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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Vegetation Warming Experiment: Thaw depth and dGPS locations, Utqiagvik, Alaska, 2019

Thaw depth measurements within and around warming chambers, and in ambient plots located on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska. Measurements were taken at the start and end of chamber deployment, and two intermediate times during the 2019 growth season. dGPS measurements of chamber and ambient plot locations are also included. The files included in this data package are in .csv format, and include 3 data files and 3 metadata files. This data was recorded as part of the Zero Power Warming (ZPW) vegetation warming experiment. See related data files for environmental conditions, leaf physiology, leaf traits, and landscape and plot phenocam images. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Basin & Range Investigation for Developing Geothermal Energy

Hidden geothermal systems represent a potentially prolific energy resource that could support critical U.S. public and government energy priorities. Basin and Range Investigations for Developing Geothermal Energy (BRIDGE) addressed some the challenges associated with hidden system exploration by prioritizing cost-effective exploration early on through strategic workflow and informed decision-making that mitigates early risk and shifts resources to later exploration stages (e.g., drilling). Sandia National Laboratories partnered with U.S. Navy Geothermal Office, Geologic Geothermal Group, and independent consultants, with additional collaboration with U.S. Geological Survey and private industry. The primary tool of the BRIDGE project was to deploy a regional-scale airborne electromagnetic method to investigate the shallow resistivity structure in areas with high prospectivity. This was followed up at several prospects by a multidisciplinary exploration approach, including additional geologic, geophysical and geochemical studies. A central tenet to the BRIDGE methodology is that zones of low resistivity frequently occur over geothermal systems in the Basin and Range, and when paired with other data constraints, imaging these zones can enable discovery of these systems. In addition to exploring greenfield areas (i.e., Grover Point), the BRIDGE project also flew HTEM resistivity surveys over known geothermal systems including those with established power plants (Don A. Campbell and Salt Wells) and prospects that are known to the literature but remain undeveloped, at least in part, due to a lack of understanding on the location of their producible reservoirs. BRIDGE produced a comprehensive set of data from prospects identified in the Nevada Play Fairway Analysis along with conceptual models for top ranking prospects, wherein all of the observations are used to inform an interpreted model of the system. These models present a range of possible system parameters such as temperature and size, and they are further informed by system analogues in the Basin and Range province and elsewhere. The results of this work leave space for further exploration that may now occur at prospects ‘down the list’ rather than distribution exploration resources evenly across all prospects.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Connected and Learning Based Optimal Freight Management for Efficiency

The management of the future heterogenous fleet is a complex decision-making problem. The heterogenous fleet is emerging as decarbonization technologies are deployed by fleets toward lowering the freight operation emissions in Medium and Heavy-duty vehicles. Traditionally, in fleets characterized by a homogeneous Diesel Internal Combustion Engine (ICE) powertrain, the process of fleet planning and operational optimization unfolds sequentially without the necessity to account for powertrain and vehicle-specific characteristics during dispatch decisions. Fleets with trucks less than 5 years old tend to maintain stable vehicle efficiency with minimal operational reliability risks for fleet managers. However, the landscape changes with the incorporation of emerging powertrain technologies, which lack extensive operational data and service experiences. This includes technologies like hybrid, Electric, Fuel Cell, or alternative fuel ICE. Operational decisions for fleets featuring heterogeneous powertrain technologies and facing limited access to alternative fueling and charging stations become intricate, requiring careful consideration and optimization at each dispatch. The difference in efficiency characteristics of emerging technologies, their range limitations, and the restricted availability of charging/alternative fueling infrastructure, coupled with sensitivity to driving conditions (e.g., EV range reduction in low temperatures) and their impact on component aging (such as batteries), become pivotal factors influencing the reliable and efficient freight transportation. To make the path toward low emission freight transportation efficient and reliable, an AI-assisted fleet management software is developed in this project to help fleet managers in optimizing both adoption of emerging powertrain decarbonization, connected and automated technologies and also operating the fleet after such technologies are deployed as schematically. Freight transportation requirements are different depending on the cargos to be shipped, customer requirements and regions of operations. This further highlights the need for software and digital solutions to tailor deployment and operation of emerging powertrain, connectivity, and automation technologies toward the specific fleet operation requirements. The fleet management optimizer was also integrated with a model of the fleet to simulate the operation of the fleet over 1 year of the baseline fleet operation (250,000+ shipments) indicating the significance of day-to-day variations on emissions and energy consumption of a freight transportation fleet. The results demonstrate ≥20% improvement in freight efficiency in terms of WTW CO2 per ton-mile of cargo shipments while all fleet operation constraints are enforced, and the cost (CapEx and OpEx) is minimized.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Soil Temperature and Moisture, Council Road Mile Marker 71, Seward Peninsula, Alaska, beginning 2016

