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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Effects of a nuclear-disturbed environment on electromagnetic wave propagation through the atmosphere

This paper investigates the effects of a nuclear-disturbed environment on the transmission of electromagnetic (EM) waves through the atmosphere. An atmospheric nuclear detonation can produce heightened free electron densities in the surrounding atmosphere that can disrupt EM waves that propagate through the disturbed region. Radiation transport models simulated the ionization and free electron densities created in the atmosphere from a 1 MT detonation at heights of burst of 5 km, 25 km, and 75 km. Recombination rates for the free electrons in the atmosphere were applied, from previous work in the literature, to determine the nuclear-induced electron densities as a function of time and space after the detonation. A ray-tracing algorithm was applied to determine the refraction and reflection of waves propagating in the different nuclear-disturbed environments. The simulation results show that the free electron plasma created from an atmospheric nuclear detonation depend on the height of burst of the weapon, the weapon yield, and the time after detonation. Detonations at higher altitudes produce higher free electron densities for greater durations and over larger ranges. The larger the free electron densities, the greater the impact on EM wavelengths in regards to refraction, reflection, and absorption in the atmosphere. An analysis of modern infrastructure and the effects of nuclear-disturbed atmospheres on different signal wavelengths and systems is discussed.

42 ENGINEERING↗
DOE OSTI2017

On coincident loop transient electromagnetic induction logging

Coincident loop transient induction wireline logging is examined as the borehole analog of the well-known land and airborne time-domain electromagnetic (EM) method. The concept of whole-space late-time apparent resistivity is modified from the half-space version commonly used in land and airborne geophysics and applied to the coincident loop voltages produced from various formation, borehole, and invasion models. Given typical tool diameters, off-time measurements with such an instrument must be made on the order of nanoseconds to microseconds — much more rapidly than for surface methods. Departure curves of the apparent resistivity for thin beds, calculated using an algorithm developed to model the transient response of a loop in a multilayered earth, indicate that the depth of investigation scales with the bed thickness. Modeled resistivity logs are comparable in accuracy and resolution with standard frequency-domain focused induction logs. However, if measurement times are longer than a few microseconds, the thicknesses of conductors can be overestimated, whereas resistors are underestimated. Thin-bed resolution characteristics are explained by visualizing snapshots of the EM fields in the formation, where a conductor traps the electric field while two current maxima are produced in the shoulder beds surrounding a resistor. Radial profiling is studied using a concentric cylinder earth model. Results found that true formation resistivity can be determined in the presence of either oil- or water-based mud, although in the latter case, measurements must be taken several orders of magnitude later in time. Lastly, the ability to determine true formation resistivity is governed by the degree that the EM field heals after being distorted by borehole fluid and invasion, a process visualized and particularly evident in the case of conductive water-based mud.

58 GEOSCIENCES↗
DOE OSTI2015

On specular reflectivity measurements in high and low-contrast relativistic laser-plasma interactions

Using both experiment and 2D3V particle-in-cell (PIC) simulations, we describe the use of specular reflectivity measurements to study relativistic (Iλ{sup 2 }> 10{sup 18 }W/cm{sup 2}⋅μm{sup 2}) laser-plasma interactions for both high and low-contrast 527 nm laser pulses on initially solid density aluminum targets. In the context of hot-electron generation, studies typically rely on diagnostics which, more-often-than-not, represent indirect processes driven by fast electrons transiting through solid density materials. Specular reflectivity measurements, however, can provide a direct measure of the interaction that is highly sensitive to how the EM fields and plasma profiles, critical input parameters for modeling of hot-electron generation, evolve near the interaction region. While the fields of interest occur near the relativistic critical electron density, experimental reflectivity measurements are obtained centimeters away from the interaction region, well after diffraction has fully manifested itself. Using a combination of PIC simulations with experimentally inspired conditions and an analytic, non-paraxial, pulse propagation algorithm, we calculate reflected pulse properties, both near and far from the interaction region, and compare with specular reflectivity measurements. The experiment results and PIC simulations demonstrate that specular reflectivity measurements are an extremely sensitive qualitative, and partially quantitative, indicator of initial laser/target conditions, ionization effects, and other details of intense laser-matter interactions. The techniques described can provide strong constraints on many systems of importance in ultra-intense laser interactions with matter.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Fractional modeling of viscoelasticity in 3D cerebral arteries and aneurysms

