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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

WHONDRS River Corridor Sediment and Water Geochemistry and In Situ Sensor Data from Machine-Learning-Informed Sites across the Contiguous United States (v6)

This dataset supports a broader study examining hyporheic zone respiration rates to improve predictive models at a contiguous United States (CONUS) scale. The CONUS-Scale Model-Sample Study (CM) was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the CONUS. New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Sampling began in April 2022 and ended in October 2023. In addition to the widely distributed CONUS sites, a more spatially focused sampling occurred in the Yakima River Basin, WA in summer 2022. Data from this more spatially intensive sampling occurred under the label “Second Spatial Study (SSS)” and were also included in the machine learning models. Other data types collected from SSS that were not part of CM were published in a separate data package (https://data.ess-dive.lbl.gov/view/doi:10.15485/1969566). This data package was originally published in February 2023. It was updated in June 2023 (v2; new and modified files); December 2023 (v3; new and modified files); June 2024 (v4; new and modified files); April 2024 (v5; new and modified files); and September 2025 (v6; modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of two folders of field photos and videos, one folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data and one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) international generic sample number (IGSN) mapping file; (6) field protocols; (7) a subfolder with sample data; and (8) a subfolder with sensor data. The sample data subfolder contains (1) surface water and sediment dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) surface water and sediment total nitrogen data and averages; (3) surface water major cations and anions and averages; (4) sediment grain size data; (5) sediment iron (II) data and averages; (6) wet sediment mass, dry sediment mass, water mass, and wet sediment volume in incubation and sediment ICR vials; (7) sediment incubation respiration rate data and averages; (8) normalized respiration rate data and averages; (9) methods codes; (10) sediment specific surface area; (11) sediment percent carbon and nitrogen; (12) sediment gravimetric moisture and averages; (15) sediment X-ray diffraction (XRD) data; (16) sediment adenosine triphosphate (ATP) and averages; (17) a subfolder with sediment incubation respiration data, scripts, and plots; (18) surface water and sediment FTICR methods; and (19) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains five subfolders, one containing the sediment .xml data files, one containing the water .xml files, one containing the sediment CoreMS output files, one containing the water CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS).The sensor data subfolder contains (1) a subfolder with miniDOT dissolved oxygen and temperature data and plots; (2) miniDOT dissolved oxygen and temperature summary data; and (3) miniDOT installation methods. All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4. CORRECTION: Carbon and nitrogen content are reported as percentages. The current column headers "01395_C_percent_per_mg" and "01397_N_percent_per_mg" are incorrect. These should read "01395_C_percent" and "01397_N_percent" and will be corrected in the next version of this data package. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Case Study: Nuclear-Renewable-Water Integration in Arizona

