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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A section 110 evaluation of the huron king test chamber, area 3, nevada national security site, nye county, nevada

The U.S. Department of Energy, National Nuclear Security Administration Nevada Field Office tasked Desert Research Institute (DRI) with the identification and evaluation of the Huron King Test Chamber as part of their cultural resources program obligations under Section 110 of the National Historic Preservation Act. The Huron King nuclear test was a vertical line-of-sight weapons effects test that took place in Area 3 on June 24, 1980. Unique to this event was a specially designed aboveground test chamber that held a model defense communications satellite in a vacuum tank meant to replicate the space environment. Sponsored by the Defense Nuclear Agency, the purpose of the experiment was to understand the response of the satellite and its materials and equipment to an electromagnetic pulse and attendant radiation. Between July and August 2022, DRI conducted an archival review on the Huron King experiment and its associated test chamber. Subsequently, pedestrian fieldwork was undertaken at the Huron King Test Chamber by DRI on September 29, 2022. During fieldwork, the outside of the test chamber was documented using a Nevada State Historic Preservation Office Architectural Resource Assessment form. This effort included verifying the substructures that compose the test chamber as determined by the archival review, as well as obtaining a detailed photographic recordation of the exterior and assessing its current condition. Notably, the site where the Huron King test took place has been abandoned and almost entirely naturalized. All of the portable instrumentation trailers, communications and data cabling, and winch systems used to retract the test chamber following the detonation were removed after completion of the experiment in 1980. Only the subsidence crater and the test chamber remain as physical evidence of this experiment. Based on these findings, DRI recommends that the Huron King Test Chamber is eligible for listing in the National Register of Historic Places (NRHP) at the local level under Significance Criteria A and C and that it meets Criteria Consideration G for properties less than 50 years old.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Finding Of Adverse Effect and Proposed Mitigation for the Removal of Joshua Trees at Building 23-210, Mercury, Area 23, Nevada National Security Site, Nye County, Nevada

The U.S. Department of Energy (DOE), National Nuclear Security Administration Nevada Field Office (NNSA/NFO) recently removed two fallen Joshua trees that were part of an Accessory Resource (AR) to the former Department of Defense (DOD) Motor Pool Maintenance Compound in Mercury (Nevada State Historic Preservation Office [SHPO] Resource No. C307) on the Nevada National Security Site (NNSS) in Nye County, Nevada (Figure 1). The Joshua trees were part of AR3, a landscaped feature at the entrance to the compound. There are two remaining Joshua trees at risk of falling which may require removal in the future. The NNSA/NFO is proactively consulting with the SHPO regarding the potential removal of the remaining two trees. The purpose of the undertaking is to ensure that dead or dying Joshua trees do not cause damage to the remaining landscape elements, nor injury to NNSS employees. The NNSA/NFO will implement this undertaking in accordance with the Programmatic Agreement between the National Nuclear Security Administration Nevada Field Office and the Nevada State Historic Preservation Officer Regarding Modernization and Operational Maintenance of the Nevada National Security Site, at Mercury in Nye County, Nevada, hereafter referred to as the Mercury PA. Building 23-210 was the DOD’s motor vehicle maintenance compound constructed in 1951. The compound included its own repair shop and fuel station. The town of Mercury and the immediate surrounding area have been formally determined eligible for listing in the National Register of Historic Places (National Register, NRHP) as the Mercury Historic District (MHD, SHPO Resource No. D230) under Criteria A and C for their importance in supporting nuclear testing and scientific research from 1951 through 1992. The former DOD Motor Pool Compound was identified as a contributing element to the MHD in a 2018 architectural survey of the district (Reno et al. 2018) and recorded on a Nevada Architectural Resource Assessment (ARA) form (Reno et al. 2017). The foundation for Building 23-210 was also identified in Appendix C of the Mercury PA as a Category III contributing element with three contributing ARs. Category III properties are those that may include elements for which there are several representatives in the MHD, such as foundations, and those elements which possess characteristics that are not unique to the MHD and are commonly found in other non-NNSS facilities. The former DOD compound, which is identified by the foundation for the Building 23-210 and its ARs, is a historic property for the purposes of compliance with Section 106 of the National Historic Preservation Act (NHPA) and subject to the stipulations of the Mercury PA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The Prospects for Pumped Storage Hydropower in Alaska

