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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC Using Quantum Computer Simulators and Quantum Computer Hardware

Machine learning enjoys widespread success in High Energy Physics (HEP) analyses at LHC. However the ambitious HL-LHC program will require much more computing resources in the next two decades. Quantum computing may offer speed-up for HEP physics analyses at HL-LHC, and can be a new computational paradigm for big data analyses in High Energy Physics.We have successfully employed three methods (1) Variational Quantum Classifier (VQC) method, (2) Quantum Support Vector Machine Kernel (QSVM-kernel) method and (3) Quantum Neural Network (QNN) method for two LHC flagship analyses: ttH (Higgs production in association with two top quarks) and H->mumu (Higgs decay to two muons, the second generation fermions). We shall address the progressive improvements in performance from method (1) to method (3).We will present our experiences and results of a study on LHC High Energy Physics data analyses with IBM Quantum Simulator and Quantum Hardware (using IBM Qiskit framework), Google Quantum Simulator (using Google Cirq framework), and Amazon Quantum Simulator (using Amazon Braket cloud service). The work is in the context of a Qubit platform (a gate-model quantum computer). Taking into account the present limitation of hardware access, different quantum machine learning methods are studied on simulators and the results are compared with classical machine learning methods (BDT, classical Support Vector Machine and classical Neural Network). Furthermore, we do apply quantum machine learning on IBM quantum hardware to compare performance between quantum simulator and quantum hardware. The work is performed by an international and interdisciplinary collaboration with the Department of Physics and Department of Computer Sciences of University of Wisconsin, CERN Quantum Technology Initiative, IBM Research Zurich, IBM T.J. Watson Research Center, Fermilab Quantum Institute, BNL Computational Science Initiative, State University of New York at Stony Brook, and Quantum Computing and AI Research of Amazon Web Services. This work pioneers a close collaboration of academic institutions with industrial corporations in the High Energy Physics analyses effort. Though the size of event samples in future HL-LHC physics and the limited number of qubits pose some challenges to the Quantum Machine learning studies for High Energy Physics, more advanced quantum computers with larger number of qubits, reduced noise and improved running time (as envisioned by IBM and Google) may outperform classical machine learning in both classification power and in speed.Although the era of efficient quantum computing may still be years away, we have made promising progress and obtained preliminary results in applying quantum machine learning to High Energy Physics. A PROOF OF PRINCIPLE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1977

Hydrocarbon group type determination in jet fuels by high performance liquid chromatography

Results are given for the analysis of some jet and diesel fuel samples which were prepared from oil shale and coal syncrudes. Thirty-two samples of varying chemical composition and physical properties were obtained. Hydrocarbon types in these samples were determined by fluorescent indicator adsorption (FIA) analysis, and the results from three laboratories are presented and compared. Recently, rapid high performance liquid chromatography (HPLC) methods have been proposed for hydrocarbon group type analysis, with some suggestion for their use as a replacement of the FIA technique. Two of these methods were used to analyze some of the samples, and these results are also presented and compared. Two samples of petroleum-based Jet A fuel are similarly analyzed.

Antoine, A. C.↗
DOE OSTI2026

Improving statistical precision in Monte Carlo samples with negative weights via reweighting and uncertainty quantification

High statistical precision is critical for Monte Carlo (MC) samples in high energy physics and is degraded by negatively weighted events. This paper investigates a procedure to learn the relationship between the negative and positive weight distributions of any sample, allowing the reduction of statistical uncertainty by reweighting kinematically equivalent events with the same sign. A robust uncertainty quantification method is required for the practical application of such method. Two methods for the estimation of the reweighting uncertainty are developed: one at the event and another one at the final observable level. The latter method is strongly favored. The gains in statistical precision are then quantified. The method is demonstrated on Sherpa vector boson plus jets samples when using all generated events and when restricted to the signal region of a mock analysis. It is demonstrated to significantly reduce stochastic behavior in sparse MC samples while decreasing the overall uncertainty with a sufficiently well-known reweighting function.

