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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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95 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A Computationally Efficient Algorithm for Computing Convex Hull Prices

Electricity markets worldwide allow participants to bid non-convex production offers. While non-convex offers can more accurately reflect a resource's capabilities, they create challenges for market clearing processes. For example, system operators may execute side payments when a participant’s cost is not covered through energy sale settlements from locational marginal pricing schemes, or when a participant incurs lost opportunity costs to follow the dispatch signal. Convex hull pricing minimizes these and other types of side payments while providing uniform (i.e., locationally and temporally consistent) prices. However, computing convex hull prices involves solving either a large-scale linear program - which in turn requires explicit descriptions of market participants’ convex hulls -or the Lagrangian dual of the corresponding non-convex scheduling problem. Here, we propose a computationally feasible and industrially scalable Benders decomposition approach to computing convex hull prices at least an order of magnitude faster than the current state-of-the-art while leveraging recent advances in convex hull formulations for thermal generating units.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2023

Addendum 2 to the Closure Report for CAU 577 Area 5 Chromium Containing Waste Disposal Cells, NNSS, Nevada

Corrective Action Unit (CAU) 577, “Area 5 Chromium Containing Waste Disposal Cells,” includes five low-level waste cells at the Nevada National Security Site Area 5 Radioactive Waste Management Site where buried waste received from Nuclear Fuel Services, Inc., was subsequently determined to contain chromium that exceeded the toxicity characteristic leaching procedure regulatory limit, which would require the waste to carry hazardous waste code D007. CAU 577 was created to satisfy the requirements of the Settlement Agreement (SA) executed between the Nevada Division of Environmental Protection (NDEP) and the U.S. Department of Energy, National Nuclear Security Administration Nevada Field Office (NNSA/NFO) on April 25, 2019 (NDEP 2019). The SA required that the chromium-containing waste received from NFS would be addressed following the closure process laid out in the Federal Facility Agreement and Consent Order (FFACO). The FFACO process ensures proper closure of the chromium-containing waste and documentation of that closure through FFACO-type documents. The following three Corrective Action Sites (CASs) were closed, and their closure was documented in the Closure Report for Corrective Action Unit 577: Area 5 Chromium Containing Waste Disposal Cells, Nevada National Security Site, Nevada, DOE/EMNV--0030, dated September 2021 (U.S. Department of Energy [DOE] Environmental Management [EM] Nevada Program 2021a): • CAS 05-21-02, Waste Disposal Cell 12 • CAS 05-21-03, Waste Disposal Cell 15 • CAS 05-21-04, Waste Disposal Cell 17 Closure of the following CAS was previously documented in the Addendum to the Closure Report for Corrective Action Unit 577: Area 5 Chromium Containing Waste Disposal Cells, Nevada National Security Site, Nevada, DOE/EMNV--0030-ADD, dated September 2022 (DOE EM Nevada Program 2022): • CAS 05-21-05, Waste Disposal Cell 20 This second addendum to the Closure Report documents the closure activities that have occurred for CAS 05-21-06, Waste Disposal Cell 21. This is the last CAS in CAU 577. The final waste shipment was placed in the waste disposal cell on November 21, 2022. Following this, closure activities began on November 28, 2022, and were conducted according to the Corrective Action Decision Document/Corrective Action Plan (CADD/CAP) for CAU 577 (DOE EM Nevada Program 2021b). The following closure activities were performed: • Constructing an engineered evapotranspiration cover • Installing two subsidence monuments and vadose zone monitoring equipment • Seeding the cover with a mixture of native plant species • Installing four concrete monuments on the corners of the cover and placing two use restriction (UR) warning signs on each monument These activities fulfill applicable federal and state regulations for closure of CAS 05-21-06 and minimize potential future exposure pathways to buried waste. Completed closure activities are also consistent with closure of the nine historical Resource Conservation and Recovery Act (RCRA) units included in Section 10.2.2 of the RCRA Permit that governs hazardous waste management activities at the Nevada National Security Site (Permit NEV HW0101) (NDEP 2023). UR documentation for this CAS is included in Appendix B of this report. The post-closure plan is presented in detail in the CADD/CAP for CAU 577 (DOE EM Nevada Program 2021b), and the requirements are summarized in Section 5.2 of this document. In accordance with paragraph 5D of the SA, a request to incorporate the requirements for post-closure monitoring of CAU 577 was included with the permit application for RCRA Permit NEV HW0101 that was submitted in January 2022 (NNSA/NFO 2022). The request included the post-closure requirements for the three CASs that had been closed at the time of submittal of the application as well as requirements that would be implemented upon future approval of closure of the remaining two CASs. All CAU 577 post-closure monitoring requirements have been captured in the April 4, 2023, Revision 7 of the RCRA Permit (NDEP 2023). As the RCRA Permit NEV HW0101 has been revised since the submittal of the original CAU 577 Closure Report (DOE EM Nevada Program 2021a) and Addendum 1 (DOE EM Nevada Program 2022), the post-closure requirements in this Addendum 2 report do not align with the previous documents. Specific changes resulting from the issuance of Revision 7 of the RCRA Permit are discussed in Section 5.2 of this report. The requirements in this report are consistent with the current permit (NDEP 2023) and supersede all requirements listed in the CAU 577 Closure Report and Addendum 1. All CAU 577 post-closure requirements should be conducted in accordance with the version of the RCRA Permit that is current at the time of the activities being performed. The DOE EM Nevada Program is requesting a Notice of Completion from NDEP for closure of CAU 577. Although CAU 577 is not a legacy site, the FFACO process is being followed to ensure proper closure of the chromium-containing waste. Therefore, transfer of CAU 577 from Appendix III of the FFACO to Appendix IV, Closed Corrective Action Units, is requested, as all closure activities have been completed.

