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Results for “Dynamic Timing Analysis”

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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Real-Time Modal Analysis of Electric Power Grids - The Need for Dynamic State Estimation

We articulate the reason why dynamic state estimation is needed to push the boundaries of the modal analysis of electric power grids in real-time operation. Then, we demonstrate how to unravel linear and nonlinear modes by using the extended dynamic mode decomposition along with estimates of the synchronous generators’ rotor angles and rotor speed deviations from nominal speed. The estimated modes are associated with electromechanical oscillations that take place continuously in electric power grids because of imbalances between power generation and demand. The numerical simulations are performed on a synthetic, albeit realistic, 2,000-bus network that was designed to resemble the electric power grid of Texas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

State-based network similarity visualization

We introduce an approach for the interactive visual analysis of weighted, dynamic networks. These networks arise in areas such as computational neuroscience, sociology, and biology. Network analysis remains challenging due to complex time-varying network behavior. For example, edges disappear/reappear, communities grow/vanish, or overall network topology changes. Our technique, TimeSum, detects the important topological changes in graph data to abstract the dynamic network and visualize one summary representation for each temporal phase, a state. We define a network state as a graph with similar topology over a specific time interval. To enable a holistic comparison of networks, we use a difference network to depict edge and community changes. Here, we present case studies to demonstrate that our methods are effective and useful for extracting and exploring complex dynamic behavior of networks.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Momentum-resolved analysis of condensate dynamic and Higgs oscillations in quenched superconductors with time-resolved ARPES

Higgs oscillations in nonequilibrium superconductors provide a unique tool to obtain information about the underlying order parameter. Several properties like the absolute value, existence of multiple gaps, and the symmetry of the order parameter can be encoded in the Higgs oscillation spectrum. Studying Higgs oscillations with time-resolved angle-resolved photoemission spectroscopy (ARPES) has the advantage over optical measurements that a momentum-resolved analysis of the condensate dynamic is possible. In this paper, we investigate the time-resolved spectral function measured in ARPES for different quench protocols. We find that analyzing amplitude oscillations of the ARPES intensity in the whole Brillouin zone allows to understand how the condensate dynamic contributes to the emerging of collective Higgs oscillations. Furthermore, by evaluating the phase of these oscillations, the symmetry deformation dynamic of the condensate can be revealed, which gives insight about the ground state symmetry of the system. With such an analysis, time-resolved ARPES experiments might be used in the future as a powerful tool in the field of Higgs spectroscopy.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dynamics retrieval from stochastically weighted incomplete data by low-pass spectral analysis

Time-resolved serial femtosecond crystallography (TR-SFX) provides access to protein dynamics on sub-picosecond timescales, and with atomic resolution. Due to the nature of the experiment, these datasets are often highly incomplete and the measured diffracted intensities are affected by partiality. To tackle these issues, one established procedure is that of splitting the data into time bins, and averaging the multiple measurements of equivalent reflections within each bin. This binning and averaging often involve a loss of information. Here, we propose an alternative approach, which we call low-pass spectral analysis (LPSA). In this method, the data are projected onto the subspace defined by a set of trigonometric functions, with frequencies up to a certain cutoff. This approach attenuates undesirable high-frequency features and facilitates retrieving the underlying dynamics. A time-lagged embedding step can be included prior to subspace projection to improve the stability of the results with respect to the parameters involved. Subsequent modal decomposition allows to produce a low-rank description of the system's evolution. Using a synthetic time-evolving model with incomplete and partial observations, we analyze the LPSA results in terms of quality of the retrieved signal, as a function of the parameters involved. We compare the performance of LPSA to that of a range of other sophisticated data analysis techniques. We show that LPSA allows to achieve excellent dynamics reconstruction at modest computational cost. Finally, we demonstrate the superiority of dynamics retrieval by LPSA compared to time binning and merging, which is, to date, the most commonly used method to extract dynamical information from TR-SFX data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Safety Risk Reliability Model Library

