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At least 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Microreactor Automated Control System Test Bed Digital Architecture for Real-Time, Hardware-in-the-Loop Simulation

This work describes progress made towards the development of a real-time hardware-in-the-loop (HIL) test bed for non-nuclear testing of microreactor control schemes and failure modes. Non-nuclear testing is a crucial step in developing robust control algorithms for managing microreactor dynamics. The creation of an HIL simulation harnesses the realistic dynamics of physical analogue systems while additionally considering the challenges of variable communication delay. This collaborative effort between Oak Ridge National Laboratory and Idaho National Laboratory has resulted in a LabVIEW-based gRPC communication protocol which couples a TRANSFORM Modelica simulation of nuclear components to the ViBRANT physical hardware for realistic feedback and visual representation of control action in real time. A modular python client structure is developed to manage FMU-based Modelica simulation and real-time gRPC communication. HIL testing suggests that the modeled reactor with natural convection molten salt loop coolant configuration responds well to PID control of drum positioning for modulation of reactor core power, however, future efforts will be made to explore the added thermal inertial delay of system level control and downstream demand changes. Development of this platform with a generalized methodology provides a foundation for exploring a variety of reactor configurations and failure modes in rapid order to provide insight into the most effective avenues of study for further research and development.

McConnell, Jono [ORNL] (ORCID:0000000238984741)↗
DOE OSTI2022

Exponential time differencing scheme for mass transport and depletion in molten salt reactors

This work extends the capability previously shown for addressing the problem of computing depletion and mass transport calculations in molten salt reactors (MSRs) by calculating matrix exponentials. Additional algorithms are implemented to compute the matrix exponential and the action of the matrix exponential on a matrix. These algorithms include two methods based on the Pade approximation, a Taylor series method, and three methods based on Cauchy's integral formula. In addition to the added matrix exponential solvers, a variable-order total variation diminishing scheme is applied to the convective flux approximation to provide enhanced accuracy. Finally, a simplified MSR problem is shown for each of the exponential time differencing solvers along with classical backwards differencing integrators. The results show excellent convergence for exponential time differencing methods. Computation time is a key element for selecting the optimal solver in these problems, and this work shows that Pade and Cauchy-based solvers may provided the fastest and most accurate solutions. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Time reflection of light from a quantum perspective and vacuum entanglement

If a boundary between two static media is moving with a constant superluminal velocity, or there is a sudden change of the refractive index with time, this yields generation of entangled pairs of photons out of vacuum propagating in the opposite directions. Here we show that during this process, entanglement of Minkowski vacuum is transferred to the entanglement of the generated photon pairs. If initially an electromagnetic pulse is present in the medium the photon generation is stimulated into the pulse mode, and since photons are created as entangled pairs the counter-propagating photon partners produce a pulse moving in the opposite direction, which is known as time reflection. Thus, time reflection occurs due to stimulated generation of the entangled photon pairs out of entangled vacuum and no photons in the original pulse are in fact being reflected. This is different from the mechanism of light reflection from spatial inhomogeneities for which no photons are generated.

Optics↗
DOE OSTI2020

Real-time jitter correction in a photonic analog-to-digital converter

A real-time jitter meter is used to measure and digitally sample the pulse-to-pulse timing error in a laser pulse train. The jitter meter is self-referenced using a single-pulse delay line interferometer and measures timing jitter using optical heterodyne detection between two frequency channels of the pulse train. Jitter sensitivity down to 3×10 -10 fs 2 /Hz at 500 MHz has been demonstrated with a pulse-to-pulse noise floor of 1.6 fs. Furthermore, as a proof of principle, the digital correction of the output of a high-frequency photonic analog-to-digital converter (PADC) is demonstrated with an emulated jitter signal. Up to 23 dB of jitter correction, down to the noise floor of the PADC, is accomplished with radio-frequency modulation up to 40 GHz.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

High-rate multiplexed entanglement source based on time-bin qubits for advanced quantum networks

Entanglement distribution based on time-bin qubits is an attractive option for emerging quantum networks. We demonstrate a 4.09-GHz repetition rate source of photon pairs entangled across early and late time bins separated by 80 ps. Simultaneous high rates and high visibilities are achieved through frequency multiplexing the spontaneous parametric down conversion output into eight time-bin entangled channel pairs. We demonstrate entanglement visibilities as high as 99.4%, total entanglement rates up to 3.55×10 6 coincidences/s, and predict a straightforward path towards achieving up to an order of magnitude improvement in rates without compromising visibility. Finally, we resolve the density matrices of the entangled states for each multiplexed channel and express distillable entanglement rates in ebit/s, thereby quantifying the trade-off between visibility and coincidence rates that contributes to useful entanglement distribution. This source is a fundamental building block for high-rate entanglement-based quantum key distribution systems or advanced quantum networks.