Key Takeaways: The resource mapping analysis confirmed that numerous locations in Alaska are suitable for the development of pumped storage hydropower (PSH) projects, both larger grid scale projects and smaller projects that could be suitable for remote communities; The resource assessment for larger, grid-scale projects showed the potential for more than 1,800 closed-loop systems in Alaska, with a total energy storage capacity of about 4 terawatt hours (TWh); Because of their small reservoir sizes and dam heights, many locations were identified as potentially suitable for small-scale PSH systems. Nearly 50% of the identified potentially suitable small-scale PSH sites are in Southeast Alaska; PSH candidate sites were part of the optimal capacity expansion solution in all scenarios analyzed for the Railbelt system. Depending on the scenario, the new PSH capacity that the model selected for the analysis period until 2046 ranged from 300 MW to 600 MW. The locations and timing of new PSH investments vary in different scenarios; Lithium-ion batteries were also selected a source of new generating capacity in all analyzed scenarios for the Railbelt system, indicating that the system will need a mix of short- and long-duration energy storage to support variable renewable energy sources and provide system reliability in the future; For rural communities, analysis results showed that PSH suitability is very site-specific; in addition to diesel fuel costs and PSH capital costs, suitability depends heavily on available renewable resources and existing infrastructure (e.g., reservoirs, transmission access and construction road access); The analysis for rural communities also showed that PSH projects with 10-hour energy storage are likely to be more economical for remote community applications in Alaska than those with larger reservoirs that could provide 10 days of energy storage; Lithium-ion batteries seem to be an economically more viable energy storage option for small, remote communities in Alaska.

13 HYDRO ENERGY↗
DOE OSTI2022

Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer

Abstract. With the increasing level of offshore wind energy investment, it is correspondingly important to be able to accurately characterize the wind resource in terms of energy potential as well as operating conditions affecting wind plant performance, maintenance, and lifespan. Accurate resource assessment at a particular site supports investment decisions. Following construction, accurate wind forecasts are needed to support efficient power markets and integration of wind power with the electrical grid. To optimize the design of wind turbines, it is necessary to accurately describe the environmental characteristics, such as precipitation and waves, that erode turbine surfaces and generate structural loads as a complicated response to the combined impact of shear, atmospheric turbulence, and wave stresses. Despite recent considerable progress both in improvements to numerical weather prediction models and in coupling these models to turbulent flows within wind plants, major challenges remain, especially in the offshore environment. Accurately simulating the interactions among winds, waves, wakes, and their structural interactions with offshore wind turbines requires accounting for spatial (and associated temporal) scales from O(1 m) to O(100 km). Computing capabilities for the foreseeable future will not be able to resolve all of these scales simultaneously, necessitating continuing improvement in subgrid-scale parameterizations within highly nonlinear models. In addition, observations to constrain and validate these models, especially in the rotor-swept area of turbines over the ocean, remains largely absent. Thus, gaining sufficient understanding of the physics of atmospheric flow within and around wind plants remains one of the grand challenges of wind energy, particularly in the offshore environment. This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. Such phenomena include horizontal temperature gradients that lead to strong vertical stratification; consequent features such as low-level jets and internal boundary layers; highly nonstationary conditions, which occur with both extratropical storms (e.g., nor'easters) and tropical storms; air–sea interaction, including deformation of conventional wind profiles by the wave boundary layer; and precipitation with its contributions to leading-edge erosion of wind turbine blades. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.

