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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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Life cycle assessment of suspended algal cultures in outdoor open pond systems: investigating carbon utilization efficiency and CO2 sourcing
Gas sparging is the most commonly used method for delivering CO2 to algal cultures in Open Raceway Ponds (ORPs). While this method is well-tested and effective, these systems experience large amounts of outgassing to the surrounding atmosphere and often achieve carbon utilization efficiencies (CUE) lower than 30%. With such large losses, system are overdesigned to provide CO2 gas in extreme excess, resulting in increased capital and operational expenses. This research is focused on the development, testing, and implementation of a hydrogel membrane seeded with the enzyme carbonic anhydrase and designed to convert gaseous CO2 into aqueous bicarbonate and deliver this inorganic carbon (iC) directly to the algae pond. Despite increased capital expenses for the module, bicarbonate storage tank, and added pumps and infrastructure, preliminary results suggest that the membrane module has the potential to reduce the minimum biomass selling price by 35% by significantly reducing the operational expenditure for CO2.
The specialized ontology for the Carbon Storage Open Database will enable more rapid assignment of appropriate symbology standards for visualization improvements, optimize topical and spatial tagging within keywords, and improve flexibility for utilization in existing data repositories such as EDX. This effort also aims to establish a foundation for utilization of ontologies for organization of other data related to geologic carbon storage in the future.
Geologic carbon storage (GCS) offers a way to capture and permanently store CO₂ from fossil fuel operations in underground geologic structures, aiding in the transition to a carbon-neutral energy economy. However, CO₂ injection sites can experience gas leakage through existing wells that penetrate storage reservoirs, making knowledge of well locations and characteristics crucial for permitting, infrastructure reusability, and risk assessment in GCS. Currently, public wellbore data from state, federal, and tribal entities are inconsistent and fragmented, with gaps and redundancies. To address this, the National Energy Technology Laboratory (NETL) developed CO2-Locate, an open-source, geospatial database and online application. CO2-Locate integrates over 50 data sources from federal, state, and tribal entities, creating a standardized national well database. Funded by the Bipartisan Infrastructure Law, the database is publicly available through the Energy Data eXchange (EDX) and viewable via the CO2-Locate web mapping application. This tool allows users to query, filter, and visualize well data to support GCS planning, permitting, and risk assessments. This presentation covers the methods used to create CO2-Locate, including data acquisition, processing, attribute mapping, and integration, much of which is automated for future updates. The web mapping application and its role in GCS site selection will also be discussed.
The Open Energy Data Initiative Solar Systems Integration (OEDI SI) Data and Modeling Platform offers a comprehensive set of use cases tailored for power systems analysis. Each use case is centered around a specific power system analysis problem, supported by composite input data and reference algorithms. These composite input datasets are meticulously assembled using OEDI SI's data preprocessing tools, which integrate raw data from various sources. The primary objectives of the OEDI SI Platform include facilitating access to composite input data through widely accepted input/output formats and verified results. This accessibility enables power system network researchers and developers to validate their algorithms and showcase their applications' capabilities to the broader community. Moreover, the platform strives to promote reproducible, robust, replicable, and generalizable solar systems integration research.
The project led by the Donald Danforth Plant Science Center, in collaboration with Arizona State University, George Washington University, and Saint Louis University, has made significant strides in advancing the phenotypic analysis of bioenergy crops through the development of an innovative AI processing pipeline. This initiative was primarily funded by ARPA-E, with additional cost-sharing provided by the participating institutions. The project successfully utilized a variety of sensors—3D scanners, thermal, RGB, and hyperspectral—to refine algorithms for data-driven trait signature identification and improve the classification and visualization of plant traits. The developed AI processing pipeline is capable of handling the complex, multidimensional data characteristic of dynamic agricultural environments. 1) Contributions to understanding: The research has advanced the field of plant phenomics by showcasing the synergistic use of various sensor data to enhance the precision of trait analysis in bioenergy crops. Through the integration of 3D scanners, thermal, RGB, and hyperspectral sensors, the project has developed robust data-driven trait signature algorithms and visualization techniques. These innovations have facilitated detailed monitoring and management of plant traits, providing vital insights into plant growth dynamics and stress responses. Further, the project has broadened our understanding of how machine learning can be effectively applied in multi-sensor environments to refine trait analysis. By leveraging diverse datasets, the research has not only improved the accuracy of phenotypic assessments but also established a versatile methodological framework that can be extended beyond agriculture to other fields requiring detailed phenotypic analysis. 2) Technical effectiveness and economic feasibility: The AI processing pipeline developed in this project demonstrated significant technical effectiveness, achieving high throughput analysis of extensive phenotypic data and meeting targeted accuracies. This system exemplified the capability of advanced machine learning technologies to efficiently manage and analyze large, complex datasets. Economically, the implementation of the project-developed pipelines may offer substantial cost savings across multiple sectors. It enhances data analysis processes and significantly reduces the need for manual data interpretation, thereby decreasing both the time and resources required. 3) Public benefit: The project has significantly broadened the scope of agricultural methodologies to enhance phenotypic analysis, with potential applications in various sectors beyond agriculture. Additionally, the initiative fostered an enriching educational and collaborative environment, significantly enhancing the technical skills of participants. It also made substantial contributions to the scientific community by providing open-access data sets and tools, encouraging ongoing research and development across various disciplines. Overall, the project not only met its scientific goals but also showcased the extensive utility of integrating advanced machine learning and sensor data analysis technologies. These advancements have proven instrumental in driving forward both theoretical research and practical applications, setting a strong foundation for future explorations and innovations in data-driven science.
