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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Hierarchical semi-Markov models with duration-aware dynamics for activity sequences

Residential electricity demand at granular scales is driven by what people do and for how long. Accurately forecasting this demand for applications like microgrid management and demand response therefore requires generative models for activities that can produce realistic daily activity sequences, capturing both the timing and duration of human behavior. This paper develops a generative model of human activity sequences using nationally representative time-use diaries at a 10-min resolution. We use this model to quantify which demographic factors are most critical for improving predictive performance. We propose a hierarchical semi-Markov framework that addresses two key modeling challenges. First, a time-inhomogeneous Markov router learns the patterns of “which activity comes next.” Second, a semi-Markov hazard component explicitly models activity durations, capturing “how long” activities realistically last. To ensure statistical stability when data are sparse, the model pools information across related demographic groups and time blocks. The entire framework is trained and evaluated using survey design weights to ensure our findings are representative of the U.S. population. On a held-out test set, we demonstrate that explicitly modeling durations with the hazard component provides a substantial and statistically significant improvement over purely Markovian models. Furthermore, our analysis reveals a clear hierarchy of demographic factors: Sex, Day-Type, and Household Size provide the largest predictive gains, while Region and Season, though important for energy calculations, contribute little to predicting the activity sequence itself. The result is an interpretable and robust generator of synthetic activity traces, providing a high-fidelity foundation for downstream energy systems modeling.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Granger Causality for prediction in Dynamic Mode Decomposition: Application to power systems

Here, the dynamic mode decomposition (DMD) technique extracts the dominant modes characterizing the innate dynamical behavior of the system within the measurement data. For appropriate identification of dominant modes from the measurement data, the DMD algorithm necessitates ensuring the quality of the input measurement data sequences. On that account, for validating the usability of the dataset for the DMD algorithm, the paper proposed two conditions: Persistence of excitation (PE) and the Granger Causality Test (GCT). The virtual data sequences are designed with the hankel matrix representation such that the dimensions of the subspace spanning the essential system modes are increased with the addition of new state variables. The PE condition provides the lower bound for the trajectory length, and the GCT provides the order of the model. Satisfying the PE condition enables estimating an approximate linear model, but the predictability with the identified model is only assured with the temporal causation among data searched with GCT. The proposed methodology is validated with the application for coherency identification (CI) in a multi-machine power system (MMPS), an essential phenomenon in transient stability analysis. The significance of PE condition and GCT is demonstrated through various case studies implemented on 22 bus six generator system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1995

On-line estimation of error covariance parameters for atmospheric data assimilation

A simple scheme is presented for on-line estimation of covariance parameters in statistical data assimilation systems. The scheme is based on a maximum-likelihood approach in which estimates are produced on the basis of a single batch of simultaneous observations. Simple-sample covariance estimation is reasonable as long as the number of available observations exceeds the number of tunable parameters by two or three orders of magnitude. Not much is known at present about model error associated with actual forecast systems. Our scheme can be used to estimate some important statistical model error parameters such as regionally averaged variances or characteristic correlation length scales. The advantage of the single-sample approach is that it does not rely on any assumptions about the temporal behavior of the covariance parameters: time-dependent parameter estimates can be continuously adjusted on the basis of current observations. This is of practical importance since it is likely to be the case that both model error and observation error strongly depend on the actual state of the atmosphere. The single-sample estimation scheme can be incorporated into any four-dimensional statistical data assimilation system that involves explicit calculation of forecast error covariances, including optimal interpolation (OI) and the simplified Kalman filter (SKF). The computational cost of the scheme is high but not prohibitive; on-line estimation of one or two covariance parameters in each analysis box of an operational bozed-OI system is currently feasible. A number of numerical experiments performed with an adaptive SKF and an adaptive version of OI, using a linear two-dimensional shallow-water model and artificially generated model error are described. The performance of the nonadaptive versions of these methods turns out to depend rather strongly on correct specification of model error parameters. These parameters are estimated under a variety of conditions, including uniformly distributed model error and time-dependent model error statistics.

