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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Autonomous sputter synthesis of thin film nitrides with composition controlled by Bayesian optimization of optical plasma emission

Autonomous experimentation has emerged as an efficient approach to accelerate the pace of material discovery. Although instruments for autonomous synthesis have become popular in molecular and polymer science, solution processing of hybrid materials, and nanoparticles, examples of autonomous tools for physical vapor deposition are scarce yet important for the semiconductor industry. Here, we report the design and implementation of an autonomous workflow for sputter deposition of thin films with controlled composition, leveraging a highly automated sputtering reactor custom-controlled by Python, optical emission spectroscopy (OES), and a Bayesian optimization algorithm. We modeled film composition, measured by x-ray fluorescence, as a linear function of plasma emission lines monitored during co-sputtering from elemental Zn and Ti targets in an N 2 and Ar atmosphere. A Bayesian control algorithm, informed by OES, navigates the space of sputtering power to fabricate films with user-defined compositions by minimizing the absolute error between desired and measured optical emission signals. We validated our approach by autonomously fabricating Zn x Ti 1-x N y films that deviate from the targeted cation composition by a relative ±3.5%, even for 15 nm thin films, demonstrating that the proposed approach can reliably synthesize thin films with a specific composition and minimal human interference. Moreover, the proposed method can be extended to more difficult synthesis experiments where plasma intensity lines depend non-linearly on pressure, or the elemental sticking coefficients strongly depend on the substrate temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Collective neutrino oscillations in three flavors on qubit and qutrit processors

Collective neutrino flavor oscillations are of primary importance in understanding the dynamic evolution of core-collapse supernovae and subsequent terrestrial detection, but also among the most challenging aspects of numerical simulations. This situation is complicated by the quantum many-body nature of the problem due to neutrino-neutrino interactions, which demands a quantum treatment. An additional complication is the presence of three flavors, which often is approximated by the electron flavor and a heavy lepton flavor. In this work, we provide both qubit and qutrit encodings for all three flavors, and develop optimized quantum circuits for the time evolution and analyze the Trotter error. We conclude our study with a hardware experiment of a system of two neutrinos with superconducting hardware: the IBM Torino device for qubits and Advanced Quantum Testbed device at the Lawrence Berkeley National Laboratory for qutrits. We find that error mitigation greatly helps in obtaining a signal consistent with simulations. Finally, while hardware results are comparable at this stage, we expect the qutrit setup to be more convenient for large-scale simulations since it does not suffer from probability leakage into nonphysical qubit space, unlike the qubit setup.

Neutrino oscillations↗
DOE OSTI2022

Automated Stellar Spectra Classification with Ensemble Convolutional Neural Network

Large sky survey telescopes have produced a tremendous amount of astronomical data, including spectra. Machine learning methods must be employed to automatically process the spectral data obtained by these telescopes. Classification of stellar spectra by applying deep learning is an important research direction for the automatic classification of high-dimensional celestial spectra. In this paper, a robust ensemble convolutional neural network (ECNN) was designed and applied to improve the classification accuracy of massive stellar spectra from the Sloan digital sky survey. We designed six classifiers which consist six different convolutional neural networks (CNN), respectively, to recognize the spectra in DR16. Then, according the cross-entropy testing error of the spectra at different signal-to-noise ratios, we integrate the results of different classifiers in an ensemble learning way to improve the effect of classification. The experimental result proved that our one-dimensional ECNN strategy could achieve 95.0% accuracy in the classification task of the stellar spectra, a level of accuracy that exceeds that of the classical principal component analysis and support vector machine model.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Charged Pion Production Explorations Using the NOvA Near Detector

Measurements of neutrino-nucleus interactions in the few-GeV region provide crucial inputs to the neutrino oscillation program being carried out by currently running experiments (NOvA, SBN, T2K), and next-generation experiments (DUNE, 2HK). This poster presents the status of a measurement using the high-intensity NuMI beam and the NOvA Near Detector of the flux-integrated differential cross-section in pion kinetic energy for $\nu_\mu + N \rightarrow \mu^- + \pi^\pm + X$, where X does not include any charged pions. This signal is sensitive to inelastic processes with Delta resonance production, which dominate at neutrino energies relevant for NOvA and DUNE. To overcome the difficulty of reconstructing the energy of charged pions in a lower-spatial-resolution detector, a novel analysis technique is used that incorporates a multi-template fit to extract the number of events for two signal categories that differ from one another by their purity and resolution. The two signal categories are combined leveraging their complementary errors into a more informative cross section result.

