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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

NASA's High-Resolution GEOS Forecasting and Reanalysis Products: Support for TOLNet

Stratospheric intrusions (SIs) the introduction of ozone-rich stratospheric air into the troposphere – have been the interest of decades of research for their link with surface ozone air quality exceedances, especially at the high elevations in the western USA in springtime; however, the impact of SIs in the remaining seasons and over the rest of the USA is less clear. We can expect MERRA-2 to realistically represent both atmospheric dynamics and composition. The operational GEOS weather forecasting system, GEOS-FP, has a similar ozone observing system to MERRA-2, while NASA's new global high-resolution air quality forecast system, GEOS-CF, combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12), simulating a wide range of additional air pollutants and tracers which strengthens this detailed analysis of the intrusions and the sources for the high ozone concentrations. Using a multitude of observational datasets, including lidar, air craft, ozonesondes and air quality monitoring surface sites, in combination with the GEOS forecast and reanalysis products, we aim to provide the public with tools which are available in near-real time to enhance their capability to identify the impact of stratospheric air on surface ozone concentrations separate from anthropogenic sources. In particular, improved understanding of the connections between large-scale climate variability and local-scale dynamically-driven air quality events may support improved seasonal prediction of SI events.

Knowland, K. Emma↗
NASA NTRSDate not supplied

Science Objectives of the ACCP/AOS Millimeter- and Submillimeter-Wave Radiometers

Passive microwave radiometers provide highly useful information regarding the Earth system by measuring thermal emission (from the surface and atmosphere) that is reflected, absorbed, and scattered by the surface and the atmosphere. As such, radiometers have been instrumental across Earth-observing concepts with heritage tracing back to the Nimbus era, and clouds and precipitation have been key geophysical phenomena these sensors have targeted. As millimeter-wave (particularly ≥85 GHz) and submillimeter-wave technologies have advanced, the applicability of microwave radiometers has expanded to encompass falling snow and ice clouds, respectively. Given the strong heritage of microwave radiometry and the emergence of submillimeter-wave sensors for sensing falling snow and ice clouds, the National Academies’ 2017 Decadal Survey for Earth Science and Applications from Space recommended passive sensors covering these wavelengths be included in observing systems that address the Aerosols, Clouds, Convection, and Precipitation (ACCP) combined designated observable. To showcase the capabilities for addressing the ACCP science objectives, we will provide an overview of the passive microwave capabilities envisioned for the Atmosphere Observing System (AOS, part of the recently unveiled NASA Earth System Observatory), including descriptions of the radiometers for the inclined and polar orbits and the primary geophysical variables of interest related to ice-phase clouds and precipitation. We will also discuss secondary science objectives, such as precipitation mapping, that can be achieved with the ACCP/AOS radiometers. Examples from the analyses performed for the ACCP/AOS architecture study and subsequent sensor definition exercises will be presented, outlining the basis for the radiometer configuration within the context of the overarching ACCP/AOS goals to elucidate the connections between atmospheric dynamics, weather (including extreme events), air quality, and climate.

Ian S Adams↗
NASA NTRS2022

Demonstrating a New Flood Observing Strategy on the Nos Testbed

A new observing strategy for floods was demonstrated and evaluated in a testbed environment. The strategy coordinates several observing platforms, including in situ and space based, to observe a flood from multiple vantage points and to dynamically target predicted flood events with high- resolution observations. The coordinated observations were assimilated back into the model to improve forecasts and future observation selection. This demonstration shows the potential for coordinated, model-driven observing strategies and the feasibility of the NOS Testbed for demonstrating and evaluating new observing strategies.

Capra, Leigha↗
NASA NTRSDate not supplied

Orion Artemis I Descent and Landing Performance

The Artemis I mission successfully demonstrated the Orion Multi-Purpose Crew Module ability to trigger parachute deployment, control angular rate and touch-down heading using reaction control jets while under parachutes, and automatically detect splashdown. Guidance and control logic is implemented to initiate para-chute events in a favorable dynamic state and select a touchdown heading direction that will minimize impact loads at splashdown. Detection of splashdown inhibits control to prevent firing reaction control jets under water and begin the transition into post-landing activities that cut parachutes and deploy the Crew Module Up-righting System (CMUS). This paper presents analysis of Orion descent and landing guidance and control performance for the Artemis I mission utilizing imagery taken from the vehicle, recorded flight data, and results from high fidelity simulations.

