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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Voltage Inference Framework for Real-Time Observability in Active Distribution Grids

Active distribution grids are gaining traction to meet the growing environmental, socio-economic, and sustainability targets. Various advanced smart grid technologies facilitate the integration of Distributed Energy Resources (DERs) by supporting the bi-directional power flow. The limited observability of distribution grids, primarily related to their location at the very edge of power system infrastructure, brings challenges to optimal grid management. Moreover, only a limited number of measurements at regular intervals are usually available. This paper presents a novel inference framework, referred to as “Voltage Inference”, to overcome the observability issues. The proposed framework employs a prediction step based on the Multivariate Taylor series approximation, followed by a corrector step that minimizes the estimation error to infer the otherwise unknown voltages from the available measurements. Furthermore, numerical results on the IEEE 13-bus test feeder validate the accuracy and computational performance of the proposed framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

A distributed voltage inference framework for cyber-physical attacks detection and localization in active distribution grids

The transition to active distribution grids with real-time monitoring and control depends on the proliferation of advanced communication networks and devices. This paradigm shift towards a cyber-physical architecture also introduces new vulnerabilities for adversaries to exploit and launch sophisticated cyber-physical attacks targeting grid observability. Current research highlights the challenges in distinguishing attacks on voltage phasor or nodal injection measurements and isolating multi-source attack locations in a multiphase distribution grid. The attack detection and localization methods in literature face accuracy issues, applications across diverse attack scenarios, or scalability limits. Here, to bridge these gaps, this paper proposes a distributed Voltage Inference framework for real-time detection and localization of cyber-physical attacks, addressing scalability, adaptability, and accuracy challenges in state-of-the-art methods. The proposed methodology leverages the distributed nature of the Voltage Inference framework through a two-step process of prediction and correction, together with a tractable graph partitioning approach, providing a reliable solution to identify compromised measurement sources and facilitate isolation. Extensive testing on IEEE 13 and 123-node distribution feeders underscores the algorithm’s efficacy, enhancing the security and resilience of active distribution grids against evolving cyber threats. Additionally, Hardware-in-the-Loop (HIL) implementation validates the proposed strategy’s practical applicability in real-world scenarios.

active distribution grids↗
DOE OSTI2022

Artificial intelligence inferred microstructural properties from voltage–capacity curves

Abstract The quantification of microstructural properties to optimize battery design and performance, to maintain product quality, or to track the degradation of LIBs remains expensive and slow when performed through currently used characterization approaches. In this paper, a convolution neural network-based deep learning approach (CNN) is reported to infer electrode microstructural properties from the inexpensive, easy to measure cell voltage versus capacity data. The developed framework combines two CNN models to balance the bias and variance of the overall predictions. As an example application, the method was demonstrated against porous electrode theory-generated voltage versus capacity plots. For the graphite|LiMn $$_2$$ 2 O $$_4$$ 4 chemistry, each voltage curve was parameterized as a function of the cathode microstructure tortuosity and area density, delivering CNN predictions of Bruggeman’s exponent and shape factor with 0.97 $$R^2$$ R 2 score within 2 s each, enabling to distinguish between different types of particle morphologies, anisotropies, and particle alignments. The developed neural network model can readily accelerate the processing-properties-performance and degradation characteristics of the existing and emerging LIB chemistries.

25 ENERGY STORAGE↗
DOE OSTI2020

Optimization of DARHT Axis 1 Injector Voltage

The Dual-Axis Radiographic Hydrodynamic Test (DARHT) facility provides flash radiography capabilities using two electron Linear Induction Accelerators (LIA’s). Axis-1 of DARHT produces a 20- MeV, 2-kA, 80-ns-FWHM electron beam. The strict requirements for flash radiography require a detailed understanding of the LIA’s performance, including precision measurements of the injector electron beam energy. The technique for time resolved measurement of the electron beam energy using electron Permanent Magnet Spectrometer (ePMSpec) has been developed. The electron energy then is used to infer the voltage produced by the injector pulsed power. The injector pulsed power of DARHT I consists of a 1.5-MV, glycol-insulated Blumlein that is pulsecharged by a step-up transformer and switched by four, laser-triggered spark gaps. A series of increasing impedance transmission lines are used to transform the output voltage of the Blumlein to a maximum of 3.8MV at the diode. Figure 1a shows a graphic of the Axis-1 prime power tank, Blumlein and downstream transmission lines. The injector pulse power is designed to produce voltage on a velvet cathode located in the vacuum vessel. The specifications of the voltage-pulse flat-top over 60ns is +/-1%. A key element in the high-voltage circuit is the ethylene glycol Blumlein. The outer volume, adjacent to the laser triggered switches of the Blumlein, is a peaking capacitor. This is an independent volume that contains a mixture of ethylene glycol and water to make it an adjustable capacitor of 380 to 750 pF.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Analytic Neural Network Gaussian Process Enabled Chance-Constrained Voltage Regulation for Active Distribution Systems with PVs, Batteries and EVs