17 WIND ENERGY↗
DOE OSTI2020

Outcomes of the DOE Workshop on Atmospheric Challenges for the Wind Energy Industry

The U.S. Department of Energy-funded Mesoscale-to-Microscale Coupling (MMC) project team planned and conducted a virtual Workshop on Atmospheric Challenges for the Wind Energy Industry on October 19 and 20, 2020. The goal of the workshop was to forge a dialog with the community, including industry representatives, on how modeling tools are currently being used, the present active atmospheric modeling research in support of wind energy, and required advancements in capabilities and technology to continue to advance wind energy deployment. The workshop was planned in collaboration with an industry advisory panel that included representatives from wind power plant developers, turbine manufacturers, and companies that provide resource assessment and forecasting services. The format of the workshop included panels from government research sponsors, visionaries from industry, and mixed panels of researchers discussing research status and needs. A shared keynote presentation from the Technical University of Denmark experts anchored the second day of the workshop. An emphasis was placed on understanding the research needs in the offshore environment. In addition, breakout opportunities were provided each day. On the first day, the breakout discussions addressed predesigned questions configured to elicit participants’ thoughts on needed research directions. The second-day breakouts treated three important technical topics through a combination of presentations and group conversations. Each workshop participant chose their breakout preference from among downscaling details, modeling for turbines, and using artificial intelligence for atmospheric modeling. The discussions were robust and productive. The outcomes of the workshop include archiving a series of recommendations from industry and the research community on research directions required to further advance wind energy deployment. Discussions confirmed the need for high-fidelity modeling but that there are specific areas of applicability and other areas where the time and cost of computation is prohibitive. In those cases, the high-fidelity models can inform low-order models that are more practical for real-time or widely deployed applications. Industry must consider the financial cost of performing more expensive modeling approaches, but industry engineers and researchers are using these approaches where there appears to be a return on investment. An emerging type of low-order model is based on machine learning (ML). Participants confirmed that there are many atmospheric phenomena that need to be modeled better, including low-level jets, cold air outbreaks, land-sea induced circulations, diurnal variability, thin stable boundary layers, dynamic changes such as from frontal passage, interaction of wakes and blockage, and more. For the offshore environment, there is wide agreement that some level of ocean-wave-atmospheric coupling is necessary to capture variations in rotor-level winds needed to plan and operate offshore wind plants. Another recurring recommendation is that more observations are needed, particularly for the offshore environment. Those observations should consider the needs for model improvement, both for physically based models and for ML models. Observations must capture atmospheric profiles of variables that are important to understanding and modeling atmospheric and oceanic phenomena that impact boundary layer winds. Models must be validated with data and the uncertainty quantified, particularly those that are sensitive to initial and boundary conditions. Finally, a repeated request was to consider the holistic needs of hybrid plants of wind, solar, and storage resources because those types of plants are likely to be the wave of the future. In addition, industry wishes to understand impacts of the resource under a changing climate for long-term planning.

17 WIND ENERGY↗
DOE OSTI2023

Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic

Abstract. As offshore wind farm development expands, accurate wind resource forecasting over the ocean is needed. One important yet relatively unexplored aspect of offshore wind resource assessment is the role of sea surface temperature (SST). Models are generally forced with reanalysis data sets, which employ daily SST products. Compared with observations, significant variations in SSTs that occur on finer timescales are often not captured. Consequently, shorter-lived events such as sea breezes and low-level jets (among others), which are influenced by SSTs, may not be correctly represented in model results. The use of hourly SST products may improve the forecasting of these events. In this study, we examine the sensitivity of model output from the Weather Research and Forecasting model (WRF) 4.2.1 to different SST products. We first evaluate three different data sets: the Multiscale Ultrahigh Resolution (MUR25) SST analysis, a daily, 0.25∘ × 0.25∘ resolution product; the Operational Sea Surface Temperature and Ice Analysis (OSTIA), a daily, 0.054∘ × 0.054∘ resolution product; and SSTs from the Geostationary Operational Environmental Satellite 16 (GOES-16), an hourly, 0.02∘ × 0.02∘ resolution product. GOES-16 is not processed at the same level as OSTIA and MUR25; therefore, the product requires gap-filling using an interpolation method to create a complete map with no missing data points. OSTIA and GOES-16 SSTs validate markedly better against buoy observations than MUR25, so these two products are selected for use with model simulations, while MUR25 is at this point removed from consideration. We run the model for June and July of 2020 and find that for this time period, in the Mid-Atlantic, although OSTIA SSTs overall validate better against in situ observations taken via a buoy array in the area, the two products result in comparable hub-height (140 m) wind characterization performance on monthly timescales. Additionally, during hours-long flagged events (< 30 h each) that show statistically significant wind speed deviations between the two simulations, both simulations once again demonstrate similar validation performance (differences in bias, earth mover's distance, correlation, and root mean square error on the order of 10−1 or less), with GOES-16 winds validating nominally better than OSTIA winds. With a more refined GOES-16 product, which has been not only gap-filled but also assimilated with in situ SST measurements in the region, it is likely that hub-height winds characterized by GOES-16-informed simulations would definitively validate better than those informed by OSTIA SSTs.

