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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

On the additivity of climate responses to the volcanic and solar forcing in the early 19th century

The early 19th century was the coldest period over the past 500 years, when strong tropical volcanic events and a solar minimum coincided. The 1809 unidentified eruption and the 1815 Tambora eruption happened consecutively during the Dalton minimum of solar irradiance; however, the relative role of the two forcing (volcano and solar) agents is still unclear. In this study, we examine the responses from a set of early 19th century simulations with combined and separated volcanic and solar forcing agents, as suggested in the protocol for the past1000 experiment of the Paleoclimate Modelling Intercomparison Project – Phase 4 (PMIP4). From 20-member ensemble simulations with the Max Planck Institute Earth system model (MPI-ESM1.2-LR), we find that the volcano- and solar-induced surface cooling is additive in the global mean/large scale, regardless of combining or separating the forcing agents. The two solar reconstructions (SATIRE (Spectral and Total Irradiance REconstruction-Millennia model) and PMOD (Physikalisch-Meteorologisches Observatorium Davos)) contribute to a cooling before and after 1815 of ~0.05 and ~0.15 K monthly average near-surface air cooling, respectively, indicating a limited solar contribution to the early 19th century cold period. The volcanic events provide the main cooling contributions, inducing a surface cooling that peaks at ~0.82 K for the 1809 event and ~1.35 K for Tambora. After the Tambora eruption, the temperature in most regions increases toward climatology largely within 5 years, along with the reduction of volcanic forcing. In the northern extratropical oceans, the temperature increases slowly at a constant rate until 1830, which is related to the reduction of seasonality and the concurrent changes in Arctic sea-ice extent. The albedo feedback of Arctic sea ice is found to be the main contributor to the Arctic amplification of the cooling signal. Several non-additive responses to solar and volcanic forcing happen on regional scales. In the atmosphere, the stratospheric polar vortex tends to strengthen when combining both volcano and solar forcing, even though the two forcing agents separately induce opposite-sign changes in stratospheric temperatures and zonal winds. In the ocean, when combining the two forcings, additional surface cold water propagates to the northern extratropics from the additional solar cooling in the tropics, which results in regional cooling along the propagation. Overall, this study not only quantifies the surface responses from combinations of the volcano and solar forcing, but also highlights the components that cannot be simply added from the responses of the individual forcing agents, indicating that a relatively small forcing agent (such as solar in early 19th century) can impact the response from the large forcing (such as the 1815 Tambora eruption) when considering regional climates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Transactive Energy Systems: The Market-based Coordination of Distributed Energy Resources

This paper synthesizes a unifying framework that captures a large class of transactive energy systems studied in the literature. The framework consists of four key elements: agent preferences, control decisions, information structure, and solution concept. Among them, agent preference specifies the preferred outcome by each agent and models the conflict of interest in decision making. Control decision specifies the set of decisions for each agent to maximize its payoff. Information structure describes the information available to each agent before it makes a decision. Solution concept encodes the rationality assumptions of each agent in the system. These elements are important in identifying and distinguishing different transactive energy systems. In this paper, we use them to analyze a number of important ones, including the competitive equilibrium, Stackelberg game, reverse Stackelberg game, and mechanism design. Each class of these problems is formulated as a special case of the proposed transactive energy systems. Their connections and differences are discussed. Available tools and results for each category of problems are surveyed.

Li, Sen↗
DOE OSTI2023

Data exchange and processing synchronization in distributed systems

Systems, methods, techniques and apparatuses of asynchronous communication is distributed systems are disclosed. One exemplary embodiment is a method determining, with a plurality of agent nodes structured to communicate asynchronously in a distributed system, a first set of iterations including an iteration determined by each of the plurality of agent nodes; determining, with a first agent node of the plurality of agent nodes, a local vector clock; receiving, with the first agent node, a first iteration of the first set of iterations and a remote vector clock determined based on the first iteration; updating, with the first agent node, the local vector clock based on the received remote vector clock; and determining a first iteration of a second set of iterations based on the first set of iterations after determining all iterations of the first set of iterations have been received based on the local vector clock.

