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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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At least 217 records · Page 12

Composite Overwrap Pressure Vessels: Mechanics and Stress Rupture Lifing Philosophy

The NASA Engineering and Safety Center (NESC) has been conducting an independent technical assessment to address safety concerns related to the known stress rupture failure mode of filament wound pressure vessels in use on Shuttle and the International Space Station. The Shuttle's Kevlar-49 fiber overwrapped tanks are of particular concern due to their long usage and the poorly understood stress rupture process in Kevlar-49 filaments. Existing long term data show that the rupture process is a function of stress, temperature and time. However due to the presence of load sharing liners and the complex manufacturing procedures, the state of actual fiber stress in flight hardware and test articles is not clearly known. Indeed non-conservative life predictions have been made where stress rupture data and lifing procedures have ignored the contribution of the liner in favor of applied pressure as the controlling load parameter. With the aid of analytical and finite element results, this paper examines the fundamental mechanical response of composite overwrapped pressure vessels including the influence of elastic-plastic liners and degraded/creeping overwrap properties. Graphical methods are presented describing the non-linear relationship of applied pressure to Kevlar-49 fiber stress/strain during manufacturing, operations and burst loadings. These are applied to experimental measurements made on a variety of vessel systems to demonstrate the correct calibration of fiber stress as a function of pressure. Applying this analysis to the actual qualification burst data for Shuttle flight hardware revealed that the nominal fiber stress at burst was in some cases 23% lower than what had previously been used to predict stress rupture life. These results motivate a detailed discussion of the appropriate stress rupture lifing philosophy for COPVs including the correct transference of stress rupture life data between dissimilar vessels and test articles.

Thesken, John C.↗

Miscellaneous Electric Loads: Characterization and Energy Savings Potential

Over time, miscellaneous electric loads (MELs) are expected to increase both in magnitude and share of residential and commercial building energy consumption. This trend is most apparent in North America, but it is also occurring in Japan and Europe. However, the contribution of MELs to building energy use is not currently well understood, both because the products in this category are transforming rapidly and the definition and classification of MELs is ambiguous. This study estimated the national energy consumption of 36 MELs using best-available data and found them to comprise 12% of delivered electricity to the U.S. residential and commercial building sectors. If 26 of these MELs were replaced with the most energy-efficient product models available on the market, their energy consumption could be halved to 6% of delivered electricity. National energy models will better account for building energy consumption by incorporating the MELs data collected and analyzed for this study, leading to improved policy decisions.

Miscellaneous electric loads, Plug loads, Taxonomy↗

Simulation-based assessment on stochastic load scheduling for building cooling systems

Here, to fill knowledge gaps related to stochastic load scheduling, we performed a comprehensive evaluation of the stochastic load scheduling for building cooling systems. Specifically, we studied the common uncertain variables in the load scheduling process for building cooling systems and categorized those variables based on their dynamic patterns. We then developed a generic stochastic load scheduling framework and applied it to building cooling systems that served a simulated community. This community consists of 100 heterogeneous houses and serves as a virtual testbed for evaluating the performance of stochastic load scheduling. In this evaluation, we considered representatives of uncertain variables with different dynamic patterns and included 100 realizations of the considered uncertainty in the evaluation to better catch the probability distribution of the control performance. The evaluation results suggest that deterministic load scheduling can reduce the operating energy cost by 18% but its performance can be affected by uncertainty. Stochastic load scheduling can further decrease the operating energy cost under uncertainty compared to deterministic load scheduling. We also found that the effectiveness of stochastic load scheduling in handling uncertainty is not directly associated with the number of uncertainty scenarios that are considered in its formulation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

EV Profile Capture

NextGen Profiles' EV profile capture efforts aimed to explore the variance in performance and evaluate how different operational conditions influence production EV charging behavior. Data were collected at a frequency of 10 Hz from both the EV and EVSE during each charge session. These charge session parameters were then entered into a time-series database for further analysis. The data were gathered under different operational conditions to examine the effects of various factors such as battery state of charge, battery temperature, vehicle condition, smart charge management, and EVSE limitations. The EV profile capture dataset includes extensive high-power charging data from 16 different EVs—comprising light-, medium-, and heavy-duty vehicles—along with EVSE from various suppliers. To protect confidentiality, the EV and EVSE metadata are anonymized, and the publicly released datasets are aggregated to 0.1-Hz frequency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

SWS Building Electric Demand Profile

This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

SWS Building Electric Demand Profile

This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

SWS Building Electric Demand Profile

This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

SWS Building Electric Demand Profile

This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

SWS Building Electric Demand Profile

This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

SWS Building Electric Demand Profile

This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

SWS Building Electric Demand Profile

This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

SWS Building Electric Demand Profile

This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

SWS Building Electric Demand Profile

This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

SWS Building Electric Demand Profile

This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

SWS Building Electric Demand Profile

This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

SWS Building Electric Demand Profile

This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

SWS Building Electric Demand Profile

This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