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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Thermodynamic analysis of hydrogel swelling in aqueous sodium chloride solutions

Here, a comprehensive thermodynamic model is presented for swelling and salt partitioning of poly (n-isopropyl acrylamide) hydrogels in aqueous sodium chloride (NaCl) solutions at 298 K. Together with a Helmholtz energy expression for network elastic energy, a modified electrolyte Nonradom Two-Liquid activity coefficient model is used to represent both the long-range electrostatic interactions and the short-range van der Waals interactions present in the gel phase. The model parameters include one network parameter per hydrogel system plus two binary parameters per interaction pair in the polymer–solvent-salt systems. With semiquantitave agreement with experimental data for both hydrogel swelling and salt partitioning up to salt saturation, the analysis suggests the ion hydration between NaCl and water is the root cause for hydrogel deswelling in aqueous NaCl solution at high NaCl concentrations, i.e., NaCl weight fraction >0.03. On the other hand, the strong repulsive interaction between NaCl and hydrogel polymer is the dominant factor for salt partitioning.

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

Spin excitations and dynamics in 2D magnets: An overview of magnons and magnetic skyrmions

van der Waals magnetic materials open up exciting possibilities to investigate fundamental spin properties in low-dimensional systems and to build compact functional spintronic structures. This review focuses on the recent progress in two-dimensional (2D) magnets that explore beyond the homogenous magnetically ordered state, including magnons (spin waves), magnetic skyrmions, and complex magnetic domains. Properties of these spin and topology excitations in 2D magnets provide insights into spin-orbit interactions and other forms of coupling between electrons, phonons, and spin-dependent excitations. Such spin-based quasiparticles can also serve as information carriers for next-generation high-speed computing elements. Furthermore, we will first lay out the general theoretical basis of dynamical responses in magnetic systems, followed by detailed descriptions of experimental progress in magnons and spin textures (including magnetic domains and skyrmions). Discussion on the experimental techniques and future perspectives are also included.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Hydrogen bonding sewing interface

Hydrogen bonding and van der Waals (vdW) forces have been precisely measured and distinguished by an in-house nanomechanical testing system.

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

Construction of Fractal Order and Phase Transition with Rydberg Atoms

We propose the construction of a many-body phase of matter with fractal structure using arrays of Rydberg atoms. The degenerate low energy excited states of this phase form a self-similar fractal structure. This phase is analogous to the so-called “type-II fracton topological states.” The main challenge in realizing fractonlike models in standard condensed matter platforms is the creation of multispin interactions, since realistic systems are typically dominated by two-body interactions. In this work, we demonstrate that the van der Waals interaction and experimental tunability of Rydberg-based platforms enable the simulation of exotic phases of matter with fractal structures, and the study of a quantum phase transition involving a fractal ordered phase.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Nature of Excitons and Their Ligand-Mediated Delocalization in Nickel Dihalide Charge-Transfer Insulators

The fundamental optical excitations of correlated transition-metal compounds are typically identified with multielectronic transitions localized at the transition-metal site, such as d d transitions. In this vein, intense interest has surrounded the appearance of sharp, below-band-gap optical transitions, i.e., excitons, within the magnetic phase of correlated Ni 2 + van der Waals magnets. The interplay of magnetic and charge-transfer insulating ground states in Ni 2 + systems raises intriguing questions on the roles of long-range magnetic order and of metal-ligand charge transfer in the exciton nature, which inspired microscopic descriptions beyond typical d d excitations. Here we study the impact of charge transfer and magnetic order on the excitation spectrum of the nickel dihalides ( Ni X 2 , X = Cl , Br, and I) using Ni- L 3 edge resonant inelastic x-ray scattering (RIXS). In all compounds, we detect sharp excitations, analogous to the recently reported excitons, and assign them to spin-singlet multiplets of octahedrally coordinated Ni 2 + stabilized by intra-atomic Hund’s exchange. Additionally, we demonstrate that these excitons are dispersive using momentum-resolved RIXS. Our data evidence a ligand-mediated multiplet dispersion, which is tuned by the charge-transfer gap and independent of the presence of long-range magnetic order. This reveals the mechanisms governing nonlocal interactions of on-site d d excitations with the surrounding crystal or magnetic structure, in analogy to ground-state superexchange. These measurements thus establish the roles of magnetic order, self-doped ligand holes, and intersite-coupling mechanisms for the properties of d d excitations in charge-transfer insulators. Published by the American Physical Society 2024

