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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Active Learning of Microgrid Frequency Dynamics Using Neural Ordinary Differential Equations

Accurate frequency modelling of inverter‐based resource (IBR)‐dominated power systems is crucial for ensuring stable, reliable and resilient operations, particularly given their inherent low‐inertia characteristics and fast dynamics that traditional swing equation‐based models inadequately capture. This paper explores neural ordinary differential equations (Neural ODEs) as a computationally efficient, data‐driven framework for modelling power system frequency dynamics, specifically within microgrids integrating high penetrations of distributed energy resources (DERs). The developed neural ODEs framework incorporates a neural network architecture designed to capture input dynamics. By actively perturbing the system with a known signal, the Python‐based neural ODEs framework was trained using measured system states and inputs, without the need for detailed system information. The framework, tested on a model of the Cordova, AK, microgrid, achieved a goodness of fit ranging from 60% to 99% across different state variables and maintained a mean square error in the 10 -6 p.u. range under square and step excitation signals. The proposed approach demonstrated robustness to measurement noise and initial condition variations while maintaining low computational complexity suitable for real‐time power system control applications. Furthermore, transfer learning enabled the neural ODEs model to adapt to the following changes in system topology or generator dispatch, highlighting its effectiveness for dynamic microgrids with frequently evolving configurations and diverse DERs.

Aryal, Tara [South Dakota State Univ., Brookings, ↗
DOE OSTI2025

Effects of stoichiometry and epitaxial strain on the stabilization of infinite-layer nickelates

The discovery of superconductivity in infinite-layer nickelates has sparked great interest due to their potential analogy with the unconventional cuprate superconductors. However, investigations of this system have been limited by the challenges in materials control and synthesis driven by substantial thermodynamic instability, making it difficult to reach an experimental consensus. Hence, establishing a robust synthetic route to highly crystalline infinite-layer nickelates is of paramount importance. Here, we present and discuss recent progress on the reproducible two-step synthesis of (Nd,Sr)NiO2 via the stabilization of high-quality perovskite nickelates and the subsequent topotactic transition to the infinite-layer phase. In particular, we discuss the important factors, such as cation stoichiometry and epitaxial strain, which significantly impact the crystallinity of both phases, accompanied by careful structural characterization. These results on robust synthesis can help accelerate the experimental investigation of the intrinsic physical properties of these complex strongly correlated materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Development of Stable Solid Oxide Electrolysis Cells for Low-Cost Hydrogen Production

The project objective was to demonstrate a solid oxide cell-based steam electrolysis stack that exhibits robustness, reliability, endurance, hydrogen purity, and produces hydrogen at elevated pressure of 2 to 3 bar. Innovative materials and processing methods were evaluated to improve degradation characteristics. Performance improvement focused on nearly all layers involved in the cell and stack assembly. Primary attention was paid to zirconia-ceria interface resistance control via sintering optimization and decrease in degradation from the oxygen electrode by evaluating low strontium (Sr) or Sr-free composition for both the oxygen electrode and current collection layer. Stack robustness was addressed by validating redox tolerance of fuel electrode, confirming capability of cells to survive repeated thermal cycles, studying the effect of pressure on performance and degradation, evaluating the effect of contamination on fuel and oxygen electrode performance and degradation, and identifying mitigation strategies to improve performance. The characterization included evaluation of electrochemical performance and stability followed by microstructural analysis. At the cell level, performance and stability improvements were achieved by incorporating a Sr-free oxygen electrode and a denser oxygen electrode barrier layer. At the stack level, pressurized operation reduces demand on first stage compression, the redox tolerant fuel electrode mitigates risk from service interruptions, and improvements to interconnect coating alleviate chromium (Cr) contamination effects. The denser barrier layer was achieved by adding a sintering aid to the samaria-doped ceria (SDC) composition that reduced sintering temperature by 150 °C. The resulting density was on par with the baseline SDC barrier layer density and the lower sintering temperature resulted in less resistive phase formation during sintering. Button cell tests did not demonstrate a change in performance when exposed to silicon (Si) or manganese (Mn) impurities to the fuel electrode and Cr impurity to the oxygen electrode. More detailed study however is warranted. The project addressed SOEC performance and stability at the cell and stack levels through a systematic approach to known sources of degradation that were combined and tested in three stack tests using an electrolyte supported cell design to allow for evaluation of a variety of fuel and oxygen electrode compositions. STK-82 and STK-83 had identical compositions. STK-100 incorporated the best materials and processing variables developed under this and concurrent projects, and was tested at elevated pressure in steam electrolysis. • STK-82 recovered performance after redox and thermal cycling, demonstrating the robustness of the stack and seals. It exhibited stable performance in testing for 500 hours in SOEC mode, followed by 300 hours of cycling between SOEC and SOFC tests. Degradation during SOEC operation was 1.8 %/ 1,000 hours. • STK-83 generated hydrogen at >80% steam conversion, and oxygen above 98.5 % purity during pressurized operation. Both hydrogen and oxygen were generated at 3 barg pressure without the use of a pressure vessel. In addition to balanced pressure, electrolysis operation at 1 bar differential pressure across anode and cathode was also demonstrated to substantial the robustness of the cell and seal. • STK-100 measured at initial ambient pressure conditions showed an area specific resistance of 1.1 ohm-cm 2 , and STK-83 had 1.3 ohm-cm 2 .