This document reports the application of the N-R HES software framework to a case study for Arizona Public Service (APS); the manager and part owner of the Palo Verde (PV) nuclear power plant. The case study is a work in progress of which this report presents a detailed description of the current model input data, assumptions and the corresponding results produced by the developed software framework. The goal of the report is, that with this information APS (together with INL) should be able to identify where more detailed data and more accurate model assumptions are necessary to reproduce all driving physical and economic phenomena in order to capture enough complexity of the reality to allow APS to use the results for their strategic decisions. The N-R HES software framework started being developed at INL two years ago [ref]. The framework has reached some level of maturity so that it can be applied to more than simple demonstration cases, e.g. real industry problems. Nevertheless, more capabilities are constantly added to accommodate the special needs of these challenging real life problems. The N-R HES framework is built on top of the RAVEN code [ref] and uses it as driver and workflow manager for all calculations. The framework has specifically been developed for the economic assessment of N-R HES systems. There are four main cornerstones of the N-R HES simulation framework: 1) generation of stochastic time series, 2) a probabilistic analysis and optimization set of algorithms available in RAVEN, 3) a set of models for representation of the physical behavior of N-R HES, and 4) a RAVEN plug-in that maps physical performance into economic performance called CashFlow. Within this framework, a broad spectrum of questions related to N-R HES can be addressed. One of the challenges currently of high interest is that the increasing penetration of variable renewables is altering the profile of the net demand (demand after removing all non curtailable renewable energy sources), with which the other generators on the grid have to cope. The N-R HES software framework is capable to analyze the potential feasibility of mitigating the resultant volatility in the net electricity demand. One possible solution to that problem currently intensively studied by the energy industry is adding stabilizing loads to the grid. These loads can be external industrial processes that are able to ramp production up and down quickly or adding variable internal loads that will lift the base load of the power plant. The latter is a possible solution currently studied by APS. APS if currently anticipating several challenges that will likely arise in the near future. One of them is, as mentioned, coping with the rapid growth of variable renewable energy (VRE) sources put on the grid. Although APS can sell its electricity for a fixed retail price to cover the local demand first, it also sells electricity in excess of the local demand at the Palo Verde energy hub. Due to the growing VRE penetration, the Palo Verde hub electricity spot price is negative more and more frequently. To mitigate this price volatility, APS is seeking to add more base load, so that the quantity of excess energy to be sold for prices potentially less than the internal retail price or even negative prices at the hub is reduced. A second challenge APS is facing is that their cooling water acquisition contract with the Sub Regional Operating Group (SROG) will expire soon and a renewal can only be done for a significantly higher price of the water. Therefore, APS is also seeking for alternative sources for their cooling water. An opportunity currently under investigation is to pump a limited quantity of brackish water from the regional ground water. Although much cheaper than the water from the new SROG contract, the salinity of the brackish water is so high that a blend of brackish and SROG water would need additional treatment to improve its quality for use in the cooling towers. An on-site reverse osmosis (RO) desalination plant is envisaged to reduce the salinity to an acceptable level. This report investigates the economic impact of such an RO plant. As one can see, the RO plant would help with both problems APS is facing: First, it would rise the APS base load helping to mitigate VRE induced hub price volatility. From this perspective, the RO plant can be seen as a stabilizing load on the grid as would be any other industrial process. Second, the RO’s goods, e.g. the clean water produced can be used by APS itself for a (hopefully) lower cost than 100% SROG water would be. The report will first describe the APS water procurement strategy in detail and lay out the possible alternative scenarios studied in this document. This is covered in Chapter 2. Next, in Chapter 3, the APS case model developed within the N-R HES software framework is described. This chapter includes also a description of the needed framework developments to accommodate the APS study.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Exascale Virtualized and Programmable Distributed Cyber Resource Control: Final Scientific Technical Report

Extreme-science drives the need for distributed exascale processing and communications that are carefully, yet flexibly, managed. Exponential growth of data for scientific simulations, experimental data, collaborative data analyses, remote visualization and GRID computing requirements of scientists in fields as diverse as high energy physics, climate change, genomics, fusion, synchrotron radiation, material science, medicine, and other scientific disciplines cannot be accommodated by simply applying existing transport protocols to faster pipes. Further, scientific challenges today demand diverse research teams, heightening the need for and increasing the complexity of collaboration. To address these issues within the network layer and physical layer, we have performed a number of research activities surrounding effective allocation and management of elastic optical network (EON) resources, particularly focusing on FlexGrid transponders. FlexGrid transponders support the opportunity to build Layer-1 connections at a wide range of bandwidths and to reconfigure them rapidly. The new flexibility supports complex new ways of using the physical layer that must be carefully managed and hidden from the scientist end-users. FlexGrid networks utilize flexible (or elastic) spectral bandwidths for each data link without using fixed wavelength grids. The flexibility in spectrum allocation brings many appealing features to network operations. Current networks are designed for the worst case impairments in transmission performance and the assigned spectrum is over-provisioned. In contrast, the FlexGrid networks can operate with the highest spectral efficiency and minimum bandwidth for the given traffic demand while meeting the minimum quality of transmission (QoT) requirement. Two primary focuses of our research are: (1) resource and spectrum allocation (RSA) for IP traffic over EONs, and (2) RSA for cross-domain optical networks. Previous work concentrates primarily on large file transfers within a single domain. Adding support for IP traffic changes the nature of the RSA problem: instead of choosing to accept or deny each request for network support, IP traffic is inherently elastic and thus lends itself to a bandwidth maximization formulation. We developed a number of algorithms that could be easily deployed within existing and new FlexGrid networks, leading to networks that better support scientific collaboration. Cross-domain RSA research is essential to support large-scale FlexGrid networks, since configuration information is generally not shared or coordinated across domains. The results presented here are in their early stages. They are technically feasible and practical, but still require coordination among organizations and equipment owners and a higher-layer framework for managing network requests.

