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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 OSTI2021

Evaluation of Low Cost Infrasound Sensor Packages

Sandia National Laboratories has tested and evaluated the performance of the following five models of low-cost infrasound sensors and sensor packages: Camas microphone, Gem Infrasound Logger, InfraBSU sensor, Raspberry Boom, and the Samsung S10 smartphone utilizing the Redvox app. The purpose of this infrasound sensor evaluation is to measure the performance characteristics in such areas as power consumption, sensitivity, self-noise, dynamic range, response, passband, linearity, sensitivity variation due to changes in static pressure and temperature, and sensitivity to vertical acceleration. The infrasound monitoring community has leveraged such sensors and integrated packages in novel ways; better understanding the performance of these units serves the geophysical monitoring community.

42 ENGINEERING↗
DOE OSTI2023

Likely accelerated weakening of Atlantic overturning circulation emerges in optimal salinity fingerprint

The long-term response of the Atlantic meridional overturning circulation (AMOC) to anthropogenic forcing has been difficult to detect from the short direct measurements available due to strong interdecadal variability. Here, we present observational and modeling evidence for a likely accelerated weakening of the AMOC since the 1980s under the combined forcing of anthropogenic greenhouse gases and aerosols. This likely accelerated AMOC weakening signal can be detected in the AMOC fingerprint of salinity pileup remotely in the South Atlantic, but not in the classic warming hole fingerprint locally in the North Atlantic, because the latter is contaminated by the “noise” of interdecadal variability. Our optimal salinity fingerprint retains much of the signal of the long-term AMOC trend response to anthropogenic forcing, while dynamically filtering out shorter climate variability. Given the ongoing anthropogenic forcing, our study indicates a potential further acceleration of AMOC weakening with associated climate impacts in the coming decades.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Fermilab PIP-II Master Oscillator and Precision Reference Line Design and Construction

The phase averaging reference line system pro- vides the RF phase reference, LO and clock sig- nals to the LLRF and other accelerator sub- systems. The PIP-II linac has RF systems at three frequencies – 162.5 MHz, 325 MHz and 650 MHz. A temperature-stabilized, low-phase- noise oscillator is used as the master oscil- lator. Phase reference signals at 162.5 MHz, 325 MHz, and 650 MHz, along with LO signals at 182.5 MHz, 345 MHz, 670 MHz and LLRF clocks at 1320 MHz and 1300 MHz, are generated in temperature-controlled RF modules at each fre- quency section. A phase reference from each module travels to the next section, where it is doubled to produce required frequencies. The reference also travels alongside the accelerating cavities in the tunnel, allowing cavity probe and reference cables to temperature track and reduce measurement errors from temperature changes or phase drift.

Syed, Ahmed [Fermilab]↗
DOE OSTI2025

Master Oscillator and Phase Reference Line Design for the PIP-II Linac

The phase averaging reference line system provides the RF phase reference, LO and clock signals to the LLRF and other accelerator sub-systems. The PIP-II linac has RF systems at three frequencies - 162.5 MHz, 325 MHz and 650 MHz. A temperature-stabilized, low-phase-noise oscillator is used as the master oscillator. Phase reference signals at 162.5 MHz, 325 MHz, and 650 MHz, along with LO signals at 182.5 MHz, 345 MHz, 670 MHz and LLRF clocks at 1320 MHz and 1300 MHz, are generated in temperature-controlled RF modules at each frequency section. A phase reference from each module travels to the next section, where it is doubled to produce required frequencies. The reference also travels alongside the accelerating cavities in the tunnel, allowing cavity probe and reference cables to temperature track and reduce measurement errors from temperature changes or phase drift.

Syed, A. [Fermilab]↗
DOE OSTI2025

Analysis strategy for ultrafast X-ray photon correlation spectroscopy

We explain an analysis strategy for ultrafast X-ray photon correlation spectroscopy, a technique enabled by X-ray free electron lasers to probe nano- and atomic-scale dynamics in complex systems on nanosecond timescales or faster. Central to the technique is the methodology for extracting contrast in coherent X-ray diffraction patterns, known as speckle patterns. Guided by simulations, we analyze common challenges and examine errors encountered in contrast extraction. A method for accurately determining contrast amid shot noise across a wide range of count rates is presented.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Time-temperature-superposition analysis of diverse datasets by the minimum-arclength method: long-term prediction with uncertainty margins