2-dimensional systems↗
DOE OSTI2022

Dynamic Role-Based Access Control Policy for Smart Grid Applications: An Offline Deep Reinforcement Learning Approach

Role-based access control (RBAC) is adopted in the information and communication technology domain for authentication purposes. However, due to a very large number of entities within organizational access control (AC) systems, static RBAC management can be inefficient, costly, and can lead to cybersecurity threats. In this paper, a novel hybrid RBAC model is proposed, based on the principles of offline deep reinforcement learning (RL) and Bayesian belief networks. The considered framework utilizes a fully offline RL agent, which models the behavioral history of users as a Bayesian belief-based trust indicator. Thus, the initial static RBAC policy is improved in a dynamic manner through off-policy learning while guaranteeing compliance of the internal users with the security rules of the system. By deploying our implementation within the smart grid domain and specifically within a Distributed Energy Resources (DER) ecosystem, we provide an end-to-end proof of concept of our model. Finally, detailed analysis and evaluation regarding the offline training phase of the RL agent are provided, while the online deployment of the hybrid RL-based RBAC model into the DER ecosystem highlights its key operation features and salient benefits over traditional RBAC models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Multi-agent voltage control in distribution systems using GAN-DRL-based approach

Active distribution grids can experience voltage fluctuations and violations due to the high penetration of variable distributed energy resources (DERs). These problems might occur because of the uncertain and variable generation natures of these resources, especially solar photovoltaic resources, during panel shadowing scenarios. Volt-VAR control (VVC) is an efficient method that controls the reactive power set-points of the inverters to regulate the voltage of distribution grids. Although several VVC approaches have been proposed recently, the performance of these approaches degrades significantly if behind-the-meter solar generation data are unobservable/missing. Therefore, it is necessary to impute missing/unobservable PV data accurately to be utilized in VVC approaches. Further, this paper proposes a model-free, data-driven, centrally trained, and decentrally executed multi-agent deep reinforcement learning-based VVC architecture to regulate the voltage of distribution networks. A generative adversarial network (GAN) is incorporated to impute the unobservable PV data accurately, which improves the performance of the proposed control architecture. The proposed multi-agent-soft-actor–critic algorithm (MASAC)-based VVC technique utilizes the actual PV dataset as well as the imputed dataset from the GAN framework to learn the optimal coordinated control policy for controlling the optimal reactive power set-points of PV inverters. The effectiveness of the proposed approach is analyzed on a modified IEEE 34-bus test case with added PV inverters. The results are compared and analyzed with a base case model with no VVC and VVC with a local droop control approach, genetic algorithm optimization, and a centralized soft actor–critic-based approach. Moreover, the performance of the proposed approach is compared with that of a multi-agent VVC framework without using the PV generation data and load information as the system state. The results illustrate that the proposed method with more state input improves the voltage profile and reduces the power loss of the network across various loading and PV generation scenarios.