Daily averages of soil temperature and moisture measured once every hour at different heights, as well as daily averages of hourly measured snow depths located at Intensive Monitoring Stations at Council Road Mile Marker 71 site. Deployed at each site is an Onset HOBO U30 data logger with five smart temperature sensors and three smart soil moisture sensors (10HS). Three sites (CN_IS_4, CN_IS_6A, CN_IS_7) are equipped with a snow depth sensor. Data are retrieved annually since 2016. Contains 53 *.CSV files including a file inventory list by year. Data files have header rows, NaN fields indicate invalid or missing data, and negative vertical offsets are above ground. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Vegetation Warming Experiment: Thaw depth and dGPS locations, Utqiagvik, Alaska, 2021

Thaw depth measurements within and around warming chambers, and in ambient plots located on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska. Measurements were taken at the start and end of chamber deployment, and two intermediate times during the 2021 growth season. dGPS measurements of chamber and ambient plot locations are also included. The files included in this data package are in .csv format, and include 2 data files and 3 metadata files. This data was recorded as part of the Zero Power Warming (ZPW) vegetation warming experiment. Other datasets under the Vegetation Warming Experiment include data for environmental conditions, leaf physiology, leaf traits, and landscape and plot phenocam images. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

U.S. Agrivoltaics Irradiance Database

This is a foundational data set for research and deployment of agrivoltaics, which is the co-location of agriculture and solar power plants on the same land. This irradiance and shading dataset can be utilized to determine the suitability of agrivoltaics configurations for a given region and crop-type. The data is hourly, 4x4 km resolution across the contiguous United States and Hawaii. It is calculated from the National Solar Radiation Database sites, using the System Advisor Model (SAM) to simulate the shading patterns for 10 common agrivoltaics configurations. Sunlight availability data is reported for 10 locations on the ground between adjacent rows of solar panels, as well as averaged across areas of interest such as the average irradiance in the edge-to-edge open area or across 3-6 planting beds. Other available metrics include the input meteorological data from the NSRDB (e.g. global horizontal irradiance, wind speed, etc.) and estimates for comparing energy and agricultural characteristic across the 10 configurations, including power output per acre or per kW installed capacity and farmable land area per acre.

14 SOLAR ENERGY↗
DOE OSTI2025

Learning from Arctic Microgrids: Cost and Resiliency Projections for Renewable Energy Expansion with Hydrogen and Battery Storage

Electricity in rural Alaska is provided by more than 200 standalone microgrid systems powered predominantly by diesel generators. Incorporating renewable energy generation and storage to these systems can reduce their reliance on costly imported fuel and improve sustainability; however, uncertainty remains about optimal grid architectures to minimize cost, including how and when to incorporate long-duration energy storage. This study implements a novel, multi-pronged approach to assess the techno-economic feasibility of future energy pathways in the community of Kotzebue, which has already successfully deployed solar photovoltaics, wind turbines, and battery storage systems. Using real community load, resource, and generation data, we develop a series of comparison models using the HOMER Pro software tool to evaluate microgrid architectures to meet over 90% of the annual community electricity demand with renewable generation, considering both battery and hydrogen energy storage. We find that near-term planned capacity expansions in the community could enable over 50% renewable generation and reduce the total cost of energy. Additional build-outs to reach 75% renewable generation are shown to be competitive with current costs, but further capacity expansion is not currently economical. We additionally include a cost sensitivity analysis and a storage capacity sizing assessment that suggest hydrogen storage may be economically viable if battery costs increase, but large-scale seasonal storage via hydrogen is currently unlikely to be cost-effective nor practical for the region considered. While these findings are based on data and community priorities in Kotzebue, we expect this approach to be relevant to many communities in the Arctic and Sub-Arctic regions working to improve energy reliability, sustainability, and security.