The Open-Source Offshore (OSO) airfoils have been developed for research purposes for offshore wind turbines, offering a set of airfoils that align with modern turbine design requirements and industry design practices without proprietary constraints on research use. The eventual airfoil family will target the IEA 22 MW reference wind turbine, which was originally developed with the FFA airfoils. The two airfoils summarized in Table 1 (OSO-21-WT1 and OSO-30-WT1) started development as part of a family of airfoils being designed to target the IEA 22 MW wind turbine. The criteria used to design these airfoils are summarized in Table 1, which aim to encapsulate requirements of modern airfoils for offshore wind turbine applications, and were developed with feedback from industry and research experts. The airfoils were designed using XFOIL and candidate airfoils were then analyzed in RFOIL, which is considered more accurate than XFoil for high lift predictions of thicker airfoils. The design process for a preliminary family of airfoils is available, including a more detailed explanation of the design requirements and metrics similar to those used for these airfoils. Most of the design criteria are met for these two airfoils, with two exceptions. For both airfoils, the L/D Roughness Loss metric is exceeded (42% > 40% goal) and the desired lift coefficient margin over the design value (“CL_Margin”) was moderately exceeded (0.43 > 0.3) while smooth-stall characteristics (computed) were achieved. Note that all of the metrics were computed using RFOIL, and like other new airfoils, these will need to be experimentally validated at a range of Reynolds numbers. The airfoil coordinates will be shared publicly on Sadia National Laboratories’ public Github repository:
FuelLib is an open-source Python-based fuel library, developed by NREL, that leverages the group contribution method (GCM) of [1] to systematically estimate the thermodynamic and transport properties of hydrocarbon fuels. FuelLib predicts these properties based on the molecular structure of individual compounds or compound families, using weight percentages of a fuel's composition, typically measured using techniques such as gas chromatography (GC). FuelLib enables property estimation over a wide range of temperatures and pressures of multi-component fuels in the absence of detailed molecular composition data, making it particularly valuable for complex fuel mixtures where detailed experimental characterization of fuel composition is unavailable. These capabilities contribute directly to synthetic aviation turbine fuels (SATF) development, supporting the short-term American Society for Testing and Materials (ASTM) qualification of drop-in fuels while potentially expanding ASTM boundaries to certify a broader range of fuels.
Open loop cooling towers play a pivotal role in rejecting heat for chilled water systems serving industrial processes, heating, ventilation, and air conditioning loads. Exposure to outside conditions can present certain operating challenges to system performance including scaling, corrosion, and biological growth. These issues are mitigated through the use of effective cooling tower water filtration and treatment systems to reduce contaminants—along with other total suspended solids (TSS) and total dissolved solids (TDS)—in the system. The U.S. Department of Energy’s Industrial Efficiency and Decarbonization Office is interested in an electrochemical-based filtration system with the ability to remove TSS particulate down to 1 micron. The removal of finer particulates reduces the need for blowdown and, in turn, makeup water use and chemical treatment in the cooling tower. Additionally, the removal process can reduce fouling at the condensers, resulting in better heat exchange and energy savings. Two Industrial Technology Validation program projects validated the electrochemical water filtration system at the Toyota Motor Manufacturing, Mississippi plant in Blue Springs, Mississippi, and the Nissan Canton Vehicle Assembly Plant in Canton, Mississippi. Researchers found that installation of both systems resulted in water and chemical treatment savings with minimal impact on energy use.
