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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Probabilistic Survivability Versus Time Modeling

This technical paper documents Kennedy Space Centers Independent Assessment team work completed on three assessments for the Ground Systems Development and Operations (GSDO) Program to assist the Chief Safety and Mission Assurance Officer (CSO) and GSDO management during key programmatic reviews. The assessments provided the GSDO Program with an analysis of how egress time affects the likelihood of astronaut and worker survival during an emergency. For each assessment, the team developed probability distributions for hazard scenarios to address statistical uncertainty, resulting in survivability plots over time. The first assessment developed a mathematical model of probabilistic survivability versus time to reach a safe location using an ideal Emergency Egress System at Launch Complex 39B (LC-39B); the second used the first model to evaluate and compare various egress systems under consideration at LC-39B. The third used a modified LC-39B model to determine if a specific hazard decreased survivability more rapidly than other events during flight hardware processing in Kennedys Vehicle Assembly Building (VAB).Based on the composite survivability versus time graphs from the first two assessments, there was a soft knee in the Figure of Merit graphs at eight minutes (ten minutes after egress ordered). Thus, the graphs illustrated to the decision makers that the final emergency egress design selected should have the capability of transporting the flight crew from the top of LC 39B to a safe location in eight minutes or less. Results for the third assessment were dominated by hazards that were classified as instantaneous in nature (e.g. stacking mishaps) and therefore had no effect on survivability vs time to egress the VAB. VAB emergency scenarios that degraded over time (e.g. fire) produced survivability vs time graphs that were line with aerospace industry norms.

Joyner, James J., Sr.↗
NASA NTRSDate not supplied

A Field Campaign to Study Lightning that Ignites the Bush

The impact of recent wildland fires in the United States and Australia have received much attention in the past several years. As a result, NASA has developed a new Earth Science program to better understand, predict, and manage this phenomenon, as well as a future suborbital mission to studying pyro-cumulonimbus clouds and their effects on the Earth system. In Australia, government and philanthropic stakeholders are supporting a series of field campaigns over the next few years to inform science requirements and advance technology for a future satellite mission to monitor Bushfires. Lightning flashes are a major source of wildland fires, but uncertainties remain about the physical characteristics of lightning and their parent thunderstorms responsible for igniting wildfires. It was long believed that most lightning-ignited wildfires are largely caused by positive flashes to the ground (CGs) and multi-stroke CGs, but a recent study looking at 26-years of NLDN data indicate otherwise. Perhaps this finding is due to statistical chance—there are more negative CGs that occur globally—or perhaps there is some unknown electrical property of the thundercloud from which the igniting flashes emanate. Regardless, new observing strategies are needed. Additionally, long continuing current CGs, which are more likely to ignite a fire, can elude detection by operational ground-based lightning location systems, making it difficult to efficiently identify potential wildland fires and manage them before they have adverse impacts. To address these science and operational gaps, NASA’s Lightning Mapping Array along with electric and magnetic field change meters will be deployed for the Australian Bushfire campaign to document the electrical structure and properties of thunderstorms and lightning that occur in a wildland fire susceptible region. Another aspect of the campaign will be the use of NASA’s airborne lightning observatory, which includes a spectrometer and high-speed imager, to document the radiometric attributes of these flashes coincident with the ground-based RF observations. These observations will be used to better understand the properties of lightning that ignite wildland fires as well as inform design of a lightning detection system for the future Bushfire Monitoring satellite mission.

lightning↗
NASA NTRS2021

Highland Lakes Water Resources: Using NASA Earth Observations to Improve Detection Systems for Harmful Algal Events in the Highland Lakes in Central Texas