Artemis 1↗
NASA NTRSDate not supplied

Determination of Interfacial Energy for Solid-State Precipitation

Precipitation of strengthening phases from solid state is a critical process for many materials. For example, the high-temperature creep resistance of Ni-based superalloys is governed by ’ precipitates. However, the interfacial energy which controls the precipitation is expensive to determine experimentally. For simulations, the atomic nature of precipitation events necessitates using Molecular Dynamics (MD) so relying on fitting to a-priori experimental behavior is not required. However, the solid-state precipitation takes orders of magnitude longer time than is accessible for traditional MD simulation. Taking inspiration from Kinetic Monte-Carlo (KMC) methods for simulating diffusion on a rigid lattice, we have developed a hybrid KMC-MD approach to enable simulation of solid-state precipitation (and determination of the interfacial energy). Implementing these methods in LAMMPS, we calculated the interfacial energy for the ’ precipitation out of a super-saturated  phase in Ni-based superalloys. The resulting interfacial energy value for the Ni-Al system is well within the accuracy of experimental values, demonstrating the capability of this method. Enabling the calculation of the interfacial energy without a-priori experimental knowledge of the precipitation unlocks a more efficient ICME-based approach for guiding annealing protocols in Ni-based superalloys and other solid-state precipitation systems.

Precipitation↗
DOE OSTI2023

Resilient Distribution System Restoration with Equitable Load Shedding

A methodology is proposed for the improvement of electric distribution system resilience to high-impact, low-probability catastrophic events. An approach for dynamic network reconfiguration and coordination of distributed energy resources is introduced to assist in restoration efforts. The problem is formulated as a mixed-integer linear program that minimizes generation costs, the cost of lost load, and costs associated with equitable load shedding, while respecting operational limits of generation, loads, and the network. Constraints are imposed on binary switching variables to ensure equitable load shedding in emergency situations. Numerical validation of the proposed approach is conducted on an example distribution feeder, and case studies are performed to analyze the impact of various parameters in the optimization problem formulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI1995

Nuclear Safety [Vol. 36, No. 2, July-December 1995]

Nuclear Safety is a journal that covers significant issues in the field of nuclear safety. Its primary scope is safety in the design, construction, operation, and decommissioning of nuclear power reactors worldwide and the research and analysis activities that promote this goal, but it also encompasses the safety aspects of the entire nuclear fuel cycle, including fuel fabrication, spent-fuel processing and handling, and nuclear waste disposal, the handling of fissionable materials and radioisotopes, and the environmental effects of all these activities. Table of Contents for this issue follows. THE CHORNOBYL ACCIDENT: 195 The Chornobyl Accident Revisited, Part III: Chernobyl Source Term Release Dynamics and Reconstruction of Events During the Active Phase, A. R. Sich; GENERAL SAFETY CONSIDERATIONS: 218 Second ANS Workshop on the Safety of Soviet-Designed Nuclear Power Plants, R. A. Bari; 234 Elements of a Nuclear Criticality Safety Program, C. M. Hopper; 243 Rickover, Excellence, and Criticality Safety Programs, R. E. Wilson; ACCIDENT ANALYSIS: 249 Transient Analysis of the PIUS Advanced Reactor Design with the TRAC-PF1/MOD2 Code, B. E. Boyack, J. L Steiner, S. C. Harmony, H. J. Stumpf, and J. F. Lime; 278 The Hierarchy-By-Interval Approach to Identifying Important Models that Need Improvement in Severe-Accident Simulation Codes, T. J. Heames, M. Khatib-Rahbar, J. E. Kelly, R. P. Jenks-Johnson, and Y.-S. Chen; 290 RELAP5/MOD3 Code Coupling Model, R. P. Martin; 299 Missiles Caused by Severe Pressurized-Water Reactor Accidents, R. Krieg; DESIGN FEATURES: 310 Validation of COMMIX with Westinghouse AP-600 PCCS Test Data, J. G. Sun, T. H. Chien, J. Ding, and W T. Sha; ENVIRONMENTAL EFFECTS: 321 Spent Nuclear Fuel Characterization for a Bounding Reference Assembly for the Receiving Basin for Off-Site Fuel, S. D. Kahook, R. L. Garrett, L. R. Canas, and M J. Beckum; OPERATING EXPERIENCES: 332 Reactor Shutdown Experience, Compiled by J. W. Cletcher; U.S. NUCLEAR REGULATORY COMMISSION INFORMATION AND ANALYSES: 335 Reactor Coolant System Blowdown at Wolf Creek on September 17, 1994, J. V. Kauffman and S. L. Israel; RECENT DEVELOPMENTS: 344 Reports, Standards, and Safety Guides, D. S. Oueener; 349 Proposed Rule Changes as of June 30, 1995; ANNOUNCEMENTS: 320 Symposium on Acceptability of Risk From Radiation—Application to Manned Space Flight; 320 24th DOE/NRC Nuclear Air Cleaning and Treatment Conference; 361 Radiation Biology and Radiation Protection— Modern Developments and Tendencies in Radiation Biology; 362 1997 IEEE Sixth Conference on Human Factors and Power Plants; 354 The Authors; 360 Reviewers of Nuclear Safety, Vol. 36.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

