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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

DUNE ND-LAr 2x2 Demonstrator Tests

A modular liquid argon (LAr) Time Projection Chamber (TPC) will be part of the near-detector facility for the Deep Underground Neutrino Experiment (DUNE). Such a TPC is being developed by the ArgonCube collaboration. To demonstrate the feasibility of the LAr DUNE Near Detector, a medium size prototype has been built: the ArgonCube 2x2 Demonstrator. The detector will be placed in the MINOS-ND hall, upstream of the current MINERνA/MINOS-ND locations, at Fermilab. Currently two modules are being tested at the Liquid Argon Test Facility (LArTF), before being place on-axis in the NuMI neutrino beam. The present report will be focused on the work done within the 2x2 Demonstrator group: the first part of the project is focused on the Low Noise AC Power distribution at LArTF, whereas the second is related to the 2x2 demonstrator light readout system tests.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

RF Digitizer for EIC LLRF

With the shutdown of Relativisitc Heavy Ion Collider (RHIC) in FY26, the former RHIC facility will be upgraded to the new Electron-Ion Collider (EIC). An RF Digitizer for the EIC LLRF system has been developed and tested on a RHIC 197 MHz cavity in Q2 2026 for a baseline per formance. Firmware and software has been developed and validated during the 197 MHz cavity test. Preliminary data from the cavity test shows phase noise performance signifi cantly better than the RHIC LLRF system.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

NUMERICAL NOISE STUDY IN EIC BEAM-BEAM SIMULATIONS

In the Electron-Ion Collider (EIC) design, a flat beam collision scheme is adopted to achieve 1034 cm -2 s -1 luminosity. We found that the vertical growth of the proton beam is much larger than of the round beam. In this article we present the numerical noise study about the number of macroparticles, the electron slice number, and the electron bunch length. Both weak-strong and strong-strong simulation methods are used. It turns out the proton emittance growth in the strong-strong simulation mainly comes from the numerical noise. This study helps us to perform beam-beam simulation correctly for the EIC.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Multi-modal effects on indirect noise induced by turbulent entropy fields

Planar entropy waves are commonly assumed for predicting indirect combustion noise. However, the non-planar and turbulent nature of flows found in most practical combustors challenges this assumption. In the present paper, we examine the indirect noise generated by non-planar and turbulent entropy fields through subsonic nozzles. Firstly, we introduce a new transfer function framework that accounts for the contribution of non-planar Fourier modes of the entropy field to the indirect noise spectra. When applied to a turbulent flow field, this method demonstrates a significant improvement in spectral predictions compared with a conventional approach that only considers the planar mode. Secondly, simulations show that non-planar Fourier modes become significant above a threshold frequency ƒ thresh , found in the mid- to high-frequency range. This contribution of non-planar modes is explained by two-dimensional shear effects that distort the entropy waves. A scaling relation that uses residence times along streamlines is developed for ƒ thresh , showing good agreement with simulation results. Finally, we show that the indirect noise from non-planar entropy modes found in aviation combustors can be significant at frequencies below 1 kHz, which might be relevant in situations of thermo-acoustic instabilities coupled to indirect noise.

42 ENGINEERING↗
DOE OSTI2025

Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration

This paper proposes novel noise-free Bayesian optimization strategies that rely on a random exploration step to enhance the accuracy of Gaussian process surrogate models. The new algorithms retain the ease of implementation of the classical GP-UCB algorithm, but the additional random exploration step accelerates their convergence, nearly achieving the optimal convergence rate. Furthermore, to facilitate Bayesian inference with intractable likelihoods, we propose to utilize optimization iterates for maximum a posteriori estimation to build a Gaussian process surrogate model for the unnormalized log-posterior density. We provide bounds for the Hellinger distance between the true and the approximate posterior distributions in terms of the number of design points. We demonstrate the effectiveness of our Bayesian optimization algorithms in nonconvex benchmark objective functions, in a machine learning hyperparameter tuning problem, and in a black-box engineering design problem. The effectiveness of our posterior approximation approach is demonstrated in two Bayesian inference problems for parameters of dynamical systems.