Mueller, Andrew (ORCID:0000000265989732)↗
DOE OSTI2022

A deep learning approach to real-time HIV outbreak detection using genetic data

Pathogen genomic sequence data are increasingly made available for epidemiological monitoring. A main interest is to identify and assess the potential of infectious disease outbreaks. While popular methods to analyze sequence data often involve phylogenetic tree inference, they are vulnerable to errors from recombination and impose a high computational cost, making it difficult to obtain real-time results when the number of sequences is in or above the thousands. Here, we propose an alternative strategy to outbreak detection using genomic data based on deep learning methods developed for image classification. The key idea is to use a pairwise genetic distance matrix calculated from viral sequences as an image, and develop convolutional neutral network (CNN) models to classify areas of the images that show signatures of active outbreak, leading to identification of subsets of sequences taken from an active outbreak. We showed that our method is efficient in finding HIV-1 outbreaks with R0 ≥ 2.5, and overall a specificity exceeding 98% and sensitivity better than 92%. We validated our approach using data from HIV-1 CRF01 in Europe, containing both endemic sequences and a well-known dual outbreak in intravenous drug users. Our model accurately identified known outbreak sequences in the background of slower spreading HIV. Importantly, we detected both outbreaks early on, before they were over, implying that had this method been applied in real-time as data became available, one would have been able to intervene and possibly prevent the extent of these outbreaks. This approach is scalable to processing hundreds of thousands of sequences, making it useful for current and future real-time epidemiological investigations, including public health monitoring using large databases and especially for rapid outbreak identification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Rolling Horizon Based Temporal Decomposition for the Offline Pickup and Delivery Problem with Time Windows

The offline pickup and delivery problem with time windows (PDPTW) is a classical combinatorial optimization problem in the transportation community, which has proven to be very challenging computationally. Due to the complexity of the problem, practical problem instances can be solved only via heuristics, which trade-off solution quality for computational tractability. Among the various heuristics, a common strategy is problem decomposition, that is, the reduction of a large-scale problem into a collection of smaller sub-problems, with spatial and temporal decompositions being two natural approaches. While spatial decomposition has been successful in certain settings, effective temporal decomposition has been challenging due to the difficulty of stitching together the sub-problem solutions across the decomposition boundaries. In this work, we introduce a novel temporal decomposition scheme for solving a class of PDPTWs that have narrow time windows, for which it is able to provide both fast and high-quality solutions. We utilize techniques that have been popularized recently in the context of online dial-a-ride problems along with the general idea of rolling horizon optimization. To the best of our knowledge, this is the first attempt to solve offline PDPTWs using such an approach. To show the performance and scalability of our framework, we use the optimization of paratransit services as a motivating example. Due to the lack of benchmark solvers similar to ours (i.e., temporal decomposition with an online solver), we compare our results with an offline heuristic algorithm using Google OR-Tools. In smaller problem instances (with an average of 129 requests per instance), the baseline approach is as competitive as our framework. However, in larger problem instances (approximately 2,500 requests per instance), our framework is more scalable and can provide good solutions to problem instances of varying degrees of difficulty, while the baseline algorithm often fails to find a feasible solution within comparable compute times.

Kim, Youngseo↗
DOE OSTI2021

Conditioned Simulation of Ground-Motion Time Series at Uninstrumented Sites Using Gaussian Process Regression

Ground-motion time series are essential input data in seismic analysis and performance assessment of the built environment. Because instruments to record free-field ground motions are generally sparse, methods are needed to estimate motions at locations with no available ground-motion recording instrumentation. In this study, given a set of observed motions, ground-motion time series at target sites are constructed using a Gaussian process regression (GPR) approach, which treats the real and imaginary parts of the Fourier spectrum as random Gaussian variables. Model training, verification, and applicability studies are carried out using the physics-based simulated ground motions of the 1906 Mw 7.9 San Francisco earthquake and Mw 7.0 Hayward fault scenario earthquake in northern California. Additionally, the method’s performance is further evaluated using the 2019 Mw 7.1 Ridgecrest earthquake ground motions recorded by the Community Seismic Network stations located in southern California. These evaluations indicate that the trained GPR model is able to adequately estimate the ground-motion time series for frequency ranges that are pertinent for most earthquake engineering applications. The trained GPR model exhibits proper performance in predicting the long-period content of the ground motions as well as directivity pulses.

