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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Emergence and dynamics of unconfined self-organised vortices in active magnetic roller liquids

Actively driven colloids demonstrate complex out-of-equilibrium dynamics often rivaling self-organized patterns and collective behavior observed in living systems. Recent studies revealed the emergence of steady macroscopic states with multiple interacting vortices in an unconfined environment that emerge from the coupling between microscale particle rotation and translation. Yet, insights into the microscopic behavior during the vortex emergence, growth, and formation of a multi-vortical state remain lacking. Here, we investigate in experiments and simulations how the microscale magnetic roller behavior leads to the emergence of seed vortices, their aggregation or annihilation, and the formation of stable large-scale vortical structures. We reveal that the coupling of roller-induced hydrodynamic flows guides the local self-densifications and self-organization of the micro-rollers into seed vortices. Here, the resulting multi-vortical state is sensitive to the external magnetic field amplitude and allows tuning the rollers' number density in a vortex and its characteristic size.

36 MATERIALS SCIENCE↗
DOE OSTI2025

From disorganized data to emergent dynamic models: Questionnaires to partial differential equations

Starting with sets of disorganized observations of spatially varying and temporally evolving systems, obtained at different (also disorganized) sets of parameters, we demonstrate the data-driven derivation of parameter dependent, evolutionary partial differential equation (PDE) models capable of generating the data. This tensor type of data is reminiscent of shuffled (multidimensional) puzzle tiles. The independent variables for the evolution equations (their “space” and “time”) as well as their effective parameters are all emergent , i.e. determined in a data-driven way from our disorganized observations of behavior in them. We use a diffusion map based questionnaire approach to build a smooth parametrization of our emergent space/time/parameter space for the data. This approach iteratively processes the data by successively observing them on the “space,” the “time” and the “parameter” axes of a tensor. Once the data become organized, we use machine learning (here, neural networks) to approximate the operators governing the evolution equations in this emergent space. Our illustrative examples are based (i) on a simple advection–diffusion model; (ii) on a previously developed vertex-plus-signaling model of Drosophila embryonic development; and (iii) on two complex dynamic network models (one neuronal and one coupled oscillator model) for which no obvious smooth embedding geometry is known a priori. This allows us to discuss features of the process like symmetry breaking, translational invariance, and autonomousness of the emergent PDE model, as well as its interpretability.

generative models↗
DOE OSTI2022

Charaterization of Emerging Computing Architectures for Dynamic Simulation of Future Power Grids with Large-Scale Power Electronics

The increasing penetration of power electronics in power grids significantly raises the computing requirements in a real-time (and/or fast) simulation of the power grid. The real-time simulation is an enabler for evaluating controllers, protection systems, new equipment, and twinning. In this paper, emerging computing architectures such as tensor processing units (TPU), neural/neuromorphic processing units (NPU), and quantum processing units (QPU) are introduced and characterized for the real-time (and/or fast) simulation of power electronics-dominated power grids. The metrics and the process to characterize emerging computing architectures to perform real-time (and/or fast) simulations of future power grids with power electronics are discussed. Three of the emerging computing units are characterized based on these metrics and the process developed. This characterization will enable identification and comparison of emerging computing architectures that can perform real-time (and/or fast) simulation of future power grids.

Choi, Jongchan↗
DOE OSTI2021

Adaptive Load Shedding for Grid Emergency Control via Deep Reinforcement Learning

Emergency control, typically such as under-voltage load shedding (UVLS), is broadly used to grapple with low voltage and voltage instability issues in real-world power systems under contingencies. However, existing emergency control schemes are rule-based and cannot be adaptively applied to uncertain and floating operating conditions. Here, we propose an adaptive UVLS algorithm for emergency control via deep reinforcement learning (DRL) and expert systems. We first construct dynamic components for picturing the power system operation as the environment. The transient voltage recovery criteria, which poses time-varying requirements to UVLS, is integrated into the states and reward function to advise the learning of deep neural networks. The proposed method has no tuning issue of coefficients in reward functions, and this issue was regarded as a deficiency in the existing DRL-based algorithms. Case studies illustrate that the proposed method outperforms the traditional UVLS relay in both the timeliness and efficacy for emergency control.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