Cintuglu, Mehmet H.↗
DOE OSTI2020

Novel Foaming Solutions for High Level Waste (HLW) Processing - 20195

Foaming occurs during treatment of high level waste (HLW) at Savannah River Site's Defense Waste Processing Facility (DWPF) in the Chemical Process Cell (CPC) due to high gas generation from process steam and chemical offgas products [1]. The presence of amphiphilic particles in the waste slurry stabilizes the foam [2], [3]. Efficient processing of HLW requires foam control, as foam-overs lead to lower productivity and potential radioactive contamination of condensate streams. DWPF currently employs Siovation Antifoam 747, a surfactant produced by Momentive Performance Materials, as an antifoaming agent during waste treatment. Despite its ability to control foam generation, processing issues have arisen from its use [4]. During DWPF chemical processing, a chemical antifoam agent must be effective at temperatures up to 103 deg. C between pH 3-13. Antifoam 747 is effective at pH 7 but degrades as pH deviates [5]. Antifoam 747 is 90% by weight Momentive{sup TM} L77 and 10% by weight Momentive{sup TM} Y-17580. These ingredients are trisiloxane products, which are especially vulnerable to attack in acidic and caustic environments. Since Antifoam 747 readily degrades, periodic addition is required. Several processing concerns related to Antifoam 747 have affected DWPF operation. Its overuse warranted concern of potential flammable melter offgas under upset conditions due to the generation of carbon dioxide and hydrogen. As a result, a Potential Inadequacy in the Safety Analysis (PISA) was declared [6]. Antifoam contribution has been included in the melter offgas flammability strategy and the quantity of antifoam is monitored to ensure limits are not exceeded. Testing at Savannah River National Laboratory (SRNL) determined that propanal, tri-methylsilanol, and hexamethyldisiloxane are generated as Antifoam 747 degrades, leading to a second PISA [7]. The formation of these byproducts poses additional flammability risks. Furthermore, the methyl and ethyl groups of these compounds may be responsible for the formation of the methyl and ethyl mercury discovered in DWPF [8], [9]. The formation of organomercury has specifically been called out by DOE as a need for increased research. The effectiveness of non-chemical methods for foam control and the efficiency of alternative antifoam agents were evaluated as part of an effort to improve HLW treatment operations at DWPF and to reduce, or eliminate, the flammability hazards associated with Antifoam 747 currently in use. Foaming of high-level waste (HLW) slurries is an issue at the Defense Waste Processing Facility (DWPF) Chemical Process Cell (CPC) which is currently mitigated with a chemical antifoam agent. The effectiveness of non-chemical methods for foam control and the efficiency of alternative antifoams were examined to improve HLW treatment at DWPF and to eliminate the flammability hazards associated with Siovation Antifoam 747 currently in use. Non-chemical foam control methods were deemed unviable for chemical processing at DWPF, as the spray/mist technique requires too much water and space limitations make installation and implementation of headspace agitators unfeasible. The use of ultrasonic energy thickened and further stabilized the foam. Two suitable alternative defoaming agents, Momentive{sup TM} Y- 17112 and Evonik Surfynol{sup R} MD-20, were identified. Both defoamers control foam over a pH range of 4- 13 at boiling, with no flammable degradation products, and only minimal quantities (100-200 mg/kg) are required under glycolic acid conditions. In order to validate the effectiveness of these defoaming agents, SRNL recommends irradiation testing and laboratory scale SRAT/SME experiments with actual radioactive waste. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Investigating uncertainties in human adaptation and their impacts on water scarcity in the Colorado river Basin, United States