08 HYDROGEN↗
DOE OSTI2026

Gaussian processes for inferring parton distributions

The extraction of parton distribution functions (PDFs) from experimental or lattice QCD data is an ill-posed inverse problem, where regularization strongly impacts both systematic uncertainties and the reliability of the results. We study a framework based on Gaussian Process Regression (GPR) to reconstruct PDFs from lattice QCD matrix elements. Within a Bayesian framework, Gaussian processes serve as flexible priors that encode uncertainties, correlations, and constraints without imposing rigid functional forms. We investigate a wide range of kernel choices, mean functions, and hyperparameter treatments. We quantify information gained from the data using the Kullback-Leibler divergence. Synthetic data tests demonstrate the consistency and robustness of the method. Our study establishes GPR as a systematic and non-parametric approach to PDF reconstruction, offering controlled uncertainty estimates and reduced model bias in lattice QCD analyses.

hadronic spectroscopy↗
DOE OSTI2024

Microscopic origin of tunable assembly forces in chiral active environments

Across a variety of spatial scales, from nanoscale biological systems to micron-scale colloidal systems, equilibrium self-assembly is entirely dictated by—and therefore limited by—the thermodynamic properties of the constituent materials. In contrast, nonequilibrium materials, such as self-propelled active matter, expand the possibilities for driving the assemblies that are inaccessible in equilibrium conditions. Recently, a number of works have suggested that active matter drives or accelerates self-organization, but the emergent interactions that arise between solutes immersed in actively driven environments are complex and poorly understood. Here, we analyze and resolve two crucial questions concerning actively driven self-assembly: (i) how, mechanistically, do active environments drive self-assembly of passive solutes? (ii) Under which conditions is this assembly robust? We employ the framework of odd hydrodynamics to theoretically explain numerical and experimental observations that chiral active matter, i.e., particles driven with a directional torque, produces robust and long-ranged assembly forces. Overall, these developments constitute an important step towards a comprehensive theoretical framework for controlling self-assembly in nonequilibrium environments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Tuning Catalytically Active Single Sites in Nonstoichiometric, Mixed Metal Oxides for Oxygen Electrocatalysis (Final Technical Report)

The objective of the proposed work is to employ controlled synthesis, advanced characterization, detailed electrochemical testing and theoretical calculations to develop a framework that would guide the design of robust, non-stoichiometric mixed metal oxides for oxygen electrocatalysis. In this research plan, we focus around the idea of tuning the metal ion composition and environment to create single atom centers with the utmost electrocatalytic activity. We hypothesized that tuning the cationic sites in nonstoichiometric mixed metal oxides will lead to single 4d/5d metal surface sites with optimal catalytic activity for low temperature oxygen reduction (ORR) and oxygen evolution (OER) at solid/liquid interfaces. We will focus on different crystal structures of these oxides including Ruddlesden-Popper (R-P) oxides, simple perovskites and pyrochlores due to their flexibility in accommodating different metal cation dopants, and the fact that they represent variations in the cationic arrangements in non-stoichiometric mixed metal oxide structures, which will lead to an understanding of how bulk crystal structure effects catalytic activity and stability of these systems.