In a recent publication, we carried out an extensive analysis of an unsupervised method of determining optimum shift factors in time-temperature-superposition of accelerated-aging data that involves minimizing the vertical arclength to obtain the master curve. For synthetic Arrhenius data with a variety of noise distributions, the work showed that, in conjunction with bootstrap-resampling, the method can produce reliable estimates of the mean activation energy along with uncertainty quantification. Here, we apply the above method to six different datasets taken from the published literature and demonstrate accurate prediction of mean activation energy from the data as-is without the need for any pre-processing or fitting. We also compare uncertainty margins computed by second-order bootstrap with that by linear regression theory and show that the former appears to provide consistent margins in the presence of common noise types in real data, including intra-isotherm measurement-errors, sample-to-sample variations, and intrinsic deviation from perfect Arrhenius behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Robust and optimal alignment of high-dimensional data using maximum likelihood estimation through a random sample consensus framework

Abstract Correcting spatial orientations of groups of high-dimensional data sets such that they are all in a consistent coordinate system is often a time-consuming and error-prone process. Automation of this process can be accomplished by using Generalized Procrustes Analysis to estimate the relative orientations among a population of high-dimensional data sets. A least squares Procrustes solution is applied through a maximum likelihood estimation and random sample consensus framework for robustness. The likelihood model is comprised of a mixture distribution where inliers are modeled using t -distribution and outliers from a uniform distribution. Applications will focus on a synthetic data set that emulates triaxial acceleration data and also real shock data from a population of triaxial accelerometers. Outliers represent either non-rigid body responses, environmental noise, and/or sensor and data acquisition issues. The intended application for the methodology is to robustly automate the rotation of populations of experimentally collected triaxial accelerometer data sets to a single global coordinate system.

LOSAC↗
DOE OSTI2021

Secure LoRa Firmware Update with Adaptive Data Rate Techniques

Internet of Things (IoT) devices rely upon remote firmware updates to fix bugs, update embedded algorithms, and make security enhancements. Remote firmware updates are a significant burden to wireless IoT devices that operate using low-power wide-area network (LPWAN) technologies due to slow data rates. One LPWAN technology, Long Range (LoRa), has the ability to increase the data rate at the expense of range and noise immunity. The optimization of communications for maximum speed is known as adaptive data rate (ADR) techniques, which can be applied to accelerate the firmware update process for any LoRa-enabled IoT device. In this paper, we investigate ADR techniques in an application that provides remote monitoring of cattle using small, battery-powered devices that transmit data on cattle location and health using LoRa. In addition to issues related to firmware update speed, there are significant concerns regarding reliability and security when updating firmware on mobile, energy-constrained devices. A malicious actor could attempt to steal the firmware to gain access to embedded algorithms or enable faulty behavior by injecting their own code into the device. A firmware update could be subverted due to cattle moving out of the LPWAN range or the device battery not being sufficiently charged to complete the update process. To address these concerns, we propose a secure and reliable firmware update process using ADR techniques that is applicable to any mobile or energy-constrained LoRa device. The proposed system is simulated and then implemented to evaluate its performance and security properties.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Retargetable Optimizing Compilers for Quantum Accelerators via a Multi-Level Intermediate Representation

In this work, we present a multi-level quantum-classical intermediate representation (IR) that enables an optimizing, retargetable compiler for available quantum languages. Our work builds upon the Multi-level Intermediate Representation (MLIR) framework and leverages its unique progressive lowering capabilities to map quantum languages to the LLVM machine-level IR. We provide both quantum and classical optimizations via the MLIR pattern rewriting sub-system and standard LLVM optimization passes, and demonstrate the programmability, compilation, and execution of our approach via standard benchmarks and test cases. In comparison to other standalone language and compiler efforts available today, our work results in compile times that are 1000x faster than standard Pythonic approaches, and 5-10x faster than comparative standalone quantum language compilers. Our compiler provides quantum resource optimizations via standard programming patterns that result in a 10x reduction in entangling operations, a common source of program noise. We see this work as a vehicle for rapid quantum compiler prototyping.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Identifying Suspect Instrument Intervals Using Midnight Noise Time Histories