On the compatibility of established methods with emerging artificial intelligence and machine learning methods for disaster risk analysis

Abstract There is growing interest in leveraging advanced analytics, including artificial intelligence (AI) and machine learning (ML), for disaster risk analysis (RA) applications. These emerging methods offer unprecedented abilities to assess risk in settings where threats can emerge and transform quickly by relying on “learning” through datasets. There is a need to understand these emerging methods in comparison to the more established set of risk assessment methods commonly used in practice. These existing methods are generally accepted by the risk community and are grounded in use across various risk application areas. The next frontier in RA with emerging methods is to develop insights for evaluating the compatibility of those risk methods with more recent advancements in AI/ML, particularly with consideration of usefulness, trust, explainability, and other factors. This article leverages inputs from RA and AI experts to investigate the compatibility of various risk assessment methods, including both established methods and an example of a commonly used AI‐based method for disaster RA applications. This article utilizes empirical evidence from expert perspectives to support key insights on those methods and the compatibility of those methods. This article will be of interest to researchers and practitioners in risk‐analytics disciplines who leverage AI/ML methods.

Mathematical Methods In Social Sciences↗
DOE OSTI2023

Advancing the central role of non-model biorepositories in predictive modeling of emerging pathogens

The COVID-19 pandemic demonstrated the insufficiency of a reactive approach to emerging zoonotic pathogens. With spillover increasing in frequency as environments change and the human footprint continues to grow, pandemic prevention will require predictive models that can identify (i) potential zoonoses with a high likelihood of emergence and (ii) environmental or other features that may trigger a shift in host, vector, or pathogen baselines associated with emergence and/or spillover. Artificial intelligence (AI), and particularly its machine learning and deep learning branches, holds enormous potential for detecting shifts in large-scale biodiversity and disease datasets (genomic, ecological, geospatial, etc.). Such algorithms can be trained to identify subtle patterns in large volumes of data to yield insights into complex phenomena for which we have limited knowledge of the true cause(s) or predictor(s), as is the case for emerging infectious diseases.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Modeling the emergence of viral resistance for SARS-CoV-2 during treatment with an anti-spike monoclonal antibody

To mitigate the loss of lives during the COVID-19 pandemic, emergency use authorization was given to several anti-SARS-CoV-2 monoclonal antibody (mAb) therapies for the treatment of mild-to-moderate COVID-19 in patients with a high risk of progressing to severe disease. Monoclonal antibodies used to treat SARS-CoV-2 target the spike protein of the virus and block its ability to enter and infect target cells. Monoclonal antibody therapy can thus accelerate the decline in viral load and lower hospitalization rates among high-risk patients with variants susceptible to mAb therapy. However, viral resistance has been observed, in some cases leading to a transient viral rebound that can be as large as 3–4 orders of magnitude. As mAbs represent a proven treatment choice for SARS-CoV-2 and other viral infections, evaluation of treatment-emergent mAb resistance can help uncover underlying pathobiology of SARS-CoV-2 infection and may also help in the development of the next generation of mAb therapies. Although resistance can be expected, the large rebounds observed are much more difficult to explain. We hypothesize replenishment of target cells is necessary to generate the high transient viral rebound. Thus, we formulated two models with different mechanisms for target cell replenishment (homeostatic proliferation and return from an innate immune response antiviral state) and fit them to data from persons with SARS-CoV-2 treated with a mAb. We showed that both models can explain the emergence of resistant virus associated with high transient viral rebounds. We found that variations in the target cell supply rate and adaptive immunity parameters have a strong impact on the magnitude or observability of the viral rebound associated with the emergence of resistant virus. Both variations in target cell supply rate and adaptive immunity parameters may explain why only some individuals develop observable transient resistant viral rebound. Our study highlights the conditions that can lead to resistance and subsequent viral rebound in mAb treatments during acute infection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Adapting to a Changing World: Managing the Promise and Peril of Emerging Technologies and the Nuclear Nexus [Slides]

