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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Efficient sampling of free energy landscapes with functions in Sobolev spaces

Molecular simulations of biological and physical phenomena generally involve sampling complicated, rough energy landscapes characterized by multiple local minima. In this work, we introduce a new family of methods for advanced sampling that draw inspiration from functional representations used in machine learning and approximation theory. As shown here, such representations are particularly well suited for learning free energies using artificial neural networks. As a system evolves through phase space, the proposed methods gradually build a model for the free energy as a function of one or more collective variables, from both the frequency of visits to distinct states and generalized force estimates corresponding to such states. Implementation of the methods is relatively simple and, more importantly, for the representative examples considered in this work, they provide computational efficiency gains of up to several orders of magnitude over other widely used simulation techniques.

Approximation theory↗
DOE OSTI2025

Absolute Hugoniot measurements in low-density plastic foams on the NIKE shock compression platform

The propagation of Mbar-range shock waves in low-density foam materials is of interest to target design in all approaches to inertial confinement fusion, high energy density physics, and laboratory astrophysics. We report absolute Hugoniot measurements for CH 1.72 N 0.086 O 0.37 plastic foams with 73 and 94 mg/cm 3 densities in the 32–107 km/s shock velocity range. The experiments were performed on the shock compression platform developed on the NIKE KrF laser facility at the U.S. Naval Research Laboratory. NIKE's 4 or 8-ns long flat-top laser pulses drive steady shock waves into foam targets at the ablative pressures of 1–7 Mbar. The propagation of the ablation and shock fronts is tracked continuously in time using streaked side-on monochromatic x-ray imaging radiography. The straight x−t trajectories of the shock and ablation fronts in the recorded streak images confirmed their steadiness. The SESAME tabulated equation of state predictions generally agrees with our Hugoniot data within a 95% confidence band. The experimental uncertainty of the evaluated shock density compression ratios remains large, indicating the need for more experiments and improved theoretical understanding of the strong shock propagation mechanisms in dry foams.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Increased extreme rains intensify erosional nitrogen and phosphorus fluxes to the northern Gulf of Mexico in recent decades

Abstract Soil erosion delivers enormous amounts of macro-nutrients including nitrogen (N) and phosphorus (P) from land to rivers, potentially sustaining water column bioavailable nutrient levels for decades. In this study, we represent erosional N and P fluxes in the Energy Exascale Earth System Model (E3SM) and apply the model to the continental United States. We estimate that during 1991–2019 soil erosion delivers 775 Gg yr −1 (1 Gg = 10 9 g) of particulate N (PN) and 328 Gg yr −1 of particulate P (PP) on average to the drainage basins of the northern Gulf of Mexico, including the Mississippi/Atchafalaya River and other rivers draining to the Texas Gulf and the Eastern Gulf. Our model simulation shows that in these rivers PP is the dominant P constituent and over 55% of P exported by erosion comes from soil P pools that could become bioavailable within decades. More importantly, we find that during 1991–2019 erosional N and P fluxes increase at rates of about 15 Gg N yr −1 and 6 Gg P yr −1 , respectively, due to increased extreme rains in the Mississippi/Atchafalaya river basin, and this intensification of erosional N and P fluxes drive the significant increase of riverine PN and PP yields to the northern Gulf of Mexico. With extreme rains projected to increase with warming, erosional nutrient fluxes in the region would likely continue to rise in the future, thus complicating the effort of reducing eutrophication in the inland and coastal waters.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Quantum error mitigation by hidden inverses protocol in superconducting quantum devices

We present a method to improve the convergence of variational algorithms based on hidden inverses (HIs) to mitigate coherent errors. In the context of error mitigation, this means replacing the hardware implementation of certain Hermitian gates with their inverses. Doing so results in noise cancellation and a more resilient quantum circuit. This approach improves performance in a variety of two-qubit error models where the noise operator also inverts with the gate inversion. We apply the mitigation scheme on superconducting quantum processors running the variational quantum eigensolver (VQE) algorithm to find the H 2 ground-state energy. When implemented on superconducting hardware we find that the mitigation scheme effectively reduces the energy fluctuations in the parameter learning path in VQE, reducing the number of iterations for a converged value. We also provide a detailed numerical simulation of VQE performance under different noise models and explore how HIs and randomized compiling affect the underlying loss landscape of the learning problem. These simulations help explain our experimental hardware outcomes, helping to connect lower-level gate performance to application-specific behavior in contrast to metrics like fidelity which often do not provide an intuitive insight into observed high level performance.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Spectroscopy on the A 1 Π←X 1 Σ + Transition of Buffer-Gas Cooled AlCl

