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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Self-Aware Local Autonomous and Semi-Cooperative Control for Cross-Layered Resilience (SLAC3R)

The objective of this work is to develop and demonstrate novel, adaptive, lightweight algorithms that enable the decision-making agents in a large cyber-physical network to act both autonomously and in collaborative harmony to enforce assured resilience across spatiotemporal layers, even under unforeseen adversarial scenarios (e.g., high- impact-low-probability events). Towards this end, the proposed solution will serve as minimally invasive add-on layers that bridge the existing (faster, reactive) local myopic controls and (slower, predictive) centralized optimization. Importantly, the proposed algorithms will enable the multi-agent network to autonomously and collaboratively enforce resilient operation under no or limited communication environment typical of severe cyber- physical adversarial events. The expected outcome of this effort is a suite of prototype, open-source, software algorithms for safety-aware local autonomous and semi-cooperative control (SLAC3R), demonstrated on networked microgrids (via RD2C/Thrust-1 OPAL-RT testbed).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning

This paper presents a novel federated reinforcement learning (Fed-RL) methodology to inject sufficient resiliency into the operations of the network of microgrids. We consider adversarial actions to the voltage and power control loop reference signals at the grid forming (GFM) inverters in the microgrids which are essential to integrate renewable resources. Therefore, we formulate a resilient reinforcement learning training setup that uses these adversarial injections to generate episodic trajectories and train the RL agents to alleviate their impact on performance. To circumvent the concerns about data-sharing and privacy for different owners of the microgrids in the networked setting, we bring in the aspects of the federated operation to propose novel Fed-RL algorithms. As the dynamics of each microgrid are coupled due to electrical interlinks, the conventional federated RL approaches using decoupled independent environments are not applicable, which leads us to propose a multi-agent vertically federated variation of actor-critic algorithms, namely federated soft actor-critic (FedSAC). We have performed numerical simulations on an IEEE 123-bus benchmark test feeder with three microgrids by creating a customized simulation setup by encapsulating the microgrid dynamic simulations in GridLAB-D/HELICS co-simulation platform with the OpenAI Gym environment and validated the proposed resilient and secured learning methodology.

Artificial Intelligence (AI), reinforcement learni↗
DOE OSTI2026

Overview of IMPACT Data Acquisition System and Data Reduction Process

This report documents the development of the data acquisition system (DAS) and data reduction methodologies for the Irradiated Material Property Accelerated Characterization Test (IMPACT) experiment at the Advanced Test Reactor (ATR). The IMPACT experiment is designed to enable in-pile measurement of thermal conductivity in metallic nuclear fuels, specifically U-10Zr, using an instrumented thermal conductivity probe. The DAS supports both passive temperature monitoring and active thermal interrogation of the probe through controlled AC and DC excitation. Significant modifications to laboratory-scale systems were required to accommodate the higher resistance paths associated with the in-pile application. Custom electronics and relay-controlled measurement sequencing were developed to enable the measurement and sufficient power delivery to the sensing region. A reduced-order, axisymmetric thermal model based on the thermal quadrupoles method is presented to support data interpretation. This model enables efficient evaluation of transient heat transfer behavior and facilitates solution of the inverse problem required to extract thermal properties from measured signals. Multiple boundary condition formulations are discussed to address varying experimental time scales and geometries. Additionally, machine learning techniques are introduced to support data reduction and improve confidence in inverse solutions. Convolutional neural networks are applied to identify the presence of gas gaps and other evolving geometric features that significantly impact thermal response during irradiation. These efforts contribute to the broader integration of digital twin frameworks and real-time modeling capabilities within the Advanced Fuels Campaign.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2020

Roles of D1-Glu189 and D1-Glu329 in O 2 Formation by the Water-Splitting Mn 4 Ca Cluster in Photosystem II