Dee, Dick P.↗
NASA NTRS2023

Extensive Global Wetland Loss Over the Past Three Centuries

Wetlands have long been drained for human use, thereby strongly affecting greenhouse gas fluxes, flood control, nutrient cycling and biodiversity. Nevertheless, the global extent of natural wetland loss remains remarkably uncertain. Here, we reconstruct the spatial distribution and timing of wetland loss through conversion to seven human land uses between 1700 and 2020, by combining national and subnational records of drainage and conversion with land-use maps and simulated wetland extents. We estimate that 3.4 million km 2 (confidence interval 2.9–3.8) of inland wetlands have been lost since 1700, primarily for conversion to croplands. This net loss of 21% (confidence interval 16–23%) of global wetland area is lower than that suggested previously by extrapolations of data disproportionately from high-loss regions. Wetland loss has been concentrated in Europe, the United States and China, and rapidly expanded during the mid-twentieth century. Our reconstruction elucidates the timing and land-use drivers of global wetland losses, providing an improved historical baseline to guide assessment of wetland loss impact on Earth system processes, conservation planning to protect remaining wetlands and prioritization of sites for wetland restoration.

Etienne Fluet-Chouinard↗
NASA NTRS1998

Fiber Pulling Apparatus

The fiber optics industry has grown into a multi-billion marketplace that will continue to grow into the 21st century. Optical fiber communications is currently dominated by silica glass technology. Successful efforts to improve upon the low loss transmission characteristics of silica fibers have propelled the technology into the forefront of the communications industry. However, reaching the theoretical transmission capability of silica fiber through improved processing has still left a few application areas in which other fiber systems can provide an influential role due to specific characteristics of high theoretical transmission in the 2 - 3 micron wavelength region. One of the other major materials used for optical fibers is the systems based upon Heavy Metal Fluoride Glass (HMFG). Commercial interest is driven primarily by the potential for low loss repeaterless infrared fibers. An example of the major communications marketplace which would benefit from the long distance repeaterless capability of infrared fibers is the submarine cables which link the continents. When considering commercial interests, optical fiber systems provide a healthy industrial position which continues to expand. Major investments in the systems used for optical fiber communications have continued to increase each year and are predicted to continue well into the next century. Estimates of 8.5% compounded annually are predicted through 1999 for the North American market and 1 1 % worldwide. The growth for the optical fiber cable itself is expected to continue between 44 and 50 per cent of the optical fiber communications budget through 1999. The total budget in 1999 world-wide is expected to be in the neighborhood of $9 billion. Another survey predicts that long haul telecommunications represents 15% of a world-wide fiber optics market in 1998. The actual amount allotted to cable was not specified. However, another market research had predicted that the cable costs alone represents more than 50% of the total budget each year through 1998. A newly emerging activity is the commercial development of doped optical fibers which can be pumped by laser diodes to provide amplification of the communication signals. This technology is newly emerging and will be developed for commercial interests in the United States by Galileo Electro-optical Incorporated in Sturbridge, MA on a license from British Telecom. Long repeaterless communication links provide the biggest stimulus for this technology. As an example of the of the revenues involved in the optical fiber communications 3 industry, the current trade journal lists that for the fiscal years, 1991 - 1994, 185 separate undersea links were established. In addition, another 105 links are planned through 1998. The distribution of revenues involved in the undersea installations is roughly $8.5 billion through 1993 and another $13 billion planned through 1998. A large portion of the future activity (34%) is planned for Southeast Asia and the Pacific Region. Other examples of the commercial utility of optical fiber networks is given in a recent scientific symposium in which the outlook for HMFG infrared fiber was determined to be very bright.Another area of interest lies in the use of fiber optics for laser surgery delivery systems.