14 SOLAR ENERGY↗
DOE OSTI2023

Bayesian GAN-Based False Data Injection Attack Detection in Active Distribution Grids With DERs

Advancements in information and communication technologies have revolutionized monitoring and control capabilities within smart grids. However, it also brings new vulnerabilities to data acquisition systems and state estimation functions, which attackers can subtly tamper with the measurement data through compromising the communication network. Moreover, the high penetration of renewable energy sources with the inherited characteristics of uncertainty and variability further complicates the design of effective intrusion detection systems. In this paper, a Bayesian deep learning-based approach is developed to detect cyber attacks and maintain the security of smart grids. Our method specifically addresses the prevalent issue of imbalanced data in real power systems, which arises from the predominance of normal system operations over compromised or attacked states. Employing a novel Bayesian GAN-based technique, our approach successfully discriminates between secure and compromised measurement data, even in scenarios with significant data imbalance. Furthermore, the proposed method accommodates various practical application factors, ensuring accurate intrusion detection despite the presence of measurement noise. The feasibility and effectiveness of the proposed detection mechanism are validated by testing on IEEE 13-node and 123-node test systems. Simulation results and comparisons with literature methods demonstrate the superiority of proposed cybersecurity solutions.

Bayesian GAN↗
DOE OSTI2024

How Accurate Are Simulations and Experiments for the Lattice Energies of Molecular Crystals?

Molecular crystals play a central role in a wide range of scientific fields, including pharmaceuticals and organic semiconductor devices. However, they are challenging systems to model accurately with computational approaches because of a delicate interplay of intermolecular interactions such as hydrogen bonding and Van der Waals dispersion forces. Here, by exploiting recent algorithmic developments, we report the first set of diffusion Monte Carlo lattice energies for all 23 molecular crystals in the popular and widely used X23 dataset. Comparisons with previous state-of-the-art lattice energy predictions (on a subset of the dataset) and a careful analysis of experimental sublimation enthalpies reveals that high-accuracy computational methods are now at least as reliable as (computationally derived) experiments for the lattice energies of molecular crystals. Overall, this work demonstrates the feasibility of high-level explicitly correlated electronic structure methods for broad benchmarking studies in complex condensed phase systems, and signposts a route towards closer agreement between experiment and simulation. Published by the American Physical Society 2024

Physics↗
DOE OSTI2021

Distributed Optimization in Distribution Systems: Use Cases, Limitations, and Research Needs

We report electric distribution grid operations typically rely on both centralized optimization and local non-optimal control techniques. As an alternative, distribution system operational practices can consider distributed optimization techniques that leverage communications among various neighboring agents to achieve optimal operation. With the rapidly increasing integration of distributed energy resources (DERs), distributed optimization algorithms are growing in importance due to their potential advantages in scalability, flexibility, privacy, and robustness relative to centralized optimization. Implementation of distributed optimization offers multiple challenges and also opportunities. This paper provides a comprehensive review of the recent advancements in distributed optimization for electric distribution systems and classifications using key attributes. Problem formulations and distributed optimization algorithms are provided for example use cases, including volt/var control, market clearing process, loss minimization, and conservation voltage reduction. Finally, this paper also presents future research needs for the applicability of distributed optimization algorithms in the distribution system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Evaporated Se x Te 1-x Thin Films with Tunable Bandgaps for Short-Wave Infrared Photodetectors

Semiconducting absorbers in high-performance short-wave infrared (SWIR) photodetectors and imaging sensor arrays are dominated by single-crystalline germanium and III-V semiconductors. However, these materials require complex growth and device fabrication procedures. In this work, thermally evaporated Se x Te 1- x alloy thin films with tunable bandgaps for the fabrication of high-performance SWIR photodetectors are reported. From absorption measurements, it is shown that the bandgaps of Se x Te 1- x films can be tuned continuously from 0.31 eV (Te) to 1.87 eV (Se). Owing to their tunable bandgaps, the peak responsivity position and photoresponse edge of Se x Te 1- x film-based photoconductors can be tuned in the SWIR regime. By using an optical cavity substrate consisting of Au/Al 2 O 3 to enhance its absorption near the bandgap edge, the Se 0.32 Te 0.68 film (an optical bandgap of ≈0.8 eV)-based photoconductor exhibits a cut-off wavelength at ≈1.7 μm and gives a responsivity of 1.5 AW -1 and implied detectivity of 6.5 × 10 10 cm Hz 1/2 W -1 at 1.55 μm at room temperature. Importantly, the nature of the thermal evaporation process enables the fabrication of Se 0.32 Te 0.68 -based 42 × 42 focal plane arrays with good pixel uniformity, demonstrating the potential of this unique material system used for infrared imaging sensor systems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