The Colorado River Basin (CRB) supports the water supply for seven states and forty million people in the Western United States (US) and has been suffering an extensive drought for more than two decades. As climate change continues to reshape water resources distribution in the CRB, its impact can differ in intensity and location, resulting in variations in human adaptation behaviors. The feedback from human systems in response to the environmental changes and the associated uncertainty is critical to water resources management, especially for water-stressed basins. This paper investigates how human adaptation affects water scarcity uncertainty in the CRB and highlights the uncertainties in human behavior modeling. Our focus is on agricultural water consumption, as approximately 80% of the water consumption in the CRB is used in agriculture. We adopted a coupled agent-based and water resources modeling approach for exploring human-water system dynamics, in which an agent is a human behavior model that simulates a farmer’s water consumption decisions. We examined uncertainties at the system, agent, and parameter levels through uncertainty, clustering, and sensitivity analyses. The uncertainty analysis results suggest that the CRB water system may experience 13 to 30 years of water shortage during the 2019–2060 simulation period, depending on the paths of farmers’ adaptation. The clustering analysis identified three decision-making classes: bold, prudent, and forward-looking, and quantified the probabilities of an agent belonging to each class. The sensitivity analysis results indicated agents whose decision-making models require further investigation and the parameters with the higher uncertainty reduction potentials. Here, by conducting numerical experiments with the coupled model, this paper presents quantitative and qualitative information about farmers’ adaptation, water scarcity uncertainties, and future research directions for improving human behavior modeling.

Agent-based modeling↗
DOE OSTI2022

Quinoa protein extract: an effective alternative for the fining of wine phenolics

Abstract BACKGROUND Lately, there has been an increasing interest in using plant‐derived proteins for wine phenolic fining. Proteins extracted from cereals, potatoes, and legumes have been proposed as effective fining agents, but only those from pea, wheat, and potatoes have been approved for their use in wine. This work aimed at determining the fining ability of quinoa ( Chenopodium quinoa Willd.) protein extracts (QP), compared to commercial fining agents, on red wines. RESULTS The trials compared the performance of QP (30 and 50 g/hL), two potato protein extracts and gelatin, at two different contact times (48 and 96 h), using Petit Verdot, Malbec, and Cabernet Sauvignon wines. Turbidity, total phenolics, precipitable tannins, catechins, and color characteristics were determined. QP reduced the turbidity of all wines in a similar way to commercial fining agents. Both doses of QP significantly reduced tannins and other phenolic measures, including color intensity reductions, in a similar way to commercial fining agents. CONCLUSION QP behaved as an effective fining agent that deserves further studies in order to improve its performance and advance its characterization. © 2022 Society of Chemical Industry.

Pino‐Ramos, Liudis L.↗
DOE OSTI2023

A Simplified Approach Based on Cellular Automata for Describing Direct Reduced Iron Production in Different Reducing Conditions

A quick computation approach based on cellular automata is developed and implemented to describe the reduction of iron ore pellets by a mixture of reducing agents featured by different H 2 /CO ratios. The evolution of oxygen concentration inside the pellet is followed from the beginning to the end of contact between reducing agent and pellet. The variation of thermal state of pellets and gas mixture is computed based on their initial temperature, considering the heat involved and the convective heat exchange between pellet and gas mixture. The use of cellular automata and finite‐difference method to solve the diffusion equation point out the absence of any diffusion coefficient value, allowing to make the model fit the experimental trial, because the problem is that it is not ruled just by diffusion but also by the concentration variation of reducing agent inside the pellet due to porosity increasing during reduction. The updating of the reducing agents concentration implies a sharp decrease of oxygen concentration that the cellular automata model considers. The developed model is able to provide the in‐line control of reduction process and could be used to adjust the chemical concentration and temperature of injected reducing agents.

Metallurgy & Metallurgical Engineering↗
DOE OSTI2025

Unsupported and carbon-supported silver catalysts for oxygen reduction reaction in alkaline media