Monte Carlo methods↗
DOE OSTI2021

WHOLESCALE Catalog of Rock Samples at San Emidio Nevada collected in January 2021

This submission contains information on thirty-six rock samples collected from San Emidio, Nevada during January, 2021 for Subtask 2.3 of the WHOLESCALE project. The following resources include a .zip of rock sample photos taken in the field, a .zip of rock sample photos taken in the laboratory at UW-Madison, and an excel catalog of rock samples with information on sample name, rock type, coordinates of sample location, structural measurements, field notes, observations for plug preparation (e.g., weathering, ability to be cut and cored), and rock descriptions. It should be noted that not every sample was photographed in the field. Names and descriptions of rock formation units are taken from Rhodes et al. (2011). The README.txt file is a description of this submission.

15 GEOTHERMAL ENERGY↗
NASA NTRS2024

Preliminary Analyses of Asteroid Bennu Samples Returned by NASA’s OSIRIS-REx Mission

NASA’s OSIRIS-REx mission returned ~122 g of pristine carbonaceous regolith from Bennu on September 24, 2023. The science team is performing analyses of the mineralogy, chemical and isotopic compositions, and physical properties of the samples. The returned samples consist of particles ranging up to 3.5 cm in size with angular and hummocky morphologies predominating. Reflectance spectra over the visible and infrared wavelengths collected during the spacecraft encounter contained features indicating abundant hydrated phyllosilicates, carbonates, magnetite, organic matter, and rare anhydrous silicates. Laboratory analyses confirmed these detections and identified several other major and minor phases including abundant FeNi sulfides, minor hydrated Mg-rich phosphates as encrustations and in veins, and presolar grains. The Bennu samples show evidence for extensive aqueous alteration and resemble the rare and chemically primitive CI carbonaceous chondrites and samples returned from Ryugu by JAXA’s Hayabusa2 mission. Samples of Bennu’s uppermost surface, collected by contact pads, help us to understand how recent impacts and irradiation processes have changed the optical properties of Bennu surface materials measured by remote sensing. The analyses thus far highlight the value of sample return by illustrating the pristine nature of the samples compared to meteorites that have been compromised and altered by exposure to the terrestrial environment.

Lindsay P Keller↗
DOE OSTI2025

A machine-learning approach to measure 3D sample properties from 2D Transmission Electron Microscopy images

Transmission Electron Microscopy (TEM) is a powerful tool for the characterization of materials at the nanoscale; however, its inherent two-dimensional (2D) nature poses significant challenges to accurately measure three-dimensional (3D) properties. We introduce a supervised machine-learning model that predicts 3D structural information, such as sample thickness and curvature, from a series of conventional 2D TEM images. The model, a U-Net convolutional neural network, is trained on a large synthetic dataset generated from dynamical diffraction simulations that model TEM’s complex, nonlinear image formation, accounting for sample thickness and curvature. This physically realistic framework enables exploration of a broad parameter space impractical to sample experimentally. We demonstrate that the trained model has accurate predictions for experimental single-crystal silicon samples, achieving performance comparable to established measurement techniques. This work highlights the critical role of robust, simulation-based training in overcoming the limitations of real-world imaging artifacts and inconsistent sample geometries. By integrating machine learning with numerical simulations, we offer an efficient and scalable framework for quantitative TEM analysis, paving the way for more sophisticated 3D characterization of complex materials.

Dynamical diffraction↗
NASA NTRS1983

The use of heterodyne speckle photogrammetry to measure high-temperature strain distributions

Thermal and mechanical strains have been measured on samples of a common material used in jet engine burner liners, which were heated from room temperature to 870 C and cooled back to 220 C, in a laboratory furnace. The physical geometry of the sample surface was recorded to select temperatures by means of a set of twelve single-exposure specklegrams. Sequential pairs of specklegrams were compared in a heterodyne interferometer which allowed high-precision measurement of differential displacements. Good speckle correlation was observed between the first and last specklegrams also, which showed the durability of the surface microstructure, and permitted a check on accumulated errors. Agreement with calculated thermal expansion was to within a few hundred microstrain over a range of fourteen thousand.

Stetson, K. A.↗
NASA NTRS1982

Laser speckle technique for burner liner strain measurements

Thermal and mechanical strains were measured on samples of a common material used in jet engine burner liners, which were heated from room temperature to 870 C and cooled back to 220 C, in a laboratory furnance. The physical geometry of the sample surface was recorded at selected temperatures by a set of 12 single exposure speckle-grams. Sequential pairs of specklegrams were compared in a heterodyne interferometer which give high precision measurement of differential displacements. Good speckle correlation between the first and last specklegrams is noted which allows a check on accumulate errors.

