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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Multi-Area Distribution System State Estimation Using Decentralized Physics-Aware Neural Networks

The development of active distribution grids requires more accurate and lower computational cost state estimation. In this paper, the authors investigate a decentralized learning-based distribution system state estimation (DSSE) approach for large distribution grids. The proposed approach decomposes the feeder-level DSSE into subarea-level estimation problems that can be solved independently. The proposed method is decentralized pruned physics-aware neural network (D-P2N2). The physical grid topology is used to parsimoniously design the connections between different hidden layers of the D-P2N2. Monte Carlo simulations based on one-year of load consumption data collected from smart meters for a three-phase distribution system power flow are developed to generate the measurement and voltage state data. The IEEE 123-node system is selected as the test network to benchmark the proposed algorithm against the classic weighted least squares and state-of-the-art learning-based DSSE approaches. Numerical results show that the D-P2N2 outperforms the state-of-the-art methods in terms of estimation accuracy and computational efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Subnanometer Thick Native sp 2 Carbon on Oxidized Diamond Surfaces

Oxygen-terminated diamond has a wide breadth of applications, which include stabilizing near-surface color centers, semiconductor devices, and biological sensors. Despite the vast literature on characterizing functionalization groups on diamond, the chemical composition of the shallowest portion of the surface (<1 nm) is challenging to probe with conventional techniques like XPS and FTIR. In this work, we demonstrate the use of angleresolved XPS to probe the first ten nanometers of both oxygen and hydrogen terminated (100) single-crystalline diamond grown via chemical vapor deposition (CVD). With the use of consistent peakfitting methods, the peak identities and relative peak binding energies were identified for sp 2 carbon, ether, hydroxyl, carbonyl, and C−H groups for both of these diamond surface terminations. For the oxygen-terminated sample, we also quantified the thickness of the sp 2 carbon layer situated on top of the bulk sp 3 diamond bonded carbon to be 0.3 ± 0.1 nm, based on the analysis of the Auger electron spectra and D-parameter calculations. These results indicate that the majority of the oxygen is bonded to the sp 2 carbon layer on the diamond, and not directly to the sp 3 diamond bonded carbon.

Carbon↗
DOE OSTI2021

Enhancement of Distribution System State Estimation Using Pruned Physics-Aware Neural Networks

Realizing complete observability in the three-phase distribution system remains a challenge that hinders the implementation of classic state estimation algorithms. In this paper, a new method, called the pruned physics-aware neural network (P2N2), is developed to improve the voltage estimation accuracy in the distribution system. The method relies on the physical grid topology, which is used to design the connections between different hidden layers of a neural network model. To verify the proposed method, a numerical simulation based on one- year smart meter data of load consumptions for three-phase power flow is developed to generate the measurement and voltage state data. The IEEE 123-node system is selected as the test network to benchmark the proposed algorithm against the classic weighted least squares (WLS). Numerical results show that P2N2 outperforms WLS in terms of data redundancy and estimation accuracy.

distribution system state estimation↗
DOE OSTI2021

Enhancement of Distribution System State Estimation Using Pruned Physics-Aware Neural Networks: Preprint

Realizing complete observability in the three-phase distribution system remains a challenge that hinders the implementation of classical state estimation algorithms. In this paper, a new method so-called pruned physics-aware neural network (P2N2) is developed to improve the voltage estimation accuracy in the distribution system. The method relies on the physical grid topology, which is used to design the connections between different hidden layers of a neural network model. To verify the proposed method, a numerical simulation based on one-year smart meter data of load consumptions for threephase power flow is developed to generate the measurement and voltage state data. The IEEE 123 node system is selected as the test network to benchmark the proposed algorithm against the classical weighted least squares (WLS). Numerical results show that P2N2 outperforms WLS, in terms of data redundancy and estimation accuracy.

distribution systems state estimation↗
DOE OSTI2023

Scrutinizing formally Ni IV centers through the lenses of core spectroscopy, molecular orbital theory, and valence bond theory

Nickel K- and L 2,3 -edge X-ray absorption spectra (XAS) are discussed for 16 complexes and complex ions with nickel centers spanning a range of formal oxidation states from II to IV. K-edge XAS alone is shown to be an ambiguous metric of physical oxidation state for these Ni complexes. Meanwhile, L 2,3 -edge XAS reveals that the physical d-counts of the formally Ni IV compounds measured lie well above the d 6 count implied by the oxidation state formalism. The generality of this phenomenon is explored computationally by scrutinizing 8 additional complexes. The extreme case of NiF 6 2- is considered using high-level molecular orbital approaches as well as advanced valence bond methods. The emergent electronic structure picture reveals that even highly electronegative F-donors are incapable of supporting a physical d 6 Ni IV center. The reactivity of Ni IV complexes is then discussed, highlighting the dominant role of the ligands in this chemistry over that of the metal centers.