17 WIND ENERGY↗
DOE OSTI2025

Visualizing and analyzing 3D biomolecular structures using Mol* at RCSB.org: Influenza A H5N1 virus proteome case study

The easiest and often most useful way to work with experimentally determined or computationally predicted structures of biomolecules is by viewing their three-dimensional (3D) shapes using a molecular visualization tool. Mol* was collaboratively developed by RCSB Protein Data Bank (RCSB PDB, RCSB.org) and Protein Data Bank in Europe (PDBe, PDBe.org) as an open-source, web-based, 3D visualization software suite for examination and analyses of biostructures. It is capable of displaying atomic coordinates and related experimental data of biomolecular structures together with a variety of annotations, facilitating basic and applied research, training, education, and information dissemination. Across RCSB.org, the RCSB PDB research-focused web portal, Mol* has been implemented to support single-mouse-click atomic-level visualization of biomolecules (e.g., proteins, nucleic acids, carbohydrates) with bound cofactors, small-molecule ligands, ions, water molecules, or other macromolecules. RCSB.org Mol* can seamlessly display 3D structures from various sources, allowing structure interrogation, superimposition, and comparison. Using influenza A H5N1 virus as a topical case study of an important pathogen, we exemplify how Mol* has been embedded within various RCSB.org tools—allowing users to view polymer sequence and structure-based annotations integrated from trusted bioinformatics data resources, assess patterns and trends in groups of structures, and view structures of any size and compositional complexity. In addition to being linked to every experimentally determined biostructure and Computed Structure Model made available at RCSB.org, Standalone Mol* is freely available for visualizing any atomic-level or multi-scale biostructure at rcsb.org/3d-view.

3D biostructure↗
DOE OSTI2021

Chapter 2: Global Value Chain and Manufacturing Analysis on Geothermal Power Plant Turbines

The global geothermal power market has shown significant growth since the last decade and is expected to reach a total installed capacity of 18.4 gigawatts electric (GWe) by the end of 2021 (GEA, 2016). The global geothermal power plant turbine market is dominated by a small number of manufacturers. Between 2005 and 2015, 82% of the geothermal steam turbines were manufactured in Japan, and 74% of the geothermal binary cycle turboexpanders were manufactured in Israel. During this period, the United States played an important role in the global trade flow of fully assembled turbine units and turbine parts, with a high volume of imports and exports. Another significant growth area was in Italian turbine/turboexpander manufacturers, who have increased their market share in the last couple of years. One other important change in the manufacturing market was in Turkey, where the bonus on feed-in-tariff (FIT) for domestic hardware components boosted the national manufacturing sector between 2010 and 2020. When planning geothermal power projects, developers customize their power plant size to fit the available geothermal resource capacity. The turbine is designed and sized to optimize the efficiency and utilization of resource and revenue production. The rest of the power plant components such as heat exchangers (HX), water-cooled cooling towers (WCCT), or air-cooled condensers (ACC) are then chosen to complement the turbine size and design. These one-off manufacturing custom design turbines have relatively higher manufacturing set-up costs, longer lead times, and higher capital costs than the standard design turbines manufactured in larger volumes. However, turbines produced in standard increments and in larger manufacturing volumes could result in lower costs per turbine, but potentially lower efficiency. Based on pipeline projects and resource assessments, there is significant potential value in creating standard turbine sizes that could offer an economic advantage, as is done for modular microturbines.

