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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

Improving Spectral Resolution from Real-time Evolution for Correlated Systems

Abstract The quality of numerically simulated spectra using real-time evolution methods for strongly correlated systems is affected by both the length of simulation time and the system size, limiting resolution in both frequency and momentum. In this work, we propose a computationally cheap, linear autoregressive machine learning-based framework to extend short-time and short-distance results over a wider range. We use the proposed method to extend the lesser Green’s function for both the Hubbard model and the much more computationally challenging Hubbard-extended Holstein model. This technique significantly improves both the frequency and momentum resolution of the single-particle removal spectrum $${\mathcal{A}}(k,\omega )$$ A ( k , ω ) , allowing the observation of otherwise obscured spectral features due to electron-phonon coupling.

Tang, Ta↗

Machine learning based systems and methods for real time, model based diagnosis

The disclosure following relates generally to complex simulations, and fault diagnosis. In some embodiments, a component that is causing a delayed simulation time of a system is determined. A component of reduced complexity is designed, and the component of reduced complexity is used to replace the original component in the system. Fault diagnosis may then be conducted using the updated system with the reduced complexity component, thus decreasing the time taken to diagnose the fault.

Matei, Ion↗

Accessible Real-Time Surveillance Radar System for Object Detection

As unmanned ground and aerial vehicles become more accessible and their usage covers a wider area of application, including for threatening purposes which can cause connected catastrophe, a surveillance system for the public places is being considered more essential to respond to those possible threats. We propose an inexpensive, lighter, safer, and smaller radar system than military-grade radar systems while keeping reasonable capability for use in monitoring public places. The paper details the iterative process on the system design and improvements with experiments to realize the system used for surveillance. The experiments show the practical use of the system and configuration for a better understanding of using the system. Cyber-physical systems for outdoor environments can benefit from the system as a sensor for sensing objects as well as monitoring.

47 OTHER INSTRUMENTATION↗

Low-latency NuMI Trigger for the CHIPS-5 Neutrino Detector

The CHIPS R&D project aims to develop affordable water Cherenkov detectors for large-scale underwater installations. In 2019, a 5kt prototype detector CHIPS-5 was deployed in northern Minnesota to study neutrinos generated by the nearby NuMI beam. This contribution presents a dedicated low-latency time distribution system for CHIPS-5 that delivers timing signals from the Fermilab accelerator to the detector with sub-nanosecond precision. Exploiting existing NOvA infrastructure, the time distribution system achieves this only with open-source software and conventional network elements. In a time-of-flight study, the presented system has reliably offered a time budget of $610 \pm 330\text{ ms}$ for on-site triggering. This permits advanced analysis in real-time as well as a novel hardware-assisted active triggering mode, which reduces DAQ computing load and network bandwidth outside triggered time windows.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

National Ignition Facility Opacity Time Resolved Spectrometer Systems Engineering Final Project

The National Ignition Facility (NIF) is the world’s largest and most energetic laser facility. The NIF system is designed to produce high energy density (temperature and pressure) conditions through the application of its 192 laser beams. One of the users of NIF is the opacity platform developed to study the opacities at temperatures and densities relevant to the solar interior and stellar evolution. The platform was developed to study iron (Fe) opacity at temperatures relevant to the solar interior. The opacity campaign uses spectrometers to gather data. Spectrometers utilize crystals to produce x-ray spectra that are recorded on time-integrated and time-resolved detectors. The opacity spectrometer (OpSpec) currently fielded and in use at NIF uses a time integrated film channel to collect data. The opacity spectrometer time resolved (OpSpecTR) will utilize novel hCMOS detectors to capture time resolved images of spectra of interest. The key stakeholders identified for OpSpecTR included the physicists responsible for OpSpec and OpSpecTR, the Target Area Science and Engineering (TASE) department at NIF, the NIF and Photon Science (NIF & PS) Opacity program, the Nevada National Security Site (NNSS) Physics and Engineering program, the Sandia hCMOS manufacturing and testing program, and the Los Alamos National Laboratory (LANL) program sponsor. The Target and Experimental Operations (TEXOPS) was identified as a key stakeholder because the group includes the individuals that will physically interact with the OpSpecTR system as it participates in NIF experiments. The opacity platform collects data in a unique orientation relative to the existing diagnostics fielded at NIF. The existing infrastructure at NIF uses a diagnostic manipulator (DIM) to insert the diagnostic near the target chamber center to collect data during a NIF shot. Existing diagnostics collect data through the center line of the DIM axis and collect relevant data perpendicular to this axis. The opacity platform requires crystals mounted in a specific orientation which requires data collection parallel to the DIM axis. This deviation from standard NIF practices was a key factor in developing requirements.

42 ENGINEERING↗

Virtual sensing-enabled digital twin framework for real-time monitoring of nuclear systems leveraging deep neural operators

Abstract Real-time monitoring is a foundation of nuclear digital twin technology, crucial for detecting material degradation and maintaining nuclear system integrity. Traditional physical sensor systems face limitations, particularly in measuring critical parameters in hard-to-reach or harsh environments, often resulting in incomplete data coverage. Machine learning-driven virtual sensors offer a transformative solution by complementing physical sensors in monitoring critical degradation indicators. This paper introduces the use of Deep Operator Networks (DeepONet) to predict key thermal-hydraulic parameters in the hot leg of pressurized water reactor. DeepONet acts as a virtual sensor, mapping operational inputs to spatially distributed system behaviors without requiring frequent retraining. Our results show that DeepONet achieves low mean squared and Relative L2 error, making predictions 1400 times faster than traditional CFD simulations . These characteristics enable DeepONet to function as a real-time virtual sensor, synchronizing with the physical system to track degradation conditions and provide insights within the digital twin framework for nuclear systems.