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

Practical quantum computation of chemical and nuclear energy levels using quantum imaginary time evolution and Lanczos algorithms

Various methods have been developed for the quantum computation of the ground and excited states of physical and chemical systems, but many of them require either large numbers of ancilla qubits or high-dimensional optimization in the presence of noise. The quantum imaginary-time evolution (QITE) and quantum Lanczos (QLanczos) methods proposed in Motta et al. (2020) eschew the aforementioned issues. In this study, we demonstrate the practical application of these algorithms to challenging quantum computations of relevance for chemistry and nuclear physics, using the deuteron-binding energy and molecular hydrogen binding and excited state energies as examples. With the correct choice of initial and final states, we show that the number of timesteps in QITE and QLanczos can be reduced significantly, which commensurately simplifies the required quantum circuit and improves compatibility with NISQ devices. We have performed these calculations on cloud-accessible IBM Q quantum computers. With the application of readout-error mitigation and Richardson error extrapolation, we have obtained ground and excited state energies that agree well with exact results obtained from diagonalization.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Responses in the Subpolar North Atlantic in Two Climate Model Sensitivity Experiments with Increased Stratospheric Aerosols

Abstract The subpolar North Atlantic (SPNA) shows contrasting responses in two sensitivity experiments with increased stratospheric aerosols, offering insight into the physical processes that may impact the Atlantic meridional overturning circulation (AMOC) in a warmer climate. In one, the upper ocean becomes warm and salty, but in the other it becomes cold and fresh. The changes are accompanied by diverging AMOC responses. The first experiment strengthens the AMOC, opposing the weakening trend in the reference simulation. The second experiment shows a much smaller impact. Both simulations use the Community Earth System Model with the Whole Atmosphere Community Climate Model component (CESM-WACCM) but differ in model versions and stratospheric aerosol specifications. Despite both experiments using similar approaches to increase stratospheric aerosols to counteract the rising global temperature, the contrasting SPNA and AMOC responses indicate a considerable dependency on model physics, climate states, and model responses to forcings. This study focuses on examining the physical processes involved with the impact of stratospheric aerosols on the SPNA salinity changes and their potential connections with the AMOC and the Arctic. We find that in both cases, increased stratospheric aerosols act to enhance the SPNA upper-ocean salinity by reducing freshwater export from the Arctic, which is closely tied to the Arctic sea ice changes. The impact on AMOC is primarily through the thermal component of the surface buoyancy fluxes, with negligible contributions from the freshwater component. These experiments shed light on the physical processes that dictate the important connections between the SPNA, the Arctic, the AMOC, and their subsequent feedbacks on the climate system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Chiral population analysis: a real space visualization of X-ray circular dichroism

The microscopic understanding of probing and controlling molecular chirality is of considerable interest. Numerous spectroscopic techniques are capable of monitoring molecular asymmetry and its consequences, ranging from the infrared to the X-ray regime. Resonant X-rays have long been used to investigate local atomic sites within molecules thanks to the localized nature of core electronic transitions. These techniques can be used to determine the extent to which chirality is a local versus a delocalized property. However, how to systematically partition dichroic contributions from the point of view of electronic structure simulations remains an open question. Here, we introduce the concept of chiral population analysis that connects chirality to the atomic orbital picture. In analogy with Mulliken population analysis, which assigns charges to atomic orbitals, chiral populations allow the dichroic response to be distributed among the participating atomic orbitals. This decomposition can be further visualized in real space by representing it in terms of isosurface plots, providing an intuitive way to connect the dichroic response to its origins. Thus chiral population analysis can be particularly useful to assess the extent to which a given electronic transition is sensitive to chirality as a local or global feature of the molecular geometry.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Solving Optimal Power Flow for Distribution Networks with State Estimation Feedback

Conventional optimal power flow (OPF) solvers assume full observability of the involved system states. However in practice, there is a lack of reliable system monitoring devices in the distribution networks. To close the gap between the theoretic algorithm design and practical implementation, this work proposes to solve the OPF problems based on the state estimation (SE) feedback for the distribution networks where only a part of the involved system states are physically measured. The SE feedback increases the observability of the under-measured system and provides more accurate system states monitoring when the measurements are noisy. We analytically investigate the convergence of the proposed algorithm. The numerical results demonstrate that the proposed approach is more robust to large pseudo measurement variability and inherent sensor noise in comparison to the other frameworks without SE feedback.