Workman, Gary L.↗
NASA NTRS2002

NASA Tech Briefs, April 2002

The contents include: 1) Application Briefs; 2) Sneak Preview of Sensors Expo; 3) The Complexity of the Diagnosis Problem; 4) Design Concepts for the ISS TransHab Module; 5) Characteristics of Supercritical Transitional Mixing Layers; 6) Electrometer for Triboelectric Evaluation of Materials; 7) Infrared CO2 Sensor With Built-In Calibration Chambers; 8) Solid-State Potentiometric CO Sensor; 9) Planetary Rover Absolute Heading Detection Using a Sun Sensor; 10) Concept for Utilizing Full Areas of STJ Photodetector Arrays; 11) Development of Cognitive Sensors; 12) Enabling Higher-Voltage Operation of SOl CMOS Transistors; 13) Estimating Antenna-Pointing Errors From Beam Squints; 14) Advanced-Fatigue-Crack-Growth and Fracture- Mechanics Program; 15) Software for Sequencing Spacecraft Actions; 16) Program Distributes and Tracks Organizational Memoranda; 16) Flat Membrane Device for Dehumidification of Air; 17) Inverted Hindle Mount Reduces Sag of a Large, Precise Mirror; 18) Heart-Pump-Outlet/Cannula Coupling; 19) Externally Triggered Microcapsules Release Drugs In Situ; 20) Combinatorial Drug Design Augmented by Information Theory; 21) Multiple-Path-Length Optical Absorbance Cell; 22) Model of a Fluidized Bed Containing a Mixture of Particles; 23) Refractive Secondary Concentrators for Solar Thermal Systems; 24) Cold Flow Calorimeter; 25) Methodology for Tracking Hazards and Predicting Failures; 26) Estimating Heterodyne-Interferometer Polarization Leakage; 27) An Efficient Algorithm for Propagation of Temporal- Constraint Networks; 28) Software for Continuous Replanning During Execution; 29) Surface-Launched Explorers for Reconnaissance/Scouting; 30) Firmware for a Small Motion-Control Processor; 31) Gear Bearings and Gear-Bearing Transmissions; and 32) Linear Dynamometer With Variable Stroke and Frequency.

Source record↗
NASA NTRSDate not supplied

Spaceborne Lidar Retrievals of PM2.5 for Air Quality Studies and Applications

Fine particulate matter (PM2.5) substantially contributes to air pollution and negatively affects human health. While many studies have investigated the use of passive column-integrated aerosol optical depth to infer surface PM2.5, the use of lidar observations for air quality characterization is not nearly as extensive. Lidar measurements are critical, however, due to the vertical aerosol information they provide, including near the surface. In this presentation, we first provide an overview of various lidar-based approaches for estimating PM2.5 concentrations and then discuss how lidar measurements can assist other air quality applications. For example, estimates of PM2.5 have been obtained in a physics-based approach through CALIOP near-surface aerosol extinction retrievals, assumptions on the mass extinction efficiency, and incorporating other parameters (an aerosol hygroscopic growth factor and PM2.5/PM10 ratio). Application of this algorithm over the contiguous United States (CONUS) from 2006 to 2018 yielded larger PM2.5 values over the eastern and western CONUS (~10-15 μg/m³) and lower PM2.5 levels in the central CONUS (~5 μg/m³). These spatial patterns were similar to those from gridded PM2.5 concentrations obtained through in situ measurements at ground stations operated by the US Environmental Protection Agency. In another approach, the Cloud Aerosol Transport System (CATS) lidar was used with the Goddard Earth Observing System (GEOS) model in a 1D ensemble-based variational technique to obtain PM2.5 over the US and Europe, and the spatial patterns of the CATS/GEOS based PM2.5 concentrations generally captured those from surface stations (with corresponding hourly EPA PM2.5 vs CATS PM2.5 statistics of R=0.4 and bias=1.5 μg/m³). In our recent work, as part of the Models, In situ, and Remote sensing of Aerosols (MIRA) Working Group, we have applied both the CALIOP and CATS/GEOS based approaches over the highly polluted country of India during the post-monsoon season (September-October 2016). We derived elevated levels of two-month mean PM2.5 (~100 μg/m³) in northern India, especially near New Delhi. These high PM2.5 concentrations in the Indo-Gangetic plain are driven in large part from the seasonal burning of crop residue and meteorological conditions typical at this time of the year, such as low wind speeds and a shallow boundary layer. While the satellite-derived PM2.5 moderately replicates (R = ~0.7-0.9) the spatial variability in the two-month mean of surface in situ PM2.5 from monitoring sites operated by the Central and State Pollution Control Boards, we show results from specific scenes for which there are large deviations between the satellite-derived PM2.5 and in situ measurements. Other current work on this topic focuses on developing PM2.5 estimates using airborne high spectral resolution lidar measurements through machine learning regression algorithms and involves several parameters (e.g., aerosol extinction, color ratio, lidar ratio). Application of this method over major metropolitan areas in the US and Asia have resulted in high correlations (R = 0.93) with surface measurements. This airborne lidar approach can be adapted to spaceborne lidar measurements, and all three of these approaches can be applied to ESA’s EarthCARE Atmospheric Lidar instrument, setting the stage for the future Cloud Aerosol Lidar for Global Scale Observations of the Ocean-Land Atmosphere System (CALIGOLA) mission. Ultimately, beyond estimates of PM2.5, the aerosol vertical distribution from lidars can benefit studies involving passive sensor approaches for PM2.5 proxies (including from geostationary satellites), wildfire smoke plume injection heights, volcanic emissions (e.g., ash height retrievals), and aerosol/air quality model assimilation, evaluation, and forecasts.