Spacely is an open-source framework for the post-silicon validation of analog, digital, and mixed-signal ASICs (Application-Specific Integrated Circuits) which maximizes the reuse of hardware and software, reducing the time taken to achieve meaningful test results. Spacely specifically addresses the needs of small, flexible ASIC design teams commonly found in academia or research institutions which benefit most from sharing the overhead of test stand creation between many unique ASIC designs. Spacely is a set of software, firmware, and design practices. It targets two primary hardware platforms (NI-PXI and Caribou) as well as offering extensible support for bench instruments. Spacely provides a high-level Python interface to all test hardware for accessibility, while also giving more sophisticated teams the opportunity to integrate custom test firmware. The design principles of Spacely are presented in brief. Current documentation is available at https://github.com/SpacelyProject/spacely-docs.
Currently, no tool exists to detect daily change across the globe, let alone to do so cost- and resource-efficiently. The proposed tool will use open-source data to detect change in near-real-time to identify targets of interest for tasking, providing a unique capability for constant global situational awareness.
In this talk we introduce Kynema, an open-source general flexible-multibody-dynamics solver that is well suited for simulating wind turbine structural dynamics. Kynema uses a Lie-group time integrator for constrained systems and runs on both CPUs and GPUs. Timing results for simulations are presented for the IEA 15-MW turbine with and without aerodynamic forces.
This report describes the design basis and design details for an instrumented marine turbine system intended to serve as the DOE’s marine tidal turbine test bed for foundational open-source R&D and data generation to advance our understanding and to identify knowledge gaps on the techno-economic performance of tidal energy converters (TEC) under real tidal flow conditions and at sufficiently large scale to enable upscaling of fluid-structure-interactions and component and material load responses
NREL OpenPATH is an open-source, extensible platform that allows communities to instrument their own travel behavior data. The platform consists of a smartphone app, server and analysis pipeline, and enables collection of opt-in, multi-modal, end-to-end travel diaries. It makes the aggregate statistics available via a public dashboard, and allows deployers to download and visualize trip and trajectory data through the admin dashboard. It also allows for customization of the initial demographic survey and the trip-level qualitative information collected. Our goal is to provide an easy-to-use tool that can democratize travel behavior data collection by empower communities of all sizes to recruit participants and obtain a holistic picture of their travel patterns. The platform has been used by close to 40 partners, to collect data from thousands of participants. Upon signing a simple MOU, it is currently available for free to universities, non-profits and public agencies in the United States.
This report categorizes the top 10 security weaknesses in electric vehicle supply equipment (EVSE) using a similar approach by the Open Worldwide Application Security Project (OWASP) Top 10, and it describes example exploitations, methods for prevention, and related common weakness enumerations (CWEs) for each category. The weaknesses listed in this report could lead to security issues or vulnerabilities in EVSE if they are exploited by an attacker to compromise the confidentiality, integrity, or availability of a system. This report also recommends EVSE industry best practices and remediations for the identified weaknesses to use as guidelines for their secure products and systems.
Open and new Geant4 requirements and usage of Geant4 by HEP Intensity and Cosmic Frontier experiments are presented.
The U.S. Department of Energy transmits to Congress the following Report to Congress on the Feasibility of Incorporating Engineering within Sea Water Air Conditioning (SWAC) and Ocean Thermal Energy Conversation (OTEC) that would Enhance Open-Ocean Aquaculture, as requested in the 2022 House Committee on Appropriations Report 117-98, an update to the previously requested Report on Ocean Thermal Energy Conversion (OTEC) and Sea Water Air Conditioning (SWAC) systems in noncontiguous states and U.S. territories, as requested in the 2021 House Committee on Appropriations Report 116-449, and more recently requested again in the 2023 House Committee on Appropriations Report 117-394.
The integration of Non-Energy Benefits (NEBs) into energy efficiency initiatives is essential for operational excellence in manufacturing. This presentation and software demonstration explore how quantifying NEBs such as improved safety, increased quality, and enhanced productivity, can strengthen business cases for energy investments, leading to better payback periods and alignment with organizational goals. We introduce JUSTIFI, a free, open-source software by the U.S. Department of Energy that aids in the measurement of NEBs and enhances understanding of their impact on Key Performance Indicators (KPIs) and return on investment (ROI). JUSTIFI features an intuitive interface for identifying NEBs, customizable reporting tools, and comprehensive system cataloging, empowering companies to effectively communicate the value of energy efficiency projects. By leveraging this innovative tool, organizations can better navigate energy efficiency assessments and drive support for their energy management initiatives.