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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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This final technical report documents the work performed under DOE Award DE‑EE0008799 by GTI Energy over the course of the project period. The project focused on advanced fueling methods for compressed natural gas (CNG) vehicles which have historically suffered from underfilling.
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The Muckleshoot Indian Tribe (MIT) collaborated with our Project Partner, GRID Alternatives (GRID), to install 132 kilowatts of direct current (kW-DC) of rooftop solar on three Tribal facilities. The three facilities are the Tribe’s Youth Drop-In Center, Canoe Shed, and Water Treatment Facility. The solar PV systems were originally anticipated to offset approximately 45% of the aggregate annual electricity usage of the three buildings. A major aspect of the MIT-ED project was providing hands-on paid training to five Muckleshoot Building Maintenance workers in solar PV installations, operations, and maintenance. The scope of work aligns with the Tribe’s goals of building local capacity and providing real world work experience and potential career opportunities in solar PV to its citizens. The Building Maintenance Department committed five of its current FTE employees to the project. GRID provided guidance for the MIT project team on identifying paid trainees as well as end goals of skill development through training, including a long-term Operations and Maintenance (O&M) plan tailored to the Tribe’s goals of local capacity building and stewardship of natural resources.
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Recent progress towards the implementation of renewable bioenergy production has included microalgae, which have potential to significantly contribute to a viable future bioeconomy. In a current challenging energy landscape, where an increased demand for renewable fuels is projected and accompanied by plummeting fossil fuels' prices, economical production of algae-based fuels becomes more challenging. However, in the context of mitigating carbon emissions with the potential of algae to assimilate large quantities of CO2, there is a route to drive carbon sequestration and utilization to support a sustainable and secure global energy future. This chapter places international energy policy in the context of the current and projected energy landscape. The contribution that algae can make, is summarized as both a conceptual contribution as well as an overview of the commercial infrastructure installed globally. Some of the major recent developments and crucial technology innovations are the results of global government support for the development of algae-based bioenergy, biofuels and bioproduct applications, which have been awarded as public private partnerships and are summarized in this chapter.
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Abstract not provided.
Paenibacillus polymyxa is a Gram-positive bacterium commonly found associated with plant roots. P. polymyxa can exhibit two forms of flagellar motility: swimming in liquid culture and swarming on a surface. Here, swimming cells were compared to swarming cells using an integrated proteomic and lipidomic approach, yielding information about how lipid modifications and protein/enzyme pathways are tailored for these specific phenotypes. Observed differences in both phospholipid composition and metabolism between the two conditions suggest membrane remodeling in response to the surrounding environment. Key enzymes involved in glycerophospholipid metabolism were abundant in swimming bacteria, while enzymes associated with glycerol-3-phosphate metabolism were more abundant in swarming bacteria. Several glycoside hydrolases were either unique to or more abundant during swarming. This likely reflects the degradation of their own exopolysaccharides to both enhance swarming and supply the necessary chemical energy to compensate for increased flagellar synthesis. The observed upregulation of biosynthetic gene clusters (polyketides, lantibiotics, and surfactin) in swarming bacteria suggest the importance of signaling, antimicrobial activity, and surfactin production during this mode of motility – the latter of which is confirmed via RT-PCR.
The growing energy demand of the industrial sector and the need for sustainable solutions highlight the importance of efficient decision making in solar photovoltaic (PV) implementation. Selecting optimal PV configuration is complex due to the interdependent technical, economic, environmental, and social factors involved. This study introduces an integrated decision-making method combining a morphological matrix and fuzzy TOPSIS to systematically select and rank optimal PV system configurations for manufacturing firms. While the morphological matrix exhaustively examines possible design solutions based on sensing, smart, sustainable, and social (S4) attributes, the fuzzy TOPSIS method ranks the alternatives by handling uncertainty in decision making. A case study conducted in a Mexican manufacturing company validates the methodology’s effectiveness. The optimal PV configuration identified comprehensively addresses operational and sustainability criteria, covering all lifecycle stages. This approach demonstrates quantitative superiority and greater robustness compared to existing fuzzy TOPSIS-based methods for solar PV applications. The findings highlight the practical value of data-driven, multi-criteria decision making for industrial solar energy adoption, enhancing project feasibility, cost efficiency, and environmental compliance. Future research will incorporate discrete event simulation (DES) to further refine energy consumption strategies in manufacturing.
Sleep disturbances are common in older adults and may contribute to disease progression in certain populations (e.g., Alzheimer’s disease). Light therapy is a simple and cost-effective intervention to improve sleep. Primary barriers to light therapy are: (1) poor acceptability of the use of devices, and (2) inflexibility of current devices to deliver beyond a fixed light spectrum and throughout the entirety of the day. However, dynamic, tunable lighting integrated into the native home lighting system can potentially overcome these limitations. Herein, we describe our protocol to implement a whole-home tunable lighting system installed throughout the homes of healthy older adults already enrolled in an existing study with embedded home assessment platforms (Oregon Center for Aging & Technology—ORCATECH). Within ORCATECH, continuous data on room location, activity, sleep, and general health parameters are collected at a minute-to-minute resolution over years of participation. This single-arm longitudinal protocol collected participants’ light usage in addition to ORCATECH outcome measures over a several month period before and after light installation. The protocol was implemented with four subjects living in three ORCATECH homes. Technical/usability challenges and feasibility/acceptability outcomes were explored. The successful implementation of our protocol supports the feasibility of implementing and integrating tunable whole-home lighting systems into an automated home-based assessment platform for continuous data collection of outcome variables, including long-term sleep measures. Challenges and iterative approaches are discussed. This protocol will inform the implementation of future clinical intervention trials using light therapy in patients at risk for developing Alzheimer’s disease and related conditions.
Not provided.
With support from the U.S. Department of Energy's Vehicle Technologies Office, the National Renewable Energy Laboratory developed BLAST-V -- the Battery Lifetime Analysis and Simulation Tool for Vehicles. The addition of high-resolution spatial-temporal travel histories enables BLAST-V to investigate user-defined infrastructure rollouts of publically accessible charging infrastructure, as well as quantify impacts on vehicle and station owners in terms of improved vehicle utility and station throughput. This paper presents simulation outputs from BLAST-V that quantify the utility improvements of multiple distinct rollouts of publically available Level 2 electric vehicle supply equipment (EVSE) in the Seattle, Washington, metropolitan area. Publically available data on existing Level 2 EVSE are also used as an input to BLAST-V. The resulting vehicle utility is compared to a number of mock rollout scenarios. Discussion focuses on the estimated number of Level 2 stations necessary to substantially increase vehicle utility and how stations can be strategically sited to maximize their potential benefit to prospective electric vehicle owners.
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ReEDS™ is a capacity planning and dispatch model for the U.S. electricity system. As the National Laboratory of the Rockies (NLR's) flagship long-term power sector model, ReEDS has served as the primary analytic tool for many studies of electricity sector research questions. Example model results are available in the Scenario Viewer here: https://scenarioviewer.nlr.gov/. Quick-start guide The ReEDS model is written in Python, GAMS, and Julia. Python and Julia are free, open-source languages; GAMS requires a software license from the vendor. A step-by-step guide for getting started with ReEDS is available here: https://pages.github.nrel.gov/ReEDS/ReEDS-2.0/setup.html.
Abstract not provided.