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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Local control of perpendicular magnetic anisotropy of Pd/Co multilayers via ion bombardment

In this work, we have controlled locally the magnetic anisotropy of PdCo multilayers using ion beam modification. First, we have used techniques with different probing depths to confirm that cobalt is partially oxidized in the Pd/Co multilayers exhibiting PMA fabricated by sputtering, presumably due to the diffusion of atmospheric oxygen migrated through the Pd capping layer. Then, low energy ion bombardment has been employed to induce a structural disorder in the upper layers of the multilayer, gradually reducing the PMA at local scale with increasing ion doses. Medium ion doses lead to a marked decline in PMA, resulting in equivalent magnetic anisotropy directions and the loss of characteristic labyrinthine magnetic domain morphology. High ion doses completely suppress PMA, yielding a typical in-plane shape anisotropy. Furthermore, by combining controlled PMA reduction via lithographic techniques, we create elongated structures displaying distinct magnetic responses; bombarded regions exhibit reduced PMA, whereas adjacent regions maintain their PMA. Notably, regions with reduced PMA do not disrupt the distribution of perpendicular magnetic domains in neighboring PMA regions, even at widths as narrow as 500 nm, demonstrating the robustness of the PMA domain structure.

Sebastiani-Tofano, Eugenia [Universidad Complutens↗
DOE OSTI2025

Hardware-Efficient Quantum Phase Estimation via Local Control

Quantum phase estimation plays a central role in quantum simulation as it enables the study of spectral properties of many-body quantum systems. Most variants of the phase estimation algorithm require the application of the global unitary evolution conditioned on the state of one or more auxiliary qubits, posing a significant challenge for current quantum devices. In this work, we present an approach to quantum phase estimation that uses only locally controlled operations, resulting in a significantly reduced circuit depth. At the heart of our approach are efficient routines to measure the complex phase of the expectation value of the time-evolution operator, the so-called Loschmidt echo, for both circuit dynamics and Hamiltonian dynamics. By tracking changes in the phase during the dynamics, the routines trade circuit depth for increased sampling cost and classical postprocessing. Our approach does not rely on reference states and is applicable to any efficiently preparable state, regardless of its correlations. We provide a comprehensive analysis of the sample complexity and illustrate the results with numerical simulations. Our methods offer a practical pathway for measuring spectral properties in large many-body quantum systems using current quantum devices.

Schiffer, Benjamin F. [Max Planck Institute of Qua↗
DOE OSTI2025

Subsurface Interface Structure Controlling Local Electronic Properties of Epitaxial Graphene on SiC(0001)

Recently realized high-mobility semiconducting epitaxial graphene on silicon carbide (SiC) [Zhao, J. Nature 2024, 625 (7993), 60−65, 10.1038/s41586-023-06811-0] provided an important step toward integration of the graphene-based system into active components in postsilicon micro- and nanoelectronics. However, the exact atomic-scale structure and complex bonding configurations of the first epitaxial graphene carbon layer (C buffer ) remain an open problem. Our recent report [Kolmer, M. Communications Physics 2024, 7 (1), 16, 10.1038/s42005-023-01515-3] has shed new light on understanding this interface, where the external transverse electric field-dependent dynamic switching behavior of the C buffer –SiC bonds was observed. Here, using scanning tunneling microscopy and spectroscopy (STM and STS), we present direct evidence of silicon (Si) vacancies at the interface and provide their distribution at the topmost reconstructed SiC(0001) layer. Bias voltage and epitaxial graphene thickness-dependent characterization of the collective C buffer –SiC interface showed that “Si” vacancy sites beneath C buffer are stable under STM electric fields. Moreover, the vacancies introduce localized electronic states below the Fermi level, thereby enhancing the charge-transfer phenomenon across the interface.