SR2ML is a software package which contains a set of safety and reliability models designed to be interfaced with the INL developed RAVEN code. These models can be employed to perform both static and dynamic system risk analysis and determine risk importance of specific elements of the considered system. Two classes of reliability models have been developed; the first class includes all classical reliability models (Fault-Trees, Event-Trees, Markov models and Reliability Block Diagrams) which have been extended to deal not only with Boolean logic values but also time dependent values. The second class includes several components aging models. Models of these two classes are designed to be included in a RAVEN ensemble model to perform time dependent system reliability analysis (dynamic analysis). Similarly, these models can be interfaced with system analysis codes to determine failure time of systems and evaluate accident progression (static analysis).

Wang, Congjian↗
DOE OSTI2019

High definition large area mapping of geological samples using a Maia detector array in the Nuclear Microprobe

A Maia detector array has been utilized and installed on the CSIRO-MARC Nuclear Microprobe. Maia uses an annular array of 384 silicon detectors to provide a solid-angle of 1.3 sr to improve the collection of X-rays induced by Particle Induced X-ray Emission (PIXE) using a micron focussed MeV proton beam. It features event-by-event data acquisition with dead-time correction and pileup rejection and a total count-rate capacity above 10 M/s, versatile stage and beam scanning modes (coupled to the DAQ-36 system described previously) to cater for a variety of sample configuration and experimental needs, precise measurement of transit time and integration of beam fluence per pixel, and spectral deconvolution of event data in real-time using the Dynamic Analysis method to provide element images during data collection. In this work we demonstrate the system on thin sections made from geological samples extracted from active seafloor hydrothermal vents in the PACMANUS hydrothermal field in Papua New Guinea.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Aliovalent Anion Incorporation in Halide Na-ion Conductors for Enhanced Ionic Conductivity

Halide-based solid electrolytes (SEs), particularly zirconium (Zr)-centered halides, are attractive from a material cost perspective. Nevertheless, Zr-centered halide SEs are hindered by their low ionic conductivity. Here, in this study, we report on the cubic Na 3 ZrCl 5 S superionic conductor through strategic sulfur anion incorporation, achieving 10 times higher ionic conductivity than that of Na 2 ZrCl 6 . With the optimal composition, the highest ionic conductivity of 0.753 mS cm –1 is obtained for the 0.6Na 2 S–1.4NaCl–ZrCl 4 compound. When paired with a NaCrO 2 cathode, the assembled all-solid-state batteries (ASSBs) achieve a specific discharge capacity of 110 mA h g –1 at 0.1C and exhibit long-term cycling stability at 0.3C at room temperature over 1000 cycles (with 83% capacity retention). Moreover, in situ electrochemical impedance spectroscopy combined with distribution of relaxation times analysis reveal the dynamically interfacial stability between Na halide with electrodes. In conclusion, this work highlights the design and synthesis of advanced halide electrolytes through anion incorporation, paving the way for the development of next-generation ASSBs.

Guo, Xiaolin [Univ. of Louisville, KY (United Stat↗
DOE OSTI2019

Time-resolved, nonequilibrium carrier and coherent acoustic phonon dynamics in (Cd, Mg)Te single crystals for radiation detectors

We present time-resolved nonequilibrium carrier and coherent acoustic phonon dynamics in (Cd, Mg)Te single crystals intended for detection of high-energy x-ray photons. Our (Cd, Mg)Te crystals were grown using a vertical Bridgman method and during the process were doped either with In (a shallow dopant) or Ge (a deep impurity). Next they were cut into platelets and, subsequently, tested as as-grown specimens, or, before testing, long-term annealed in a temperature range of 750 °C–800 °C to improve their crystalline properties. All samples exhibited high resistivities of 10 9 –10 10 Ω cm, as required for x-ray detection applications. We have performed femtosecond optical spectroscopy, pump-probe measurements on all samples in both one-color (high-intensity 800 nm pump and low-intensity 800 nm probe) and two-color (high-intensity 400 nm pump and low-intensity 800 nm probe) configurations, and analyzed the results using our carrier dynamics rate-equation model. The model allowed us to perform a comprehensive, time-resolved analysis of relaxation dynamics of photoexcited carriers and investigate the role of carrier traps present in all tested samples. Two-color measurements also allowed us to observe and analyze the propagation of acoustic coherent phonons excited by the high-energy pump pulse. We observed that the dependence of phonon mode frequency, after including a correction of the experimentally measured crystal index of refraction dependence on the probe wavelength, was dispersionless and led to the value for the sound velocity of 3367 m s –1 , which is in excellent agreement with the literature data. The intrinsic lifetime of coherent phonons in (Cd, Mg)Te was estimated to be of the order of 10 ns. Lastly, we conclude that among our tested samples, the In-doped, annealed (Cd, Mg)Te crystal exhibits the best characteristics for radiation detection applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Spectral Methods in Time-dependent Data Analysis