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

Popnet : computer vision based deep learning model for forecasting gridded population

Here, this study introduces Popnet, a deep learning model for forecasting 1 km-gridded populations, integrating U-Net, ConvLSTM, a Spatial Autocorrelation module and deep ensemble methods. Using spatial variables and population data from 2000 to 2020, Popnet predicts South Korea’s population trends by age groups (under 14, 15-64 and over 65) up to 2040. In validation, it outperforms traditional machine learning and state-of-the-art computer vision models. The output of this model discovered significant polarisation: population growth in urban areas, especially the capital region, and severe depopulation in rural areas. Popnet is a robust tool for offering significant insights to policymakers and related stakeholders about the detailed future population, which allows them to establish detailed, localised planning and resource allocations.

computer vision↗
DOE OSTI2021

Large-Scale Classification of Urban Structural Units From Remote Sensing Imagery

Remote sensing in combination with deep learning has become instrumental for efficiently and accurately classifying land-use and land-cover across large geographic areas. These technologies have also been successful in characterizing urban environments in terms of their structural units, structure types, or morphological regions. In these approaches, an urban area is partitioned into regions that exhibit homogeneous physical characteristics. However, existing approaches are typically limited to a single city, use inconsistent typologies, and lack scalability and generalization capacity. In this article, we propose an urban structural units categorization scheme and demonstrate its utility by applying it to 13 cities. Inspired by the lack of scalability and generalization capacity in urban structural units mapping, we extend the reach of deep learning and conduct a set of classification experiments in all 13 cities. These experiments offer insights into the strengths and limitations of deep neural networks for classifying urban structural units over diverse geographic regions and on heterogeneous collections of satellite imagery. The efficacy of the proposed deep learning approach is compared to a baseline method of multiscale image features and support vector machines. Our validation on five cities shows that better performance is achieved with deep neural networks. Additionally, we evaluate the impact of input size, model depth, and spatial pyramid pooling to assess the generalization capacity of deep neural networks.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Urban Parameters Maricopa County 100m Grid Spacing

132 urban parameters for Maricopa County are provided in WRF-readable binary form at 100m grid spacing with index. These parameters allow regional weather modelers to account for the contribution of built structures in modeling the urban heat island. This data is also provided in CSV format. Data are generated using 2023 Model America Version 1 data (https://www.osti.gov/biblio/1774134) and will be used to represent the urban morphology in Maricopa County to the WRF model for experiments conducted for the Southwest Urban Corridor Integrated Field Laboratory (SW-IFL). Three files associated with these parameters are included in this repository: index, binary file 00001-01549.00001-01796, Maricopa_Parameters_03072024.csv . To use the binary file with the Weather Research and Forecasting (WRF) model, the binary file and the index file must be placed in their own directory in WRF_GEOG and accessed in the same way the National Urban Database and Access Portal Tool data (NUDAPT44) would be accessed. The name of the binary file must be in the format: 00001-02715.00001-01803. If, upon download, the binary filename has additional or different characters, please change them to match this format.

54 ENVIRONMENTAL SCIENCES↗