58 GEOSCIENCES↗
DOE OSTI2025

Effect of Time Window and Spectral Measurement Options on Empirical Green’s Function Analysis Using DAS Array and Seismic Stations

The recorded seismic waveform is a convolution of event source term, path term, and station term. Removing high-frequency attenuation due to path effect is a challenging problem. Empirical Green’s function (EGF) method uses nearly collocated small earthquakes to correct the path and station terms for larger events recorded at the same station. However, this method is subject to variability due to many factors. Here, we focus on three events that were well recorded by the seismic network and a rapid response distributed acoustic sensing (DAS) array. Using a suite of high-quality EGF events, we assess the influence of time window, spectral measurement options, and types of data on the spectral ratio and relative source time function (RSTF) results. Increased number of tapers (from 2 to 16) tends to increase the measured corner frequency and reduce the source complexity. Extended long time window (e.g., 30 s) tends to produce larger variability of corner frequency. The multitaper algorithm that simultaneously optimizes both target and EGF spectra produces the most stable corner-frequency measurements. The stacked spectral ratio and RSTF from the DAS array are more stable than two nearby seismic stations, and are comparable to stacked results from the seismic network, suggesting that DAS array has strong potential in source characterization.

58 GEOSCIENCES↗
DOE OSTI2023

The Seismic Signature of a High-Energy Density Physics Laboratory and Its Potential for Measuring Time-Dependent Velocity Structure

The Z Machine at Sandia National Laboratories is a pulsed power facility for high-energy density physics experiments that can shock materials to extreme temperatures and pressures through a focused energy release of up to ~ 25 MJ in < 100 nanoseconds. It has been in operation for more than two decades and conducts up to ~ 100 experiments, or “shots,” per year. Based on a set of 74 known shot times from 2018, we determined that Z Machine shots produce detectable ~ 3–17 Hz ground motion 12 km away at the Albuquerque Seismological Laboratory, New Mexico (ANMO), borehole seismograph, with peak signal at ~ 7 Hz. The known shot waveforms were used to create a three-component template, leading to the detection of 2339 Z Machine shots since 1998 through single-station cross-correlation. Local seismic magnitude estimates range from local magnitude (M L ) —2 to —1.3 and indicate that only a small fraction of the shot energy is transmitted by seismic phases observable at 12 km distance. The most recent major facility renovation, which was intended to decrease mechanical dissipation, is associated with an abrupt decrease in observed seismic amplitudes at ANMO despite stable maximum shot energy. The highly repetitive impulsive sources are well suited to coda-wave interferometry to investigate time-dependent velocity structures. Relative velocity variations (dv/v) show an annual cycle with amplitude of ~ 0.2%. Local minima are observed in the late spring, and dv/v increases through the summer monsoon rainfall, possibly reflecting patchy saturation as rainfall infiltrates near the eastern edge of the Albuquerque basin. Importantly, the cumulative results demonstrate that forensic seismology can provide insight into long-term operation of facilities such as pulsed-power laboratories, and that their recurring signals may be valuable for studies of time-dependent structure

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Rapid Analyses of Sparse Seismoacoustic Data Reveals the Timing and Size of the Accurate Energetic Systems Explosion

On 10 October 2025 an explosion occurred at a facility operated by Accurate Energetic Systems in Humphreys County, Tennessee. The incident resulted in 16 fatalities and created a debris field over several square kilometers. To address remaining questions about explosion timing and size, we collected about 20 seismic and 19 acoustic records of the blast from sensors up to hundreds of kilometers away. We then deployed 10 distinct physics-based, reduced order models (ROMs) that used validated geological structure and atmospheric conditions from the time of the event, along with observations of body- and surface-wave energy, as well as acoustic overpressure and phase duration. Each ROM predicted either timing, yield estimates, or both. We binned these estimates and their uncertainties according to each ROMs’ assumptions about confinement (aboveground, buried fully coupled, and buried partially coupled) and combined these estimates with other forensic data to conclude that the event occurred as a single, aboveground explosion on 10 December 2025 12:47:50.8 ±0.1 s with a yield equivalent to 11.8 [2.3,16.5] tons of Trinitrotoluene. Our estimates align with the Bureau of Alcohol, Tobacco, Firearms and Explosives inventory reports of 11–13 tons. This multimethod approach demonstrates the use of remotely observed geophysical data to rapidly aid conventional forensic investigations of accidental explosions.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