Hossain, Raisa↗

A time-resolved imaging system for the diagnosis of x-ray self-emission in high energy density physics experiments

A diagnostic capable of recording spatially and temporally resolved x-ray self-emission data was developed to characterize experiments on the MAGPIE pulsed-power generator. The diagnostic used two separate imaging systems: a pinhole imaging system with two-dimensional spatial resolution and a slit imaging system with one-dimensional spatial resolution. The two-dimensional imaging system imaged light onto the image plate. The one-dimensional imaging system imaged light onto the same piece of image plate and a linear array of silicon photodiodes. This design allowed the cross-comparison of different images, allowing a picture of the spatial and temporal distribution of x-ray self-emission to be established. The design was tested in a series of pulsed-power-driven magnetic-reconnection experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Symmetry-breaking-induced rare fluctuations in a time-delay dynamic system

Inspired by the experimental and numerical findings, we study the dynamic instabilities of two coupled nonlinear delay differential equations that are used to describe the coherent oscillations between the top and bottom boundary layers in turbulent Rayleigh–Bénard convection. By introducing two sensitivity parameters for the instabilities of the top and bottom boundary layers, we find three different types of solutions, namely in-phase single-period oscillations, multi-period oscillations and chaos. The chaos solution contains rare but large amplitude fluctuations. The statistical properties of these fluctuations are consistent with those observed in the experiment for the massive eruption of thermal plumes, which causes random reversals of the large-scale circulation in turbulent Rayleigh–Bénard convection. Finally, our study thus provides new insights into the origin of rare massive eruptions and sudden changes of large-scale flow pattern that are often observed in convection systems of geophysical and astrophysical scales.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

A Simple Lagrangian Parcel Model for the Initiation of Summer-time Mesoscale Convective Systems over the Central United States

Mesoscale convective systems (MCSs) account for more than 50% of summertime precipitation over the central United States and have a significant impact on local weather and hydrologic cycle. It is hypothesized that the inadequate treatment of MCSs is responsible for the long-standing warm and dry bias over the central United States in coarse-resolution general circulation model (GCM) simulations. In particular, a better understanding of MCS initiation is still lacking. Here a single-column Lagrangian parcel model is first developed to simulate the basic features of a rising parcel. This simple model demonstrates the collective effects of boundary layer moistening and dynamical lifting in triggering convective initiation and reproduces successfully its early afternoon peak with surface equivalent potential temperature as a controlling factor. It also predicts that convection is harder to trigger in the future climate under global warming, consistent with the results from convection-permitting regional climate simulations. Then, a multicolumn model that includes an array of single-column models aligned in the east–west direction and incorporates idealized cold pool interaction mechanisms is developed. The multicolumn model captures readily the cold pool–induced upscale growth feature in MCS genesis from initially scattered convection that is organized into a mesoscale cluster in a few hours. It also highlights the crucial role of lifting effects due to cold pool collision and spreading, subsidence effect, and gust front propagation speed in controlling the final size of mesoscale clusters and cold pool regions. This simple model should be useful for understanding fundamental mechanisms of MCS initiation and providing guidance for improving MCS simulations in GCMs.

54 ENVIRONMENTAL SCIENCES↗

Development of a hardware-in-the-loop testbed for laboratory performance verification of flexible building equipment in typical commercial buildings

The goals of reducing energy costs, shifting electricity peaks, increasing the use of renewable energy, and enhancing the stability of the electric grid can be met in part by fully exploiting the energy flexibility potential of buildings and building equipment. The development of strategies that exploit these flexibilities could be facilitated by publicly available high-resolution datasets illustrating how control of HVAC systems in commercial buildings can be used in different climate zones to shape the energy use profile of a building for grid needs. This article presents the development and integration of a Hardware-In-the-Loop Flexible load Testbed (HILFT) that integrates physical HVAC systems with a simulated building model and simulated occupants with the goal of generating datasets to verify load flexibility of typical commercial buildings. Compared to simulation-only experiments, the hardware-in-the-loop approach captures the dynamics of the physical systems while also allowing efficient testing of various boundary conditions. The HILFT integration in this article is achieved through the co-simulation among various software environments including LabVIEW, MATLAB, and EnergyPlus. Although theoretically viable, such integration has encountered many real-world challenges, such as: 1) how to design the overall data infrastructure to ensure effective, robust, and efficient integration; 2) how to avoid closed-loop hunting between simulated and emulated variables; 3) how to quantify system response times and minimize system delays; and 4) how to assess the overall integration quality. Lessons-learned using the examples of an AHU-VAV system, an air-source heat pump system, and a water-source heat pump system are presented.

Chen, zhelun↗

Online real-time learning of dynamical systems from noisy streaming data

Abstract Recent advancements in sensing and communication facilitate obtaining high-frequency real-time data from various physical systems like power networks, climate systems, biological networks, etc. However, since the data are recorded by physical sensors, it is natural that the obtained data is corrupted by measurement noise. In this paper, we present a novel algorithm for online real-time learning of dynamical systems from noisy time-series data, which employs the Robust Koopman operator framework to mitigate the effect of measurement noise. The proposed algorithm has three main advantages: (a) it allows for online real-time monitoring of a dynamical system; (b) it obtains a linear representation of the underlying dynamical system, thus enabling the user to use linear systems theory for analysis and control of the system; (c) it is computationally fast and less intensive than the popular extended dynamic mode decomposition (EDMD) algorithm. We illustrate the efficiency of the proposed algorithm by applying it to identify the Van der Pol oscillator, the chaotic attractor of the Henon map, the IEEE 68 bus system, and a ring network of Van der Pol oscillators.

97 MATHEMATICS AND COMPUTING↗