40 EE - Geothermal Technologies Office (EE-4G)↗
DOE OSTI2020

A lightweight method for evaluating in situ workflow efficiency

Performance evaluation is crucial to understanding the behavior of scientific workflows. In this study, we target an emerging type of workflow, called in situ workflows. These workflows tightly couple components such as simulation and analysis to improve overall workflow performance. To understand the tradeoffs of various configurable parameters for coupling these heterogeneous tasks, namely simulation stride, and component placement, separately monitoring each component is insufficient to gain insights into the entire workflow behavior. Through an analysis of the state-of-the-art research, we propose a lightweight metric, derived from a defined in situ step, for assessing resource usage efficiency of an in situ workflow execution. By applying this metric to a synthetic workflow, which is parameterized to emulate behaviors of a molecular dynamics simulation, we explore two possible scenarios (Idle Simulation and Idle Analyzer) for the characterization of in situ workflow execution. In addition to preliminary results from a recently published study [11], we further exploit the proposed metric to evaluate a practical in situ workflow with a real molecular dynamics application, i.e., GROMACS. Here, experimental results show that the in transit placement (analytics on dedicated nodes) sustains a higher frequency for performing in situ analysis compared to the helper-core configuration (analytics co-allocated with simulation).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Impact of automated battery sorting for mineral recovery from lithium-ion battery recycling in the United States

The United States has identified several lithium-ion battery materials as critical for reaching the national emission reduction targets that have been set in accordance with the 2015 Paris Agreement. However, with few natural resources available domestically, there is a rapidly growing focus on the development of a domestic recycling industry to recover these materials from end-of-life batteries. Here, we use the Lithium-Ion Battery Resources Assessment (LIBRA) system dynamics model to evaluate the impact of automated battery sorting technology in terms of the shares of cobalt and nickel that are recovered through recycling. Findings show that automated sorting has clear benefits over manual sorting methods by helping recyclers selectively process high-cobalt batteries. By maximizing cobalt recovery, recycling becomes more profitable and drives greater investment in recycling capacity, resulting in a higher share of nickel and cobalt recovered from EOL batteries over time.

25 ENERGY STORAGE↗
DOE OSTI2021

Active microbial biomass decreases, but microbial growth potential remains similar across soil depth profiles under deeply-vs. shallow-rooted plants

Climate-smart land management practices that replace shallow-rooted annual crop systems with deeply-rooted perennial plants can contribute to soil carbon sequestration. However, deep soil carbon accrual may be influenced by active microbial biomass and their capacity to assimilate fresh carbon at depth. Incorporating active microbial biomass, dormancy, and growth in microbially-explicit models can improve our ability to predict soil's capacity to store carbon. But, so far, the microbial parameters that are needed for such modeling are poorly constrained, especially in deep soil layers. Here, we used a lab incubation experiment and growth kinetics model to estimate how microbial parameters vary along 240 cm of soil depth in profiles under shallow- (soy) and deeply-rooted (switchgrass) plants 11 years after plant cover conversion. We also assessed resource origin and availability (total organic carbon, 14 C, extractable organic carbon, specific UV absorbance of K 2 SO 4 extractable organic C, total nitrogen, total dissolved nitrogen) along the soil profiles to examine associations between soil chemical and biological parameters. Even though root biomass was greater and rooting depth was deeper under switchgrass than soy, resource availability and microbial growth parameters were generally similar between vegetation types. Instead, depth significantly influenced soil chemical and biological parameters. For example, resource availability and total and relative active microbial biomass decreased with soil depth. Decreases in the relative active microbial biomass coincided with increased lag time (response time to external carbon inputs) along the soil profiles. Even at a depth of 210–240 cm, microbial communities were activated to grow by added resources within a day. Maximum specific growth rate decreased to a depth of 90 cm and then remained consistent in deeper layers. Our findings show that >10 years of vegetation and rooting depth changes may not be long enough to alter microbial growth parameters, and suggest that at least a portion of the microbial community in deep soils can grow rapidly in response to added resources. Our study determined microbial growth parameters that can be used in microbially-explicit models to simulate carbon dynamics in deep soil layers.