POWER DATA PIPELINE

SF-25-081 Utility software for creating high-performance data pipelines to extract, load, and transform raw electric power systems measurements. For use with anomaly detection models training workflows. The software supports the project: Adaptive Cybersecurity for DER: A Game-Theoretic and Machine Learning approach for Real-Time Threat Detection and Mitigation

Plathottam, Silby Jose [Argonne National Laborator↗
DOE OSTI2023

Distribution Network Capacity Market Design: Marginal Distribution Capacity Pricing Mechanism for Efficient Investment and Cost Allocation

As electricity markets begin to shift from reliance on large, centralized power plants and towards distributed energy resources (DERs), there is a growing acknowledgement that more efficient planning, operations, and oversight is needed in the distribution system. This paper addresses one step in that direction by proposing an auction mechanism that uses a detailed distribution system planning model that allocates permits to end-used customers who request capacity to install new devices at their location and network upgrade contracts to utilities or 3rd-party companies who offer to upgrade system components. The resulting plan maximizes market surplus, that is, maximizes the total benefit to consumers minus the cost of network upgrades. We apply a marginal pricing scheme to the auction’s results such that the cost of each permits or contracts is differentiated by time and location, based on the Lagrangian multipliers of binding network constraints. These prices are shown to be no greater than the bid price of any awarded contract and no less than the offered cost of any awarded upgrade contract. Furthermore, the nonlinearity of power flows in the planning model result in an additional surplus that would be collected by the entity that hosts the auction, which could then be refunded to market participants or used to cover overhead costs of running the market. We provide four example auction results in a simple three-node distribution feeder to demonstrate the properties of the design. Results suggest that larger or more realistic case studies could be a promising next step.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Hardware-in-the-loop Testing of Network Protectors for Low-Voltage Networks with Distributed Energy Resources

In this report, we developed and validated a network protector relay digital twin model and interfaced a commonly used network protector relay hardware with our real-time simulation system. Hardware-in-the-loop protection studies are performed to assess the impact of distributed energy resources (DER) and benchmark a rate-of-change-based mitigation strategy. Simulation results suggest that the network protector reverse trip and auto-reclose functions are negatively impacted by the high distributed energy resource penetration. To accommodate DER backfeed while remaining secure and reliable for faults on primary feeders, we recommend options for a rate-of-change-based blocking scheme and a protection setting change. Finally, future mitigation ideas and standard revisions are discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

EVs-at-RISC: A Secure and Resilient Interoperable SCM Control System Architecture for Electric Vehicle’s-at-Scale (Final Technical Report)

The EVs-at-RISC project was a five-year research, development, and demonstration initiative to create foundational tools for utility-scale fleet aggregation and Smart Charge Management (SCM) of Electric Vehicles (EV), Electric Vehicle Charging Infrastructure (EVCI), and related Distributed Energy Resources (DER). Rather than seeking to develop and demonstrate highly perfected SCM algorithms and control strategies, this project instead focused on creating foundational software solutions that enable unprecedented digital interoperability across the communications technologies and vendor platforms used to manage EV , EVCI, and DER, as well as existing energy management infrastructure operated by utilities, grid operators, and aggregators. This project then extends these novel interoperability capabilities to develop and deploy powerful middleware abstractions across grid edge networks and EVCI/DER fleet aggregations incorporating modern software tools and best practices, such as CI/CD, to bring the immense capabilities of infrastructure-as-code and policy-as-code to modern grid edge network environments. This addresses the foremost systemic issues preventing realization of any net operational benefits from scaled deployment of behind-the-meter EV, EVCI, and DER assets in electric power grids and markets today. The results of this approach and project unlock massive potential for new SCM capabilities to be easily prototyped, evaluated, and deployed at-scale within the existing grid edge network infrastructure and EVCI/DER technology ecosystem. The EVs-at-RISC project achieves this by extending Open Field Message Bus (OpenFMB), a conceptual model for digital interoperability and distributed intelligence in traditional front-of-meter utility SCADA networks, validating our hypothesis that OpenFMB could be similarly used to solve systemic digital interoperability issues in behind-the-meter environments and unlock real-world utility-scale SCM capabilities without requiring any new proprietary vendor solutions or significant infrastructure reconfiguration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Extraordinary phase transition revealed in a van der Waals antiferromagnet