During the catalytic step that precedes O–O bond formation in Photosystem II, a water molecule deprotonates and moves next to the water-splitting Mn 4 Ca cluster’s O5 oxo bridge. The relocated oxygen, known as O6 or Ox, may serve as a substrate, combining with O5 to form O 2 during the final step in the catalytic cycle, or may be positioned to become a substrate during the next catalytic cycle. Recent serial femtosecond X-ray crystallographic studies show that the flexibility of D1-E189 plays a critical role in facilitating the relocation of O 6 /Ox. In this study, the D1-E189G and D1-E189S mutations were characterized with FTIR difference spectroscopy. The data show that both mutations support Mn 4 Ca cluster assembly, substantially inhibit advancement beyond the S 2 state, and alter the network of H bonds that surrounds the Mn 4 Ca cluster. Previously, the D1-E189Q, D1-E189K, and D1-E189R mutations were shown to have little impact on the activity, electron transfer rates, or spectral properties of Photosystem II. A rationale for this behavior is presented. The residue D1-E329 interacts with water molecules in the O1 water network that has been suggested recently to supply substrate during the catalytic cycle. Characterization of the D1-E329A mutant with FTIR difference spectroscopy shows that this mutation does not substantially perturb the structure of PSII or the water molecules whose O–H stretching modes change during the catalytic cycle. Furthermore, this result provides additional evidence that the water molecules whose vibrational properties change during the S 1 to S 2 transition are confined approximately to the region bounded by D1-N87, D1-N298, and D2-K317.

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

From tides to seasons: How cyclic tidal drivers and plant physiology interact to affect carbon cycling at the terrestrial-estuarine boundary (Final technical report)

Coastal ecosystems are among the most biologically and biogeochemically active and diverse systems on Earth. Because they act as important linkages between terrestrial ecosystems and the open ocean, their incorporation in Earth system models (ESMs) is critical to predict coastal and global responses to environmental changes. However, they vary greatly in the magnitude of tides and the volume and timing of freshwater input from land, making it challenging to model the major biogeochemical reactions that control productivity and greenhouse gas emissions across coastal terrestrial aquatic interfaces (TAIs). Our overall objective was to improve mechanistic process understanding and modeling of tidal wetland hydro-biogeochemistry in coastal TAIs. We established a new flux tower site (Ameriflux US-PLo) in the oligohaline part of the Parker River to continuously monitor ecosystem-scale carbon fluxes under temporally varying salinity conditions. The site is co-located with long-term monitoring plots of the Plum Island Ecosystems LTER project. We installed wells and redox sensors in the marsh interior and creek bank, established biomass monitoring plots and deployed novel optode sensors in both locations. We used this data to parameterize plant-mediated transport in PFLOTRAN and tested the impact of soil heterogeneity on porewater constituents and gas fluxes. We collected observations of root oxygen release with a novel planar optode system in the field. Flux data collected during the measurement period encompasses a large variation in salinity ranging from drought to record precipitation years. We developed a method to extract functional relationships from the flux data using artificial neural networks, identifying salinity thresholds for CH 4 fluxes. Finally, we are using the coupled ELM-PFLOTRAN model to test the impact of antecedent hydrological conditions on the salinity-CH 4 flux relationship. This grant contributed to the professional development of one postdoc, three research assistants and one graduate student. The sensor data has been shared with external collaborators.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Keep it short: Exploring the impacts of configuration choices on the recent economics of solar-plus-battery and wind-plus-battery hybrid energy plants