This paper proposes an analytic neural network Gaussian process (NNGP)-based chance-constrained real-time voltage regulation method for active distribution systems with photovoltaics (PVs), batteries, and electric vehicles (EVs). NNGP can utilize historical measurement data to achieve real-time probabilistic node voltage estimation through Bayesian inference. Then, NNGP is fully analytically embedded into the optimal power flow model to perform voltage regulation and adapt to various topological changes. The uncertainties of voltage estimations are easily considered via the chance constraint, and it has been shown that the adoption of this chance constraint can significantly improve the reliability of voltage regulation under various scenarios. The comparison results with other methods, carried out on a real 759-node distribution system located in western Colorado, U.S., show that the proposed method can achieve accurate voltage estimation across different topologies and reliably perform voltage regulation considering PVs, batteries, and EVs.

active distribution systems↗
DOE OSTI2021

Enhanced electro-osmosis in propylene carbonate salt solutions

Properties of solid–liquid interfaces and surface charge characteristics mediate ionic and molecular transport through porous systems, affecting many processes such as separations. Herein, we report experiments designed to probe the electrochemical properties of solid–liquid interfaces using a model system of a single polyethylene terephthalate (PET) pore in contact with aqueous and propylene carbonate solutions of LiClO 4 . First, the existence and polarity of surface charges were inferred from current–voltage curves recorded when a pore was placed in contact with a LiClO4 concentration gradient. Second, the electro-osmotic transport of uncharged polystyrene particles through the PET pore provided information on the polarity and the magnitude of the pore walls’ zeta potential. Our experiments show that the PET pores become effectively positively charged when in contact with LiClO 4 solutions in propylene carbonate, even though in aqueous LiClO 4 , the same pores are negatively charged. Additionally, the electro-osmotic velocity of the particles revealed a significantly higher magnitude of the positive zeta potential of the pores in propylene carbonate compared to the magnitude of the negative zeta potential in water. The presented methods of probing the properties of solid–liquid interfaces are expected to be applicable to a wide variety of solid and liquid systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

AI Driven Experiment Calibration and Control

One critical step on the path from data taking to physics analysis is calibration. For many experiments this step is both time consuming and computationally expensive. The AI Experimental Calibration and Control project seeks to address these issues, starting first with the GlueX Central Drift Chamber (CDC). We demonstrate the ability of a Gaussian Process to estimate the gain correction factor (GCF) of the GlueX CDC accurately, and also the uncertainty of this estimate. Using the estimated GCF, the developed system infers a new high voltage (HV) setting that stabilizes the GCF in the face of changing environmental conditions. This happens in near real time during data taking and produces data which are already approximately gain-calibrated, eliminating the cost of performing those calibrations which vary ±15% with fixed HV. We also demonstrate an implementation of an uncertainty aware system which exploits a key feature of a Gaussian process.

Britton, Thomas↗
DOE OSTI2020

Basic factors for acquiring, correcting, and interpreting probe current-voltage characteristic in moderate-collisional plasma for determining electron energy distribution

Methods for inferring the electron distribution function (EDF) from a probe current–voltage trace are reviewed for moderate-collisional plasma. The explicit accounting of particle collisions, modeling sheath depletion, and electron refilling processes are rarely performed when analyzing probe measurements, yet they are critical for regime validity, EDF-profile accuracy, and space potential quantification precision. Furthermore, the advantages, limitations, and utilization of large-area wall probes in moderate-collisional plasma, capable of highly resolved indications of energetic electrons in plasma, are outlined.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Probabilistic Physics-Informed Graph Convolutional Network for Active Distribution System Voltage Prediction

Here this letter proposes a novel data-driven probabilistic physics-informed graph convolutional network (GCN) for active distribution system voltage prediction with PVs and EVs. It leverages both measurements and network topology to accurately and efficiently predict node voltages without the need for an accurate distribution system power flow model. The dropout-enabled Bayesian inference is developed to achieve uncertainty quantification of the voltage prediction. Thanks to the network model embedding, it also has robustness against topology changes, a key difference with existing machine learning-based approaches. Comparison results with other state-of-the-art machine learning methods on a realistic 759-node distribution system demonstrate that the proposed method can achieve better accuracy and robustness under different scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

BMINN: Learning chemical potentials and parameters from voltage data for multi-phase battery modeling