Ewart, Erin [Indiana U.]↗
DOE OSTI2020

Mitigation of LEO Satellite Brightness and Trail Effects on the Rubin Observatory LSST

In this work, we report studies on the mitigation of optical effects of bright low-Earth-orbit (LEO) satellites on Vera C. Rubin Observatory and its Legacy Survey of Space and Time (LSST). These include options for pointing the telescope to avoid satellites, laboratory investigations of bright trails on the Rubin Observatory LSST camera sensors, algorithms for correcting image artifacts caused by bright trails, experiments on darkening SpaceX Starlink satellites, and ground-based follow-up observations. The original Starlink v0.9 satellites are g ~ 4.5 mag, and the initial experiment "DarkSat" is g ~ 6.1 mag. Future Starlink darkening plans may reach g ~ 7 mag, a brightness level that enables nonlinear image artifact correction to well below background noise. However, the satellite trails will still exist at a signal-to-noise ratio ~ 100, generating systematic errors that may impact data analysis and limit some science. For the Rubin Observatory 8.4 m mirror and a satellite at 550 km, the full width at half maximum of the trail is about 3'' as the result of an out-of-focus effect, which helps avoid saturation by decreasing the peak surface brightness of the trail. For 48,000 LEOsats of apparent magnitude 4.5, about 1% of pixels in LSST nautical twilight images would need to be masked.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

End-to-End Encryption for Cyber-Physical Systems Using Fully Homomorphic Encryption

Cyber-physical systems require reliable, safe, and secure control of critical infrastructure, combining computational and networking capabilities, which heighten the risk of cyber attacks. These attacks can disrupt the physical process, causing unforeseen consequences. One solution is the use of fully homomorphic encryption (FHE) to protect the control loop, allowing for secure computations and communications without compromising signal and control system privacy. The challenge with FHE, however, is its requirement for inputs to be integers. This presentation introduces a modified Learning With Errors (LWE) FHE approach that encodes control system dynamics and signals into integers. Our proposed scheme leverages a generalized LWE encoding function and modifies the Gentry-Sahai-Waters gadget decomposition tool to encrypt the control system. Using the modified LWE scheme, we formalize a fully encrypted control system, supported by simulated results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Ultra-Long Distance BOTDA Sensor System Employing Hybrid Amplification and Advanced Noise Reduction Techniques

Brillouin Optical Time Domain Analysis (BOTDA) sensor system play a pivotal role in distributed sensing, which enables precise measurements of strain and temperature across extensive fiber lengths. Nonetheless, challenges emerge as distances grow due to signal attenuation and noise interference resulting in measurement errors. This research offers a comprehensive strategy to extend the sensing range of BOTDA systems beyond 10’s of kilometers while maintaining high spatial resolutions. Such enhanced sensing is realized through the integration of distributed Raman amplification, inline amplification using erbium-doped fiber amplifiers (EDFA), and advanced noise reduction techniques. Leveraging inherent redundancy in measured data as a function of frequency and fiber distance, the non-local means (NLM) filter removes noise while preserving essential physical information. This approach proves particularly advantageous in BOTDA systems, where accurate measurement of Brillouin scattering signals is paramount for long-range sensing, while concurrently safeguarding high spatial resolutions. In summary, this research has shown a holistic exploration of extending BOTDA's distance sensing capabilities up to 150 km with spatial resolutions of 8 meters.

Bhatta, Hari↗
DOE OSTI2022

Vacuum-ultraviolet absorption cross-sections of functionalized four-carbon species