distirbution systems↗
DOE OSTI2022

Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow

We report, in fluid physics, data-driven models to enhance or accelerate time to solution are becoming increasingly popular for many application domains, such as alternatives to turbulence closures, system surrogates, or for new physics discovery. In the context of reduced order models of high-dimensional time-dependent fluid systems, machine learning methods grant the benefit of automated learning from data, but the burden of a model lies on its reduced-order representation of both the fluid state and physical dynamics. In this work, we build a physics-constrained, data-driven reduced order model for Navier–Stokes equations to approximate spatiotemporal fluid dynamics in the canonical case of isotropic turbulence in a triply periodic box. The model design choices mimic numerical and physical constraints by, for example, implicitly enforcing the incompressibility constraint and utilizing continuous neural ordinary differential equations for tracking the evolution of the governing differential equation. We demonstrate this technique on a three-dimensional, moderate Reynolds number turbulent fluid flow. In assessing the statistical quality and characteristics of the machine-learned model through rigorous diagnostic tests, we find that our model is capable of reconstructing the dynamics of the flow over large integral timescales, favoring accuracy at the larger length scales. More significantly, comprehensive diagnostics suggest that physically interpretable model parameters, corresponding to the representations of the fluid state and dynamics, have attributable and quantifiable impact on the quality of the model predictions and computational complexity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

The development of thermodynamically consistent and physics-informed equation-of-state model through machine learning

Ab initio molecular dynamics (AIMD) simulations have become an important tool used in the construction of equations of state (EOS) tables for warm dense matter. Due to computational costs, only a limited number of system state conditions can be simulated, and the remaining EOS surface must be interpolated for use in radiation-hydrodynamic simulations of experiments. In this work, we develop a thermodynamically consistent EOS model that utilizes a physics-informed machine learning approach to implicitly learn the underlying Helmholtz free-energy from AIMD generated energies and pressures. The model, referred to as PIML-EOS, was trained and tested on warm dense polystyrene producing a fit within a 1% relative error for both energy and pressure and is shown to satisfy both the Maxwell and Gibbs–Duhem relations. In addition, we provide a path toward obtaining thermodynamic quantities, such as the total entropy and chemical potential (containing both ionic and electronic contributions), which are not available from current AIMD simulations.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Chern Fermi pocket, topological pair density wave, and charge-4e and charge-6e superconductivity in kagomé superconductors

Abstract The recent discovery of novel charge density wave (CDW) and pair density wave (PDW) in kagomé lattice superconductors A V 3 Sb 5 ( A = K, Rb, Cs) hints at unexpected time-reversal symmetry breaking correlated and topological states whose physical origin and broader implications are not understood. Here, we make conceptual advances toward a mechanism behind the striking observations and new predictions for novel macroscopic phase coherent quantum states. We show that the metallic CDW state with circulating loop currents is a doped orbital Chern insulator near van Hove filling. The emergent Chern Fermi pockets (CFPs) carry concentrated Berry curvature and orbital magnetic moment. We find that the pairing of electrons on the CFPs leads to a superconducting state with an emergent vortex-antivortex lattice and the formation of a complex triple- Q PDW. A plethora of correlated and topological states emerge, including a never-before-encountered chiral topological PDW superconductor, a loop-current pseudogap phase, and vestigial charge-4 e and charge-6 e superconductivity in staged melting of the vortex-antivortex lattice and hexatic liquid crystal. Our findings reveal previously unknown nature of the superconducting state of a current-carrying Chern metal, with broad implications for correlated and topological materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Joint state-parameter estimation for the reduced fracture model via the united filter

Here, in this paper, we introduce an effective United Filter method for jointly estimating the solution state and physical parameters in flow and transport problems within fractured porous media. Fluid flow and transport in fractured porous media are critical in subsurface hydrology, geophysics, and reservoir geomechanics. Reduced fracture models, which represent fractures as lower-dimensional interfaces, enable efficient multi-scale simulations. However, reduced fracture models also face accuracy challenges due to modeling errors and uncertainties in physical parameters such as permeability and fracture geometry. To address these challenges, we propose a United Filter method, which integrates the Ensemble Score Filter (EnSF) for state estimation with the Direct Filter for parameter estimation. EnSF, based on a score-based diffusion model framework, produces ensemble representations of the state distribution without deep learning. Meanwhile, the Direct Filter, a recursive Bayesian inference method, estimates parameters directly from state observations. The United Filter combines these methods iteratively: EnSF estimates are used to refine parameter values, which are then fed back to improve state estimation. Numerical experiments demonstrate that the United Filter method surpasses the state-of-the-art Augmented Ensemble Kalman Filter, delivering more accurate state and parameter estimation for reduced fracture models. This framework also provides a robust and efficient solution for PDE-constrained inverse problems with uncertainties and sparse observations.