Quick and easy Ag catalysts preparation via wet chemical synthesis method using only reducing agent (pure-Ag); reducing agent and citric acid as the capping agent (Ag-CA); and carbon support (KetjenBlack 600J), capping agent, and the reducing agent (Ag/C) is demonstrated. The Ag-based electrocatalysts are characterized by high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) with energy-dispersive X-ray spectroscopy (EDS), scanning electron microscopy (SEM), X-ray diffraction (XRD) analysis, and X-ray photoelectron spectroscopy (XPS). The electrocatalytic activity of Ag catalysts for O 2 reduction reaction (ORR) in 1 M KOH is evaluated using the rotating (ring)-disc electrode method. SEM and HAADF-STEM results show that the unsupported pure-Ag and Ag-CA catalysts consist mainly of big agglomerates, and Ag/C has the smallest agglomerates and some sub-3 nm Ag nanoparticles. The XPS results reveal that Ag in all the catalysts is in the metallic form (Ag 0 ). Despite consisting of big agglomerates, the Ag-CA catalyst exhibits similar ORR electrocatalytic activity to that of Ag/C. Ag-CA (unsupported) shows the lowest hydrogen peroxide yield. These results are of great importance for the development of Ag-based catalysts that can be prepared in a fast, simple and easily up scalable fashion, for anion exchange membrane fuel cells.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Molecular Interaction Regulates the Performance and Longevity of Defect Passivation for Metal Halide Perovskite Solar Cells

Defect passivation constitutes one of the most commonly used strategies to fabricate highly efficient perovskite solar cells (PSCs). However, the durability of the passivation effects under harsh operational conditions has not been extensively studied regardless of the weak and vulnerable secondary bonding between the molecular passivation agents and perovskite crystals. Here, we incorporated strategically designed passivating agents to investigate the effect of their interaction energies on the perovskite crystals and correlated these with the performance and longevity of the passivation effects. We unraveled that the passivation agents with a stronger interaction energy are advantageous not only for effective defect passivation but also to suppress defect migration. The prototypical PSCs treated with the optimal passivation agent exhibited superior performance and operational stability, retaining 81.9 and 85.3% of their initial performance under continuous illumination or nitrogen at 85 °C after 1008 h, respectively, while the reference device completely degraded during that time. Furthermore, this work provides important insights into designing operationally durable defect passivation agents for perovskite optoelectronic devices.

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

A Reinforcement Learning Approach to Augment Conventional PID Control in Nuclear Power Plant Transient Operation

The ability of nuclear reactors to operate their power conversion cycles more flexibly will enhance their value to energy grids with variable pricing. Current nuclear control systems are typically classical controllers that are often based on proportional-integral-derivative (PID) control. This paper presents a method of augmenting the existing PID control for difficult transient operations in nuclear power plants using a reinforcement learning–derived feedforward signal applied in real time. The agents, which are trained on a test thermal load-following problem, are designed to improve steam generator outlet temperature control for a range of fast load-following scenarios covering ramp rates from 9%/min to 15%/min. Several reinforcement learning algorithms were initially investigated for the training of the feedforward agents with deep Q-learning (DQN) and proximal policy optimization (PPO) networks, which were found to be the most promising. The DQN controllers utilize discrete actions, giving them a better disturbance rejection at steady state but inconsistent response to initial temperature deviations. In contrast, PPO-trained agents, which take continuous actions except for a dead zone around zero, were shown to have the best combination of high disturbance rejection at steady state and good tracking of the desired temperature value. The ability of the PPO agent was also examined, with the average time of decision making found to be on the order of 1 ms. The fault properties of the controller under the loss of the reinforcement learning agent feedforward signal were also examined. The controller showed strong performance in situations of “no-signal” faults. but was less good at handling “stuck-at” faults, where the feedforward signal remains at a set value. In both cases, however, the PID was able to successfully maintain stability, eventually returning the system to a steady state. It is hoped that this work will allow for the proposed control architecture to be examined for more difficult control problems such that it may eventually be used to adapt existing nuclear plants for more aggressive load-following on grids of the future.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Monotonicity in the averaging process

In this work, we investigate an averaging process that describes how interacting agents approach consensus through binary interactions. In each elementary step, two agents are selected at random and they reach compromise by adopting their opinion average. We show that the fraction of agents with a monotonically decreasing opinion decays as e - αt , and that the exponent is selected as the extremum from a continuous spectrum of possible values. The opinion distribution of monotonic agents is asymmetric, and it becomes self-similar at large times. Furthermore, the tails of the opinion distribution are algebraic, and they are characterized by two distinct and nontrivial exponents. We also explore statistical properties of agents with an opinion strictly above average.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Characterization of Classical Sheep Scrapie in White-tailed Deer after Experimental Oronasal Exposure