Thupakula, Umamahesh [Ames Laboratory (AMES), Ames↗
DOE OSTI2025

Traffic Smoothing Using Explicit Local Controllers: Experimental Evidence for Dissipating Stop-and-go Waves with a Single Automated Vehicle in Dense Traffic

This article presents experimental evidence of the ability of a single automated vehicle acting as a controller to effectively dissipate stop-and-go waves in real traffic. The automated vehicle succeeded in stabilizing the speed profile by reducing oscillations in time and speed variations between vehicles during rush hour on I-24 in the Nashville area. Here, we detail the control design, deployment and results obtained in this experiment, conducted as part of the CIRCLES consortium’s “MegaVanderTest” 2022, which involved a total of 100 automated vehicles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Data for: Subsurface Interface Structure Controlling Local Electronic Properties of Epitaxial Graphene on SiC(0001)

Recently realized high-mobility semiconducting epitaxial graphene on silicon carbide, provided an important step towards integration of the graphene-based system into active components in post-silicon micro- and nano-electronics. However, the exact atomic-scale structure and the complex bonding configurations of the first epitaxial graphene carbon layer remain an open problem. Our recent report has shed new light on understanding this interface, where the external transverse electric field-dependent dynamic switching behavior of the Cbuffer-SiC bonds was observed. Here, using scanning tunneling microscopy and spectroscopy (STM and STS), we present the direct evidence of silicon (Si) vacancies at the interface and provide their distribution at the topmost reconstructed SiC(0001) layer. Experimental STM and density functional theory modeling data were used in the preparation of figures in a published article in the Journal of Physical Chemistry Letters. Files related to the figures and supplementary materials in the article are present in this dataset in .txt format.

Condensed matter imaging↗
DOE OSTI2025

Data for: Subsurface Interface Structure Controlling Local Electronic Properties of Epitaxial Graphene on SiC(0001)

Recently realized high-mobility semiconducting epitaxial graphene on silicon carbide, provided an important step towards integration of the graphene-based system into active components in post-silicon micro- and nano-electronics. However, the exact atomic-scale structure and the complex bonding configurations of the first epitaxial graphene carbon layer remain an open problem. Our recent report has shed new light on understanding this interface, where the external transverse electric field-dependent dynamic switching behavior of the Cbuffer-SiC bonds was observed. Here, using scanning tunneling microscopy and spectroscopy (STM and STS), we present the direct evidence of silicon (Si) vacancies at the interface and provide their distribution at the topmost reconstructed SiC(0001) layer. Experimental STM and density functional theory modeling data were used in the preparation of figures in a published article in the Journal of Physical Chemistry Letters. Files related to the figures and supplementary materials in the article are present in this dataset in .txt format.

Condensed matter imaging↗
DOE OSTI2023

"Traffic smoothing using explicit local controllers"

The dissipation of stop-and-go waves attracted recent attention as a traffic management problem, which can be efficiently addressed by automated driving. As part of the 100 automated vehicles experiment named MegaVanderTest, feedback controls were used to induce strong dissipation via velocity smoothing. More precisely, a single vehicle driving differently in one of the four lanes of I-24 in the Nashville area was able to regularize the velocity profile by reducing oscillations in time and velocity differences among vehicles. Quantitative measures of this effect were possible due to the innovative I-24 MOTION system capable of monitoring the traffic conditions for all vehicles on the roadway. This paper presents the control design, the technological aspects involved in its deployment, and, finally, the results achieved by the experiment.

Hayat, Amaury↗
DOE OSTI2023

High‐Nickel Heterostructured Cathodes with Local Stoichiometry Control for High‐Voltage Operation

The growing demand for lithium‐ion batteries to power electric vehicles and other energy‐dense devices continues to fuel the need for cathodes of increasingly higher nickel in cathodes. The relentless pursuit of high Ni content, however, raises concerns on compromising cell lifetime and safety, especially under high‐voltage operation. Alternative to the traditional design of uniform or core–shell composition, we report a rational control of local stoichiometry in high‐Ni cathodes, enabling their high thermal and cycling stabilities—up to 258 °C at the fully charged state and 91.4% capacity retention for 100 cycles between 2.7 and 4.4 V. Multimodal synchrotron X‐ray characterization unveils the heterostructure of secondary particles, featuring a high‐Ni core (LiNi 0.90 Mn 0.05 Co 0.05 O 2 ) covered by a thin Ni‐gradient layer that remains stable over prolonged cycling due to suppressed oxygen release and structural deterioration. This work underlines, the intricate interplay between local stoichiometry and redox reactions in stabilizing high‐Ni cathodes for high‐voltage operation while ensuring safety.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Swarm Intelligence Based Optimal Design of Local Volt/Var Control Function for Distributed Energy Resources