14C↗
DOE OSTI2023

How do North American weather regimes drive wind energy at the sub-seasonal to seasonal timescales?

Abstract There has been an increasing need for forecasting power generation at the subseasonal to seasonal (S2S) timescales to support the operation, management, and planning of the wind-energy system. At the S2S timescales, atmospheric variability is largely related to recurrent and persistent weather patterns, referred to as weather regimes (WRs). In this study, we identify four WRs that influence wind resources over North America using a universal two-stage procedure approach. These WRs are responsible for large-scale wind and power production anomalies over the CONUS at the S2S timescales. The WR-based reconstruction explains up to 40% of the monthly variance of power production over the western United States, and the explanatory power of WRs generally increases with the increase of timescales. The identified relationship between WRs and power production reveals the potential and limitations of the regional WR-based wind resource assessment over different regions of the CONUS across multiple timescales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Land-based wind plant wake characterization using dual-Doppler radar measurements at AWAKEN

Wind plant wakes have been shown to persist for tens of kilometers downstream in offshore environments, reducing the power output of neighboring plants, but their behavior on land remains relatively unexplored through observation. This study capitalizes on the unique and extensive field data collected for the American WAKE ExperimeNt (AWAKEN) project underway in northern Oklahoma. X-band dual-Doppler radars deployed at this site measure wind speed and direction at 25-m and 2-min resolution within a 30-km range, capturing the interactions between three neighboring wind plants. These measurements show that the wake of one wind plant extends at least 15 km downstream under easterly wind and stable atmospheric conditions. Though the wake wind speed increases within the first 10 km, it plateaus at 90% of the freestream wind speed. The spanwise velocity distribution within the wake initially shows the clear signature of the wind plant layout, which is smoothed as it propagates downstream, indicating spanwise momentum transfer is a key mechanism in wind plant wake development and recovery. These findings have important implications for wind plant siting decisions and resource assessments, and provide insights into atmospheric interactions at the wind plant scale.

17 WIND ENERGY↗
DOE OSTI2022

Smoke from 2020 United States wildfires responsible for substantial solar energy forecast errors

Abstract The 2020 wildfire season (May through December) in the United States was exceptionally active, with the National Interagency Fire Center reporting over 10 million acres ( > 40 000 km 2 ) burned. During the September 2020 wildfire events, large concentrations of smoke particulates were emitted into the atmosphere. As a result, smoke was responsible for ∼10%–30% reduction in solar power production during peak hours as recorded by the California Independent System Operator (CAISO) sites. In this study, we focus on a 9 d period in September when wildfire smoke had a profound impact on solar energy production. During the smoke episodes, hour-ahead forecasts utilized by CAISO did not include the effects of smoke and therefore overestimated the expected power production by ∼10%–50%. Here we use multiple observational networks and a numerical weather prediction (NWP) model to show that the wildfire events of 2020 had a significantly detrimental influence on solar energy production due to high aerosol loading. We find that including the contribution of biomass burning particles greatly improves the day-ahead solar energy bias forecast of both global horizontal irradiance and direct normal irradiance by nearly ∼50%. Our results suggest that a more comprehensive treatment of aerosols, including biomass burning aerosols, in NWP models may be an important consideration for energy grid balancing, in addition to solar resource assessment, as solar power reliance increases.