We aim to create a new model for time-dependent data analysis, named dynamical learning, that integrates data-driven manifold learning techniques with operator-theoretic methods from dynamical systems theory. This approach has the potential to deliver more efficient methods for analyzing time-dependent data, such as video streams, by naturally separating out the temporal and spatial features of the data. We aim to apply the newly developed methods to video surveillance data related to Sandia mission applications, and particularly focus on the problems of image segmentation and object tracking. This project ended early due to the departure of the PI from Sandia about 18 months into the project. Therefore, this document reports on partial progress towards the initial goals of the project. In addition, this document reports on part of the work conducted during the project; see the Appendix for a summary of all the work conducted during the 18 months.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Non-autoregressive time-series methods for stable parametric reduced-order models

Advection-dominated dynamical systems, characterized by partial differential equations, are found in applications ranging from weather forecasting to engineering design where accuracy and robustness are crucial. There has been significant interest in the use of techniques borrowed from machine learning to reduce the computational expense and/or improve the accuracy of predictions for these systems. These rely on the identification of a basis that reduces the dimensionality of the problem and the subsequent use of time series and sequential learning methods to forecast the evolution of the reduced state. Often, however, machine-learned predictions after reduced-basis projection are plagued by issues of stability stemming from incomplete capture of multiscale processes as well as due to error growth for long forecast durations. To address these issues, we have developed a non-autoregressive time series approach for predicting linear reduced-basis time histories of forward models. In particular, we demonstrate that non-autoregressive counterparts of sequential learning methods such as long short-term memory (LSTM) considerably improve the stability of machine-learned reduced-order models. Further, we evaluate our approach on the inviscid shallow water equations and show that a non-autoregressive variant of the standard LSTM approach that is bidirectional in the principal component directions obtains the best accuracy for recreating the nonlinear dynamics of partial observations. Moreover-and critical for many applications of these surrogates-inference times are reduced by three orders of magnitude using our approach, compared with both the equation-based Galerkin projection method and the standard LSTM approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Anomaly analysis and visualization for dynamic networks through spatiotemporal graph segmentations

With recent technology advance in Internet of Things (IoT) that involves human, sensors, and mobile devices, networks are not only growing much larger but more complex and dynamic in nature. The spatiotemporal dynamics of networks are represented by both topological changes and temporal shifts of attribute information associated with network components. Understanding the pattern and trend of dynamic networks is increasingly important. While data mining approaches are generally useful in analyzing the statistical properties of networks, there has been recent trend to consider bringing human into the loop, and to examine how feedback from visualizations of large-scale dynamic networks can further improve data mining and machine learning. Traditional visualization methods based on animation and sequences of snapshot graphs are also limited by human cognitive capability. Here, we present a dynamic network analysis and visualization (DNAV) tool which explores the spatiotemporal dimensions of graph components. In particular, nodes and edges are augmented with spatiotemporal segmentation based on both topological and attribute dynamics (e.g., time and locations of connectivity). To further facilitate analysis of large dynamic networks, DNAV includes statistical dynamic overviews alongside graph views, as well as data filtering modules for scalable analysis. Using case studies on public datasets, we demonstrate the effectiveness of DNAV in understanding and analyzing anomalies in dynamic networks such as computer communication networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Feasibility Assessment of Long-Term Electrical Resistivity Monitoring of a Nitrate Plume