42 ENGINEERING↗
DOE OSTI2016

Direct Feed Low Activity Waste Program Integration - 16638

The mission of Program Integration is to champion and steer the development and implementation of common and consistent programs between the Tank Operating Contract (TOC) contractor and the Waste Treatment Plant (WTP) contractor. TOC is managed by Washington River Protection Solutions LLC (WRPS) and WTP is managed by Bechtel National, Inc. (BNI). The DOE Office of River Protection (DOE ORP) is the Program Owner. WRPS has been actively managing the TOC at the Hanford Site Tank Farms (TF) since 2008. Programs are already developed, verified, and in use for the TOC, whereas WTP is still in the construction phase. WTP Programs are established for construction only, but not developed for commissioning and future operations. An extensive effort is underway at WTP to develop commissioning programs. In order to drive consistency and minimize differences in program requirements between TF, which will feed the tank waste, and WTP, which will receive and treat the tank waste, DOE-ORP requires the two contractors to integrate and collaborate in program development. Advantages of integration are: - Achieves cost and time savings as WTP is able to review existing TOC programs as starting points in development of its programs. - Promotes interfaces with industry counterparts to share experiences, issues, and lessons learned. ? Minimizes difference in program requirements between the companies. - Helps prepare for and ultimately succeed in Operational Readiness Reviews (ORR). Program Integration is a priority for TOC and WTP, and in order to maintain the focus and drive to this effort, a unified One System Program Integration Council (OSPIC) was created in 2012. Membership of the OSPIC consists of senior managers from both companies and the OSPIC chair's report directly to their respective President. Thirty (30) programs are subject to integration, including but not limited to Conduct of Operations, Worker Safety and Health, Quality Assurance, Contractor Assurance, and Environmental Management. Progress of program development and integration efforts are tracked and monitored, and barriers to integration are resolved in a collaborative fashion within both companies. (authors)

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

Understanding EV Charging Pain Points Through Deep Learning Analysis

Current and potential electric vehicle (EV) owners express concerns about the charging infrastructure, mentioning non-functional chargers, prolonged charging times, inconvenient charger locations, long wait times, and high costs as major barriers. Addressing these issues often requires analyzing actual vehicle charging data, which is typically proprietary and inconsistent due to diverse standards and protocols. To understand and improve the EV charging experience, customer reviews are typically used to identify common customer pain points (CPPs). However, there is not a comprehensive method to map customer reviews to a standardized set of CPPs. In collaboration with the National Charging Experience (ChargeX) Consortium, this study bridges these gaps by proposing a Systematic Categorization and Analysis of Large-scale EV-charging Reviews (SCALER) framework. SCALER is an integrated, deep learning framework that segments, actively labels, analyzes, and classifies EV charging customer reviews into six CPP categories. To test its effectiveness, we used SCALER to analyze over 72,000 reviews from customers charging various EV models on different networks across the United States. SCALER achieves a classification accuracy of 92.5%, with an F1 score exceeding 85.7%. By demonstrating real-world applications of SCALER, we enhance the industry’s ability to understand and address CPPs to improve the EV charging experience.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2015

Integrating remediation and resource recovery: On the economic conditions of landfill mining

Highlights: • We compare two remediation scenarios; one with resource recovery and one without. • Economic analysis includes relevant direct costs and revenues for the landfill owner. • High degrees of metal and/or combustible contents are important economic factors. • Landfill tax and the access to a CHP can have a large impact on the result. • Combining landfill mining and remediation may decrease the project cost. - Abstract: This article analyzes the economic potential of integrating material separation and resource recovery into a landfill remediation project, and discusses the result and the largest impact factors. The analysis is done using a direct costs/revenues approach and the stochastic uncertainties are handled using Monte Carlo simulation. Two remediation scenarios are applied to a hypothetical landfill. One scenario includes only remediation, while the second scenario adds resource recovery to the remediation project. Moreover, the second scenario is divided into two cases, case A and B. In case A, the landfill tax needs to be paid for re-deposited material and the landfill holder does not own a combined heat and power plant (CHP), which leads to disposal costs in the form of gate fees. In case B, the landfill tax is waived on the re-deposited material and the landfill holder owns its own CHP. Results show that the remediation project in the first scenario costs about €23/ton. Adding resource recovery as in case A worsens the result to −€36/ton, while for case B the result improves to −€14/ton. This shows the importance of landfill tax and the access to a CHP. Other important factors for the result are the material composition in the landfill, the efficiency of the separation technology used, and the price of the saleable material.