14 SOLAR ENERGY↗
DOE OSTI2022

Addressing Market Issues in Electrical Power Systems with Large Shares of Variable Renewable Energy

This paper reports recent findings from IEA Wind TCP Task 25, which compiles international experiences and research related to large-scale integration of wind and other renewable energy. In the paper, we address the main challenges for market integration of variable renewable energy, relating to price formation, cost recovery, balancing and other grid services. The paper gives an overview of recent scenario studies on electricity price impacts of (1) various generation, energy storage and demand types in different markets, and (2) different market designs and energy/climate policies. Studying markets with very high shares of variable renewable energy requires an improved set of analysis tools for forecasting market outcomes, estimating flexibility needs and sources, and assessing resource adequacy. Key market features need to be investigated within these improved analytical capabilities for systems transitioning to high shares of variable renewable energy, storage and flexible demand. System services that can be supported by markets will likely need to be revisited. Finally, this paper identifies open questions and suggested future market design work for supporting systems with very high shares of variable renewable energy, which are to be addressed in follow-up work of Task 25 collaborative research.

cost recovery↗
DOE OSTI2020

CA-ResidueRetentionAssumptionsBT16-BaseCase

As part of the Billion Ton resource assessment projections created in 2016 (see https://www.energy.gov/sites/prod/files/2016/12/f34/2016_billion_ton_report_12.2.16_0.pdf, DOI: 10.2172/1435342) -henceforth "BT16", this dataset was used as an assumption to limit the availability of residues under a "base-case" scenario (BC1). Crop residues that had this assumption applied include corn stover, cereal (wheat, oats, and barley) straws, and sorghum stubble. What is the purpose of the data set? Why were the data collected? Per request for use in subsequent research, we have summarized assumptions for California only and selected years (2020, 2030, 2040) that were used in the BT16's base-case projections for agricultural residues and have provided details by tillage class that limited residue availability for harvest (dry tons of residues that must remain). Note: '10' is a high number that assures that no residue harvested could occur. Note: Tillage classification assumptions are also of importance: a low flexibility was applied, allowing a moderate deviation from a baseline situation (using historic CTIC data on tillage type used in counties for each crop). A moderate flexibility, allows farmers to put land into another tillage type (no till, conservation till, and reduced till) where a higher net present value was calculated. This dataset includes all allowable tillage types by crop, but each tillage type may not have occurred in BT16 simulations.

Davis, Maggie↗
DOE OSTI2026

Decision Support System

Forest-based value chains involve decisions that begin at the landscape level and extend through processing, product manufacturing, and end-use markets. However, these decisions are often made independently across sectors, with limited visibility into how upstream resource conditions, incentives, and land management choices influence downstream production systems. In forested regions of the United States, wildfire risk, fragmented ownership, and uncertain markets for low-value residues complicate efforts to align extraction, processing, and utilization decisions. Without tools that link these stages, stakeholders may overlook opportunities to improve resource utilization or inadvertently shift impacts elsewhere in the value chain. This repository introduces a decision support system (DSS) that applies a system-impact-analysis approach to forest biomass residues and co-products. The framework integrates forest inventory data, geospatial resource assessments, and economic modeling to evaluate how biomass extraction decisions influence downstream product pathways. By linking regional feedstock avail- ability with market incentives and processing options—such as fuels, wood products, or soil amendments like biochar—the tool allows decision-makers to compare value chain outcomes across multiple utilization strategies.

Davis, Maggie [Oak Ridge National Laboratory (ORNL↗
DOE OSTI2020

WindWatts Computational Framework and Web UI [SWR-20-100]

This software provides a collection of algorithms, an API, and a functional Web UI for the Distributed Wind’s WindWatts project. DW WindWatts is a DOE WETO-funded project aimed at the development of tools to supporting the distributed wind industry, particularly with respect to siting and resource assessment. The computational framework and the back-end API are powering the easy to use front-end services available at: https://windwatts.nrel.gov. https://github.com/NREL/dw-tap-api; https://github.com/NREL/dw-tap; https://github.com/NREL/windwatts-data For reference, this software was previously known as DW TAP Computational Framework.

Phillips, Caleb↗