While the surface-bulk correspondence has been ubiquitously shown in topological phases, the relationship between surface and bulk in Landau-like phases is much less explored. Theoretical investigations since 1970s for semi-infinite systems have predicted the possibility of the surface order emerging at a higher temperature than the bulk, clearly illustrating a counterintuitive situation and greatly enriching phase transitions. But experimental realizations of this prediction remain missing. Here, we demonstrate the higher-temperature surface and lower-temperature bulk phase transitions in CrSBr, a van der Waals (vdW) layered antiferromagnet. We leverage the surface sensitivity of electric dipole second harmonic generation (SHG) to resolve surface magnetism, the bulk nature of electric quadrupole SHG to probe bulk spin correlations, and their interference to capture the two magnetic domain states. Our density functional theory calculations show the suppression of ferromagnetic-antiferromagnetic competition at the surface is responsible for this enhanced surface magnetism. Our results not only show counterintuitive, richer phase transitions in vdW magnets, but also provide viable ways to enhance magnetism in their 2D form.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Decentralised Reinforcement Learning for Dynamic Cyberattack Response in Microgrid Networks

Microgrids rely on communication networks for reliable operation, which makes them inherently vulnerable to cyberattacks. Such attacks can destabilise system dynamics and drive states away from their nominal operating trajectories. Although several physics-informed and machine learning-based strategies have been developed to counter these threats, the rapidly evolving cyber landscape enables adversaries to bypass static defences or rules-based mitigation approaches. This paper proposes a dynamic, online-trained and fully decentralised reinforcement learning (RL)-based cyberattack response framework to protect microgrids from evolving cyberattacks. The proposed framework deploys multiple deep Q-networks (DQNs), each associated with a distributed energy resource (DER), to enable localised and adaptive attack mitigation. In this framework, each DQN processes local voltage and frequency measurements—combined with intrusion detection system (IDS) alerts—as observations and rewards to guide decision-making. Extensive simulation studies demonstrate the robustness of the proposed framework under diverse attack scenarios and varying IDS-induced detection delays. Comparative analysis highlights its superiority over existing static or preexisting rules-based mitigation approaches. Finally, we present an analysis that shows the framework's scalability to real-life microgrids with more interacting agents.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Substrate-controlled dynamics of spin qubits in low dimensional van der Waals materials

We report a theoretical study of the coherence dynamics of spin qubits in two-dimensional materials (2DMs) and van der Waals heterostructures, as a function of the host thickness and the composition of the surrounding environment. We focus on MoS2 and WS2, two promising systems for quantum technology applications, and we consider the decoherence arising from the interaction of the spin qubit with nuclear spins. We show that the Hahn-echo coherence time is determined by a complex interplay between the source of decoherence in the qubit host and in the environment, which in turn determines whether the noise evolution is in a classical or quantum mechanical regime. We suggest that the composition and thickness of van der Waals heterostructures encapsulating a qubit host can be engineered to maximize coherence times. Finally, we discuss how quantum sensors may be able to probe the dynamics of the nuclear bath in 2DMs.

Onizhuk, Mykyta (ORCID:0000000304344575)↗