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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Electron correlations in the cubic paramagnetic perovskite Sr ( V , Mn ) O 3 : Results from fully self-consistent self-energy embedding calculations

In this study, we use the thermodynamically consistent and conserving self-energy embedding theory (SEET) to study the spectra of the prototypical undistorted cubic perovskites SrVO 3 and SrMnO 3 . In the strongly correlated metallic SrVO 3 we find that the usual attribution of the satellite peaks at –1.8 eV to Hund or Hubbard physics in the t 2 g orbitals is inconsistent with our calculations. In the strongly correlated insulator SrMnO 3 we recover insulating behavior due to a feedback effect between the strongly correlated orbitals and the weakly correlated environment. Our calculation shows a systematic convergence of spectral features as the space of strongly correlated orbitals is enlarged, paving the way to a systematic parameter-free study of correlated perovskites.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Quantum embedding study of strain- and electric-field-induced Stark effects on the NV - center in diamond

The NV - color center in diamond has been demonstrated as a powerful nanosensor for quantum metrology due to the sensitivity of its optical and spin properties to external electric, magnetic, and strain fields. In view of these applications, we use quantum embedding to derive a many-body description of strain- and electric-field-induced Stark effects on the NV - center. Here, we quantify how strain longitudinal to the axis of NV - shifts the excited states in energy, while strain with a component transverse to the NV - axis splits the degeneracies of the 3 E and 1 E states. The largest effects are for the optically relevant 3 E manifold, which splits into E x and E y with transverse strain. From these responses we extract strain susceptibilities for the E x/y states within the quasilinear regime. Additionally, we study the many-body dipole matrix elements of the NV - and find a permanent dipole 1.6 D at zero strain, which is somewhat smaller than that obtained from recent density functional theory calculations. We also determine the transition dipole between the E x and E y and how it evolves with strain.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Embedding space approach to Lorentzian CFT amplitudes and causal spherical functions

Conformal field theory in a Minkowski setting is discussed in an embedding space approach, paying special attention to causality constraints for four-point amplitudes. The physics of dilatation and Lorentz boost is emphasized in specifying the noncompact maximal Abelian subgroup of S O ( d , 2 ) . Reduction of a conformal field theory four-point amplitudes as functions of cross ratios is shown to be equivalent to enforcing H bi-invariance, i.e., F ( h g h ′ ) = F ( g ) , with g ∈ S O ( d , 2 ) and H an appropriate subgroup. Causality is imposed by introducing appropriate semigroups. Causal zonal spherical functions are constructed, making contact with Minkowski conformal blocks introduced previously. Published by the American Physical Society 2024

Agarwal, Pulkit (ORCID:0000000346581691)↗
DOE OSTI2021

Embedded training of neural-network subgrid-scale turbulence models

We report that the weights of a deep neural-network model are optimized in conjunction with the governing flow equations to provide a model for subgrid-scale stresses in a temporally developing plane turbulent jet at Reynolds number Re 0 = 6000 . The objective function for training is first based on the instantaneous filtered velocity fields from a corresponding direct numerical simulation, and the training is by a stochastic gradient descent method, which uses the adjoint Navier-Stokes equations to provide the end-to-end sensitivities of the model weights to the velocity fields. In-sample and out-of-sample testing on multiple dual-jet configurations show that its required mesh density in each coordinate direction for prediction of mean flow, Reynolds stresses, and spectra is half that needed by the dynamic Smagorinsky model for comparable accuracy. The same neural-network model trained directly to match filtered subgrid-scale stresses, without the constraint of being embedded within the flow equations during the training, fails to provide a qualitatively correct prediction. The coupled formulation is generalized to train based only on mean-flow and Reynolds stresses, which are more readily available in experiments. The mean-flow training provides a robust model, which is important, though a somewhat less accurate prediction for the same coarse meshes, as might be anticipated due to the reduced information available for training in this case. The anticipated advantage of the formulation is that the inclusion of resolved physics in the training increases its capacity to extrapolate. This is assessed for the case of passive scalar transport, for which it outperforms established models due to improved mixing predictions.