We develop efficient numerical methods for fractional order PDEs, and employ them to investigate viscoelastic constitutive laws for arterial wall mechanics. Recent simulations using one-dimensional models [1] have indicated that fractional order models may offer a more powerful alternative for modeling the arterial wall response, exhibiting reduced sensitivity to parametric uncertainties compared with the integer-calculus-based models. Here, we study three-dimensional (3D) fractional PDEs that naturally model the continuous relaxation properties of soft tissue, and for the first time employ them to simulate flow structure interactions for patient-specific brain aneurysms. To deal with the high memory requirements and in order to accelerate the numerical evaluation of hereditary integrals, we employ a fast convolution method [2] that reduces the memory cost to O(log(N)) and the computational complexity to O(N log(N)). Furthermore, we combine the fast convolution with high-order backward differentiation to achieve third-order time integration accuracy. We confirm that in 3D viscoelastic simulations, the integer order models strongly depends on the relaxation parameters, while the fractional order models are less sensitive. As an application to long-time simulations in complex geometries, we also apply the method to modeling fluid-structure interaction of a 3D patient-specific compliant cerebral artery with an aneurysm. Taken together, our findings demonstrate that fractional calculus can be employed effectively in modeling complex behavior of materials in realistic 3D time-dependent problems if properly designed efficient algorithms are employed to overcome the extra memory requirements and computational complexity associated with the non-local character of fractional derivatives.

Computer Science↗
DOE OSTI2019

Advances in Multi-Physics Modeling for Parallel Finite Element Code Suite ACE3P

SLAC developed ACE3P code suite is a comprehensive set of parallel finite-element codes for multi-physics modeling of particle accelerators. Running on massively parallel computer platforms for high fidelity and high accuracy simulation, ACE3P enables rapid virtual prototyping of accelerator and RF component design, optimization and analysis. Furthermore, recent advances of ACE3P have been focused on the implementation of advanced numerical algorithms, enhancement of multi-physics capabilities, and improvement of code performance on state-of-the-art high performance computing (HPC) platforms for large-scale RF modeling in accelerator applications and beyond. In this paper, we will present a nonlinear electromagnetic (EM) eigensolver for damping calculation of resonant modes for cavities coupled with waveguides, the thermal solver for accurate evaluation of heat loads in superconducting RF (SRF) cavity power coupler, the elastic solver for investigation of SRF cavity cryomodule (CM) bowing due to thermal effect, the frequency domain mechanical solver for studying SRF cavity mechanical oscillation modes, the particle tracking code for dark current simulation in SRF CM using a hybrid MPI+OpenMP parallel programing model, the time domain solver for high fidelity modeling of EM wave propagation for medical device in human body.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Analytical and Computational Studies of Correlations of Hydrodynamic Fluctuations in Shear Flow

Correlations of hydrodynamic fluctuations in shear flow analytically and also by dissipative particle dynamics (DPD) simulations are studied. The hydrodynamic equations are linearized around the macroscopic velocity field and then solved by a perturbation method in Fourier-transformed space. The autocorrelation functions (ACFs) from the analytical method are compared with results obtained from DPD simulations under the same shear-flow conditions. Up to a moderate shear rate, various ACFs from the two approaches agree with each other well. At large shear rates, discrepancies between the two methods are observed, hence revealing strong additional coupling between different fluctuating variables, which is not considered in the analytical approach. In addition, the results at low and moderate shear rates can serve as benchmarks for developing multiscale algorithms for coupling of heterogeneous solvers, such as a hybrid simulation of molecular dynamics and fluctuating hydrodynamics solver, where thermal fluctuations are indispensable.