Beginning in 2019, harmful algal events in Austin, Texas, caused canine deaths in the Lady Bird Lake and Lake Travis reservoirs. These reservoirs are part of the larger Highland Lakes chain, managed by the Lower Colorado River Authority (LCRA) and the City of Austin Department of Watershed Protection (CoA DWP), which fulfill municipal, commercial, and agricultural water demands. Given the recent increase in favorable algal event conditions in central Texas, the LCRA and CoA DWP partnered with NASA DEVELOP to improve algal event early-warning systems through the application of remote sensing and machine learning. An Earth observation-based algal monitoring system will assist the responsible agencies in predicting algal conditions and communicating hazards to the public. The NASA DEVELOP team utilized Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data to produce products including chlorophyll-a concentrations, cyanobacteria detections, turbidity, and water surface temperature. Chlorophyll-a concentrations were retrieved with a pre-trained machine learning model (mixture density network) and spectral indices, while the other products were derived from spectral indices. In situ field data were used to validate and quantify uncertainties for each product. The validations show strong correlations for chlorophyll-a and water surface temperature. Time series analyses of chlorophyll-a concentrations show peaks in the severe drought years (2015 and 2016). This project's resulting products enable monitoring of environmental proxies relevant to algal event presence in the Highland Lakes chain and will ultimately support water management, decision making, and risk communication.

Kaitlynn Hietpas↗
DOE OSTI2022

A multistage distributionally robust optimization approach to water allocation under climate uncertainty

This paper investigates a Multistage Distributionally Robust Optimization (MDRO) approach to water allocation under climate uncertainty. The MDRO is formed by creating sets of conditional distributions (called conditional ambiguity sets) on a finite scenario tree. The distributions in the conditional ambiguity sets remain close to a nominal conditional distribution according a ø-divergence (e.g., Kullback-Leibler divergence, Hellinger distance, Burg entropy, etc.). Here, the paper discusses a decomposition algorithm to solve the resulting MDRO with ø-divergences, which uses the dual formulation and solves only linear subproblems instead of convex ones. Some properties of the algorithm such as generating feasible policies and valid upper/lower bounds are established. The paper then applies the modeling and solution techniques to allocate water in a rapidly-developing area of Tucson, Arizona. Tucson, like many arid and semi-arid regions around the world, faces considerable uncertainty in its ability to provide water for its citizens in the future. The primary sources of uncertainty in the Tucson region include (1) unpredictable population growth, (2) the availability of water from the Colorado River, and (3) the effects of climate variability on water consumption. This paper integrates forecasts for all these sources of uncertainty into a single optimization model for robust and sustainable water allocation. Then, it uses this model to analyze the value of constructing additional treatment facilities to reduce future water shortages. The results indicate that the MDRO approach can be very valuable for water managers by providing insights to minimize their risks and help them plan for the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

AI Improves the Accuracy, Reliability, and Economic Value of Continental‐Scale Flood Predictions

Accurate flood early warnings are critical to minimize damage and loss of life. Current large‐scale operational forecasting systems, however, have limited accuracy, description of uncertainty, and computational efficiency. While Artificial intelligence (AI) can address these limitations in principle, the accuracy and reliability of AI forecasts have thus far proven insufficient. Here we present a novel hybrid framework that integrates AI‐based machinery termed Errorcastnet (ECN) with the National Water Model (NWM) to showcase the potential of ensemble AI flood forecasts over the contiguous U.S. ECN boosts prediction accuracy four‐ to six‐fold across lead times of 1–10 days, while providing uncertainty quantification. It also outperforms Google's state‐of‐the‐art global AI model. ECN‐based forecasts offer superior economic value (up to four‐fold) for decision‐making as compared to those from NWM alone. ECN performs well in varied ecoregions, physiography, and land management conditions. The framework is computationally efficient, enabling national‐scale ensemble forecasts in minutes.

artificial intelligence↗
NASA NTRS2019

How Much Testing is Needed to Manage Supportability Risks for Beyond-LEO Missions?

Supportability will be a significantly greater driver of cost and risk for future deep-space crewed missions than it has been in the past. Spares requirements and maintenance risk mitigation in particular present an unprecedented challenge for missions beyond Low Earth Orbit (LEO), since, for the first time in human spaceflight history, crews will be weeks or months away from resupply or a safe return to Earth in the event of an abort. Under these conditions, failure rates are a critical parameter that must be well-understood in order to manage logistics and risk effectively. However, failure rates cannot be measured directly, and can only be estimated based on past experience and test results. Previous research has shown that International Space Station (ISS) operational experience has provided significant benefits to future missions by reducing uncertainty and improving accuracy in failure rate estimates, resulting in significant reductions in mass and risk for beyond-LEO missions. This paper updates and expands on that research and quantifies the potential value of continued testing for future mission supportability. Frequentist and Bayesian models for evaluating, validating, and updating failure rate estimates are described, and are combined with supportability models to examine potential impacts of additional operating experience for future missions in terms of logistics mass reduction. The implications of these results for technology development, system design, and program planning are discussed along with lessons learned and recommendations for future system development. In the end, there is no simple answer to the question of how much testing is required, but the models described in this paper provide a way to evaluate the potential impacts of testing in order to inform test planning.