42 ENGINEERING↗
DOE OSTI2023

Embedding hard physical constraints in neural network coarse-graining of three-dimensional turbulence

In recent years, deep learning approaches have shown much promise in modeling complex systems in the physical sciences. A major challenge in deep learning of partial differential equations is enforcing physical constraints and boundary conditions. In this work, we propose a general framework to directly embed the notion of an incompressible fluid into convolutional neural networks, and apply this to coarse-graining of turbulent flow. These physics-embedded neural networks leverage interpretable strategies from numerical methods and computational fluid dynamics to enforce physical laws and boundary conditions by taking advantage the mathematical properties of the underlying equations. Here, we demonstrate results on three-dimensional fully developed turbulence, showing that this technique drastically improves local conservation of mass, without sacrificing performance according to several other metrics characterizing the fluid flow.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Towards excitations and dynamical quantities in correlated lattices with density matrix embedding theory

Density matrix embedding theory (DMET) provides a framework to describe ground-state expectation values in strongly correlated systems, but its extension to dynamical quantities is still an open problem. We show one route to obtaining excitations and dynamical spectral functions by using the techniques of DMET to approximate the matrix elements that arise in a single-mode inspired excitation ansatz. We demonstrate this approach in the one-dimensional Hubbard model, comparing the neutral excitations, single-particle density of states, charge, and spin dynamical structure factors to benchmarks from the Bethe ansatz and density matrix renormalization group. Finally, our work highlights the potential of these ideas in building computationally efficient approaches for dynamical quantities.

1-dimensional systems↗
DOE OSTI2022

Small-angle X-ray microdiffraction from fibrils embedded in tissue thin sections

Small-angle X-ray scattering (SAXS) from fibrils embedded in a fixed, thin section of tissue includes contributions from the fibrils, the polymeric matrix surrounding the fibrils, other constituents of the tissue, and cross-terms due to the spatial correlation between fibrils and neighboring molecules. This complex mixture severely limits the amount of information that can be extracted from scattering studies. However, availability of micro- and nano-beams has made the measurement of scattering from very small volumes possible, which, in some cases, may be dominated by a single fibrillar constituent. In such cases, information about the predominant species may be accessible. Nevertheless, even in these cases, the correlations between the positions of fibrils and other constituents have a significant impact on the observed scattering. Here, strategies are proposed to extract partial information about fibril structure and tissue organization on the basis of SAXS from samples of this type. It is shown that the spatial correlation function of the fibril in the direction perpendicular to the fibril axis can be computed and contains information about the predominant fibril structure and the organization of the surrounding tissue matrix. This has significant advantages over approaches based on techniques developed for X-ray solution scattering. Examples of correlation calculations in different types of samples are given to demonstrate the information that can be obtained from these measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Spot pattern welding scanning strategy for sensor embedding and residual stress reduction in laser-foil-printing additive manufacturing

Here, this paper aims to present spot pattern welding (SPW) as a scanning strategy for laser-foil-printing (LFP) additive manufacturing (AM) in place of the previously used continuous pattern welding (CPW) (line-raster scanning). The SPW strategy involves generating a sequence of overlapping spot welds on the metal foil, allowing the laser to form dense and uniform weld beads. This in turn reduces thermal gradients, promotes material consolidation and helps mitigate process-related risks such as thermal cracking, porosity, keyholing and Marangoni effects. 304L stainless steel (SS) feedstock is used to fabricate test specimens using the LFP system. Imaging techniques are used to examine the melt pool dimensions and layer bonding. In addition, the parts are evaluated for residual stresses, mechanical strength and grain size. Compared to CPW, SPW provides a more reliable heating/cooling relationship that is less dependent on part geometry. The overlapping spot welds distribute heat more evenly, minimizing the risk of elevated temperatures during the AM process. In addition, the resulting dense and uniform weld beads contribute to lower residual stresses in the printed part. To the best of the authors’ knowledge, this is the first study to thoroughly investigate SPW as a scanning strategy using the LFP process. In general, SPW presents a promising strategy for securing embedded sensors into LFP parts while minimizing residual stresses.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Adaptive Internal Model Based Current Control with Embedded Active Damping of a Three-Phase Grid-Connected Inverter with LCL Filter for PV Application

This paper presents an internal model based controller with embedded active damping for a three-phase grid-connected inverter with LCL filter. A strategy to control the grid current based on the internal model principle is presented in this paper. It is known that a third-order LCL filter apart from providing better attenuation also poses the problem of initiating unwanted resonance oscillations especially during transients. To address this problem, an active damping strategy based on virtual resistance emulation on the grid side filter is implemented. Also, to ensure effective active damping, an online parameter update strategy is implemented for the filter parameters. The proposed method provides better attenuation to oscillation damping and achieves good tracking performance without adversely affecting the bandwidth. The architecture is verified based on computer simulations via MATLAB/Simulink and PLECS domain and various important case study results are presented.

grid-connected inverter↗
DOE OSTI2022

Secure Firmware Update and Device Authentication for Smart Inverters using Blockchain and Physically Uncloable Function (PUF)-Embedded Security Module