Free-energy landscapes and chemical potentials govern the dynamics of phase transitions, transport, and stability in functional materials, yet they remain experimentally inaccessible under realistic operating conditions. Here we introduce a Bayesian model-integrated neural network (BMINN) that embeds physics-based formulations of non-autonomous partial differential-algebraic equations into probabilistic learning. This approach reconstructs hidden thermodynamics directly from macroscopic current-voltage data, providing quantitative access to metastable states, staging transitions, and energy barriers without synchrotron probes. Demonstrated on lithium-graphite electrodes, BMINN recovers full Gibbs free-energy landscapes with fidelity validated against operando X-ray diffraction. The framework generalizes across dynamical regimes, enabling accurate voltage prediction, internal state estimation, and inference of governing parameters. Beyond batteries, BMINN exemplifies a broadly applicable strategy for learning missing physics in multiphase, non-equilibrium systems, offering a new pathway to uncover hidden thermodynamic functions across condensed matter and materials physics.

25 ENERGY STORAGE↗
DOE OSTI2019

Technique for inferring angle change as a function of time for high-current electron beams using a dose-rate monitor array

Intense electron beams striking a high-atomic number target produce high-output pulsed photon fluxes for flash x-ray experiments. Without an external guide field, such beams are subject to the dynamics of high-current electron beam propagation, including changes to electron trajectories either from self-fields or from development of beam instabilities. The bremsstrahlung output (dose-rate) scales approximately as IV x , where I is the beam current, V the electron energy, and x is in the range 2.0–2.65 and depends upon the electron angle on the converter. Using experimental beam data (dose-rate, I and V), this equation can be solved for x, a process known as “inverting the radiographer’s equation.” Inversion methods that rely on thermoluminescent dosimeters, which are time-integrated, yield no information about evolution of the electron beam angle in time. We propose here an inversion method that uses several dose-rate monitors at different angles with respect to the beam axis. By measuring dose-rates at different angles, one can infer the time-dependent beam voltage and angle. Furthermore, this method compares well with estimates of corrected voltage and results in a self-consistent picture of beam dynamics. Techniques are demonstrated using data from self-magnetic pinch experiments at the RITS-6 facility at Sandia National Laboratories.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Dual-ion ECRAM as a stable and accurate analog synapse

Electrochemical random-access memory (ECRAM) works by tuning the bulk electronic conductance of functional materials via reversible, electrochemical insertion of ions, resulting in stable analog resistive switching, attractive for analog in-memory and neuromorphic computing. However, achieving fast programming for training and long retention for inference has been elusive. Protonic ECRAM demonstrates fast programming but insufficient retention, while oxygen-based ECRAM with excellent retention requires elevated programming temperatures. Cu-based ECRAM offers a compromise, with an activation energy (E A ) of ≈0.76 eV between protons (E A ≈ 0.4 eV) and oxygen (E A > 1 eV), enabling extensive retention and room temperature programming. Combining Cu 2+ ions with protons to form a dual-ion ECRAM, we demonstrate two distinct switching behaviors: fast switching at ≤5 V, (E A ≈ 0.45 eV) via protons, and nonvolatile, room temperature switching at ≥8 V, with E A ≈ 0.76 eV via Cu 2+ ions. In conclusion, the Cu-based state exhibits a wide conductance range, with excellent retention, low noise, and linear current-voltage behavior, achieving digital-equivalent ImageNet inference accuracy.

analog in-memory computing↗
DOE OSTI2023

Electropotential Verification for Nuclear Safeguards

The international safeguards regime desires methods to efficiently verify that facilities are only performing declared activities. Electropotential verification (EPV) is a newly proposed technique that was tested for its feasibility to perform facility design information verification (DIV) and verification of spent nuclear fuel while in a cooling pool. EPV works by passing a constant, low voltage current through a conductive system (facility infrastructure of nuclear fuel assembly) and measuring the resulting voltage at various places throughout the infrastructure in order to establish a baseline. Changes made to the system affect these voltage readings, which will deviate from the baseline and indicate that a change to the system was made. For facility DIV, it appears feasible that changes in configuration of the system’s grounding can be detected in real-time, and the location of the change can be inferred from the measured intensity of the change in voltage. Determination of whether or not spent fuel was present in a fuel rod, as well as the presence/absence of a fuel rod from an assembly using EPV, proved unsuccessful with the sensitivity of instrumentation used in this study.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Algorithm-Based Fault Tolerance for Convolutional Neural Networks