With an intense work ethic, and high levels of devotion to their craft, seismologists continue to battle the elements and wrestle with complex logistics in their efforts to field instrumentation over the entire globe. We are fortunate to see data volumes accelerating in size, and quality continuing to improve; for example, universal timing issues can now be considered rare. However, instrument response issues are still common, and can hinder research that seeks to understand amplitudes of seismic wavefields. We are interested in using the accumulations of seismic data to develop models that will predict high frequency (0.2-20 Hz, and higher) signal amplitudes for explosion monitoring purposes over broad areas, which requires large data sets and extensive quality control. To identify response and station health issues, we have collected noise time histories for global seismic data, focusing on measurements near (but not restricted to) midnight to eliminate diurnal variations, and have manually determined time intervals that appear inconsistent with background behavior. We assign descriptive labels, but do not attempt to diagnose causes. We use these intervals to discard data. To date, we have examined 39,260 channels from 11,105 stations, heavily weighted toward IRIS holdings, dates through 2017 (depending on station, roughly the date we started our manual review), bands between 1 and 8 Hz, finding 24,733 anomalous time intervals. The great majority (90%) of these intervals appear to be shifts of constant offset, often bounded by times of known instrument changes, likely the result of poor documentation of response parameters at one of many stages between the field and the plotter. We hope these results can be of use to our colleagues, and would encourage community efforts to diagnose anomalous behavior, and fix poor responses. We also hope these results will support automation efforts, including application of supervised learning techniques.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

CEBAF Accelerator Tunnel Robot Project

Advancements in the field of robotics allow for the use of remotely controlled robots to perform tasks that have typically required human intervention. Jefferson Lab has explored the possibilities of robots placed in the accelerator tunnel to perform remote inspections that would otherwise require powering down equipment and completing lengthy radiological surveys that impact beam operations. A robotic solution ? driven by a human operator ? could not only reduce accelerator down time but also support the Lab's radiation exposure policy (known as "ALARA" - As Low As Reasonably Acceptable) by handling routine tunnel and beamline investigations in place of a human being. However, operating a remote device in a radioactive, high electronic-noise area is not a trivial task and drop-in solutions are rarely as simple as they seem. We will present the past prototypes that were tested as well follow-on technology developed from the project. We will also briefly survey current applications of robots at accelerator facilities with an eye toward future applications.

McGuckin, Theodore↗
DOE OSTI2026

Beam test results of the Intermediate Silicon Tracker for sPHENIX

The Intermediate Silicon Tracker (INTT), a two-layer barrel silicon strip tracker, is a key component of the tracking system for sPHENIX at the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory. The INTT is designed to enable the association of reconstructed tracks with individual RHIC bunch crossings. To evaluate the performance of preproduction INTT ladders and the readout chain, a beam test was conducted at the Research Center for Accelerator and Radioisotope Science, Tohoku University, Japan. This paper presents the performance of the INTT evaluated through studies of the signal-to-noise ratio, residual distribution, spatial resolution, hit-detection efficiency, and multiple track reconstruction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Selecting XFEL single-particle snapshots by geometric machine learning

A promising new route for structural biology is single-particle imaging with an X-ray Free-Electron Laser (XFEL). This method has the advantage that the samples do not require crystallization and can be examined at room temperature. However, high-resolution structures can only be obtained from a sufficiently large number of diffraction patterns of individual molecules, so-called single particles. Here, we present a method that allows for efficient identification of single particles in very large XFEL datasets, operates at low signal levels, and is tolerant to background. This method uses supervised Geometric Machine Learning (GML) to extract low-dimensional feature vectors from a training dataset, fuse test datasets into the feature space of training datasets, and separate the data into binary distributions of “single particles” and “non-single particles.” As a proof of principle, we tested simulated and experimental datasets of the Coliphage PR772 virus. We created a training dataset and classified three types of test datasets: First, a noise-free simulated test dataset, which gave near perfect separation. Second, simulated test datasets that were modified to reflect different levels of photon counts and background noise. These modified datasets were used to quantify the predictive limits of our approach. Third, an experimental dataset collected at the Stanford Linear Accelerator Center. The single-particle identification for this experimental dataset was compared with previously published results and it was found that GML covers a wide photon-count range, outperforming other single-particle identification methods. Moreover, a major advantage of GML is its ability to retrieve single particles in the presence of structural variability.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Comparing smoothing techniques for extracting MOSFET threshold voltage