Xopt and Badger: a machine learning ecosystem for real-time accelerator control and optimization

Machine learning (ML)-based black-box optimization algorithms have demonstrated significant improvements in accelerator optimization speed, often by orders of magnitude. However, deploying these algorithms in real-time facility control remains challenging due to the specialized expertise and infrastructure required. To bridge this gap, we introduce the Xopt ecosystem, a versatile suite of tools designed to make advanced ML-based optimization accessible to the broader accelerator community. This ecosystem includes Xopt, a modular Python framework that facilitates the integration of ML-based optimization algorithms with arbitrary control problems, and Badger, a graphical user interface built on top of Xopt, which enables seamless deployment of ML algorithms in real-time control systems. The Xopt ecosystem has been successfully applied towards solving challenging real-time control problems at leading international accelerator facilities, including SLAC, LBNL, Argonne, Fermilab, BNL, DESY, and ESRF, demonstrating its effectiveness in real-world optimization tasks. In this presentation, we provide an overview of Xopt’s capabilities and illustrate its impact through case studies from SLAC accelerator facilities including LCLS, LCLS-II, and FACET-II.

Roussel, Ryan [SLAC]↗
DOE OSTI2021

Real-Time Edge AI for Distributed Systems (READS): Progress on Beam Loss De-Blending for the Fermilab Main Injector and Recycler

The Fermilab Main Injector enclosure houses two accelerators, the Main Injector and Recycler. During normal operation, high intensity proton beams exist simultaneously in both. The two accelerators share the same beam loss monitors (BLM) and monitoring system. Beam losses in the Main Injector enclosure are monitored for tuning the accelerators and machine protection. Losses are currently attributed to a specific machine based on timing. However, this method alone is insufficient and often inaccurate, resulting in more difficult machine tuning and unnecessary machine downtime. Machine experts can often distinguish the correct source of beam loss. This suggests a machine learning (ML) model may be producible to help de-blend losses between machines. Work is underway as part of the Fermilab Real-time Edge AI for Distributed Systems Project (READS) to develop a ML empowered system that collects streamed BLM data and additional machine readings to infer in real-time, which machine generated beam loss.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

Report on Task USA A 0931 (A.252) Implementation of Fast, Front-End Electronics for Improved Low-Dead Time Neutron Counting

The International Atomic Energy Agency's (IAEA) Department of Safeguards is working on the elaboration and implementation of advanced Non-Destructive Assay (NDA) methods, generally known as neutron noise analysis, for the verification of critical and sub-critical cores containing substantial quantities of direct use nuclear material. These verification methods rely on interpreting time properties of the neutron multiplication process, which are to be extracted from measured neutron pulse trains. The interpretations are highly dependent on the quality of the measured data, which is directly related to the capability of an employed neutron detector to resolve very close detection events originating from neutron bursts produced in a highly multiplying medium. Therefore, the application of detectors with minimum dead-time, pile-up, and other distortion effects is required. This report describes the work under Task USA A 0931 (A.252 Implementation of fast, front-end electronics for improved low-dead time neutron counting).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

TRANSENSOR: Transformer Real-time Assessment INtelligent System with Embedded Network of Sensors and Optical Readout. Final Report