Absorption cross-sections were measured in the vacuum ultraviolet from 5.17 – 9.92 eV using differential absorption spectroscopy for 33 four-carbon species: n-butane, trans-2-butene, cis-2-butene, butanal, butyric acid, ethyloxirane, trans-2,3-dimethyloxirane, cis-2,3-dimethyloxirane, 2-methyloxetane, 2,2'-bioxirane, vinyl oxirane, 3,4-epoxybutan-2-one, diacetyl, diethyl ether, ethyl vinyl ether, vinyl acetate, acetic anhydride, 1-butanol, 3-buten-1-ol, 1–hydroxy-butan-2-one, 1–hydroxy-butan-3-one, 2,3-epoxybutan-1-ol, 3,4-epoxybutan-1-ol, 2-oxetanemethanol, butanone, methyl vinyl ketone, 4H-1,3-dioxine, allyl formate, 2-oxobutanal, 4-hydroxybutanal, 2-methyloxetan-3-one, cis–but-2-en-1-ol, and trans-2–but-enal. Uncertainties were quantified in all cases by accounting for errors in gas-phase concentration, experimental repeatability, and signal-to-noise ratio as a function of photon energy. With the exception of 2-oxobutanal, which is reported with an uncertainty of 10%, convolving the sources of error using the root-sum-square method led to an upper limit of 5% uncertainty above the detection limit, which is largely attributable to chemical purity. Here, the primary objective of the present work is to provide absolute cross-sections along with quantified uncertainty limits. The majority of the absorption spectra, which reflect electronic transitions such as σ → σ* and n → σ*, are reported for the first time and provide insight into fundamental chemical physics, such as vibrational band structure and Rydberg transitions. The quantitative spectra in the present work facilitates the discovery of chemical intermediates that support improvement in the accuracy of computational models for low-temperature combustion and atmospheric chemistry.

biofuels↗
DOE OSTI2022

Early arrival waveform inversion using data uncertainties and matching filters with application to near-surface seismic refraction data

We develop an early arrival waveform inversion (EAWI) technique for high-resolution near-surface velocity estimation by iteratively updating the P-wave velocity model to minimize the difference between the observed and calculated seismic refraction data. Traditional EAWI uses a least-squares penalty function and an acoustic forward-modeling engine. Conventional least-squares error is sensitive to data with low signal-to-noise ratio (S/N) and iterations of EAWI stop at a local-minimum data misfit or at the preassigned maximum number of iterations. These stopping criteria can result in overfitting the data. In addition, fitting the elastic field data with an acoustic modeling engine can introduce artifacts in velocity estimation, especially in land data with significant elastic effects. To overcome these challenges, we develop a robust EAWI (REAWI) method by (1) incorporating the data uncertainties into the penalty function and (2) mitigating the elastic effects using a matching filter workflow. The data uncertainties are estimated from waveform reciprocal errors. When full-waveform reciprocity is not available, trace interpolation is applied. The proposed method prevents closely fitting data with low S/N, avoids overall overfitting by stopping the iterations when a normalized chi-square ([Formula: see text]) waveform misfit of one is achieved, and is less affected by elastic effects. Numerical examples and application to near-surface refraction data at a groundwater contamination site suggest that the final REAWI models are more accurate than the corresponding EAWI models, at the same level of misfit. This is the first known application of a matching filter workflow to real land data. The final REAWI models satisfy an appropriate misfit between the real data and predicted elastic P-wave data, making this approach in this respect equivalent to elastic waveform inversion. We also develop a method to analyze model constraint by examining the energy of the wavefield Fréchet derivative thereby avoiding the influence of the data residuals in traditional Fréchet kernels.

Geochemistry & Geophysics↗
DOE OSTI2024

Redshift-dependent RSD bias from intrinsic alignment with DESI Year 1 spectra

ABSTRACT We estimate the redshift-dependent, anisotropic clustering signal in the Dark Energy Spectroscopic Instrument (DESI) Year 1 Survey created by tidal alignments of Luminous Red Galaxies (LRGs) and a selection-induced galaxy orientation bias. To this end, we measured the correlation between LRG shapes and the tidal field with DESI’s Year 1 redshifts, as traced by LRGs and Emission-Line Galaxies. We also estimate the galaxy orientation bias of LRGs caused by DESI’s aperture-based selection, and find it to increase by a factor of seven between redshifts 0.4−1.1 due to redder, fainter galaxies falling closer to DESI’s imaging selection cuts. These effects combine to dampen measurements of the quadrupole of the correlation function (ξ2) caused by structure growth on scales of 10–80 h−1 Mpc by about 0.15 per cent for low redshifts (0.4 < z < 0.6) and 0.8 per cent for high (0.8 < z < 1.1), a significant fraction of DESI’s error budget. We provide estimates of the ξ2 signal created by intrinsic alignments that can be used to correct this effect, which is necessary to meet DESI’s forecasted precision on measuring the growth rate of structure. While imaging quality varies across DESI’s footprint, we find no significant difference in this effect between imaging regions in the Legacy Imaging Survey.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Machine learning for continuous quantum error correction on superconducting qubits

