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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Probing Coherent States of Light and Matter in Two-Dimensional Semiconductors. Final Report

The ability to enhance light-matter interactions in engineered optical environments is well-established in micro- and nano-photonics. New classes of materials bring new rich correlations between spin, momentum, and light polarization that can be exploited in these photonic systems for information processing and low-power electronics. Interfacing these novel material features with strong optical interactions suggests the compelling capability to create hybrid light-matter systems harnessing low-energy and protected material properties such as spin. The long-term objective of this program was to investigate the interplay between pseudospin in 2D materials and light, revealing new coherent phenomena. The materials studied are the monolayer transition metal dichalcogenides (TMDs), which support polarization-sensitive optical transitions when isolated to a single sub-nanometer crystal layer in thickness. In particular, monolayer TMD semiconductors such as MoS 2 exhibit degenerate valleys in momentum space with distinct spin character. These valleys can be separately addressed by circularly polarized light, providing a tool for manipulation. Integrating this valley pseudospin of excitons in monolayer TMDs with engineered strong optical excitations to create novel coherent phenomena was a key long-term goal of this research. The scope of this research primarily focused on studying light-matter interactions of valley-sensitive excitations in monolayer TMDs. The scope also included the integration of monolayer materials that support valley-polarized emission into photonic devices. This approach extends prior work on exciton-polaritons in 2D materials toward a platform with far more control and improved light input and output characteristics to aid scalability of these polarization-sensitive phenomena to many-body, long-range optical networks. During the course of the five-year Early Career program, the approaches led to successful progress on each proposed aim. Initial successful work seeded additional research that developed from the main themes, while this program focus evolved toward coherent manipulation of valley polaritons and integration with photonics. Both of these advanced topics set the stage for new capabilities in future innovative research. The results of this project provide advances in materials and methods relevant for new visions of opto-electronics and photonics that can improve information processing in an era of increased focus on quantum and coherent phenomena. The research has also contributed to the training of two postdoctoral scholars and five graduate students.

2D Materials↗
DOE OSTI2024

Development of Steady-State and Dynamic Mass and Energy Constrained Neural Networks for Distributed Chemical Systems Using Noisy Transient Data

The paper presents the development of algorithms for mass and energy constrained neural network models that can exactly conserve the overall mass and energy of distributed chemical process systems, even though the noisy transient data used for optimal model training violate the same. In contrast to approximately satisfying mass and energy balance constraints of a system by soft penalization of objective function, algorithms have been developed for solving equality-constrained nonlinear optimization problems, thus providing the guarantee of exactly satisfying the system mass and energy conservation laws. For developing dynamic mass-energy constrained network models for distributed systems, hybrid series and parallel dynamic-static neural networks have been leveraged. The developed algorithms for solving both the training and forward problems are validated using both steady-state and dynamic data in the presence of various noise characteristics. The developed data-driven algorithms are flexible to exactly satisfy mass and energy balance constraints for dynamic chemical processes if the system holdup information is available. The proposed network structures and algorithms are applied to the development of data-driven lumped and distributed models of an adiabatic superheater/reheater system, a nonisothermal continuous stirred tank reactor, as well as an electrically heated plug-flow reactor system where one form of energy gets transformed to another. It has been observed that the mass-energy constrained neural networks yield a root mean squared error of <1% with respect to the system truth for the case studies evaluated in this work.

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

Incorporating Local Extreme Weather Events into Reliability Assessments

Presentation discussing modeling extreme weather effects on the Bulk power system. Using a Production cost model to analyze weather related demand increases, generator outages, transmission outages, and fuel supply constraints. These results can be analyzed to inform on system reliability effects of extreme weather. This presentation was developed for the 2026 NERC Probabilistic Assessment Forum at EPRI in Charlotte NC.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

The Durability of Piston Seals in Hydraulic Power Take-off Systems in Wave Energy Converters