The Variability of the Black Hole Image in M87 at the Dynamical Timescale

The black hole images obtained with the Event Horizon Telescope (EHT) are expected to be variable at the dynamical timescale near their horizons. For the black hole at the center of the M87 galaxy, this timescale (5–61 days) is comparable to the 6 day extent of the 2017 EHT observations. Closure phases along baseline triangles are robust interferometric observables that are sensitive to the expected structural changes of the images but are free of station-based atmospheric and instrumental errors. We explored the day-to-day variability in closure-phase measurements on all six linearly independent nontrivial baseline triangles that can be formed from the 2017 observations. We showed that three triangles exhibit very low day-to-day variability, with a dispersion of ∼3°–5°. The only triangles that exhibit substantially higher variability (∼90°–180°) are the ones with baselines that cross the visibility amplitude minima on the u–v plane, as expected from theoretical modeling. We used two sets of general relativistic magnetohydrodynamic simulations to explore the dependence of the predicted variability on various black hole and accretion-flow parameters. We found that changing the magnetic field configuration, electron temperature model, or black hole spin has a marginal effect on the model consistency with the observed level of variability. On the other hand, the most discriminating image characteristic of models is the fractional width of the bright ring of emission. Models that best reproduce the observed small level of variability are characterized by thin ring-like images with structures dominated by gravitational lensing effects and thus least affected by turbulence in the accreting plasmas.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

A Non‐Intrusive Machine Learning Framework for Debiasing Long‐Time Coarse Resolution Climate Simulations and Quantifying Rare Events Statistics

Abstract Due to the rapidly changing climate, the frequency and severity of extreme weather is expected to increase over the coming decades. As fully‐resolved climate simulations remain computationally intractable, policy makers must rely on coarse‐models to quantify risk for extremes. However, coarse models suffer from inherent bias due to the ignored “sub‐grid” scales. We propose a framework to non‐intrusively debias coarse‐resolution climate predictions using neural‐network (NN) correction operators. Previous efforts have attempted to train such operators using loss functions that match statistics. However, this approach falls short with events that have longer return period than that of the training data, since the reference statistics have not converged. Here, the scope is to formulate a learning method that allows for correction of dynamics and quantification of extreme events with longer return period than the training data. The key obstacle is the chaotic nature of the underlying dynamics. To overcome this challenge, we introduce a dynamical systems approach where the correction operator is trained using reference data and a coarse model simulation nudged toward that reference. The method is demonstrated on debiasing an under‐resolved quasi‐geostrophic model and the Energy Exascale Earth System Model (E3SM). For the former, our method enables the quantification of events that have return period two orders longer than the training data. For the latter, when trained on 8 years of ERA5 data, our approach is able to correct the coarse E3SM output to closely reflect the 36‐year ERA5 statistics for all prognostic variables and significantly reduce their spatial biases.