Travis D Toth↗
DOE OSTI2012

Final Report on HOLODEC 2 Technology Readiness Level

During the period of this project, the Holographic Detector for Clouds 2 (HOLODEC 2) instrument has advanced from a laboratory-proven instrument with some initial field testing to a fully flight-tested instrument capable of providing useful cloud microphysics measurements. This can be summarized as 'Technology Readiness Level 8: Technology is proven to work - Actual technology completed and qualified through test and demonstration.' As part of this project, improvements and upgrades have been made to the optical system, the instrument power control system, the data acquisition computer, the instrument control software, the data reconstruction and analysis software, and some of the basic algorithms for estimating basic microphysical variables like droplet diameter. Near the end of the project, the instrument flew on several research flights as part of the IDEAS 2011 project, and a small sample of data from the project is included as an example. There is one caveat in the technology readiness level stated above: the upgrades to the instrument power system were made after the flight testing, so they are not fully field proven. We anticipate that there will be an opportunity to fly the instrument as part of the IDEAS project in fall 2012.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

HAMscope: a snapshot Hyperspectral Autofluorescence Miniscope for real-time molecular imaging

We introduce HAMscope, a compact, snapshot hyperspectral autofluorescence miniscope that enables real-time, label-free molecular imaging in a wide range of biological systems. By integrating a thin polymer diffuser into a widefield miniscope, HAMscope spectrally encodes each frame and employs a probabilistic deep learning framework to reconstruct 30-channel hyperspectral stacks (452-703 nm) or directly infer molecular composition maps from single images. A scalable multi-pass U-Net architecture with transformer-based attention and per pixel uncertainty estimation enables high spatio-spectral fidelity (mean absolute error ∼0.0048) at video rates. While initially demonstrated in plant systems, including lignin, chlorophyll, and suberin imaging in intact poplar and cork tissues, the platform is readily adaptable to other applications such as neural activity mapping, metabolic profiling, and histopathology. We show that the system generalizes to out-of-distribution tissue types and supports direct molecular mapping without the need for spectral unmixing. HAMscope establishes a general framework for compact, uncertainty-aware spectral imaging that combines minimal optics with advanced deep learning, offering broad utility for real-time biochemical imaging across neuroscience, environmental monitoring, and biomedicine.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2001

Application of Remote Sensing to Assess the Impact of Short Term Climate Variability on Coastal Sedimentation

The University of Wisconsin and Louisiana State University have teamed to study the forcing of winter season cold frontal wind systems on sediment distribution patterns and geomorphology in the Louisiana coastal zone. Wind systems associated with cold fronts have been shown to modify coastal circulation and resuspend sediments along the microtidal Louisiana coast. The assessment includes quantifying the influence of cumulative winter season atmospheric forcing (through surface wind observations) from year to year in response to short term climate variability, such as El Nino events. A correlation between winter cyclone frequency and the strength of El Nino events has been suggested. The atmospheric forcing data are being correlated to geomorphic measurements along western Louisiana's prograding muddy coast. Remote sensing data is being used to map and track sediment distribution patterns for various wind conditions. Transferring a suspended sediment concentration (SSC) algorithm to EOS MODIS observations will enable estimates of SSC in case 2 waters over the global domain. Progress in Year 1 of this study has included data collection and analysis of wind observations for atmospheric forcing characterization, a field activity (TX-2001) to collect in situ water samples with co-incident remote sensing measurements from the NASA ER-2 based MODIS Airborne Simulator (MAS) and the EOS Terra based MODerate resolution Imaging Spectroradiometer (MODIS) instruments, aerial photography and of sediment burial pipe field measurements along the prograding muddy Chenier Plain coast of western Louisiana for documenting coastal change in that dynamic region, and routine collection of MODIS 250 in resolution data for monitoring coastal sediment patterns. The data sets are being used in a process to transfer an SSC estimation algorithm to the MODIS platform. Work is underway on assessing coastal transport for the winter 2000-01 season. Water level data for use in a Geomorphic Impact Index, which relates wind energy, water level conditions, and geomorphic change along the microtidal western Louisiana coastline is being assembled.