Hydraulic cylinder seals are a critical component of hydraulic power take-off (PTO) systems in wave energy converters (WECs). Primary hydraulic piston seal wear is a major concern for the longevity of hydraulic PTOs, especially in the context of the effort and expense associated with seal replacement. Piston seals, made from polymeric elastomers, are used to contain and isolate high pressure fluids within PTO systems. A specific challenge for WEC designers is knowing, with confidence, the relative expected lifetimes of commercially available seals and seal materials for the unique long travel and continuous use case of WEC hydraulic systems. This information is critical to accurately determine operating expense (OPEX) and levelized cost of electricity (LCOE). If failures of seals occur earlier than their designed lifetime, the estimated operations, and maintenance (O&M) and LCOE costs may double based on estimation. Unfortunately, information from seal manufacturers on longevity in these applications is not generally available and quantitative performance comparison between different manufacturers is not available, creating significant uncertainty on use of hydraulic PTO system in wave power generation. In this project, PNNL, with advice from different WEC device and seal manufacturers, has created a framework to address the industry need for available, dependable and comparative data for seals and seals materials for WEC hydraulic applications including piston seals, glide rings, and shaft seals. Commonly used and candidate seal materials were identified and available information on the materials such as mechanical and fatigue performance, chemical (fluid) compatibility, and cost has been compiled. Hardware and strategy for bench scale measurement of key materials and seal performance and pathway for publicly available library of hydraulic seal materials, properties, suppliers, and options have also been identified for future implementation. The results of this project were presented at WPTO Seedling Symposium 2023 and OCEANS 2023. The results of the literature review including identified polymer seals and ideal operating conditions were summaried and compiled to a database WEC-SealsDB hosted locally at PNNL.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Adaptive cache management based on programming model information

A processing system selectively allocates space to store a group of one or more cache lines at a cache level of a cache hierarchy having a plurality of cache levels based on memory access patterns of a software application executing at the processing system. The processing system generates bit vectors indicating which cache levels are to allocate space to store groups of one or more cache lines based on the memory access patterns, which are derived from data granularity and movement information. Based on the bit vectors, the processing system provides hints to the cache hierarchy indicating the lowest cache level that can exploit the reuse potential for a particular data.

Na, Weon Taek↗
DOE OSTI2021

A Comprehensive Method to Mitigate Forced Oscillations in Large Interconnected Power Grids

Multiple severe forced oscillation events have recently been observed across the North American interconnections and around the world. These forced oscillations have caused power swings, limited power transfer capability, damaged equipment, and persisted indefinitely until the driving source was located and removed. This paper proposes a comprehensive method to mitigate forced oscillations. Once a forced oscillation is detected, a new source location algorithm can be used to locate the source based on the oscillation mode angle without requiring system topology information. If the source cannot be quickly located and removed, a control strategy can be activated to modulate the active power of utility-scale inverterbased battery energy storage systems (BESSs) to reduce the energy of forced oscillations to a safe level and allow sufficient time for locating the exact source. The proposed source location algorithm is validated using measurements collected during the January 11, 2019 forced oscillation event in North America and other actual grid events, while the proposed control strategy is verified using the Eastern Interconnection dynamic model under the replicated January 11, 2019 forced oscillation event. The simulation results demonstrated that the proposed source location algorithm can accurately identify the forced oscillation source, and the proposed control strategy can significantly reduce forced oscillation energy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Design and implementation of dynamic I/O control scheme for large scale distributed file systems

In this paper, we have analyzed the input/output (I/O) activities of Cori, which is a high-performance computing system at the National Energy Research Scientific Computing Center at Lawrence Berkeley National Laboratory. Our analysis results indicate that most users do not adjust storage configurations but rather use the default settings. In addition, owing to the interference from many applications running simultaneously, the performance varies based on the system status. To configure file systems autonomously in complex environments, we developed DCA-IO, a dynamic distributed file system configuration adjustment algorithm that utilizes the system log information to adjust storage configurations automatically. Our scheme aims to improve the application performance and avoid interference from other applications without user intervention. Moreover, DCA-IO uses the existing system logs and does not require code modifications, an additional library, or user intervention. To demonstrate the effectiveness of DCA-IO, we performed experiments using I/O kernels of real applications in both an isolated small-sized Lustre environment and Cori. Our experimental results shows that our scheme can improve the performance of HPC applications by up to 263% with the default Lustre configuration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

PowerModelsGAT-AI: Physics-Informed Graph Attention for Multi-System Power Flow With Continual Learning

Solving the alternating current power flow equations in real time is essential for secure grid operation, yet classical Newton–Raphson solvers can be slow under stressed conditions. Existing graph neural networks for power flow are typically trained on a single system and often degrade on different systems. We present PowerModelsGAT-AI, a physics-informed graph attention network that predicts bus voltages and generator injections. The model uses bus-type-aware masking to handle different bus types and balances multiple loss terms, including a power-mismatch penalty, using learned weights. We evaluate the model on 14 benchmark systems (4 to 6,470 buses) and train a unified model on 13 of these under contingency conditions with up to two branch outages, achieving an average normalized mean absolute error of 0.89% for voltage magnitudes and R 2 >0.99 for voltage angles. We also show continual learning: when adapting a base model to a new 1,354-bus system, standard fine-tuning causes severe forgetting with error increases exceeding 1000% on base systems, while our experience replay and elastic weight consolidation strategy keeps error increases below 2% and in some cases improves base-system performance. Interpretability analysis shows that learned attention weights correlate with physical branch parameters (susceptance: r=0.38 ; thermal limits: r=0.22 ), and feature importance analysis supports that the model captures established power flow relationships.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Dragonstone Strategy – State of Cybersecurity in the Oil & Natural Gas Sector