08 HYDROGEN↗
DOE OSTI2026

Beyond sequence similarity: toward function-based screening of nucleic acid synthesis

Synthetic nucleic acids are a key input to modern biotechnology, yet they represent dual-use materials that require robust screening to mitigate biosecurity risks. The prevailing screening paradigm, which identifies sequences of concern (SoCs) through sequence similarity to controlled pathogens and toxins, may not fully capture risks posed by AI tools that can decouple biomolecular function from reliance on known sequences. Rapidly advancing biodesign capabilities enable the generation of genes and proteins that might evade sequence-based detection. We highlight the critical need for function-based screening approaches that can detect sequences capable of hazardous biological functions, regardless of similarity to known SoCs. We examine the feasibility of function-based screening with an initial focus on proteins, arguing that, while protein sequence space is vast, biologically functional proteins are significantly constrained by biophysical and biochemical requirements that can be learned and modeled. We propose a concrete implementation framework organized along a continuum of complexity, starting with toxins as the most tractable targets before expanding to more complex pathogenic functions. We then discuss open challenges and describe a research and development strategy to address them.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Latent heat thermal energy storage performance maps enabling fast & accurate building energy simulations

Thermal energy storage (TES) using phase change materials (PCMs) has gained attention as an effective approach to manage energy demand fluctuations and shift peak building loads. PCM embedded heat exchangers (PCM-HXs) offer high energy storage density and low temperature variation during phase change, being suitable for load-shifting applications. However, this component is typically evaluated using computationally expensive methods, which present significant challenges when the ultimate goal is to assess the performance of PCM-HX integrated thermal energy storage systems in the full building context. In this paper, we present a methodology to generate highly accurate and computationally efficient PCM-HX performance maps which can be easily integrated into building energy simulation tools to analyze the feasibility of space conditioning systems with latent heat PCM-based TES. The performance maps are generated using a computationally efficient PCM-HX simulation tool based on a Generalized Resistance-Capacitance Model (GRCM) which can simulate arbitrary PCM-HXs with high accuracy and significantly less computational effort compared to full CFD simulations. The methodology was verified for a case study considering a 5-ton (~17.5 kW) air-to-water heat pump-thermal energy storage system (HP-TES), which was co-simulated in Modelica for a DOE prototype small-office building in Vienna, Austria, using Spawn of EnergyPlus™. The TES performance maps provided accurate predictions of PCM-HX behavior when used as Modelica component, with deviations within 2-4% while also achieving at least 103 computational time reduction. Leveraging this faster prediction capability, four PCMs with different melting temperatures for cooling (12°C, 16°C) and heating (31°C, 36°C) were assessed to investigate their impact on system performance. This work highlights the importance of robust PCM-HX models for efficient and high-fidelity building-level simulations, presenting new opportunities for advanced control strategy development and parametric analysis of TES configurations in a computationally efficient manner

Modelica Building Simulations↗
DOE OSTI2025

Deep Reinforcement Learning-Based Control of Energy Storage for Interarea Oscillation Damping

With the increasing electricity consumption and lack of transmission investment, today's power systems are operated much closer to their limits, raising concerns of inter-area oscillations that deteriorate the system stability. Here, this article presents a novel energy storage placement and control approach for enhanced damping of interarea oscillations. Combining the residual analysis and dominant mode analysis, we are able to identify the advantageous locations for placing energy storage that achieve improved damping performance. To overcome the challenges, such as fixed control parameters and insufficient damping, we propose to use a deep reinforcement learning-based approach for energy storage control. A state-of-the-art guided surrogate-gradient-based evolutionary strategy is used to train a learning agent in a robust, efficient, and reproducible manner. Parallel computing is also adopted to speed up the training process. The proposed strategy has been tested on both medium and large-scale systems. The proposed methods have demonstrated their effectiveness in mitigating various interarea oscillations within a timeframe of 20 s, thereby averting system collapse and enhancing power grid stability effectively.