Abstract Continuous quantum error correction has been found to have certain advantages over discrete quantum error correction, such as a reduction in hardware resources and the elimination of error mechanisms introduced by having entangling gates and ancilla qubits. We propose a machine learning algorithm for continuous quantum error correction that is based on the use of a recurrent neural network to identify bit-flip errors from continuous noisy syndrome measurements. The algorithm is designed to operate on measurement signals deviating from the ideal behavior in which the mean value corresponds to a code syndrome value and the measurement has white noise. We analyze continuous measurements taken from a superconducting architecture using three transmon qubits to identify three significant practical examples of non-ideal behavior, namely auto-correlation at temporal short lags, transient syndrome dynamics after each bit-flip, and drift in the steady-state syndrome values over the course of many experiments. Based on these real-world imperfections, we generate synthetic measurement signals from which to train the recurrent neural network, and then test its proficiency when implementing active error correction, comparing this with a traditional double threshold scheme and a discrete Bayesian classifier. The results show that our machine learning protocol is able to outperform the double threshold protocol across all tests, achieving a final state fidelity comparable to the discrete Bayesian classifier.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

A Systems Approach to Estimating the Uncertainty Limits of X-Ray Radiographic Metrology

Micro- and nanomanufacturing capabilities have rapidly expanded over the past decade to include complex three-dimensional (3D) structure fabrication; however, the metrology required to accurately assess these processes via part inspection and characterization has struggled to keep pace. X-ray computed tomography (CT) is considered an ideal candidate for providing the critically needed metrology on the smallest scales, especially internal features, or inaccessible regions. X-ray CT supporting micro- and nanomanufacturing often push against the poorly understood resolution and variation limits inherent to the machines, which can distort or hide fine structures. In this study, we have developed and experimentally verify a comprehensive analytical uncertainty propagation signal variation flow graph (SVFG) model for X-ray radiography in this work to better understand resolution and image variability limits on the small scale. The SVFG approach captures, quantifies, and predicts variations occurring in the system that limit metrology capabilities, particularly in the micro/nanodomain. This work is the first step to achieving full uncertainty modeling of CT reconstructions and provides insight into improving X-ray attenuation imaging systems. The SVFG methodology framework is applied to generate a complete basis set of functions describing the major sources of variation in radiographs. Five models are identified, covering variation in energy, intensity, length, blur, and position. Radiographic system experiments are defined to measure the parameters required by the SVFGs. Best practices are identified for these measurements. The SVFG models are confirmed via direct measurement of variation to predict variation within 30% on average.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Error Correction Decoding for Underwater Acoustic Tag Detection

This paper presents a decoding method applied to a set of underwater acoustic tags that employ very short signal beacons periodically transmitted. The tags use the CRC8- Dallas linear block code for error detection originally. Though the CRC8-Dallas is quite weak, syndrome-based error correction is applied to correct the decoding errors. Meanwhile, multi-channel array receivers are utilized to find the common decodes of the tag IDs, and the list of the true tag IDs is used to test the detection accuracy and false alarm rate. The proposed syndrome decoding method is applied to over 2 million files recorded in field experiments. The results demonstrate that the syndrome method yields around 9.85% more true positive decodes at the cost of a 2 - 5 times increase in false positive rates. Further insights are also gained to enable future improvement of the detection algorithms

Hall, Joseph↗
DOE OSTI2022

Estimating Energy Market Schedules using Historical Price Data

The global climate crisis is expected to reshape the energy generation landscape in the coming decades. Increasing integration of non-dispatchable renewable energy resources into energy infrastructures and markets creates uncertainty as well as new opportunities for flexible energy systems. To conduct proper economic evaluation of flexible energy systems, such as integrated energy systems (IES), advancements in modelling of market interactions, such as bidding, is crucial. This work presents a shortcut algorithm which uses two mixed integer linear programs to compute dispatch schedules (e.g., hourly power production targets) that are constrained by the resource's bid information and characteristics (e.g., minimum up and down times) based on historical locational marginal price (LMP) data. The proposed algorithm is approximately 100 times faster and uses orders of magnitude less data than a full production cost model (PCM). We find the shortcut simulator recapitulates generator dispatch signals for the Prescient PCM with approximately 4% error for the RTS-GMLC test system.

electricity generation↗
DOE OSTI2021

Constrained non-negative matrix factorization enabling real-time insights of in situ and high-throughput experiments