25 ENERGY STORAGE↗
DOE OSTI2024

Digital camera imagery for vegetation phenology, Seward Peninsula, Alaska, 2019-2021

Time lapse camera images from Kougarok mile marker (MM) 64 and Teller MM 27 NGEE-Arctic field sites on the Seward Peninsula, Alaska, captured from July 2019 to August 2021. Thirty three Wingscape Timelapse Pro cameras were deployed targeting patches of low and tall shrubs (including Alnus sp. and Salix sp.) and general vegetation and landscape views. Images from were recorded at hourly intervals from 11 AM to 2 PM, continuously for 25 months and capture vegetation phenology, snow accumulation and snow melt events. This data package includes images (*.jpg), organized by site and camera ID, and metadata with details of the cameras used, number of images recorded, start and end dates, GPS locations and example fields of view. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Hydrogen Education for a Decarbonized Global Economy (H 2 EDGE) (Final Technical Report)

The energy sector is undergoing rapid growth and transformation, creating urgent demand for a skilled workforce to support emerging technologies and resilient systems. Hydrogen is an emerging solution for “hard to abate” sectors, including heavy industry and transportation that also provides optionality for the electric system and enables long‑duration energy storage. As hydrogen scales from traditional industrial uses to new applications, success depends on knowledgeable engineers, technicians, planners, and operators safely deploying hydrogen technologies across the full value chain: production, delivery, storage, and end use. The H 2 EDGE initiative, funded by the U.S. Department of Energy’s Hydrogen and Fuel Cell Technologies Office from 2020 to 2025, was launched to prepare people for careers hydrogen and related technologies. Through its interactive approach, H 2 EDGE equipped stakeholders with knowledge and tools to build a reliable and sustainable energy future together. H 2 EDGE built a national network of academic and industry partners to deliver modular training, share open-access curricula, and engage diverse talent pipelines. The program leveraged EPRI’s extensive industry membership and expanded on the train-the-trainer model of the GridEd program to connect educators, employers, and community organizations across all regions of the U.S. New methods were introduced to more systematically map hydrogen competencies and assess curricula using traditional and AI-assisted approaches. Outcomes included delivery of professional short courses, developing university curriculum, sponsoring faculty and student projects, and engaging stakeholders through workshops, site tours, and webinars.

08 HYDROGEN↗
DOE OSTI2023

Expanded modelling scenarios to understand the role of offshore wind in decarbonizing the United States

An assessment of decarbonization pathways in energy models reveals fundamental limitations in representing factors that are relevant for practical decision-making. Although these modelling limitations are widely acknowledged, their impact on the deployment of individual power generation types is not well understood. As a result, the societal value from such generation types could be vastly misrepresented. Here we explore a wide spectrum of factors that impact offshore wind deployment in the United States using a detailed capacity expansion model. Many factors prescribe a large future role for offshore wind, yet this diverges from what models often show. We extend the typically narrow modelling context through high spatial resolution, several cost and transmission possibilities and various energy-sector policies. Further, we estimate offshore wind to constitute 1-8% (31-256 gigawatts) of total US generation by 2050. This wide range suggests an uncertain but potentially important regional role. Our expansive scenarios demonstrate how to address many limitations of decarbonization modelling.

17 WIND ENERGY↗
DOE OSTI2020

An Optimization-Based Planning Tool for On-Demand Mobility Service Operations

Regions worldwide are adopting and exploring low-speed automated electric shuttle (AES) service as an on-demand shared mobility service in dense geofenced urban areas. Building on this concept, the National Renewable Energy Laboratory (NREL) recently developed the Automated Mobility District (AMD) toolkit. The AMD toolkit—comprising of a travel micro-simulation model and an energy estimation model—estimates the mobility and energy impacts of a given shuttle configuration within an AMD. Early-stage AMD deployments need to find optimal operational configurations that include: (a) passenger capacity of an AES, (b) time-dependent routes, and (c) fleet size (AES units) to satisfy the demand for the region. This research extends the AMD toolkit functionality by developing an optimization-based planning module that will assist in the operations of AES units. We developed a constrained mixed-integer program accounting for passenger waiting time, battery range, and passenger capacity of AES units. For scalability, we demonstrated the Tabu search-based solution technique for a real-world network—a proposed AMD deployment in Greenville, South Carolina, USA. Compared to rule-based operations, our developed solution yields higher travel time and energy savings for the network at different demand levels. The sensitivity analyses for waiting time thresholds indicate nonlinearity in the system performance, underscoring the need to meet shared-use mobility user-level expectations. The developed optimization framework can be adapted and extended to accommodate different categories of shared-use on-demand mobility services.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2024

Machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado and New Mexico

Temporally continuous snow depth estimates are vital for understanding changing snow patterns in the Arctic and impacts on permafrost. We trained random forest machine learning models to predict snow depth from temperature data recorded at or just below the ground surface. Training data was collected at the Teller 27 Watershed and Kougarok 64 Hillslope during the 2021 - 2022 water year on the Seward Peninsula, Alaska using distributed temperature profiling (DTP) systems. We then applied this model to other sites where ground surface or shallow soil temperature data was available for at least one water year (see Related Datasets). Many of these temperature measurements were collocated with snow depth observations. Ground surface temperature (i.e. snow-ground interface temperature) is easy to measure using small, cheap and easy-to-deploy temperature sensors such as iButtons and TinyTags, and such measurements have previously been used to calculate a variety of snow metrics (e.g. snow onset date). However, this is the first study to estimate snow depth directly from ground surface temperature data. The present dataset contains one *.csv file which includes machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado, and New Mexico and one *.kml file including the locations of sites with snow depth predictions. No training data predictions are included in the *.csv file. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Assessing Impacts of Waves on Hub-Height Winds off the U.S. West Coast Using Lidar Buoys and Coupled Modeling Approaches