The increasing penetration of renewable based distributed energy resources (DERs) in distribution network (DN) leads to larger and more frequent voltage variation in distributions network (DN), thus posing challenges on voltage control. Real-time local voltage control method is a promising solution for the above issue. However, the local voltage control function needs to be customized and optimized according to real distribution system condition. In this paper, a swarm intelligence based Volt/Var control optimal design method (SO-VVC) is proposed to optimize the control function. Compared with existing approaches, the proposed method can not only represent the nonlinear behaviour of power flow but is also computation efficient. The performance of the proposed SO-VVC is demonstrated by case studies on a modified IEEE-123 bus system.

Zhang, Zhengfa [University of Tennessee, Knoxville↗
DOE OSTI2023

Learning Provably Stable Local Volt/Var Controllers for Efficient Network Operation

Here this paper develops a data-driven framework to synthesize local Volt/Var control strategies for distributed energy resources (DERs) in power distribution grids (DGs). Aiming to improve DG operational efficiency, as quantified by a generic optimal reactive power flow (ORPF) problem, we propose a two-stage approach. The first stage involves learning the manifold of optimal operating points determined by an ORPF instance. To synthesize local Volt/Var controllers, the learning task is partitioned into learning local surrogates (one per DER) of the optimal manifold with voltage input and reactive power output. Since these surrogates characterize efficient DG operating points, in the second stage, we develop local control schemes that steer the DG to these operating points. We identify the conditions on the surrogates and control parameters to ensure that the locally acting controllers collectively converge, in a global asymptotic sense, to a DG operating point agreeing with the local surrogates. We use neural networks to model the surrogates and enforce the identified conditions in the training phase. AC power flow simulations on the IEEE 37-bus network empirically bolster the theoretical stability guarantees obtained under linearized power flow assumptions. The tests further highlight the optimality improvement compared to prevalent benchmark methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Bioinspired mineralizing microenvironments generated by liquid-liquid phase coexistence

Biominerals such as bones, teeth and shells exhibit improved mechanical properties and intricate morphologies not seen in nonbiologically-produced minerals of ostensibly the same composition. These remarkable properties of biogenic minerals are thought to arise due to precise local control over the mineral deposition process, including organic and inorganic inclusions. Understanding how Biology controls the local reaction environment during formation of these materials to control their composition, structure, and properties is a grand challenge that promises to enable new routes to high-performance materials. This project developed multi-compartment bioinspired microreactors as artificial mineralizing vesicles, and used them to understand and control formation of inorganic/organic composite solid materials. A major emphasis was on developing all-aqueous emulsions in which each droplet was a structured microreactor with two or more adjacent phases. This approach provided local control over reaction environment including availability of inclusions such as polypeptides and metal ions, while being sufficiently simple to produce large populations of essentially identical multiphase reactor droplets simultaneously within a batch. Organic/inorganic composite materials could be produced with very high organic content that stabilized the inorganic portions as amorphous materials (e.g., amorphous calcium carbonate) by preventing the typical conversion to more thermodynamically crystalline forms (e.g., calcite). These stabilized amorphous composites could be stored indefinitely and converted to crystalline forms later by removal of the organic inclusions via, for example, heating. Compositional gradients in the organic and inorganic components were embedded during synthesis due to the evolution of the reaction microenvironment, and despite the complexity of this process it occurred similarly across the population of reactive droplets and was hence encoded into the population of resulting composite particles. The approach developed here allows pre-structuring of reactive microenvironments to control the spatiotemporal reaction environment, which is an important step towards rational design and on-demand production of complex functional materials with desired composition, optical properties, and mechanical response.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Beneficial Integration of Energy Storage and Load Management with Photovoltaic (PV)