Coupled renewable-battery powerplants differ from the traditional concept of independent siting of electricity resources within transmission networks. Prior research on the value proposition and cost savings from coupling did not consider the geographic constraint of co-location. This paper fills the gap by assessing how pricing volatility differences between nodes within electricity markets impact the system value of coupled renewable-battery projects as compared to independent VRE and battery installations. We use wholesale power market prices from 2012–2019 across the seven main U.S. independent system operators (ISOs) with a linear optimization program to compare the electricity market value of coupled projects to the value of the same underlying sub-components, deployed separately. We find that additional value from adding a 4-hour battery sized to 50% of renewable-plant nameplate capacity is $\$10$/MWh across ISOs on average. The highest boost occurs in California ($\$15$/MWh), where the value of adding storage to solar rises over time in tandem with increased solar penetration in the region. If renewables and batteries are deployed independently, we estimate that $\$12.5$/MWh of additional value could be achieved because of more flexibility on battery siting and operation. The $\$12.5$/MWh coupling penalty is reduced to $\$1.6$/MWh when considering alternative approaches to integrating battery storage. This result implies that renewable-battery power plants will play an increasing role in electricity systems if they can be built for $\$2$–$\$13$/MWh less than independent projects of comparable size. However, the wide regional variation in coupling penalties, along with the importance of conditions captured in our sensitivity cases, suggests the tradeoff between coupling penalties and savings will vary by situation. Therefore, roles exist for independent and coupled projects from a system optimization perspective.

14 SOLAR ENERGY↗
DOE OSTI2022

Technoeconomic Analysis of Infrastructure Buildout Scenarios

The success of CCUS deployment in the southeast will depend on the optimized pipeline network from CO 2 point sources to geologic sinks for CO 2 storage. An optimal pipeline framework will reduce both environmental impacts and pipeline building cost. However, finding an optimal pipeline/transport infrastructure design is a non-trivial task and requires simultaneous usage of large volumes of data, computational resources, and a state-of-the-art simulator. Such a transport infrastructure model must consider point sources for CO 2 capture and associated volumes, sinks for CO 2 storage, and transportation from source to sink via pipeline networks. These considerations must be addressed simultaneously and systemically in an optimization process, in which a defined objective function (i.e., capital, variable CO 2 capture, transport, and storage cost function) is required to be minimized with the consideration of practical constraints (e.g., CO 2 flow through pipelines is less than the maximum capacity). Although optimization has been widely used in subsurface resources production and CO 2 storage, its application in large scale CCUS infrastructure design is rarely reported in the literature. SimCCS, developed by Los Alamos National Laboratory, is an open-source CCUS pipeline infrastructure design toolset that facilitates the optimization of pipeline infrastructure networks.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Leveraging inter-firm influence in the diffusion of energy efficiency technologies: An agent-based model

Energy efficiency technologies (EETs) are crucial for saving energy and reducing carbon dioxide emissions. However, the diffusion of EETs in small and medium-sized enterprises is rather slow. Literature shows the interactions between innovation adopters and potential adopters have significant impacts on innovation diffusion. Enterprises lack the motivation to share information, and EETs usually lack observability, which suppress the inter-firm influence. Thus, an information platform, together with proper policies encouraging or forcing enterprises to disclose EET-related information, should help harness inter-firm influence to accelerate EETs' diffusion. To explore whether and how such an information platform affects EETs' diffusion in small and medium-sized enterprises, this report builds an agent-based model to mimic EET diffusion processes. Based on a series of controlled numerical experiments, some counter-intuitive phenomena are discovered and explained. The results show that the information platform is a double-edged sword that notably accelerates EETs' diffusion by approximately 47% but may also boost negative information to diffuse even faster and delay massive adoption of EETs. Increasing network density and the intensity of inter-firm influence are effective to speed EET diffusion, but their impacts diminish drastically after reaching some critical values (0.05 and 0.15 respectively) and eventually harm the stability of the system. Ultimately, the findings implicate that EET suppliers should carefully launch their promising but immature products; policies that can reduce the perceived risk by enterprises and the effort to maintain an informative rather than judgmental information platform can prominently mitigate the negative side effects brought by high fluidity of information.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Analysis of mobility data to build contact networks for COVID-19