Andrew C Owens↗
DOE OSTI2021

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

System Model Calculations for the Active Trenches of the Low-Level Burial Grounds, Hanford Site, Washington

Two lined trenches (Trench 31 and 34) located within the 200 West Low-Level Burial Ground (LLBG) area of the U.S. Department of Energy’s (DOE) Hanford Site are designated for permanent disposal of low-level radioactive waste (LLW) and mixed low-level radioactive waste (MLLW). In accordance with DOE O 435.1, Radioactive Waste Management, radioactive waste shall be managed and disposed in a manner that is protective of worker, public health and safety, and the environment. DOE O 435.1 requires a site-specific radiological performance assessment (PA) that includes calculations of potential releases and subsequent doses to members of the public for a period of 1,000 years after closure of a low-level waste (LLW) disposal facility. The purpose of this environmental calculation file (ECF) is to document the various dose calculations performed to support DOE’s LLBG PA. The dose assessments are performed to evaluate the potential exposure of an all-pathway representative person to radionuclide contaminants of potential concern (COPCs) that may be released from the LLBG Trenches 31 and 34 to the point of calculation (POC) located at the outer edge of a 100 m buffer zone surrounding the trench boundary. This ECF uses inputs from other ECFs and their associated model package reports (MPRs) supporting the LLBG PA. The five major objectives of this ECF are to present the results of the following: (1) Deterministic dose assessments for both groundwater and atmospheric exposure scenarios; (2) Radon-222 (Rn-222) flux emanating at the surface from the disposed wastes; (3) Uncertainty analyses of the groundwater pathway annual dose due to uncertainty in input parameters; (4) Sensitivity analyses of the input parameters that could potentially impact the fate and transport of the contaminants and doses; (5) Doses arising from inadvertent intruder exposure scenarios.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Probabilistic Look-ahead Contingency Analysis Integration with Commercial Tool and Practical Data

This paper presents an initial effort of integrating a smart sampling-based probabilistic look-ahead contingency analysis algorithm with General Electric (GE) Grid Solutions’ commercial energy management system (EMS) tool as a proof-of-concept for a seamless research tool integration using real world large-scale grid data. With the increasing impact of random forces such as variable generation and load, their stochastic behaviors cannot be ignored. However, the current practices are still dominated by deterministic tools. They are becoming increasingly inadequate for the future grid. The developed look-ahead contingency analysis algorithm incorporates forecast errors of variable energy and load to address the challenges brought by the increasing uncertainty of power system. The algorithm can reveal the potential violations caused by the variance of variable energy and load that are not normally detected by traditional deterministic approaches. To test its performance under practical environments ( real data with real commercial tool), significant efforts have been made to prepare test cases, modify GE EMS tool, and adapt an extreme value distribution algorithm to analyze the GE EMS’s violation-only outputs. The test results clearly demonstrate the effectiveness of the developed algorithm as new transformer violations that were not previously detected have been identified. This performance provides better situational awareness to engineers for their decision-making process under uncertainty. Moreover, with the discussion of computational performance and future work, this paper has shown a clear path for integrating the probabilistic algorithm with commercial tools to make us better equipped for the changing power system.