Given the importance of offshore wind energy development to the U.S. clean energy targets, it is vital to be able to characterize the wind resource in that environment accurately. Toward that end, two Bureau of Ocean Energy Management buoys equipped with Doppler lidar are being maintained by Pacific Northwest National Laboratory on behalf of the Department of Energy and deployed to regions of potential offshore wind development. In addition to standard meteorological and oceanographic measurements, the buoys document the wind profile between about 40 m and 250 m above the sea surface through Doppler lidar retrievals. After a multiyear deployment of two buoys along the U.S. East Coast, the buoys were redeployed to the U.S. West coast from 2020 – 2022 to locations near the Humboldt and Morro Bay lease areas. The buoys provide nearly continuous, multiyear datasets that can be used to evaluate predictions of hub-height (~100 m) wind speed for standard atmospheric models in the region. In the absence of measurements at the study site, offshore wind developers rely on model-based data to assess site conditions. Potential sources of model error in this environment include under-resolution or misrepresentation of coastal topographically forced flows, marine boundary layer dynamics and the evolution of their associated cloud and turbulence fields, the role of upwelling and other currents on surface heat fluxes into the boundary layer, and the impact of wave fields on surface momentum fluxes and thus the wind speed profile. In particular, most predictive models of wind speed do not predict wave fields at all, relying on parameterizations to represent their effects. In thus study, we focus on evaluating the role of wind / wave interactions on modeled hub-height wind speed and error by using the Coupled Ocean–Atmosphere–Wave–Sediment–Transport Modeling System to capture two-way interactions between an atmospheric model (Weather Research and Forecasting (WRF)) and a wave model (WAVEWATCHIII (WW3)) and compare to both stand-alone WRF and one-way coupled WRF / WW3 configurations. Our approach is similar to that used in Gaudet et al. (2022) to evaluate wind / wave coupling over the U.S. East Coast, but applied to the very different environment of the U.S. West Coast. We show examples for two cases, a cold-season frontal case and a warm-season low-level jet case. We find that wind / wave coupling makes little impact on model error for these cases at the location of the lidar buoys, for which other misrepresentations of model physics seems to be responsible for model-observation discrepancies. However, domain-wide evaluations, which also make use of the National Buoy Data Center network, show that a two-way coupling approach is less prone to systematic errors in the hub-height wind field than the one-way coupled approach. WRF resolution of kilometer-scale or less is needed to properly capture the sharp wind speed gradients that can be found along the coastline, and WW3 simulations driven by the downscaled WRF produce better bulk and spectral wave fields when compared to observations. Implications of the results for wind resource characterization are then discussed.

17 WIND ENERGY↗
DOE OSTI2022

Global biomass supply modeling for long-run management of the climate system

Bioenergy is projected to have a prominent, valuable, and maybe essential, role in climate management. However, there is significant variation in projected bioenergy deployment results, as well as concerns about the potential environmental and social implications of supplying biomass. Bioenergy deployment projections are market equilibrium solutions from integrated modeling, yet little is known about the underlying modeling of the supply of biomass as a feedstock for energy use in these modeling frameworks. We undertake a novel diagnostic analysis with ten global models to elucidate, compare, and assess how biomass is supplied within the models used to inform long-run climate management. With experiments that isolate and reveal biomass supply modeling behavior and characteristics (costs, emissions, land use, market effects), we learn about biomass supply tendencies and differences. The insights provide a new level of modeling transparency and understanding of estimated global biomass supplies that informs evaluation of the potential for bioenergy in managing the climate and interpretation of integrated modeling. For each model, we characterize the potential distributions of global biomass supply across regions and feedstock types for increasing levels of quantity supplied, as well as some of the potential societal externalities of supplying biomass. We also evaluate the biomass supply implications of managing these externalities. Finally, we interpret biomass market results from integrated modeling in terms of our new understanding of biomass supply. Overall, we find little consensus between models on where biomass could be cost-effectively produced and the implications. We also reveal model specific biomass supply narratives, with results providing new insights into integrated modeling bioenergy outcomes and differences. The analysis finds that many integrated models are considering and managing emissions and land use externalities of supplying biomass and estimating that environmental and societal trade-offs in the form of land emissions, land conversion, and higher agricultural prices are cost-effective, and to some degree a reality of using biomass, to address climate change.

