifetime entail the development of a correlation between test and application conditions; this has been accomplished through an analytical procedure for the correlation of time-varying field exposures to constant-stress accelerated environments.

Otth, D. H.↗
DOE OSTI2024

Rapidly convergent quantum Monte Carlo using a Chebyshev projector

The multireference coupled-cluster Monte Carlo (MR-CCMC) algorithm is a determinant-based quantum Monte Carlo (QMC) algorithm that is conceptually similar to Full Configuration Interaction QMC (FCIQMC). It has been shown to offer a balanced treatment of both static and dynamic correlation while retaining polynomial scaling, although application to large systems with significant strong correlation remained impractical. In this paper, we document recent algorithmic advances that enable rapid convergence and a more black-box approach to the multireference problem. These include a logarithmically scaling metric-tree-based excitation acceptance algorithm to search for determinants connected to the reference space at the desired excitation level and a symmetry-screening procedure for the reference space. We show that, for moderately sized reference spaces, the new search algorithm brings about an approximately 8-fold acceleration of one MR-CCMC iteration, while the symmetry screening procedure reduces the number of active reference space determinants with essentially no loss of accuracy. We also introduce a stochastic implementation of an approximate wall projector, which is the infinite imaginary time limit of the exponential projector, using a truncated expansion of the wall function in Chebyshev polynomials. Notably, this wall-Chebyshev projector can be used to accelerate any projector-based QMC algorithm. We show that it requires significantly fewer applications of the Hamiltonian to achieve the same statistical convergence. We benchmark these acceleration methods on the beryllium and carbon dimers, using initiator FCIQMC and MR-CCMC with basis sets up to cc-pVQZ quality.

Zhao, Zijun↗
DOE OSTI2024

Multichannel meta-imagers for accelerating machine vision

Rapid developments in machine vision technology have impacted a variety of applications, such as medical devices and autonomous driving systems. These achievements, however, typically necessitate digital neural networks with the downside of heavy computational requirements and consequent high energy consumption. As a result, real-time decision-making is hindered when computational resources are not readily accessible. Here we report a meta-imager designed to work together with a digital back end to offload computationally expensive convolution operations into high-speed, low-power optics. Further, in this architecture, metasurfaces enable both angle and polarization multiplexing to create multiple information channels that perform positively and negatively valued convolution operations in a single shot. We use our meta-imager for object classification, achieving 98.6% accuracy in handwritten digits and 88.8% accuracy in fashion images. Owing to its compactness, high speed and low power consumption, our approach could find a wide range of applications in artificial intelligence and machine vision applications.

47 OTHER INSTRUMENTATION↗
NASA NTRS1990

Efficient detection and signal parameter estimation with application to high dynamic GPS receiver

In a system for deriving position, velocity, and acceleration information from a received signal emitted from an object to be tracked wherein the signal comprises a carrier signal phase modulated by unknown binary data and experiencing very high Doppler and Doppler rate, this invention provides combined estimation/detection apparatus for simultaneously detecting data bits and obtaining estimates of signal parameters such as carrier phase and frequency related to receiver dynamics in a sequential manner. There is a first stage for obtaining estimates of the signal parameters related to phase and frequency in the vicinity of possible data transitions on the basis of measurements obtained within a current data bit. A second stage uses the estimates from the first stage to decide whether or not a data transition has actually occurred. There is a third stage for removing data modulation from the received signal when a data transition has occurred and a fourth stage for using the received signal with data modulation removed therefrom to update global parameters which are dependent only upon receiver dynamics and independent of data modulation. Finally, there is a fifth stage for using the global parameters to determine the position, velocity, and acceleration of the object.

Kumar, Rajendra↗
DOE OSTI2024

Gridded RF Gun Design for SRF Linac Applications

The thermionic gridded gun is described which generates short electron bunches for further acceleration in a Nb3Sn conduction cooled SRF linac. The gun is built into the first cavity of the 250 keV injector [1] for the 20 kW, 10 MeV, 1.3 GHz CW conduction cooled one-cavity linac. The beam current is 2 mA. The RF gun design is presented as well as the results of perveance measurements, which are in a good agreement with the design parameters. The design of the RF resonator of the gun is presented also. The beam generated by the gun is matched to the injector to provide lack of current interception in the SRF cavity.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Machine-Learning Accelerated Studies of Materials with High Performance and Edge Computing

In the studies of materials, experimental measurements often serve as the reference to verify physics theory and modeling; while theory and modeling provide a fundamental understanding of the physics and principles behind. However, the interactions and cross validation between them have long been a challenge even to-date. Not only that inferring a physics model from experimental data is itself a difficult inverse problem, another major challenge is the orders-of-magnitude longer wall-clock time required to carry out high-fidelity computer modeling to match the timescale of experiments. We envisage that by combining high performance computing, data science, and edge computing technology, the current predicament can be alleviated, and a new paradigm of data-driven physics research will open up. For example, we can accelerate computer simulations by first performing the large-scale modeling on high performance computers and train a machine-learned surrogate model. This computationally inexpensive surrogate model can then be transferred to the computing units residing closely to the experimental facilities to perform high-fidelity simulations at a much higher throughout. The model will also be more amenable to analyzing and validating experimental observations in comparable time scales at a much lower computational cost. Further integration of these accelerated computer simulations with an outer machine learning loop can also inform and direct future experiments, while making the inverse problem of physics model inference more tractable. We will demonstrate a proof-of-concept by using a quantum Monte Carlo application, Dynamical Cluster Approximation (DCA++), to machine-learn a surrogate model and accelerate the study of quantum correlated materials.

Li, Ying Wai↗
DOE OSTI2025

Surrogate optimization of variational quantum circuits

Variational quantum eigensolvers are touted as a near-term algorithm capable of impacting many applications. However, the potential has not yet been realized, with few claims of quantum advantage and high resource estimates, especially due to the need for optimization in the presence of noise. Finding algorithms and methods to improve convergence is important to accelerate the capabilities of near-term hardware for VQE or more broad applications of hybrid methods in which optimization is required. To this goal, we look to use modern approaches developed in circuit simulations and stochastic classical optimization, which can be combined to form a surrogate optimization approach to quantum circuits. Using an approximate (classical CPU/GPU) state vector simulator as a surrogate model, we efficiently calculate an approximate Hessian, passed as an input for a quantum processing unit or exact circuit simulator. This method will lend itself well to parallelization across quantum processing units. We demonstrate the capabilities of such an approach with and without sampling noise and a proof-of-principle demonstration on a quantum processing unit utilizing 40 qubits.

Gustafson, Erik J. [RIACS, Mtn. View] (ORCID:00000↗