Modeling and simulation of power systems, constrai↗
DOE OSTI2021

A machine-learning inverse model framework for rapid forecasting and history matching in unconventional reservoirs

Model-based optimization for real-time forecasting in unconventional reser-voirs requires novel methods and work?ows since the strategies and work?ows used in conventional reservoirs are either inapplicable, or prohibitively expen-sive and time-consuming. Insu?cient site data and computational expense of high-?delity simulations mean that work?ows with high-?delity simulations are not ideal for usage in comprehensive uncertainty quanti?cation stud-ies that require 1000s of forward model runs. We present an alternative, novel work?ow for unconventional reservoirs, based on the interplay between reduced-order models and machine-learning. Our physics-informed machine-learning (PIML) work?ow addresses the challenges to real-time reservoir management in uncoventionals, namely lack of data (the time-frame for which the wells have been producing), and computational expense of high-?delity modeling. We use the machine-learning paradigm of transfer-learning to bind together fast but less accurate reduced-order models with slow, but accurate high-?delity models and circumvent the di?culties inherent in the current state-of-the-art for unconventionals. Such a PIML work?ow, grounded in physics, is a viable candidate for real-time history matching and production forecasting in a fractured shale gas reservoir. The signi?cance of our approach is that while it is developed for a particu-lar well and site in the Marcelus Shale gas reservoir of the Appalachian basin (MSEEL), it is not wedded to it. We expect the same work?ow can be ap-plied to other shale formations (e.g., Woodford, Barnett, Utica, EagleFord) should site-data become available, using the same set of machine-learning techniques from transfer learning. Some ?ne-tuning (or minimal retraining of the neural networks) will be required to transfer knowledge across shale gas sites/formations but it is a clearly superior alternative to developing a new machine-learning model altogether when considering a di?erent site.

Srinivasan, Shriram↗
DOE OSTI2024

Impurity gas detection for SNF canisters using probabilistic deep learning and acoustic sensing *

Abstract Monitoring impurity gases in spent nuclear fuel (SNF) canisters is a novel structural health monitoring approach for SNF in dry storage. The SNF canisters are sealed containers that do not facilitate visual access to the inside. Acoustic sensing can be deployed by taking advantage of the pathways unobstructed by internal hardware. Although the ultrasonic time-of-flight measurement can provide valuable information, it is limited in its ability to discern the concentration of only one impurity gas. As such, deep learning algorithms, particularly convolutional neural networks (CNNs), offer a promising solution. In this study, CNN-based probabilistic deep learning models were implemented to detect and quantify multiple impurity gases in helium. An experimental platform was established to simulate canister conditions, and ultrasonic test data were collected. The presence of argon and air in helium at concentrations ranging from 0% to 1.2% at increments of 0.05% was considered. The multi-layer perceptron, decision tree, and logistic regression classifiers achieved high accuracies when distinguishing pure helium from helium with impurities. CNN with dropout layers and CNN using maximum likelihood estimation showed a similar performance, indicating their ability to capture uncertainties. The ensemble CNN model exhibited improved predictions and the ability to balance individual gas concentration by integrating 1D- and 2D-CNN models. These findings contribute probabilistic deep learning solutions for impurity gas detection and analysis within SNF canisters, thus ensuring safe storage and management of SNFs.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Sample Preparation Method for Low-Level Total 129 I Measurements by ICP-MS

Trace-level measurements of iodine’s isotopic ( 129 I and 127 I) and chemical species distributions are needed for an accurate understanding of radioiodine migration in the Hanford subsurface. Pacific Northwest National Laboratory (PNNL) previously developed a novel analytical method for iodine characterization that uses ion chromatography (IC) coupled to inductively coupled mass spectrometry (ICP-MS). While the method can measure speciated forms of 129 I at levels below the drinking water standard, an interference from molybdenum (Mo) prevents the assay from quantifying the $\underline{total}$ 129 I concentration in many Hanford sample matrices. In this work, solvent extraction was evaluated as a sample preparation method for eliminating the Mo interference. A series of 10 experiments was conducted in which solutions containing known amounts of iodate or iodide were treated by solvent extraction, and the extracted solutions were analyzed for total iodine concentrations by ICP MS. Several extraction parameters such as reagent concentrations and chemical reaction times were systematically adjusted in attempts to optimize the extraction process. While solvent extraction was shown to be effective at removing Mo, there was a consistent inability to recover more than approximately 75% of the total iodine in most experiments. This would reduce the ability to detect 129 I at levels near the drinking water standard. Additionally, the extraction efficiencies in several experiments were highly variable, suggesting that solvent extraction could add significant uncertainty to radioiodine measurements. We recommend evaluating ion exchange as an alternative sample preparation approach in fiscal year (FY) 2024.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Interconnected Risks in Electricity Systems: Understanding Research Challenges, Needs, and Partnerships