The Promise and Peril of Emerging Technologies: New and emerging technologies offer countless advances for just above everything; They also have the potential to be used for more destructive purposes; These technologies remain largely ungoverned by laws or norms; and, The nexus of emerging technologies with nuclear threats demands special attention. Overview, questions: How should we be thinking about these issues? What can we do to reduce the risk that accidents or miscalculation will lead to inadvertent escalation—especially at the nuclear nexus? This talk will examine emerging threats, the state of play of international discussions on these issues, and offer recommendations for the future.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Emergency Management Overview [Slides]

Introdution: LANL's Emergency Management Program is designed and managed in accordance with standards and requirements mandated by the DOE. LANL's primary driver and foundation of this course is DOE O 151.1D, Comprehensive Emergency Management System ; Established the requirements for emergency preparedness, response, and recovery for DOE facilities; Implemented by Emergency Management Division (EMD) on behalf of the Laboratory.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Development of the Table of Initial Isolation and Protective Action Distances for the 2024 Emergency Response Guidebook

The transportation of hazardous materials creates numerous opportunities for the release of toxic substances into the environment, whether caused by traffic accidents, train derailments, equipment failures, or human error. Such releases can pose acute hazards to the general public and to emergency response personnel who are the first to arrive at the scene. To help first responders determine whether a shipment is potentially hazardous and decide what actions should be taken if a toxic spill does occur, the Emergency Response Guidebook (ERG) is published by the U.S. Department of Transportation (DOT), Transport Canada, and the Secretariat of Transport and Communications of Mexico; with contributions from Centro de Informaciòn Quìmica para Emergencias of Argentina. The most recent version is the 2024 edition of the ERG (ERG 2024), titled 2024 Emergency Response Guidebook (ERG2024). The ERG provides essential information about firefighting, spill response, and potential public health effects. For chemicals that are toxic by inhalation (TIH) and chemicals that produce TIH gases upon reaction with water (TIH by water reactivity or TIHWR), the ERG provides initial isolation distances (IIDs) and protective action distances (PADs). The IID defines the radius of the zone around the spill that should be accessed solely by people who are directly involved in emergency response. The PAD is the distance downwind of the source of the release within which persons should be either evacuated or sheltered in place, depending on the severity of the incident and the nature of the population (e.g., density, age, health).

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2022

Energy Emergency and Preparedness Data: Frequently Asked Questions (FAQs) and Quick Guidance on Crisis Communications

State Energy Offices and Public Utility Commissions rely on timely, accurate, and actionable information to perform their energy emergency response duties and execute their roles as state energy security planners. In support of this need, the National Association of State Energy Officials (NASEO) and the National Association of Regulatory Utility Commissions (NARUC) hosted an Energy Security and Data Analysis Workshop in Washington, DC to identify energy security response and planning data sources; and to share successful methods of data use and integration in state, federal, and private sector tools. Following the workshop, NASEO and NARUC hosted two topical data-centric webinars based on state priorities to identify best practices in Geographic Information Systems (GIS) and Crisis Communications programs leveraged in energy assurance planning and response. The Crisis Communications webinar covered best practice tactics for how states can respond during energy emergencies and other crises. Based on the workshop and webinars, this document summarizes commonly used data sources and includes frequently asked questions which may be used by state energy officials (i.e., consisting of staff from Public Utility Commissions and Governor-designated State Energy Offices) to help guide them in developing or improving their Crisis Communications capabilities. A strong public information program is a key crisis management tool. Timely and accurate information helps prevent confusion and uncertainty and encourages public support and cooperation. As energy subject matter experts, state energy officials are vital in the distilling, clarifying, and conveying energy sector information and implications to decision-makers and the public. Other participants in an effective public information program include the Governor’s Office, other state agencies, local governments, energy providers, local businesses, state legislature, and the federal government. It is essential to provide stakeholders and the public with information about the nature, severity, and duration of an emergency because inadequate understanding and awareness can lead to undesirable actions that could further exacerbate the situation. Before a state government can provide information to the public, it must gather information, describe the emergency accurately, and develop recommendations to manage the situation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Adapting Technology Learning Curves for Prospective Techno-Economic and Life Cycle Assessments of Emerging Carbon Capture and Utilization Pathways