Moeller, Christopher C.↗
DOE OSTI2013

Nationwide water availability data for energy-water modeling

The purpose of this effort is to explore where the availability of water could be a limiting factor in the siting of new electric power generation. To support this analysis, water availability is mapped at the county level for the conterminous United States (3109 counties). Five water sources are individually considered, including unappropriated surface water, unappropriated groundwater, appropriated water (western U.S. only), municipal wastewater and brackish groundwater. Also mapped is projected growth in non-thermoelectric consumptive water demand to 2035. Finally, the water availability metrics are accompanied by estimated costs associated with utilizing that particular supply of water. Ultimately these data sets are being developed for use in the National Renewable Energy Laboratories' (NREL) Regional Energy Deployment System (ReEDS) model, designed to investigate the likely deployment of new energy installations in the U.S., subject to a number of constraints, particularly water.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Insights into the year-round vertical distribution of chlorophyll concentration in high-latitude Arctic Ocean: implications for primary production

Climate-induced rapid changes in the Arctic Ocean, such as decreasing sea ice extent and increasing water temperature, are altering nutrient and light availability, profoundly impacting primary producer growth. However, access to the high-latitude Arctic Ocean is limited, and satellite data are primarily available only during summer, making continuous in-situ data collection challenging. We collected year-round chlorophyll-a (Chl-a) concentration data in high-latitude regions using a mooring system and performed a comparative analysis with reanalysis data. Unlike previous satellite-based studies, which typically rely on surface measurements, we used the annual vertical distribution of Chl-a. These data were applied to the vertically generalized production model to accurately estimate annual primary production. The moored Chl-a concentration data showed that phytoplankton exhibited a typical subsurface chlorophyll maximum (SCM) layer as sea ice retreated in June. Contrary to the gradually deepening SCM distribution predicted by model-based reanalysis data, the SCM layer persisted for approximately 4 months. This indicates that light and nutrient conditions within the SCM layer remained stable, sustaining continuous phytoplankton growth. Annual primary production, reflecting this vertical distribution of Chl-a concentration, was 6.85 gC m −2 yr −1 . This exceeded satellite-based estimates by at least two-fold, highlighting the significant underestimation of primary production by satellite approaches. Estimating primary production while accounting for the vertical distribution of phytoplankton and light is essential for improving ecological models to better understand carbon cycle and food web changes in the Arctic Ocean, with important implications for climate change predictions.

Arctic Ocean↗
DOE OSTI2018

The Importance of Capturing Topographic Features for Modeling Groundwater Flow and Transport in Mountainous Watersheds

Abstract Physics‐based distributed hydrological models that include groundwater are widely used to understand and predict physical and biogeochemical processes within watersheds. Typically, due to computational limitations, watershed modelers minimize the number of elements used in domain discretization, smoothing or even ignoring critical topographic features. We use an idealized model to investigate the implications of mesh refinement along streams and ridges for modeling three‐dimensional groundwater flow and transport in mountainous watersheds. For varying degrees of topographic complexity level (TCL), which increases with the level of mesh refinement, and geological heterogeneity, we estimate and compare steady state baseflow discharge, mean age, and concentration of subsurface weathering products. Results show that ignoring lower‐order streams or ridges diminishes flow through local flow paths and biases higher the contribution of intermediate and regional flow paths, and biases baseflow older. The magnitude of the bias increases for systems where permeability rapidly decreases with depth and is dominated by shallow flow paths. Based on a simple geochemical model, the concentration of weathering products is less sensitive to the TCL, partially due to the thermodynamic constraints on chemical reactions. Our idealized model also reproduces the observed emergent scaling relationship between the groundwater contribution to streamflow and drainage area, and finds that this scaling relationship is not sensitive to mesh TCL. The bias effects have important implications for the use of hydrological models in the interpretation of environmental tracer data and the prediction of biogeochemical evolution of stream water in mountainous watersheds.