Physics↗
DOE OSTI2014

Efficient modeling of photonic crystals with local Hermite polynomials

Developing compact algorithms for accurate electrodynamic calculations with minimal computational cost is an active area of research given the increasing complexity in the design of electromagnetic composite structures such as photonic crystals, metamaterials, optical interconnects, and on-chip routing. We show that electric and magnetic (EM) fields can be calculated using scalar Hermite interpolation polynomials as the numerical basis functions without having to invoke edge-based vector finite elements to suppress spurious solutions or to satisfy boundary conditions. This approach offers several fundamental advantages as evidenced through band structure solutions for periodic systems and through waveguide analysis. Compared with reciprocal space (plane wave expansion) methods for periodic systems, advantages are shown in computational costs, the ability to capture spatial complexity in the dielectric distributions, the demonstration of numerical convergence with scaling, and variational eigenfunctions free of numerical artifacts that arise from mixed-order real space basis sets or the inherent aberrations from transforming reciprocal space solutions of finite expansions. The photonic band structure of a simple crystal is used as a benchmark comparison and the ability to capture the effects of spatially complex dielectric distributions is treated using a complex pattern with highly irregular features that would stress spatial transform limits. This general method is applicable to a broad class of physical systems, e.g., to semiconducting lasers which require simultaneous modeling of transitions in quantum wells or dots together with EM cavity calculations, to modeling plasmonic structures in the presence of EM field emissions, and to on-chip propagation within monolithic integrated circuits.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Design of an advanced energy management system for microgrid control using a state machine

A state machine is proposed as the solution for an automated microgrid energy management system (EMS) to improve transient performance during transition operations. It characterizes microgrid operation by seven states that cover all the operating modes: two for steady-state operation (grid-connected and islanded), four for transition operation (preparing for disconnection, transitioning to islanding, preparing for reconnection, and transitioning to grid-connected), and one for emergency operation (black-start operation). A unique dispatch algorithm is developed for each state to achieve the control objective, and the transition function is implemented in the state machine as control logics to transition the system from one state to the next. The feasibility and effectiveness of the developed state machine is validated by simulation in MATLAB with an example microgrid, and the test results show excellent performance of the state machine to achieve the target control objective in each state and to improve the system's transient performance during transition operation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms?

Variational Quantum Algorithms (VQAs) are often viewed as the best hope for near-term quantum advantage. However, recent studies have shown that noise can severely limit the trainability of VQAs, e.g., by exponentially flattening the cost landscape and suppressing the magnitudes of cost gradients. Error Mitigation (EM) shows promise in reducing the impact of noise on near-term devices. Thus, it is natural to ask whether EM can improve the trainability of VQAs. In this work, we first show that, for a broad class of EM strategies, exponential cost concentration cannot be resolved without committing exponential resources elsewhere. This class of strategies includes as special cases Zero Noise Extrapolation, Virtual Distillation, Probabilistic Error Cancellation, and Clifford Data Regression. Second, we perform analytical and numerical analysis of these EM protocols, and we find that some of them (e.g., Virtual Distillation) can make it harder to resolve cost function values compared to running no EM at all. As a positive result, we do find numerical evidence that Clifford Data Regression (CDR) can aid the training process in certain settings where cost concentration is not too severe. Our results show that care should be taken in applying EM protocols as they can either worsen or not improve trainability. On the other hand, our positive results for CDR highlight the possibility of engineering error mitigation methods to improve trainability.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

Watershed Merge Tree Classification for Electron Microscopy Image Segmentation

Automated segmentation of electron microscopy (EM) images is a challenging problem. In this paper, we present a novel method that utilizes a hierarchical structure and boundary classification for 2D neuron segmentation. With a membrane detection probability map, a watershed merge tree is built for the representation of hierarchical region merging from the watershed algorithm. A boundary classifier is learned with non-local image features to predict each potential merge in the tree, upon which merge decisions are made with consistency constraints in the sense of optimization to acquire the final segmentation. Independent of classifiers and decision strategies, our approach proposes a general framework for efficient hierarchical segmentation with statistical learning. We demonstrate that our method leads to a substantial improvement in segmentation accuracy.