25 ENERGY STORAGE↗
DOE OSTI2026

Generating entangled steady states in multistable open quantum systems via initial state control

Entanglement underpins the power of quantum technologies, yet it is fragile and typically destroyed by dissipation. Paradoxically, the same dissipation, when carefully engineered, can drive a system toward robust entangled steady states. However, this engineering task is nontrivial, as dissipative many-body systems are complex, particularly when they support multiple steady states. Here, we derive analytic expressions that predict how the steady state of a system evolving under a Lindblad equation depends on the initial state, without requiring integration of the dynamics. These results extend Refs. [V. V. Albert and L. Jiang, Phys. Rev. A 89, 022118 (2014); V. V. Albert et al., Phys. Rev. X 6, 041031 (2016)], showing that while the steady-state manifold is determined by the Liouvillian kernel, the weights within it depend on both the Liouvillian and the initial state. We identify a special class of Liouvillians for which the steady state depends only on the initial overlap with the kernel. Our framework provides analytical insight and a computationally efficient tool for predicting steady states in open quantum systems. As an application, we propose schemes to generate metrologically useful entangled steady states in spin ensembles via balanced collective decay.

Dissipative dynamics↗
DOE OSTI2025

Aligning thermal and current quenches with a high density low-Z injection

The conventional approach for thermal quench (TQ) mitigation in a tokamak disruption is through a high- Z impurity injection that radiates away the plasma’s thermal energy before it reaches the wall. The downside is a robust Ohmic-to-runaway current conversion due to the radiatively clamped low post-thermal-quench electron temperature. An alternative approach is to deploy a low- Z (either deuterium or hydrogen) injection that aims to slow down the TQ, and ideally aligns it with the current quench (CQ). This approach has been investigated here via 3D MHD simulations using the PIXIE3D code. By boosting the hydrogen density, a fusion-grade plasma is dilutionally cooled at approximately the original pressure. Energy loss to the wall is controlled by a Bohm outflow condition at the boundary where the magnetic field intercepts a thin plasma sheath at the wall, in addition to Bremsstrahlung bulk losses. Robust MHD instabilities proceed as usual, while the collisionality of the plasma has been greatly increased and parallel transport is now in the Braginskii regime. The main conclusion of this study is that the decreased transport loss along open field lines due to a sufficient low- Z injection slows down the TQ rate to the order of 20 ms, aligned with the CQ timescale for a 15 MA ITER plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Robust quantification of the diamond nitrogen-vacancy center charge state via photoluminescence spectroscopy

Nitrogen vacancy (NV) centers in diamond are at the heart of many emerging quantum technologies, all of which require control over the NV charge state. Hence, methods for quantification of the relative photoluminescence intensities of the NV 0 and NV − charge states, i.e., a charge state ratio, are vital. Several approaches to quantify NV charge state ratios have been reported but are either limited to bulk-like NV diamond samples or yield qualitative results. We propose an NV charge state quantification protocol based on the determination of sample- and experimental setup-specific NV 0 and NV − reference spectra. The approach employs blue (400–470 nm) and green (480–570 nm) excitation to infer pure NV 0 and NV − spectra, which are then used to quantify NV charge state ratios in subsequent experiments via least squares fitting. We test our dual excitation protocol (DEP) for a bulk diamond NV sample and 20 and 100 nm nanodiamond particles and compare results with those obtained via other commonly used techniques such as zero-phonon line fitting and non-negative matrix factorization. We find that DEP can be employed across different samples and experimental setups and yields consistent and quantitative results for NV charge state ratios that are in agreement with our understanding of NV photophysics. By providing robust NV charge state quantification across sample types and measurement platforms, DEP will support the development of NV-based quantum technologies.