Bayesian inference↗
DOE OSTI2023

Bridge-Mediated Metal-to-Metal Electron and Hole Transfer in a Supermolecular Dinuclear Complex: A Computational Study Using Quantum Electron–Nuclear Dynamics

Bimetallic electron donor–acceptor complexes can facilitate electron and energy transfer with excellent structural control through synthetic design. In this work, we investigate the photochemical dynamics in a Ru–Cu bimetallic complex after photoexcitation of the Ru-centered charge transfer state. The physical underpinnings of the metal-to-metal directional charge transfer process are unraveled via analyses of the quantum electronic dynamics and electron–nuclear trajectories. Here, the effects of molecular vibrations in the photoexcited state on the charge transfer processes are also analyzed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Machine Learned Empirical Numerical Integrator from Simulated Data

Recently, a number of state-of-the-art surrogate machine learning (ML) models have been designed for global weather and climate prediction, which have been trained using reanalysis data products. Reanalysis data products are constructed using numerical model simulations that combine numerical integration of partial differential equations and parameterization schemes. These products are typically only archived and made available using coarsened spatial and temporal resolutions. This study explores the impact of the numerical generation methods used to produce the training datasets and the temporal resolution of those datasets on machine learning surrogate models. Using the nonlinear vector autoregression (NVAR) machine as an explainable ML technique, simple dynamical systems are emulated with ML models trained on data produced by three classical numerical integration schemes. NVAR is validated as a skillful ML method, capable of producing accurate predictions and, more importantly, reconstructing both the underlying dynamics and the numerical integration scheme used to generate the training data. However, the machine fails to generalize predictions on unseen test data generated by different numerical integration schemes, despite the underlying dynamical system being the same. This result provides a word of caution for the growing field of machine learning emulation of weather and climate dynamics. Furthermore, we illustrate using NVAR that training on temporally coarsened data may increase the required complexity of ML models and potentially introduce new numerical challenges. Finally, we discover that empirical integration schemes with arbitrary time-stepping sizes can be constructed directly from the data, which implies a potential for the development of empirical numerical integration schemes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Communication Network Layer State Estimation Measurement Model for a Cyber-Secure Smart Grid

Network communication has been proven to be a very important tool and a key factor in the recent development and progress of the power grid operation. It is also considered as the foundation for the smart grid because information and communication are integrated into electricity distribution to achieve reliable and accurate knowledge of the power grid. In previous years, absorbing energy from substations and delivering it to customers was the only type of interaction we knew between utility companies and customers. Presently, the growing connections of small distributed generation units caused by the cost reduction of most of the technologies used in generation and storage of electrical energy, along with the potential benefits of renewable energy have pushed many researchers to look into the improvement of information and communication technologies (ICT) in order to ensure a bidirectional flow of power and data. Moreover, the evolution of information and communication technologies and its applications to smart grid have converted the smart grid into a cyber-physical system where vulnerabilities and additional security challenges such as cyber-threats and cyber-attacks have emerged. Previously, we have demonstrated that using machine learning-based processing on data gathered from communication networks and the power grid was a promising solution for detecting cyber threats by implementing a co-simulation of cyber-security for cross-layer strategy. Since the majority of the challenges observed can only be solved in the network communication layer, we present in this work a physics-based state estimation model of the communication network system towards enhanced cyber-physical security of the smart grid. Information integration with the previously developed machine learning model is developed, providing a enhanced cyber-physical security application for the smart grid. Easy-to-implement model, without hard-to-derive parameters, highlight potential aspects of the model for real-life applications.

Mathieu, Reynold↗
DOE OSTI2024

Extracting free-energy landscapes of AgMn spin glass thin films with 1/$f$ resistance fluctuations

The temperature-dependent 1/f noise is reported for AgMn (12 at. %) thin films with multiple thicknesses ranging from 15 to 80 nm. A significant increase in the noise magnitude occurs in the spin-glass state. Analysis of the temperature-dependent exponent provides information on the energy barrier distribution and height, which in turn are related to the spin-glass coherence length. Here, we compare our results with those obtained previously on CuMn (13.5 at. %) thin films with similar thicknesses. We observe a similar trend of the increase of the barrier height with respect to thickness, but with relatively lower values for the AgMn barriers as compared to CuMn.

36 MATERIALS SCIENCE↗