Cybersecurity of inverters has been significantly important as inverters become smarter in cyber-physical environments. However, firmware security of smart inverters against firmware attacks from various attack vectors has been less studied. Furthermore, this paper proposes a secure firmware update and device authentication method using a blockchain-based public key infrastructure (PKI) management system and a physically unclonable function (PUF)-embedded security module in a smart inverter. The proposed method is validated by experiments.

blockchain↗
DOE OSTI2025

A Graph-Net with Node Embeddings to Detect False Data Injection Attacks in Photovoltaic Systems

Distributed energy resources (DER) contribute to the operational stability of the larger power grid both at utility-scale as well as commercial and residential scales in aggregated forms. These DER in-turn are susceptible to increasing cyber threats. An adversary can plug into the same local network that a field photovoltaic (PV) system uses to interconnect its data loggers and inverters and manipulate certain measurements collected from the network or trick existing irradiance and inverter readings through false data injection attacks (FDIA). Control routines that rely on these measurements can propagate the false data, impacting critical decisions that result in a suboptimal operation or even cause intentional harm leading to inverter-tripping or unscheduled loads that need to be shed. To detect FDIA in PV systems, the paper introduces an attention-based graph neural network with node embeddings and applied it to a simple prototypical DC-coupled microgrid with PV, energy storage, and load. The algorithm shows a detection accuracy of up to 98.95%. The proposed FDIA detection technique will provide micro-grid operators with an effective method to safeguard their systems, guaranteeing the secure and reliable operation.

Parvez, Imtiaz [Utah Valley University]↗
DOE OSTI2025

Amorphous Indium Oxide Channel FEFETs With Write Voltage of 0.9 V and Endurance >10 12 for Refresh-Free Embedded Memory

This work presents, for the first time, a back-end-of-the-line (BEOL)-compatible W-doped indium oxide (IWO) ferroelectric field-effect transistor (FEFET) with a record-low operating voltage below 0.9 V and a write speed of 20 ns while achieving a transient read current window (CW) ratio ( I LVT /I HVT ) greater than 10 4 . The device also exhibits exceptional reliability characteristics such as: 1) measured bipolar write endurance up to 10 12 cycles; 2) a fast read speed of 50 ns; 3) read endurance surpassing 10 12 cycles; and 4) retention exceeding 10 4 s at 85 ∘ C. Furthermore, a physics-based numerical model has been developed to investigate the nanoscale characteristics of BEOL FEFET devices, leveraging nucleation-limited switching in HfO 2 ferroelectrics and dc characterization to extract material and channel parameters for accurate device simulation. The simulation uncovers the stochastic switching behavior of BEOL amorphous oxide semiconductor (AOS) FEFETs and demonstrates an intrinsic switching time as low as 1 ps, highlighting the potential of BEOL AOS FEFETs for ultrafast memory applications. These results establish AOS FEFETs as a compelling candidate for high-density embedded memory applications for last-level cache (LLC) (L4) in advanced CMOS technology nodes.

1-V ferroelectric field-effect transistor (FEFET)↗
DOE OSTI2020

Efficient and Robust Dynamic Simulation of Power Systems With Holomorphic Embedding

Dynamic simulation is vitally important in power system analysis, but traditional approaches based on numerical integration over small time steps are time-consuming. Also, the Newton-Raphson method suffers from difficulty in convergence when solving nonlinear algebraic equations. In this paper, we propose a novel dynamic simulation approach based on holomorphic embedding. By obtaining a high-order approximation of system dynamics, it achieves a much larger time step and thus enhances the computational efficiency significantly. In addition, the new approach avoids non-convergence issues in solving algebraic equations, which improves robustness. The approach includes flexible modeling of synchronous generators and controllers, and we propose a method for modeling generator coordinate transformations. The approach is tested on the IEEE 39-bus, 10-generator system and a Polish 2383-bus, 327-generator system. The results demonstrate promising computational efficiency and satisfactory numerical robustness for the analysis of large-scale power systems.