Color center laser spectroscopy↗
DOE OSTI2026

Detector Interface for Streaming, Control, and Open-source integration (DISCO) v1.0.0

This suite consists of a multi-package ecosystem featuring detector emulators, EPICS areaDetector drivers, and remote server frameworks designed for the Advanced Light Source (ALS). Engineered for high-bandwidth devices—including VFCCD, Timepix3, Timepix4, and related pixel detectors—the software simulates hardware, wraps vendor SDKs into remote-callable servers, and integrates with open-source control systems. Key Capabilities: Distributed SDK Architecture: Server packages wrap hardware-specific SDKs, allowing areaDetector drivers to execute remote framework calls. This isolates proprietary libraries from the EPICS IOC, enhancing stability and enabling distributed computing across beamline networks. Device Support: Custom drivers for VFCCD, the Timepix family, and similar sensors optimize the data path from hardware control to high-speed transport. Full-Stack Emulation: Sophisticated emulator packages allow end-to-end pipeline testing and software development without requiring physical hardware or beam time. Integrated Workflows: Supports high-bandwidth streaming for real-time analysis and robust, metadata-rich file-based workflows (e.g., HDF5/NeXus). By standardizing interfaces across heterogeneous hardware, this suite reduces technical debt. It provides the ALS with a scalable, open-source solution to manage massive data rates within a unified control environment.

Mahl, Johannes [Lawrence Berkeley National Laborat↗
DOE OSTI2026

Operating Experience Data Analysis for Digital Instrumentation and Control System Reliability and Risk Assessment in Nuclear Power Plants

The implementation of advanced digital instrumentation and control (DI&C) systems in U.S. nuclear power plants (NPPs) can bring significant advancements in reliability, monitoring, and control capabilities. However, these systems also introduce new challenges, particularly in assessing risks such as common-cause failures (CCFs) and establishing robust reliability estimates for DI&C components. Addressing these challenges is critical for ensuring the safe and efficient operation of NPPs. Recently, Idaho National Laboratory was tasked by the U.S. Nuclear Regulatory Commission (NRC) to conduct a DI&C reliability study using operating experience data from the nuclear industry. The two operating experience data sources for the study are the Institute of Nuclear Power Operations’ Industry Reporting and Information System (IRIS) and the NRC’s Licensee Event Report database which is hosted at Idaho National Laboratory at https://lersearch.inl.gov/LERSearchCriteria.aspx. This report provides a comprehensive examination of DI&C systems, including their architecture, operational advantages, and associated challenges. It reviews existing industry DI&C studies and failure mode taxonomies, along with reliability data from various industries. Through a detailed analysis of these databases, the study provides insights into DI&C system performance. Considerations should be given to incorporate DI&C failure data into the NRC's Integrated Data Collection and Coding System and updating the Reliability and Availability Data System to support ongoing DI&C reliability studies. Recommendations are also provided for modeling DI&C reliability and CCF in probabilistic risk assessment, thereby supporting risk-informed decision-making and enhancing the reliability and safety of NPPs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

CO2 response screen in grass Brachypodium reveals the key role of a MAP kinase in CO2-triggered stomatal closure

Abstract Plants respond to increased CO2 concentrations through stomatal closure, which can contribute to increased water use efficiency. Grasses display faster stomatal responses than eudicots due to dumbbell-shaped guard cells flanked by subsidiary cells working in opposition. However, forward genetic screening for stomatal CO2 signal transduction mutants in grasses has yet to be reported. The grass model Brachypodium distachyon is closely related to agronomically important cereal crops, sharing largely collinear genomes. To gain insights into CO2 control mechanisms of stomatal movements in grasses, we developed an unbiased forward genetic screen with an EMS-mutagenized B. distachyon M5 generation population using infrared imaging to identify plants with altered leaf temperatures at elevated CO2. Among isolated mutants, a “chill1” mutant exhibited cooler leaf temperatures than wild-type Bd21-3 parent control plants after exposure to increased CO2. chill1 plants showed strongly impaired high CO2-induced stomatal closure despite retaining a robust abscisic acid-induced stomatal closing response. Through bulked segregant whole-genome sequencing analyses followed by analyses of further backcrossed F4 generation plants and generation and characterization of sodium azide and CRISPR-cas9 mutants, chill1 was mapped to a protein kinase, Mitogen-Activated Protein Kinase 5 (BdMPK5). The chill1 mutation impaired BdMPK5 protein-mediated CO2/HCO3− sensing together with the High Temperature 1 (HT1) Raf-like kinase in vitro. Furthermore, AlphaFold2-directed structural modeling predicted that the identified BdMPK5-D90N chill1 mutant residue is located at the interface of BdMPK5 with the BdHT1 Raf-like kinase. BdMPK5 is a key signaling component that mediates CO2-induced stomatal movements and is proposed to function as a component of the primary CO2 sensor in grasses.