Wang, Chao↗
DOE OSTI2014

New superconducting toroidal magnet system for IAXO, the international AXion observatory

Axions are hypothetical particles that were postulated to solve one of the puzzles arising in the standard model of particle physics, namely the strong CP (Charge conjugation and Parity) problem. The new International AXion Observatory (IAXO) will incorporate the most promising solar axions detector to date, which is designed to enhance the sensitivity to the axion-photon coupling by one order of magnitude beyond the limits of the current state-of-the-art detector, the CERN Axion Solar Telescope (CAST). The IAXO detector relies on a high-magnetic field distributed over a very large volume to convert solar axions into X-ray photons. Inspired by the successful realization of the ATLAS barrel and end-cap toroids, a very large superconducting toroid is currently designed at CERN to provide the required magnetic field. This toroid will comprise eight, one meter wide and twenty one meter long, racetrack coils. The system is sized 5.2 m in diameter and 25 m in length. Its peak magnetic field is 5.4 T with a stored energy of 500 MJ. The magnetic field optimization process to arrive at maximum detector yield is described. In addition, materials selection and their structure and sizing has been determined by force and stress calculations. Thermal loads are estimated to size the necessary cryogenic power and the concept of a forced flow supercritical helium based cryogenic system is given. A quench simulation confirmed the quench protection scheme.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

Halo formation and self-pinching of an electron beam undergoing the Weibel instability

The collisionless Maxwellization of the energy distribution of an electron beam undergoing Weibel filamentation instability in a dense background plasma is demonstrated. While binary collisions between discrete charged particles are usually responsible for establishing the Maxwell-Boltzmann distribution (MBD) of non-equilibrium plasmas, we demonstrate that the same effect is achieved through collective collisions between multiple beam filaments. The final state of the filaments' merger is a single pinched beam surrounded by a wide halo. An analytic model for the equilibrated beam is developed and used to estimate spatial profiles of the pinched beam and its halo, the temperature, and the magnetic field. Results of analytical theory agree well with those of particle-in-cell simulations. Deviations from the MBD are explained by incomplete Maxwellization of the electrons with high and low transverse energies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

Investigation on the Core Bypass Flow in a Very High Temperature Reactor

Uncertainties associated with the core bypass flow are some of the key issues that directly influence the coolant mass flow distribution and magnitude, and thus the operational core temperature profiles, in the very high-temperature reactor (VHTR). Designers will attempt to configure the core geometry so the core cooling flow rate magnitude and distribution conform to the design values. The objective of this project is to study the bypass flow both experimentally and computationally. Researchers will develop experimental data using state-of-the-art particle image velocimetry in a small test facility. The team will attempt to obtain full field temperature distribution using racks of thermocouples. The experimental data are intended to benchmark computational fluid dynamics (CFD) codes by providing detailed information. These experimental data are urgently needed for validation of the CFD codes. The following are the project tasks: (1) Construct a small-scale bench-top experiment to resemble the bypass flow between the graphite blocks, varying parameters to address their impact on bypass flow. Wall roughness of the graphite block walls, spacing between the blocks, and temperature of the blocks are some of the parameters to be tested. (2) Perform CFD to evaluate pre- and post-test calculations and turbulence models, including sensitivity studies to achieve high accuracy. (3) Develop the state-of-the art large eddy simulation (LES) using appropriate subgrid modeling. (4) Develop models to be used in systems thermal hydraulics codes to account and estimate the bypass flows. These computer programs include, among others, RELAP3D, MELCOR, GAMMA, and GAS-NET. Actual core bypass flow rate may vary considerably from the design value. Although the uncertainty of the bypass flow rate is not known, some sources have stated that the bypass flow rates in the Fort St. Vrain reactor were between 8 and 25 percent of the total reactor mass flow rate. If bypass flow rates are on the high side, the quantity of cooling flow through the core may be considerably less than the nominal design value, causing some regions of the core to operate at temperatures in excess of the design values. These effects are postulated to lead to localized hot regions in the core that must be considered when evaluating the VHTR operational and accident scenarios.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2013