Liu, TIng↗
DOE OSTI2017

Performance of GeantV EM Physics Models

The recent progress in parallel hardware architectures with deeper vector pipelines or many-cores technologies brings opportunities for HEP experiments to take advantage of SIMD and SIMT computing models. Launched in 2013, the GeantV project studies performance gains in propagating multiple particles in parallel, improving instruction throughput and data locality in HEP event simulation on modern parallel hardware architecture. Due to the complexity of geometry description and physics algorithms of a typical HEP application, performance analysis is indispensable in identifying factors limiting parallel execution. In this report, we will present design considerations and preliminary computing performance of GeantV physics models on coprocessors (Intel Xeon Phi and NVidia GPUs) as well as on mainstream CPUs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Attack-resilient algorithms and testbed federation for wide-area protection and control in smart grid

Today’s energy infrastructure is undergoing a massive transformation across all generation, transmission, and distribution systems to provide reliability, efficiency, and sustainability to the power system network. During the past 10 years, there has been a remarkable enhancement in developing close-loop wide-area protection and control (WAPAC) applications, such as automatic generation control (AGC), wide-area protection scheme (WAPS), synchrophasor-based wide-area voltage control system (WAVCS), etc., in the energy management system (EMS) to provide an adequate situational awareness to control center operators during physical disturbances and natural events. The rapid digitalization, unencrypted communication, and numerous data-sharing devices have increased digital access points that have exposed the grid network to countless cyberattacks pertinent to the WAPAC cybersecurity. Given the amount of complex grid network coupled with legacy infrastructures, it is untrustworthy to completely rely on information technology (IT)-based conventional security measures to defend against evolving cyberattacks. Hence, there is a compelling need to go beyond the traditional paradigm of ‘security by obscurity’ and ‘bolt-on’ security measures, and develop a suite of innovative security solutions at multiple layers to transform the current “fault-resilient” grid into “fault and attack-resilient” grid of the future. This dissertation specifically focuses on developing application-specific attack-resilient solutions for critical WAPAC applications in the smart grid. The first part of the study, Attack-Resilient Wide-Area Protection Scheme, presents a cyber-physical attack-resilient system (CPARS) for WAPS, where two anomaly detection methods are proposed to detect multi-level cyberattacks followed by the rules-based intrusion mitigation system (RIMS) to initiate effective mitigation strategies, tailored to these attacks, to restore the normal grid operation after disturbances. The first anomaly detection component, Machine learning-based Anomaly Detection for Centralized WAPS, discusses a novel architecture and methodology for developing a machine-learning-based anomaly detection system (ADS) for the centralized wide-area protection system (CWAPS) by applying the variational mode decomposition (VMD) technique and decision tree (DT) algorithms using cyber logs and synchrophasor measurements. The second component, Multi-Agent-based Anomaly Detection for Decentralized WAPS, presents a two-level hierarchical multi-agent-based decentralized WAPS against the system-aware stealthy cyber-attacks. Finally, for the RIMS, the state transition diagram-based mitigation measures are defined based on the grid operation states and experimental testing and evaluation are presented in a cyber-physical testbed environment. The second part of the study, Attack-Resilient Wide-Area Voltage Control System, addresses the existing vulnerabilities in the wide-area voltage control system (WAVCS), presents the methodology for performing an impact analysis during data integrity attacks, and proposes a data-driven attackresilient system (DARS) by incorporating machine learning-based ADS and rules-based anomaly mitigation system (RAMS) to detect these attacks and provide necessary mitigation actions to maintain the transient voltage stability after disturbances. Finally, the third part, Networked Federation Testbed (NEFTSec) for Smart Grid Cybersecurity, focuses on developing an interconnected federated testbed through a common network like the internet, describes its conceptual architectures, and also presents its design components for experimental testing, validation, and evaluation. Further, it presents the co-simulation interface algorithm (CIA) to facilitate the geographically-dispersed real-time distributed simulation (GD-RTDS) during the cyber-physical federation. In general, this dissertation presents innovative attack-resilient solutions for WAPAC applications that are experimentally tested and evaluated using the PowerCyber testbed and NEFTSec platform to accelerate the transition of state-of-the-art research works to the real-field deployments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A Butterfly-Accelerated Volume Integral Equation Solver for Broad Permittivity and Large-Scale Electromagnetic Analysis