Utilities across the world are wrestling with evolving market dynamics, population growth and climate change. Distributed Energy Resources (DERs) such as solar photovoltaics, distributed generators and energy storage systems are becoming important parts of the U.S. energy mix. These factors are driving an increasing need for low-cost grid asset monitoring. Aside from being costly, traditional utility monitoring systems are not sufficiently robust and do not provide real-time visibility into the condition of grid assets such as transformers. Lack of accurate real-time measurements on performance has resulted in the use of lagging indicators, such as oil sample analysis. To address this need, an innovative embedded optical sensing technology, Transformer Real-time Assessment Intelligent System (TRANSENSOR) was developed, validated and demonstrated in this project. To date, TRANSENSOR has focused on transformers but is extendable to other grid assets. In addition to the technology being embedded into new transformers during manufacturing, a retrofit configuration that can be installed on existing transformers in the field was also developed. It is anticipated that the ability to retrofit existing transformers will help accelerate adoption of the underlying TRANSENSOR technology. Phase 1 of the project focused on laboratory development and qualification of the technology. Following iterations and exploration of relevant optical sensing modalities and multiplexed configurations of interest for the transformer environment, an effective candidate configuration was agreed upon, down-selected and custom-designed for embedding into General Electric (GE) network transformers. Two new GE 500 kilovolt ampere (kVA), 27 kilovolt (kV) distribution network transformers were built with embedded fiber-optic (FO) sensors and successfully qualified per industry standards at GE’s Shreveport, Louisiana facility during Phase 1 of the project. Following the successful completion of Phase 1, the team proceeded to Phase 2, which focused on a field demonstration of the technology. Over Phase 2, the first new GE transformer built with embedded fiber-optic sensors was installed in an above-ground cage and connected to the grid at Con Edison’s Astoria facility. A second transformer equipped with TRANSENSOR was installed in an underground vault. Additionally, an older (1982 year model) GE transformer in an above-ground cage was retrofitted with fiber- optic sensors and reconnected to the grid at ConEd’s facility. Analysis of data acquired from the sensors showed interesting correlations with transformer loading. Additionally, key events such as transformer low-voltage network connection, primary-side energizing, and a pressure loss event from an oil sampling were detected by the TRANSENSOR system. Online data processing/feature extraction algorithms for the second transformer in the underground vault detected key features and event alerts that were transmitted through a wireless 4G connection. The remote deployment concept showed promising results with data collection running for a total of 8 months across the three (3) GE transformers instrumented for the Phase 2 field demonstration. This sets the stage well for further development and commercialization.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Reaction History Uncertainty Propagation from Flux to α and from Time to α [Slides]

We discuss uncertainty propagation in reaction history data from underground nuclear tests. γ reaction history detectors measured flux as a function of time. After data analysis we report α as a function of time. Flux uncertainty and time uncertainty propagation into α uncertainty are discussed in detail. Discretization of the formulas is discussed.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

Parallel-in-Time Simulation of Lindblad's Equation

Constructing fast quantum logic gates is critical to building a scalable quantum computer. We consider a qudit, a quantum version of a bit that can take an arbitrary number of states, coupled with a cavity. In this project, we wish to force the qudit to reach the 0-state, for any possible initial state. The coupled system changes in time according to Lindblad’s equation, an ordinary differential equation on the density matrix of the quantum system. Lindblad’s equation contains some parameters that we can control, so-called control functions. We seek control functions which force the qudit to the 0-state within 2 microseconds, which is much faster than what is currently done in practice. The search method is gradient descent, a numerical optimization method that uses gradient information to iteratively improve the control parameters. My contribution to this project is an attempt to speed up the computation of the gradient. It currently takes about 40 seconds to compute the gradient which involves solving a set of ODEs sequentially. Current supercomputers have thousands of cores, but sequential computations can only make use of 1 core at a time. We wish to divide up the work better, so that we can use many more cores at once. To this end, we have implemented the Multigrid Reduction in Time (MGRIT) algorithm. We perform a systematic parameter search on how to best apply this algorithm. Results indicate a 25 percent speed up for solving Lindblad’s equation and determining how close the final state is the 0-state.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Shockwave Arrival Times from Operation Redwing and Operation Upshot-Knothole

Back in the 1940s, 50s, and 60s, 210 nuclear tests were conducted in the atmosphere. As part of the testing program, EG&G performed shockwave arrival-time measurements for different shots in different operations using pressure transducers at different distances from ground zero. They then developed correlations unique to each set of data. They did not attempt to correlate all data onto one curve. As a fireball moves, material is picked up and pulled in, which is called entrainment. This extra material increases the mass of the fireball, and as the energy is spread out over a larger and larger mass, the specific internal energy of the fireball gases drops, which causes the temperature to drop as well. When the energy in the solid angle of a spherical shockwave hits the surface, it is reflected. Shortly thereafter, it catches up with the outward-moving shockwave and re-combines with it, causing it to expand at a slightly faster rate. As the shockwave grows, it approaches a hemispherical shape. The purpose of this project is to demonstrate that the shockwave arrival-time data can, in fact, be correlated onto a single function when the shockwave time-dependent solution is modified to include a geometric factor, θ, and is corrected for an entrainment coefficient.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