In recent years, a number of industry activities have aimed at addressing the integration challenges posed by the variability and uncertainty of higher penetration of renewable generation sources, like solar photovoltaic (PV) – one of the key objectives of the Sustainable and Holistic Integration of Energy Storage and Solar PV (SHINES) program launched by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE). This EPRI led Beneficial Integration of Energy Storge, and Load Management with PV project aimed to design, develop, and demonstrate end-to-end distributed energy resource (DER) integration solution to build on these activities. EPRI led project team designed and implemented a local controller that uses model predictive control (MPC) algorithm to optimally manage DERs on site by planning for a receding horizon while executing the control settings for the first step of its plan. The team has also developed a system controller to interface with the local controller to demonstrate the hierarchical control and how it can leverage DER capabilities to address challenges like over voltage and thermal limit violations which typically limits the DER hosting capacity of distribution feeders. Team has demonstrated how the local controller with optimization algorithm can effectively manage controllable loads like HVAC, water heater, and pool pumps to allow for greater integration of PV with relatively smaller energy storage system requirements. Optimal utilization of the load control can also reduce the depth of discharge of batteries to meet grid export/import limit from behind-the-meter (BTM) DERs. Proper utilization of DER capabilities via local control intelligence, like the one developed and demonstrated in this project can help the industry to address integration challenges of higher penetration of solar PV in economically efficient manner. This can help to accelerate deployment of clean renewable energy systems at lower societal cost.

14 SOLAR ENERGY↗
DOE OSTI2023

Constraints on OPF Surrogates for Learning Stable Local Volt/Var Controllers

We consider the problem of learning local Volt/Var controllers in distribution grids (DGs). Our approach starts from learning separable surrogates that take both local voltages and reactive powers as arguments and predict the reactive power setpoints that approximate optimal power flow (OPF) solutions. We propose an incremental control algorithm and identify two different sets of slope conditions on the local surrogates such that the network is collectively steered toward desired configurations asymptotically. Our results reveal the trade-offs between each set of conditions, with coupled voltage-power slope constraints allowing an arbitrary shape of surrogate functions but risking limitations on exploiting generation capabilities, and reactive power slope constraints taking full advantage of generation capabilities but constraining the shape of surrogate functions. AC power flow simulations on the IEEE 37-bus feeder illustrate their guaranteed stability properties and respective advantages in two DG scenarios.

asymptotic stability↗
DOE OSTI2024

Experimental Analysis of Distribution Network Voltage Regulation Using Smart Inverters

Smart inverters (SIs) have demonstrated their potential to provide grid services for both transmission and distribution systems. One of these grid services, distribution network voltage regulation by SIs, has the potential to improve network voltage regulation through controlling the reactive and active power output of the SIs. Voltage regulation by SIs will be distributed and might be better suited to controlling local conditions to complement traditional voltage-regulating assets, e.g., tap-changing transformers, capacitor banks, and line voltage regulators. There is a gap in the literature on comparing the SI response characteristics when the SIs are controlled by a local controller or external control signals. This paper presents an experimental study to characterize SI reactive power regulation responses to two different control methods: autonomous control and remote dispatch. We found that SI reactive power regulation responses exhibit important differences between these methods in terms of delays and ramp rate. Finally, power-hardware-in-the-loop (PHIL) tests were conducted to evaluate the performance of these two methods. The PHIL test results show that the SI response characteristics for autonomous control and remote dispatch need to be considered when planning for distribution network voltage regulation using SIs.

autonomous control↗
DOE OSTI2024

Chapter 7: Learning Stable Local Volt/Var Controllers in Distribution Grids

This chapter describes a framework to synthesize provably stable local Volt/Var controllers for distributed energy resources (DERs) in power distribution grids (DGs). The goal is to control the reactive power injections of DERs to improve the system performance as quantified by a generic optimal reactive power flow (ORPF) problem. To achieve this, we jointly design for each DER the control function, which prescribes the reactive power update rule, and the equilibrium function, which approximates the ORPF solutions from local measurements of voltages and powers. We provide conditions on the equilibrium functions and the control parameters ensuring the stability of the closed-loop system. In particular, we discuss the trade-offs between each set of conditions accounting for practical considerations, like fully exploiting the DERs' generation capabilities and reducing the optimality gap. These conditions are then translated into learning constraints on the neural networks' parameters that are enforced in the training phase. We validate our framework with numerical simulations on the IEEE 37-bus network and through a comparison with an optimized version of standard piece wise linear control rules.

closed-loop asymptotic stability↗