In this work, a butterfly-accelerated volume integral equation (VIE) solver is proposed for fast and accurate electromagnetic (EM) analysis of scattering from heterogeneous objects. The proposed solver leverages the hierarchical off-diagonal butterfly (HOD-BF) scheme to construct the system matrix and obtain its approximate inverse, used as a preconditioner. Complexity analysis and numerical experiments validate the O(N log 2 N) construction cost of the HOD-BF-compressed system matrix and O(N log 1.5 N) inversion cost for the preconditioner, where N is the number of unknowns in the high-frequency EM scattering problem. For many practical scenarios, the proposed VIE solver requires less memory and computational time to construct the system matrix and obtain its approximate inverse compared to a H matrix-accelerated VIE solver. The accuracy and efficiency of the proposed solver have been demonstrated via its application to the EM analysis of large-scale canonical and real-world structures comprising of broad permittivity values and involving millions of unknowns.

42 ENGINEERING↗
DOE OSTI2019

Chance-Constrained Energy Management System for Power Grids With High Proliferation of Renewables and Electric Vehicles

This paper proposes a two-stage energy management system (EMS) for power grids with massive integration of electric vehicles (EVs) and renewable energy resources. The first stage economic dispatch determines the optimal operating points of charging stations and battery swapping stations (BSS) for EVs under plug-in and battery swapping modes, respectively. The proposed stochastic model predictive control (SMPC) problem in this stage is characterized through a chance-constrained optimization formulation that can effectively capture the system and the forecast uncertainties. A distributed algorithm, the alternating direction method of multipliers (ADMM), is applied to accelerate the optimization computation through parallel computing. The second stage is aimed in coordinating the EV charging mechanisms to continuously follow the first-stage solutions, i.e., the target operating points, and meeting the EV customers' charging demands captured via the Advanced Metering Infrastructure (AMI). In this work, the proposed solution offers a holistic control strategy for large-scale centralized power grids in which the aggregated individual parameters are predictable and the system dynamics do not vary sharply within a short time-interval.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Probabilistic Data Association for Orbital-Element Estimation Using Multistage Expectation–Maximization

Tracking space objects is important for managing space traffic and predicting collisions, but is difficult in part due to data association and orbit model uncertainty. Expectation–maximization (EM) is a commonly used tracking method that has not been widely considered for tracking space objects. The technique consists of iteratively computing data association probabilities with a set of current element estimates, and updating estimates of the elements by solving a nonlinear weighted least-squares regression problem where the weights are the data association probabilities. In this paper, we demonstrate the use of EM for probabilistic data association and orbital-element estimation by applying the technique to simulated data from two angles-only tracking scenarios. In both scenarios, EM provides correct data associations and accurate maximum likelihood estimates of orbital elements. One scenario considers tracking a single object in clutter and quantifies the improvement of the orbital-element estimates and data associations as the detection probability increases. However, standard application of EM requires knowing the number of objects or may fail when a large number of objects are present. To address these issues, this paper employs a multistage version of EM that is applicable when there are a large and possibly unknown number of objects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

hp -VPINNs: Variational physics-informed neural networks with domain decomposition

We formulate a general framework for hp-variational physics-informed neural networks (hp-VPINNs) based on the nonlinear approximation of shallow and deep neural networks and hp-refinement via domain decomposition and projection onto the space of high-order polynomials. The trial space is the space of neural network, which is defined globally over the entire computational domain, while the test space contains piecewise polynomials. Specifically in this study, the hp-refinement corresponds to a global approximation with a local learning algorithm that can efficiently localize the network parameter optimization. Here, we demonstrate the advantages of hp-VPINNs in both accuracy and training cost for several numerical examples of function approximation and in solving differential equations.