Measurement noise acts as a barrier to the accurate calculation of threshold voltage by derivative-based extraction methods. We examined several smoothing techniques and their effects on measurement noise in threshold voltage extracted by the linear extrapolation method and the g m /I d method. We applied these techniques on a set of SiC power MOSFET devices before and after they had undergone accelerated thermal testing. The smoothing methods examined are least-squares polynomial fitting, low-pass filtering using Butterworth and Chebyshev fillers, and differential smoothing. These methods are compared by their shift in threshold voltage compared to unsmoothed data and their standard deviation among the measurements of each device. All methods performed poorly at smoothing when extracting via the g m /I d due to the large amounts of high-magnitude subthreshold noise, mostly tending to extract threshold voltages far below the expected value. Differential smoothing provided results closest to the expected value with an absolute shift of 1.2 V on average. The smoothing methods performed better with the LE method. On a failed device, all smoothing methods except polynomial smoothing preserved the large threshold voltage anomaly for four out of five measurements. These results show that choice of smoothing method, if used must be evaluated based on the device and extraction method.

42 ENGINEERING↗
DOE OSTI2025

Peeking into the next decade in large-scale structure cosmology with its Effective Field Theory

After the successful full-shape analyses of BOSS data using the Effective Field Theory of Large-Scale Structure, we investigate what upcoming galaxy surveys might achieve. We introduce a “perturbativity prior” that ensures that loop terms are as large as theoretically expected, which is effective in the case of a large number of EFT parameters. After validating our technique by comparison with already-performed analyses of BOSS data, we provide Fisher forecasts using the one-loop prediction for power spectrum and bispectrum for two benchmark surveys: DESI and MegaMapper. We find overall great improvements on the cosmological parameters. In particular, we find that MegaMapper (DESI) should obtain at least a 12σ (2σ) evidence for non-vanishing neutrino masses, bound the curvature Ω k to 0.0012 (0.012), and primordial inflationary non-Gaussianities as follows: f NL loc. to ± 0.26 (3.3), f NL eq. to ±16 (92), f NL orth. to ± 4.2 (27). Such measurements would provide much insight on the theory of Inflation. We investigate the limiting factor of shot noise and ignorance of the EFT parameters.

cosmological parameters from LSS↗
DOE OSTI2020

Integrative Quantitative-Phase and Airy Light-Sheet Imaging

Light-sheet microscopy enables considerable speed and phototoxicity gains, while quantitative-phase imaging confers label-free organelle recognition and metabolic information that are inaccessible by conventional methods. We report the fusion of these two modalities onto a standard inverted microscope that retains compatibility with microfluidics. We describe the utilization of an accelerating Airy-beam light-sheet yielding identical imaging areas with interferometry, and an application in unmasking the effects of cellular noise on metabolic compartmentalization.

Biological sciences, Biological techniques, Micros↗
DOE OSTI2022

B-DeepONet: An enhanced Bayesian DeepONet for solving noisy parametric PDEs using accelerated replica exchange SGLD

Here, the Deep Operator Network (DeepONet) is a neural network architecture used to approximate operators, including the solution operator of parametric PDEs. DeepONets have shown remarkable approximation ability. However, the performance of DeepONets deteriorates when the training data is polluted with noise, a scenario that occurs in practice. To handle noisy data, we propose a Bayesian DeepONet based on replica exchange Langevin diffusion (reLD). Replica exchange uses two particles. The first particle trains a DeepONet to exploit the loss landscape and make predictions. The other particle trains a different DeepONet to explore the loss landscape and escape local minima via swapping. Compared to DeepONets trained with state-of-the-art gradient-based algorithms (e.g., Adam), the proposed Bayesian DeepONet greatly improves the training convergence for noisy scenarios and accurately estimates the uncertainty. To further reduce the high computational cost of the reLD training of DeepONets, we propose (1) an accelerated training framework that exploits the DeepONet's architecture to reduce its computational cost up to 25% without compromising performance and (2) a transfer learning strategy that accelerates training DeepONets for PDEs with different parameter values. Finally, we illustrate the effectiveness of the proposed Bayesian DeepONet using four parametric PDE problems.

97 MATHEMATICS AND COMPUTING↗