Bayesian inference↗
DOE OSTI2025

Noise limits for dc SQUID readout of high- Q resonators below 300 MHz

We present the limits on noise for the readout of cryogenic high-Q resonators using dc Superconducting Quantum Interference Devices (SQUIDs) below 300 MHz. This analysis uses realized first-stage SQUIDs (previously published), whose performance is well described by Tesche–Clarke (TC) theory, coupled directly to the resonators. We also present data from a prototype second-stage dc SQUID array designed to couple to this first-stage SQUID as a follow-on amplifier with high system bandwidth. This analysis is the first full consideration of dc SQUID noise performance referred to a high-Q resonator over this frequency range and is presented relative to the standard quantum limit. We include imprecision, backaction, and backaction–imprecision noise correlations from TC theory, the noise contributed by the second-stage SQUIDs, wiring, and preamplifiers, and optimizations for both on-resonance measurements and off-resonance scan sensitivity. This architecture has modern relevance due to the increased interest in axion searches and the requirements of the DMRadio-m 3 axion search, which uses dc SQUIDs in this frequency range.

Bosons↗
DOE OSTI2021

Particle track classification using quantum associative memory

Pattern recognition algorithms are commonly employed to simplify the challenging and necessary step of track reconstruction in sub-atomic physics experiments. Aiding in the discrimination of relevant interactions, pattern recognition seeks to accelerate track reconstruction by isolating signals of interest. In high collision rate experiments, such algorithms can be particularly crucial for determining whether to retain or discard information from a given interaction even before the data is transferred to tape. As data rates, detector resolution, noise, and inefficiencies increase, pattern recognition becomes more computationally challenging, motivating the development of higher efficiency algorithms and techniques. Quantum associative memory is an approach that seeks to exploits quantum mechanical phenomena to gain advantage in learning capacity, or the number of patterns that can be stored and accurately recalled. Here, we study quantum associative memory based on quantum annealing and apply it to the particle track classification. We focus on discrimination models based on Ising formulations of quantum associative memory model (QAMM) recall and quantum content-addressable memory (QCAM) recall. We characterize classification performance of these approaches as a function detector resolution, pattern library size, and detector inefficiencies, using the D-Wave 2000Q processor as a testbed. Discrimination criteria is set using both solution-state energy and classification labels embedded in solution states. We find that energy-based QAMM classification performs well in regimes of small pattern density and low detector inefficiency. In contrast, state-based QCAM achieves reasonably high accuracy recall for large pattern density and the greatest recall accuracy robustness to a variety of detector noise sources.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

EnZymClass: Substrate specificity prediction tool of plant acyl-ACP thioesterases based on ensemble learning

Characterizing the functional properties of plant acyl-ACP thioesterases (TEs), a key enzyme class used in the production of renewable oleochemicals in microbial hosts, experimentally, can be an expensive and time consuming process since it requires manual screening of thousands of candidates in a database. Using amino acid sequence to computationally predict an enzyme’s function might accelerate this process; however obtaining the necessary amount of information on previously characterized enzymes and their respective sequences required by standard Machine Learning (ML) based approaches to accurately infer sequence-function relationships can be prohibitive, especially with a low-throughput testing cycle. Experimental noise, unbalanced dataset where high sequence similarity does not always imply identical functional properties will further prevent robust prediction performance. Herein we present a ML method, Ensemble method for enZyme Classification (EnZymClass), that is specifically designed to address these issues. We used EnZymClass to classify TEs into short, long and mixed free fatty acid substrate specificity categories. While general guidelines for inferring substrate specificity have been proposed before, prediction of chain-length preference from primary sequence has remained elusive for plant acyl-ACP TEs. By applying EnZymClass to a subset of TEs in the ThYme database, we identified two medium chain TEs, ClFatB3 and CwFatB2, with previously uncharacterized activity in E. coli fatty acid production hosts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Radiation Testing for High-Resolution Radiation-Hardened Camera System (Final CRADA Report)