High-Performance Computing for Real-Time Grid Analysis and Operation

Power grids worldwide are undergoing an unprecedented transition as a result of grid evolution meeting information revolution. The grid evolution is largely driven by the desire for green energy. Emerging grid technologies such as renewable generation, smart loads, plug-in hybrid vehicles, and distributed generation provide opportunities to generate energy from green sources and to manage energy use for better system efficiency. With utility companies actively deploying these technologies, a high level of penetration of these new technologies is expected in the next 5-10 years, bringing in a level of intermittency, uncertainties, and complexity that the grid did not see nor design for. On the other hand, the information infrastructure in the power grid is being revolutionized with large-scale deployment of sensors and meters in both the transmission and distribution networks. The future grid will have two-way flows of both electrons and information. The challenge is how to take advantage of the information revolution: pull the large amount of data in, process it in real time, and put information out to manage grid evolution. Without addressing this challenge, the opportunities in grid evolution will remain unfulfilled. This transition poses grand challenges in grid modeling, simulation, and information presentation. The computational complexity of underlying power grid modeling and simulation will significantly increase in the next decade due to an increased model size and a decreased time window allowed to compute model solutions. High-performance computing is essential to enable this transition. The essential technical barrier is to vastly increase the computational speed so operation response time can be reduced from minutes to seconds and sub-seconds. The speed at which key functions such as state estimation and contingency analysis are conducted (typically every 3-5 minutes) needs to be dramatically increased so that the analysis of contingencies is both comprehensive and real time. An even bigger challenge is how to incorporate dynamic information into real-time grid operation. Today’s online grid operation is based on a static grid model and can only provide a static snapshot of current system operation status, while dynamic analysis is conducted offline because of low computational efficiency. The offline analysis uses a worst-case scenario to determine transmission limits, resulting in under-utilization of grid assets. This conservative approach does not necessarily lead to reliability. Many times, actual power grid scenarios are not studied, and they will push the grid over the edge and resulting in outages and blackouts. This chapter addresses the HPC needs in power grid analysis and operations. Example applications such as state estimation and contingency analysis are given to demonstrate the value of HPC in power grid applications. Future research directions are suggested for high performance computing applications in power grids to improve the transparency, efficiency, and reliability of power grids.

Contingency Analysis↗
DOE OSTI2017

Spatial representation of organic carbon and active-layer thickness of high latitude soils in CMIP5 earth system models

Soil properties such as soil organic carbon (SOC) stocks and active-layer thickness are used in earth system models (F.SMs) to predict anthropogenic and climatic impacts on soil carbon dynamics, future changes in atmospheric greenhouse gas concentrations, and associated climate changes in the permafrost regions. Accurate representation of spatial and vertical distribution of these soil properties in ESMs is a prerequisite for redudng existing uncertainty in predicting carbon-climate feedbacks. We compared the spatial representation of SOC stocks and active-layer thicknesses predicted by the coupled Modellntercomparison Project Phase 5 { CMIP5) ESMs with those predicted from geospatial predictions, based on observation data for the state of Alaska, USA. For the geospatial modeling. we used soil profile observations {585 for SOC stocks and 153 for active-layer thickness) and environmental variables (climate, topography, land cover, and surficial geology types) and generated fine-resolution (50-m spatial resolution) predictions of SOC stocks (to 1-m depth) and active-layer thickness across Alaska. We found large inter-quartile range (2.5-5.5 m) in predicted active-layer thickness of CMIP5 modeled results and small inter-quartile range (11.5-22 kg m-2) in predicted SOC stocks. The spatial coefficient of variability of active-layer thickness and SOC stocks were lower in CMIP5 predictions compared to our geospatial estimates when gridded at similar spatial resolutions (24.7 compared to 30% and 29 compared to 38%, respectively). However, prediction errors. when calculated for independent validation sites, were several times larger in ESM predictions compared to geospatial predictions. Primaly factors leading to observed differences were ( 1) lack of spatial heterogeneity in ESM predictions, (2) differences in assumptions concerning environmental controls, and (3) the absence of pedogenic processes in ESM model structures. Our results suggest that efforts to incorporate these factors in F.SMs should reduce current uncertainties associated with ESM predictions of carbon-climate feedbacks.

Active-layer thickness↗