Barthel Sorensen, B.↗
NASA NTRS2019

North American Extreme Precipitation Events and Related Large-Scale Meteorological Patterns: a Review of Statistical Methods, Dynamics, Modeling, and Trends

This paper surveys the current state of knowledge regarding Large-Scale Meteorological Patterns (LSMPs) associated with short-duration (less than one week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defined based on the characteristic spatial variations of a meteorological field or on the remote circulation response to a known forcing, LSMPs are defined relative to the occurrence of a specific phenomenon—here, extreme precipitation—and with an emphasis on the synoptic scales that have a primary influence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to definitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many different definitions of “extreme” are in use, making it difficult to directly compare different studies. Dynamically, several types of meteorological systems—extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs—and several mechanisms—fronts, atmospheric rivers, and orographic ascent—have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some 4 promising analysis techniques have been identified and the LSMP perspective is useful for evaluating model dynamics.

Mathew Barlow↗
DOE OSTI2024

Transmission Data-Driven User-Defined Model for Inverter-based and Conventional Power Plants

Recent events in Odessa [1], [2] have shed light on the complexities of integrating large Inverter-Based Resource (IBR) plants with the transmission system, prompting NERC to stress continuous performance monitoring by transmission operators. Challenges such as plant control updates, IBR model revisions, Phase-locked loop loss of synchronism, and protection events have been identified, underscoring the need for enhanced monitoring protocols by regulatory bodies. The recent FERC 901 order underscores the importance of accurate data exchange regarding IBRs for reliability studies. However, limited access to IBR plant-related data hampers effective decision-making for transmission operators (TOP). This paper proposes a method for constructing data-driven User-Defined dynamic Models (UDM) for power plants for validating multiple-event data using field measurements from interconnection bus locations. The problem is formulated as a power plant model identification problem and a multi-task learning approach under partial input observability assumptions is proposed in this work. This approach aims to predict aggregated responses of conventional and IBR power plants during various dynamic physical events which is useful for planning studies under diverse disturbance conditions. Ultimately, this methodology emphasizes the importance of plant visibility to operators in addressing power system challenges, facilitating improved planning and operational studies.

Mahapatra, Kaveri [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2025

Demographic Microsimulator for Integrated Urban Systems: Adapting Panel Survey of Income Dynamics to Capture the Continuum of Life

Agent-based models (ABMs) in transportation modeling simulate activity and travel decisions at the disaggregate level of households and individuals. To do this, ABMs require detailed and realistic information on agents’ socioeconomic and demographic characteristics. Various synthetic population generators have been proposed to address this need. However, most of those currently in practice are cross-sectional in nature and do not account for the dynamics within households and individuals as they progress through life events over time. This is a major shortcoming, as literature has shown that transportation decisions are affected by the transition between and co-occurrence of life cycle events. While some demographic evolution simulators have been proposed to address this issue, they are developed using cross-sectional data and capture only a small set of life cycle events and their interdependence. Addressing these drawbacks, we propose a demographic microsimulator (DEMOS) that captures the “continuum of life” by considering a range of household- and individual-level life cycle events. DEMOS is developed using the Panel Survey of Income Dynamics, one of the world’s longest-running longitudinal surveys. The DEMOS submodels consider key life cycle events that are influenced by agents’ demographic variables. DEMOS is applied to evolve the population of the San Francisco Bay Area over a 9-year horizon. Results demonstrate how DEMOS generates life trajectories and how DEMOS outputs match the observed demographic trends. DEMOS is expected to enable longitudinal analysis in the context of ABMs and expand ABMs analyses relating to dynamic processes such as household-level vehicle transactions.

Demographic evolution↗
DOE OSTI2021

Online PMU Missing Value Replacement Via Event-Participation Decomposition

We introduce a new method for online Phasor Measurement Unit (PMU) missing value replacement. Our approach allows us to decompose PMU event responses into a non-dynamic component (denoted the participation factor) that can be inferred directly from the past and a dynamic component that can be inferred directly from all other PMUs (denoted the event strength). When missing values occur, we can use these two components, which do not rely on the missing index, to estimate the correct value. The method is extremely fast and can easily be used for online applications. Furthermore, extensive testing on real power system event data reveals that our approach achieves state-of-the-art performance in terms of Mean Absolute Percent Errors (MAPEs) for PMU data dropped during event periods. Here, the method also yields an interpretable and simplified view of events for further analysis and applications. The method relies only on PMU data and does not take outside information such as network topology.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1986

Dynamic pressure model derived from an observation by Sakigake for Comet Halley on 31 December, 1985

An outstanding disconnection event (DE)-like knot was observed on 31 Dec. 1985 in P/Halley's tail. Analysis of the Sakigake/IMF data reveals that comet Halley did not encounter the heliospheric neutral sheet on the day, demanding a new explanation of the DE-like event. During this event, the comet encountered a high-speed solar wind from a coronal hole tongue of the Sun. The event can be explained by a dynamic pressure model, according to which the DE-like plasmoid was caused by a sudden increase in the dynamic pressure of the solar wind. A simulation result is found to support this interpretation. The dynamic pressure model for a comet can be compared with the mechanism of a possible geotail disturbance during a spacecraft triggered auroral substorm.