Abstract Background Classic scrapie is a prion disease of sheep and goats that is associated with accumulation of abnormal prion protein (PrPSc) in the central nervous and lymphoid tissues. Chronic wasting disease (CWD) is the prion disease of cervids. This study was conducted to determine the susceptibility of white-tailed deer (WTD) to the classic scrapie agent. Methods We inoculated WTD (n = 5) by means of a concurrent oral/intranasal exposure with the classic scrapie agent from sheep or oronasally with the classic scrapie agent from goats (n = 6). Results All deer exposed to the agent of classic scrapie from sheep accumulated PrPSc. PrPSc was detected in lymphoid tissues at preclinical time points, and necropsies in deer 28 months after inoculation showed clinical signs, spongiform lesions, and widespread PrPSc in neural and lymphoid tissues. Western blots on samples from the brainstem, cerebellum, and lymph nodes of scrapie-infected WTD have a molecular profile similar to CWD and distinct from samples from the cerebral cortex, retina, or the original classic scrapie inoculum. There was no evidence of PrPSc in any of the WTD inoculated with classic scrapie prions from goats. Conclusions WTD are susceptible to the agent of classic scrapie from sheep, and differentiation from CWD may be difficult.

Immunology↗
DOE OSTI2021

Emergency Voltage Regulation in Power Systems via Ripple-Type Control

With increasing penetrations of volatile renewable generation and cyber-physical disruptions, ensuring the safe operation of bulk power systems has become unprecedentedly challenging. Because communication and computational costs restrict centralized system dispatch to being called upon every few minutes, and because purely local schemes are shown to be insufficient, distributed controls have been advocated for handling unanticipated system conditions in real time. The applicability of distributed control schemes, however, is fundamentally limited by their need for widespread communication and model cognizance. In this context, we put forth a hybrid, low-communication, saturation-driven protocol for the coordination of control agents that are distributed over a physical system and are allowed to communicate with peers over a "hotline" communication network. Under this protocol, when agents observe a constraint violation based on local measurements, they respond locally until their control resources saturate, in which case they send a beacon for assistance to peer agents. The scheme ensures that minor violations are efficiently mitigated via fast local controls, whereas severe violations can be handled by collaboration among a relatively small set of agents. We evaluate the performance of this scheme via numerical tests on the IEEE 14-bus test feeder, where agents act upon noisy measurements under diverse scenarios of load variations and severe low-/high-voltage events.

collaboration↗
DOE OSTI2020

A New Approach to Distributed Hypothesis Testing and Non-Bayesian Learning: Improved Learning Rate and Byzantine-Resilience

Here, we study a setting where a group of agents, each receiving partially informative private signals, seek to collaboratively learn the true underlying state of the world (from a finite set of hypotheses) that generates their joint observation profiles. To solve this problem, we propose a distributed learning rule that differs fundamentally from existing approaches, in that it does not employ any form of “belief-averaging”. Instead, agents update their beliefs based on a min-rule. Under standard assumptions on the observation model and the network structure, we establish that each agent learns the truth asymptotically almost surely. As our main contribution, we prove that with probability 1, each false hypothesis is ruled out by every agent exponentially fast, at a network-independent rate that is strictly larger than existing rates. We then develop a computationally-efficient variant of our learning rule that is provably resilient to agents who do not behave as expected (as represented by a Byzantine adversary model) and deliberately try to spread misinformation.