As social distancing policies and recommendations went into effect in response to COVID-19, people made rapid changes to the places they visit. These changes are clearly seen in mobility data, which records foot traffic using location trackers in cell phones. While mobility data is often used to extract the number of customers that visit a particular business or business type, it is the frequency and duration of concurrent occupancy at those sites that governs transmission. Understanding the way people interact at different locations can help target policies and inform contact tracing and prevention strategies. This paper outlines methods to extract interactions from mobility data and build networks that can be used in epidemiological models. Several measures of interaction are extracted: interactions between people, the cumulative interactions for a single person, and cumulative interactions that occur at particular businesses. Network metrics are computed to identify structural trends which show clear changes based on the timing of stay-at-home orders. Measures of interaction and structural trends in the resulting networks can be used to better understand potential spreading events, the percent of interactions that can be classified as close contacts, and the impact of policy choices to control transmission.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Artificial Intelligence-Aided Wind Plant Optimization for Nationwide Evaluation of Land Use and Economic Benefits of Wake Steering

If clean energy pathways are to harness massive increases in wind power, innovations with broad geographic viability will be needed to support buildout in diverse locations. However, geodiversity in impact potential is seldom captured in technology assessment. Here we propose a scalable approach to plant-level optimization using artificial intelligence to evaluate land sparing and economic benefits of wake steering at more than 6,800 plausible onshore wind locations in the USA. This emerging controls strategy optimizes plant energy production by directing turbine wakes. On the basis of estimates from our artificial intelligence model trained on engineering wind flow simulations, co-optimizing plant layouts with wake steering can reduce land requirements by an average of 18% per plant (site-specific benefits range from 2% to 34%), subject to errors and uncertainties in the flow model, wind resource estimates, buildout scenario and geographic factors. According to model estimates, wake steering is predicted to increase power production during high-value (relatively low wind) periods, boosting the annual revenue of individual plants by up to US$3.7 million (equivalent to US$13,000 MW-1 yr-1) but producing negligible gains in some settings. Consideration of wake steering’s geographic potential reveals divergent nationwide prospects for improved economics and siting flexibility.

deployment↗
DOE OSTI2020

Real-time power management technique for microgrid with flexible boundaries

In order to diminish the impacts brought by high penetration of renewable energy on the reliability of distribution systems, some distribution networks (e.g. Chattanooga electric power board system) have deployed smart switches (SSs) to island some areas to mitigate outage losses. However, due to intermittency and sharply changing rate of renewable energy, it is likely to experience insufficient or excessive power for islanded areas. Therefore, a microgrid controller featured with flexible boundaries is proposed. With proposed microgrid controller, the microgrid can not only shrink or expand its boundaries according to current renewable energy supply, but also disconnect/connect to the main grid with a designated SS. Furthermore, to ensure the microgrid controller could obtain suitable boundaries on the time scale of seconds, a real-time power management technique with alternative generating algorithm is designed to generate all possible alternative boundaries and choose the optimal one, which is scalable to any topology. In addition, in order to maintain state of charge of batteries within a desirable range, anti-overcharge/discharge strategies are designed. Four comprehensive experiments verify that the implementation of the microgrid controllers can realise flexible boundaries and deal with sharply changing rate of renewable generation or load on the time scale of seconds.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Comparison of machine learning systems trained to detect Alfvén eigenmodes using the CO 2 interferometer on DIII-D

Abstract A Machine-Learning (ML) based detection scheme that automatically detects Alfvén Eigenmodes (AE) in a labelled DIII-D database is presented here. Controlling AEs is important for the success of planned burning plasma devices such as ITER, since resonant fast ions can drive AEs unstable and degrade the performance of the plasma or damage the first walls of the machine vessel. Artificial Intelligence could be useful for real-time detection and control of AEs in steady-state plasma scenarios by implementing ML-based models into control algorithms that drive actuators for mitigation of AE impacts. Thus, the objective is to compare differences in performance between using two different recurrent neural network systems (Reservoir Computing Network and Long Short Term Memory Network) and two different representations of the C O 2 phase data (simple and crosspower spectrograms). All C O 2 interferometer chords are used to train both models, but only one is processed during each training step. The results from the model and data comparison show higher performance for the RCN model (True Positive Rate = 90% and False Positive Rate = 14%), and that using simple magnitude spectrograms is sufficient to detect AEs. Also, the vertical C O 2 interferometer chord passing near the center is better for ML-based detection of AEs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Hidden frustration in the triangular-lattice antiferromagnet NdCd 3 ⁢P 3