Saito, Takao↗
DOE OSTI2021

Event-Based Analysis of Solar Power Distribution Feeder Using Micro-PMU Measurements

Solar distribution feeders are commonly used in solar farms that are integrated into distribution substations. In this paper, we focus on a real-world solar distribution feeder and conduct an event-based analysis by using micro-PMU measurements. The solar distribution feeder of interest is a behind-the-meter solar farm with a generation capacity of over 4 MW that has about 200 low-voltage distributed photovoltaic (PV) inverters. The event-based analysis in this study seeks to address the following practical matters. First, we conduct event detection by using an unsupervised machine learning approach. For each event, we determine the event’s source region by an impedancebased analysis, coupled with a descriptive analytic method. We segregate the events that are caused by the solar farm, i.e., locallyinduced events, versus the events that are initiated in the grid, i.e., grid-induced events, which caused a response by the solar farm. Second, for the locally-induced events, we examine the impact of solar production level and other significant parameters to make statistical conclusions. Third, for the grid-induced events, we characterize the response of the solar farm; and make comparisons with the response of an auxiliary neighboring feeder to the same events. Fourth, we scrutinize multiple specific events; such as by revealing the dynamics to the control system of the solar distribution feeder. The results and discoveries in this study are informative to utilities and solar power industry.

14 SOLAR ENERGY↗
DOE OSTI2025

Propagating synthetic populations with dynamic Bayesian networks: a framework for long-horizon demographic forecasting

This study presents a dynamic demographic microsimulator using dynamic Bayesian networks to forecast long–term changes in household and individual life events. Leveraging longitudinal Panel Study of Income Dynamics (PSID) data, two networks for individuals and households were modeled to simulate transitions in employment, income, education, marriage, childbirth, leaving the parental home, home ownership, mortality, and household formation or dissolution. Across 1,000 simulation runs spanning 24 years, household–level outcomes remain highly accurate and individual–level predictions reasonable. Although accuracy naturally declines with projection horizon, performance remains promising at both levels. This study addresses a key limitation of existing population synthesis models, which typically generate only a single static snapshot of the population. In conclusion, by introducing a framework that propagates cross-sectional outputs into the future, the microsimulator enables the tracking of demographic evolution over time, enhances realism in population-based simulations, and supplies credible inputs to agent-based travel demand models.

Demographic modeling↗
NASA NTRS2001

Adjustable Autonomy Testbed

The Adjustable Autonomy Testbed (AAT) is a simulation-based testbed located in the Intelligent Systems Laboratory in the Automation, Robotics and Simulation Division at NASA Johnson Space Center. The purpose of the testbed is to support evaluation and validation of prototypes of adjustable autonomous agent software for control and fault management for complex systems. The AA T project has developed prototype adjustable autonomous agent software and human interfaces for cooperative fault management. This software builds on current autonomous agent technology by altering the architecture, components and interfaces for effective teamwork between autonomous systems and human experts. Autonomous agents include a planner, flexible executive, low level control and deductive model-based fault isolation. Adjustable autonomy is intended to increase the flexibility and effectiveness of fault management with an autonomous system. The test domain for this work is control of advanced life support systems for habitats for planetary exploration. The CONFIG hybrid discrete event simulation environment provides flexible and dynamically reconfigurable models of the behavior of components and fluids in the life support systems. Both discrete event and continuous (discrete time) simulation are supported, and flows and pressures are computed globally. This provides fast dynamic simulations of interacting hardware systems in closed loops that can be reconfigured during operations scenarios, producing complex cascading effects of operations and failures. Current object-oriented model libraries support modeling of fluid systems, and models have been developed of physico-chemical and biological subsystems for processing advanced life support gases. In FY01, water recovery system models will be developed.

Malin, Jane T.↗