42 ENGINEERING↗
DOE OSTI2026

Two datasets are better than one: method of double moments for 3D reconstruction in cryo-EM

Cryo-electron microscopy is a powerful imaging technique for reconstructing three-dimensional molecular structures from noisy tomographic projection images of randomly oriented particles. We introduce a new data fusion framework, termed the method of double moments, which reconstructs molecular structures from two instances of the second-order moment of projection images obtained under distinct orientation distributions: one uniform, the other non-uniform and unknown. We prove that these moments generically uniquely determine the underlying structure, up to a global rotation and reflection, and we develop a convex-relaxation-based algorithm that achieves accurate recovery using only second-order statistics. Our results demonstrate the advantage of collecting and modeling multiple datasets under different experimental conditions, illustrating that leveraging dataset diversity can substantially enhance reconstruction quality in computational imaging tasks.

Kam’s method↗
DOE OSTI2021

Autonomous Tools for Attack Surface Reduction (Final Report)

The electric power grid is a complex critical infrastructure that forms the lifeline of modern society, and its secure and reliable operation is of paramount importance to national security and economic wellbeing. However, recent findings documented in authoritative sources indicate the threat of cyber-based attacks growing in numbers and sophistication. However, securing the grid against stealthy cyberattacks is a challenging task due to legacy nature of the infrastructure coupled with dynamic nature of threat landscape and ever-growing sophistication of the adversaries. Additionally, the grid’s attack surface continues to grow with the increased dependence on digital communications and control that now extends to each consumer through smart meters and distributed energy resources. Unfortunately, this expansive surface increases the grid’s vulnerability and further exposes critical control systems in both substations and control centers. To respond to this emerging need, we had successfully assembled an interdisciplinary team with academic- industry partnership to successfully conduct research, development, evaluation, demonstration, and commercialization of attack surface reduction tools, whose goal was to significantly reduce the cyber attack surface in the North American power grid. Our proposed project was a synergistic collaborative effort leveraging the synergistic expertise of the team members across power systems, cyber security and CPS security, testbeds, field deployments and demonstration, and successful commercialization. The following are the specific tasks that have been successfully completed two phases (2016-2020). Phase I: Task 1: Developed and implemented a robust Project Management and Data Management Plan, coupled with a well thought out Risk Mitigation Plan. Task 2.1: Developed a comprehensive framework that continually assesses and autonomously reduces the attack surface for the power grid control environment spanning across substations, control center and the SCADA network to significantly reduce the risks of cyber attacks. Task 2.2: Developed attack surface analysis techniques, metrics, and tools that assess the attack surface at multiple levels including the control center, substations, and the SCADA network. Task 2.3: Developed attack surface reduction techniques and tools that dynamically reduce attack surface and hence increase attacker’s cost without interfering in the critical functions of the system. Task 2.4: Prototyped, implemented, and quantitatively evaluated/validated the techniques and tools on a realistic industrial CPS security testbed environment by leveraging the unique resources of the team. Task 3: Developed Commercialization plan to transition the developed tools into power system industry stakeholders for a broader adoption by leveraging the expertise of our industrial members. Phase II: Task 4: Successfully completed field demonstration, verification, and evaluation of the effectiveness of the attack surface analysis and reduction techniques on a realistic utility testbed environment. This also involved the development of realistic scenarios, sound metrics, data sets, evaluation criteria, and documentation. Technology integration & Field demonstration: The project had significantly advanced the state-of-the-art research and practice in improving the cybersecurity of our nation’s power grid infrastructure against cyber threats. In particular, the proposed, designed, and deployed attack surface analysis and reduction algorithms and tools have contributed to significantly reducing the exposure and risk of the devices, substations, and the integrated SCADA/EMS/ DMS grid environment to cyber threat. Strong demonstration and evaluation techniques have verified the feasibility of the developed techniques on realistic cyber-physical testbeds and utility partner's real grid environment, and collaborative research and evaluation of attack surface reduction techniques (for wide-are monitoring and control) within a vendor (GE) EMS platform. The Attack Host Analyzer (AHA) tool that was developed through this project was made available through GitHub.

24 POWER TRANSMISSION AND DISTRIBUTION↗