Long-term monitoring solutions at contaminated sites are necessary to track plume migration and evaluate the performance of remediation efforts. Electrical resistivity imaging (ERI) can potentially provide information about plume dynamics; however, the feasibility and likelihood of success is seldom evaluated before conducting a field study. Coupling flow and transport models with geoelectrical models provides a powerful way to assess the potential effectiveness of an actual ERI field campaign. in this paper, we present a coupled approach for evaluating the feasibility of monitoring nitrate migration and remediation using 4D time-lapse ERI at a legacy nuclear waste facility. This kilometer-scale study focuses on depths below the water table (~70 m). A flow and transport model is developed to perform simulations of nitrate migration and removal via a hypothetical pump-and-treat system. A tracer injection is also simulated at the leading edge of the nitrate plume to enhance the conductivity contrast between the native subsurface and the groundwater fluids. Images of bulk conductivity provide limited information concerning plume migration while time-lapse difference images, which remove the static effects of geology, provide more useful information concerning plume dynamics over time. A spatial moment analysis performed on flow and transport and ERI models matches well during the tracer injection; however, inversion regularization smoothing otherwise limits the value in terms of locating the center of mass. We find that the addition of a tracer enables ERI to characterize plume dynamics during pump-and-treat operations, and late-time ERI monitoring provides a conservative estimate of nitrate plume boundaries in this synthetic study.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Combined, time-resolved, in situ neutron reflectometry and X-ray diffraction analysis of dynamic SEI formation during electrochemical N 2 reduction

One means of improving performance for electrochemical ammonia production through the Li-mediated N 2 reduction reaction (Li-NRR) is by cycling the current driving the reaction between open-circuit conditions and periods of applied current density. Herein, we have investigated the dynamics of the electrode–electrolyte interface under Li-NRR conditions during current cycling using in situ time-resolved neutron reflectometry and grazing-incidence synchrotron X-ray diffraction. During cycling, measured neutron reflectivity curves indicated bilayer formation in which Li-containing species such as LiOH, Li 2 O, and small quantities of Li 3 N and metallic Li primarily appeared in a thin layer at the cathode surface, above which formed a much larger, porous, ‘solid–electrolyte interface’ (SEI) layer. Upon return to open-circuit conditions, Li-containing species quickly moved out of the thin layer, leaving a compact, stable layer of decomposition products underneath the SEI layer. This SEI layer concomitantly filled with electrolyte or dissolved, becoming indistinguishable from the electrolyte via contrast in scattering-length density (SLD). During the second current cycle, Li-containing species again preferentially deposited directly atop the cathode, with the thick SEI-like layer again appearing within a minute. This SEI layer exhibited a lower SLD more quickly than in the first cycle, which might suggest that Li-containing species become distributed within the porous SEI layer. Furthermore, these time-resolved observations of SEI and plated layers during current cycling suggest that benefits associated with return to open-circuit conditions between periods of applied current density may be related to the concomitant loss of Li-containing species from a thin layer at the cathode surface into a porous SEI layer that becomes filled with electrolyte or dissolves.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Engineered thermalization and cooling of quantum many-body systems

We develop a scheme for engineering genuine thermal states in analog quantum simulation platforms by coupling local degrees of freedom to driven, dissipative ancilla pseudospins. We demonstrate the scheme in a many-body quantum spin lattice simulation setting. A Born-Markov master equation describing the dynamics of the many-body system is developed, and we show that if the ancilla energies are periodically modulated, with a carefully chosen hierarchy of timescales, one can effectively thermalize the many-body system. Through analysis of the time-dependent dynamical generator, we determine the conditions under which the true thermal state is an approximate dynamical fixed point for general system Hamiltonians. Finally, we evaluate the thermalization protocol through numerical simulation and discuss prospects for implementation on current quantum simulation hardware.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Understanding latent timescales in neural ordinary differential equation models of advection-dominated dynamical systems