Comparisons of emerging carbon capture and utilization (CCU) technologies with equivalent incumbent technologies are necessary to support technology developers and to help policy-makers design appropriate long-term incentives to mitigate climate change through the deployment of CCU. In particular, early-stage CCU technologies must prove their economic viability and environmental reduction potential compared to already-deployed technologies. These comparisons can be misleading, as emerging technologies typically experience a drastic increase in performance and decrease in cost and greenhouse gas emissions as they develop from research to mass-market deployment due to various forms of learning. These changes complicate the interpretation of early techno-economic assessments (TEAs) and life cycle assessments (LCAs) of emerging CCU technologies. The effects of learning over time or cumulative production themselves can be quantitatively described using technology learning curves (TLCs). While learning curve approaches have been developed for various technologies, a harmonized methodology for using TLCs in TEA and LCA for CCU in particular is required. To address this, we describe a methodology that incorporates TLCs into TEA and LCA to forecast the environmental and economic performance of emerging CCU technologies. This methodology is based on both an evaluation of the state of the art of learning curve assessment and a literature review of TLC approaches developed in various manufacturing and energy generation sectors. Additionally, we demonstrate how to implement this methodology using a case study on a CO 2 mineralization pathway. Finally, commentary is provided on how researchers, technology developers, and LCA and TEA practitioners can advance the use of TLCs to allow for consistent, high-resolution modeling of technological learning for CCU going forward and enable holistic assessments and fairer comparisons with other climate technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems

Multiscale systems are ubiquitous in science and technology, but are notoriously challenging to simulate as short spatiotemporal scales must be appropriately linked to emergent bulk physics. When expensive high-dimensional dynamical systems are coarse-grained into low-dimensional models, the entropic loss of information leads to emergent physics which are dissipative, history-dependent, and stochastic. To machine learn coarse-grained dynamics from time-series observations of particle trajectories, we propose a framework using the metriplectic bracket formalism that preserves these properties by construction; most notably, the framework guarantees discrete notions of the first and second laws of thermodynamics, conservation of momentum, and a discrete fluctuation-dissipation balance crucial for capturing non-equilibrium statistics. We introduce the mathematical framework abstractly before specializing to a particle discretization. As labels are generally unavailable for entropic state variables, we introduce a novel self-supervised learning strategy to identify emergent structural variables. We validate the method on benchmark systems and demonstrate its utility on two challenging examples: (1) coarse-graining star polymers at challenging levels of coarse-graining while preserving non-equilibrium statistics, and (2) learning models from high-speed video of colloidal suspensions that capture coupling between local rearrangement events and emergent stochastic dynamics. We provide open-source implementations in both PyTorch and LAMMPS, enabling large-scale inference and extensibility to diverse particle-based systems.

Computational Engineering, Finance, and Science (c↗
DOE OSTI2024

Emergence of catalytic function in prebiotic information-coding polymers

Life as we know it relies on the interplay between catalytic activity and information processing carried out by biological polymers. Here we present a plausible pathway by which a pool of prebiotic information-coding oligomers could acquire an early catalytic function, namely sequence-specific cleavage activity. Starting with a system capable of non-enzymatic templated replication, we demonstrate that even non-catalyzed spontaneous cleavage would promote proliferation by generating short fragments that act as primers. Furthermore, we show that catalytic cleavage function can naturally emerge and proliferate in this system. Specifically, a cooperative catalytic network with four subpopulations of oligomers is selected by the evolution in competition with chains lacking catalytic activity. The cooperative system emerges through the functional differentiation of oligomers into catalysts and their substrates. The model is inspired by the structure of the hammerhead RNA enzyme as well as other DNA- and RNA-based enzymes with cleavage activity that readily emerge through natural or artificial selection. We identify the conditions necessary for the emergence of the cooperative catalytic network. In particular, we show that it requires the catalytic rate enhancement over the spontaneous cleavage rate to be at least 10 2 –10 3 , a factor consistent with the existing experiments. The evolutionary pressure leads to a further increase in catalytic efficiency. The presented mechanism provides an escape route from a relatively simple pairwise replication of oligomers toward a more complex behavior involving catalytic function. This provides a bridge between the information-first origin of life scenarios and the paradigm of autocatalytic sets and hypercycles, albeit based on cleavage rather than synthesis of reactants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Emergence of catalytic function in prebiotic information-coding polymers