Aluminum monochloride (AlCl) has been proposed as an excellent candidate for laser cooling. Here we present absorption spectroscopy measurements on the A 1 Π←X 1 Σ + transition in AlCl inside a cryogenic helium buffer-gas beam cell. The high resolution absorption data enables a rigorous, quantitative comparison with our high-level ab initio calculations of the electronic and rovibronic energies, providing a comprehensive picture of the AlCl quantum structure. Further, the combination of high resolution spectral data and theory permits the evaluation of spectroscopic constants and associated properties, like equilibrium bond length, with an order of magnitude higher precision. Based on the measured molecular equilibrium constants of the A 1 Π state, we estimate a Franck-Condon factor of the A 1 Π←X 1 Σ + of 99.88%, which confirms that AlCl is amenable to laser cooling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Model-independent determination of the dipole response of 66 Zn using quasimonoenergetic and linearly polarized photon beams

Background: Photon strength functions are an important ingredient in calculations relevant for the nucleosynthesis of heavy elements. The relation to the photoabsorption cross section allows to experimentally constrain photon strength functions by investigating the photoresponse of atomic nuclei. Purpose: Here, we determine the photoresponse of 66 Zn in the energy region of 5.6 MeV to 9.9 MeV and analyze the contribution of the 'elastic' decay channel back to the ground state. In addition, for the elastic channel electric and magnetic dipole transitions were separated. Methods: Nuclear resonance fluorescence experiments were performed using a linearly polarized quasi-monoenergetic photon beam at the High Intensity γ-ray Source. Photon beam energies from 5.6 to 9.9 MeV with an energy spread of about 3% were selected in steps of 200–300 keV. Two high purity germanium detectors were used for the subsequent γ-ray spectroscopy. Results: Full photoabsorption cross sections are extracted from the data making use of the monoenergetic character of the photon beam. For the ground-state decay channel, the average contribution of electric and magnetic dipole strengths is disentangled. The average branching ratio back to the ground state is determined as well. Conclusions: The new results indicate lower cross sections when compared to the values extracted from a former experiment using bremsstrahlung on 66 Zn. In the latter, the average branching ratio to the ground state is estimated from statistical-model calculations in order to analyze the data. Corresponding estimates from statistical-model calculations underestimate this branching ratio compared to the values extracted from the present analysis, which would partly explain the high cross sections determined from the bremsstrahlung data.

59 ≤ A ≤ 89↗
DOE OSTI2024

Global Data-Driven Determination of Baryon Transition Form Factors

Hadronic resonances emerge from strong interactions encoding the dynamics of quarks and gluons. The structure of these resonances can be probed by virtual photons parametrized in transition form factors. Here, in this study, twelve N* and Δ transition form factors at the pole are extracted from data with the center-of-mass energy from πN threshold to 1.8 GeV, and the photon virtuality 0 ≤ Q 2 /GeV 2 ≤ 8. For the first time, these results are determined from a simultaneous analysis of more than one state, i.e., ~10 5 π⁢N, η⁢N, and K⁢Λ electroproduction data. In addition, about 5×10 4 data in the hadronic sector as well as photoproduction serve as boundary conditions. For the Δ⁡(1232) and N⁡(1440) states our results are in qualitative agreement with previous studies, while the transition form factors at the poles of some higher excited states are estimated for the first time. Realistic uncertainties are determined by further exploring the parameter space.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Optimization-Based Dynamic Voltage Support of Microgrids Using Energy Storage Systems