Stetson, K. A.↗
DOE OSTI2026

Crystal Growth of Quaternary RE 2 EuSi 2 S 8 ( RE = Ce–Nd, Sm, Gd, Tb) Using Flux-Assisted Boron Chalcogen Mixture (BCM) Method: Investigation of Magnetic and Luminescence Properties

A series of quaternary rare-earth containing thiosilicates with the general formula RE 2 EuSi 2 S 8 (RE = Ce–Nd, Sm, Gd, Tb) has been synthesized via the flux-assisted boron chalcogen mixture (BCM) crystal growth method. High-quality single crystals were obtained, and their crystal structures were determined by single-crystal X-ray diffraction. The RE 2 EuSi 2 S 8 series crystallizes in the trigonal system, adopting the space group R-3c. Polycrystalline samples were employed for physical property measurements, including magnetic susceptibility measurements, UV–visible diffuse reflectance, and photoluminescent response. Magnetic data of RE 2 EuSi 2 S 8 ( RE = Ce, Nd, and Gd) were collected over the 2–300 K temperature range. The samples were paramagnetic behavior with negative Weiss constants (θ W = −11.05, −10.55, and −1.35 K respectively). Their thermal stability was investigated using thermogravimetric analysis (TGA). Optical band gaps, estimated from diffuse reflectance spectra, were determined to be 2.2(1) eV for Ce 2 EuSi 2 S 8 , 1.8(1) eV for Nd 2 EuSi 2 S 8 , and 1.7(1) eV for Gd 2 EuSi 2 S 8 respectively. Finally, photoluminescence measurements were collected on Ce 2 EuSi 2 S 8 and Tb 2 EuSi 2 S 8 single crystals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2014

Nanophase Carbonates on Mars: Formation, Detection, and Implications

Despite having an atmosphere composed primarily of CO2 and evidence for abundant water in the past, carbonate minerals have only been discovered on Mars in small amounts in martian dust, in soils in the Northern Plains, and in outcrops of limited spatial extent. Recently, carbonates have been identified as the possible source of CO2 released during thermal analysis of material from an aeolian deposit named Rocknest and drilled sample from the sheepbed mudstone, both samples analyzed by the Mars Science Laboratory (MSL) in Gale Crater. Both the Phoenix lander and MSL carry thermal analysis instruments, the Thermal and Evolved Gas Analyzer (TEGA) on Phoenix and the Sample Analysis at Mars (SAM) instrument on MSL. While thermal analysis does not provide definitive mineralogy, it can detect volatile-bearing minerals present at very low abundance and the temperature profiles of evolved gases can reveal physical properties of the sample. For example, the decomposition temperature of volatilebearing minerals depends heavily on the particle size of the mineral.

Archer, P. D.↗
DOE OSTI2022

Descriptor Aided Bayesian Optimization for Many-Level Qualitative Variables With Materials Design Applications

Abstract Engineering design often involves qualitative and quantitative design variables, which requires systematic methods for the exploration of these mixed-variable design spaces. Expensive simulation techniques, such as those required to evaluate optimization objectives in materials design applications, constitute the main portion of the cost of the design process and underline the need for efficient search strategies—Bayesian optimization (BO) being one of the most widely adopted. Although recent developments in mixed-variable Bayesian optimization have shown promise, the effects of dimensionality of qualitative variables have not been well studied. High-dimensional qualitative variables, i.e., with many levels, impose a large design cost as they typically require a larger dataset to quantify the effect of each level on the optimization objective. We address this challenge by leveraging domain knowledge about underlying physical descriptors, which embody the physics of the underlying physical phenomena, to infer the effect of unobserved levels that have not been sampled yet. We show that physical descriptors can be intuitively embedded into the latent variable Gaussian process approach—a mixed-variable GP modeling technique—and used to selectively explore levels of qualitative variables in the Bayesian optimization framework. This physics-informed approach is particularly useful when one or more qualitative variables are high dimensional (many-level) and the modeling dataset is small, containing observations for only a subset of levels. Through a combination of mathematical test functions and materials design applications, our method is shown to be robust to certain types of incomplete domain knowledge and significantly reduces the design cost for problems with high-dimensional qualitative variables.