09 BIOMASS FUELS↗
DOE OSTI2022

Design of a supervisory control system for autonomous operation of advanced reactors

Advanced reactors to be deployed in the coming decades will face deregulated energy markets, and may adopt flexible operation to boost profitability. To aid in the transition from baseload to flexible operation paradigm, autonomous operation is sought. This work focuses on the control aspect of autonomous operation. Specifically, a hierarchical control system is designed to support constraint enforcement during routine operational transients. Within the system, data-driven modeling, physics-based state observation, and classical control algorithms are integrated to provide an adaptable and robust solution. A 320 MW Fluoride-cooled High-temperature Pebble-bed Reactor is the design basis for demonstrating the proposed control system. The hierarchical control system consists of a supervisory layer and low-level layer. The supervisory layer receives requests to change the system's operating conditions (e.g., the current reactor power to meet a load -follow), and accepts or rejects them based on constraints that have been assigned. Constraints are issued to keep the plant within an optimal operating region. The low-level layer interfaces with the actuators of the system to fulfill requested changes, while maintaining tracking and regulation duties. Further, to accept requests at the supervisory layer, the Reference Governor algorithm was adopted. To model the dynamics of the reactor, a system identification algorithm, Dynamic Mode Decomposition, was utilized. To estimate the evolution of process variables that cannot be directly measured (e.g., the propagation of delayed neutron precursors), the Unscented Kalman Filter, incorporating a nonlinear model of nuclear dynamics, was adopted. The composition of these algorithms led to a numerical demonstration of constraint enforcement during a 40% power drop transient (at a rate of 5 %/min). Uncontrolled secondary-side temperatures were successfully constrained. Adaptability of the proposed system was demonstrated by modifying the constraint values, and enforcing them during the transient. Robustness was also demonstrated by enforcing constraints under noisy environments.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Data-driven emulation of modal aerosol microphysics via neural operator-based modeling

The complexity and the small characteristic scales of aerosol microphysical processes pose a big challenge for accurate and efficient Earth system simulations at regional and global scales. In this work, we construct and evaluate a surrogate model: the aerosol deep operator network (ADON), a physics-inspired dual-net architecture for emulating the aerosol microphysics parameterization suite in the version 2 of the Energy Earth System Model (E3SMv2). The current version of the surrogate model is trained on a dataset comprising 9.8 million samples obtained from a global E3SMv2 simulation with the horizontal resolution of about one degree under cloud-free conditions. Incorporating domain spatial and temporal coordinates, as well as principle components extracted from training data, the dual-net surrogate model effectively captures the intricate representations of aerosol and the relationship with atmospheric state variables, achieving an R-squared score over $$95.7\%$$ for all the lognormal aerosol modes in the extrapolated regime. The validated model provides feature importance of input variables and their impact on the predictive capacity of the surrogate model in relation to the E3SM. The computational cost of online inference time deployed on CPUs and GPUs with lower precisions highlights ADON’s efficiency and potential in robust predictive modeling for large-scale Earth system computations.

Bai, Zhe↗
DOE OSTI2021

Methods for Representing Flexible, Energy-Constrained Technologies in Utility Planning Tools

Capacity expansion models are widely used by power system researchers, planners, and policy analysts to evaluate alternative power system investment scenarios. With the increasing deployment of wind and solar in the US, there has been much focus on improving the representations of variable generation (VG) technologies within capacity expansion models. Models that capture the variable net-load profiles and larger reserve requirements associated with high penetration VG systems represent an improvement to classic capacity expansion models, but fall short of capturing the complexities associated with storage technologies, such as battery energy storage (BES) and concentrating solar power with thermal energy storage (CSP with TES). While difficult to model in a capacity expansion setting, these technologies are potentially competitive sources of flexibility with the intriguing characteristics of being able to absorb VG that would otherwise be curtailed and directly contribute to renewable energy goals, respectively. In this paper we present methods for accurately representing these technologies in a large-scale capacity expansion model with high electrical and geospatial resolution. VG modeling techniques, including novel methods for capturing curtailment due to unit commitment and other hourly dispatch phenomena, are also reviewed. Modeling for a region in the southwestern United States demonstrates the economic relevance of being able to explicitly trade off the costs and capabilities of energy-constrained technologies, especially BES, against other resources in the near-term, in time to make plans for the coming decade

24 POWER TRANSMISSION AND DISTRIBUTION↗