A microgrid network is characterized by a high R/X ratio, making the voltage more sensitive to active power changes compared to bulk power systems, where the voltage is regulated primarily by reactive power. Due to its sensitivity, voltage control approaches for microgrids should also consider the active power input coupling, making it very different from conventional power systems. Additionally, as the energy costs associated with active and reactive powers are different and the operational conditions of microgrids connected to active distribution systems vary over time, the ideal controller to provide voltage support must be flexible enough to handle these technical and operational constraints. This paper proposes a model predictive control approach to provide dynamic voltage support using energy storage systems. This approach uses a simplified predictive model of the system to solve the model predictive control problem. By proper selection of model predictive control weighting parameters, the quality of service provided can be adjusted to achieve the desired performance. A simulation study in MATLAB/Simulink validates the proposed approach for the Cordova, Alaska microgrid. Results show that the performance of the voltage support can be adjusted depending on the choice of weight and constraints of the controller.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Optimization-Based Data-Driven Approach for Detecting Fault Location in Power Systems

In grids with large penetration of converterinterfaced resources (CIRs), measurements of voltage, current, and line parameters can fluctuate significantly during fault conditions. These fluctuations, combined with complex network topologies and extensive system branching, make accurate fault location challenging. Faults, such as short circuits, can cause prolonged outages with serious socio-economic impacts, highlighting the need for rapid fault identification to minimize downtime. However, current fault detection methods—such as relays and digital fault recorders—often relay information too slowly, impeding swift corrective action. Given the limited availability of high-resolution phasor measurement units, this paper introduces an optimization-based observer to estimate fault locations, grid line parameters, and voltages using local CIR measurements. To preserve the confidentiality of CIRs and enhance estimation accuracy, this study uses a black-box model of CIRs. This bottom-up, event-driven approach can enhances protection and control systems through optimized and real-time fault detection. Simulation results show that the optimization-based data-driven observer can accurately detect fault locations and estimate grid states and parameters, providing valuable insights for utilities and operators in grid applications.

Subedi, Sunil [ORNL] (ORCID:000000034069090X)↗
DOE OSTI2022

Hybrid Data-Driven Physics-Based Model Framework Implementation: Towards a Secure Cyber-Physical Operation of the Smart Grid

False data injection cyber-attack detection models on smart grid operation have been much explored recently, considering analytical physics-based and data-driven solutions. Recently, a hybrid data-driven physics-based model framework for monitoring the smart grid is developed. However, the framework has not been implemented in real-time environment yet. In this paper, the framework of the hybrid model is developed within a real-time simulation environment. OPAL-RT real-time simulator is used to enable Hardware-in-the-Loop testing of the framework. IEEE 9-bus system is considered as a testing grid for gaining insight. The process of building the framework and the challenges faced during development are presented. The performance of the framework is investigated under various false data injection attacks.

false data injection attack, machine learning, sta↗
DOE OSTI2022

Security Enhancement of Network Constraint Grid-Edge Energy Management System

Network constrained grid edge energy management system (EMS) provides economic solution for active and reactive power dispatch of distributed energy resources (DERs) at the grid edge level. Grid edge EMS ensures secure interconnection of a circuit segment to the distribution system by maintaining grid code requirements (e.g. IEEE 1547–2018). Grid edge EMS is dependent on communication to receive load measurement, which brings a risk of unobservable false data injection attacks (FDIAs). To mitigate the risk, this paper proposes a framework to enhance resilient operation of grid edge EMS by detecting the unobservable FDIAs on loads and replacing them with forecasted values. In this work, a two-step detection algorithm is proposed. In first step, conventional residual based algorithm is deployed. Autoencoder (AE) based data driven mechanism is included in second step to detect the presence of unobservable FDIAs. After ensuring the presence of FDIA, its specific location is detected by checking the maximum residue values till the predefined threshold value is reached. Detected false data injected loads are then replaced with forecasted load values following long-short term memory (LSTM) based forecast to ensure resilient performance of grid edge EMS in the presence of attacks. This proposed security enhancement framework for grid edge EMS is evaluated in IEEE 13 bus system with three integrated DERs. Numerical simulation shows the validation of the proposed framework by reducing voltage violation in real operation of grid edge EMS.

cyber attack detection↗
DOE OSTI2021

Detecting False Data Injection Attacks in Smart Grids: A Semi-Supervised Deep Learning Approach