Engineering↗
DOE OSTI2021

Power Grid Reliability Estimation via Adaptive Importance Sampling

Electricity production currently generates approximately 25% of greenhouse gas emissions in the USA. Thus, increasing the amount of renewable energy is a key step to carbon neutrality. However, integrating a large amount of fluctuating renewable generation is a significant challenge for power grid operating and planning. Grid reliability, i.e., an ability to meet operational constraints under power fluctuations, is probably the most important of them. In this letter, we propose computationally efficient and accurate methods to estimate the probability of line overflow, i.e., reliability constraints violation, under a known distribution of renewable energy generation. To this end, we investigate an importance sampling approach, a flexible extension of Monte-Carlo methods, which adaptively changes the sampling distribution to generate more samples near the reliability boundary. The approach allows to estimate overload probability in real-time based only on a few dozens of random samples, compared to thousands required by the plain Monte-Carlo. Our study focuses on high voltage direct current power transmission grids with linear reliability constraints on power injections and line currents. Herein, we propose a novel theoretically justified physics-informed adaptive importance sampling algorithm and compare its performance to state-of-the-art methods on multiple IEEE power grid test cases.

power system faults↗
NASA NTRS1990

Probing the tides in interacting galaxy pairs

Detailed spectroscopic and imaging observations of colliding elliptical galaxies revealed unmistakable diagnostic signatures of the tidal interactions. It is possible to compare both the distorted luminosity distributions and the disturbed internal rotation profiles with numerical simulations in order to model the strength of the tidal gravitational field acting within a given pair of galaxies. Using the best-fit numerical model, one can then measure directly the mass of a specific interacting binary system. This technique applies to individual pairs and therefore complements the classical methods of measuring the masses of galaxy pairs in well-defined statistical samples. The 'personalized' modeling of galaxy pairs also permits the derivation of each binary's orbit, spatial orientation, and interaction timescale. Similarly, one can probe the tides in less-detailed observations of disturbed galaxies in order to estimate some of the physical parameters for larger samples of interacting galaxy pairs. These parameters are useful inputs to the more universal problems of (1) the galaxy merger rate, (2) the strength and duration of the driving forces behind tidally stimulated phenomena (e.g., starbursts and maybe quasi steller objects), and (3) the identification of long-lived signatures of interaction/merger events.

Borne, Kirk D.↗
DOE OSTI2023

Formation and evolution of carbonaceous asteroid Ryugu: Direct evidence from returned samples

Samples of the carbonaceous asteroid Ryugu were brought to Earth by the Hayabusa2 spacecraft. We analyzed 17 Ryugu samples measuring 1 to 8 millimeters. Carbon dioxide–bearing water inclusions are present within a pyrrhotite crystal, indicating that Ryugu’s parent asteroid formed in the outer Solar System. The samples contain low abundances of materials that formed at high temperatures, such as chondrules and calcium- and aluminum-rich inclusions. The samples are rich in phyllosilicates and carbonates, which formed through aqueous alteration reactions at low temperature, high pH, and water/rock ratios of <1 (by mass). Less altered fragments contain olivine, pyroxene, amorphous silicates, calcite, and phosphide. Numerical simulations, based on the mineralogical and physical properties of the samples, indicate that Ryugu’s parent body formed ~2 million years after the beginning of Solar System formation.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2014