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

Packages of Distributed Energy Technologies Demonstrating Demand Flexibility at Community Scale

The combination of increased electric load growth across all sectors, deferred electrical infrastructure investment, and other factors resulting in variable electric power supply, has created technical challenges to maintaining a resilient and reliable grid. Many federal, regional, and local efforts are in play to modernize the electric grid, including advancing building technologies and distributed energy resources (DERs) that are utilizing smarter controls to become responsive to both occupant and grid needs. This report reviews ten pilot projects demonstrating how groups of buildings combined with behind-the-meter (BTM) DERs such as electric vehicle (EV) charging, battery storage, flexible HVAC and domestic hot water systems, and photovoltaic systems can reliably and cost effectively provide grid services. Each of the ten pilot projects aim to deliver both energy efficiency and demand flexibility (DF) while supporting load growth. The ten demonstration teams are piloting flexible DER packages across diverse communities of residential and commercial buildings to address a variety of regional grid needs. The outcomes of these pilot projects will be used to inform future scaling through utility program development. This paper characterizes the ten teams, showcasing the decision-making process used by each group to develop their packages (Section 2), the grid services they plan to deliver (Section 3), the types of DER packages selected for deployment within building sectors (Section 4) and trends between building sector, DER types, and grid services In order to achieve community scale benefits, the pilot projects must utilize aggregated control mechanisms for coordinating buildings and DERs together. Several types of coordinated control architectures have evolved amongst the teams, influenced by use type, existing market conditions, and integration type. Three coordinated controls architectures have been characterized, highlighting their use cases, benefits, challenges, and tradeoffs in their design. These insights can aid utilities, control vendors, and developers in scaling community-level energy systems (Paul, 2024). Ultimately, the technology packages selected by the ten teams will be coordinated to provide power system services, also known as grid services. Insights from these demonstrations will be useful for grid operators, regulators, aggregators and other stakeholders as they look to deploy demand flexible resources as grid services in the future. The grid services that each team is targeting for demonstration are described in Section 3 and Section 4. Methods for evaluating the grid services have been described in the paper Metrics for Evaluating Grid Service Provision from Communities of Grid-interactive and Efficient Buildings and other DER (MacDonald, 2023). To identify technology packages for demonstration, Section 2 shows that project teams used a range of analysis approaches, including building energy modeling, AMI data analysis, cost-benefit frameworks, and utility pilot data. Some teams emphasized technical modeling to quantify grid impacts and demand reduction potential, while others prioritized economic evaluations, stakeholder input, or exploratory pilots to inform deployment decisions. This diversity reflects the need to tailor selection methods to project goals, available data, and organizational context. Section 5 discusses trends between the DER technologies deployed and the grid service provisions from each team. Residential buildings (multifamily and single family) lean towards technologies that enhance energy efficiency (e.g. weatherization upgrades, smart thermostats) and onsite power generation integration (e.g. solar PV). Commercial building demonstrations prioritize technologies that ensure operational reliability (e.g. battery storage) and centralized energy management systems and optimization solutions. Teams that are deploying controllable storage-based technologies are more likely to provide grid services that require a near real-time response. Teams incorporating load shifting technologies like smart thermostats with HEMs are likely to include energy markets participation and customer bill management offerings. Campus demonstrations are adopting diverse sets of DERs to emphasize renewable generation, paired with centralized control. This section also describes technologies that were considered during project planning but ultimately excluded from final deployment. These demonstrations reveal that effective DER package design should be tailored to building type, customer segment, and construction vintage. Multifamily buildings benefit from centralized HVAC upgrades and supervisory controls, while single-family homes are well-suited for individualized technologies like solar, storage, and smart home energy monitors. Commercial and campus settings prioritize EMIS integration and load optimization. New construction enables cost-effective integration of DER-ready infrastructure, whereas retrofits require deployments aligned with owner and tenant value streams. For utility program planners, early coordination with developers and building owners, paired with segmented and modular program offerings, can improve adoption, scalability, and grid impact.

24 POWER TRANSMISSION AND DISTRIBUTION↗