The oil & natural gas (ONG) system touches every corner of the nation and increased communications & control capabilities have not only allowed for greater efficiency of system operation, but have also created a massive target for adversaries to launch cyber-attacks. Like any other heavy industrial process, the ONG system relies on a complex system of information technology (IT) and operational technology (OT) devices. The current state of cyber-preparedness across the ONG industry varies from organization to organization. Among the most common challenges that ONG companies face are remote locations, longlived field assets, and lacking capabilities to find and track malware on their systems. ONG companies tend to be concerned with lack of cyber-awareness from employees, risk stemming from remote access for operations & maintenance, and software vulnerabilities within third-party equipment. Various industry and government organizations are performing research and development activities to address some of these challenges, but in many cases industry stakeholders are not aware of solutions that already exist. It is clear that a need exists for a coherent, comprehensive, multi-layered strategy for assuring the security and resilience of the nation's pipeline infrastructure against cyber threats. For a variety of reasons, the general consensus from stakeholders interviewed by LLNL is that the state of cyber-security within the electric grid is currently outpacing its ONG cousin. Existing strategies for the resilience and cyber-security of the electric grid can and should be leveraged to provide immediate benefits to the ONG system. In LLNL’s view, there are two key factors currently limiting the development of necessary cyber-practices within the ONG industry: The sheer number of differing regulatory bodies and trade groups offering both standards and best-practice recommendations for ONG cyber-security makes it difficult to create a comprehensive, directed, and coherent strategy that is applicable to all players within the ONG industry. The ONG industry is unaware of potentially useful technologies that have been developed for ensuring cyber-security of other infrastructure systems, such as the electric grid. Leveraging these technologies—and the science and engineering behind them—can provide some low-hanging fruit that can greatly improve cyber-security in the ONG industry without significant investments in terms of time and money. In the months following this report, LLNL will continue to perform outreach to key oil & gas industry stakeholders in a continual effort to identify the most pressing cyber-resilience issues in the industry. This outreach will be supplemented with LLNL’s threat intelligence capabilities to begin painting a clearer picture of the overall threat landscape faced by this sector. This assessment will be threat-informed and while the strategy itself will not be classified we will leverage intelligence analysis and adversary capabilities to identify gaps in current cybersecurity practices for oil & gas pipeline systems. Recommended efforts will be compiled into a cyber-resilience roadmap for the oil & gas pipeline sector, in which LLNL will highlight priority activities to immediately improve the state of cyber-resilience in the industry.

02 PETROLEUM↗
DOE OSTI2025

On-chip frequency-bin quantum photonics

Abstract Frequency-bin encoding furnishes a compelling pathway for quantum information processing systems compatible with established lightwave infrastructures based on fiber-optic transmission and wavelength-division multiplexing. Yet although significant progress has been realized in proof-of-principle tabletop demonstrations, ranging from arbitrary single-qubit gates to controllable multiphoton interference, challenges in scaling frequency-bin processors to larger systems remain. In this Perspective, we highlight recent advances at the intersection of frequency-bin encoding and integrated photonics that are fundamentally transforming the outlook for scalable frequency-based quantum information. Focusing specifically on results on sources, state manipulation, and hyperentanglement, we envision a possible future in which on-chip frequency-bin circuits fulfill critical roles in quantum information processing, particularly in communications and networking.

electro-optic modulators↗
DOE OSTI2024

Hydrogen Production, Grid Integration, and Scaling for the Future

The project will explore near and long-term visions towards the commercialization of grid integrated electrolysis systems to inform deployment across the planning, procurement, and operation stages of hydrogen production on the grid. It will leverage NREL's state-of-the-art 1.25 MW polymer electrolyte membrane (PEM) electrolyzer system to characterize system performance in relevant scenarios, also creating a digital twin for emulation in the Advanced Research on Integrated Energy Systems (ARIES) virtual environment and performing hardware-in-the-loop (HIL) testing of pilot scale, decentralized, and centralized hydrogen systems.

electrolysis systems↗
DOE OSTI2020

A global analysis on the systemic effects of antibiotics in cultured fish and their potential human health risk: a review