The U.S. federal government has a potential role in mitigating the most poorly addressed risks across electricity markets and interdependent systems. Electricity system risks stem from extreme acute shocks (e.g., cyberattacks, extreme weather, and supply chain disruptions) and chronic stressors (e.g., global economic competition, uncertainties surrounding emerging technologies, climate change, and related conflicts). These acute shocks and chronic stressors can result in dire consequences for society, underscoring the need for better information for decision makers in both public and private sectors. Best practices and a shared understanding of risk management and risk quantification must be rapidly developed and disseminated. Informed by a quantitative treatment of risk to electrical infrastructure and interdependent systems, decision makers can more prudently and efficiently prioritize mitigation initiatives to maximize benefits to government, businesses, and the public.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2006

Integrated Vehicle Health Management (IVHM) for Aerospace Systems

To achieve NASA's ambitious Integrated Space Transportation Program objectives, aerospace systems will implement a variety of new concept in health management. System level integration of IVHM technologies for real-time control and system maintenance will have significant impact on system safety and lifecycle costs. IVHM technologies will enhance the safety and success of complex missions despite component failures, degraded performance, operator errors, and environment uncertainty. IVHM also has the potential to reduce, or even eliminate many of the costly inspections and operations activities required by current and future aerospace systems. This presentation will describe the array of NASA programs participating in the development of IVHM technologies for NASA missions. Future vehicle systems will use models of the system, its environment, and other intelligent agents with which they may interact. IVHM will be incorporated into future mission planners, reasoning engines, and adaptive control systems that can recommend or execute commands enabling the system to respond intelligently in real time. In the past, software errors and/or faulty sensors have been identified as significant contributors to mission failures. This presentation will also address the development and utilization of highly dependable sohare and sensor technologies, which are key components to ensure the reliability of IVHM systems.

Baroth, Edmund C.↗
NASA NTRS2014

Concepts to Support HRP Integration Using Publications and Modeling

Initial efforts are underway to enhance the Human Research Program (HRP)'s identification and support of potential cross-disciplinary scientific collaborations. To increase the emphasis on integration in HRP's science portfolio management, concepts are being explored through the development of a set of tools. These tools are intended to enable modeling, analysis, and visualization of the state of the human system in the spaceflight environment; HRP's current understanding of that state with an indication of uncertainties; and how that state changes due to HRP programmatic progress and design reference mission definitions. In this talk, we will discuss proof-of-concept work performed using a subset of publications captured in the HRP publications database. The publications were tagged in the database with words representing factors influencing health and performance in spaceflight, as well as with words representing the risks HRP research is reducing. Analysis was performed on the publication tag data to identify relationships between factors and between risks. Network representations were then created as one type of visualization of these relationships. This enables future analyses of the structure of the networks based on results from network theory. Such analyses can provide insights into HRP's current human system knowledge state as informed by the publication data. The network structure analyses can also elucidate potential improvements by identifying network connections to establish or strengthen for maximized information flow. The relationships identified in the publication data were subsequently used as inputs to a model captured in the Systems Modeling Language (SysML), which functions as a repository for relationship information to be gleaned from multiple sources. Example network visualization outputs from a simple SysML model were then also created to compare to the visualizations based on the publication data only. We will also discuss ideas for building upon this proof-of-concept work to further support an integrated approach to human spaceflight risk reduction.