42 ENGINEERING↗
DOE OSTI2021

Observation of bound states in the continuum embedded in symmetry bandgaps

In the past decade, symmetry-protected bound states in the continuum (BICs) have proven to be an important design principle for creating and enhancing devices reliant upon states with high-quality (Q) factors, such as sensors, lasers, and those for harmonic generation. However, as we show, current implementations of symmetry-protected BICs in photonic crystal slabs can only be found at the center of the Brillouin zone and below the Bragg diffraction limit, which fundamentally restricts their use to single-frequency applications. By microprinting a three-dimensional (3D) photonic crystal structure using two-photon polymerization, we demonstrate that this limitation can be overcome by altering the radiative environment surrounding the slab to be a 3D photonic crystal. This allows for the protection of a line of BICs by embedding it in a symmetry bandgap of the crystal. This concept substantially expands the design freedom available for developing next-generation devices with high-Q states.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

FTL: Transfer Learning Nonlinear Plasma Dynamic Transitions in Low Dimensional Embeddings (FTL) v1.0

Fusion Transfer Learning (FTL) model provides a new paradigm to study high-dimensional dynamical behaviors, such as those in fusion plasma systems. The knowledge transfer process leverages a pre-trained neural encoder-decoder network, initially trained on linear simulations, to effectively capture nonlinear dynamics. The low-dimensional embeddings extract the coherent structures of interest, while preserving the inherent dynamics of the complex system. Experimental results highlight FTL's capacity to capture transitional behaviors and dynamical features in plasma dynamics -- a task often challenging for conventional methods. The model developed in this study is generalizable and can be extended broadly through transfer learning to address various magnetohydrodynamics (MHD) modes.

Bai, Zhe↗
DOE OSTI2026

Hybrid-BPR (Bayesian Personalized Ranking with Feature Embeddings and Explicit Negative Sampling) [SWR-26-039]

Hybrid-BPR is a Python library for Bayesian Personalized Ranking (BPR) with two key capabilities that go beyond standard BPR implementations: 1. User and item feature embeddings - incorporate content-based signals (genres, tags, metadata) alongside collaborative filtering. 2. Implicit negative interactions - use observed non-interactions (e.g. viewed-but-not-clicked) as negative training signal instead of random sampling from the full item space. The software is built for recommender systems research with MLflow experiment tracking, parallel hyperparameter sweeps, and standard ranking metrics.

Sandhu, Rimple [National Laboratory of the Rockies↗
DOE OSTI2024

Fatigue performance of the Deeply Embedded Ring Anchor

Existing anchor solutions are limited in their applicability to the floating offshore wind industry, which demands prolonged load endurance in extreme weather conditions and varied seabed soils. This limitation restricts project feasibility and increases costs. In order to address these challenges, the Deeply Embedded Ring Anchor (DERA) is proposed to achieve substantial reductions in anchor costs through efficiency enhancements. Ensuring the long-term safety and reliability of the DERA, especially considering a minimum operational period of 25 years, necessitates a comprehensive understanding of fatigue damage. Induced by repetitive loading below material yield strength, fatigue poses a significant threat to structures exposed to waves and currents. Anchors, vital for station keeping, are susceptible to fatigue damage, particularly in the padeye area. This paper conducts a fatigue analysis, focusing on factors such as the wall thickness of the ring anchor, inner stiffeners, and stiffener thickness, to evaluate the performance of DERA in this context. The results showed that the stiffener significantly affects the elongation of the total life of the anchor. In conclusion, this paper focuses on advancing innovations like DERA, enabling the efficient utilization of offshore wind resources, and addressing safety concerns through meticulous fatigue analysis.

17 WIND ENERGY↗
DOE OSTI2024

Optimal load attachment of a deeply embedded ring anchor in clay

A Deeply Embedded Ring Anchor (DERA) system has been developed as a cost-effective solution for mooring arrays of floating offshore wind turbines (FOWTs) to the seabed. The DERA boasts several key features, including its versatility in various soil types, compact size, compatibility with diverse mooring systems, multi-line potential, and robust performance even under unintentional loading conditions. While prior preliminary studies have provided valuable insights into how the DERA can enhance cost-effectiveness by offering a high load capacity, these studies have predominantly focused on optimizing anchor performance under translational horizontal and vertical loading. However, to design the DERA optimally, we must also consider its ability to handle inclined loading conditions in addition to lateral and axial loadings. Due to its shorter length compared to a conventional caisson, the DERA has less resistance to moments, making it more sensitive to horizontal load capacity and the optimal load attachment depth concerning load angle. For this reason, our study introduces an analytical approach to evaluate the effects of inclined loading on anchor performance, utilizing the previously validated upper bound plastic limit analysis (PLA) method. In investigating the optimal load attachment of the DERA, this paper conducts a parametric study to analyze how factors such as load attachment depth, anchor aspect ratio, and load inclination affect the DERA’s load capacity. Our findings indicate that PLA can serve as a valuable analytical tool for assessing the ultimate load capacity of the DERA, particularly under inclined loading conditions.

42 ENGINEERING↗