The dependence on advanced information and communication technology increases the vulnerability in smart grids under cyber-attacks. Recent research on unobservable false data injection attacks (FDIAs) reveals the high risk of secure system operation, since these attacks can bypass current bad data detection mechanisms. To mitigate this risk, this paper proposes a data-driven learning-based algorithm for detecting unobservable FDIAs in distribution systems. We use autoencoders for efficient dimension reduction and feature extraction of measurement datasets. Further, we integrate the autoencoders into an advanced generative adversarial network (GAN) framework, which successfully detects anomalies under FDIAs by capturing the unconformity between abnormal and secure measurements. Also, considering that the datasets collected from practical power systems are partially labeled due to expensive labeling costs and missing labels, the proposed method only requires a few labeled measurement data in addition to unlabeled data for training. Numerical simulations in three-phase unbalanced IEEE 13-bus and 123-bus distribution systems validate the detection accuracy and efficiency of this method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Machine Learning–Based Tire Life Prediction Framework for Increasing Life of Commercial Vehicle Tires

In the commercial freight industry, tire retreading decisions are often conservative due to limited knowledge of a tire’s remaining service life. This practice leads to increased costs and material waste. This paper proposes a machine learning–based approach for estimating tire casing life and retreadability, focusing on usage data rather than wear information. This approach could extend the tire’s lifespan and reduce landfill waste. Data integration from diverse tire casing measurement sources presents challenges, including imbalanced removal data. Our methodology addresses these challenges by using historical inspection, telematics, and finite element modeling (FEM) datasets. We introduce “Tire Casing Energy” as a comprehensive usage input and apply a Variance-Reduction Synthetic Minority Oversampling Technique (VR-SMOTE) for data imbalance rectification. A random forest model is used to estimate the state of the tire casing and the casing removal probability, with Bayesian optimization applied for hyperparameter tuning, enhancing model accuracy. Here, the proposed prediction framework is able to differentiate different truck fleets and tire locations based on their usage parameters. With the aid of this machine learning model, the importance and sensitivity of different tire usage parameters can be obtained, which is beneficial to maximize tire life.

Data balancing↗
DOE OSTI2023

Leveraging on-shell interference to search for FCNCs of the top quark and the Z boson

Abstract Flavour-changing-neutral currents (FCNCs) involving the top quark are highly suppressed within the Standard Model (SM). Hence, any signal in current or planned future collider experiments would constitute a clear manifestation of physics beyond the SM. We propose a novel, interference-based strategy to search for top-quark FCNCs involving the Z boson that has the potential to complement traditional search strategies due to a more favourable luminosity scaling. The strategy leverages on-shell interference between the FCNC and SM decay of the top quark into hadronic final states. We estimate the feasibility of the most promising case of anomalous tZc couplings using Monte Carlo simulations and a simplified detector simulation. We consider the main background processes and discriminate the signal from the background with a deep neural network that is parametrised in the value of the anomalous tZc coupling. We present sensitivity projections for the HL-LHC and the FCC-hh. We find an expected 95% CL upper limit of $${\mathcal {B}}_{\textrm{excl}}(t\rightarrow Zc) = 6.4 \times 10^{-5}$$ B excl ( t → Z c ) = 6.4 × 10 - 5 for the HL-LHC. In general, we conclude that the interference-based approach has the potential to provide both competitive and complementary constraints to traditional multi-lepton searches and other strategies that have been proposed to search for tZc FCNCs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Ultrasonic Nondestructive Diagnosis of Cylindrical Batteries Under Various Charging Rates

Lithium-ion batteries have been used increasingly as electrochemical energy storage systems for electronic devices and vehicles. It is important to accurately estimate the state of charge (SoC) of a battery management system to control the battery operation to optimize performance, lifetime, and safety. The current work experimentally leverages ultrasonic diagnostic technology to investigate the SoC of lithium-ion batteries during the charge/discharge processes. A cylindrical-type nickel-cobalt-aluminum (NCA)–based 2500mAh 20A (INR18650-25R) battery was used for ultrasonic measurements with various charge/discharge rates of C/10.4, C/5.2, and C/1.3 at constant currents. The ultrasonic signals were analyzed for extracting wave velocity and wave attenuation. For all the testing rates, wave velocity increased in the charge process and decreased in the discharge process. Further, velocity profiles corresponding to lower rates of C/10.4 and C/5.2 exhibited primary peaks at the maximum SoCs, whereas the absolute wave velocity of C/1.3 rate showed primary peaks that occurred slightly after the SoC peak, indicating a delayed maximum Young's modulus. The wave attenuation computed for the C/10.4 rate had local maxima in the charge and discharge processes and depicted negative correlations with SoC, ranging from 0% to 18%, and positive correlations with SoC from 18% to 85%. On the other hand, the wave attenuation curves of the C/1.3 rate showed no local peaks and had negative correlations with SoC, ranging from 0% to 28%, and positive correlations with SoC ranging from 28% to 53%.