The neural ordinary differential equation (ODE) framework has shown considerable promise in recent years in developing highly accelerated surrogate models for complex physical systems characterized by partial differential equations (PDEs). For PDE-based systems, state-of-the-art neural ODE strategies leverage a two-step procedure to achieve this acceleration: a nonlinear dimensionality reduction step provided by an autoencoder, and a time integration step provided by a neural-network based model for the resultant latent space dynamics (the neural ODE). This work explores the applicability of such autoencoder-based neural ODE strategies for PDEs in which advection terms play a critical role. More specifically, alongside predictive demonstrations, physical insight into the sources of model acceleration (i.e., how the neural ODE achieves its acceleration) is the scope of the current study. Such investigations are performed by quantifying the effects of both autoencoder and neural ODE components on latent system time-scales using eigenvalue analysis of dynamical system Jacobians. To this end, the sensitivity of various critical training parameters – de-coupled versus end-to-end training, latent space dimensionality, and the role of training trajectory length, for example – to both model accuracy and the discovered latent system timescales is quantified. Furthermore, this work specifically uncovers the key role played by the training trajectory length (the number of rollout steps in the loss function during training) on the latent system timescales: larger trajectory lengths correlate with an increase in limiting neural ODE time-scales, and optimal neural ODEs are found to recover the largest time-scales of the full-order (ground-truth) system. Demonstrations are performed across fundamentally different unsteady fluid dynamics configurations influenced by advection: (1) the Kuramoto–Sivashinsky equations (2) Hydrogen-Air channel detonations (the compressible reacting Navier–Stokes equations with detailed chemistry), and (3) 2D Atmospheric flow.

Advection-dominated dynamical systems↗
DOE OSTI2022

Noisy-Intermediate-Scale Quantum Electromagnetic Transients Program

Quantum-empowered electromagnetic transients program (QEMTP) is a promising paradigm for tackling EMTP's computational burdens. Nevertheless, no existing studies truly achieve a practical and scalable QEMTP operable on today's noisy-intermediate-scale quantum (NISQ) computers. The strong reliance on noise-free and fault-tolerant quantum devices--which appears to be decades away--hinder practical applications of current QEMTP methods. Here, we devise a NISQ-QEMTP methodology which for the first time transitions the QEMTP operations from ideal, noise-free quantum simulators to real, noisy quantum computers. The main contributions lie in: (1) a shallow-depth QEMTP quantum circuit for mitigating noises on NISQ quantum devices; (2) practical QEMTP linear solvers incorporating executable quantum state preparation and measurements for nodal voltage computations; (3) a noise-resilient QEMTP algorithm leveraging quantum resources logarithmically scaled with power system dimension; (4) a quantum shifted frequency analysis (QSFA) for accelerating QEMTP by exploiting dynamic phasor simulations with larger time steps; (5) a systematical analysis on QEMTPs performance under various noisy quantum environments. Extensive experiments systematically verify the accuracy, efficacy, universality and noise-resilience of QEMTP on both noise-free simulators and IBM real quantum computers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Machine Intelligence-Centered System for Automated Characterization of Functional Materials and Interfaces

Classic design of experiment relies on a time-intensive workflow that requires planning, data interpretation, and hypothesis building by experienced researchers. Here, in this paper, we describe an integrated, machine-intelligent experimental system which enables simultaneous dynamic tests of electrical, optical, gravimetric, and viscoelastic properties of materials under a programmable dynamic environment. Specially designed software controls the experiment and performs on-the-fly extensive data analysis and dynamic modeling, real-time iterative feedback for dynamic control of experimental conditions, and rapid visualization of experimental results. The system operates with minimal human intervention and enables time-efficient characterization of complex dynamic multifunctional environmental responses of materials with simultaneous data processing and analytics. The system provides a viable platform for artificial intelligence (AI)-centered material characterization, which, when coupled with an AI-controlled synthesis system, could lead to accelerated discovery of multifunctional materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