We report a study of the magnetic ground state and crystal electric field (CEF) scheme in the triangular-lattice antiferromagnet NdCd 3 ⁢P 3 . Combined neutron scattering, magnetization, and heat capacity measurements demonstrate that the Nd 3+ moments occupying the triangular lattice in this material harbor hidden signs of frustration not detected in typical Curie-Weiss-based parametrization of the frustration index (𝑓 = Θ 𝐶⁢𝑊 /𝑇 𝑁 ). This is evidenced by a zero-field splitting of the Kramers' ground state and first excited state doublets at temperatures far in excess of 𝑇 𝑁 as well as signatures of low-energy fluctuations for 𝑇 ≫ 𝑇 𝑁 . As a result, a suppression of the zero-field ordered moment relative to its field saturation value is observed, and the impacts of this magnetic frustration as well as the coexisting bond frustration in the CdP honeycomb network on the physical properties of NdCd 3 ⁢P3 are discussed.

Frustrated magnetism↗
DOE OSTI2021

Few-shot Learning for Post-disaster Structure Damage Assessment

Automating post-disaster damage assessment with remote sensing data is critical for faster surveys of structures impacted by natural disasters. One significant obstacle to training state-of-the-art deep neural networks to support this automation is that large quantities of labelled data are often required. However, obtaining those labels is particularly unrealistic to support post-disaster damage assessment in a timely manner. Few-shot learning methods could help to mitigate this by reducing the amount of labelled data required to successfully train a model while achieving satisfactory results. To this end, we explore a feature reweighting method to the YOLOv3 object detection architecture to achieve few-shot learning of damage assessment models on the xBD dataset. Our results show that the feature reweighting approach yield improved mAP over the baseline with significantly fewer labelled samples. In addition, we use t-SNE to analyze the class-specific reweighting vectors generated by the reweighting module in order to evaluate their inter-class and intra-class similarity. We find that the vectors form clusters based on class, and that these clusters overlap with visually similar classes. Those results show the potential to employ this few-shot learning strategy for rapid damage assessment with post-event remote sensing images.

Bowman, Jordan↗
DOE OSTI2021

Locating Seismic Events with Local-Distance Data

As the seismic monitoring community advances toward detecting, identifying, and locating ever-smaller natural and anthropogenic events, the need is constantly increasing for higher resolution, higher fidelity data, models, and methods for accurately characterizing events. Local-distance seismic data provide robust constraints on event locations, but also introduce complexity due to the significant geologic heterogeneity of the Earth’s crust and upper mantle, and the relative sparsity of data that often occurs with small events recorded on regional seismic networks. Identifying the critical characteristics for improving local-scale event locations and the factors that impact location accuracy and reliability is an ongoing challenge for the seismic community. Using Utah as a test case, we examine three data sets of varying duration, finesse, and magnitude to investigate the effects of local earth structure and modeling parameters on local-distance event location precision and accuracy. We observe that the most critical elements controlling relocation precision are azimuthal coverage and local-scale velocity structure, with tradeoffs based on event depth, type, location, and range.

42 ENGINEERING↗
DOE OSTI2025

A Graph-Net with Node Embeddings to Detect False Data Injection Attacks in Photovoltaic Systems

Distributed energy resources (DER) contribute to the operational stability of the larger power grid both at utility-scale as well as commercial and residential scales in aggregated forms. These DER in-turn are susceptible to increasing cyber threats. An adversary can plug into the same local network that a field photovoltaic (PV) system uses to interconnect its data loggers and inverters and manipulate certain measurements collected from the network or trick existing irradiance and inverter readings through false data injection attacks (FDIA). Control routines that rely on these measurements can propagate the false data, impacting critical decisions that result in a suboptimal operation or even cause intentional harm leading to inverter-tripping or unscheduled loads that need to be shed. To detect FDIA in PV systems, the paper introduces an attention-based graph neural network with node embeddings and applied it to a simple prototypical DC-coupled microgrid with PV, energy storage, and load. The algorithm shows a detection accuracy of up to 98.95%. The proposed FDIA detection technique will provide micro-grid operators with an effective method to safeguard their systems, guaranteeing the secure and reliable operation.