Convolutional neural networks (CNNs) are becoming more and more important for solving challenging and critical problems in many fields. CNN inference applications have been deployed in safety-critical systems, which may suffer from soft errors caused by high-energy particles, high temperature, or abnormal voltage. Of critical importance is ensuring the stability of the CNN inference process against soft errors. Traditional fault tolerance methods are not suitable for CNN inference because error-correcting code is unable to protect computational components, instruction duplication techniques incur high overhead, and existing algorithm-based fault tolerance (ABFT) techniques cannot protect all convolution implementations. In this paper, we focus on how to protect the CNN inference process against soft errors as efficiently as possible, with the following three contributions. (1) We propose several systematic ABFT schemes based on checksum techniques and analyze their fault protection ability and runtime thoroughly. Unlike traditional ABFT based on matrix-matrix multiplication, our schemes support any convolution implementations. (2) We design a novel workflow integrating all the proposed schemes to obtain a high detection/correction ability with limited total runtime overhead. (3) We perform our evaluation using ImageNet with well-known CNN models including AlexNet, VGG-19, ResNet-18, and YOLOv2. Here, experimental results demonstrate that our implementation can handle soft errors with very limited runtime overhead (4%~8% in both error-free and error-injected situations).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Electropotential Verification for Nuclear Safeguards

The international safeguards regime desires methods to efficiently verify that facilities are only performing declared activities. Electropotential verification (EPV) is a newly proposed technique that was tested for its feasibility to perform facility design information verification (DIV). EPV works by passing a constant, low voltage current through a conductive system (facility infrastructure of nuclear fuel assembly) and measuring the resulting voltage at various places throughout the infrastructure in order to establish a baseline. Changes made to the system affect these voltage readings, which will deviate from the baseline and indicate that a change to the system was made. For large scale infrastructure such as a nuclear facility DIV, it appears feasible that changes in configuration of the system’s grounding can be detected in real-time, and the location of the change can be inferred from the measured intensity of the change in voltage.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Modeling of convective cells, turbulence, and transport induced by a radio-frequency antenna in the tokamak boundary plasma

The edge turbulence model Hermes (Dudson et al 2017 Plasma Phys. Control. Fusion 59 05401) is set up for plasma boundary simulations with an radiofrequency (RF) antenna, using parameters characteristic of a tokamak edge. Cartesian slab geometry is used with thin plate limiters representing the ion cyclotron range of frequency (ICRF) antenna side-wall limiters. Ad-hoc DC electric biasing of the limiters, motivated by calculations with VSim (Nieter et al 2004 J. Comput. Phys. 196 448), represents an induced RF sheath rectified potential in the plasma turbulence model. Flux-driven turbulence simulations demonstrate a realistic distribution of plasma profiles and fluctuations. There is a clear effect of the antenna sheath voltage leading to formation of convective cells; bias-induced convective transport flattens the scrape-off layer density profile and fluctuations penetrate into the shadow region of the limiters as the bias voltage increases. Turbulent transport for impurity ions is inferred by following ion trajectories in the simulated plasma turbulence fields, showing Bohm-like effective diffusion rates. All in all, the model elucidates the key physical phenomena governing the effects of ICRF-induced antenna biasing on the tokamak boundary plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Analysis and mitigation of parasitic resistance effects for analog in-memory neural network acceleration

To support the increasing demands for efficient deep neural network processing, accelerators based on analog in-memory computation of matrix multiplication have recently gained significant attention for reducing the energy of neural network inference. However, analog processing within memory arrays must contend with the issue of parasitic voltage drops across the metal interconnects, which distort the results of the computation and limit the array size. This work analyzes how parasitic resistance affects the end-to-end inference accuracy of state-of-the-art convolutional neural networks, and comprehensively studies how various design decisions at the device, circuit, architecture, and algorithm levels affect the system's sensitivity to parasitic resistance effects. Here, a set of guidelines are provided for how to design analog accelerator hardware that is intrinsically robust to parasitic resistance, without any explicit compensation or re-training of the network parameters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Multiple Operando Fields Can Identify a Predictive Mass Transport Theory in Electrolytes

An electrolyte transport theory connects its transport properties, evolution of spatiotemporal fields (e.g., concentration), and corresponding macroscopic current and voltage responses. Given this interconnection, the transport properties are typically inferred by analyzing the macroscopic response through the lens of a chosen electrolyte transport theory. Unfortunately, the same macroscopic measurements can be analyzed by using different theories to arrive at seemingly dissimilar properties that are inconsistent with each other. Here, we offer a resolution to this dilemma by analyzing multiple spatiotemporal (i.e., operando) fields for a given electrolyte. We show that predictive transport theory can analyze operando fields to estimate underlying transport properties and subsequently predict another operando field. A theory passing this test identifies meaningful transport properties such that the electrolyte behavior can be accurately predicted over a wide range of excitations, and is critical to property-based electrolyte screening and electrolyte discovery efforts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