Abstract The rearing of fish in intensive systems reduces their immunity leading to eruption of diseases, consequently prompting the use of antibiotics. Antibiotics are currently used in cultured fish globally to treat/prevent diseases. On the one hand, antibiotics used in aquaculture help to improve fish health by killing or inhibiting pathogenic bacteria proliferation. However, on the other hand, they cause multiple side effects. To date, information on systemic effects of antibiotics in cultured fish is scattered and their potential human health risk remains elusive. This review critically analyses and discusses the available information in the literature on the positive and negative effects of antibiotics in cultured fish and their potential human health risk via fish consumption. The review found 44 different antibiotics dominated by oxytetracycline were studied in various fish species worldwide. Antibiotics used in global aquaculture either decrease or increase fish growth and survival rates. They induce oxidative stress, which affect antioxidant and detoxification responses and cause host–microbiota dysbiosis. These changes compromise the fish immunity system, which ultimately lead to cellular damage. The toxicities generated inhibit the fish aerobic glycolysis, suppress lipogenesis and fatty acid β‐oxidation while increase energy demand by initiating gluconeogenesis and anaerobic glycolysis. Consumption of fish treated with antibiotics causes human health risk. Future studies are required on agents to restore dysfunctions induced by antibiotics in cultured fish, while attempts to limit their utilisation in aquaculture production are underway. [Correction added on 21 November 2020, after first online publication: The abstract has been amended to clarify the scope of the review.]

Limbu, Samwel Mchele↗
DOE OSTI2020

Recovery of Damaged Information and the Out-of-Time-Ordered Correlators

The evolution with a complex Hamiltonian generally leads to information scrambling. A time-reversed dynamics unwinds this scrambling and thus leads to the original information recovery. In this work, we show that if the scrambled information is, in addition, partially damaged by a local measurement, then such a damage can still be treated by application of the time-reversed protocol. This information recovery is described by the long-time saturation value of a certain out-of-time-ordered correlator of local variables. We also propose a simple test that distinguishes between quantum and reversible classical chaotic information scrambling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Power Application Developer’s Guide to the Common Information Model: An Introduction for Power Systems Engineers and Application Developers – CIM17v40

A key issue in creating the next generation of energy management system (EMS) and advanced distribution management system (ADMS) platforms will be the ability to represent and exchange power system network model data in a consistent manner. To this end, the Common Information Model (CIM) stands out as the only standardized vocabulary (or ontology) for defining power system network models and asset data in a comprehensive, consistent manner across the generation-transmission-distribution boundary. The CIM is freely available to use and extend. The CIM is maintained by the UCAiug (informally known as the CIM User’s Group) under an Apache 2.0 license. The CIM Users Group collaborates with the IEC and other standards communities for the development of technical and informative specifications. Although portions of the information model are referred to by the corresponding IEC standards naming, it is not necessary to purchase any of the IEC standards to use the CIM information model. This document provides a roadmap for power system engineers and application developers not familiar with semantic modeling to start using the CIM for modeling, simulation, optimization, and development of advanced power applications. The key classes needed for defining power system topology and equipment are explained systematically. Key focus areas include modeling of lines, transformers, generators, switching equipment, loads, and distributed energy resources (DERs).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

GFINNs: GENERIC formalism informed neural networks for deterministic and stochastic dynamical systems

Here we propose the GENERIC formalism informed neural networks (GFINNs) that obey the symmetric degeneracy conditions of the GENERIC formalism. GFINNs comprise two modules, each of which contains two components. We model each component using a neural network whose architecture is designed to satisfy the required conditions. The component-wise architecture design provides flexible ways of leveraging available physics information into neural networks. We prove theoretically that GFINNs are sufficiently expressive to learn the underlying equations, hence establishing the universal approximation theorem. We demonstrate the performance of GFINNs in three simulation problems: gas containers exchanging heat and volume, thermoelastic double pendulum and the Langevin dynamics. In all the examples, GFINNs outperform existing methods, hence demonstrating good accuracy in predictions for both deterministic and stochastic systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

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

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

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Cyber-Informed Engineering (CIE) Power Generation Guide [Slides]

This guide offers suggestions for applying CIE principles to technologies used to generate electric power. It addresses issues of design, implementation, and maintenance, preemptively addressing cybersecurity threats to electric generation. The intended audience for this guide includes practitioners across the energy and cybersecurity sectors, such as energy industry professionals (e.g., engineers, system designers, operators, and researchers) and cybersecurity experts (e.g., communication system designers, information technology/operational technology [IT/OT] administrators, and penetration testers).

13 HYDRO ENERGY↗