Life as we know it relies on the interplay between catalytic activity and information processing carried out by biological polymers. Here we present a plausible pathway by which a pool of prebiotic information-coding oligomers could acquire an early catalytic function, namely sequence-specific cleavage activity. Starting with a system capable of non-enzymatic templated replication, we demonstrate that even non-catalyzed spontaneous cleavage would promote proliferation by generating short fragments that act as primers. Furthermore, we show that catalytic cleavage function can naturally emerge and proliferate in this system. Specifically, a cooperative catalytic network with four subpopulations of oligomers is selected by the evolution in competition with chains lacking catalytic activity. The cooperative system emerges through the functional differentiation of oligomers into catalysts and their substrates. The model is inspired by the structure of the hammerhead RNA enzyme as well as other DNA- and RNA-based enzymes with cleavage activity that readily emerge through natural or artificial selection. We identify the conditions necessary for the emergence of the cooperative catalytic network. In particular, we show that it requires the catalytic rate enhancement over the spontaneous cleavage rate to be at least 10 2 –10 3 , a factor consistent with the existing experiments. The evolutionary pressure leads to a further increase in catalytic efficiency. The presented mechanism provides an escape route from a relatively simple pairwise replication of oligomers toward a more complex behavior involving catalytic function. This provides a bridge between the information-first origin of life scenarios and the paradigm of autocatalytic sets and hypercycles, albeit based on cleavage rather than synthesis of reactants.

Tkachenko, Alexei V. (ORCID:000000031291243X)↗
NASA NTRS1974

The potential for congressional use of emergent telecommunications: An exploratory assessment

A study of the use of newly emerging communications technology for improving the understanding between members of Congress and their constituents was conducted. The study employed a number of specific methodologies such as interdisciplinary systems model building, technology analysis, a sample survey, and semi-structured interviews using sketches of the emergent channels. The following configurations were identified as representative of emergent channel characteristics: (1) the teleconference, (2) the videoconference, (3) the videophone, (4) cable television, (5) cable television polling, and (6) information retrieval. Analysis of the interview data resulted in an overview of the current congressional-constituent communication system and an assessment of the potential for emergent telecommunications, as perceived by congressmen and senior staff from 40 offices in the stratified judgement sample.

Wood, F. B.↗
NASA NTRS1976

Observation of spatial and temporal variations in X-ray bright point emergence patterns

Observations of X-ray bright points (XBP) over a six-month interval in 1973 show significant variations in both the number density of XBP as a function of heliographic longitude and in the full-sun average number of XBP from one rotation to the next. The observed increases in XBP emergence are estimated to be equivalent to several large active regions emerging per day for several months. The number of XBP emerging at high latitudes varies in phase with the low-latitude variation and reaches a maximum approximately simultaneous with a major outbreak of active regions. The quantity of magnetic flux emerging in the form of XBP at high latitudes alone is estimated to be as large as the contribution from all active regions.

Golub, L.↗
NASA NTRS1980

Evidence for globally coherent variability in solar magnetic flux emergence

We examine the large-scale spatial and temporal variations in the emergence of X-ray bright points on the sun, in order to study the global properties of magnetic flux emergence. Major variations in the rate of flux emergence are observed at all solar latitudes, on a time scale of 3-5 months. The most economical explanation of the observations is that the full sun participated in a single large eruptive event during the available 8 month observing period from Skylab in 1973. The peak of this global event corresponds in time to the eruption of a major complex of activity. Moreover, it appears that the only portion of the solar surface which deviates from the above pattern of behavior is the low latitude region in the vicinity of the AR complex; this area shows a temporary depletion immediately following the AR outburst. The high-latitude regions in both hemispheres show the same variation and appear to lead the low-latitude emergence by approximately 1 month.

Golub, L.↗