Mindock, J.↗
NASA NTRS2021

Perceptions of Climate Risk and Use of Climate Risk Information by Natural Resource Conservation Stakeholders Participating in ADVANCE Projects in Asia and Latin America

Integrating climate risk information into resilience-building activities in the field is important to ensure that adaptation is based on the best available science. Despite this, many challenges exist when developing, communicating, and incorporating climate risk information. There are limited resources on how stakeholders perceive risks, use risk information, and what barriers exist to limit knowledge integration. This paper seeks to define the following: 1) What do conservation stakeholders consider to be the most significant climate risks they face now and possibly in the future? 2) What have been the most significant barriers to their using climate risk information? 3) What sources and types of knowledge would be most useful for these managers to overcome these barriers? A survey was conducted among stakeholders (n = 224) associated with World Wildlife Fund projects in tropical and subtropical countries. A very high proportion of stakeholders used climate risk information and yet faced integration-related challenges, which included too much uncertainty and the lack of a relevant scale for planning. The main factors preventing the use of climate risk information in decision-making were unavailability of climate risk information, no or limited financial or human resources available to respond, lack of organizational mandate or support, and no or limited institutional incentives. Comparing perceived current and future risks revealed a decline in concern for some future climate hazards. Survey respondents identified scientific reports, climate scientists, and online sources as the most useful information sources of climate risk information, while (i) maps and illustrations; (ii) scenarios format; and (iii) data tables, graphs, and charts were identified as user-friendly formats.

Climate risk↗
NASA NTRSDate not supplied

Medium-Range River Flood Forecasts Using a Long Short-Term Memory Network

River flooding and the impacts are a concern for decision makers throughout the United States. Accurate medium-range forecasts (~3-7 days) are critical for providing advanced outlooks to emergency management officials. Unfortunately, accurately forecasting rainfall-runoff and the subsequent rise and fall within rivers remain a challenge in hydrological modeling. While complex physical modeling systems are the standard for representing the hydrological processes, they are computationally demanding and can require extensive calibration. Further, uncertainties remain in the model parameters and input data. The use of machine learning can reduce some of the computational demand while maintaining high accuracy. Therefore, this project makes use of a Long Short-Term Memory (LSTM) network which explicitly accounts for the time-dependent nature of rainfall-runoff modeling. The developed LSTM was trained to predict river gauge height, or stage height, based on time-lagged input features which include: gauge height to initialize the model, the NASA Short-term Prediction Research and Transition Center’s instance of the Land Information System (SPoRT-LIS) relative soil moisture to describe the rainfall infiltration rate, and 6-hr Multi-Radar Multi-Sensor quantitative precipitation estimate (MRMS QPE). The developed LSTM based system is then used to produce 7-day forecasts with a 6-hr temporal resolution using three different quantitative precipitation forecasts (QPF) from the NWS’s Weather Prediction Center (WPC), the NCEP Global Forecast System (GFS) model and the National Blend of Models (NBM). This trained modeling system has been implemented as an experimental product at over 100 different rivers in collaboration with at multiple National Weather Service (NWS) Forecast Offices and River Forecast Centers (RFC) across the eastern half of the United States. The developed LSTM model achieved average Nash-Sutcliffe efficiency (NSE) 0.89 higher than the equivalent medium-range National Water Model ensemble member forecast over a 7-day forecast. In addition to the initial development and evaluation, this project has continued to expand. While the initial model was developed for precipitation dominated basins, expansion of the project has taken it to basins effected by snow melt. This presentation will provide an overview of the project with focus on recent developments on incorporating snow melt processes into the model.

Andrew T. White↗
DOE OSTI2026

Buildings Sector Scenarios: Demand-side data to support energy system planning in the United States

The US energy system is in a period of high uncertainty about load growth, its implications for the energy generation mix, and downstream impacts on customer energy costs. In this context, there is a need for comprehensive, credible, and readily-customized projections of energy demand to ensure that planning decisions account for end-use management opportunities to improve system reliability and affordability. Here we introduce the Buildings Sector Scenarios (BSS) dataset, which includes a benchmark suite of such projections for the buildings sector — a key source of energy consumption, peak electricity demand, and consumer energy expenditures. The dataset contains projections through 2050 covering the contiguous United States (CONUS) resolved down to the county, hourly level by sector and end use for electricity demand and to the state, annual level by sector and end use for non-electric fuels. We summarize the BSS analysis workflow and the tools and datasets that support it, document key BSS scenario inputs and modeling assumptions, and outline BSS scenario outputs. We assess the technical quality of the dataset against historical surveys and projected estimates of buildings sector demand. Finally, we provide guidance on how stakeholders can access, use, and reproduce the dataset, and/or create new scenarios to explore their own analysis questions.

Langevin, Jared↗