Non-negative matrix factorization (NMF) is an appealing class of methods for performing unsupervised learning on streaming spectral data, particularly in time-sensitive applications such as in situ characterization of materials. These methods seek to decompose a dataset into a small number of components and weights that can compactly represent the underlying signal while effectively reconstructing the observations with minimal error. However, canonical NMF methods have no underlying requirement that the reconstruction uses components or weights that are representative of the true physical processes. In this work, we demonstrate how constraining a subset of the NMF weights or components as rigid priors, provided as known or assumed values, can provide significant improvement in revealing true underlying phenomena. We present a PyTorch-based method for efficiently applying constrained NMF and demonstrate its application to several synthetic examples. Our implementation allows an expert researcher-in-the-loop to provide and dynamically adjust the constraints during a live experiment involving streaming spectral data. Such interactive priors allow researchers to specify known or identified independent components, as well as functional expectations about the mixing or transitions between the components. We further demonstrate the application of this method to measured synchrotron x-ray total scattering data from in situ beamline experiments. In such a context, constrained NMF can result in a more interpretive and scientifically relevant decomposition than canonical NMF or other decomposition techniques. As a result, the details of the method are provided, along with general guidance for employing constrained NMF in the extraction of critical information and insights during time-sensitive experimental applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Intracardiac Electrical Imaging using the 12-lead ECG: A Machine Learning Approach using Synthetic Data

Current state-of-the-art techniques for non-invasive imaging of cardiac electrical phenomena require voltage recordings from dozens of different torso locations and anatomical models built from expensive medical diagnostic imaging procedures. Here this study aimed to assess if recent machine learning advances could alternatively reconstruct electroanatomical maps at clinically relevant resolutions using only the standard 12-lead electrocardiogram (ECG) as input. To that end, a computational study was conducted to generate a dataset of over 16000 detailed cardiac simulations, which was then used to train neural network (NN) architectures designed to exploit both spatial and temporal correlations in the ECG signal. Analysis over a validation set showed average errors in activation map reconstruction below 1.7 msec over 75 intracardiac locations. Furthermore, phenotypical patterns of activation and the morphology of the activation potential were correctly reconstructed. The approach offers opportunities to stratify patients non-invasively, both retrospectively and prospectively, using metrics otherwise only available through invasive clinical procedures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Evaluating Machine Learning-Based MRI Reconstruction Using Digital Image Quality Phantoms

Quantitative and objective evaluation tools are essential for assessing the performance of machine learning (ML)-based magnetic resonance imaging (MRI) reconstruction methods. However, the commonly used fidelity metrics, such as mean squared error (MSE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR), often fail to capture fundamental and clinically relevant MR image quality aspects. To address this, we propose evaluation of ML-based MRI reconstruction using digital image quality phantoms and automated evaluation methods. Our phantoms are based upon the American College of Radiology (ACR) large physical phantom but created in k-space to simulate their MR images, and they can vary in object size, signal-to-noise ratio, resolution, and image contrast. Our evaluation pipeline incorporates evaluation metrics of geometric accuracy, intensity uniformity, percentage ghosting, sharpness, signal-to-noise ratio, resolution, and low-contrast detectability. We demonstrate the utility of our proposed pipeline by assessing an example ML-based reconstruction model across various training and testing scenarios. The performance results indicate that training data acquired with a lower undersampling factor and coils of larger anatomical coverage yield a better performing model. The comprehensive and standardized pipeline introduced in this study can help to facilitate a better understanding of the performance and guide future development and advancement of ML-based reconstruction algorithms.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Estimating Energy Market Schedules Using Historical Price Data: Preprint

The global climate crisis is expected to reshape the energy generation landscape in the coming decades. Increasing integration of non-dispatchable renewable energy resources into energy infrastructures and markets increases uncertainty and creates new opportunities for flexible energy systems. To conduct proper economic evaluation of flexible energy systems, such as integrated energy systems (IES), advancements in modelling of market interactions, such as bidding, is crucial. This work presents a shortcut algorithm which uses two mixed integer linear programs to compute dispatch schedules (e.g., hourly power production targets) that are constrained by the resource's bid information and characteristics (e.g., minimum up and down times) based on historical locational marginal price (LMP) data. This is orders of magnitude less data than required for a market clearing calculation with a full production cost model (PCM). We find the shortcut simulator recapitulates generator dispatch signals for the Prescient PCM with approximately 4% error for the RTS-GMLC test system.

electricity generation↗