Lopez, Bryn N. K. (ORCID:0009000937288216)↗
DOE OSTI2024

Robust Restoration From Cyber-Physical Attacks in Active Distribution Grids With Grid-Edge IBRs

The inverter-based resources (IBRs) have enabled the integration of renewable energy at the grid edge with enhanced control capabilities to support the reliable operation of power grids. Different control frameworks, such as hierarchical or distributed architecture, have been proposed with the expansion of cyber networks for real-time monitoring and control. This evolution of critical infrastructure into cyber-physical systems also brings more vulnerabilities for the broadened attack surfaces, and significantly increases the possibility of physical system failures or outages caused by cyberattacks. Among tremendous efforts in the defense-in-depth approach, it remains challenging to provide prompt detection and accurate location of attack entry points or paths. Therefore, the prevailing restoration framework may struggle to fully consider the cyber-physical interdependence, successfully isolate the compromised cyber and physical components, and safely recover the systems without the potential risks leading to secondary outages. This paper is motivated to develop a cyber-physical restoration framework for distribution grids to recover from cyber attacks by harnessing grid-edge IBRs. The framework is first built on the operational guidelines of IBRs considering the compromised cyber layer. Then, an ambiguity set is established to represent the uncertainty of attack scenarios and their possibility levels. Next, a distributionally robust optimization model is developed to provide the optimal load restoration strategy across all scenarios. The effectiveness of the proposed model is demonstrated through various use cases on the modified IEEE 13-node and 123-node test systems. Finally, simulation results demonstrate the effectiveness and advancement of developed post-attack restoration strategies.

Cybersecurity↗
DOE OSTI2025

Stochastic Adaptive Droop Control in Frequency Regulation of Power Systems With Intermittent Generators

Modern power systems (MPSs), including microgrids (MGs), are increasingly incorporating multiple renewable energy sources (RESs) such as wind and solar power, as well as battery storage and controllable loads. While environmentally beneficial, these sources pose challenges for control and management due to their intermittent and stochastic nature, especially in maintaining frequency stability with multiple interconnected generators of varying capacities. Traditional droop control methods are effective in systems with generators that are dispatchable and have fixed generation capacities, but they fall short when applied to systems with RESs, where generation capacities are dynamic and affected by unpredictable environmental conditions. To address these challenges, this paper introduces a novel stochastic adaptive droop control (SADC) method for load frequency control (LFC). The proposed method adapts droop coefficients in real time, based on the measured stochastic data of power generation capacities, enabling more effective frequency regulation in systems with variable and intermittent power generation. Unlike traditional adaptive control methods, which assume constant or slowly-varying system parameters, this approach accounts for stochastic processes by modeling them as Markov chains, enabling robust performance under highly dynamic and unpredictable conditions. The key contributions of this work include the development of real-time droop coefficient adaptation algorithms, derivation of their stability and convergence properties, and the demonstration of the advantages of the method through simulations. Case studies highlight the improved performance of frequency regulation, particularly in addressing the impact of stochastic weather conditions and the benefits of reducing dependence on battery reserves in dealing with intermittency of RESs. Finally, this paper provides a comprehensive analysis of the theoretical foundations of the method, as well as practical implementation insights for future power systems with high penetration of RESs.

24 POWER TRANSMISSION AND DISTRIBUTION↗