42 ENGINEERING↗
DOE OSTI2021

Decentralized Failure-Tolerant Optimization of Electric Vehicle Charging

We present a decentralized failure-tolerant algorithm for optimizing electric vehicle (EV) charging, using charging stations as computing agents. The algorithm is based on the alternating direction method of multipliers (ADMM) and it has the following features: (i) It handles capacity, peak demand, and ancillary services coupling constraints. (ii) It does not require a central agent collecting information and performing coordination (e.g. an aggregator), instead all agents exchange information and computations are carried out in a fully decentralized fashion. (iii) It can withstand the failure of any number of computing agents, as long as the remaining computing agents are in a connected communications network. We construct this algorithm by reformulating the optimal EV charging problem in a decomposable form, amenable to ADMM, and then developing efficient decentralized solution methods for the subproblems dealing with coupling constraints. We conduct numerical experiments on industry-scale synthetic EV charging datasets, with up to 1,152 charging stations, using a high performance computing cluster. The experiments demonstrate that the proposed algorithm can solve the optimal EV charging problem fast enough to permit the integration of EV charging with real-time electricity markets, even in the presence of failures.

42 ENGINEERING↗
DOE OSTI2021

Deep Reinforcement Learning Enabled Physical-Model-Free Two-Timescale Voltage Control Method for Active Distribution Systems

Active distribution networks are being challenged by frequent and rapid voltage violations due to renewable energy integration. Conventional model-based voltage control methods rely on accurate parameters of the distribution networks, which are difficult to achieve in practice. This paper proposes a novel physical-model-free two-timescale voltage control framework for active distribution systems. To achieve fast control of PV inverters, the whole network is first partitioned into several subnetworks using voltage-reactive power sensitivity. Then, the scheduling of PV inverters in the multiple sub-networks is formulated as Markov games and solved by a multi-agent soft actor-critic (MASAC) algorithm, where each subnetwork is modeled as an intelligent agent. All agents are trained in a centralized manner to learn a coordinated strategy while being executed based on only local information for fast response. For the slower time-scale control, OLTCs and switched capacitors are coordinated by a single agent-based SAC algorithm using the global information with considering control behaviors of the inverters. Particularly, the two-level agents are trained concurrently with information exchange according to the reward signal calculated from the data-driven surrogate model. Comparative tests with different benchmark methods on IEEE 33-and 123-bus systems and 342-node low voltage distribution system demonstrate that the proposed method can effectively mitigate the fast voltage violations and achieve systematical coordination of different voltage regulation assets without the knowledge of accurate system model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Cyberwheel

Cyberwheel is a high fidelity training environment for autonomous cyber defense agents that includes both simulation and emulation capabilities. Cyberwheel simplifies customization of the training network and easily allows redefining the agent’s reward function, observation space, and action space to support rapid experimentation of novel approaches to agent design. It also provides visibility into agent behaviors necessary for agent evaluation and sufficient documentation and examples to lower the barrier to entry.

Oesch, TimothySean [Oak Ridge National Laboratory ↗
DOE OSTI2025

EVI-EnSitePy (Electric Vehicle Infrastructure – Energy Estimation and Site Optimization Tool in Python) [EVI-X Modeling Suite] [SWR-25-07]

EVI-EnSitePy is a comprehensive agent-based tool designed for the analysis and design of high-power charging sites, encompassing a wide array of site agents including Electric Vehicles (EVs), chargers, energy storage units (ESS), renewable energy resources (DER), and loads. This versatile tool offers diverse functionalities and a modular modeling approach, allowing detailed configuration of agents based on power ratings, port numbers, energy capacities, demand requirements, charger interfaces, and flexibility to customize the tool for project specific goals. By simulating agent interactions and employing various metrics, EVI-EnSitePy enables the assessment of site performance, exploration of energy management systems (EMS), and implementation of innovative EV charging policies. Utilizing EV charge schedules and arrival states, the tool performs thorough charging site simulations, with outputs consisting of agent and site-level power profiles and statistical metrics. Employing a tree graph structure, EVI-EnSitePy supports nested site structures and power distribution modeling. The tool's ability to generate charging schedules deterministically or via stochastic analysis further enhances its versatility. Through its features and capabilities, EVI-EnSitePy offers a powerful platform for informed decision-making in the realm of high-power charging site design and operation.

Jackson, Derek [National Renewable Energy Laborato↗