25 ENERGY STORAGE↗
DOE OSTI2026

Non-Invasive Electrode Impedance Estimation for Optimized Charge Profile Parameterization of Lithium-Ion Batteries

This work presents a non-invasive method for parameterizing a physically motivated equivalent circuit model of lithium-ion batteries using operando electrochemical impedance spectroscopy and time-domain data. The proposed model consists exclusively of linear circuit elements, enabling computationally efficient simulation and real-time implementation on battery management system chips. By integrating frequency- and time-domain measurements, the model accurately estimates internal states such as the negative electrode potential, achieving a root mean square error of 12.3 mV during fast charging validation. Parameterization requires only rate tests with sinusoidal perturbations at three different ambient temperatures, making the approach experimentally accessible. The model reveals key insights into battery behavior, including rate-dependent overpotentials primarily governed by charge transfer kinetics at the positive electrode, and temperature-dependent impedance contributions from both charge transfer and solid-state diffusion processes. Validation using reference electrodes confirms the model’s ability to detect lithium plating onset and reproduce impedance behavior across a wide range of operating conditions. The approach enables in situ optimization of fast charging profiles and lays the foundation for future extensions incorporating aging effects and plating dynamics.

25 ENERGY STORAGE↗
DOE OSTI2020

The Technical and Economic Potential of the H2@Scale Hydrogen Concept within the United States

The U.S. energy system is evolving as society and technologies change. Renewable electricity generation - especially from wind and solar - is growing rapidly, and alternative energy sources are being developed and implemented across the residential, commercial, transportation, and industrial sectors to take advantage of their cost, security, and health benefits. Systemic changes present numerous challenges to grid resiliency and energy affordability, creating a need for synergistic solutions that satisfy multiple applications while yielding system-wide cost and emissions benefits. One such solution is an integrated hydrogen energy system. This is the focus of H2@Scale - a U.S. Department of Energy (DOE) initiative led by the Office of Energy Efficiency and Renewable Energy’s Hydrogen and Fuel Technologies Office. H2@Scale brings together stakeholders to advance affordable hydrogen production, transport, storage, and utilization in multiple energy sectors. The H2@Scale concept involves hydrogen as an energy intermediate. Hydrogen can be produced from various conventional and renewable energy sources including as a responsive load on the electric grid. Hydrogen has many current applications and many more potential applications, such as energy for transportation - used directly in fuel cell electric vehicles (FCEVs), as a feedstock for synthetic fuels, and to upgrade oil and biomass - feedstock for industry (e.g., for ammonia production, metals refining, and other end uses), heat for industry and buildings, and electricity storage. Owing to its flexibility and fungibility, a hydrogen intermediate could link energy sources that have surplus availability to markets that require energy or chemical feedstocks, benefiting both. This document builds upon a growing body of analyses of hydrogen as an energy intermediate by reporting the results from our initial analysis of the potential impacts of the H2@Scale vision by the mid-21 st century for the 48 contiguous U.S. states. Previous estimates have been based on expert elicitation and focused on hydrogen demands. We build upon them, first, by estimating hydrogen’s serviceable consumption potential for possible hydrogen applications and the technical potential for producing hydrogen from various resources. We define the serviceable consumption potential as the quantity of hydrogen that would be consumed to serve the portion of the market that could be captured without considering economics (i.e., if the price of hydrogen were $0/kg over an extended period); thus, it can be considered an upper bound for the size of the market. We define the technical potential as the resource potential constrained by real-world geography and system performance, but not by economics. We then compare the cumulative serviceable consumption potential with the technical potential of a number of possible sources. Second, we estimate economic potential: the quantity of hydrogen at an equilibrium price at which suppliers are willing to sell and consumers are willing to buy the same quantity of hydrogen. We believe this method provides a deeper understanding than was available in the previous analyses. We develop economic potentials for multiple scenarios across various market and technology-advancement assumptions.

08 HYDROGEN↗