Parvez, Imtiaz [Utah Valley University]↗
DOE OSTI2023

Performance of hybrid Innegra-carbon fiber composites

Abstract In this work material synergy with high stiffness carbon fiber with ductile high strength polypropylene fiber (Innegra S), (referred to as Innegra, herein) have been evaluated in a range of laminate designs. Both woven and discontinuous carbon fiber have been considered. The discontinuous fibers are based on three-dimensional deposition (3DEP) (referred to as 3DEP, herein) carbon fiber preform process. Eleven (11) variants of Innegra-carbon fiber hybrid laminates were investigated for tensile, flexure, compression, in-plane shear and low velocity impact response. The effect of position of Innegra within the laminate was studied and found to influence strength and stiffness properties. In terms of overall trends, Innegra provides upward of 18% improvement in strain (ductility) to the composite and eliminates brittle fracture of carbon fiber. The moduli trends follow the proportionality of Innegra fiber to carbon fiber plies. However, the strength is controlled by the interface between Innegra and carbon fiber. The primary failure mode in tension and compression is via onset of debonding between Innegra and carbon fiber. The 3DEP carbon fiber constituents provided highest values of in-plane shear indicating that three-dimensional (3D) network of carbon fiber provides higher shear resistance. The Innegra intensive variants exhibited superior energy absorption under low velocity impact, at energy levels 15–60 J. This work provides insight for designers to incorporate Innegra and carbon fiber hybrids in composite structures.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Interior Athabascan Energy Capacity Building Network (Final Report)

Tanana Chiefs Conference (TCC) is the traditional tribal consortium for the 37 federally recognized Tribes and villages of Interior Alaska. TCC is based on a belief in tribal self-determination and the need for regional Native unity. TCC is a nonprofit organization that works toward meeting the needs and challenges for more than 10,000 Alaska Natives (mostly Alaskan Athabaskans) in Interior Alaska. With support from DOE-OIE’s Inter-Tribal Technical Assistance grant, TCC’s goal was to expand and build on the solid foundation of work that the existing TCC Energy Program established through the creation of the Interior Athabascan Energy Network (IAEN). More specifically, the IAEN was designed to create a network of village-based energy champions across the interior and hold quarterly teleconferences and annual inperson meetings. Among the greatest impacts of the IAEN were the creation and support of realistic, communitybased energy projects that produced tangible results such as fuel savings and skills and capacity development among local staff as well as establishment of an energy team that included TCC staff, Tribal participants, technical consultants, and support agencies. The DOE-TA funding allowed us to provide technical assistance to our Tribal communities to develop energy projects, and to foster a network consisting of Tribal community members and energy experts that has strengthened the ability in the region to troubleshoot energy issues, develop meaningful projects, envision a clean energy future, and to share project successes and challenges. The funding from DOE was a catalyst for growth, has created opportunities for learning and knowledge sharing, and has led to the development of cost saving energy projects. Most importantly, the DOE project allowed energy champions around the region to share their knowledge, experiences, and expertise to create place-based solutions and has forged inter-Tribal relationships that will continue to be an asset for years to come. Through information exchange that occurred at out IAEN annual meetings and other events, this technical assistance grant award has spurred a spinoff project that is focused on establishing a collaborative of independent utilities that work together to solve energy problems and share services to operate and maintain their electric utilities. We have indentified at least 10 and possibly 12 communities that are interested in collaborating in managing their utilities to reduce costs and increase reliability. As well, by identifying and supporting Community Energy Champions in the Interior villages and adding capacity to the TCC Energy Program, we were able to create actionable community energy plans, develop specific projects, and increase local capacity, skills, and literacy around clean energy initiatives.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