Astromaterials Acquisition and Curation Office (KT) Overview

The Astromaterials Acquisition and Curation Office has the unique responsibility to curate NASA's extraterrestrial samples - from past and forthcoming missions - into the indefinite future. Currently, curation includes documentation, preservation, physical security, preparation, and distribution of samples from the Moon, asteroids, comets, the solar wind, and the planet Mars. Each of these sample sets has a unique history and comes from a unique environment. The curation laboratories and procedures developed over 40 years have proven both necessary and sufficient to serve the evolving needs of a worldwide research community. A new generation of sample return missions to destinations across the solar system is being planned and proposed. The curators are developing the tools and techniques to meet the challenges of these new samples. Extraterrestrial samples pose unique curation requirements. These samples were formed and exist under conditions strikingly different from those on the Earth's surface. Terrestrial contamination would destroy much of the scientific significance of extraterrestrial materials. To preserve the research value of these precious samples, contamination must be minimized, understood, and documented. In addition, the samples must be preserved - as far as possible - from physical and chemical alteration. The elaborate curation facilities at JSC were designed and constructed, and have been operated for many years, to keep sample contamination and alteration to a minimum. Currently, JSC curates seven collections of extraterrestrial samples: (a)) Lunar rocks and soils collected by the Apollo astronauts, (b) Meteorites collected on dedicated expeditions to Antarctica, (c) Cosmic dust collected by high-altitude NASA aircraft,t (d) Solar wind atoms collected by the Genesis spacecraft, (e) Comet particles collected by the Stardust spacecraft, (f) Interstellar dust particles collected by the Stardust spacecraft, and (g) Asteroid soil particles collected by the Japan Aerospace Exploration Agency (JAXA) Hayabusa spacecraft Each of these sample sets has a unique history and comes from a unique environment. We have developed specialized laboratories and practices over many years to preserve and protect the samples, not only for current research but for studies that may be carried out in the indefinite future.

Allen, Carlton↗
NASA NTRS1976

Diffusive isothermal partitioning in a layered medium with geologic applications

The diffusive isothermal partitioning of solute in a layered two-phase material has been analyzed to help elucidate the phenomenon of solute partitioning in multiphase lunar and terrestrial materials and to estimate the cooling history of samples. After reviewing the physical chemistry of partitioning and the case of an infinite one-dimensional diffusion couple, we solve in analytic form the case of a finite one-dimensional couple. The solution can be used to estimate cooling histories or to interpret laboratory experiments on partitioning. A sample calculation is included.

Hopper, R. W.↗
DOE OSTI2022

Classical Simulation of Boson Sampling Based on Graph Structure

Boson sampling is a fundamentally and practically important task that can be used to demonstrate quantum supremacy using noisy intermediate-scale quantum devices. In this Letter, we present classical sampling algorithms for single-photon and Gaussian input states that take advantage of a graph structure of a linear-optical circuit. The algorithms’ complexity grows as so-called treewidth, which is closely related to the connectivity of a given linear-optical circuit. Using the algorithms, we study approximated simulations for local Haar-random linear-optical circuits. For equally spaced initial sources, we show that, when the circuit depth is less than the quadratic in the lattice spacing, the efficient simulation is possible with an exponentially small error. Notably, right after this depth, photons start to interfere each other and the algorithms’ complexity becomes subexponential in the number of sources, implying that there is a sharp transition of its complexity. Finally, when a circuit is sufficiently deep enough for photons to typically propagate to all modes, the complexity becomes exponential as generic sampling algorithms. We numerically implement a likelihood test with a recent Gaussian boson sampling experiment and show that the treewidth-based algorithm with a limited treewidth renders a larger likelihood than the experimental data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1990

Thermal and electric properties of Nd(1.85)Ce(0.15)CuO(4-y) and Pr(1.85)Ce(0.15)CuO(4-y)

Electric resistivity, magnetic susceptibility, thermoelectric power, and Hall coefficient of Nd(1.85)Ce(0.15)CuO(4-y) and Pr(1.85)Ce(0.15)CuO(4-y) whose onset temperature of the superconductivity are 24 K and 23 K were measured. Experimental results show many interesting features. In particular, the Hall coefficients are negative and relatively flat as a function of temperature. However, the temperature dependence of the thermoelectric power (TEP) for these two samples shows the positive sign for both samples in contrast to the previous results. Moreover TEP for both samples remains flat in the normal state below 250 K, but decreases rapidly above 250 K. TEP of only Pr(1.85)Ce(0.15)CuO(4-y) shows a peak near 50 K. Finally onset temperatures of sudden drop of TEP are higher than those of resistance drop. The physical properties of these samples produced at different conditions such as different heat treatment temperatures, atmospheres were also measured. TEP and resistance measurement show that oxygen deficiency is essential to produce better superconducting samples. Correlation between TEP and superconductivity for these different samples will be discussed.

Lim, Z. S.↗