The nuclear energy industry needs higher-image quality and higher rad-hard vision systems for refueling and inspection operations that are required every 12-18 months for all operating commercial reactors. During refueling operations, the serial numbers of fuel assemblies need to be visually verified in a challenging high radiation environment with dose rates on the order of 1kGy/hr. at the top of the core (approximately 10 days after shutdown). Vega Wave Systems has developed and built a radiation hard vision system that: 1. has high radiation hardness, 2. demonstrated best in the industry image quality, 3. has small size and weight, and 4. does not suffer from radiation-induced noise. ANL-1060 (02/13/2023) The proprietary Enduray vision system from Vega Wave Systems, Inc. is designed to withstand more than 400x the radiation level of the Vidicon-based systems, currently the highest radiation tolerant system in use today. The Van De Graaff (VDG) electron accelerator at Argonne National Laboratory is ideally suited for testing of Enduray vision system. VDG provides a broad band (up to 3 MeV) x-ray source that reasonably approximates the energy and spectrum of the gamma radiation expected at the top of a nuclear core 10 days after shutdown (~1kGy/hr.), is easily controlled for variable tests, and will not activate the parts under radiation for ease of timely and close inspection and characterization of the system in case of operational issues.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

deeprob/ThioesteraseEnzymeSpecificity: EnZymClass-first-release

Characterizing the functional properties of plant acyl-ACP thioesterases (TEs), a key enzyme class used in the production of renewable oleochemicals in microbial hosts, experimentally, can be an expensive and time consuming process since it requires manual screening of thousands of candidates in a database. Using amino acid sequence to computationally predict an enzyme’s function might accelerate this process; however obtaining the necessary amount of information on previously characterized enzymes and their respective sequences required by standard Machine Learning (ML) based approaches to accurately infer sequence-function relationships can be prohibitive, especially with a low-throughput testing cycle. Experimental noise, unbalanced dataset where high sequence similarity does not always imply identical functional properties will further prevent robust prediction performance. Herein we present a ML method, Ensemble method for enZyme Classification (EnZymClass), that is specifically designed to address these issues. We used EnZymClass to classify TEs into short, long and mixed free fatty acid substrate specificity categories. While general guidelines for inferring substrate specificity have been proposed before, prediction of chain-length preference from primary sequence has remained elusive for plant acyl-ACP TEs. By applying EnZymClass to a subset of TEs in the ThYme database, we identified two medium chain TEs, ClFatB3 and CwFatB2, with previously uncharacterized activity in E. coli fatty acid production hosts.

Banerjee, Deepro↗
DOE OSTI2024

Beam Loss Assessment Through Use of Photomultiplier Tubes

Modern accelerators aim to deliver maximal beam current at stable energy with minimal beam loss. Environmental changes, among other factors, can result in increased beam loss and decreased beam throughput, prompting daily retuning of the accelerator. The compact nature of the oldest part of the linear accelerator limits the available beam instrumentation, making beam loss assessment and tuning difficult. Thus, additional devices for beam loss monitoring must be considered. Photomultiplier tube-based beam loss monitors (BLMs) were installed along the Fermilab drift tube Linac to assess beam loss. Due to noise, the data from the installed photomultiplier tubes was difficult to assess. After noise reduction and signal analysis, it was found that the signals produced by the photomultiplier tubes in response to beam loss were consistent for a given configuration and therefore a reasonable measure of beam loss. This project lays groundwork for future work in beam loss assessment using photomultiplier tubes, with the automation of the process developed in this project being the next step in this effort.

Waggoner, Alexander↗
DOE OSTI2026

Particle Acceleration and Pitch-angle Evolution in Relativistic Turbulence

Synchrotron radiation detected from relativistic astrophysical objects such as pulsar-wind nebulae and jets from active galactic nuclei depends on the magnetic fields and the distribution functions of energetic electrons in these systems. Relativistic magnetically dominated turbulence has been recognized as an efficient mechanism for structure formation and nonthermal particle acceleration in these environments. Recent numerical simulations of relativistic turbulence have provided insights into the energy distribution functions of accelerated electrons. Much less is currently understood about their pitch angle distributions, which are crucial for accurately interpreting the spectra of synchrotron radiation. We perform a detailed case study of the pitch angle distributions formed during the process of turbulent acceleration for B 0 /δB 0 = 10 and $\tilde{σ}$ 0 ~40, where B0 is the uniform component of the magnetic field, δB0 is the fluctuating component, and $\tilde{σ}$ 0 is the plasma magnetization based on the magnetic fluctuations. We find that even minimal numerical noise can cause substantial pitch angle scattering, but we demonstrate techniques for overcoming the numerical challenges associated with the evolution of very small pitch angles. Our numerical results are consistent with the phenomenological considerations found in C. Vega et al. (2024a, 2025).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks

Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretical data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) patterns of metal–organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental data and shuffled; then it was merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. For the first time, one-to-one material identification was achieved. Theoretical MOFs patterns (1012) were augmented to a whole data set of 72 864 samples. It was then randomly shuffled and split into training (58 292 samples) and validation (14 572 samples) data sets at a ratio of 4:1. For the task of discriminating, the optimized model showed the highest identification accuracy of 96.7% for the top 5 ranking on a test data set of 30 hold-out samples. Neighborhood component analysis (NCA) on the experimental XRD samples shows that the samples from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD patterns. Furthermore, this CNN model trained by the data augmentation technique would not only open numerous potential applications for identifying XRD patterns for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR spectroscopies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Facile Measurement of the Rotation of a Single Optically Trapped Nanoparticle Using the Diagonal Ratio of a Quadrant Photodiode

Optical tweezers are a powerful tool for exploring physical properties of particles in various environments through analysis of their dynamics in a trapping potential. Analysis of the trapped particle's position is often done using interferometric back-focal-plane detection microscopy in which interference between the trapping laser and the light interacting with the trapped particle is projected on a quadrant photo-diode (QPD). The QPD measurements are almost universally the voltage ratios between the transverse components of the detector (left/right; up/down) that are calibrated to ascertain particle positions and orientations (i.e., rotation). In this paper, we demonstrate how measuring the diagonal ratio of the QPD allows tracking the spinning and orbital motion of optically trapped objects with enhanced sensitivity compared to the conventional transverse ratio and reveals additional information about particle dynamics. The diagonal ratio "balances" the signal from opposite sides of the QPD, allowing common mode noise reduction and increased measurement sensitivity of the rotating nanospheres at frequencies from below 200 Hz to above 15 kHz. We show that the variability in particle rotation rates is due to slight differences in the aspect ratio of the nanoparticles. We also measure in situ a gradual acceleration in the spinning frequency of trapped nanoparticles due to photothermal shedding of surface ligands and the resulting decrease of rotational friction in solution. The accelerating particles spin at rates exceeding 30 kHz and are eventually "optically printed" onto the glass coverslip on top of the liquid cell enclosure. The diagonal ratio is a simple, readily available, and powerful enabler for measuring rotational dynamics with a 5-fold sensitivity increase over conventional QPD measurements. This affords opportunities for controlling the orientational dynamics of trapped nanoparticles, which is crucial for various applications, including nanoscale mechanical motors.

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

Accelerated Probabilistic Marching Cubes by Deep Learning for Time-Varying Scalar Ensembles

Visualizing the uncertainty of ensemble simulations is challenging due to the large size and multivariate and temporal features of en-semble data sets. One popular approach to studying the uncertainty of ensembles is analyzing the positional uncertainty of the level sets. Probabilistic marching cubes is a technique that performs Monte Carlo sampling of multivariate Gaussian noise distributions for positional uncertainty visualization of level sets. However, the technique suffers from high computational time, making interactive visualization and analysis impossible to achieve. This paper introduces a deep-learning-based approach to learning the level-set uncertainty for two-dimensional ensemble data with a multivariate Gaussian noise assumption. We train the model using the first few time steps from time-varying ensemble data in our workflow. We demonstrate that our trained model accurately infers uncertainty in level sets for new time steps and is up to 170X faster than that of the original probabilistic model with serial computation and 10X faster than that of the original parallel computation.

Han, Mengjiao↗
DOE OSTI2021

Transverse Beam Emittance Measurement by Undulator Radiation Power Noise

Generally, turn-to-turn power fluctuations of incoherent spontaneous synchrotron radiation in a storage ring depend on the 6D phase-space distribution of the electron bunch. In some cases, if only one parameter of the distribution is unknown, this parameter can be determined from the measured magnitude of these power fluctuations. In this contribution, we report the results of our experiment at the Integrable Optics Test Accelerator (IOTA) storage ring, where we carried out an absolute measurement (no free parameters or calibration) of a small vertical emittance (5–15 nm rms) of a flat beam by this new method, under conditions, when the small vertical emittance is unresolvable by a conventional synchrotron light beam size monitor.

43 PARTICLE ACCELERATORS↗